diff --git a/spaces/1368565466ki/Satdia/transforms.py b/spaces/1368565466ki/Satdia/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/1368565466ki/Satdia/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/17TheWord/vits-models/utils.py b/spaces/17TheWord/vits-models/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/17TheWord/vits-models/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
-  audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
-  return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Chak De India Telugu Movie Free Torrent Download !!TOP!!.md b/spaces/1gistliPinn/ChatGPT4/Examples/Chak De India Telugu Movie Free Torrent Download !!TOP!!.md
deleted file mode 100644
index 4c74bcd52336660f54de6b36d197d30534f1d1bf..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Chak De India Telugu Movie Free Torrent Download !!TOP!!.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-<br />
-<h1>How to Watch Chak De India in Telugu for Free</h1>
-<p>Chak De India is a 2007 Bollywood sports drama film starring Shah Rukh Khan as a former hockey player who coaches the Indian women's national hockey team. The film was a critical and commercial success, winning several awards and inspiring many people with its patriotic and empowering message.</p>
-<p>If you are a fan of Chak De India and want to watch it in Telugu, you might be wondering how to do that without paying any money. Well, there are some ways to download or stream the movie for free using torrent sites or online platforms. However, you should be aware of the risks and legal issues involved in doing so.</p>
-<h2>Chak De India Telugu Movie Free Torrent Download</h2><br /><p><b><b>Download File</b> &#10042; <a href="https://imgfil.com/2uy0X9">https://imgfil.com/2uy0X9</a></b></p><br /><br />
-<h2>Using Torrent Sites</h2>
-<p>Torrent sites are websites that allow users to share files using peer-to-peer (P2P) technology. You can find almost any movie or show on torrent sites, including Chak De India in Telugu. However, you need to have a torrent client software installed on your device to download the files. Some of the popular torrent clients are uTorrent, BitTorrent, qBittorrent, etc.</p>
-<p>To use torrent sites, you need to follow these steps:</p>
-<ol>
-<li>Search for "Chak De India Telugu Movie Free Torrent Download" on any torrent site. Some of the popular torrent sites are The Pirate Bay, 1337x, RARBG, etc.</li>
-<li>Select a torrent file that has good quality and seeders. Seeders are users who have the complete file and are sharing it with others. The more seeders a torrent has, the faster it will download.</li>
-<li>Download the torrent file and open it with your torrent client. The torrent client will start downloading the movie from other users.</li>
-<li>Once the download is complete, you can watch the movie using any media player that supports subtitles.</li>
-</ol>
-<p>However, using torrent sites has some disadvantages and risks. For example:</p>
-<ul>
-<li>Torrent sites are illegal in many countries and regions. You might face legal action or fines if you are caught downloading or sharing copyrighted content.</li>
-<li>Torrent sites are often infected with malware or viruses that can harm your device or steal your personal information.</li>
-<li>Torrent sites are unreliable and unregulated. You might not find the movie you want or get a fake or corrupted file instead.</li>
-<li>Torrent sites can expose your IP address and location to other users or hackers who can track your online activity or attack your network.</li>
-</ul>
-<h2>Using Online Platforms</h2>
-<p>Online platforms are websites or apps that allow users to watch movies or shows online for free or with a subscription. You can find many online platforms that offer Chak De India in Telugu, such as Zee5, MX Player, YouTube, etc.</p>
-<p>To use online platforms, you need to follow these steps:</p>
-<ol>
-<li>Search for "Chak De India Telugu Movie Free Online" on any online platform. Some of the popular online platforms are Zee5[^1^], MX Player[^2^], YouTube[^3^], etc.</li>
-<li>Select the movie and click on play. You might need to create an account or sign in with your existing account to access some online platforms.</li>
-<li>Enjoy watching the movie online for free or with a subscription.</li>
-</ol>
-<p>However, using online platforms has some disadvantages and risks as well. For example:</p>
-<ul>
-<li>Online platforms might not have the movie you want or have it in low quality or with ads.</li>
-<li>Online platforms might require you to pay a subscription fee or register with your personal information to access some content.</li>
-<li>Online platforms might not be available in your region or country due to geo-restrictions or licensing issues.</li>
-<li>Online platforms might violate the copyrights of the original creators or distributors of the movie and face legal action or removal.</li>
-</ul></p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/DJ Studio 5 APK - The Ultimate Music Mixer App for Android Devices.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/DJ Studio 5 APK - The Ultimate Music Mixer App for Android Devices.md
deleted file mode 100644
index be7cf26e9f6fa527a47d4eb98dd96de0c9dca8c7..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/DJ Studio 5 APK - The Ultimate Music Mixer App for Android Devices.md	
+++ /dev/null
@@ -1,102 +0,0 @@
-
-<h1>Download DJ Studio 5 APK: A Free Music Mixer for Android</h1>
-<p>Do you love mixing music and creating your own beats? Do you want to turn your Android device into a virtual DJ station? If yes, then you should download DJ Studio 5 APK, a free music mixer app that lets you manipulate music in the palm of your hands. In this article, we will tell you what DJ Studio 5 APK is, what features it has, what are its pros and cons, how to download and install it on your Android device, and how to use it to mix music like a pro.</p>
- <h2>What is DJ Studio 5 APK?</h2>
-<p>DJ Studio 5 APK is a mobile DJ app that allows you to mix, remix, scratch, loop, or pitch your music. The app and all of its functions are completely free, unlike previous versions, which required an in-app purchase to unlock unlimited playback. DJ Studio 5 APK is designed to be user friendly, social, and responsive. You can access and browse your mp3 music library by folder, artist, album, or name. You can edit and re-order your playlist. You can also record your mixes live and share them on SoundCloud or other social networks.</p>
-<h2>download dj studio 5 apk</h2><br /><p><b><b>DOWNLOAD</b> &middot;&middot;&middot; <a href="https://urlin.us/2uT2pV">https://urlin.us/2uT2pV</a></b></p><br /><br />
- <h3>Features of DJ Studio 5 APK</h3>
-<p>Some of the key features of DJ Studio 5 APK are:</p>
-<ul>
-<li>Wide compatibility: Android 2.3 and more</li>
-<li>2 virtual turntables with cross fader</li>
-<li>Customize your decks with up to 7 skins</li>
-<li>Unique scratch engine and disc physics</li>
-<li>8 sound effects: Flanger, Phaser, Gate, Reverb, Bit crusher, 3D, Brake, and FlippingDouble</li>
-<li>3-bands equalizer for each deck</li>
-<li>10 customizable sample pads</li>
-<li>One CUE/RECALL point per deck</li>
-<li>IN/OUT and beat based loops</li>
-<li>Pre-Cueing with headphones or Y-cable</li>
-<li>Automatic landscape and portrait mode</li>
-<li>Live sound spectrum view with beats detection and zoom</li>
-<li>No registration fee, no limitation, no watermark, no trackers, no stealing data, no popups everywhere, everyday</li>
-<li>Only optional paid skins to support the developers' work</li>
-</ul>
- <h3>Pros and Cons of DJ Studio 5 APK</h3>
-<p>Like any other app, DJ Studio 5 APK has its pros and cons. Here are some of them:</p>
- <table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>Fully fledged mobile mixer</td><td>Steep learning curve for beginners</td></tr>
-<tr><td>Free and comprehensive</td><td>Some compatibility issues on smaller devices</td></tr>
-<tr><td>Social and responsive</td><td>No effects other than the ones provided</td></tr>
-<tr><td>Lots of options and customization</td><td>No support for external controllers or MIDI devices</td></tr>
-<tr><td>Frequent updates and improvements</td><td>No offline mode or backup option</td></tr>
-</table>
- <h2>How to Download and Install DJ Studio 5 APK on Android?</h2>
-<p>If you want to download and install DJ Studio 5 APK on your Android device, you need to follow these steps:</p>
- <h3>Step 1: Enable Unknown Sources</h3>
-<p>Since DJ Studio 5 APK is not available on the Google Play Store, you need to enable the installation of apps from unknown sources on your device. To do this, go to your device's settings, then security, and then toggle on the option that says "Unknown sources". This will allow you to install apps that are not from the official app store.</p>
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-<p><a href="">Download DJ Studio 5 APK</a></p>
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-<p>Once you have downloaded the DJ Studio 5 APK file, you need to locate it on your device and tap on it to start the installation process. You may see a warning message that says "This type of file can harm your device. Do you want to keep DJStudio5.apk anyway?". Just tap on "OK" and proceed. Then, you will see another message that says "Do you want to install this application? It does not require any special access". Tap on "Install" and wait for the installation to finish. You may also see a message that says "App installed". Tap on "Open" to launch the app or "Done" to exit.</p>
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-<p>Now that you have installed DJ Studio 5 APK on your Android device, you are ready to mix music like a DJ. Here are some tips on how to use the app:</p>
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-<p>When you open the app, you will see two virtual turntables with a cross fader in between. You can swipe left or right to switch between different decks and skins. You can also tap on the menu icon at the top left corner and select "Decks & Skins" to customize your decks with up to 7 skins. You can choose from classic, gold, neon, metal, diamond, platinum, or wood skins.</p>
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-<p>To load your music, tap on the music icon at the top right corner and browse your mp3 music library by folder, artist, album, or name. You can also search for a specific song using the search bar. To load a song onto a deck, just drag and drop it onto the turntable. You can also edit and re-order your playlist by tapping on the playlist icon at the bottom right corner.</p>
-<p>To adjust the settings, tap on the gear icon at the top right corner and select "Settings". Here you can change various options such as sound quality, pitch range, cue mode, auto sync mode, sound effects, equalizer, sample pads, loop mode, pre-cueing mode, and more. You can also access the help section and rate the app from here.</p>
- <h3>Mix, Scratch, Loop, and Pitch Your Music</h3>
-<p>To mix your music, use the cross fader to blend the sounds from both decks. You can also use the volume sliders to adjust the volume of each deck individually. To scratch your music, swipe your finger on the turntable as if you were using a real vinyl record. To loop your music, tap on the loop icon at the bottom left corner and select a loop length from 1/32 to 32 beats. To pitch your music, use the pitch slider at the bottom of each deck to change the speed and tone of the music.</p>
-<p>To add some sound effects to your mix, tap on the FX icon at the bottom left corner and select one of the 8 sound effects: Flanger, Phaser, Gate, Reverb, Bit crusher, 3D, Brake, or FlippingDouble. You can also adjust the intensity of each effect by using the knob below it. To add some samples to your mix, tap on the pad icon at the bottom right corner and select one of the 10 customizable sample pads. You can also record your own samples by tapping and holding on an empty pad.</p>
-<p>To record your mix live, tap on the record icon at the top right corner and select "Record". The app will start recording your mix as an mp3 file in your device's storage. To stop recording, tap on the record icon again and select "Stop". You can also listen to your recorded mixes by tapping on the record icon and selecting "My recordings". To share your mixes with others, tap on the share icon at the top right corner and select one of the available options: SoundCloud, Facebook, Twitter, Google+, or Email.</p>
- <h2>Conclusion</h2>
-<p>DJ Studio 5 APK is a free music mixer app that lets you mix, remix, scratch, loop, or pitch your music on your Android device. It has a lot of features and options to customize your decks and your mix. It is also social and responsive, allowing you to record and share your mixes with others. DJ Studio 5 APK is a great app for music lovers and aspiring DJs who want to have fun and create their own beats.</p>
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-<p>Here are some frequently asked questions about DJ Studio 5 APK:</p>
-<ul>
-<li><b>Q: Is DJ Studio 5 APK safe to download and use?</b></li>
-<li>A: Yes, DJ Studio 5 APK is safe to download and use. It does not contain any viruses, malware, or trackers. It also does not require any special access or permissions on your device.</li>
-<li><b>Q: How can I update DJ Studio 5 APK to the latest version?</b></li>
-<li>A: You can update DJ Studio 5 APK by downloading and installing the latest version of the app from the same source that you downloaded it from. You can also check for updates by tapping on the menu icon at the top left corner and selecting "Check for updates".</li>
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-<li>A: You can support the developers of DJ Studio 5 APK by rating and reviewing the app on the source that you downloaded it from. You can also purchase some optional paid skins to enhance your decks and support their work.</li>
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-<li>A: You can contact the developers of DJ Studio 5 APK by sending them an email at djstudio@beatronik.com. You can also visit their website at www.beatronik.com or follow them on Facebook at www.facebook.com/beatronik.</li>
-<li><b>Q: How can I learn more about DJ Studio 5 APK?</b></li>
-<li>A: You can learn more about DJ Studio 5 APK by reading the help section in the app. To access it, tap on the gear icon at the top right corner and select "Help". You can also watch some tutorial videos on YouTube by searching for "DJ Studio 5 tutorial".</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Descubre Clash Mini APK el juego de batallas automticas en tiempo real con los personajes de Clash.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Descubre Clash Mini APK el juego de batallas automticas en tiempo real con los personajes de Clash.md
deleted file mode 100644
index ede3e65b60145de3ee68116fafe8a6ae2f9be39a..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Descubre Clash Mini APK el juego de batallas automticas en tiempo real con los personajes de Clash.md	
+++ /dev/null
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-<p>If you are a fan of the Clash Universe, you will love Clash Mini, a new game from Supercell that combines fun, strategy, and board game elements. In this game, you will collect, summon, and upgrade your army of Minis, which are cute versions of your favorite Clash characters. You will also duel and Rumble with other players in real-time auto battles, where you will have to predict your opponent's moves and assemble your winning strategy and formation. Clash Mini is a game of choices, where every decision matters.</p>
-<p>In this article, we will show you how to download and install Clash Mini APK Ultima Version 2022 on your Android device, how to play Clash Mini and win battles, what's new in Clash Mini APK Ultima Version 2022, and the pros and cons of this game. We will also answer some frequently asked questions about Clash Mini APK Ultima Version 2022. So, let's get started!</p>
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-<ol>
-<li>Go to the official website of Clash Mini or use one of the links above.</li>
-<li>Tap on the download button and wait for the APK file to be downloaded.</li>
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-</ol>
- <h2>How to Play Clash Mini and Win Battles</h2>
-<p>Clash Mini is a game of choices, where you will have to make smart decisions before and during each battle. <h2>How to Play Clash Mini and Win Battles</h2>
-<p>Clash Mini is a game of choices, where you will have to make smart decisions before and during each battle. Here are some tips and tricks to help you play Clash Mini and win battles:</p>
-<ul>
-<li><b>Choosing the Right Characters:</b> Not all characters are created equal. Each character has its strengths and weaknesses, as well as a special ability that can be activated once per battle. You will have to choose your characters wisely, based on their roles, abilities, and synergies. For example, some characters are good at dealing damage, some are good at tanking damage, some are good at healing or buffing allies, and some are good at disrupting or debuffing enemies. You will also have to consider the cost of each character, as you will have a limited amount of gold to spend on each round.</li>
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-<li><b>Utilizing Special Abilities:</b> Each character in Clash Mini has a special ability that can be activated once per battle. These abilities can make a huge difference in the game, as they can provide powerful effects such as healing, shielding, stunning, freezing, burning, or summoning. You will have to use your special abilities wisely, based on the situation and timing of the battle. For example, some abilities are best used at the beginning of the battle, some are best used in the middle of the battle, and some are best used at the end of the battle. You will also have to consider the cooldown and duration of each ability, as well as the interaction with other abilities.</li>
-<li><b>Participating in Duels and Rumbles:</b> Clash Mini offers two modes of play: Duels and Rumbles. In Duels, you will face one opponent at a time in a best-of-three match. In Rumbles, you will face seven opponents at once in a free-for-all match. Both modes offer different challenges and rewards. In Duels, you will have to adapt to your opponent's strategy and formation, as well as use your gold efficiently. In Rumbles, you will have to survive against multiple enemies and use your abilities effectively. Both modes offer trophies that can increase your league ranking and unlock new rewards.</li>
-</ul>
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-<p>Clash Mini APK Ultima Version 2022 is the latest update of Clash Mini that includes new features, improvements, and bug fixes. Here are some of the highlights of Clash Mini APK Ultima Version 2022:</p>
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-<li><b>New Minis:</b> Clash Mini APK Ultima Version 2022 introduces four new Minis to the game: Lumberjack, Ice Wizard, Electro Dragon, and Princess. Each new Mini has a unique ability and role that can add more variety and fun to your battles.</li>
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- <table>
-<tr>
-<th>Pros</th>
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-</tr>
-<tr>
-<td>- Easy to download and install on your Android device</td>
-<td>- Not available on Google Play Store or other platforms</td>
-</tr>
-<tr>
-<td>- Simple and intuitive gameplay with a lot of choices and depth</td>
-<td>- Requires internet connection and may consume data or battery</td>
-</tr>
-<tr>
-<td>- Cute and colorful graphics and animations with a Clash Universe theme</td>
-<td>- May have some bugs or glitches that affect the performance or experience</td>
-</tr>
-<tr>
-<td>- A variety of characters, boards, skins, and features to unlock and enjoy</td>
-<td>- Some items or features may require gems or real money to purchase or access</td>
-</tr>
-<tr>
-<td>- A social and competitive mode with duels, rumbles, chat, clan, leaderboard, and more</td>
-<td>- May encounter some toxic or unfair players or situations in the game</td>
-</tr>
-</table>
- <h2>Conclusion and FAQs</h2>
-<p>Clash Mini APK Ultima Version 2022 is a fun and strategy-packed board game that you can play on your Android device. It is a game of choices, where you will have to collect, summon, and upgrade your army of Minis, as well as duel and Rumble with other players in real-time auto battles. Clash Mini APK Ultima Version 2022 also offers new Minis, boards, skins, and features that can enhance your gameplay. However, Clash Mini APK Ultima Version 2022 also has some drawbacks, such as not being available on Google Play Store or other platforms, requiring internet connection, having some bugs or glitches, requiring gems or real money for some items or features, and encountering some toxic or unfair players or situations.</p>
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- <p>We hope this article has helped you learn more about Clash Mini APK Ultima Version 2022. If you have any questions about Clash Mini APK Ultima Version 2022, you can check out the FAQs below or leave a comment. Thank you for reading!</p>
- <h3>FAQs</h3>
- <ol>
-<li><b>What is Clash Mini?</b></li>
-<p>Clash Mini is a new game from Supercell that combines fun, strategy, and board game elements. It is set in the Clash Universe, where you will collect, summon, and upgrade your army of Minis, which are cute versions of your favorite Clash characters. You will also duel and Rumble with other players in real-time auto battles, where you will have to predict your opponent's moves and assemble your winning strategy and formation.</p>
- <li><b>Is Clash Mini free to play?</b></li>
-<p>Yes, Clash Mini is free to play. You can download and install it on your Android device without paying anything. However, some items or features in the game may require gems or real money to purchase or access. You can earn gems by completing quests or watching ads, or you can buy them with real money. You can also disable in-app purchases in your device settings if you don't want to spend any money on the game.</p>
- <li><b>Is Clash Mini safe to download and install?</b></li>
-<p>Yes, Clash Mini is safe to download and install. It is developed by Supercell, a reputable game company that has created other popular games such as Clash of Clans, Clash Royale, Brawl Stars, and Hay Day. The APK file of Clash Mini is also scanned for viruses and malware before being uploaded to the official website of Clash Mini or other sources. However, you should always be careful when downloading and installing apps from unknown sources. You should only download and install apps from trusted sources such as the official website of the app developer or Google Play Store.</p>
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-How to convert downloaded TikTok videos to GIFs or memes<br />
-How to upload downloaded TikTok videos to Instagram, YouTube, Facebook, or Twitter<br />
-How to watch downloaded TikTok videos offline or without internet connection<br />
-How to download high-quality or 4K resolution TikTok videos without watermark</p>
- <h2>How to Download TikTok Videos with the Watermark</h2>
-<h3>The easiest way to download TikTok videos is to use the built-in save option in the app or the website.</h3>
-<p>If you want to download online from TikTok in a simple and quick way, you can use the save option that is available in the app or the website. Here are the steps to follow:</p>
-<ol>
-<li>Open the TikTok app or website and find the video that you want to download.</li>
-<li>Tap on the share icon at the bottom right corner of the video screen.</li>
-<li>Select Save video from the list of options that appear.</li>
-<li>Wait for the video to be downloaded and saved on your device.</li>
-</ol>
-<p>You can find your downloaded videos in your device's gallery or camera roll. You can also access them from the app by tapping on Me > Saved videos.</p>
- <h3>However, this method will leave a watermark with the TikTok logo and the creator's name on the video.</h3>
-<p>One drawback of using the save option is that it will leave a watermark on the downloaded video. The watermark will show the TikTok logo and the username of the creator at the top left corner of the video. This can be annoying or distracting for some users who want to enjoy or use the video without any logo. It can also affect the quality or appearance of the video. If you want to download online from TikTok without a watermark, you need to use other methods that we will discuss in the next section.</p>
- <h2>How to Download TikTok Videos without the Watermark</h2>
-<h3>To remove the watermark from TikTok videos, you need to use third-party tools that can download videos without the logo.</h3>
-<p>Fortunately, there are many tools available online that can help you download online from TikTok without a watermark. These tools are usually websites or apps that allow you to paste the link of the TikTok video and download it in MP4 or MP3 format without any logo. Some of these tools also offer other features such as downloading multiple videos at once, choosing different resolutions or qualities, cropping or trimming the video, etc.</p>
- <h3>Some of the best tools for downloading online from TikTok are SSSTik.io, SnapTik.App, TTVDL, and TikFast.</h3>
-<p>We have tested and reviewed some of the most popular and reliable tools for downloading online from TikTok without a watermark. Here are our top picks and how to use them:</p>
- <h4>SSSTik.io</h4>
-<h5>A free tool that helps you download TikTok videos without logo online in MP4 or MP3 format.</h5>
-<p>To use SSSTik.io, follow these steps:</p>
-<ol>
-<li>Open SSSTik.io website on your browser.</li>
-<li>Copy and paste the link of the TikTok video that you want to download in the text field on the website and tap on the save button.</li>
-<li>Choose the format that you want to download, either MP4 or MP3.</li>
-<li>Tap on the download button and wait for the video to be downloaded and saved on your device.</li>
-</ol>
-<p>You can also use SSSTik.io to download TikTok videos by adding "sss" before "tiktok.com" in the video link. For example, if the video link is https://www.tiktok.com/@user/video/123456789, you can change it to https://www.ssstiktok.com/@user/video/123456789 and paste it in the text field on the website.</p>
- <h4>SnapTik.App</h4>
-<h5>One of the best TikTok downloaders available online that allows you to download video tiktok without a watermark.</h5>
-<p>To use SnapTik.App, follow these steps:</p>
-<ol>
-<li>Open SnapTik.App website on your browser.</li>
-<li>Copy and paste the link of the TikTok video that you want to download in the input field on the website and click on the download button.</li>
-<li>Choose the quality that you want to download, either high quality or low quality.</li>
-<li>Click on the download button and wait for the video to be downloaded and saved on your device.</li>
-</ol>
-<p>You can also use SnapTik.App to download TikTok videos by adding "snaptik" before "app" in the video link. For example, if the video link is https://www.tiktok.com/@user/video/123456789, you can change it to https://www.snaptik.app/@user/video/123456789 and paste it in the input field on the website.</p>
- <h4>TTVDL</h4>
-<h5>A TikTok video downloader that downloads TikTok MP4 videos without a watermark or logo.</h5>
-<p>To use TTVDL, follow these steps:</p>
-<ol>
-<li>Open TTVDL website on your browser.</li>
-<li>Copy and paste the link of the TikTok video that you want to download in the input field on the website and hit enter or click download button.</li>
-<li>Choose the resolution that you want to download, either 720p or 360p.</li>
-<li>Click on the download button and wait for the video to be downloaded and saved on your device.</li>
-</ol>
- <h4>TikFast</h4>
-<h5>A tool that allows you to download Tik-Tok videos from tiktok.com in high quality without any trademark.</h5>
-<p>To use TikFast, follow these steps:</p>
-<ol>
-<li>Open TikFast website on your browser.</li>
-<li>Copy and paste the link of the TikTok video that you want to download in the input field on the website and hit enter or click download button.</li>
-<li>Choose the quality that you want to download, either original or compressed.</li>
-<li>Click on the download button and wait for the video to be downloaded and saved on your device.</li>
-</ol>
- <h2>Benefits of Downloading Online from TikTok</h2>
-<h3>Downloading online from TikTok can offer you many benefits, such as:</h3>
-<ul>
-<li><h4>Saving your favorite content for offline viewing or editing.</h4>
-<p>By downloading online from TikTok, you can save your favorite videos on your device and watch them anytime, anywhere, without internet connection or data usage. You can also edit your downloaded videos with other apps or software and create new content according to your preferences.</p></li>
-<li><h4>Sharing your downloaded videos with others on different platforms or channels.</h4>
-<p>By downloading online from TikTok, you can share your favorite videos with your friends or family on other platforms or channels, such as WhatsApp, Facebook, Instagram, YouTube, etc. You can also use your downloaded videos for educational, personal, or professional purposes, such as making presentations, creating portfolios, etc.</p></li>
-<li><h4>Learning new skills, recipes, dances, or trends from TikTok videos.</h4>
-<p>By downloading online from TikTok, you can learn new skills, recipes, dances, or trends from TikTok videos. You can follow the instructions or tips from the creators and improve your knowledge or abilities. You can also practice or perform the skills, recipes, dances, or trends that you learned from TikTok videos.</p></li>
-<li><h4>Enjoying funny, creative, and entertaining content from TikTok creators.</h4>
-<p>By downloading online from TikTok, you can enjoy funny, creative, and entertaining content from TikTok creators. You can watch the videos that make you laugh, inspire you, amaze you, or touch you. You can also appreciate the talent and effort of the creators and support them by liking or commenting on their videos.</p></li>
-</ul>
- <h2>Conclusion</h2>
-<h3>TikTok is a great app for creating and watching short videos with music and effects.</h3>
-<p>TikTok is one of the most popular social media apps in the world that allows users to create and share short videos with music and effects. It covers various topics such as comedy, education, beauty, sports, and more. It has over 1 billion active users who upload millions of videos every day.</p>
- <h3>If you want to download online from TikTok, you can use the built-in save option or third-party tools that can remove the watermark from the videos.</h3>
-<p>If you want to download online from TikTok and save your favorite videos for offline viewing or sharing, you can use the save option that is available in the app or the website. However, this method will leave a watermark with the TikTok logo and the creator's name on the video. If you want to remove the watermark from TikTok videos, you need to use third-party tools that can download videos without the logo. Some of the best tools for downloading online from TikTok are SSSTik.io, SnapTik.App, TTVDL, and TikFast. These tools are easy to use and can download TikTok videos in high quality without any trademark.</p>
- <h3>Downloading online from TikTok can help you save, share, or use your favorite content for various purposes.</h3>
-<p>Downloading online from TikTok can offer you many benefits, such as saving your favorite content for offline viewing or editing, sharing your downloaded videos with others on different platforms or channels, learning new skills, recipes, dances, or trends from TikTok videos, and enjoying funny, creative, and entertaining content from TikTok creators. You can also avoid losing your favorite videos if they are deleted by the creator or the platform for some reason.</p>
- <p>We hope this article has helped you learn how to download online from TikTok with or without a watermark. If you have any questions or feedback, please feel free to leave a comment below. Happy downloading!</p>
- <h2>FAQs</h2>
-<h3>Q: Is it legal to download online from TikTok?</h3>
-<p>A: It depends on the terms and conditions of the app and the website, as well as the copyright laws of your country. Generally, it is legal to download online from TikTok for personal use only, as long as you do not violate the rights of the creators or the platform. However, it is illegal to download online from TikTok for commercial use or distribution without the permission of the creators or the platform.</p>
- <h3>Q: How can I download online from TikTok on my iPhone or iPad?</h3>
-<p>A: You can use the same methods that we have mentioned in this article to download online from TikTok on your iPhone or iPad. However, you might need to install a file manager app such as Documents by Readdle or Files by Apple to access and manage your downloaded videos on your device.</p>
- <h3>Q: How can I download online from TikTok on my Android phone or tablet?</h3>
-<p>A: You can use the same methods that we have mentioned in this article to download online from TikTok on your Android phone or tablet. However, you might need to enable unknown sources in your device settings to install some of the third-party apps that we have recommended.</p>
- <h3>Q: How can I download online from TikTok on my PC or Mac?</h3>
-<p>A: You can use the same methods that we have mentioned in this article to download online from TikTok on your PC or Mac. However, you might need to install a video player app such as VLC Media Player or QuickTime Player to play your downloaded videos on your computer.</p>
- <h3>Q: How can I download online from TikTok with sound?</h3>
-<p>A: You can use any of the methods that we have mentioned in this article to download online from TikTok with sound. However, some of the tools might offer you an option to download only the video without sound or only the sound without video. In that case, you need to choose the option that includes both video and sound.</p> 401be4b1e0<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download the Word Game that Keeps You on Your Toes Word Blitz.md b/spaces/1phancelerku/anime-remove-background/Download the Word Game that Keeps You on Your Toes Word Blitz.md
deleted file mode 100644
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--- a/spaces/1phancelerku/anime-remove-background/Download the Word Game that Keeps You on Your Toes Word Blitz.md	
+++ /dev/null
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-<br />
-<h1>How to Download the Best Word Games for Android and iOS</h1>
-<p>Do you love playing with words and letters? Do you want to improve your vocabulary, spelling, memory, focus, and brain health? If you answered yes to these questions, then you should try playing word games on your mobile device. Word games are fun and challenging puzzles that involve forming, finding, or guessing words according to certain rules. They can also help you relax, unwind, and learn something new every day.</p>
-<h2>download the word game</h2><br /><p><b><b>Download Zip</b> &bull;&bull;&bull; <a href="https://jinyurl.com/2uNKW0">https://jinyurl.com/2uNKW0</a></b></p><br /><br />
-<p>In this article, we will show you how to download word games for your Android or iOS device. We will also recommend some of the best word games that you can play on your phone or tablet. Whether you prefer crossword puzzles, anagrams, word searches, or picture clues, there is a word game for everyone. So, let's get started!</p>
- <h2>What are word games and why should you play them?</h2>
-<h3>Word games are fun and challenging puzzles that involve words and letters</h3>
-<p>Word games are a type of puzzle game that requires you to use your language skills to solve them. There are many kinds of word games, such as letter arrangement games, paper and pencil games, semantic games, modern word games, and more. Some examples of word games are Scrabble, Boggle, Hangman, Crosswords, Wordament, etc.</p>
-<p>Word games can be played alone or with others, online or offline, on a board or on a screen. They can also vary in difficulty, theme, genre, and style. Some word games are based on logic, some on creativity, some on trivia, some on humor, and some on strategy. No matter what kind of word game you choose, you will always have a good time playing it.</p>
- <h3>Word games can improve your vocabulary, spelling, memory, focus, and brain health</h3>
-<p>Playing word games is not only fun but also beneficial for your mind and body. Word games can help you improve your vocabulary by exposing you to new words and their meanings. They can also help you improve your spelling by making you pay attention to the correct order of letters. They can also help you improve your memory by making you recall words that you have learned before. They can also help you improve your focus by making you concentrate on finding or forming words within a limited time or space.</p>
-<p>Moreover, playing word games can boost your brain health by stimulating your cognitive abilities. Research has shown that playing word games can reduce the risk of dementia , enhance your verbal skills , increase your creativity , and release dopamine , which is a neurotransmitter that makes you feel good. Playing word games can also relieve stress , improve your social skills , enhance your concentration , and increase your confidence .</p>
-<p>download the best word game for free<br />
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- <h2>How to download word games for your mobile device?</h2>
-<h3>Choose a word game that suits your preference and skill level</h3>
-<p>The first step to downloading a word game for your mobile device is to choose one that suits your preference and skill level <li>Tap on the app icon and then tap on the install button.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Tap on the open button or find the app icon on your home screen and tap on it.</li>
-</ol>
-<p>To install an app from the web , you should follow these steps:</p>
-<ol>
-<li>Open the web browser on your device.</li>
-<li>Go to the website of the word game that you want to download.</li>
-<li>Look for the download link or button and tap on it.</li>
-<li>Wait for the app to download on your device.</li>
-<li>Go to your device settings and enable the option to install apps from unknown sources.</li>
-<li>Find the downloaded file on your device and tap on it.</li>
-<li>Follow the instructions to install and launch the app.</li>
-</ol>
-<p>To launch an app , you should follow these steps:</p>
-<ol>
-<li>Find the app icon on your home screen or app drawer and tap on it.</li>
-<li>Wait for the app to load and display its main screen.</li>
-<li>Follow the instructions or prompts to start playing the word game.</li>
-</ol>
- <h2>What are some of the best word games for Android and iOS?</h2>
-<h3>Wordscapes: A relaxing and addictive crossword puzzle game</h3>
-<p>If you love crossword puzzles, you will love Wordscapes. Wordscapes is a word game that combines the best of crossword and word search games. You have to swipe letters to form words that fit into a crossword grid. You can also use hints, shuffles, or coins to help you solve the puzzles. Wordscapes has over 10,000 levels with beautiful backgrounds and themes. You can also play daily puzzles and challenges to earn rewards and bonuses. Wordscapes is a relaxing and addictive word game that will keep you entertained for hours.</p>
- <h3>Wordalot: A unique and challenging word game with pictures</h3>
-<p>If you love picture clues, you will love Wordalot. Wordalot is a word game that challenges you to find words hidden in pictures. You have to look at the picture carefully and use your imagination and logic to figure out the words. You can also use hints or coins to reveal letters or words. Wordalot has over 1,000 levels with stunning graphics and animations. You can also play with friends and compare your scores and progress. Wordalot is a unique and challenging word game that will test your visual and verbal skills.</p>
- <h3>Pictoword: A fun and creative word game that combines two images</h3>
-<p>If you love word association, you will love Pictoword. Pictoword is a word game that asks you to guess a word or phrase based on two images. You have to look at the images and think of how they can be combined to form a new word or phrase. For example, if you see a picture of a sand and a witch, you can guess the word "sandwich". You can also use hints or coins to reveal letters or words. Pictoword has over 300 levels with different categories, such as celebrities, movies, brands, etc. You can also play with friends and family in multiplayer mode or create your own puzzles. Pictoword is a fun and creative word game that will make you think outside the box.</p>
- <h3>Bonza Word Puzzle: A clever and original word game that mixes crossword and jigsaw</h3>
-<p>If you love jigsaw puzzles, you will love Bonza Word Puzzle. Bonza Word Puzzle is a word game that blends crossword and jigsaw puzzles. You have to arrange fragments of words to form a complete crossword puzzle. You can also rotate, move, or zoom in on the fragments to fit them better. Bonza Word Puzzle has hundreds of levels with different themes, such as animals, music, food, etc. You can also play daily puzzles and challenges to earn coins and badges. You can also create your own puzzles and share them with other players. Bonza Word Puzzle is a clever and original word game that will challenge your brain and vocabulary.</p>
- <h3>Words with Friends: A popular and social word game that lets you play with friends online</h3>
-<p>If you love Scrabble, you will love Words with Friends. Words with Friends is a word game that lets you play with friends online. You have to form words on a board using letter tiles. You can also use special tiles, such as blanks, double letters, triple words, etc., to score more points. Words with Friends has millions of players around the world that you can chat and compete with. You can also play solo games against the computer or join tournaments and events. Words with Friends is a popular and social word game that will connect you with other word lovers.</p>
- <h2>Conclusion</h2>
-<h3>Word games are a great way to have fun and learn new words on your mobile device</h3>
-<p>Word games are one of the most popular and enjoyable genres of games that you can play on your mobile device. They are fun and challenging puzzles that involve words and letters. They can also help you improve your vocabulary, spelling, memory, focus, and brain health. They can also help you relax, unwind, and learn something new every day.</p>
- <h3>You can download word games easily from the app store or the web</h3>
-<p>Downloading word games for your mobile device is easy and convenient. You can download word games from the app store or the web. You just need to choose a word game that suits your preference and skill level, check its compatibility, ratings, reviews, and permissions, and follow the instructions to install and launch it. You can also update, delete, or reinstall the app anytime you want.</p>
- <h3>You can choose from a variety of word games that suit your taste and skill level</h3>
-<p>There are hundreds of word games available on the app store or the web, so you have plenty of options to choose from. You can choose from different kinds of word games, such as crossword puzzles, anagrams, word searches, picture clues, etc. You can also choose from different levels of difficulty, themes, genres, and styles. You can also play with friends or strangers online or offline. You can also create your own puzzles or play puzzles created by other players.</p>
-<p>Some of the best word games for Android and iOS are Wordscapes, Wordalot, Pictoword, Bonza Word Puzzle, and Words with Friends. These word games are fun, addictive, unique, challenging, and social. They will keep you entertained for hours and make you fall in love with words.</p>
- <h2>FAQs</h2>
-<h4>Q: How do I download word games for free?</h4>
-<p>A: Most word games are free to download from the app store or the web. However, some word games may have in-app purchases or ads that require you to pay money to access certain features or functions. You can also look for word games that offer free trials or discounts.</p>
- <h4>Q: How do I play word games offline?</h4>
-<p>A: Some word games can be played offline without an internet connection. However, some word games may require an internet connection to access certain features or functions, such as multiplayer modes, daily puzzles, updates, etc. You can check the description or settings of the app to see if it supports offline mode.</p>
- <h4>Q: How do I improve my word game skills?</h4>
-<p>A: The best way to improve your word game skills is to practice regularly and learn from your mistakes. You can also use hints or coins to help you solve difficult puzzles. You can also read books, magazines, newspapers, or websites to expand your vocabulary and knowledge. You can also play with friends or other players online to learn from their strategies and tips.</p>
- <h4>Q: How do I find new word games to play?</h4>
-<p>A: The easiest way to find new word games to play is to browse through the app store or the web. You can also search for keywords or categories that interest you. You can also read reviews or recommendations from other users or experts. You can also join online communities or forums that discuss word games.</p>
- <h4>Q: How do I create my own word game puzzles?</h4>
-<p>A: Some word games allow you to create your own puzzles and share them with other players. You can use your creativity and imagination to come up with interesting words and clues. You can also use online tools or generators to help you create puzzles. You can also edit or customize existing puzzles to make them more challenging or fun.</p> 197e85843d<br />
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diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/inference/tts/GenerSpeech.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/inference/tts/GenerSpeech.py
deleted file mode 100644
index 50527337e218248c763cf7504bc13814cca0696c..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/inference/tts/GenerSpeech.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import torch
-import os
-import importlib
-from inference.tts.base_tts_infer import BaseTTSInfer
-from utils.ckpt_utils import load_ckpt, get_last_checkpoint
-from modules.GenerSpeech.model.generspeech import GenerSpeech
-from data_gen.tts.emotion import inference as EmotionEncoder
-from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance
-from data_gen.tts.emotion.inference import preprocess_wav
-from data_gen.tts.data_gen_utils import is_sil_phoneme
-from resemblyzer import VoiceEncoder
-from utils import audio
-class GenerSpeechInfer(BaseTTSInfer):
-    def build_model(self):
-        model = GenerSpeech(self.ph_encoder)
-        model.eval()
-        load_ckpt(model, self.hparams['work_dir'], 'model')
-        return model
-
-    def preprocess_input(self, inp):
-        """
-        :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)}
-        :return:
-        """
-        # processed text
-        preprocessor, preprocess_args = self.preprocessor, self.preprocess_args
-        text_raw = inp['text']
-        item_name = inp.get('item_name', '<ITEM_NAME>')
-        ph, txt, word, ph2word, ph_gb_word = preprocessor.txt_to_ph(preprocessor.txt_processor, text_raw, preprocess_args)
-        ph_token = self.ph_encoder.encode(ph)
-
-        # processed ref audio
-        ref_audio = inp['ref_audio']
-        processed_ref_audio = 'example/temp.wav'
-        voice_encoder = VoiceEncoder().cuda()
-        encoder = [self.ph_encoder, self.word_encoder]
-        EmotionEncoder.load_model(self.hparams['emotion_encoder_path'])
-        binarizer_cls = self.hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer')
-        pkg = ".".join(binarizer_cls.split(".")[:-1])
-        cls_name = binarizer_cls.split(".")[-1]
-        binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
-
-        ref_audio_raw, ref_text_raw = self.asr(ref_audio)  # prepare text
-        ph_ref, txt_ref, word_ref, ph2word_ref, ph_gb_word_ref = preprocessor.txt_to_ph(preprocessor.txt_processor, ref_text_raw, preprocess_args)
-        ph_gb_word_nosil = ["_".join([p for p in w.split("_") if not is_sil_phoneme(p)]) for w in ph_gb_word_ref.split(" ") if not is_sil_phoneme(w)]
-        phs_for_align = ['SIL'] + ph_gb_word_nosil + ['SIL']
-        phs_for_align = " ".join(phs_for_align)
-
-        # prepare files for alignment
-        os.system('rm -r example/; mkdir example/')
-        audio.save_wav(ref_audio_raw, processed_ref_audio, self.hparams['audio_sample_rate'])
-        with open(f'example/temp.lab', 'w') as f_txt:
-            f_txt.write(phs_for_align)
-        os.system(f'mfa align example/ {self.hparams["binary_data_dir"]}/mfa_dict.txt {self.hparams["binary_data_dir"]}/mfa_model.zip example/textgrid/  --clean')
-        item2tgfn = 'example/textgrid/temp.TextGrid'  # prepare textgrid alignment
-
-        item = binarizer_cls.process_item(item_name, ph_ref, txt_ref, item2tgfn, processed_ref_audio, 0, 0, encoder, self.hparams['binarization_args'])
-        item['emo_embed'] = Embed_utterance(preprocess_wav(item['wav_fn']))
-        item['spk_embed'] = voice_encoder.embed_utterance(item['wav'])
-
-        item.update({
-            'ref_ph': item['ph'],
-            'ph': ph,
-            'ph_token': ph_token,
-            'text': txt
-        })
-        return item
-
-    def input_to_batch(self, item):
-        item_names = [item['item_name']]
-        text = [item['text']]
-        ph = [item['ph']]
-
-        txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device)
-        txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device)
-        mels = torch.FloatTensor(item['mel'])[None, :].to(self.device)
-        f0 = torch.FloatTensor(item['f0'])[None, :].to(self.device)
-        # uv = torch.FloatTensor(item['uv']).to(self.device)
-        mel2ph = torch.LongTensor(item['mel2ph'])[None, :].to(self.device)
-        spk_embed = torch.FloatTensor(item['spk_embed'])[None, :].to(self.device)
-        emo_embed = torch.FloatTensor(item['emo_embed'])[None, :].to(self.device)
-
-        ph2word = torch.LongTensor(item['ph2word'])[None, :].to(self.device)
-        mel2word = torch.LongTensor(item['mel2word'])[None, :].to(self.device)
-        word_tokens = torch.LongTensor(item['word_tokens'])[None, :].to(self.device)
-
-        batch = {
-            'item_name': item_names,
-            'text': text,
-            'ph': ph,
-            'mels': mels,
-            'f0': f0,
-            'txt_tokens': txt_tokens,
-            'txt_lengths': txt_lengths,
-            'spk_embed': spk_embed,
-            'emo_embed': emo_embed,
-            'mel2ph': mel2ph,
-            'ph2word': ph2word,
-            'mel2word': mel2word,
-            'word_tokens': word_tokens,
-        }
-        return batch
-
-    def forward_model(self, inp):
-        sample = self.input_to_batch(inp)
-        txt_tokens = sample['txt_tokens']  # [B, T_t]
-        with torch.no_grad():
-            output = self.model(txt_tokens, ref_mel2ph=sample['mel2ph'], ref_mel2word=sample['mel2word'], ref_mels=sample['mels'],
-                                spk_embed=sample['spk_embed'], emo_embed=sample['emo_embed'], global_steps=300000, infer=True)
-            mel_out = output['mel_out']
-            wav_out = self.run_vocoder(mel_out)
-        wav_out = wav_out.squeeze().cpu().numpy()
-        return wav_out
-
-
-
-
-if __name__ == '__main__':
-    inp = {
-        'text': 'here we go',
-        'ref_audio': 'assets/0011_001570.wav'
-    }
-    GenerSpeechInfer.example_run(inp)
diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual.py
deleted file mode 100644
index 3e3018da79c5c24d85af1687f6f0875530dcc7c6..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import sys
-
-sys.path.insert(0, '.')  # nopep8
-from ldm.modules.losses_audio.vqperceptual import *
-
-
-class LPAPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_loss="hinge"):
-
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        self.kl_weight = kl_weight
-        self.pixel_weight = pixelloss_weight
-        self.perceptual_loss = LPAPS().eval()# LPIPS用于日常图像，而LPAPS用于梅尔谱图
-        self.perceptual_weight = perceptual_weight
-        # output log variance
-        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"LPAPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", weights=None):
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            # print(f"p_loss {p_loss}")
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
-        weighted_nll_loss = nll_loss
-        if weights is not None:
-            weighted_nll_loss = weights*nll_loss
-        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
-        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        kl_loss = posteriors.kl()
-        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/logvar".format(split): self.logvar.detach(),
-                   "{}/kl_loss".format(split): kl_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
-
-
diff --git a/spaces/AIGText/GlyphControl/ldm/modules/midas/api.py b/spaces/AIGText/GlyphControl/ldm/modules/midas/api.py
deleted file mode 100644
index b58ebbffd942a2fc22264f0ab47e400c26b9f41c..0000000000000000000000000000000000000000
--- a/spaces/AIGText/GlyphControl/ldm/modules/midas/api.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# based on https://github.com/isl-org/MiDaS
-
-import cv2
-import torch
-import torch.nn as nn
-from torchvision.transforms import Compose
-
-from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
-from ldm.modules.midas.midas.midas_net import MidasNet
-from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
-from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
-
-
-ISL_PATHS = {
-    "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
-    "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
-    "midas_v21": "",
-    "midas_v21_small": "",
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def load_midas_transform(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load transform only
-    if model_type == "dpt_large":  # DPT-Large
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    elif model_type == "midas_v21_small":
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    else:
-        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return transform
-
-
-def load_model(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load network
-    model_path = ISL_PATHS[model_type]
-    if model_type == "dpt_large":  # DPT-Large
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitb_rn50_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        model = MidasNet(model_path, non_negative=True)
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    elif model_type == "midas_v21_small":
-        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
-                               non_negative=True, blocks={'expand': True})
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    else:
-        print(f"model_type '{model_type}' not implemented, use: --model_type large")
-        assert False
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return model.eval(), transform
-
-
-class MiDaSInference(nn.Module):
-    MODEL_TYPES_TORCH_HUB = [
-        "DPT_Large",
-        "DPT_Hybrid",
-        "MiDaS_small"
-    ]
-    MODEL_TYPES_ISL = [
-        "dpt_large",
-        "dpt_hybrid",
-        "midas_v21",
-        "midas_v21_small",
-    ]
-
-    def __init__(self, model_type):
-        super().__init__()
-        assert (model_type in self.MODEL_TYPES_ISL)
-        model, _ = load_model(model_type)
-        self.model = model
-        self.model.train = disabled_train
-
-    def forward(self, x):
-        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
-        # NOTE: we expect that the correct transform has been called during dataloading.
-        with torch.no_grad():
-            prediction = self.model(x)
-            prediction = torch.nn.functional.interpolate(
-                prediction.unsqueeze(1),
-                size=x.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
-        return prediction
-
diff --git a/spaces/AISuperheroes/01ST-CSV-Dataset-Analyzer/app.py b/spaces/AISuperheroes/01ST-CSV-Dataset-Analyzer/app.py
deleted file mode 100644
index 7fb051bb590ac3a937ee7ea3a2938d71820c23f1..0000000000000000000000000000000000000000
--- a/spaces/AISuperheroes/01ST-CSV-Dataset-Analyzer/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import streamlit as st
-import pandas as pd
-import traceback
-import sys
-
-from st_aggrid import AgGrid
-from st_aggrid.grid_options_builder import GridOptionsBuilder
-from st_aggrid.shared import JsCode
-from download import download_button
-from st_aggrid import GridUpdateMode, DataReturnMode
-
-# Page config is set once with icon title and display style.  Wide mode since we want screen real estate for wide CSV files
-st.set_page_config(page_icon="📝", page_title="📝CSV Data Analyzer📊", layout="wide")
-
-# Style
-def _max_width_():
-    max_width_str = f"max-width: 1800px;"
-    st.markdown(
-        f"""
-    <style>
-    .reportview-container .main .block-container{{
-        {max_width_str}
-    }}
-    </style>    
-    """,
-        unsafe_allow_html=True,
-    )
-    
-# Title Bar with Images and Icons
-col1, col2, col3 = st.columns([1,6,1])
-with col1:
-    st.image("https://cdnb.artstation.com/p/assets/images/images/054/910/875/large/aaron-wacker-cyberpunk-computer-brain-design.jpg?1665656558",width=128,)
-with col2:
-    st.title("📝 CSV Data Analyzer 📊")
-with col3:
-    st.image("https://cdna.artstation.com/p/assets/images/images/054/910/878/large/aaron-wacker-cyberpunk-computer-devices-iot.jpg?1665656564",width=128,)
-    
-# Upload
-c29, c30, c31 = st.columns([1, 6, 1])
-with c30:
-    uploaded_file = st.file_uploader("", key="1", help="To activate 'wide mode', go to the menu > Settings > turn on 'wide mode'",)
-    if uploaded_file is not None:
-        file_container = st.expander("Check your uploaded .csv")
-        #try:
-        shows = pd.read_csv(uploaded_file)
-        #except:
-        #    print(sys.exc_info()[2])
-            
-        uploaded_file.seek(0)
-        file_container.write(shows)
-    else:
-        st.info(f"""⬆️Upload a 📝.CSV file.  Examples: [Chatbot](https://huggingface.co/datasets/awacke1/Carddata.csv)  [Mindfulness](https://huggingface.co/datasets/awacke1/MindfulStory.csv)  [Wikipedia](https://huggingface.co/datasets/awacke1/WikipediaSearch)""")
-        st.stop()
-
-# DisplayGrid
-gb = GridOptionsBuilder.from_dataframe(shows)
-gb.configure_default_column(enablePivot=True, enableValue=True, enableRowGroup=True)
-gb.configure_selection(selection_mode="multiple", use_checkbox=True)
-gb.configure_side_bar()
-gridOptions = gb.build()
-st.success(f"""💡 Tip! Hold shift key when selecting rows to select multiple rows at once.""")
-response = AgGrid(
-    shows,
-    gridOptions=gridOptions,
-    enable_enterprise_modules=True,
-    update_mode=GridUpdateMode.MODEL_CHANGED,
-    data_return_mode=DataReturnMode.FILTERED_AND_SORTED,
-    fit_columns_on_grid_load=False,
-)
-
-# Filters
-df = pd.DataFrame(response["selected_rows"])
-st.subheader("Filtered data will appear below 📊 ")
-st.text("")
-st.table(df)
-st.text("")
-
-# Download
-c29, c30, c31 = st.columns([1, 1, 2])
-with c29:
-    CSVButton = download_button(df,"Dataset.csv","Download CSV file",)
-with c30:
-    CSVButton = download_button(df,"Dataset.txt","Download TXT file",)
\ No newline at end of file
diff --git a/spaces/AIZero2HeroBootcamp/Memory/README.md b/spaces/AIZero2HeroBootcamp/Memory/README.md
deleted file mode 100644
index 7c27175f50e0bf2114a1296a27d87371bd76e7f5..0000000000000000000000000000000000000000
--- a/spaces/AIZero2HeroBootcamp/Memory/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Memory
-emoji: 📚
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Acapellas/vocalinstrumentalremover/app.py b/spaces/Acapellas/vocalinstrumentalremover/app.py
deleted file mode 100644
index 84f0f0b817b0b756f873ae82f56ac5e8e9f18a4d..0000000000000000000000000000000000000000
--- a/spaces/Acapellas/vocalinstrumentalremover/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-import gradio as gr
-from scipy.io.wavfile import write
-
-
-def inference(audio):
-  os.makedirs("out", exist_ok=True)
-  write('test.wav', audio[0], audio[1])
-  os.system("python3 -m demucs.separate -n htdemucs --two-stems=vocals -d cpu test.wav -o out")
-  return "./out/htdemucs/test/vocals.wav","./out/htdemucs/test/no_vocals.wav"
-    
-title = ""
-description = ""
-article = ""
-
-examples=[['test.mp3']]
-gr.Interface(
-    inference, 
-    gr.Audio(type="numpy", label="Input"),
-    [gr.Audio(type="filepath", label="Vocals"),gr.Audio(type="filepath", label="No Vocals / Instrumental")],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples
-    ).launch(enable_queue=True,debug=True)
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/TapCell.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/TapCell.js
deleted file mode 100644
index dd9f63fb44c3350e5e7af506a9677d3b447a6ea8..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/TapCell.js
+++ /dev/null
@@ -1,20 +0,0 @@
-import Tap from '../../tap/Tap.js';
-import EmitCellEvent from './EmitCellEvent.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-var TapCell = function (table, tableConfig) {
-    var tapConfig = GetValue(tableConfig, 'tap', undefined);
-    if (tapConfig === false) {
-        return;
-    }
-
-    table._tap = new Tap(table, tapConfig);
-    table._tap
-        .on('tap', function (tap, gameObject, lastPointer) {
-            var eventName = `cell.${tap.tapsCount}tap`
-            EmitCellEvent(this.eventEmitter, eventName, tap.gameObject, tap.worldX, tap.worldY, lastPointer);
-        }, this)
-};
-
-export default TapCell;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/Menu.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/Menu.d.ts
deleted file mode 100644
index 5a662544ac3e9b47c29e781eff9d495ebb725345..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/Menu.d.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-// import * as Phaser from 'phaser';
-import Buttons from '../buttons/Buttons';
-
-
-export default Menu;
-
-declare namespace Menu {
-
-    type EaseConfigTypes = number |
-    {
-        duration?: number,
-        orientation?: 0 | 1 | 'x' | 'y' | 'h' | 'v',
-        ease?: string
-    }
-
-    type ExpandEventTypes = 'button.click' | 'button.over';
-
-    type SubMenuSideTypes = 0 | 1 | 2 | 3 | 'right' | 'down' | 'left' | 'up';
-
-    interface IConfig extends Buttons.IConfig {
-        items: any[],
-
-        createBackgroundCallback?: (items: any[]) => Phaser.GameObjects.GameObject,
-
-        createBackgroundCallbackScope?: object,
-
-        createButtonCallback?: (item: any, index: number, items: any[]) => Phaser.GameObjects.GameObject,
-
-        createButtonCallbackScope?: object,
-
-        easeIn?: EaseConfigTypes,
-        easeOut?: EaseConfigTypes,
-
-        expandEvent?: ExpandEventTypes,
-
-        subMenuSide?: SubMenuSideTypes,
-    }
-}
-
-declare class Menu extends Buttons {
-    constructor(
-        scene: Phaser.Scene,
-        config?: Menu.IConfig
-    );
-
-    collapse(): this;
-
-    collapseSubMenu(): this;
-}
\ No newline at end of file
diff --git "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py" "b/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py"
deleted file mode 100644
index e57f80f1d45bd3ec23837253848f7b32a5ccd751..0000000000000000000000000000000000000000
--- "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py"
+++ /dev/null
@@ -1,138 +0,0 @@
-import threading
-from request_llm.bridge_all import predict_no_ui_long_connection
-from toolbox import update_ui
-from toolbox import CatchException, write_results_to_file, report_execption
-from .crazy_utils import breakdown_txt_to_satisfy_token_limit
-
-def extract_code_block_carefully(txt):
-    splitted = txt.split('```')
-    n_code_block_seg = len(splitted) - 1
-    if n_code_block_seg <= 1: return txt
-    # 剩下的情况都开头除去 ``` 结尾除去一次 ```
-    txt_out = '```'.join(splitted[1:-1])
-    return txt_out
-
-
-
-def break_txt_into_half_at_some_linebreak(txt):
-    lines = txt.split('\n')
-    n_lines = len(lines)
-    pre = lines[:(n_lines//2)]
-    post = lines[(n_lines//2):]
-    return "\n".join(pre), "\n".join(post)
-
-
-@CatchException
-def 全项目切换英文(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
-    # 第1步：清空历史，以免输入溢出
-    history = []
-
-    # 第2步：尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 第3步：集合文件
-    import time, glob, os, shutil, re
-    os.makedirs('gpt_log/generated_english_version', exist_ok=True)
-    os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    # file_manifest = ['./toolbox.py']
-    i_say_show_user_buffer = []
-
-    # 第4步：随便显示点什么防止卡顿的感觉
-    for index, fp in enumerate(file_manifest):
-        # if 'test_project' in fp: continue
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文，只输出转化后的英文代码，请用代码块输出代码: {os.path.abspath(fp)}'
-        i_say_show_user_buffer.append(i_say_show_user)
-        chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作，中间过程不予显示."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-    # 第5步：Token限制下的截断与处理
-    MAX_TOKEN = 3000
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-
-
-    # 第6步：任务函数
-    mutable_return = [None for _ in file_manifest]
-    observe_window = [[""] for _ in file_manifest]
-    def thread_worker(fp,index):
-        if index > 10: 
-            time.sleep(60)
-            print('Openai 限制免费用户每分钟20次请求，降低请求频率中。')
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        i_say_template = lambda fp, file_content: f'接下来请将以下代码中包含的所有中文转化为英文，只输出代码，文件名是{fp}，文件代码是 ```{file_content}```'
-        try:
-            gpt_say = ""
-            # 分解代码文件
-            file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN)
-            for file_content_partial in file_content_breakdown:
-                i_say = i_say_template(fp, file_content_partial)
-                # # ** gpt request **
-                gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index])
-                gpt_say_partial = extract_code_block_carefully(gpt_say_partial)
-                gpt_say += gpt_say_partial
-            mutable_return[index] = gpt_say
-        except ConnectionAbortedError as token_exceed_err:
-            print('至少一个线程任务Token溢出而失败', e)
-        except Exception as e:
-            print('至少一个线程任务意外失败', e)
-
-    # 第7步：所有线程同时开始执行任务函数
-    handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]
-    for h in handles:
-        h.daemon = True
-        h.start()
-    chatbot.append(('开始了吗？', f'多线程操作已经开始'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 第8步：循环轮询各个线程是否执行完毕
-    cnt = 0
-    while True:
-        cnt += 1
-        time.sleep(0.2)
-        th_alive = [h.is_alive() for h in handles]
-        if not any(th_alive): break
-        # 更好的UI视觉效果
-        observe_win = []
-        for thread_index, alive in enumerate(th_alive): 
-            observe_win.append("[ ..."+observe_window[thread_index][0][-60:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"... ]")
-        stat = [f'执行中: {obs}\n\n' if alive else '已完成\n\n' for alive, obs in zip(th_alive, observe_win)]
-        stat_str = ''.join(stat)
-        chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1)))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 第9步：把结果写入文件
-    for index, h in enumerate(handles):
-        h.join() # 这里其实不需要join了，肯定已经都结束了
-        fp = file_manifest[index]
-        gpt_say = mutable_return[index]
-        i_say_show_user = i_say_show_user_buffer[index]
-
-        where_to_relocate = f'gpt_log/generated_english_version/{fp}'
-        if gpt_say is not None:
-            with open(where_to_relocate, 'w+', encoding='utf-8') as f:  
-                f.write(gpt_say)
-        else:  # 失败
-            shutil.copyfile(file_manifest[index], where_to_relocate)
-        chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\n\n存入{os.path.abspath(where_to_relocate)}'))
-        history.append(i_say_show_user); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        time.sleep(1)
-
-    # 第10步：备份一个文件
-    res = write_results_to_file(history)
-    chatbot.append(("生成一份任务执行报告", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/encoders/model_irse.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/encoders/model_irse.py
deleted file mode 100644
index 976ce2c61104efdc6b0015d895830346dd01bc10..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/encoders/model_irse.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
-from encoder4editing.models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
-
-"""
-Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Backbone(Module):
-    def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
-        super(Backbone, self).__init__()
-        assert input_size in [112, 224], "input_size should be 112 or 224"
-        assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
-        assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        if input_size == 112:
-            self.output_layer = Sequential(BatchNorm2d(512),
-                                           Dropout(drop_ratio),
-                                           Flatten(),
-                                           Linear(512 * 7 * 7, 512),
-                                           BatchNorm1d(512, affine=affine))
-        else:
-            self.output_layer = Sequential(BatchNorm2d(512),
-                                           Dropout(drop_ratio),
-                                           Flatten(),
-                                           Linear(512 * 14 * 14, 512),
-                                           BatchNorm1d(512, affine=affine))
-
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_layer(x)
-        return l2_norm(x)
-
-
-def IR_50(input_size):
-    """Constructs a ir-50 model."""
-    model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_101(input_size):
-    """Constructs a ir-101 model."""
-    model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_152(input_size):
-    """Constructs a ir-152 model."""
-    model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_50(input_size):
-    """Constructs a ir_se-50 model."""
-    model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_101(input_size):
-    """Constructs a ir_se-101 model."""
-    model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_152(input_size):
-    """Constructs a ir_se-152 model."""
-    model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_configs/hyperparameters.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_configs/hyperparameters.py
deleted file mode 100644
index ca3a22302a7c5b31a6aa15492a860aa367776e4b..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_configs/hyperparameters.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Architechture
-lpips_type = 'alex'
-first_inv_type = 'w+'  # 'w+'
-optim_type = 'adam'
-
-# Locality regularization
-latent_ball_num_of_samples = 1
-locality_regularization_interval = 1
-use_locality_regularization = False
-regulizer_l2_lambda = 0.1
-regulizer_lpips_lambda = 0.1
-regulizer_alpha = 30
-
-# Loss
-pt_l2_lambda = 1
-pt_lpips_lambda = 1
-
-# Steps
-LPIPS_value_threshold = 0.04
-max_pti_steps = 350
-first_inv_steps = 450
-max_images_to_invert = 30
-
-# Optimization
-pti_learning_rate = 5e-4
-first_inv_lr = 8e-3
-train_batch_size = 1
-use_last_w_pivots = False
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
deleted file mode 100644
index e1688426e6365b2194c90dce7b2c1e00945fe04a..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
+++ /dev/null
@@ -1,473 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import warnings
-from functools import partial
-from typing import Dict, List, Optional, Union
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.core.frozen_dict import FrozenDict
-from flax.jax_utils import unreplicate
-from flax.training.common_utils import shard
-from packaging import version
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
-
-from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
-from ...schedulers import (
-    FlaxDDIMScheduler,
-    FlaxDPMSolverMultistepScheduler,
-    FlaxLMSDiscreteScheduler,
-    FlaxPNDMScheduler,
-)
-from ...utils import deprecate, logging, replace_example_docstring
-from ..pipeline_flax_utils import FlaxDiffusionPipeline
-from . import FlaxStableDiffusionPipelineOutput
-from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-# Set to True to use python for loop instead of jax.fori_loop for easier debugging
-DEBUG = False
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import jax
-        >>> import numpy as np
-        >>> from flax.jax_utils import replicate
-        >>> from flax.training.common_utils import shard
-
-        >>> from diffusers import FlaxStableDiffusionPipeline
-
-        >>> pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16
-        ... )
-
-        >>> prompt = "a photo of an astronaut riding a horse on mars"
-
-        >>> prng_seed = jax.random.PRNGKey(0)
-        >>> num_inference_steps = 50
-
-        >>> num_samples = jax.device_count()
-        >>> prompt = num_samples * [prompt]
-        >>> prompt_ids = pipeline.prepare_inputs(prompt)
-        # shard inputs and rng
-
-        >>> params = replicate(params)
-        >>> prng_seed = jax.random.split(prng_seed, jax.device_count())
-        >>> prompt_ids = shard(prompt_ids)
-
-        >>> images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
-        >>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-        ```
-"""
-
-
-class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
-    r"""
-    Flax-based pipeline for text-to-image generation using Stable Diffusion.
-
-    This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        vae ([`FlaxAutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        text_encoder ([`~transformers.FlaxCLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.CLIPTokenizer`]):
-            A `CLIPTokenizer` to tokenize text.
-        unet ([`FlaxUNet2DConditionModel`]):
-            A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
-            [`FlaxDPMSolverMultistepScheduler`].
-        safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: FlaxAutoencoderKL,
-        text_encoder: FlaxCLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: FlaxUNet2DConditionModel,
-        scheduler: Union[
-            FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
-        ],
-        safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        dtype: jnp.dtype = jnp.float32,
-    ):
-        super().__init__()
-        self.dtype = dtype
-
-        if safety_checker is None:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
-    def prepare_inputs(self, prompt: Union[str, List[str]]):
-        if not isinstance(prompt, (str, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="np",
-        )
-        return text_input.input_ids
-
-    def _get_has_nsfw_concepts(self, features, params):
-        has_nsfw_concepts = self.safety_checker(features, params)
-        return has_nsfw_concepts
-
-    def _run_safety_checker(self, images, safety_model_params, jit=False):
-        # safety_model_params should already be replicated when jit is True
-        pil_images = [Image.fromarray(image) for image in images]
-        features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
-
-        if jit:
-            features = shard(features)
-            has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
-            has_nsfw_concepts = unshard(has_nsfw_concepts)
-            safety_model_params = unreplicate(safety_model_params)
-        else:
-            has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
-
-        images_was_copied = False
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                if not images_was_copied:
-                    images_was_copied = True
-                    images = images.copy()
-
-                images[idx] = np.zeros(images[idx].shape, dtype=np.uint8)  # black image
-
-            if any(has_nsfw_concepts):
-                warnings.warn(
-                    "Potential NSFW content was detected in one or more images. A black image will be returned"
-                    " instead. Try again with a different prompt and/or seed."
-                )
-
-        return images, has_nsfw_concepts
-
-    def _generate(
-        self,
-        prompt_ids: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        num_inference_steps: int,
-        height: int,
-        width: int,
-        guidance_scale: float,
-        latents: Optional[jnp.array] = None,
-        neg_prompt_ids: Optional[jnp.array] = None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # get prompt text embeddings
-        prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
-
-        # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
-        # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
-        batch_size = prompt_ids.shape[0]
-
-        max_length = prompt_ids.shape[-1]
-
-        if neg_prompt_ids is None:
-            uncond_input = self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
-            ).input_ids
-        else:
-            uncond_input = neg_prompt_ids
-        negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
-        context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
-
-        # Ensure model output will be `float32` before going into the scheduler
-        guidance_scale = jnp.array([guidance_scale], dtype=jnp.float32)
-
-        latents_shape = (
-            batch_size,
-            self.unet.config.in_channels,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if latents is None:
-            latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-
-        def loop_body(step, args):
-            latents, scheduler_state = args
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            latents_input = jnp.concatenate([latents] * 2)
-
-            t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
-            timestep = jnp.broadcast_to(t, latents_input.shape[0])
-
-            latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet.apply(
-                {"params": params["unet"]},
-                jnp.array(latents_input),
-                jnp.array(timestep, dtype=jnp.int32),
-                encoder_hidden_states=context,
-            ).sample
-            # perform guidance
-            noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
-            noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
-            return latents, scheduler_state
-
-        scheduler_state = self.scheduler.set_timesteps(
-            params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape
-        )
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * params["scheduler"].init_noise_sigma
-
-        if DEBUG:
-            # run with python for loop
-            for i in range(num_inference_steps):
-                latents, scheduler_state = loop_body(i, (latents, scheduler_state))
-        else:
-            latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))
-
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
-
-        image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
-        return image
-
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt_ids: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        num_inference_steps: int = 50,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        guidance_scale: Union[float, jnp.array] = 7.5,
-        latents: jnp.array = None,
-        neg_prompt_ids: jnp.array = None,
-        return_dict: bool = True,
-        jit: bool = False,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide image generation.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            latents (`jnp.array`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                array is generated by sampling using the supplied random `generator`.
-            jit (`bool`, defaults to `False`):
-                Whether to run `pmap` versions of the generation and safety scoring functions.
-
-                    <Tip warning={true}>
-
-                    This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
-                    future release.
-
-                    </Tip>
-
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
-                a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
-                returned, otherwise a `tuple` is returned where the first element is a list with the generated images
-                and the second element is a list of `bool`s indicating whether the corresponding generated image
-                contains "not-safe-for-work" (nsfw) content.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        if isinstance(guidance_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                guidance_scale = guidance_scale[:, None]
-
-        if jit:
-            images = _p_generate(
-                self,
-                prompt_ids,
-                params,
-                prng_seed,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                latents,
-                neg_prompt_ids,
-            )
-        else:
-            images = self._generate(
-                prompt_ids,
-                params,
-                prng_seed,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                latents,
-                neg_prompt_ids,
-            )
-
-        if self.safety_checker is not None:
-            safety_params = params["safety_checker"]
-            images_uint8_casted = (images * 255).round().astype("uint8")
-            num_devices, batch_size = images.shape[:2]
-
-            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
-            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
-            images = np.asarray(images)
-
-            # block images
-            if any(has_nsfw_concept):
-                for i, is_nsfw in enumerate(has_nsfw_concept):
-                    if is_nsfw:
-                        images[i] = np.asarray(images_uint8_casted[i])
-
-            images = images.reshape(num_devices, batch_size, height, width, 3)
-        else:
-            images = np.asarray(images)
-            has_nsfw_concept = False
-
-        if not return_dict:
-            return (images, has_nsfw_concept)
-
-        return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
-
-
-# Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation.
-# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
-@partial(
-    jax.pmap,
-    in_axes=(None, 0, 0, 0, None, None, None, 0, 0, 0),
-    static_broadcasted_argnums=(0, 4, 5, 6),
-)
-def _p_generate(
-    pipe,
-    prompt_ids,
-    params,
-    prng_seed,
-    num_inference_steps,
-    height,
-    width,
-    guidance_scale,
-    latents,
-    neg_prompt_ids,
-):
-    return pipe._generate(
-        prompt_ids,
-        params,
-        prng_seed,
-        num_inference_steps,
-        height,
-        width,
-        guidance_scale,
-        latents,
-        neg_prompt_ids,
-    )
-
-
-@partial(jax.pmap, static_broadcasted_argnums=(0,))
-def _p_get_has_nsfw_concepts(pipe, features, params):
-    return pipe._get_has_nsfw_concepts(features, params)
-
-
-def unshard(x: jnp.ndarray):
-    # einops.rearrange(x, 'd b ... -> (d b) ...')
-    num_devices, batch_size = x.shape[:2]
-    rest = x.shape[2:]
-    return x.reshape(num_devices * batch_size, *rest)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/unidiffuser/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/unidiffuser/__init__.py
deleted file mode 100644
index a774e3274030153d20618024b8c2bc6385ef367a..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/unidiffuser/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    is_torch_available,
-    is_transformers_available,
-    is_transformers_version,
-)
-
-
-try:
-    if not (is_transformers_available() and is_torch_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import (
-        ImageTextPipelineOutput,
-        UniDiffuserPipeline,
-    )
-else:
-    from .modeling_text_decoder import UniDiffuserTextDecoder
-    from .modeling_uvit import UniDiffuserModel, UTransformer2DModel
-    from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/atss/README.md b/spaces/Andy1621/uniformer_image_detection/configs/atss/README.md
deleted file mode 100644
index 4ba915002576080f1fc1b2e007420d88aeb94187..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/atss/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@article{zhang2019bridging,
-  title   =  {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection},
-  author  =  {Zhang, Shifeng and Chi, Cheng and Yao, Yongqiang and Lei, Zhen and Li, Stan Z.},
-  journal =  {arXiv preprint arXiv:1912.02424},
-  year    =  {2019}
-}
-```
-
-## Results and Models
-
-| Backbone  | Style   | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
-| R-50      | pytorch | 1x      | 3.7      | 19.7           |  39.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) |
-| R-101     | pytorch | 1x      | 5.6      | 12.3           |  41.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) |
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/post_processing/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/post_processing/__init__.py
deleted file mode 100644
index 880b3f06609b050aae163b2e38088c1ee4aa0998..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/post_processing/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .bbox_nms import fast_nms, multiclass_nms
-from .merge_augs import (merge_aug_bboxes, merge_aug_masks,
-                         merge_aug_proposals, merge_aug_scores)
-
-__all__ = [
-    'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes',
-    'merge_aug_scores', 'merge_aug_masks', 'fast_nms'
-]
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/dynamic_roi_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/dynamic_roi_head.py
deleted file mode 100644
index 89427a931f45f5a920c0e66fd88058bf9fa05f5c..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/dynamic_roi_head.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import numpy as np
-import torch
-
-from mmdet.core import bbox2roi
-from mmdet.models.losses import SmoothL1Loss
-from ..builder import HEADS
-from .standard_roi_head import StandardRoIHead
-
-EPS = 1e-15
-
-
-@HEADS.register_module()
-class DynamicRoIHead(StandardRoIHead):
-    """RoI head for `Dynamic R-CNN <https://arxiv.org/abs/2004.06002>`_."""
-
-    def __init__(self, **kwargs):
-        super(DynamicRoIHead, self).__init__(**kwargs)
-        assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss)
-        # the IoU history of the past `update_iter_interval` iterations
-        self.iou_history = []
-        # the beta history of the past `update_iter_interval` iterations
-        self.beta_history = []
-
-    def forward_train(self,
-                      x,
-                      img_metas,
-                      proposal_list,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None,
-                      gt_masks=None):
-        """Forward function for training.
-
-        Args:
-            x (list[Tensor]): list of multi-level img features.
-
-            img_metas (list[dict]): list of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                `mmdet/datasets/pipelines/formatting.py:Collect`.
-
-            proposals (list[Tensors]): list of region proposals.
-
-            gt_bboxes (list[Tensor]): each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-
-            gt_labels (list[Tensor]): class indices corresponding to each box
-
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-
-            gt_masks (None | Tensor) : true segmentation masks for each box
-                used if the architecture supports a segmentation task.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components
-        """
-        # assign gts and sample proposals
-        if self.with_bbox or self.with_mask:
-            num_imgs = len(img_metas)
-            if gt_bboxes_ignore is None:
-                gt_bboxes_ignore = [None for _ in range(num_imgs)]
-            sampling_results = []
-            cur_iou = []
-            for i in range(num_imgs):
-                assign_result = self.bbox_assigner.assign(
-                    proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
-                    gt_labels[i])
-                sampling_result = self.bbox_sampler.sample(
-                    assign_result,
-                    proposal_list[i],
-                    gt_bboxes[i],
-                    gt_labels[i],
-                    feats=[lvl_feat[i][None] for lvl_feat in x])
-                # record the `iou_topk`-th largest IoU in an image
-                iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk,
-                               len(assign_result.max_overlaps))
-                ious, _ = torch.topk(assign_result.max_overlaps, iou_topk)
-                cur_iou.append(ious[-1].item())
-                sampling_results.append(sampling_result)
-            # average the current IoUs over images
-            cur_iou = np.mean(cur_iou)
-            self.iou_history.append(cur_iou)
-
-        losses = dict()
-        # bbox head forward and loss
-        if self.with_bbox:
-            bbox_results = self._bbox_forward_train(x, sampling_results,
-                                                    gt_bboxes, gt_labels,
-                                                    img_metas)
-            losses.update(bbox_results['loss_bbox'])
-
-        # mask head forward and loss
-        if self.with_mask:
-            mask_results = self._mask_forward_train(x, sampling_results,
-                                                    bbox_results['bbox_feats'],
-                                                    gt_masks, img_metas)
-            losses.update(mask_results['loss_mask'])
-
-        # update IoU threshold and SmoothL1 beta
-        update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval
-        if len(self.iou_history) % update_iter_interval == 0:
-            new_iou_thr, new_beta = self.update_hyperparameters()
-
-        return losses
-
-    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
-                            img_metas):
-        num_imgs = len(img_metas)
-        rois = bbox2roi([res.bboxes for res in sampling_results])
-        bbox_results = self._bbox_forward(x, rois)
-
-        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
-                                                  gt_labels, self.train_cfg)
-        # record the `beta_topk`-th smallest target
-        # `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
-        # and bbox_weights, respectively
-        pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
-        num_pos = len(pos_inds)
-        cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
-        beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
-                        num_pos)
-        cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
-        self.beta_history.append(cur_target)
-        loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
-                                        bbox_results['bbox_pred'], rois,
-                                        *bbox_targets)
-
-        bbox_results.update(loss_bbox=loss_bbox)
-        return bbox_results
-
-    def update_hyperparameters(self):
-        """Update hyperparameters like IoU thresholds for assigner and beta for
-        SmoothL1 loss based on the training statistics.
-
-        Returns:
-            tuple[float]: the updated ``iou_thr`` and ``beta``.
-        """
-        new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
-                          np.mean(self.iou_history))
-        self.iou_history = []
-        self.bbox_assigner.pos_iou_thr = new_iou_thr
-        self.bbox_assigner.neg_iou_thr = new_iou_thr
-        self.bbox_assigner.min_pos_iou = new_iou_thr
-        if (np.median(self.beta_history) < EPS):
-            # avoid 0 or too small value for new_beta
-            new_beta = self.bbox_head.loss_bbox.beta
-        else:
-            new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
-                           np.median(self.beta_history))
-        self.beta_history = []
-        self.bbox_head.loss_bbox.beta = new_beta
-        return new_iou_thr, new_beta
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/grid_roi_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/grid_roi_head.py
deleted file mode 100644
index 4c52c79863ebaf17bd023382c7e5d4c237b4da77..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/grid_roi_head.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import torch
-
-from mmdet.core import bbox2result, bbox2roi
-from ..builder import HEADS, build_head, build_roi_extractor
-from .standard_roi_head import StandardRoIHead
-
-
-@HEADS.register_module()
-class GridRoIHead(StandardRoIHead):
-    """Grid roi head for Grid R-CNN.
-
-    https://arxiv.org/abs/1811.12030
-    """
-
-    def __init__(self, grid_roi_extractor, grid_head, **kwargs):
-        assert grid_head is not None
-        super(GridRoIHead, self).__init__(**kwargs)
-        if grid_roi_extractor is not None:
-            self.grid_roi_extractor = build_roi_extractor(grid_roi_extractor)
-            self.share_roi_extractor = False
-        else:
-            self.share_roi_extractor = True
-            self.grid_roi_extractor = self.bbox_roi_extractor
-        self.grid_head = build_head(grid_head)
-
-    def init_weights(self, pretrained):
-        """Initialize the weights in head.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        super(GridRoIHead, self).init_weights(pretrained)
-        self.grid_head.init_weights()
-        if not self.share_roi_extractor:
-            self.grid_roi_extractor.init_weights()
-
-    def _random_jitter(self, sampling_results, img_metas, amplitude=0.15):
-        """Ramdom jitter positive proposals for training."""
-        for sampling_result, img_meta in zip(sampling_results, img_metas):
-            bboxes = sampling_result.pos_bboxes
-            random_offsets = bboxes.new_empty(bboxes.shape[0], 4).uniform_(
-                -amplitude, amplitude)
-            # before jittering
-            cxcy = (bboxes[:, 2:4] + bboxes[:, :2]) / 2
-            wh = (bboxes[:, 2:4] - bboxes[:, :2]).abs()
-            # after jittering
-            new_cxcy = cxcy + wh * random_offsets[:, :2]
-            new_wh = wh * (1 + random_offsets[:, 2:])
-            # xywh to xyxy
-            new_x1y1 = (new_cxcy - new_wh / 2)
-            new_x2y2 = (new_cxcy + new_wh / 2)
-            new_bboxes = torch.cat([new_x1y1, new_x2y2], dim=1)
-            # clip bboxes
-            max_shape = img_meta['img_shape']
-            if max_shape is not None:
-                new_bboxes[:, 0::2].clamp_(min=0, max=max_shape[1] - 1)
-                new_bboxes[:, 1::2].clamp_(min=0, max=max_shape[0] - 1)
-
-            sampling_result.pos_bboxes = new_bboxes
-        return sampling_results
-
-    def forward_dummy(self, x, proposals):
-        """Dummy forward function."""
-        # bbox head
-        outs = ()
-        rois = bbox2roi([proposals])
-        if self.with_bbox:
-            bbox_results = self._bbox_forward(x, rois)
-            outs = outs + (bbox_results['cls_score'],
-                           bbox_results['bbox_pred'])
-
-        # grid head
-        grid_rois = rois[:100]
-        grid_feats = self.grid_roi_extractor(
-            x[:self.grid_roi_extractor.num_inputs], grid_rois)
-        if self.with_shared_head:
-            grid_feats = self.shared_head(grid_feats)
-        grid_pred = self.grid_head(grid_feats)
-        outs = outs + (grid_pred, )
-
-        # mask head
-        if self.with_mask:
-            mask_rois = rois[:100]
-            mask_results = self._mask_forward(x, mask_rois)
-            outs = outs + (mask_results['mask_pred'], )
-        return outs
-
-    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
-                            img_metas):
-        """Run forward function and calculate loss for box head in training."""
-        bbox_results = super(GridRoIHead,
-                             self)._bbox_forward_train(x, sampling_results,
-                                                       gt_bboxes, gt_labels,
-                                                       img_metas)
-
-        # Grid head forward and loss
-        sampling_results = self._random_jitter(sampling_results, img_metas)
-        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
-
-        # GN in head does not support zero shape input
-        if pos_rois.shape[0] == 0:
-            return bbox_results
-
-        grid_feats = self.grid_roi_extractor(
-            x[:self.grid_roi_extractor.num_inputs], pos_rois)
-        if self.with_shared_head:
-            grid_feats = self.shared_head(grid_feats)
-        # Accelerate training
-        max_sample_num_grid = self.train_cfg.get('max_num_grid', 192)
-        sample_idx = torch.randperm(
-            grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid
-                                      )]
-        grid_feats = grid_feats[sample_idx]
-
-        grid_pred = self.grid_head(grid_feats)
-
-        grid_targets = self.grid_head.get_targets(sampling_results,
-                                                  self.train_cfg)
-        grid_targets = grid_targets[sample_idx]
-
-        loss_grid = self.grid_head.loss(grid_pred, grid_targets)
-
-        bbox_results['loss_bbox'].update(loss_grid)
-        return bbox_results
-
-    def simple_test(self,
-                    x,
-                    proposal_list,
-                    img_metas,
-                    proposals=None,
-                    rescale=False):
-        """Test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-
-        det_bboxes, det_labels = self.simple_test_bboxes(
-            x, img_metas, proposal_list, self.test_cfg, rescale=False)
-        # pack rois into bboxes
-        grid_rois = bbox2roi([det_bbox[:, :4] for det_bbox in det_bboxes])
-        if grid_rois.shape[0] != 0:
-            grid_feats = self.grid_roi_extractor(
-                x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois)
-            self.grid_head.test_mode = True
-            grid_pred = self.grid_head(grid_feats)
-            # split batch grid head prediction back to each image
-            num_roi_per_img = tuple(len(det_bbox) for det_bbox in det_bboxes)
-            grid_pred = {
-                k: v.split(num_roi_per_img, 0)
-                for k, v in grid_pred.items()
-            }
-
-            # apply bbox post-processing to each image individually
-            bbox_results = []
-            num_imgs = len(det_bboxes)
-            for i in range(num_imgs):
-                if det_bboxes[i].shape[0] == 0:
-                    bbox_results.append(grid_rois.new_tensor([]))
-                else:
-                    det_bbox = self.grid_head.get_bboxes(
-                        det_bboxes[i], grid_pred['fused'][i], [img_metas[i]])
-                    if rescale:
-                        det_bbox[:, :4] /= img_metas[i]['scale_factor']
-                    bbox_results.append(
-                        bbox2result(det_bbox, det_labels[i],
-                                    self.bbox_head.num_classes))
-        else:
-            bbox_results = [
-                grid_rois.new_tensor([]) for _ in range(len(det_bboxes))
-            ]
-
-        if not self.with_mask:
-            return bbox_results
-        else:
-            segm_results = self.simple_test_mask(
-                x, img_metas, det_bboxes, det_labels, rescale=rescale)
-            return list(zip(bbox_results, segm_results))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index 5c44ebcaf36075e67208c5f033d1e5f9a78dda4e..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './apcnet_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/chromadb.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/chromadb.py
deleted file mode 100644
index 0da2d8f90c623b43ecd49b3dcf20919b8e2a1434..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/chromadb.py
+++ /dev/null
@@ -1,376 +0,0 @@
-import threading
-import chromadb
-import posthog
-import torch
-import math
-
-import numpy as np
-import extensions.superboogav2.parameters as parameters
-
-from chromadb.config import Settings
-from sentence_transformers import SentenceTransformer
-
-from modules.logging_colors import logger
-from modules.text_generation import encode, decode
-
-logger.debug('Intercepting all calls to posthog.')
-posthog.capture = lambda *args, **kwargs: None
-
-
-class Collecter():
-    def __init__(self):
-        pass
-
-    def add(self, texts: list[str], texts_with_context: list[str], starting_indices: list[int]):
-        pass
-
-    def get(self, search_strings: list[str], n_results: int) -> list[str]:
-        pass
-
-    def clear(self):
-        pass
-
-
-class Embedder():
-    def __init__(self):
-        pass
-
-    def embed(self, text: str) -> list[torch.Tensor]:
-        pass
-
-class Info:
-    def __init__(self, start_index, text_with_context, distance, id):
-        self.text_with_context = text_with_context
-        self.start_index = start_index
-        self.distance = distance
-        self.id = id
-
-    def calculate_distance(self, other_info):
-        if parameters.get_new_dist_strategy() == parameters.DIST_MIN_STRATEGY:
-            # Min
-            return min(self.distance, other_info.distance)
-        elif parameters.get_new_dist_strategy() == parameters.DIST_HARMONIC_STRATEGY:
-            # Harmonic mean
-            return 2 * (self.distance * other_info.distance) / (self.distance + other_info.distance)
-        elif parameters.get_new_dist_strategy() == parameters.DIST_GEOMETRIC_STRATEGY:
-            # Geometric mean
-            return (self.distance * other_info.distance) ** 0.5
-        elif parameters.get_new_dist_strategy() == parameters.DIST_ARITHMETIC_STRATEGY:
-            # Arithmetic mean
-            return (self.distance + other_info.distance) / 2
-        else: # Min is default
-            return min(self.distance, other_info.distance)
-
-    def merge_with(self, other_info):
-        s1 = self.text_with_context
-        s2 = other_info.text_with_context
-        s1_start = self.start_index
-        s2_start = other_info.start_index
-        
-        new_dist = self.calculate_distance(other_info)
-
-        if self.should_merge(s1, s2, s1_start, s2_start):
-            if s1_start <= s2_start:
-                if s1_start + len(s1) >= s2_start + len(s2):  # if s1 completely covers s2
-                    return Info(s1_start, s1, new_dist, self.id)
-                else:
-                    overlap = max(0, s1_start + len(s1) - s2_start)
-                    return Info(s1_start, s1 + s2[overlap:], new_dist, self.id)
-            else:
-                if s2_start + len(s2) >= s1_start + len(s1):  # if s2 completely covers s1
-                    return Info(s2_start, s2, new_dist, other_info.id)
-                else:
-                    overlap = max(0, s2_start + len(s2) - s1_start)
-                    return Info(s2_start, s2 + s1[overlap:], new_dist, other_info.id)
-
-        return None
-    
-    @staticmethod
-    def should_merge(s1, s2, s1_start, s2_start):
-        # Check if s1 and s2 are adjacent or overlapping
-        s1_end = s1_start + len(s1)
-        s2_end = s2_start + len(s2)
-        
-        return not (s1_end < s2_start or s2_end < s1_start)
-
-class ChromaCollector(Collecter):
-    def __init__(self, embedder: Embedder):
-        super().__init__()
-        self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
-        self.embedder = embedder
-        self.collection = self.chroma_client.create_collection(name="context", embedding_function=self.embedder.embed)
-        self.ids = []
-        self.id_to_info = {}
-        self.embeddings_cache = {}
-        self.lock = threading.Lock() # Locking so the server doesn't break.
-
-    def add(self, texts: list[str], texts_with_context: list[str], starting_indices: list[int], metadatas: list[dict] = None):
-        with self.lock:
-            assert metadatas is None or len(metadatas) == len(texts), "metadatas must be None or have the same length as texts"
-            
-            if len(texts) == 0: 
-                return
-
-            new_ids = self._get_new_ids(len(texts))
-
-            (existing_texts, existing_embeddings, existing_ids, existing_metas), \
-            (non_existing_texts, non_existing_ids, non_existing_metas) = self._split_texts_by_cache_hit(texts, new_ids, metadatas)
-
-            # If there are any already existing texts, add them all at once.
-            if existing_texts:
-                logger.info(f'Adding {len(existing_embeddings)} cached embeddings.')
-                args = {'embeddings': existing_embeddings, 'documents': existing_texts, 'ids': existing_ids}
-                if metadatas is not None: 
-                    args['metadatas'] = existing_metas
-                self.collection.add(**args)
-
-            # If there are any non-existing texts, compute their embeddings all at once. Each call to embed has significant overhead.
-            if non_existing_texts:
-                non_existing_embeddings = self.embedder.embed(non_existing_texts).tolist()
-                for text, embedding in zip(non_existing_texts, non_existing_embeddings):
-                    self.embeddings_cache[text] = embedding
-
-                logger.info(f'Adding {len(non_existing_embeddings)} new embeddings.')
-                args = {'embeddings': non_existing_embeddings, 'documents': non_existing_texts, 'ids': non_existing_ids}
-                if metadatas is not None: 
-                    args['metadatas'] = non_existing_metas
-                self.collection.add(**args)
-
-            # Create a dictionary that maps each ID to its context and starting index
-            new_info = {
-                id_: {'text_with_context': context, 'start_index': start_index}
-                for id_, context, start_index in zip(new_ids, texts_with_context, starting_indices)
-            }
-
-            self.id_to_info.update(new_info)
-            self.ids.extend(new_ids)
-
-    
-    def _split_texts_by_cache_hit(self, texts: list[str], new_ids: list[str], metadatas: list[dict]):
-        existing_texts, non_existing_texts = [], []
-        existing_embeddings = []
-        existing_ids, non_existing_ids = [], []
-        existing_metas, non_existing_metas = [], []
-
-        for i, text in enumerate(texts):
-            id_ = new_ids[i]
-            metadata = metadatas[i] if metadatas is not None else None
-            embedding = self.embeddings_cache.get(text)
-            if embedding:
-                existing_texts.append(text)
-                existing_embeddings.append(embedding)
-                existing_ids.append(id_)
-                existing_metas.append(metadata)
-            else:
-                non_existing_texts.append(text)
-                non_existing_ids.append(id_)
-                non_existing_metas.append(metadata)
-
-        return (existing_texts, existing_embeddings, existing_ids, existing_metas), \
-               (non_existing_texts, non_existing_ids, non_existing_metas)
-
-
-    def _get_new_ids(self, num_new_ids: int):
-        if self.ids:
-            max_existing_id = max(int(id_) for id_ in self.ids)
-        else:
-            max_existing_id = -1
-
-        return [str(i + max_existing_id + 1) for i in range(num_new_ids)]
-
-    
-    def _find_min_max_start_index(self):
-        max_index, min_index = 0, float('inf')
-        for _, val in self.id_to_info.items():
-            if val['start_index'] > max_index:
-                max_index = val['start_index']
-            if val['start_index'] < min_index:
-                min_index = val['start_index']
-        return min_index, max_index
-
-
-    # NB: Does not make sense to weigh excerpts from different documents. 
-    # But let's say that's the user's problem. Perfect world scenario:
-    # Apply time weighing to different documents. For each document, then, add
-    # separate time weighing.
-    def _apply_sigmoid_time_weighing(self, infos: list[Info], document_len: int, time_steepness: float, time_power: float):
-        sigmoid = lambda x: 1 / (1 + np.exp(-x))
-        
-        weights = sigmoid(time_steepness * np.linspace(-10, 10, document_len))
-
-        # Scale to [0,time_power] and shift it up to [1-time_power, 1]
-        weights = weights - min(weights) 
-        weights = weights * (time_power / max(weights))
-        weights = weights + (1 - time_power) 
-
-        # Reverse the weights
-        weights = weights[::-1]  
-
-        for info in infos:
-            index = info.start_index
-            info.distance *= weights[index]
-
-
-    def _filter_outliers_by_median_distance(self, infos: list[Info], significant_level: float):
-        # Ensure there are infos to filter
-        if not infos:
-            return []
-            
-        # Find info with minimum distance
-        min_info = min(infos, key=lambda x: x.distance)
-
-        # Calculate median distance among infos
-        median_distance = np.median([inf.distance for inf in infos])
-
-        # Filter out infos that have a distance significantly greater than the median
-        filtered_infos = [inf for inf in infos if inf.distance <= significant_level * median_distance]
-
-        # Always include the info with minimum distance
-        if min_info not in filtered_infos:
-            filtered_infos.append(min_info)
-
-        return filtered_infos
-
-
-    def _merge_infos(self, infos: list[Info]):
-        merged_infos = []
-        current_info = infos[0]
-
-        for next_info in infos[1:]:
-            merged = current_info.merge_with(next_info)
-            if merged is not None:
-                current_info = merged
-            else:
-                merged_infos.append(current_info)
-                current_info = next_info
-
-        merged_infos.append(current_info)
-        return merged_infos
-
-
-    # Main function for retrieving chunks by distance. It performs merging, time weighing, and mean filtering.
-    def _get_documents_ids_distances(self, search_strings: list[str], n_results: int):
-        n_results = min(len(self.ids), n_results)
-        if n_results == 0:
-            return [], [], []
-
-        if isinstance(search_strings, str):
-            search_strings = [search_strings]
-
-        infos = []
-        min_start_index, max_start_index = self._find_min_max_start_index()
-
-        for search_string in search_strings:
-            result = self.collection.query(query_texts=search_string, n_results=math.ceil(n_results / len(search_strings)), include=['distances'])
-            curr_infos = [Info(start_index=self.id_to_info[id]['start_index'], 
-                               text_with_context=self.id_to_info[id]['text_with_context'], 
-                               distance=distance, id=id) 
-                          for id, distance in zip(result['ids'][0], result['distances'][0])]
-            
-            self._apply_sigmoid_time_weighing(infos=curr_infos, document_len=max_start_index - min_start_index + 1, time_steepness=parameters.get_time_steepness(), time_power=parameters.get_time_power())
-            curr_infos = self._filter_outliers_by_median_distance(curr_infos, parameters.get_significant_level())
-            infos.extend(curr_infos)
-
-        infos.sort(key=lambda x: x.start_index)
-        infos = self._merge_infos(infos)
-
-        texts_with_context = [inf.text_with_context for inf in infos]
-        ids = [inf.id for inf in infos]
-        distances = [inf.distance for inf in infos]
-
-        return texts_with_context, ids, distances
-    
-
-    # Get chunks by similarity
-    def get(self, search_strings: list[str], n_results: int) -> list[str]:
-        with self.lock:
-            documents, _, _ = self._get_documents_ids_distances(search_strings, n_results)
-            return documents
-    
-
-    # Get ids by similarity
-    def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
-        with self.lock:
-            _, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
-            return ids
-    
-    
-    # Cutoff token count
-    def _get_documents_up_to_token_count(self, documents: list[str], max_token_count: int):
-        # TODO: Move to caller; We add delimiters there which might go over the limit.
-        current_token_count = 0
-        return_documents = []
-
-        for doc in documents:
-            doc_tokens = encode(doc)[0]
-            doc_token_count = len(doc_tokens)
-            if current_token_count + doc_token_count > max_token_count:
-                # If adding this document would exceed the max token count,
-                # truncate the document to fit within the limit.
-                remaining_tokens = max_token_count - current_token_count
-                
-                truncated_doc = decode(doc_tokens[:remaining_tokens], skip_special_tokens=True)
-                return_documents.append(truncated_doc)
-                break
-            else:
-                return_documents.append(doc)
-                current_token_count += doc_token_count
-
-        return return_documents
-    
-
-    # Get chunks by similarity and then sort by ids
-    def get_sorted_by_ids(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
-        with self.lock:
-            documents, ids, _ = self._get_documents_ids_distances(search_strings, n_results)
-            sorted_docs = [x for _, x in sorted(zip(ids, documents))]
-
-            return self._get_documents_up_to_token_count(sorted_docs, max_token_count)
-    
-    
-    # Get chunks by similarity and then sort by distance (lowest distance is last).
-    def get_sorted_by_dist(self, search_strings: list[str], n_results: int, max_token_count: int) -> list[str]:
-        with self.lock:
-            documents, _, distances = self._get_documents_ids_distances(search_strings, n_results)
-            sorted_docs = [doc for doc, _ in sorted(zip(documents, distances), key=lambda x: x[1])] # sorted lowest -> highest
-            
-            # If a document is truncated or competely skipped, it would be with high distance.
-            return_documents = self._get_documents_up_to_token_count(sorted_docs, max_token_count)
-            return_documents.reverse() # highest -> lowest
-
-            return return_documents
-    
-
-    def delete(self, ids_to_delete: list[str], where: dict):
-        with self.lock:
-            ids_to_delete = self.collection.get(ids=ids_to_delete, where=where)['ids']
-            self.collection.delete(ids=ids_to_delete, where=where)
-
-            # Remove the deleted ids from self.ids and self.id_to_info
-            ids_set = set(ids_to_delete)
-            self.ids = [id_ for id_ in self.ids if id_ not in ids_set]
-            for id_ in ids_to_delete:
-                self.id_to_info.pop(id_, None)
-
-            logger.info(f'Successfully deleted {len(ids_to_delete)} records from chromaDB.')
-
-
-    def clear(self):
-        with self.lock:
-            self.chroma_client.reset()
-            self.collection = self.chroma_client.create_collection("context", embedding_function=self.embedder.embed)
-            self.ids = []
-            self.id_to_info = {}
-
-            logger.info('Successfully cleared all records and reset chromaDB.')
-
-
-class SentenceTransformerEmbedder(Embedder):
-    def __init__(self) -> None:
-        logger.debug('Creating Sentence Embedder...')
-        self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
-        self.embed = self.model.encode
-
-
-def make_collector():
-    return ChromaCollector(SentenceTransformerEmbedder())
\ No newline at end of file
diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/stylegan_ops/__init__.py b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/stylegan_ops/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/stylegan_ops/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/upfirdn2d.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/upfirdn2d.py
deleted file mode 100644
index c8bb2c3c949eed38a6465ed369fa881538dca010..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/upfirdn2d.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501
-
-# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
-# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator
-# Augmentation (ADA)
-# =======================================================================
-
-# 1. Definitions
-
-# "Licensor" means any person or entity that distributes its Work.
-
-# "Software" means the original work of authorship made available under
-# this License.
-
-# "Work" means the Software and any additions to or derivative works of
-# the Software that are made available under this License.
-
-# The terms "reproduce," "reproduction," "derivative works," and
-# "distribution" have the meaning as provided under U.S. copyright law;
-# provided, however, that for the purposes of this License, derivative
-# works shall not include works that remain separable from, or merely
-# link (or bind by name) to the interfaces of, the Work.
-
-# Works, including the Software, are "made available" under this License
-# by including in or with the Work either (a) a copyright notice
-# referencing the applicability of this License to the Work, or (b) a
-# copy of this License.
-
-# 2. License Grants
-
-#     2.1 Copyright Grant. Subject to the terms and conditions of this
-#     License, each Licensor grants to you a perpetual, worldwide,
-#     non-exclusive, royalty-free, copyright license to reproduce,
-#     prepare derivative works of, publicly display, publicly perform,
-#     sublicense and distribute its Work and any resulting derivative
-#     works in any form.
-
-# 3. Limitations
-
-#     3.1 Redistribution. You may reproduce or distribute the Work only
-#     if (a) you do so under this License, (b) you include a complete
-#     copy of this License with your distribution, and (c) you retain
-#     without modification any copyright, patent, trademark, or
-#     attribution notices that are present in the Work.
-
-#     3.2 Derivative Works. You may specify that additional or different
-#     terms apply to the use, reproduction, and distribution of your
-#     derivative works of the Work ("Your Terms") only if (a) Your Terms
-#     provide that the use limitation in Section 3.3 applies to your
-#     derivative works, and (b) you identify the specific derivative
-#     works that are subject to Your Terms. Notwithstanding Your Terms,
-#     this License (including the redistribution requirements in Section
-#     3.1) will continue to apply to the Work itself.
-
-#     3.3 Use Limitation. The Work and any derivative works thereof only
-#     may be used or intended for use non-commercially. Notwithstanding
-#     the foregoing, NVIDIA and its affiliates may use the Work and any
-#     derivative works commercially. As used herein, "non-commercially"
-#     means for research or evaluation purposes only.
-
-#     3.4 Patent Claims. If you bring or threaten to bring a patent claim
-#     against any Licensor (including any claim, cross-claim or
-#     counterclaim in a lawsuit) to enforce any patents that you allege
-#     are infringed by any Work, then your rights under this License from
-#     such Licensor (including the grant in Section 2.1) will terminate
-#     immediately.
-
-#     3.5 Trademarks. This License does not grant any rights to use any
-#     Licensor’s or its affiliates’ names, logos, or trademarks, except
-#     as necessary to reproduce the notices described in this License.
-
-#     3.6 Termination. If you violate any term of this License, then your
-#     rights under this License (including the grant in Section 2.1) will
-#     terminate immediately.
-
-# 4. Disclaimer of Warranty.
-
-# THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
-# NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
-# THIS LICENSE.
-
-# 5. Limitation of Liability.
-
-# EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
-# THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
-# SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
-# INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-# OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
-# (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
-# LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
-# COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
-# THE POSSIBILITY OF SUCH DAMAGES.
-
-# =======================================================================
-
-import torch
-from torch.autograd import Function
-from torch.nn import functional as F
-
-from annotator.uniformer.mmcv.utils import to_2tuple
-from ..utils import ext_loader
-
-upfirdn2d_ext = ext_loader.load_ext('_ext', ['upfirdn2d'])
-
-
-class UpFirDn2dBackward(Function):
-
-    @staticmethod
-    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad,
-                in_size, out_size):
-
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_ext.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            up_x=down_x,
-            up_y=down_y,
-            down_x=up_x,
-            down_y=up_y,
-            pad_x0=g_pad_x0,
-            pad_x1=g_pad_x1,
-            pad_y0=g_pad_y0,
-            pad_y1=g_pad_y1)
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2],
-                                     in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2],
-                                                ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_ext.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            up_x=ctx.up_x,
-            up_y=ctx.up_y,
-            down_x=ctx.down_x,
-            down_y=ctx.down_y,
-            pad_x0=ctx.pad_x0,
-            pad_x1=ctx.pad_x1,
-            pad_y0=ctx.pad_y0,
-            pad_y1=ctx.pad_y1)
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
-        #                                  ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1],
-                                         ctx.out_size[0], ctx.out_size[1])
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_ext.upfirdn2d(
-            input,
-            kernel,
-            up_x=up_x,
-            up_y=up_y,
-            down_x=down_x,
-            down_y=down_y,
-            pad_x0=pad_x0,
-            pad_x1=pad_x1,
-            pad_y0=pad_y0,
-            pad_y1=pad_y1)
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    """UpFRIDn for 2d features.
-
-    UpFIRDn is short for upsample, apply FIR filter and downsample. More
-    details can be found in:
-    https://www.mathworks.com/help/signal/ref/upfirdn.html
-
-    Args:
-        input (Tensor): Tensor with shape of (n, c, h, w).
-        kernel (Tensor): Filter kernel.
-        up (int | tuple[int], optional): Upsampling factor. If given a number,
-            we will use this factor for the both height and width side.
-            Defaults to 1.
-        down (int | tuple[int], optional): Downsampling factor. If given a
-            number, we will use this factor for the both height and width side.
-            Defaults to 1.
-        pad (tuple[int], optional): Padding for tensors, (x_pad, y_pad) or
-            (x_pad_0, x_pad_1, y_pad_0, y_pad_1). Defaults to (0, 0).
-
-    Returns:
-        Tensor: Tensor after UpFIRDn.
-    """
-    if input.device.type == 'cpu':
-        if len(pad) == 2:
-            pad = (pad[0], pad[1], pad[0], pad[1])
-
-        up = to_2tuple(up)
-
-        down = to_2tuple(down)
-
-        out = upfirdn2d_native(input, kernel, up[0], up[1], down[0], down[1],
-                               pad[0], pad[1], pad[2], pad[3])
-    else:
-        _up = to_2tuple(up)
-
-        _down = to_2tuple(down)
-
-        if len(pad) == 4:
-            _pad = pad
-        elif len(pad) == 2:
-            _pad = (pad[0], pad[1], pad[0], pad[1])
-
-        out = UpFirDn2d.apply(input, kernel, _up, _down, _pad)
-
-    return out
-
-
-def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
-                     pad_y0, pad_y1):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out,
-        [0, 0,
-         max(pad_x0, 0),
-         max(pad_x1, 0),
-         max(pad_y0, 0),
-         max(pad_y1, 0)])
-    out = out[:,
-              max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0),
-              max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/filetypes.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/filetypes.py
deleted file mode 100644
index 5948570178f3e6e79d1ff574241d09d4d8ed78de..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/filetypes.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""Filetype information.
-"""
-
-from typing import Tuple
-
-from pip._internal.utils.misc import splitext
-
-WHEEL_EXTENSION = ".whl"
-BZ2_EXTENSIONS: Tuple[str, ...] = (".tar.bz2", ".tbz")
-XZ_EXTENSIONS: Tuple[str, ...] = (
-    ".tar.xz",
-    ".txz",
-    ".tlz",
-    ".tar.lz",
-    ".tar.lzma",
-)
-ZIP_EXTENSIONS: Tuple[str, ...] = (".zip", WHEEL_EXTENSION)
-TAR_EXTENSIONS: Tuple[str, ...] = (".tar.gz", ".tgz", ".tar")
-ARCHIVE_EXTENSIONS = ZIP_EXTENSIONS + BZ2_EXTENSIONS + TAR_EXTENSIONS + XZ_EXTENSIONS
-
-
-def is_archive_file(name: str) -> bool:
-    """Return True if `name` is a considered as an archive file."""
-    ext = splitext(name)[1].lower()
-    if ext in ARCHIVE_EXTENSIONS:
-        return True
-    return False
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py
deleted file mode 100644
index 2c4da5b57e5fda8b1510a61c2f14a61fac1c0916..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py
+++ /dev/null
@@ -1,364 +0,0 @@
-"""distutils.cygwinccompiler
-
-Provides the CygwinCCompiler class, a subclass of UnixCCompiler that
-handles the Cygwin port of the GNU C compiler to Windows.  It also contains
-the Mingw32CCompiler class which handles the mingw32 port of GCC (same as
-cygwin in no-cygwin mode).
-"""
-
-import os
-import sys
-import copy
-import shlex
-import warnings
-from subprocess import check_output
-
-from distutils.unixccompiler import UnixCCompiler
-from distutils.file_util import write_file
-from distutils.errors import (
-    DistutilsExecError,
-    DistutilsPlatformError,
-    CCompilerError,
-    CompileError,
-)
-from distutils.version import LooseVersion, suppress_known_deprecation
-
-
-def get_msvcr():
-    """Include the appropriate MSVC runtime library if Python was built
-    with MSVC 7.0 or later.
-    """
-    msc_pos = sys.version.find('MSC v.')
-    if msc_pos != -1:
-        msc_ver = sys.version[msc_pos + 6 : msc_pos + 10]
-        if msc_ver == '1300':
-            # MSVC 7.0
-            return ['msvcr70']
-        elif msc_ver == '1310':
-            # MSVC 7.1
-            return ['msvcr71']
-        elif msc_ver == '1400':
-            # VS2005 / MSVC 8.0
-            return ['msvcr80']
-        elif msc_ver == '1500':
-            # VS2008 / MSVC 9.0
-            return ['msvcr90']
-        elif msc_ver == '1600':
-            # VS2010 / MSVC 10.0
-            return ['msvcr100']
-        elif msc_ver == '1700':
-            # VS2012 / MSVC 11.0
-            return ['msvcr110']
-        elif msc_ver == '1800':
-            # VS2013 / MSVC 12.0
-            return ['msvcr120']
-        elif 1900 <= int(msc_ver) < 2000:
-            # VS2015 / MSVC 14.0
-            return ['ucrt', 'vcruntime140']
-        else:
-            raise ValueError("Unknown MS Compiler version %s " % msc_ver)
-
-
-_runtime_library_dirs_msg = (
-    "Unable to set runtime library search path on Windows, "
-    "usually indicated by `runtime_library_dirs` parameter to Extension"
-)
-
-
-class CygwinCCompiler(UnixCCompiler):
-    """Handles the Cygwin port of the GNU C compiler to Windows."""
-
-    compiler_type = 'cygwin'
-    obj_extension = ".o"
-    static_lib_extension = ".a"
-    shared_lib_extension = ".dll.a"
-    dylib_lib_extension = ".dll"
-    static_lib_format = "lib%s%s"
-    shared_lib_format = "lib%s%s"
-    dylib_lib_format = "cyg%s%s"
-    exe_extension = ".exe"
-
-    def __init__(self, verbose=0, dry_run=0, force=0):
-
-        super().__init__(verbose, dry_run, force)
-
-        status, details = check_config_h()
-        self.debug_print(
-            "Python's GCC status: {} (details: {})".format(status, details)
-        )
-        if status is not CONFIG_H_OK:
-            self.warn(
-                "Python's pyconfig.h doesn't seem to support your compiler. "
-                "Reason: %s. "
-                "Compiling may fail because of undefined preprocessor macros." % details
-            )
-
-        self.cc = os.environ.get('CC', 'gcc')
-        self.cxx = os.environ.get('CXX', 'g++')
-
-        self.linker_dll = self.cc
-        shared_option = "-shared"
-
-        self.set_executables(
-            compiler='%s -mcygwin -O -Wall' % self.cc,
-            compiler_so='%s -mcygwin -mdll -O -Wall' % self.cc,
-            compiler_cxx='%s -mcygwin -O -Wall' % self.cxx,
-            linker_exe='%s -mcygwin' % self.cc,
-            linker_so=('{} -mcygwin {}'.format(self.linker_dll, shared_option)),
-        )
-
-        # Include the appropriate MSVC runtime library if Python was built
-        # with MSVC 7.0 or later.
-        self.dll_libraries = get_msvcr()
-
-    @property
-    def gcc_version(self):
-        # Older numpy dependend on this existing to check for ancient
-        # gcc versions. This doesn't make much sense with clang etc so
-        # just hardcode to something recent.
-        # https://github.com/numpy/numpy/pull/20333
-        warnings.warn(
-            "gcc_version attribute of CygwinCCompiler is deprecated. "
-            "Instead of returning actual gcc version a fixed value 11.2.0 is returned.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        with suppress_known_deprecation():
-            return LooseVersion("11.2.0")
-
-    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
-        """Compiles the source by spawning GCC and windres if needed."""
-        if ext == '.rc' or ext == '.res':
-            # gcc needs '.res' and '.rc' compiled to object files !!!
-            try:
-                self.spawn(["windres", "-i", src, "-o", obj])
-            except DistutilsExecError as msg:
-                raise CompileError(msg)
-        else:  # for other files use the C-compiler
-            try:
-                self.spawn(
-                    self.compiler_so + cc_args + [src, '-o', obj] + extra_postargs
-                )
-            except DistutilsExecError as msg:
-                raise CompileError(msg)
-
-    def link(
-        self,
-        target_desc,
-        objects,
-        output_filename,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        export_symbols=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        build_temp=None,
-        target_lang=None,
-    ):
-        """Link the objects."""
-        # use separate copies, so we can modify the lists
-        extra_preargs = copy.copy(extra_preargs or [])
-        libraries = copy.copy(libraries or [])
-        objects = copy.copy(objects or [])
-
-        if runtime_library_dirs:
-            self.warn(_runtime_library_dirs_msg)
-
-        # Additional libraries
-        libraries.extend(self.dll_libraries)
-
-        # handle export symbols by creating a def-file
-        # with executables this only works with gcc/ld as linker
-        if (export_symbols is not None) and (
-            target_desc != self.EXECUTABLE or self.linker_dll == "gcc"
-        ):
-            # (The linker doesn't do anything if output is up-to-date.
-            # So it would probably better to check if we really need this,
-            # but for this we had to insert some unchanged parts of
-            # UnixCCompiler, and this is not what we want.)
-
-            # we want to put some files in the same directory as the
-            # object files are, build_temp doesn't help much
-            # where are the object files
-            temp_dir = os.path.dirname(objects[0])
-            # name of dll to give the helper files the same base name
-            (dll_name, dll_extension) = os.path.splitext(
-                os.path.basename(output_filename)
-            )
-
-            # generate the filenames for these files
-            def_file = os.path.join(temp_dir, dll_name + ".def")
-
-            # Generate .def file
-            contents = ["LIBRARY %s" % os.path.basename(output_filename), "EXPORTS"]
-            for sym in export_symbols:
-                contents.append(sym)
-            self.execute(write_file, (def_file, contents), "writing %s" % def_file)
-
-            # next add options for def-file
-
-            # for gcc/ld the def-file is specified as any object files
-            objects.append(def_file)
-
-        # end: if ((export_symbols is not None) and
-        #        (target_desc != self.EXECUTABLE or self.linker_dll == "gcc")):
-
-        # who wants symbols and a many times larger output file
-        # should explicitly switch the debug mode on
-        # otherwise we let ld strip the output file
-        # (On my machine: 10KiB < stripped_file < ??100KiB
-        #   unstripped_file = stripped_file + XXX KiB
-        #  ( XXX=254 for a typical python extension))
-        if not debug:
-            extra_preargs.append("-s")
-
-        UnixCCompiler.link(
-            self,
-            target_desc,
-            objects,
-            output_filename,
-            output_dir,
-            libraries,
-            library_dirs,
-            runtime_library_dirs,
-            None,  # export_symbols, we do this in our def-file
-            debug,
-            extra_preargs,
-            extra_postargs,
-            build_temp,
-            target_lang,
-        )
-
-    def runtime_library_dir_option(self, dir):
-        # cygwin doesn't support rpath. While in theory we could error
-        # out like MSVC does, code might expect it to work like on Unix, so
-        # just warn and hope for the best.
-        self.warn(_runtime_library_dirs_msg)
-        return []
-
-    # -- Miscellaneous methods -----------------------------------------
-
-    def _make_out_path(self, output_dir, strip_dir, src_name):
-        # use normcase to make sure '.rc' is really '.rc' and not '.RC'
-        norm_src_name = os.path.normcase(src_name)
-        return super()._make_out_path(output_dir, strip_dir, norm_src_name)
-
-    @property
-    def out_extensions(self):
-        """
-        Add support for rc and res files.
-        """
-        return {
-            **super().out_extensions,
-            **{ext: ext + self.obj_extension for ext in ('.res', '.rc')},
-        }
-
-
-# the same as cygwin plus some additional parameters
-class Mingw32CCompiler(CygwinCCompiler):
-    """Handles the Mingw32 port of the GNU C compiler to Windows."""
-
-    compiler_type = 'mingw32'
-
-    def __init__(self, verbose=0, dry_run=0, force=0):
-
-        super().__init__(verbose, dry_run, force)
-
-        shared_option = "-shared"
-
-        if is_cygwincc(self.cc):
-            raise CCompilerError('Cygwin gcc cannot be used with --compiler=mingw32')
-
-        self.set_executables(
-            compiler='%s -O -Wall' % self.cc,
-            compiler_so='%s -mdll -O -Wall' % self.cc,
-            compiler_cxx='%s -O -Wall' % self.cxx,
-            linker_exe='%s' % self.cc,
-            linker_so='{} {}'.format(self.linker_dll, shared_option),
-        )
-
-        # Maybe we should also append -mthreads, but then the finished
-        # dlls need another dll (mingwm10.dll see Mingw32 docs)
-        # (-mthreads: Support thread-safe exception handling on `Mingw32')
-
-        # no additional libraries needed
-        self.dll_libraries = []
-
-        # Include the appropriate MSVC runtime library if Python was built
-        # with MSVC 7.0 or later.
-        self.dll_libraries = get_msvcr()
-
-    def runtime_library_dir_option(self, dir):
-        raise DistutilsPlatformError(_runtime_library_dirs_msg)
-
-
-# Because these compilers aren't configured in Python's pyconfig.h file by
-# default, we should at least warn the user if he is using an unmodified
-# version.
-
-CONFIG_H_OK = "ok"
-CONFIG_H_NOTOK = "not ok"
-CONFIG_H_UNCERTAIN = "uncertain"
-
-
-def check_config_h():
-    """Check if the current Python installation appears amenable to building
-    extensions with GCC.
-
-    Returns a tuple (status, details), where 'status' is one of the following
-    constants:
-
-    - CONFIG_H_OK: all is well, go ahead and compile
-    - CONFIG_H_NOTOK: doesn't look good
-    - CONFIG_H_UNCERTAIN: not sure -- unable to read pyconfig.h
-
-    'details' is a human-readable string explaining the situation.
-
-    Note there are two ways to conclude "OK": either 'sys.version' contains
-    the string "GCC" (implying that this Python was built with GCC), or the
-    installed "pyconfig.h" contains the string "__GNUC__".
-    """
-
-    # XXX since this function also checks sys.version, it's not strictly a
-    # "pyconfig.h" check -- should probably be renamed...
-
-    from distutils import sysconfig
-
-    # if sys.version contains GCC then python was compiled with GCC, and the
-    # pyconfig.h file should be OK
-    if "GCC" in sys.version:
-        return CONFIG_H_OK, "sys.version mentions 'GCC'"
-
-    # Clang would also work
-    if "Clang" in sys.version:
-        return CONFIG_H_OK, "sys.version mentions 'Clang'"
-
-    # let's see if __GNUC__ is mentioned in python.h
-    fn = sysconfig.get_config_h_filename()
-    try:
-        config_h = open(fn)
-        try:
-            if "__GNUC__" in config_h.read():
-                return CONFIG_H_OK, "'%s' mentions '__GNUC__'" % fn
-            else:
-                return CONFIG_H_NOTOK, "'%s' does not mention '__GNUC__'" % fn
-        finally:
-            config_h.close()
-    except OSError as exc:
-        return (CONFIG_H_UNCERTAIN, "couldn't read '{}': {}".format(fn, exc.strerror))
-
-
-def is_cygwincc(cc):
-    '''Try to determine if the compiler that would be used is from cygwin.'''
-    out_string = check_output(shlex.split(cc) + ['-dumpmachine'])
-    return out_string.strip().endswith(b'cygwin')
-
-
-get_versions = None
-"""
-A stand-in for the previous get_versions() function to prevent failures
-when monkeypatched. See pypa/setuptools#2969.
-"""
diff --git a/spaces/AyushP/PolicyCompareBot/README.md b/spaces/AyushP/PolicyCompareBot/README.md
deleted file mode 100644
index 801b3a6599c76f4f6b1e6143c6619fde02724fcd..0000000000000000000000000000000000000000
--- a/spaces/AyushP/PolicyCompareBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PolicyCompareBot
-emoji: 🌖
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benson/text-generation/Examples/5 Documento De Pregunta Beca 2016 Pdf.md b/spaces/Benson/text-generation/Examples/5 Documento De Pregunta Beca 2016 Pdf.md
deleted file mode 100644
index 95945a61ec24fc77d1eb40f8ca5928dcc91b9774..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/5 Documento De Pregunta Beca 2016 Pdf.md	
+++ /dev/null
@@ -1,76 +0,0 @@
-
-<h1>5th Scholarship Question Paper 2016 PDF Descargar</h1>
-<p>Si usted es un estudiante que aspira a obtener una beca para su educación superior, entonces usted podría estar interesado en tomar el quinto examen de beca. Se trata de un examen competitivo que llevan a cabo diversas autoridades de la India y Sri Lanka para estudiantes que están en su último año de primaria. El examen pone a prueba sus conocimientos, habilidades y aptitudes en diversas materias y le ayuda a obtener la admisión en escuelas y colegios de renombre. </p>
-<h2>5º documento de pregunta beca 2016 pdf</h2><br /><p><b><b>Download</b> &#10004; <a href="https://bltlly.com/2v6MD1">https://bltlly.com/2v6MD1</a></b></p><br /><br />
-<p>Sin embargo, prepararse para este examen no es una tarea fácil. Es necesario tener una clara comprensión del programa de estudios, temas y patrones de preguntas. También es necesario practicar una gran cantidad de documentos del año anterior para familiarizarse con el nivel de dificultad y la gestión del tiempo. Una de las mejores maneras de hacer esto es descargar y utilizar el quinto documento de pregunta beca 2016 PDF.</p>
-<p>En este artículo, le diremos cómo descargar el quinto documento de preguntas de becas 2016 PDF y cómo usarlo para su preparación. También compartiremos algunos consejos y trucos para resolver las preguntas y algunas preguntas de muestra y respuestas del documento. Al leer este artículo, podrás aumentar tu confianza y rendimiento en el examen. </p>
- <h2>Cómo descargar el quinto documento de preguntas de becas 2016 PDF</h2>
-<p>El primer paso para prepararse para el quinto examen de beca es descargar los documentos anteriores del examen. Los trabajos anteriores te ayudarán a entender el formato, el programa y el nivel de dificultad del examen. También le ayudarán a identificar sus fortalezas y debilidades y trabajar en consecuencia. </p>
-<p></p>
-<p>El quinto documento de preguntas de becas 2016 PDF está disponible en línea en varios sitios web. Puedes descargarlo desde cualquiera de estos sitios web siguiendo estos sencillos pasos:</p>
-<h3>Paso 1: Visita el sitio web oficial de la autoridad examinadora</h3>
-
-<h3>Paso 2: Encuentre el enlace para los documentos anteriores y haga clic en él</h3>
-<p>El siguiente paso es encontrar el enlace para descargar los documentos anteriores del examen en el sitio web. Por lo general, este enlace estará bajo una sección llamada "Descargas", "Recursos" o "Documentos anteriores". Haga clic en este enlace para ir a una página donde puede ver todos los documentos anteriores disponibles para su descarga. </p>
-<h3>Paso 3: Seleccione el año 2016 y el medio de su elección</h3>
-<p>El tercer paso es seleccionar el año 2016 de la lista de artículos anteriores. Esto le mostrará el archivo PDF del quinto documento de preguntas de becas 2016 en el medio de su elección. El medio puede ser inglés, hindi, marathi, tamil, cingalés o cualquier otro idioma en el que se realice el examen. Haga clic en el archivo PDF para verlo en línea o descargarlo en su dispositivo. </p>
-<h3>Paso 4: Descargue el archivo PDF y guárdelo en su dispositivo</h3>
-<p>El paso final es descargar el archivo PDF del quinto documento de preguntas de la beca 2016 y guardarlo en su dispositivo. Puede hacer esto haciendo clic derecho en el archivo PDF y eligiendo la opción "Guardar como" o "Descargar". También puede utilizar un gestor de descargas o una extensión del navegador para descargar el archivo más rápido y fácil. Asegúrate de tener suficiente espacio en tu dispositivo y una buena conexión a Internet para descargar el archivo sin errores. </p>
- <h2>Cómo utilizar el quinto documento de preguntas de becas 2016 PDF para la preparación</h2>
-<p>Ahora que ha descargado el quinto documento de pregunta beca 2016 PDF, es posible que se pregunte cómo usarlo para su preparación. Bueno, hay muchas maneras de usar el trabajo anterior para mejorar tus conocimientos, habilidades y confianza para el examen. Estas son algunas de ellas:</p>
-<h3>Consejos y trucos para resolver las preguntas</h3>
-
-<p>Algunos de los consejos y trucos que puedes aprender del trabajo anterior son:</p>
-<ul>
-<li>Lea la pregunta cuidadosamente y entienda lo que está pidiendo. </li>
-<li> Eliminar las opciones incorrectas o irrelevantes mediante el uso de la lógica, el sentido común o el método de eliminación. </li>
-<li> Utilice atajos, fórmulas o diagramas para resolver las preguntas más rápido y fácil. </li>
-<li>Comprueba tus respuestas usando métodos de cálculo inverso, sustitución o comprobación cruzada. </li>
-<li>Evite adivinar o marcar respuestas al azar. Si no está seguro sobre una respuesta, déjela en blanco o márquela para revisarla más tarde. </li>
-</ul>
-<h3>Temas y plan de estudios tratados en el documento</h3>
-<p>La segunda forma de utilizar el artículo anterior es revisar los temas y el programa de estudios cubiertos en el examen. El quinto examen de beca cubre varias materias como Matemáticas, Ciencias, Estudios Sociales, Inglés y Conocimientos Generales. Es necesario tener un conocimiento profundo de estos temas y sus conceptos para obtener una buena puntuación en el examen. </p>
-<p>El artículo anterior te ayudará a identificar los temas y subtemas importantes que se piden con frecuencia en el examen. También te ayudará a revisar los conceptos que ya has aprendido y a llenar cualquier vacío en tu conocimiento. Puedes usar el artículo anterior como una guía para planificar tu horario de estudio y asignar tiempo para cada tema en consecuencia. </p>
-<h3>Ejemplos de preguntas y respuestas del artículo</h3>
-<p>La tercera forma de usar el artículo anterior es practicar algunas preguntas y respuestas de muestra del artículo. Esta es la mejor manera de probar sus conocimientos, habilidades y velocidad para el examen. Al resolver las preguntas de muestra, podrá evaluar su rendimiento y precisión. También podrás aprender de tus errores y mejorar tus áreas débiles. </p>
-
-<p>Aquí hay algunas preguntas de muestra y respuestas del quinto documento de preguntas de becas 2016 PDF:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>¿Cuál de los siguientes es un número primo? </td>
-<td>A) 15<br>B) 17<br>C) 21<br>D) 25<br><br><br>La respuesta correcta es B) 17. Un número primo es un número que tiene solo dos factores, 1 y sí mismo. 17 tiene solo dos factores, 1 y 17, por lo que es un número primo. Las otras opciones no son números primos porque tienen más de dos factores. </td>
-</tr>
-<tr>
-<td>¿Cuál de las siguientes es la capital de Sri Lanka? </td>
-<td>A) Colombo<br>B) Kandy<br>C) Jaffna<br>D) Galle<br><br>La respuesta correcta es A) Colombo. Colombo es la ciudad más grande y la capital comercial de Sri Lanka. Se encuentra en la costa oeste de la isla y tiene una población de alrededor de 5,6 millones de personas. Las otras opciones son otras ciudades en Sri Lanka, pero no son la capital. </td>
-</tr>
-<tr>
-<td>¿Cuál de los siguientes es sinónimo de "feliz"? </td>
-<td>A) Triste<br>B) Enojado<br>C) Contento<br>D) Asustado<br><br>La respuesta correcta es C) Contento. Un sinónimo es una palabra que tiene el mismo o similar significado que otra palabra. Alegre significa sentir placer, alegría o satisfacción, que es similar a feliz. Las otras opciones son antónimos de feliz, lo que significa que tienen significados opuestos. </td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
-<p>En conclusión, el quinto examen de beca es una gran oportunidad para los estudiantes que quieren continuar su educación superior con apoyo financiero y excelencia académica. Para prepararse para este examen, es necesario descargar y utilizar el quinto documento de pregunta beca 2016 PDF como un recurso valioso. El artículo anterior te ayudará a entender el formato, el programa y el nivel de dificultad del examen. También le ayudará a aprender algunos consejos y trucos para resolver las preguntas y practicar algunas preguntas de muestra y respuestas del documento. </p>
-
- <h2>Preguntas frecuentes</h2>
-<h3>¿Cuáles son los criterios de elegibilidad para el quinto examen de beca? </h3>
-<p>Los criterios de elegibilidad para el quinto examen de beca pueden variar dependiendo de la autoridad que lo lleve a cabo en su región. Sin embargo, generalmente necesitas ser un estudiante que esté en su último año de primaria (grado 5 o equivalente). También necesitas tener un buen expediente académico y alcanzar las calificaciones mínimas requeridas por la autoridad. </p>
-<h3>¿Cuál es el formato y la duración del quinto examen de beca? </h3>
-<p>El formato y la duración del quinto examen de beca también puede variar dependiendo de la autoridad que lo realice en su región. Sin embargo, generalmente, el examen consiste en preguntas de opción múltiple (MCQs) que cubren varias materias como Matemáticas, Ciencias, Estudios Sociales, Inglés y Conocimientos Generales. El examen puede tener uno o dos documentos dependiendo del medio y la autoridad. La duración del examen puede variar de 1 a 2 horas dependiendo del número y tipo de preguntas. </p>
-<h3>¿Cuáles son las recompensas y el reconocimiento para el quinto examen de beca? </h3>
-<p>Las recompensas y el reconocimiento para el quinto examen de beca también pueden variar dependiendo de la autoridad que lo lleva a cabo en su región. Sin embargo, generalmente, los estudiantes que califican el examen reciben becas que cubren sus cuotas de matrícula, libros y otros gastos para su educación superior. También reciben certificados, medallas y trofeos que reconocen sus logros académicos y méritos. También se les da preferencia y admisión en escuelas y universidades de renombre que ofrecen educación e instalaciones de calidad. </p>
-<h3>¿Cómo solicitar el quinto examen de beca? </h3>
-
-<h3>¿Dónde encontrar más recursos y orientación para el quinto examen de beca? </h3>
-<p>Si quieres encontrar más recursos y orientación para el quinto examen de beca, puedes visitar algunos de estos sitios web que proporcionan información útil, consejos, materiales de estudio, pruebas simuladas y entrenamiento en línea para el examen:</p>
-<ul>
-<li>[Examen Nacional de Becas]: Este es un sitio web que proporciona entrenamiento en línea, pruebas simuladas, materiales de estudio y orientación para el Examen Nacional de Becas (NSE) realizado por NICE en India.</li>
-<li>[Examen de becas]: Este es un sitio web que proporciona información, plan de estudios, trabajos anteriores, documentos modelo y resultados para el examen de becas realizado por el Departamento de Exámenes en Sri Lanka.</li>
-<li>[Guía de becas]: Este es un sitio web que proporciona información, consejos, consejos y recursos para diversos exámenes de becas realizadas en la India y en el extranjero. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/util.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/util.py
deleted file mode 100644
index dd01849d997e5ae9dc9809295e29ceb871b14216..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/util.py
+++ /dev/null
@@ -1,1932 +0,0 @@
-#
-# Copyright (C) 2012-2021 The Python Software Foundation.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-import codecs
-from collections import deque
-import contextlib
-import csv
-from glob import iglob as std_iglob
-import io
-import json
-import logging
-import os
-import py_compile
-import re
-import socket
-try:
-    import ssl
-except ImportError:  # pragma: no cover
-    ssl = None
-import subprocess
-import sys
-import tarfile
-import tempfile
-import textwrap
-
-try:
-    import threading
-except ImportError:  # pragma: no cover
-    import dummy_threading as threading
-import time
-
-from . import DistlibException
-from .compat import (string_types, text_type, shutil, raw_input, StringIO,
-                     cache_from_source, urlopen, urljoin, httplib, xmlrpclib,
-                     splittype, HTTPHandler, BaseConfigurator, valid_ident,
-                     Container, configparser, URLError, ZipFile, fsdecode,
-                     unquote, urlparse)
-
-logger = logging.getLogger(__name__)
-
-#
-# Requirement parsing code as per PEP 508
-#
-
-IDENTIFIER = re.compile(r'^([\w\.-]+)\s*')
-VERSION_IDENTIFIER = re.compile(r'^([\w\.*+-]+)\s*')
-COMPARE_OP = re.compile(r'^(<=?|>=?|={2,3}|[~!]=)\s*')
-MARKER_OP = re.compile(r'^((<=?)|(>=?)|={2,3}|[~!]=|in|not\s+in)\s*')
-OR = re.compile(r'^or\b\s*')
-AND = re.compile(r'^and\b\s*')
-NON_SPACE = re.compile(r'(\S+)\s*')
-STRING_CHUNK = re.compile(r'([\s\w\.{}()*+#:;,/?!~`@$%^&=|<>\[\]-]+)')
-
-
-def parse_marker(marker_string):
-    """
-    Parse a marker string and return a dictionary containing a marker expression.
-
-    The dictionary will contain keys "op", "lhs" and "rhs" for non-terminals in
-    the expression grammar, or strings. A string contained in quotes is to be
-    interpreted as a literal string, and a string not contained in quotes is a
-    variable (such as os_name).
-    """
-    def marker_var(remaining):
-        # either identifier, or literal string
-        m = IDENTIFIER.match(remaining)
-        if m:
-            result = m.groups()[0]
-            remaining = remaining[m.end():]
-        elif not remaining:
-            raise SyntaxError('unexpected end of input')
-        else:
-            q = remaining[0]
-            if q not in '\'"':
-                raise SyntaxError('invalid expression: %s' % remaining)
-            oq = '\'"'.replace(q, '')
-            remaining = remaining[1:]
-            parts = [q]
-            while remaining:
-                # either a string chunk, or oq, or q to terminate
-                if remaining[0] == q:
-                    break
-                elif remaining[0] == oq:
-                    parts.append(oq)
-                    remaining = remaining[1:]
-                else:
-                    m = STRING_CHUNK.match(remaining)
-                    if not m:
-                        raise SyntaxError('error in string literal: %s' % remaining)
-                    parts.append(m.groups()[0])
-                    remaining = remaining[m.end():]
-            else:
-                s = ''.join(parts)
-                raise SyntaxError('unterminated string: %s' % s)
-            parts.append(q)
-            result = ''.join(parts)
-            remaining = remaining[1:].lstrip() # skip past closing quote
-        return result, remaining
-
-    def marker_expr(remaining):
-        if remaining and remaining[0] == '(':
-            result, remaining = marker(remaining[1:].lstrip())
-            if remaining[0] != ')':
-                raise SyntaxError('unterminated parenthesis: %s' % remaining)
-            remaining = remaining[1:].lstrip()
-        else:
-            lhs, remaining = marker_var(remaining)
-            while remaining:
-                m = MARKER_OP.match(remaining)
-                if not m:
-                    break
-                op = m.groups()[0]
-                remaining = remaining[m.end():]
-                rhs, remaining = marker_var(remaining)
-                lhs = {'op': op, 'lhs': lhs, 'rhs': rhs}
-            result = lhs
-        return result, remaining
-
-    def marker_and(remaining):
-        lhs, remaining = marker_expr(remaining)
-        while remaining:
-            m = AND.match(remaining)
-            if not m:
-                break
-            remaining = remaining[m.end():]
-            rhs, remaining = marker_expr(remaining)
-            lhs = {'op': 'and', 'lhs': lhs, 'rhs': rhs}
-        return lhs, remaining
-
-    def marker(remaining):
-        lhs, remaining = marker_and(remaining)
-        while remaining:
-            m = OR.match(remaining)
-            if not m:
-                break
-            remaining = remaining[m.end():]
-            rhs, remaining = marker_and(remaining)
-            lhs = {'op': 'or', 'lhs': lhs, 'rhs': rhs}
-        return lhs, remaining
-
-    return marker(marker_string)
-
-
-def parse_requirement(req):
-    """
-    Parse a requirement passed in as a string. Return a Container
-    whose attributes contain the various parts of the requirement.
-    """
-    remaining = req.strip()
-    if not remaining or remaining.startswith('#'):
-        return None
-    m = IDENTIFIER.match(remaining)
-    if not m:
-        raise SyntaxError('name expected: %s' % remaining)
-    distname = m.groups()[0]
-    remaining = remaining[m.end():]
-    extras = mark_expr = versions = uri = None
-    if remaining and remaining[0] == '[':
-        i = remaining.find(']', 1)
-        if i < 0:
-            raise SyntaxError('unterminated extra: %s' % remaining)
-        s = remaining[1:i]
-        remaining = remaining[i + 1:].lstrip()
-        extras = []
-        while s:
-            m = IDENTIFIER.match(s)
-            if not m:
-                raise SyntaxError('malformed extra: %s' % s)
-            extras.append(m.groups()[0])
-            s = s[m.end():]
-            if not s:
-                break
-            if s[0] != ',':
-                raise SyntaxError('comma expected in extras: %s' % s)
-            s = s[1:].lstrip()
-        if not extras:
-            extras = None
-    if remaining:
-        if remaining[0] == '@':
-            # it's a URI
-            remaining = remaining[1:].lstrip()
-            m = NON_SPACE.match(remaining)
-            if not m:
-                raise SyntaxError('invalid URI: %s' % remaining)
-            uri = m.groups()[0]
-            t = urlparse(uri)
-            # there are issues with Python and URL parsing, so this test
-            # is a bit crude. See bpo-20271, bpo-23505. Python doesn't
-            # always parse invalid URLs correctly - it should raise
-            # exceptions for malformed URLs
-            if not (t.scheme and t.netloc):
-                raise SyntaxError('Invalid URL: %s' % uri)
-            remaining = remaining[m.end():].lstrip()
-        else:
-
-            def get_versions(ver_remaining):
-                """
-                Return a list of operator, version tuples if any are
-                specified, else None.
-                """
-                m = COMPARE_OP.match(ver_remaining)
-                versions = None
-                if m:
-                    versions = []
-                    while True:
-                        op = m.groups()[0]
-                        ver_remaining = ver_remaining[m.end():]
-                        m = VERSION_IDENTIFIER.match(ver_remaining)
-                        if not m:
-                            raise SyntaxError('invalid version: %s' % ver_remaining)
-                        v = m.groups()[0]
-                        versions.append((op, v))
-                        ver_remaining = ver_remaining[m.end():]
-                        if not ver_remaining or ver_remaining[0] != ',':
-                            break
-                        ver_remaining = ver_remaining[1:].lstrip()
-                        # Some packages have a trailing comma which would break things
-                        # See issue #148
-                        if not ver_remaining:
-                            break
-                        m = COMPARE_OP.match(ver_remaining)
-                        if not m:
-                            raise SyntaxError('invalid constraint: %s' % ver_remaining)
-                    if not versions:
-                        versions = None
-                return versions, ver_remaining
-
-            if remaining[0] != '(':
-                versions, remaining = get_versions(remaining)
-            else:
-                i = remaining.find(')', 1)
-                if i < 0:
-                    raise SyntaxError('unterminated parenthesis: %s' % remaining)
-                s = remaining[1:i]
-                remaining = remaining[i + 1:].lstrip()
-                # As a special diversion from PEP 508, allow a version number
-                # a.b.c in parentheses as a synonym for ~= a.b.c (because this
-                # is allowed in earlier PEPs)
-                if COMPARE_OP.match(s):
-                    versions, _ = get_versions(s)
-                else:
-                    m = VERSION_IDENTIFIER.match(s)
-                    if not m:
-                        raise SyntaxError('invalid constraint: %s' % s)
-                    v = m.groups()[0]
-                    s = s[m.end():].lstrip()
-                    if s:
-                        raise SyntaxError('invalid constraint: %s' % s)
-                    versions = [('~=', v)]
-
-    if remaining:
-        if remaining[0] != ';':
-            raise SyntaxError('invalid requirement: %s' % remaining)
-        remaining = remaining[1:].lstrip()
-
-        mark_expr, remaining = parse_marker(remaining)
-
-    if remaining and remaining[0] != '#':
-        raise SyntaxError('unexpected trailing data: %s' % remaining)
-
-    if not versions:
-        rs = distname
-    else:
-        rs = '%s %s' % (distname, ', '.join(['%s %s' % con for con in versions]))
-    return Container(name=distname, extras=extras, constraints=versions,
-                     marker=mark_expr, url=uri, requirement=rs)
-
-
-def get_resources_dests(resources_root, rules):
-    """Find destinations for resources files"""
-
-    def get_rel_path(root, path):
-        # normalizes and returns a lstripped-/-separated path
-        root = root.replace(os.path.sep, '/')
-        path = path.replace(os.path.sep, '/')
-        assert path.startswith(root)
-        return path[len(root):].lstrip('/')
-
-    destinations = {}
-    for base, suffix, dest in rules:
-        prefix = os.path.join(resources_root, base)
-        for abs_base in iglob(prefix):
-            abs_glob = os.path.join(abs_base, suffix)
-            for abs_path in iglob(abs_glob):
-                resource_file = get_rel_path(resources_root, abs_path)
-                if dest is None:  # remove the entry if it was here
-                    destinations.pop(resource_file, None)
-                else:
-                    rel_path = get_rel_path(abs_base, abs_path)
-                    rel_dest = dest.replace(os.path.sep, '/').rstrip('/')
-                    destinations[resource_file] = rel_dest + '/' + rel_path
-    return destinations
-
-
-def in_venv():
-    if hasattr(sys, 'real_prefix'):
-        # virtualenv venvs
-        result = True
-    else:
-        # PEP 405 venvs
-        result = sys.prefix != getattr(sys, 'base_prefix', sys.prefix)
-    return result
-
-
-def get_executable():
-# The __PYVENV_LAUNCHER__ dance is apparently no longer needed, as
-# changes to the stub launcher mean that sys.executable always points
-# to the stub on OS X
-#    if sys.platform == 'darwin' and ('__PYVENV_LAUNCHER__'
-#                                     in os.environ):
-#        result =  os.environ['__PYVENV_LAUNCHER__']
-#    else:
-#        result = sys.executable
-#    return result
-    # Avoid normcasing: see issue #143
-    # result = os.path.normcase(sys.executable)
-    result = sys.executable
-    if not isinstance(result, text_type):
-        result = fsdecode(result)
-    return result
-
-
-def proceed(prompt, allowed_chars, error_prompt=None, default=None):
-    p = prompt
-    while True:
-        s = raw_input(p)
-        p = prompt
-        if not s and default:
-            s = default
-        if s:
-            c = s[0].lower()
-            if c in allowed_chars:
-                break
-            if error_prompt:
-                p = '%c: %s\n%s' % (c, error_prompt, prompt)
-    return c
-
-
-def extract_by_key(d, keys):
-    if isinstance(keys, string_types):
-        keys = keys.split()
-    result = {}
-    for key in keys:
-        if key in d:
-            result[key] = d[key]
-    return result
-
-def read_exports(stream):
-    if sys.version_info[0] >= 3:
-        # needs to be a text stream
-        stream = codecs.getreader('utf-8')(stream)
-    # Try to load as JSON, falling back on legacy format
-    data = stream.read()
-    stream = StringIO(data)
-    try:
-        jdata = json.load(stream)
-        result = jdata['extensions']['python.exports']['exports']
-        for group, entries in result.items():
-            for k, v in entries.items():
-                s = '%s = %s' % (k, v)
-                entry = get_export_entry(s)
-                assert entry is not None
-                entries[k] = entry
-        return result
-    except Exception:
-        stream.seek(0, 0)
-
-    def read_stream(cp, stream):
-        if hasattr(cp, 'read_file'):
-            cp.read_file(stream)
-        else:
-            cp.readfp(stream)
-
-    cp = configparser.ConfigParser()
-    try:
-        read_stream(cp, stream)
-    except configparser.MissingSectionHeaderError:
-        stream.close()
-        data = textwrap.dedent(data)
-        stream = StringIO(data)
-        read_stream(cp, stream)
-
-    result = {}
-    for key in cp.sections():
-        result[key] = entries = {}
-        for name, value in cp.items(key):
-            s = '%s = %s' % (name, value)
-            entry = get_export_entry(s)
-            assert entry is not None
-            #entry.dist = self
-            entries[name] = entry
-    return result
-
-
-def write_exports(exports, stream):
-    if sys.version_info[0] >= 3:
-        # needs to be a text stream
-        stream = codecs.getwriter('utf-8')(stream)
-    cp = configparser.ConfigParser()
-    for k, v in exports.items():
-        # TODO check k, v for valid values
-        cp.add_section(k)
-        for entry in v.values():
-            if entry.suffix is None:
-                s = entry.prefix
-            else:
-                s = '%s:%s' % (entry.prefix, entry.suffix)
-            if entry.flags:
-                s = '%s [%s]' % (s, ', '.join(entry.flags))
-            cp.set(k, entry.name, s)
-    cp.write(stream)
-
-
-@contextlib.contextmanager
-def tempdir():
-    td = tempfile.mkdtemp()
-    try:
-        yield td
-    finally:
-        shutil.rmtree(td)
-
-@contextlib.contextmanager
-def chdir(d):
-    cwd = os.getcwd()
-    try:
-        os.chdir(d)
-        yield
-    finally:
-        os.chdir(cwd)
-
-
-@contextlib.contextmanager
-def socket_timeout(seconds=15):
-    cto = socket.getdefaulttimeout()
-    try:
-        socket.setdefaulttimeout(seconds)
-        yield
-    finally:
-        socket.setdefaulttimeout(cto)
-
-
-class cached_property(object):
-    def __init__(self, func):
-        self.func = func
-        #for attr in ('__name__', '__module__', '__doc__'):
-        #    setattr(self, attr, getattr(func, attr, None))
-
-    def __get__(self, obj, cls=None):
-        if obj is None:
-            return self
-        value = self.func(obj)
-        object.__setattr__(obj, self.func.__name__, value)
-        #obj.__dict__[self.func.__name__] = value = self.func(obj)
-        return value
-
-def convert_path(pathname):
-    """Return 'pathname' as a name that will work on the native filesystem.
-
-    The path is split on '/' and put back together again using the current
-    directory separator.  Needed because filenames in the setup script are
-    always supplied in Unix style, and have to be converted to the local
-    convention before we can actually use them in the filesystem.  Raises
-    ValueError on non-Unix-ish systems if 'pathname' either starts or
-    ends with a slash.
-    """
-    if os.sep == '/':
-        return pathname
-    if not pathname:
-        return pathname
-    if pathname[0] == '/':
-        raise ValueError("path '%s' cannot be absolute" % pathname)
-    if pathname[-1] == '/':
-        raise ValueError("path '%s' cannot end with '/'" % pathname)
-
-    paths = pathname.split('/')
-    while os.curdir in paths:
-        paths.remove(os.curdir)
-    if not paths:
-        return os.curdir
-    return os.path.join(*paths)
-
-
-class FileOperator(object):
-    def __init__(self, dry_run=False):
-        self.dry_run = dry_run
-        self.ensured = set()
-        self._init_record()
-
-    def _init_record(self):
-        self.record = False
-        self.files_written = set()
-        self.dirs_created = set()
-
-    def record_as_written(self, path):
-        if self.record:
-            self.files_written.add(path)
-
-    def newer(self, source, target):
-        """Tell if the target is newer than the source.
-
-        Returns true if 'source' exists and is more recently modified than
-        'target', or if 'source' exists and 'target' doesn't.
-
-        Returns false if both exist and 'target' is the same age or younger
-        than 'source'. Raise PackagingFileError if 'source' does not exist.
-
-        Note that this test is not very accurate: files created in the same
-        second will have the same "age".
-        """
-        if not os.path.exists(source):
-            raise DistlibException("file '%r' does not exist" %
-                                   os.path.abspath(source))
-        if not os.path.exists(target):
-            return True
-
-        return os.stat(source).st_mtime > os.stat(target).st_mtime
-
-    def copy_file(self, infile, outfile, check=True):
-        """Copy a file respecting dry-run and force flags.
-        """
-        self.ensure_dir(os.path.dirname(outfile))
-        logger.info('Copying %s to %s', infile, outfile)
-        if not self.dry_run:
-            msg = None
-            if check:
-                if os.path.islink(outfile):
-                    msg = '%s is a symlink' % outfile
-                elif os.path.exists(outfile) and not os.path.isfile(outfile):
-                    msg = '%s is a non-regular file' % outfile
-            if msg:
-                raise ValueError(msg + ' which would be overwritten')
-            shutil.copyfile(infile, outfile)
-        self.record_as_written(outfile)
-
-    def copy_stream(self, instream, outfile, encoding=None):
-        assert not os.path.isdir(outfile)
-        self.ensure_dir(os.path.dirname(outfile))
-        logger.info('Copying stream %s to %s', instream, outfile)
-        if not self.dry_run:
-            if encoding is None:
-                outstream = open(outfile, 'wb')
-            else:
-                outstream = codecs.open(outfile, 'w', encoding=encoding)
-            try:
-                shutil.copyfileobj(instream, outstream)
-            finally:
-                outstream.close()
-        self.record_as_written(outfile)
-
-    def write_binary_file(self, path, data):
-        self.ensure_dir(os.path.dirname(path))
-        if not self.dry_run:
-            if os.path.exists(path):
-                os.remove(path)
-            with open(path, 'wb') as f:
-                f.write(data)
-        self.record_as_written(path)
-
-    def write_text_file(self, path, data, encoding):
-        self.write_binary_file(path, data.encode(encoding))
-
-    def set_mode(self, bits, mask, files):
-        if os.name == 'posix' or (os.name == 'java' and os._name == 'posix'):
-            # Set the executable bits (owner, group, and world) on
-            # all the files specified.
-            for f in files:
-                if self.dry_run:
-                    logger.info("changing mode of %s", f)
-                else:
-                    mode = (os.stat(f).st_mode | bits) & mask
-                    logger.info("changing mode of %s to %o", f, mode)
-                    os.chmod(f, mode)
-
-    set_executable_mode = lambda s, f: s.set_mode(0o555, 0o7777, f)
-
-    def ensure_dir(self, path):
-        path = os.path.abspath(path)
-        if path not in self.ensured and not os.path.exists(path):
-            self.ensured.add(path)
-            d, f = os.path.split(path)
-            self.ensure_dir(d)
-            logger.info('Creating %s' % path)
-            if not self.dry_run:
-                os.mkdir(path)
-            if self.record:
-                self.dirs_created.add(path)
-
-    def byte_compile(self, path, optimize=False, force=False, prefix=None, hashed_invalidation=False):
-        dpath = cache_from_source(path, not optimize)
-        logger.info('Byte-compiling %s to %s', path, dpath)
-        if not self.dry_run:
-            if force or self.newer(path, dpath):
-                if not prefix:
-                    diagpath = None
-                else:
-                    assert path.startswith(prefix)
-                    diagpath = path[len(prefix):]
-            compile_kwargs = {}
-            if hashed_invalidation and hasattr(py_compile, 'PycInvalidationMode'):
-                compile_kwargs['invalidation_mode'] = py_compile.PycInvalidationMode.CHECKED_HASH
-            py_compile.compile(path, dpath, diagpath, True, **compile_kwargs)     # raise error
-        self.record_as_written(dpath)
-        return dpath
-
-    def ensure_removed(self, path):
-        if os.path.exists(path):
-            if os.path.isdir(path) and not os.path.islink(path):
-                logger.debug('Removing directory tree at %s', path)
-                if not self.dry_run:
-                    shutil.rmtree(path)
-                if self.record:
-                    if path in self.dirs_created:
-                        self.dirs_created.remove(path)
-            else:
-                if os.path.islink(path):
-                    s = 'link'
-                else:
-                    s = 'file'
-                logger.debug('Removing %s %s', s, path)
-                if not self.dry_run:
-                    os.remove(path)
-                if self.record:
-                    if path in self.files_written:
-                        self.files_written.remove(path)
-
-    def is_writable(self, path):
-        result = False
-        while not result:
-            if os.path.exists(path):
-                result = os.access(path, os.W_OK)
-                break
-            parent = os.path.dirname(path)
-            if parent == path:
-                break
-            path = parent
-        return result
-
-    def commit(self):
-        """
-        Commit recorded changes, turn off recording, return
-        changes.
-        """
-        assert self.record
-        result = self.files_written, self.dirs_created
-        self._init_record()
-        return result
-
-    def rollback(self):
-        if not self.dry_run:
-            for f in list(self.files_written):
-                if os.path.exists(f):
-                    os.remove(f)
-            # dirs should all be empty now, except perhaps for
-            # __pycache__ subdirs
-            # reverse so that subdirs appear before their parents
-            dirs = sorted(self.dirs_created, reverse=True)
-            for d in dirs:
-                flist = os.listdir(d)
-                if flist:
-                    assert flist == ['__pycache__']
-                    sd = os.path.join(d, flist[0])
-                    os.rmdir(sd)
-                os.rmdir(d)     # should fail if non-empty
-        self._init_record()
-
-def resolve(module_name, dotted_path):
-    if module_name in sys.modules:
-        mod = sys.modules[module_name]
-    else:
-        mod = __import__(module_name)
-    if dotted_path is None:
-        result = mod
-    else:
-        parts = dotted_path.split('.')
-        result = getattr(mod, parts.pop(0))
-        for p in parts:
-            result = getattr(result, p)
-    return result
-
-
-class ExportEntry(object):
-    def __init__(self, name, prefix, suffix, flags):
-        self.name = name
-        self.prefix = prefix
-        self.suffix = suffix
-        self.flags = flags
-
-    @cached_property
-    def value(self):
-        return resolve(self.prefix, self.suffix)
-
-    def __repr__(self):  # pragma: no cover
-        return '<ExportEntry %s = %s:%s %s>' % (self.name, self.prefix,
-                                                self.suffix, self.flags)
-
-    def __eq__(self, other):
-        if not isinstance(other, ExportEntry):
-            result = False
-        else:
-            result = (self.name == other.name and
-                      self.prefix == other.prefix and
-                      self.suffix == other.suffix and
-                      self.flags == other.flags)
-        return result
-
-    __hash__ = object.__hash__
-
-
-ENTRY_RE = re.compile(r'''(?P<name>(\w|[-.+])+)
-                      \s*=\s*(?P<callable>(\w+)([:\.]\w+)*)
-                      \s*(\[\s*(?P<flags>[\w-]+(=\w+)?(,\s*\w+(=\w+)?)*)\s*\])?
-                      ''', re.VERBOSE)
-
-def get_export_entry(specification):
-    m = ENTRY_RE.search(specification)
-    if not m:
-        result = None
-        if '[' in specification or ']' in specification:
-            raise DistlibException("Invalid specification "
-                                   "'%s'" % specification)
-    else:
-        d = m.groupdict()
-        name = d['name']
-        path = d['callable']
-        colons = path.count(':')
-        if colons == 0:
-            prefix, suffix = path, None
-        else:
-            if colons != 1:
-                raise DistlibException("Invalid specification "
-                                       "'%s'" % specification)
-            prefix, suffix = path.split(':')
-        flags = d['flags']
-        if flags is None:
-            if '[' in specification or ']' in specification:
-                raise DistlibException("Invalid specification "
-                                       "'%s'" % specification)
-            flags = []
-        else:
-            flags = [f.strip() for f in flags.split(',')]
-        result = ExportEntry(name, prefix, suffix, flags)
-    return result
-
-
-def get_cache_base(suffix=None):
-    """
-    Return the default base location for distlib caches. If the directory does
-    not exist, it is created. Use the suffix provided for the base directory,
-    and default to '.distlib' if it isn't provided.
-
-    On Windows, if LOCALAPPDATA is defined in the environment, then it is
-    assumed to be a directory, and will be the parent directory of the result.
-    On POSIX, and on Windows if LOCALAPPDATA is not defined, the user's home
-    directory - using os.expanduser('~') - will be the parent directory of
-    the result.
-
-    The result is just the directory '.distlib' in the parent directory as
-    determined above, or with the name specified with ``suffix``.
-    """
-    if suffix is None:
-        suffix = '.distlib'
-    if os.name == 'nt' and 'LOCALAPPDATA' in os.environ:
-        result = os.path.expandvars('$localappdata')
-    else:
-        # Assume posix, or old Windows
-        result = os.path.expanduser('~')
-    # we use 'isdir' instead of 'exists', because we want to
-    # fail if there's a file with that name
-    if os.path.isdir(result):
-        usable = os.access(result, os.W_OK)
-        if not usable:
-            logger.warning('Directory exists but is not writable: %s', result)
-    else:
-        try:
-            os.makedirs(result)
-            usable = True
-        except OSError:
-            logger.warning('Unable to create %s', result, exc_info=True)
-            usable = False
-    if not usable:
-        result = tempfile.mkdtemp()
-        logger.warning('Default location unusable, using %s', result)
-    return os.path.join(result, suffix)
-
-
-def path_to_cache_dir(path):
-    """
-    Convert an absolute path to a directory name for use in a cache.
-
-    The algorithm used is:
-
-    #. On Windows, any ``':'`` in the drive is replaced with ``'---'``.
-    #. Any occurrence of ``os.sep`` is replaced with ``'--'``.
-    #. ``'.cache'`` is appended.
-    """
-    d, p = os.path.splitdrive(os.path.abspath(path))
-    if d:
-        d = d.replace(':', '---')
-    p = p.replace(os.sep, '--')
-    return d + p + '.cache'
-
-
-def ensure_slash(s):
-    if not s.endswith('/'):
-        return s + '/'
-    return s
-
-
-def parse_credentials(netloc):
-    username = password = None
-    if '@' in netloc:
-        prefix, netloc = netloc.rsplit('@', 1)
-        if ':' not in prefix:
-            username = prefix
-        else:
-            username, password = prefix.split(':', 1)
-    if username:
-        username = unquote(username)
-    if password:
-        password = unquote(password)
-    return username, password, netloc
-
-
-def get_process_umask():
-    result = os.umask(0o22)
-    os.umask(result)
-    return result
-
-def is_string_sequence(seq):
-    result = True
-    i = None
-    for i, s in enumerate(seq):
-        if not isinstance(s, string_types):
-            result = False
-            break
-    assert i is not None
-    return result
-
-PROJECT_NAME_AND_VERSION = re.compile('([a-z0-9_]+([.-][a-z_][a-z0-9_]*)*)-'
-                                      '([a-z0-9_.+-]+)', re.I)
-PYTHON_VERSION = re.compile(r'-py(\d\.?\d?)')
-
-
-def split_filename(filename, project_name=None):
-    """
-    Extract name, version, python version from a filename (no extension)
-
-    Return name, version, pyver or None
-    """
-    result = None
-    pyver = None
-    filename = unquote(filename).replace(' ', '-')
-    m = PYTHON_VERSION.search(filename)
-    if m:
-        pyver = m.group(1)
-        filename = filename[:m.start()]
-    if project_name and len(filename) > len(project_name) + 1:
-        m = re.match(re.escape(project_name) + r'\b', filename)
-        if m:
-            n = m.end()
-            result = filename[:n], filename[n + 1:], pyver
-    if result is None:
-        m = PROJECT_NAME_AND_VERSION.match(filename)
-        if m:
-            result = m.group(1), m.group(3), pyver
-    return result
-
-# Allow spaces in name because of legacy dists like "Twisted Core"
-NAME_VERSION_RE = re.compile(r'(?P<name>[\w .-]+)\s*'
-                             r'\(\s*(?P<ver>[^\s)]+)\)$')
-
-def parse_name_and_version(p):
-    """
-    A utility method used to get name and version from a string.
-
-    From e.g. a Provides-Dist value.
-
-    :param p: A value in a form 'foo (1.0)'
-    :return: The name and version as a tuple.
-    """
-    m = NAME_VERSION_RE.match(p)
-    if not m:
-        raise DistlibException('Ill-formed name/version string: \'%s\'' % p)
-    d = m.groupdict()
-    return d['name'].strip().lower(), d['ver']
-
-def get_extras(requested, available):
-    result = set()
-    requested = set(requested or [])
-    available = set(available or [])
-    if '*' in requested:
-        requested.remove('*')
-        result |= available
-    for r in requested:
-        if r == '-':
-            result.add(r)
-        elif r.startswith('-'):
-            unwanted = r[1:]
-            if unwanted not in available:
-                logger.warning('undeclared extra: %s' % unwanted)
-            if unwanted in result:
-                result.remove(unwanted)
-        else:
-            if r not in available:
-                logger.warning('undeclared extra: %s' % r)
-            result.add(r)
-    return result
-#
-# Extended metadata functionality
-#
-
-def _get_external_data(url):
-    result = {}
-    try:
-        # urlopen might fail if it runs into redirections,
-        # because of Python issue #13696. Fixed in locators
-        # using a custom redirect handler.
-        resp = urlopen(url)
-        headers = resp.info()
-        ct = headers.get('Content-Type')
-        if not ct.startswith('application/json'):
-            logger.debug('Unexpected response for JSON request: %s', ct)
-        else:
-            reader = codecs.getreader('utf-8')(resp)
-            #data = reader.read().decode('utf-8')
-            #result = json.loads(data)
-            result = json.load(reader)
-    except Exception as e:
-        logger.exception('Failed to get external data for %s: %s', url, e)
-    return result
-
-_external_data_base_url = 'https://www.red-dove.com/pypi/projects/'
-
-def get_project_data(name):
-    url = '%s/%s/project.json' % (name[0].upper(), name)
-    url = urljoin(_external_data_base_url, url)
-    result = _get_external_data(url)
-    return result
-
-def get_package_data(name, version):
-    url = '%s/%s/package-%s.json' % (name[0].upper(), name, version)
-    url = urljoin(_external_data_base_url, url)
-    return _get_external_data(url)
-
-
-class Cache(object):
-    """
-    A class implementing a cache for resources that need to live in the file system
-    e.g. shared libraries. This class was moved from resources to here because it
-    could be used by other modules, e.g. the wheel module.
-    """
-
-    def __init__(self, base):
-        """
-        Initialise an instance.
-
-        :param base: The base directory where the cache should be located.
-        """
-        # we use 'isdir' instead of 'exists', because we want to
-        # fail if there's a file with that name
-        if not os.path.isdir(base):  # pragma: no cover
-            os.makedirs(base)
-        if (os.stat(base).st_mode & 0o77) != 0:
-            logger.warning('Directory \'%s\' is not private', base)
-        self.base = os.path.abspath(os.path.normpath(base))
-
-    def prefix_to_dir(self, prefix):
-        """
-        Converts a resource prefix to a directory name in the cache.
-        """
-        return path_to_cache_dir(prefix)
-
-    def clear(self):
-        """
-        Clear the cache.
-        """
-        not_removed = []
-        for fn in os.listdir(self.base):
-            fn = os.path.join(self.base, fn)
-            try:
-                if os.path.islink(fn) or os.path.isfile(fn):
-                    os.remove(fn)
-                elif os.path.isdir(fn):
-                    shutil.rmtree(fn)
-            except Exception:
-                not_removed.append(fn)
-        return not_removed
-
-
-class EventMixin(object):
-    """
-    A very simple publish/subscribe system.
-    """
-    def __init__(self):
-        self._subscribers = {}
-
-    def add(self, event, subscriber, append=True):
-        """
-        Add a subscriber for an event.
-
-        :param event: The name of an event.
-        :param subscriber: The subscriber to be added (and called when the
-                           event is published).
-        :param append: Whether to append or prepend the subscriber to an
-                       existing subscriber list for the event.
-        """
-        subs = self._subscribers
-        if event not in subs:
-            subs[event] = deque([subscriber])
-        else:
-            sq = subs[event]
-            if append:
-                sq.append(subscriber)
-            else:
-                sq.appendleft(subscriber)
-
-    def remove(self, event, subscriber):
-        """
-        Remove a subscriber for an event.
-
-        :param event: The name of an event.
-        :param subscriber: The subscriber to be removed.
-        """
-        subs = self._subscribers
-        if event not in subs:
-            raise ValueError('No subscribers: %r' % event)
-        subs[event].remove(subscriber)
-
-    def get_subscribers(self, event):
-        """
-        Return an iterator for the subscribers for an event.
-        :param event: The event to return subscribers for.
-        """
-        return iter(self._subscribers.get(event, ()))
-
-    def publish(self, event, *args, **kwargs):
-        """
-        Publish a event and return a list of values returned by its
-        subscribers.
-
-        :param event: The event to publish.
-        :param args: The positional arguments to pass to the event's
-                     subscribers.
-        :param kwargs: The keyword arguments to pass to the event's
-                       subscribers.
-        """
-        result = []
-        for subscriber in self.get_subscribers(event):
-            try:
-                value = subscriber(event, *args, **kwargs)
-            except Exception:
-                logger.exception('Exception during event publication')
-                value = None
-            result.append(value)
-        logger.debug('publish %s: args = %s, kwargs = %s, result = %s',
-                     event, args, kwargs, result)
-        return result
-
-#
-# Simple sequencing
-#
-class Sequencer(object):
-    def __init__(self):
-        self._preds = {}
-        self._succs = {}
-        self._nodes = set()     # nodes with no preds/succs
-
-    def add_node(self, node):
-        self._nodes.add(node)
-
-    def remove_node(self, node, edges=False):
-        if node in self._nodes:
-            self._nodes.remove(node)
-        if edges:
-            for p in set(self._preds.get(node, ())):
-                self.remove(p, node)
-            for s in set(self._succs.get(node, ())):
-                self.remove(node, s)
-            # Remove empties
-            for k, v in list(self._preds.items()):
-                if not v:
-                    del self._preds[k]
-            for k, v in list(self._succs.items()):
-                if not v:
-                    del self._succs[k]
-
-    def add(self, pred, succ):
-        assert pred != succ
-        self._preds.setdefault(succ, set()).add(pred)
-        self._succs.setdefault(pred, set()).add(succ)
-
-    def remove(self, pred, succ):
-        assert pred != succ
-        try:
-            preds = self._preds[succ]
-            succs = self._succs[pred]
-        except KeyError:  # pragma: no cover
-            raise ValueError('%r not a successor of anything' % succ)
-        try:
-            preds.remove(pred)
-            succs.remove(succ)
-        except KeyError:  # pragma: no cover
-            raise ValueError('%r not a successor of %r' % (succ, pred))
-
-    def is_step(self, step):
-        return (step in self._preds or step in self._succs or
-                step in self._nodes)
-
-    def get_steps(self, final):
-        if not self.is_step(final):
-            raise ValueError('Unknown: %r' % final)
-        result = []
-        todo = []
-        seen = set()
-        todo.append(final)
-        while todo:
-            step = todo.pop(0)
-            if step in seen:
-                # if a step was already seen,
-                # move it to the end (so it will appear earlier
-                # when reversed on return) ... but not for the
-                # final step, as that would be confusing for
-                # users
-                if step != final:
-                    result.remove(step)
-                    result.append(step)
-            else:
-                seen.add(step)
-                result.append(step)
-                preds = self._preds.get(step, ())
-                todo.extend(preds)
-        return reversed(result)
-
-    @property
-    def strong_connections(self):
-        #http://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm
-        index_counter = [0]
-        stack = []
-        lowlinks = {}
-        index = {}
-        result = []
-
-        graph = self._succs
-
-        def strongconnect(node):
-            # set the depth index for this node to the smallest unused index
-            index[node] = index_counter[0]
-            lowlinks[node] = index_counter[0]
-            index_counter[0] += 1
-            stack.append(node)
-
-            # Consider successors
-            try:
-                successors = graph[node]
-            except Exception:
-                successors = []
-            for successor in successors:
-                if successor not in lowlinks:
-                    # Successor has not yet been visited
-                    strongconnect(successor)
-                    lowlinks[node] = min(lowlinks[node],lowlinks[successor])
-                elif successor in stack:
-                    # the successor is in the stack and hence in the current
-                    # strongly connected component (SCC)
-                    lowlinks[node] = min(lowlinks[node],index[successor])
-
-            # If `node` is a root node, pop the stack and generate an SCC
-            if lowlinks[node] == index[node]:
-                connected_component = []
-
-                while True:
-                    successor = stack.pop()
-                    connected_component.append(successor)
-                    if successor == node: break
-                component = tuple(connected_component)
-                # storing the result
-                result.append(component)
-
-        for node in graph:
-            if node not in lowlinks:
-                strongconnect(node)
-
-        return result
-
-    @property
-    def dot(self):
-        result = ['digraph G {']
-        for succ in self._preds:
-            preds = self._preds[succ]
-            for pred in preds:
-                result.append('  %s -> %s;' % (pred, succ))
-        for node in self._nodes:
-            result.append('  %s;' % node)
-        result.append('}')
-        return '\n'.join(result)
-
-#
-# Unarchiving functionality for zip, tar, tgz, tbz, whl
-#
-
-ARCHIVE_EXTENSIONS = ('.tar.gz', '.tar.bz2', '.tar', '.zip',
-                      '.tgz', '.tbz', '.whl')
-
-def unarchive(archive_filename, dest_dir, format=None, check=True):
-
-    def check_path(path):
-        if not isinstance(path, text_type):
-            path = path.decode('utf-8')
-        p = os.path.abspath(os.path.join(dest_dir, path))
-        if not p.startswith(dest_dir) or p[plen] != os.sep:
-            raise ValueError('path outside destination: %r' % p)
-
-    dest_dir = os.path.abspath(dest_dir)
-    plen = len(dest_dir)
-    archive = None
-    if format is None:
-        if archive_filename.endswith(('.zip', '.whl')):
-            format = 'zip'
-        elif archive_filename.endswith(('.tar.gz', '.tgz')):
-            format = 'tgz'
-            mode = 'r:gz'
-        elif archive_filename.endswith(('.tar.bz2', '.tbz')):
-            format = 'tbz'
-            mode = 'r:bz2'
-        elif archive_filename.endswith('.tar'):
-            format = 'tar'
-            mode = 'r'
-        else:  # pragma: no cover
-            raise ValueError('Unknown format for %r' % archive_filename)
-    try:
-        if format == 'zip':
-            archive = ZipFile(archive_filename, 'r')
-            if check:
-                names = archive.namelist()
-                for name in names:
-                    check_path(name)
-        else:
-            archive = tarfile.open(archive_filename, mode)
-            if check:
-                names = archive.getnames()
-                for name in names:
-                    check_path(name)
-        if format != 'zip' and sys.version_info[0] < 3:
-            # See Python issue 17153. If the dest path contains Unicode,
-            # tarfile extraction fails on Python 2.x if a member path name
-            # contains non-ASCII characters - it leads to an implicit
-            # bytes -> unicode conversion using ASCII to decode.
-            for tarinfo in archive.getmembers():
-                if not isinstance(tarinfo.name, text_type):
-                    tarinfo.name = tarinfo.name.decode('utf-8')
-        archive.extractall(dest_dir)
-
-    finally:
-        if archive:
-            archive.close()
-
-
-def zip_dir(directory):
-    """zip a directory tree into a BytesIO object"""
-    result = io.BytesIO()
-    dlen = len(directory)
-    with ZipFile(result, "w") as zf:
-        for root, dirs, files in os.walk(directory):
-            for name in files:
-                full = os.path.join(root, name)
-                rel = root[dlen:]
-                dest = os.path.join(rel, name)
-                zf.write(full, dest)
-    return result
-
-#
-# Simple progress bar
-#
-
-UNITS = ('', 'K', 'M', 'G','T','P')
-
-
-class Progress(object):
-    unknown = 'UNKNOWN'
-
-    def __init__(self, minval=0, maxval=100):
-        assert maxval is None or maxval >= minval
-        self.min = self.cur = minval
-        self.max = maxval
-        self.started = None
-        self.elapsed = 0
-        self.done = False
-
-    def update(self, curval):
-        assert self.min <= curval
-        assert self.max is None or curval <= self.max
-        self.cur = curval
-        now = time.time()
-        if self.started is None:
-            self.started = now
-        else:
-            self.elapsed = now - self.started
-
-    def increment(self, incr):
-        assert incr >= 0
-        self.update(self.cur + incr)
-
-    def start(self):
-        self.update(self.min)
-        return self
-
-    def stop(self):
-        if self.max is not None:
-            self.update(self.max)
-        self.done = True
-
-    @property
-    def maximum(self):
-        return self.unknown if self.max is None else self.max
-
-    @property
-    def percentage(self):
-        if self.done:
-            result = '100 %'
-        elif self.max is None:
-            result = ' ?? %'
-        else:
-            v = 100.0 * (self.cur - self.min) / (self.max - self.min)
-            result = '%3d %%' % v
-        return result
-
-    def format_duration(self, duration):
-        if (duration <= 0) and self.max is None or self.cur == self.min:
-            result = '??:??:??'
-        #elif duration < 1:
-        #    result = '--:--:--'
-        else:
-            result = time.strftime('%H:%M:%S', time.gmtime(duration))
-        return result
-
-    @property
-    def ETA(self):
-        if self.done:
-            prefix = 'Done'
-            t = self.elapsed
-            #import pdb; pdb.set_trace()
-        else:
-            prefix = 'ETA '
-            if self.max is None:
-                t = -1
-            elif self.elapsed == 0 or (self.cur == self.min):
-                t = 0
-            else:
-                #import pdb; pdb.set_trace()
-                t = float(self.max - self.min)
-                t /= self.cur - self.min
-                t = (t - 1) * self.elapsed
-        return '%s: %s' % (prefix, self.format_duration(t))
-
-    @property
-    def speed(self):
-        if self.elapsed == 0:
-            result = 0.0
-        else:
-            result = (self.cur - self.min) / self.elapsed
-        for unit in UNITS:
-            if result < 1000:
-                break
-            result /= 1000.0
-        return '%d %sB/s' % (result, unit)
-
-#
-# Glob functionality
-#
-
-RICH_GLOB = re.compile(r'\{([^}]*)\}')
-_CHECK_RECURSIVE_GLOB = re.compile(r'[^/\\,{]\*\*|\*\*[^/\\,}]')
-_CHECK_MISMATCH_SET = re.compile(r'^[^{]*\}|\{[^}]*$')
-
-
-def iglob(path_glob):
-    """Extended globbing function that supports ** and {opt1,opt2,opt3}."""
-    if _CHECK_RECURSIVE_GLOB.search(path_glob):
-        msg = """invalid glob %r: recursive glob "**" must be used alone"""
-        raise ValueError(msg % path_glob)
-    if _CHECK_MISMATCH_SET.search(path_glob):
-        msg = """invalid glob %r: mismatching set marker '{' or '}'"""
-        raise ValueError(msg % path_glob)
-    return _iglob(path_glob)
-
-
-def _iglob(path_glob):
-    rich_path_glob = RICH_GLOB.split(path_glob, 1)
-    if len(rich_path_glob) > 1:
-        assert len(rich_path_glob) == 3, rich_path_glob
-        prefix, set, suffix = rich_path_glob
-        for item in set.split(','):
-            for path in _iglob(''.join((prefix, item, suffix))):
-                yield path
-    else:
-        if '**' not in path_glob:
-            for item in std_iglob(path_glob):
-                yield item
-        else:
-            prefix, radical = path_glob.split('**', 1)
-            if prefix == '':
-                prefix = '.'
-            if radical == '':
-                radical = '*'
-            else:
-                # we support both
-                radical = radical.lstrip('/')
-                radical = radical.lstrip('\\')
-            for path, dir, files in os.walk(prefix):
-                path = os.path.normpath(path)
-                for fn in _iglob(os.path.join(path, radical)):
-                    yield fn
-
-if ssl:
-    from .compat import (HTTPSHandler as BaseHTTPSHandler, match_hostname,
-                         CertificateError)
-
-
-#
-# HTTPSConnection which verifies certificates/matches domains
-#
-
-    class HTTPSConnection(httplib.HTTPSConnection):
-        ca_certs = None # set this to the path to the certs file (.pem)
-        check_domain = True # only used if ca_certs is not None
-
-        # noinspection PyPropertyAccess
-        def connect(self):
-            sock = socket.create_connection((self.host, self.port), self.timeout)
-            if getattr(self, '_tunnel_host', False):
-                self.sock = sock
-                self._tunnel()
-
-            context = ssl.SSLContext(ssl.PROTOCOL_SSLv23)
-            if hasattr(ssl, 'OP_NO_SSLv2'):
-                context.options |= ssl.OP_NO_SSLv2
-            if self.cert_file:
-                context.load_cert_chain(self.cert_file, self.key_file)
-            kwargs = {}
-            if self.ca_certs:
-                context.verify_mode = ssl.CERT_REQUIRED
-                context.load_verify_locations(cafile=self.ca_certs)
-                if getattr(ssl, 'HAS_SNI', False):
-                    kwargs['server_hostname'] = self.host
-
-            self.sock = context.wrap_socket(sock, **kwargs)
-            if self.ca_certs and self.check_domain:
-                try:
-                    match_hostname(self.sock.getpeercert(), self.host)
-                    logger.debug('Host verified: %s', self.host)
-                except CertificateError:  # pragma: no cover
-                    self.sock.shutdown(socket.SHUT_RDWR)
-                    self.sock.close()
-                    raise
-
-    class HTTPSHandler(BaseHTTPSHandler):
-        def __init__(self, ca_certs, check_domain=True):
-            BaseHTTPSHandler.__init__(self)
-            self.ca_certs = ca_certs
-            self.check_domain = check_domain
-
-        def _conn_maker(self, *args, **kwargs):
-            """
-            This is called to create a connection instance. Normally you'd
-            pass a connection class to do_open, but it doesn't actually check for
-            a class, and just expects a callable. As long as we behave just as a
-            constructor would have, we should be OK. If it ever changes so that
-            we *must* pass a class, we'll create an UnsafeHTTPSConnection class
-            which just sets check_domain to False in the class definition, and
-            choose which one to pass to do_open.
-            """
-            result = HTTPSConnection(*args, **kwargs)
-            if self.ca_certs:
-                result.ca_certs = self.ca_certs
-                result.check_domain = self.check_domain
-            return result
-
-        def https_open(self, req):
-            try:
-                return self.do_open(self._conn_maker, req)
-            except URLError as e:
-                if 'certificate verify failed' in str(e.reason):
-                    raise CertificateError('Unable to verify server certificate '
-                                           'for %s' % req.host)
-                else:
-                    raise
-
-    #
-    # To prevent against mixing HTTP traffic with HTTPS (examples: A Man-In-The-
-    # Middle proxy using HTTP listens on port 443, or an index mistakenly serves
-    # HTML containing a http://xyz link when it should be https://xyz),
-    # you can use the following handler class, which does not allow HTTP traffic.
-    #
-    # It works by inheriting from HTTPHandler - so build_opener won't add a
-    # handler for HTTP itself.
-    #
-    class HTTPSOnlyHandler(HTTPSHandler, HTTPHandler):
-        def http_open(self, req):
-            raise URLError('Unexpected HTTP request on what should be a secure '
-                           'connection: %s' % req)
-
-#
-# XML-RPC with timeouts
-#
-class Transport(xmlrpclib.Transport):
-    def __init__(self, timeout, use_datetime=0):
-        self.timeout = timeout
-        xmlrpclib.Transport.__init__(self, use_datetime)
-
-    def make_connection(self, host):
-        h, eh, x509 = self.get_host_info(host)
-        if not self._connection or host != self._connection[0]:
-            self._extra_headers = eh
-            self._connection = host, httplib.HTTPConnection(h)
-        return self._connection[1]
-
-if ssl:
-    class SafeTransport(xmlrpclib.SafeTransport):
-        def __init__(self, timeout, use_datetime=0):
-            self.timeout = timeout
-            xmlrpclib.SafeTransport.__init__(self, use_datetime)
-
-        def make_connection(self, host):
-            h, eh, kwargs = self.get_host_info(host)
-            if not kwargs:
-                kwargs = {}
-            kwargs['timeout'] = self.timeout
-            if not self._connection or host != self._connection[0]:
-                self._extra_headers = eh
-                self._connection = host, httplib.HTTPSConnection(h, None,
-                                                                 **kwargs)
-            return self._connection[1]
-
-
-class ServerProxy(xmlrpclib.ServerProxy):
-    def __init__(self, uri, **kwargs):
-        self.timeout = timeout = kwargs.pop('timeout', None)
-        # The above classes only come into play if a timeout
-        # is specified
-        if timeout is not None:
-            # scheme = splittype(uri)  # deprecated as of Python 3.8
-            scheme = urlparse(uri)[0]
-            use_datetime = kwargs.get('use_datetime', 0)
-            if scheme == 'https':
-                tcls = SafeTransport
-            else:
-                tcls = Transport
-            kwargs['transport'] = t = tcls(timeout, use_datetime=use_datetime)
-            self.transport = t
-        xmlrpclib.ServerProxy.__init__(self, uri, **kwargs)
-
-#
-# CSV functionality. This is provided because on 2.x, the csv module can't
-# handle Unicode. However, we need to deal with Unicode in e.g. RECORD files.
-#
-
-def _csv_open(fn, mode, **kwargs):
-    if sys.version_info[0] < 3:
-        mode += 'b'
-    else:
-        kwargs['newline'] = ''
-        # Python 3 determines encoding from locale. Force 'utf-8'
-        # file encoding to match other forced utf-8 encoding
-        kwargs['encoding'] = 'utf-8'
-    return open(fn, mode, **kwargs)
-
-
-class CSVBase(object):
-    defaults = {
-        'delimiter': str(','),      # The strs are used because we need native
-        'quotechar': str('"'),      # str in the csv API (2.x won't take
-        'lineterminator': str('\n') # Unicode)
-    }
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *exc_info):
-        self.stream.close()
-
-
-class CSVReader(CSVBase):
-    def __init__(self, **kwargs):
-        if 'stream' in kwargs:
-            stream = kwargs['stream']
-            if sys.version_info[0] >= 3:
-                # needs to be a text stream
-                stream = codecs.getreader('utf-8')(stream)
-            self.stream = stream
-        else:
-            self.stream = _csv_open(kwargs['path'], 'r')
-        self.reader = csv.reader(self.stream, **self.defaults)
-
-    def __iter__(self):
-        return self
-
-    def next(self):
-        result = next(self.reader)
-        if sys.version_info[0] < 3:
-            for i, item in enumerate(result):
-                if not isinstance(item, text_type):
-                    result[i] = item.decode('utf-8')
-        return result
-
-    __next__ = next
-
-class CSVWriter(CSVBase):
-    def __init__(self, fn, **kwargs):
-        self.stream = _csv_open(fn, 'w')
-        self.writer = csv.writer(self.stream, **self.defaults)
-
-    def writerow(self, row):
-        if sys.version_info[0] < 3:
-            r = []
-            for item in row:
-                if isinstance(item, text_type):
-                    item = item.encode('utf-8')
-                r.append(item)
-            row = r
-        self.writer.writerow(row)
-
-#
-#   Configurator functionality
-#
-
-class Configurator(BaseConfigurator):
-
-    value_converters = dict(BaseConfigurator.value_converters)
-    value_converters['inc'] = 'inc_convert'
-
-    def __init__(self, config, base=None):
-        super(Configurator, self).__init__(config)
-        self.base = base or os.getcwd()
-
-    def configure_custom(self, config):
-        def convert(o):
-            if isinstance(o, (list, tuple)):
-                result = type(o)([convert(i) for i in o])
-            elif isinstance(o, dict):
-                if '()' in o:
-                    result = self.configure_custom(o)
-                else:
-                    result = {}
-                    for k in o:
-                        result[k] = convert(o[k])
-            else:
-                result = self.convert(o)
-            return result
-
-        c = config.pop('()')
-        if not callable(c):
-            c = self.resolve(c)
-        props = config.pop('.', None)
-        # Check for valid identifiers
-        args = config.pop('[]', ())
-        if args:
-            args = tuple([convert(o) for o in args])
-        items = [(k, convert(config[k])) for k in config if valid_ident(k)]
-        kwargs = dict(items)
-        result = c(*args, **kwargs)
-        if props:
-            for n, v in props.items():
-                setattr(result, n, convert(v))
-        return result
-
-    def __getitem__(self, key):
-        result = self.config[key]
-        if isinstance(result, dict) and '()' in result:
-            self.config[key] = result = self.configure_custom(result)
-        return result
-
-    def inc_convert(self, value):
-        """Default converter for the inc:// protocol."""
-        if not os.path.isabs(value):
-            value = os.path.join(self.base, value)
-        with codecs.open(value, 'r', encoding='utf-8') as f:
-            result = json.load(f)
-        return result
-
-
-class SubprocessMixin(object):
-    """
-    Mixin for running subprocesses and capturing their output
-    """
-    def __init__(self, verbose=False, progress=None):
-        self.verbose = verbose
-        self.progress = progress
-
-    def reader(self, stream, context):
-        """
-        Read lines from a subprocess' output stream and either pass to a progress
-        callable (if specified) or write progress information to sys.stderr.
-        """
-        progress = self.progress
-        verbose = self.verbose
-        while True:
-            s = stream.readline()
-            if not s:
-                break
-            if progress is not None:
-                progress(s, context)
-            else:
-                if not verbose:
-                    sys.stderr.write('.')
-                else:
-                    sys.stderr.write(s.decode('utf-8'))
-                sys.stderr.flush()
-        stream.close()
-
-    def run_command(self, cmd, **kwargs):
-        p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
-                             stderr=subprocess.PIPE, **kwargs)
-        t1 = threading.Thread(target=self.reader, args=(p.stdout, 'stdout'))
-        t1.start()
-        t2 = threading.Thread(target=self.reader, args=(p.stderr, 'stderr'))
-        t2.start()
-        p.wait()
-        t1.join()
-        t2.join()
-        if self.progress is not None:
-            self.progress('done.', 'main')
-        elif self.verbose:
-            sys.stderr.write('done.\n')
-        return p
-
-
-def normalize_name(name):
-    """Normalize a python package name a la PEP 503"""
-    # https://www.python.org/dev/peps/pep-0503/#normalized-names
-    return re.sub('[-_.]+', '-', name).lower()
-
-# def _get_pypirc_command():
-    # """
-    # Get the distutils command for interacting with PyPI configurations.
-    # :return: the command.
-    # """
-    # from distutils.core import Distribution
-    # from distutils.config import PyPIRCCommand
-    # d = Distribution()
-    # return PyPIRCCommand(d)
-
-class PyPIRCFile(object):
-
-    DEFAULT_REPOSITORY = 'https://upload.pypi.org/legacy/'
-    DEFAULT_REALM = 'pypi'
-
-    def __init__(self, fn=None, url=None):
-        if fn is None:
-            fn = os.path.join(os.path.expanduser('~'), '.pypirc')
-        self.filename = fn
-        self.url = url
-
-    def read(self):
-        result = {}
-
-        if os.path.exists(self.filename):
-            repository = self.url or self.DEFAULT_REPOSITORY
-
-            config = configparser.RawConfigParser()
-            config.read(self.filename)
-            sections = config.sections()
-            if 'distutils' in sections:
-                # let's get the list of servers
-                index_servers = config.get('distutils', 'index-servers')
-                _servers = [server.strip() for server in
-                            index_servers.split('\n')
-                            if server.strip() != '']
-                if _servers == []:
-                    # nothing set, let's try to get the default pypi
-                    if 'pypi' in sections:
-                        _servers = ['pypi']
-                else:
-                    for server in _servers:
-                        result = {'server': server}
-                        result['username'] = config.get(server, 'username')
-
-                        # optional params
-                        for key, default in (('repository', self.DEFAULT_REPOSITORY),
-                                             ('realm', self.DEFAULT_REALM),
-                                             ('password', None)):
-                            if config.has_option(server, key):
-                                result[key] = config.get(server, key)
-                            else:
-                                result[key] = default
-
-                        # work around people having "repository" for the "pypi"
-                        # section of their config set to the HTTP (rather than
-                        # HTTPS) URL
-                        if (server == 'pypi' and
-                            repository in (self.DEFAULT_REPOSITORY, 'pypi')):
-                            result['repository'] = self.DEFAULT_REPOSITORY
-                        elif (result['server'] != repository and
-                              result['repository'] != repository):
-                            result = {}
-            elif 'server-login' in sections:
-                # old format
-                server = 'server-login'
-                if config.has_option(server, 'repository'):
-                    repository = config.get(server, 'repository')
-                else:
-                    repository = self.DEFAULT_REPOSITORY
-                result = {
-                    'username': config.get(server, 'username'),
-                    'password': config.get(server, 'password'),
-                    'repository': repository,
-                    'server': server,
-                    'realm': self.DEFAULT_REALM
-                }
-        return result
-
-    def update(self, username, password):
-        # import pdb; pdb.set_trace()
-        config = configparser.RawConfigParser()
-        fn = self.filename
-        config.read(fn)
-        if not config.has_section('pypi'):
-            config.add_section('pypi')
-        config.set('pypi', 'username', username)
-        config.set('pypi', 'password', password)
-        with open(fn, 'w') as f:
-            config.write(f)
-
-def _load_pypirc(index):
-    """
-    Read the PyPI access configuration as supported by distutils.
-    """
-    return PyPIRCFile(url=index.url).read()
-
-def _store_pypirc(index):
-    PyPIRCFile().update(index.username, index.password)
-
-#
-# get_platform()/get_host_platform() copied from Python 3.10.a0 source, with some minor
-# tweaks
-#
-
-def get_host_platform():
-    """Return a string that identifies the current platform.  This is used mainly to
-    distinguish platform-specific build directories and platform-specific built
-    distributions.  Typically includes the OS name and version and the
-    architecture (as supplied by 'os.uname()'), although the exact information
-    included depends on the OS; eg. on Linux, the kernel version isn't
-    particularly important.
-
-    Examples of returned values:
-       linux-i586
-       linux-alpha (?)
-       solaris-2.6-sun4u
-
-    Windows will return one of:
-       win-amd64 (64bit Windows on AMD64 (aka x86_64, Intel64, EM64T, etc)
-       win32 (all others - specifically, sys.platform is returned)
-
-    For other non-POSIX platforms, currently just returns 'sys.platform'.
-
-    """
-    if os.name == 'nt':
-        if 'amd64' in sys.version.lower():
-            return 'win-amd64'
-        if '(arm)' in sys.version.lower():
-            return 'win-arm32'
-        if '(arm64)' in sys.version.lower():
-            return 'win-arm64'
-        return sys.platform
-
-    # Set for cross builds explicitly
-    if "_PYTHON_HOST_PLATFORM" in os.environ:
-        return os.environ["_PYTHON_HOST_PLATFORM"]
-
-    if os.name != 'posix' or not hasattr(os, 'uname'):
-        # XXX what about the architecture? NT is Intel or Alpha,
-        # Mac OS is M68k or PPC, etc.
-        return sys.platform
-
-    # Try to distinguish various flavours of Unix
-
-    (osname, host, release, version, machine) = os.uname()
-
-    # Convert the OS name to lowercase, remove '/' characters, and translate
-    # spaces (for "Power Macintosh")
-    osname = osname.lower().replace('/', '')
-    machine = machine.replace(' ', '_').replace('/', '-')
-
-    if osname[:5] == 'linux':
-        # At least on Linux/Intel, 'machine' is the processor --
-        # i386, etc.
-        # XXX what about Alpha, SPARC, etc?
-        return  "%s-%s" % (osname, machine)
-
-    elif osname[:5] == 'sunos':
-        if release[0] >= '5':           # SunOS 5 == Solaris 2
-            osname = 'solaris'
-            release = '%d.%s' % (int(release[0]) - 3, release[2:])
-            # We can't use 'platform.architecture()[0]' because a
-            # bootstrap problem. We use a dict to get an error
-            # if some suspicious happens.
-            bitness = {2147483647:'32bit', 9223372036854775807:'64bit'}
-            machine += '.%s' % bitness[sys.maxsize]
-        # fall through to standard osname-release-machine representation
-    elif osname[:3] == 'aix':
-        from _aix_support import aix_platform
-        return aix_platform()
-    elif osname[:6] == 'cygwin':
-        osname = 'cygwin'
-        rel_re = re.compile (r'[\d.]+', re.ASCII)
-        m = rel_re.match(release)
-        if m:
-            release = m.group()
-    elif osname[:6] == 'darwin':
-        import _osx_support, distutils.sysconfig
-        osname, release, machine = _osx_support.get_platform_osx(
-                                        distutils.sysconfig.get_config_vars(),
-                                        osname, release, machine)
-
-    return '%s-%s-%s' % (osname, release, machine)
-
-
-_TARGET_TO_PLAT = {
-    'x86' : 'win32',
-    'x64' : 'win-amd64',
-    'arm' : 'win-arm32',
-}
-
-
-def get_platform():
-    if os.name != 'nt':
-        return get_host_platform()
-    cross_compilation_target = os.environ.get('VSCMD_ARG_TGT_ARCH')
-    if cross_compilation_target not in _TARGET_TO_PLAT:
-        return get_host_platform()
-    return _TARGET_TO_PLAT[cross_compilation_target]
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/_musllinux.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/_musllinux.py
deleted file mode 100644
index 8ac3059ba3c246b9a5a6fb8d14936bb07777191e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/_musllinux.py
+++ /dev/null
@@ -1,136 +0,0 @@
-"""PEP 656 support.
-
-This module implements logic to detect if the currently running Python is
-linked against musl, and what musl version is used.
-"""
-
-import contextlib
-import functools
-import operator
-import os
-import re
-import struct
-import subprocess
-import sys
-from typing import IO, Iterator, NamedTuple, Optional, Tuple
-
-
-def _read_unpacked(f: IO[bytes], fmt: str) -> Tuple[int, ...]:
-    return struct.unpack(fmt, f.read(struct.calcsize(fmt)))
-
-
-def _parse_ld_musl_from_elf(f: IO[bytes]) -> Optional[str]:
-    """Detect musl libc location by parsing the Python executable.
-
-    Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca
-    ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html
-    """
-    f.seek(0)
-    try:
-        ident = _read_unpacked(f, "16B")
-    except struct.error:
-        return None
-    if ident[:4] != tuple(b"\x7fELF"):  # Invalid magic, not ELF.
-        return None
-    f.seek(struct.calcsize("HHI"), 1)  # Skip file type, machine, and version.
-
-    try:
-        # e_fmt: Format for program header.
-        # p_fmt: Format for section header.
-        # p_idx: Indexes to find p_type, p_offset, and p_filesz.
-        e_fmt, p_fmt, p_idx = {
-            1: ("IIIIHHH", "IIIIIIII", (0, 1, 4)),  # 32-bit.
-            2: ("QQQIHHH", "IIQQQQQQ", (0, 2, 5)),  # 64-bit.
-        }[ident[4]]
-    except KeyError:
-        return None
-    else:
-        p_get = operator.itemgetter(*p_idx)
-
-    # Find the interpreter section and return its content.
-    try:
-        _, e_phoff, _, _, _, e_phentsize, e_phnum = _read_unpacked(f, e_fmt)
-    except struct.error:
-        return None
-    for i in range(e_phnum + 1):
-        f.seek(e_phoff + e_phentsize * i)
-        try:
-            p_type, p_offset, p_filesz = p_get(_read_unpacked(f, p_fmt))
-        except struct.error:
-            return None
-        if p_type != 3:  # Not PT_INTERP.
-            continue
-        f.seek(p_offset)
-        interpreter = os.fsdecode(f.read(p_filesz)).strip("\0")
-        if "musl" not in interpreter:
-            return None
-        return interpreter
-    return None
-
-
-class _MuslVersion(NamedTuple):
-    major: int
-    minor: int
-
-
-def _parse_musl_version(output: str) -> Optional[_MuslVersion]:
-    lines = [n for n in (n.strip() for n in output.splitlines()) if n]
-    if len(lines) < 2 or lines[0][:4] != "musl":
-        return None
-    m = re.match(r"Version (\d+)\.(\d+)", lines[1])
-    if not m:
-        return None
-    return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2)))
-
-
-@functools.lru_cache()
-def _get_musl_version(executable: str) -> Optional[_MuslVersion]:
-    """Detect currently-running musl runtime version.
-
-    This is done by checking the specified executable's dynamic linking
-    information, and invoking the loader to parse its output for a version
-    string. If the loader is musl, the output would be something like::
-
-        musl libc (x86_64)
-        Version 1.2.2
-        Dynamic Program Loader
-    """
-    with contextlib.ExitStack() as stack:
-        try:
-            f = stack.enter_context(open(executable, "rb"))
-        except OSError:
-            return None
-        ld = _parse_ld_musl_from_elf(f)
-    if not ld:
-        return None
-    proc = subprocess.run([ld], stderr=subprocess.PIPE, universal_newlines=True)
-    return _parse_musl_version(proc.stderr)
-
-
-def platform_tags(arch: str) -> Iterator[str]:
-    """Generate musllinux tags compatible to the current platform.
-
-    :param arch: Should be the part of platform tag after the ``linux_``
-        prefix, e.g. ``x86_64``. The ``linux_`` prefix is assumed as a
-        prerequisite for the current platform to be musllinux-compatible.
-
-    :returns: An iterator of compatible musllinux tags.
-    """
-    sys_musl = _get_musl_version(sys.executable)
-    if sys_musl is None:  # Python not dynamically linked against musl.
-        return
-    for minor in range(sys_musl.minor, -1, -1):
-        yield f"musllinux_{sys_musl.major}_{minor}_{arch}"
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import sysconfig
-
-    plat = sysconfig.get_platform()
-    assert plat.startswith("linux-"), "not linux"
-
-    print("plat:", plat)
-    print("musl:", _get_musl_version(sys.executable))
-    print("tags:", end=" ")
-    for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])):
-        print(t, end="\n      ")
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/abc.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/abc.py
deleted file mode 100644
index e6e498efabfab0dcf31cd7731f8f821cc423bc4f..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/abc.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from abc import ABC
-
-
-class RichRenderable(ABC):
-    """An abstract base class for Rich renderables.
-
-    Note that there is no need to extend this class, the intended use is to check if an
-    object supports the Rich renderable protocol. For example::
-
-        if isinstance(my_object, RichRenderable):
-            console.print(my_object)
-
-    """
-
-    @classmethod
-    def __subclasshook__(cls, other: type) -> bool:
-        """Check if this class supports the rich render protocol."""
-        return hasattr(other, "__rich_console__") or hasattr(other, "__rich__")
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from pip._vendor.rich.text import Text
-
-    t = Text()
-    print(isinstance(Text, RichRenderable))
-    print(isinstance(t, RichRenderable))
-
-    class Foo:
-        pass
-
-    f = Foo()
-    print(isinstance(f, RichRenderable))
-    print(isinstance("", RichRenderable))
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/contrib/socks.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/contrib/socks.py
deleted file mode 100644
index c326e80dd117458ff6e71741ca57359629b05ae4..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/contrib/socks.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module contains provisional support for SOCKS proxies from within
-urllib3. This module supports SOCKS4, SOCKS4A (an extension of SOCKS4), and
-SOCKS5. To enable its functionality, either install PySocks or install this
-module with the ``socks`` extra.
-
-The SOCKS implementation supports the full range of urllib3 features. It also
-supports the following SOCKS features:
-
-- SOCKS4A (``proxy_url='socks4a://...``)
-- SOCKS4 (``proxy_url='socks4://...``)
-- SOCKS5 with remote DNS (``proxy_url='socks5h://...``)
-- SOCKS5 with local DNS (``proxy_url='socks5://...``)
-- Usernames and passwords for the SOCKS proxy
-
-.. note::
-   It is recommended to use ``socks5h://`` or ``socks4a://`` schemes in
-   your ``proxy_url`` to ensure that DNS resolution is done from the remote
-   server instead of client-side when connecting to a domain name.
-
-SOCKS4 supports IPv4 and domain names with the SOCKS4A extension. SOCKS5
-supports IPv4, IPv6, and domain names.
-
-When connecting to a SOCKS4 proxy the ``username`` portion of the ``proxy_url``
-will be sent as the ``userid`` section of the SOCKS request:
-
-.. code-block:: python
-
-    proxy_url="socks4a://<userid>@proxy-host"
-
-When connecting to a SOCKS5 proxy the ``username`` and ``password`` portion
-of the ``proxy_url`` will be sent as the username/password to authenticate
-with the proxy:
-
-.. code-block:: python
-
-    proxy_url="socks5h://<username>:<password>@proxy-host"
-
-"""
-from __future__ import absolute_import
-
-try:
-    import socks
-except ImportError:
-    import warnings
-
-    from ..exceptions import DependencyWarning
-
-    warnings.warn(
-        (
-            "SOCKS support in urllib3 requires the installation of optional "
-            "dependencies: specifically, PySocks.  For more information, see "
-            "https://urllib3.readthedocs.io/en/1.26.x/contrib.html#socks-proxies"
-        ),
-        DependencyWarning,
-    )
-    raise
-
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-from ..connection import HTTPConnection, HTTPSConnection
-from ..connectionpool import HTTPConnectionPool, HTTPSConnectionPool
-from ..exceptions import ConnectTimeoutError, NewConnectionError
-from ..poolmanager import PoolManager
-from ..util.url import parse_url
-
-try:
-    import ssl
-except ImportError:
-    ssl = None
-
-
-class SOCKSConnection(HTTPConnection):
-    """
-    A plain-text HTTP connection that connects via a SOCKS proxy.
-    """
-
-    def __init__(self, *args, **kwargs):
-        self._socks_options = kwargs.pop("_socks_options")
-        super(SOCKSConnection, self).__init__(*args, **kwargs)
-
-    def _new_conn(self):
-        """
-        Establish a new connection via the SOCKS proxy.
-        """
-        extra_kw = {}
-        if self.source_address:
-            extra_kw["source_address"] = self.source_address
-
-        if self.socket_options:
-            extra_kw["socket_options"] = self.socket_options
-
-        try:
-            conn = socks.create_connection(
-                (self.host, self.port),
-                proxy_type=self._socks_options["socks_version"],
-                proxy_addr=self._socks_options["proxy_host"],
-                proxy_port=self._socks_options["proxy_port"],
-                proxy_username=self._socks_options["username"],
-                proxy_password=self._socks_options["password"],
-                proxy_rdns=self._socks_options["rdns"],
-                timeout=self.timeout,
-                **extra_kw
-            )
-
-        except SocketTimeout:
-            raise ConnectTimeoutError(
-                self,
-                "Connection to %s timed out. (connect timeout=%s)"
-                % (self.host, self.timeout),
-            )
-
-        except socks.ProxyError as e:
-            # This is fragile as hell, but it seems to be the only way to raise
-            # useful errors here.
-            if e.socket_err:
-                error = e.socket_err
-                if isinstance(error, SocketTimeout):
-                    raise ConnectTimeoutError(
-                        self,
-                        "Connection to %s timed out. (connect timeout=%s)"
-                        % (self.host, self.timeout),
-                    )
-                else:
-                    raise NewConnectionError(
-                        self, "Failed to establish a new connection: %s" % error
-                    )
-            else:
-                raise NewConnectionError(
-                    self, "Failed to establish a new connection: %s" % e
-                )
-
-        except SocketError as e:  # Defensive: PySocks should catch all these.
-            raise NewConnectionError(
-                self, "Failed to establish a new connection: %s" % e
-            )
-
-        return conn
-
-
-# We don't need to duplicate the Verified/Unverified distinction from
-# urllib3/connection.py here because the HTTPSConnection will already have been
-# correctly set to either the Verified or Unverified form by that module. This
-# means the SOCKSHTTPSConnection will automatically be the correct type.
-class SOCKSHTTPSConnection(SOCKSConnection, HTTPSConnection):
-    pass
-
-
-class SOCKSHTTPConnectionPool(HTTPConnectionPool):
-    ConnectionCls = SOCKSConnection
-
-
-class SOCKSHTTPSConnectionPool(HTTPSConnectionPool):
-    ConnectionCls = SOCKSHTTPSConnection
-
-
-class SOCKSProxyManager(PoolManager):
-    """
-    A version of the urllib3 ProxyManager that routes connections via the
-    defined SOCKS proxy.
-    """
-
-    pool_classes_by_scheme = {
-        "http": SOCKSHTTPConnectionPool,
-        "https": SOCKSHTTPSConnectionPool,
-    }
-
-    def __init__(
-        self,
-        proxy_url,
-        username=None,
-        password=None,
-        num_pools=10,
-        headers=None,
-        **connection_pool_kw
-    ):
-        parsed = parse_url(proxy_url)
-
-        if username is None and password is None and parsed.auth is not None:
-            split = parsed.auth.split(":")
-            if len(split) == 2:
-                username, password = split
-        if parsed.scheme == "socks5":
-            socks_version = socks.PROXY_TYPE_SOCKS5
-            rdns = False
-        elif parsed.scheme == "socks5h":
-            socks_version = socks.PROXY_TYPE_SOCKS5
-            rdns = True
-        elif parsed.scheme == "socks4":
-            socks_version = socks.PROXY_TYPE_SOCKS4
-            rdns = False
-        elif parsed.scheme == "socks4a":
-            socks_version = socks.PROXY_TYPE_SOCKS4
-            rdns = True
-        else:
-            raise ValueError("Unable to determine SOCKS version from %s" % proxy_url)
-
-        self.proxy_url = proxy_url
-
-        socks_options = {
-            "socks_version": socks_version,
-            "proxy_host": parsed.host,
-            "proxy_port": parsed.port,
-            "username": username,
-            "password": password,
-            "rdns": rdns,
-        }
-        connection_pool_kw["_socks_options"] = socks_options
-
-        super(SOCKSProxyManager, self).__init__(
-            num_pools, headers, **connection_pool_kw
-        )
-
-        self.pool_classes_by_scheme = SOCKSProxyManager.pool_classes_by_scheme
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/evaluation/cityscapes_evaluation.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 5af78f6854f72c0f2537e34bc056d4a8b6541da6..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import glob
-import logging
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from fvcore.common.file_io import PathManager
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
-    """
-    Evaluate instance segmentation results using cityscapes API.
-
-    Note:
-        * It does not work in multi-machine distributed training.
-        * It contains a synchronization, therefore has to be used on all ranks.
-        * Only the main process runs evaluation.
-    """
-
-    def __init__(self, dataset_name):
-        """
-        Args:
-            dataset_name (str): the name of the dataset.
-                It must have the following metadata associated with it:
-                "thing_classes", "gt_dir".
-        """
-        self._metadata = MetadataCatalog.get(dataset_name)
-        self._cpu_device = torch.device("cpu")
-        self._logger = logging.getLogger(__name__)
-
-    def reset(self):
-        self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
-        self._temp_dir = self._working_dir.name
-        # All workers will write to the same results directory
-        # TODO this does not work in distributed training
-        self._temp_dir = comm.all_gather(self._temp_dir)[0]
-        if self._temp_dir != self._working_dir.name:
-            self._working_dir.cleanup()
-        self._logger.info(
-            "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
-        )
-
-    def process(self, inputs, outputs):
-        from cityscapesscripts.helpers.labels import name2label
-
-        for input, output in zip(inputs, outputs):
-            file_name = input["file_name"]
-            basename = os.path.splitext(os.path.basename(file_name))[0]
-            pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
-            output = output["instances"].to(self._cpu_device)
-            num_instances = len(output)
-            with open(pred_txt, "w") as fout:
-                for i in range(num_instances):
-                    pred_class = output.pred_classes[i]
-                    classes = self._metadata.thing_classes[pred_class]
-                    class_id = name2label[classes].id
-                    score = output.scores[i]
-                    mask = output.pred_masks[i].numpy().astype("uint8")
-                    png_filename = os.path.join(
-                        self._temp_dir, basename + "_{}_{}.png".format(i, classes)
-                    )
-
-                    Image.fromarray(mask * 255).save(png_filename)
-                    fout.write("{} {} {}\n".format(os.path.basename(png_filename), class_id, score))
-
-    def evaluate(self):
-        """
-        Returns:
-            dict: has a key "segm", whose value is a dict of "AP" and "AP50".
-        """
-        comm.synchronize()
-        if comm.get_rank() > 0:
-            return
-        import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
-        self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
-        # set some global states in cityscapes evaluation API, before evaluating
-        cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
-        cityscapes_eval.args.predictionWalk = None
-        cityscapes_eval.args.JSONOutput = False
-        cityscapes_eval.args.colorized = False
-        cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
-        # These lines are adopted from
-        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
-        gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
-        groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
-        assert len(
-            groundTruthImgList
-        ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
-            cityscapes_eval.args.groundTruthSearch
-        )
-        predictionImgList = []
-        for gt in groundTruthImgList:
-            predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
-        results = cityscapes_eval.evaluateImgLists(
-            predictionImgList, groundTruthImgList, cityscapes_eval.args
-        )["averages"]
-
-        ret = OrderedDict()
-        ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
-        self._working_dir.cleanup()
-        return ret
diff --git a/spaces/CVPR/LIVE/thrust/thrust/allocate_unique.h b/spaces/CVPR/LIVE/thrust/thrust/allocate_unique.h
deleted file mode 100644
index 6e67d1b18a6dd8c4e8dd27a0f78531819489d6a4..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/allocate_unique.h
+++ /dev/null
@@ -1,444 +0,0 @@
-// Copyright (c) 2018 NVIDIA Corporation
-// Author: Bryce Adelstein Lelbach <brycelelbach@gmail.com>
-//
-// Distributed under the Boost Software License v1.0 (boost.org/LICENSE_1_0.txt)
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp11_required.h>
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#include <thrust/detail/raw_pointer_cast.h>
-#include <thrust/detail/type_deduction.h>
-#include <thrust/detail/memory_algorithms.h>
-#include <thrust/detail/allocator/allocator_traits.h>
-
-#include <utility>
-#include <thrust/detail/memory_wrapper.h>
-
-namespace thrust
-{
-
-// wg21.link/p0316r0
-
-///////////////////////////////////////////////////////////////////////////////
-
-namespace detail
-{
-
-template <typename Allocator, typename Pointer>
-void allocator_delete_impl(
-  Allocator const& alloc, Pointer p, std::false_type
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  if (nullptr != pointer_traits<Pointer>::get(p))
-  {
-    traits::destroy(alloc_T, thrust::raw_pointer_cast(p));
-    traits::deallocate(alloc_T, p, 1);
-  }
-}
-
-template <typename Allocator, typename Pointer>
-void allocator_delete_impl(
-  Allocator const& alloc, Pointer p, std::true_type
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  if (nullptr != pointer_traits<Pointer>::get(p))
-  {
-    traits::deallocate(alloc_T, p, 1);
-  }
-}
-
-} // namespace detail
-
-template <typename T, typename Allocator, bool Uninitialized = false>
-struct allocator_delete final
-{
-  using allocator_type
-    = typename std::remove_cv<
-        typename std::remove_reference<Allocator>::type
-      >::type::template rebind<T>::other;
-  using pointer = typename detail::allocator_traits<allocator_type>::pointer;
-
-  template <typename UAllocator>
-  allocator_delete(UAllocator&& other) noexcept
-    : alloc_(THRUST_FWD(other))
-  {}
-
-  template <typename U, typename UAllocator>
-  allocator_delete(
-      allocator_delete<U, UAllocator> const& other
-    ) noexcept
-    : alloc_(other.get_allocator())
-  {}
-  template <typename U, typename UAllocator>
-  allocator_delete(
-      allocator_delete<U, UAllocator>&& other
-    ) noexcept
-    : alloc_(std::move(other.get_allocator()))
-  {}
-
-  template <typename U, typename UAllocator>
-  allocator_delete& operator=(
-    allocator_delete<U, UAllocator> const& other
-  ) noexcept
-  {
-    alloc_ = other.get_allocator();
-    return *this;
-  }
-  template <typename U, typename UAllocator>
-  allocator_delete& operator=(
-    allocator_delete<U, UAllocator>&& other
-  ) noexcept
-  {
-    alloc_ = std::move(other.get_allocator());
-    return *this;
-  }
-
-  void operator()(pointer p)
-  {
-    std::integral_constant<bool, Uninitialized> ic;
-
-    detail::allocator_delete_impl(get_allocator(), p, ic);
-  }
-
-  allocator_type& get_allocator() noexcept { return alloc_; }
-  allocator_type const& get_allocator() const noexcept { return alloc_; }
-
-  void swap(allocator_delete& other) noexcept
-  {
-    using std::swap;
-    swap(alloc_, other.alloc_);
-  }
-
-private:
-  allocator_type alloc_;
-};
-
-template <typename T, typename Allocator>
-using uninitialized_allocator_delete = allocator_delete<T, Allocator, true>;
-
-namespace detail {
-
-template <typename Allocator, typename Pointer, typename Size>
-void array_allocator_delete_impl(
-  Allocator const& alloc, Pointer p, Size count, std::false_type
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  if (nullptr != pointer_traits<Pointer>::get(p))
-  {
-    destroy_n(alloc_T, p, count);
-    traits::deallocate(alloc_T, p, count);
-  }
-}
-
-template <typename Allocator, typename Pointer, typename Size>
-void array_allocator_delete_impl(
-  Allocator const& alloc, Pointer p, Size count, std::true_type
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  if (nullptr != pointer_traits<Pointer>::get(p))
-  {
-    traits::deallocate(alloc_T, p, count);
-  }
-}
-
-} // namespace detail
-
-template <typename T, typename Allocator, bool Uninitialized = false>
-struct array_allocator_delete final
-{
-  using allocator_type
-    = typename std::remove_cv<
-        typename std::remove_reference<Allocator>::type
-      >::type::template rebind<T>::other;
-  using pointer = typename detail::allocator_traits<allocator_type>::pointer;
-
-  template <typename UAllocator>
-  array_allocator_delete(UAllocator&& other, std::size_t n) noexcept
-    : alloc_(THRUST_FWD(other)), count_(n)
-  {}
-
-  template <typename U, typename UAllocator>
-  array_allocator_delete(
-      array_allocator_delete<U, UAllocator> const& other
-    ) noexcept
-    : alloc_(other.get_allocator()), count_(other.count_)
-  {}
-  template <typename U, typename UAllocator>
-  array_allocator_delete(
-      array_allocator_delete<U, UAllocator>&& other
-    ) noexcept
-    : alloc_(std::move(other.get_allocator())), count_(other.count_)
-  {}
-
-  template <typename U, typename UAllocator>
-  array_allocator_delete& operator=(
-    array_allocator_delete<U, UAllocator> const& other
-  ) noexcept
-  {
-    alloc_ = other.get_allocator();
-    count_ = other.count_;
-    return *this;
-  }
-  template <typename U, typename UAllocator>
-  array_allocator_delete& operator=(
-    array_allocator_delete<U, UAllocator>&& other
-  ) noexcept
-  {
-    alloc_ = std::move(other.get_allocator());
-    count_ = other.count_;
-    return *this;
-  }
-
-  void operator()(pointer p)
-  {
-    std::integral_constant<bool, Uninitialized> ic;
-
-    detail::array_allocator_delete_impl(get_allocator(), p, count_, ic);
-  }
-
-  allocator_type& get_allocator() noexcept { return alloc_; }
-  allocator_type const& get_allocator() const noexcept { return alloc_; }
-
-  void swap(array_allocator_delete& other) noexcept
-  {
-    using std::swap;
-    swap(alloc_, other.alloc_);
-    swap(count_, other.count_);
-  }
-
-private:
-  allocator_type alloc_;
-  std::size_t    count_;
-};
-  
-template <typename T, typename Allocator>
-using uninitialized_array_allocator_delete
-  = array_allocator_delete<T, Allocator, true>;
-
-///////////////////////////////////////////////////////////////////////////////
-
-template <typename Pointer, typename Lambda>
-struct tagged_deleter : Lambda
-{
-  __host__ __device__
-  tagged_deleter(Lambda&& l) : Lambda(THRUST_FWD(l)) {}
-
-  using pointer = Pointer;
-};
-
-template <typename Pointer, typename Lambda>
-__host__ __device__
-tagged_deleter<Pointer, Lambda>
-make_tagged_deleter(Lambda&& l)
-{
-  return tagged_deleter<Pointer, Lambda>(THRUST_FWD(l));
-}
-
-///////////////////////////////////////////////////////////////////////////////
-
-template <typename T, typename Allocator, typename... Args>
-__host__
-std::unique_ptr<
-  T,
-  allocator_delete<
-    T
-  , typename detail::allocator_traits<
-      typename std::remove_cv<
-        typename std::remove_reference<Allocator>::type
-      >::type
-    >::template rebind_traits<T>::allocator_type
-  >
->
-allocate_unique(
-  Allocator const& alloc, Args&&... args
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  auto hold_deleter = make_tagged_deleter<typename traits::pointer>(
-    [&alloc_T] (typename traits::pointer p) {
-      traits::deallocate(alloc_T, p, 1);
-    }
-  );
-  using hold_t = std::unique_ptr<T, decltype(hold_deleter)>;
-  auto hold = hold_t(traits::allocate(alloc_T, 1), hold_deleter);
-
-  traits::construct(
-    alloc_T, thrust::raw_pointer_cast(hold.get()), THRUST_FWD(args)...
-  );
-  auto deleter = allocator_delete<T, typename traits::allocator_type>(alloc);
-  return std::unique_ptr<T, decltype(deleter)>
-    (hold.release(), std::move(deleter));
-}
-
-template <typename T, typename Allocator>
-__host__
-std::unique_ptr<
-  T,
-  uninitialized_allocator_delete<
-    T
-  , typename detail::allocator_traits<
-      typename std::remove_cv<
-        typename std::remove_reference<Allocator>::type
-      >::type
-    >::template rebind_traits<T>::allocator_type
-  >
->
-uninitialized_allocate_unique(
-  Allocator const& alloc
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  auto hold_deleter = make_tagged_deleter<typename traits::pointer>(
-    [&alloc_T] (typename traits::pointer p) {
-      traits::deallocate(alloc_T, p, 1);
-    }
-  );
-  using hold_t = std::unique_ptr<T, decltype(hold_deleter)>;
-  auto hold = hold_t(traits::allocate(alloc_T, 1), hold_deleter);
-
-  auto deleter = uninitialized_allocator_delete<
-    T, typename traits::allocator_type
-  >(alloc_T);
-  return std::unique_ptr<T, decltype(deleter)>
-    (hold.release(), std::move(deleter));
-}
-
-template <typename T, typename Allocator, typename Size, typename... Args>
-__host__
-std::unique_ptr<
-  T[],
-  array_allocator_delete<
-    T
-  , typename detail::allocator_traits<
-      typename std::remove_cv<
-        typename std::remove_reference<Allocator>::type
-      >::type
-    >::template rebind_traits<T>::allocator_type
-  >
->
-allocate_unique_n(
-  Allocator const& alloc, Size n, Args&&... args
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  auto hold_deleter = make_tagged_deleter<typename traits::pointer>(
-    [n, &alloc_T] (typename traits::pointer p) {
-      traits::deallocate(alloc_T, p, n);
-    }
-  );
-  using hold_t = std::unique_ptr<T[], decltype(hold_deleter)>;
-  auto hold = hold_t(traits::allocate(alloc_T, n), hold_deleter);
-
-  uninitialized_construct_n_with_allocator(
-    alloc_T, hold.get(), n, THRUST_FWD(args)...
-  );
-  auto deleter = array_allocator_delete<
-    T, typename traits::allocator_type
-  >(alloc_T, n);
-  return std::unique_ptr<T[], decltype(deleter)>
-    (hold.release(), std::move(deleter));
-}
-
-template <typename T, typename Allocator, typename Size>
-__host__
-std::unique_ptr<
-  T[],
-  uninitialized_array_allocator_delete<
-    T
-  , typename detail::allocator_traits<
-      typename std::remove_cv<
-        typename std::remove_reference<Allocator>::type
-      >::type
-    >::template rebind_traits<T>::allocator_type
-  >
->
-uninitialized_allocate_unique_n(
-  Allocator const& alloc, Size n
-)
-{
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  auto hold_deleter = make_tagged_deleter<typename traits::pointer>(
-    [n, &alloc_T] (typename traits::pointer p) {
-      traits::deallocate(alloc_T, p, n);
-    }
-  );
-  using hold_t = std::unique_ptr<T[], decltype(hold_deleter)>;
-  auto hold = hold_t(traits::allocate(alloc_T, n), hold_deleter);
-
-  auto deleter = uninitialized_array_allocator_delete<
-    T, typename traits::allocator_type
-  >(alloc_T, n);
-  return std::unique_ptr<T[], decltype(deleter)>
-    (hold.release(), std::move(deleter));
-}
-
-///////////////////////////////////////////////////////////////////////////////
-
-} // end namespace thrust
-
-#endif // THRUST_CPP_DIALECT >= 2011
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/reverse.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/reverse.h
deleted file mode 100644
index f6bd8947ee4bf3715441e9516160082427aa1491..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/reverse.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the reverse.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch reverse
-
-#include <thrust/system/detail/sequential/reverse.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/reverse.h>
-#include <thrust/system/cuda/detail/reverse.h>
-#include <thrust/system/omp/detail/reverse.h>
-#include <thrust/system/tbb/detail/reverse.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_REVERSE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/reverse.h>
-#include __THRUST_HOST_SYSTEM_REVERSE_HEADER
-#undef __THRUST_HOST_SYSTEM_REVERSE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_REVERSE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/reverse.h>
-#include __THRUST_DEVICE_SYSTEM_REVERSE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_REVERSE_HEADER
-
diff --git a/spaces/ChillyFaze/runwayml-stable-diffusion-v1-5/app.py b/spaces/ChillyFaze/runwayml-stable-diffusion-v1-5/app.py
deleted file mode 100644
index a82df332731f067826d3e1ef79fabceffb74d07e..0000000000000000000000000000000000000000
--- a/spaces/ChillyFaze/runwayml-stable-diffusion-v1-5/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/runwayml/stable-diffusion-v1-5").launch()
\ No newline at end of file
diff --git a/spaces/CrabApple/prompthero-openjourney-v2/app.py b/spaces/CrabApple/prompthero-openjourney-v2/app.py
deleted file mode 100644
index 4fa45eda1d4a0af263ec59b35e375b837fe1ecf1..0000000000000000000000000000000000000000
--- a/spaces/CrabApple/prompthero-openjourney-v2/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/prompthero/openjourney-v2").launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/wsgi.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/wsgi.py
deleted file mode 100644
index c4c6a797d2675e1c13b028be977c64a822fb649b..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/wsgi.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.middleware.wsgi import WSGIMiddleware as WSGIMiddleware  # noqa
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/filterPen.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/filterPen.py
deleted file mode 100644
index 81423109ae6b0caed4b75189a0d87b64cf8d0197..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/filterPen.py
+++ /dev/null
@@ -1,164 +0,0 @@
-from fontTools.pens.basePen import AbstractPen
-from fontTools.pens.pointPen import AbstractPointPen
-from fontTools.pens.recordingPen import RecordingPen
-
-
-class _PassThruComponentsMixin(object):
-    def addComponent(self, glyphName, transformation, **kwargs):
-        self._outPen.addComponent(glyphName, transformation, **kwargs)
-
-
-class FilterPen(_PassThruComponentsMixin, AbstractPen):
-
-    """Base class for pens that apply some transformation to the coordinates
-    they receive and pass them to another pen.
-
-    You can override any of its methods. The default implementation does
-    nothing, but passes the commands unmodified to the other pen.
-
-    >>> from fontTools.pens.recordingPen import RecordingPen
-    >>> rec = RecordingPen()
-    >>> pen = FilterPen(rec)
-    >>> v = iter(rec.value)
-
-    >>> pen.moveTo((0, 0))
-    >>> next(v)
-    ('moveTo', ((0, 0),))
-
-    >>> pen.lineTo((1, 1))
-    >>> next(v)
-    ('lineTo', ((1, 1),))
-
-    >>> pen.curveTo((2, 2), (3, 3), (4, 4))
-    >>> next(v)
-    ('curveTo', ((2, 2), (3, 3), (4, 4)))
-
-    >>> pen.qCurveTo((5, 5), (6, 6), (7, 7), (8, 8))
-    >>> next(v)
-    ('qCurveTo', ((5, 5), (6, 6), (7, 7), (8, 8)))
-
-    >>> pen.closePath()
-    >>> next(v)
-    ('closePath', ())
-
-    >>> pen.moveTo((9, 9))
-    >>> next(v)
-    ('moveTo', ((9, 9),))
-
-    >>> pen.endPath()
-    >>> next(v)
-    ('endPath', ())
-
-    >>> pen.addComponent('foo', (1, 0, 0, 1, 0, 0))
-    >>> next(v)
-    ('addComponent', ('foo', (1, 0, 0, 1, 0, 0)))
-    """
-
-    def __init__(self, outPen):
-        self._outPen = outPen
-        self.current_pt = None
-
-    def moveTo(self, pt):
-        self._outPen.moveTo(pt)
-        self.current_pt = pt
-
-    def lineTo(self, pt):
-        self._outPen.lineTo(pt)
-        self.current_pt = pt
-
-    def curveTo(self, *points):
-        self._outPen.curveTo(*points)
-        self.current_pt = points[-1]
-
-    def qCurveTo(self, *points):
-        self._outPen.qCurveTo(*points)
-        self.current_pt = points[-1]
-
-    def closePath(self):
-        self._outPen.closePath()
-        self.current_pt = None
-
-    def endPath(self):
-        self._outPen.endPath()
-        self.current_pt = None
-
-
-class ContourFilterPen(_PassThruComponentsMixin, RecordingPen):
-    """A "buffered" filter pen that accumulates contour data, passes
-    it through a ``filterContour`` method when the contour is closed or ended,
-    and finally draws the result with the output pen.
-
-    Components are passed through unchanged.
-    """
-
-    def __init__(self, outPen):
-        super(ContourFilterPen, self).__init__()
-        self._outPen = outPen
-
-    def closePath(self):
-        super(ContourFilterPen, self).closePath()
-        self._flushContour()
-
-    def endPath(self):
-        super(ContourFilterPen, self).endPath()
-        self._flushContour()
-
-    def _flushContour(self):
-        result = self.filterContour(self.value)
-        if result is not None:
-            self.value = result
-        self.replay(self._outPen)
-        self.value = []
-
-    def filterContour(self, contour):
-        """Subclasses must override this to perform the filtering.
-
-        The contour is a list of pen (operator, operands) tuples.
-        Operators are strings corresponding to the AbstractPen methods:
-        "moveTo", "lineTo", "curveTo", "qCurveTo", "closePath" and
-        "endPath". The operands are the positional arguments that are
-        passed to each method.
-
-        If the method doesn't return a value (i.e. returns None), it's
-        assumed that the argument was modified in-place.
-        Otherwise, the return value is drawn with the output pen.
-        """
-        return  # or return contour
-
-
-class FilterPointPen(_PassThruComponentsMixin, AbstractPointPen):
-    """Baseclass for point pens that apply some transformation to the
-    coordinates they receive and pass them to another point pen.
-
-    You can override any of its methods. The default implementation does
-    nothing, but passes the commands unmodified to the other pen.
-
-    >>> from fontTools.pens.recordingPen import RecordingPointPen
-    >>> rec = RecordingPointPen()
-    >>> pen = FilterPointPen(rec)
-    >>> v = iter(rec.value)
-    >>> pen.beginPath(identifier="abc")
-    >>> next(v)
-    ('beginPath', (), {'identifier': 'abc'})
-    >>> pen.addPoint((1, 2), "line", False)
-    >>> next(v)
-    ('addPoint', ((1, 2), 'line', False, None), {})
-    >>> pen.addComponent("a", (2, 0, 0, 2, 10, -10), identifier="0001")
-    >>> next(v)
-    ('addComponent', ('a', (2, 0, 0, 2, 10, -10)), {'identifier': '0001'})
-    >>> pen.endPath()
-    >>> next(v)
-    ('endPath', (), {})
-    """
-
-    def __init__(self, outPointPen):
-        self._outPen = outPointPen
-
-    def beginPath(self, **kwargs):
-        self._outPen.beginPath(**kwargs)
-
-    def endPath(self):
-        self._outPen.endPath()
-
-    def addPoint(self, pt, segmentType=None, smooth=False, name=None, **kwargs):
-        self._outPen.addPoint(pt, segmentType, smooth, name, **kwargs)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-22108117.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-22108117.js
deleted file mode 100644
index 1548119864cc83116796c345b255d84f2c818d0a..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-22108117.js
+++ /dev/null
@@ -1,3727 +0,0 @@
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r=t("color-rgba"),a=t("clamp"),l=t("dtype");o.exports=function(c,i){i!=="float"&&i||(i="array"),i==="uint"&&(i="uint8"),i==="uint_clamped"&&(i="uint8_clamped");var s=new(l(i))(4),u=i!=="uint8"&&i!=="uint8_clamped";return c.length&&typeof c!="string"||((c=r(c))[0]/=255,c[1]/=255,c[2]/=255),function(h){return h instanceof Uint8Array||h instanceof Uint8ClampedArray||!!(Array.isArray(h)&&(h[0]>1||h[0]===0)&&(h[1]>1||h[1]===0)&&(h[2]>1||h[2]===0)&&(!h[3]||h[3]>1))}(c)?(s[0]=c[0],s[1]=c[1],s[2]=c[2],s[3]=c[3]!=null?c[3]:255,u&&(s[0]/=255,s[1]/=255,s[2]/=255,s[3]/=255),s):(u?(s[0]=c[0],s[1]=c[1],s[2]=c[2],s[3]=c[3]!=null?c[3]:1):(s[0]=a(Math.floor(255*c[0]),0,255),s[1]=a(Math.floor(255*c[1]),0,255),s[2]=a(Math.floor(255*c[2]),0,255),s[3]=c[3]==null?255:a(Math.floor(255*c[3]),0,255)),s)}},{clamp:86,"color-rgba":91,dtype:127}],90:[function(t,o,f){(function(r){(function(){var a=t("color-name"),l=t("is-plain-obj"),c=t("defined");o.exports=function(s){var u,h,d=[],m=1;if(typeof 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Qe.invert=function(ut,mt){return[ut/He,F(mt*He)]},Qe}function Ve(je){var He=u(je);function Qe(ut,mt){return[ut*He,(1+He)*A(mt/2)]}return Qe.invert=function(ut,mt){return[ut/He,2*i(mt/(1+He))]},Qe}function We(je,He){var Qe=O(8/(3*k));return[Qe*je*(1-c(He)/k),Qe*He]}function Ye(je,He){var Qe=O(4-3*_(c(He)));return[2/O(6*k)*je*Qe,x(He)*O(2*k/3)*(2-Qe)]}function nt(je,He){var Qe=O(k*(4+k));return[2/Qe*je*(1+O(1-4*He*He/(k*k))),4/Qe*He]}function ft(je,He){var Qe=(2+w)*_(He);He/=2;for(var ut=0,mt=1/0;ut<10&&c(mt)>b;ut++){var pt=u(He);He-=mt=(He+_(He)*(pt+2)-Qe)/(2*pt*(1+pt))}return[2/O(k*(4+k))*je*(1+u(He)),2*O(k/(4+k))*_(He)]}function yt(je,He){return[je*(1+u(He))/O(2+k),2*He/O(2+k)]}function Ot(je,He){for(var Qe=(1+w)*_(He),ut=0,mt=1/0;ut<10&&c(mt)>b;ut++)He-=mt=(He+_(He)-Qe)/(1+u(He));return Qe=O(2+k),[je*(1+u(He))/Qe,2*He/Qe]}Ke.invert=function(je,He){var Qe=3*F(He/($e*S));return[S*je/($e*(2*u(2*Qe/3)-1)),Qe]},We.invert=function(je,He){var Qe=O(8/(3*k)),ut=He/Qe;return[je/(Qe*(1-c(ut)/k)),ut]},Ye.invert=function(je,He){var Qe=2-c(He)/O(2*k/3);return[je*O(6*k)/(2*Qe),x(He)*F((4-Qe*Qe)/3)]},nt.invert=function(je,He){var Qe=O(k*(4+k))/2;return[je*Qe/(1+O(1-He*He*(4+k)/(4*k))),He*Qe/2]},ft.invert=function(je,He){var Qe=He*O((4+k)/k)/2,ut=F(Qe),mt=u(ut);return[je/(2/O(k*(4+k))*(1+mt)),F((ut+Qe*(mt+2))/(2+w))]},yt.invert=function(je,He){var Qe=O(2+k),ut=He*Qe/2;return[Qe*je/(1+u(ut)),ut]},Ot.invert=function(je,He){var Qe=1+w,ut=O(Qe/2);return[2*je*ut/(1+u(He*=ut)),F((He+_(He))/Qe)]};var Tt=3+2*E;function at(je,He){var Qe=_(je/=2),ut=u(je),mt=O(u(He)),pt=u(He/=2),Ct=_(He)/(pt+E*ut*mt),Qt=O(2/(1+Ct*Ct)),en=O((E*pt+(ut+Qe)*mt)/(E*pt+(ut-Qe)*mt));return[Tt*(Qt*(en-1/en)-2*m(en)),Tt*(Qt*Ct*(en+1/en)-2*i(Ct))]}at.invert=function(je,He){if(!(Qe=te.invert(je/1.2,1.065*He)))return null;var Qe,ut=Qe[0],mt=Qe[1],pt=20;je/=Tt,He/=Tt;do{var Ct=ut/2,Qt=mt/2,en=_(Ct),Yt=u(Ct),an=_(Qt),hn=u(Qt),xn=u(mt),_n=O(xn),On=an/(hn+E*Yt*_n),sr=On*On,mr=O(2/(1+sr)),Fr=(E*hn+(Yt+en)*_n)/(E*hn+(Yt-en)*_n),jr=O(Fr),Kr=jr-1/jr,Ur=jr+1/jr,Di=mr*Kr-2*m(jr)-je,qi=mr*On*Ur-2*i(On)-He,ha=an&&T*_n*en*sr/an,ca=(E*Yt*hn+_n)/(2*(hn+E*Yt*_n)*(hn+E*Yt*_n)*_n),da=-.5*On*mr*mr*mr,fo=da*ha,so=da*ca,za=(za=2*hn+E*_n*(Yt-en))*za*jr,Na=(E*Yt*hn*_n+xn)/za,lo=-E*en*an/(_n*za),Fo=Kr*fo-2*Na/jr+mr*(Na+Na/Fr),is=Kr*so-2*lo/jr+mr*(lo+lo/Fr),as=On*Ur*fo-2*ha/(1+sr)+mr*Ur*ha+mr*On*(Na-Na/Fr),os=On*Ur*so-2*ca/(1+sr)+mr*Ur*ca+mr*On*(lo-lo/Fr),ss=is*as-os*Fo;if(!ss)break;var ia=(qi*is-Di*os)/ss,ht=(Di*as-qi*Fo)/ss;ut-=ia,mt=p(-w,g(w,mt-ht))}while((c(ia)>b||c(ht)>b)&&--pt>0);return c(c(mt)-w)<b?[0,mt]:pt&&[ut,mt]};var et=u(35*R);function Lt(je,He){var Qe=A(He/2);return[je*et*O(1-Qe*Qe),(1+et)*Qe]}function Wt(je,He){var Qe=He/2,ut=u(Qe);return[2*je/S*u(He)*ut*ut,S*A(Qe)]}function Jt(je){var He=1-je,Qe=mt(k,0)[0]-mt(-k,0)[0],ut=O(2*(mt(0,w)[1]-mt(0,-w)[1])/Qe);function mt(Qt,en){var Yt=u(en),an=_(en);return[Yt/(He+je*Yt)*Qt,He*en+je*an]}function pt(Qt,en){var Yt=mt(Qt,en);return[Yt[0]*ut,Yt[1]/ut]}function Ct(Qt){return pt(0,Qt)[1]}return pt.invert=function(Qt,en){var Yt=ne(Ct,en);return[Qt/ut*(je+He/u(Yt)),Yt]},pt}function Be(je){return[je[0]/2,F(A(je[1]/2*R))*L]}function Ge(je){return[2*je[0],2*i(_(je[1]*R))*L]}function kt(je,He){var Qe=2*k/He,ut=je*je;function mt(pt,Ct){var Qt=a.geoAzimuthalEquidistantRaw(pt,Ct),en=Qt[0],Yt=Qt[1],an=en*en+Yt*Yt;if(an>ut){var hn=O(an),xn=s(Yt,en),_n=Qe*v(xn/Qe),On=xn-_n,sr=je*u(On),mr=(je*_(On)-On*_(sr))/(w-sr),Fr=dt(On,mr),jr=(k-je)/Oe(Fr,sr,k);en=hn;var Kr,Ur=50;do en-=Kr=(je+Oe(Fr,sr,en)*jr-hn)/(Fr(en)*jr);while(c(Kr)>b&&--Ur>0);Yt=On*_(en),en<w&&(Yt-=mr*(en-w));var Di=_(_n),qi=u(_n);Qt[0]=en*qi-Yt*Di,Qt[1]=en*Di+Yt*qi}return Qt}return mt.invert=function(pt,Ct){var Qt=pt*pt+Ct*Ct;if(Qt>ut){var en=O(Qt),Yt=s(Ct,pt),an=Qe*v(Yt/Qe),hn=Yt-an;pt=en*u(hn),Ct=en*_(hn);for(var xn=pt-w,_n=_(pt),On=Ct/_n,sr=pt<w?1/0:0,mr=10;;){var Fr=je*_(On),jr=je*u(On),Kr=_(jr),Ur=w-jr,Di=(Fr-On*Kr)/Ur,qi=dt(On,Di);if(c(sr)<1e-12||!--mr)break;On-=sr=(On*_n-Di*xn-Ct)/(_n-2*xn*(Ur*(jr+On*Fr*u(jr)-Kr)-Fr*(Fr-On*Kr))/(Ur*Ur))}pt=(en=je+Oe(qi,jr,pt)*(k-je)/Oe(qi,jr,k))*u(Yt=an+On),Ct=en*_(Yt)}return a.geoAzimuthalEquidistantRaw.invert(pt,Ct)},mt}function dt(je,He){return function(Qe){var ut=je*u(Qe);return Qe<w&&(ut-=He),O(1+ut*ut)}}function Oe(je,He,Qe){for(var ut=(Qe-He)/50,mt=je(He)+je(Qe),pt=1,Ct=He;pt<50;++pt)mt+=2*je(Ct+=ut);return .5*mt*ut}function Ie(je,He,Qe,ut,mt,pt,Ct,Qt){function en(Yt,an){if(!an)return[je*Yt/k,0];var hn=an*an,xn=je+hn*(He+hn*(Qe+hn*ut)),_n=an*(mt-1+hn*(pt-Qt+hn*Ct)),On=(xn*xn+_n*_n)/(2*_n),sr=Yt*F(xn/On)/k;return[On*_(sr),an*(1+hn*Qt)+On*(1-u(sr))]}return arguments.length<8&&(Qt=0),en.invert=function(Yt,an){var hn,xn,_n=k*Yt/je,On=an,sr=50;do{var mr=On*On,Fr=je+mr*(He+mr*(Qe+mr*ut)),jr=On*(mt-1+mr*(pt-Qt+mr*Ct)),Kr=Fr*Fr+jr*jr,Ur=2*jr,Di=Kr/Ur,qi=Di*Di,ha=F(Fr/Di)/k,ca=_n*ha,da=Fr*Fr,fo=(2*He+mr*(4*Qe+6*mr*ut))*On,so=mt+mr*(3*pt+5*mr*Ct),za=(2*(Fr*fo+jr*(so-1))*Ur-Kr*(2*(so-1)))/(Ur*Ur),Na=u(ca),lo=_(ca),Fo=Di*Na,is=Di*lo,as=_n/k*(1/O(1-da/qi))*(fo*Di-Fr*za)/qi,os=is-Yt,ss=On*(1+mr*Qt)+Di-Fo-an,ia=za*lo+Fo*as,ht=Fo*ha,zt=1+za-(za*Na-is*as),ln=is*ha,Ht=ia*ln-zt*ht;if(!Ht)break;_n-=hn=(ss*ia-os*zt)/Ht,On-=xn=(os*ln-ss*ht)/Ht}while((c(hn)>b||c(xn)>b)&&--sr>0);return[_n,On]},en}Lt.invert=function(je,He){var Qe=He/(1+et);return[je&&je/(et*O(1-Qe*Qe)),2*i(Qe)]},Wt.invert=function(je,He){var Qe=i(He/S),ut=u(Qe),mt=2*Qe;return[je*S/2/(u(mt)*ut*ut),mt]};var Te=Ie(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555),Pe=Ie(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742),qe=Ie(5/6*k,-.62636,-.0344,0,1.3493,-.05524,0,.045);function rt(je,He){var Qe=je*je,ut=He*He;return[je*(1-.162388*ut)*(.87-952426e-9*Qe*Qe),He*(1+ut/12)]}rt.invert=function(je,He){var Qe,ut=je,mt=He,pt=50;do{var Ct=mt*mt;mt-=Qe=(mt*(1+Ct/12)-He)/(1+Ct/4)}while(c(Qe)>b&&--pt>0);pt=50,je/=1-.162388*Ct;do{var Qt=(Qt=ut*ut)*Qt;ut-=Qe=(ut*(.87-952426e-9*Qt)-je)/(.87-.00476213*Qt)}while(c(Qe)>b&&--pt>0);return[ut,mt]};var lt=Ie(2.6516,-.76534,.19123,-.047094,1.36289,-.13965,.031762);function ot(je){var He=je(w,0)[0]-je(-w,0)[0];function Qe(ut,mt){var pt=ut>0?-.5:.5,Ct=je(ut+pt*k,mt);return Ct[0]-=pt*He,Ct}return je.invert&&(Qe.invert=function(ut,mt){var pt=ut>0?-.5:.5,Ct=je.invert(ut+pt*He,mt),Qt=Ct[0]-pt*k;return Qt<-k?Qt+=2*k:Qt>k&&(Qt-=2*k),Ct[0]=Qt,Ct}),Qe}function At(je,He){var Qe=x(je),ut=x(He),mt=u(He),pt=u(je)*mt,Ct=_(je)*mt,Qt=_(ut*He);je=c(s(Ct,Qt)),He=F(pt),c(je-w)>b&&(je%=w);var en=function(Yt,an){if(an===w)return[0,0];var hn,xn,_n=_(an),On=_n*_n,sr=On*On,mr=1+sr,Fr=1+3*sr,jr=1-sr,Kr=F(1/O(mr)),Ur=jr+On*mr*Kr,Di=(1-_n)/Ur,qi=O(Di),ha=Di*mr,ca=O(ha),da=qi*jr;if(Yt===0)return[0,-(da+On*ca)];var fo,so=u(an),za=1/so,Na=2*_n*so,lo=(-Ur*so-(-3*On+Kr*Fr)*Na*(1-_n))/(Ur*Ur),Fo=-za*Na,is=-za*(On*mr*lo+Di*Fr*Na),as=-2*za*(jr*(.5*lo/qi)-2*On*qi*Na),os=4*Yt/k;if(Yt>.222*k||an<k/4&&Yt>.175*k){if(hn=(da+On*O(ha*(1+sr)-da*da))/(1+sr),Yt>k/4)return[hn,hn];var ss=hn,ia=.5*hn;hn=.5*(ia+ss),xn=50;do{var ht=O(ha-hn*hn),zt=hn*(as+Fo*ht)+is*F(hn/ca)-os;if(!zt)break;zt<0?ia=hn:ss=hn,hn=.5*(ia+ss)}while(c(ss-ia)>b&&--xn>0)}else{hn=b,xn=25;do{var ln=hn*hn,Ht=O(ha-ln),un=as+Fo*Ht,Ln=hn*un+is*F(hn/ca)-os,zn=un+(is-Fo*ln)/Ht;hn-=fo=Ht?Ln/zn:0}while(c(fo)>b&&--xn>0)}return[hn,-da-On*O(ha-hn*hn)]}(je>k/4?w-je:je,He);return je>k/4&&(Qt=en[0],en[0]=-en[1],en[1]=-Qt),en[0]*=Qe,en[1]*=-ut,en}function wt(je,He){var Qe,ut,mt,pt,Ct,Qt;if(He<b)return[(pt=_(je))-(Qe=He*(je-pt*(ut=u(je)))/4)*ut,ut+Qe*pt,1-He*pt*pt/2,je-Qe];if(He>=1-b)return Qe=(1-He)/4,mt=1/(ut=B(je)),[(pt=((Qt=h(2*(Qt=je)))-1)/(Qt+1))+Qe*((Ct=ut*N(je))-je)/(ut*ut),mt-Qe*pt*mt*(Ct-je),mt+Qe*pt*mt*(Ct+je),2*i(h(je))-w+Qe*(Ct-je)/ut];var en=[1,0,0,0,0,0,0,0,0],Yt=[O(He),0,0,0,0,0,0,0,0],an=0;for(ut=O(1-He),Ct=1;c(Yt[an]/en[an])>b&&an<8;)Qe=en[an++],Yt[an]=(Qe-ut)/2,en[an]=(Qe+ut)/2,ut=O(Qe*ut),Ct*=2;mt=Ct*en[an]*je;do mt=(F(pt=Yt[an]*_(ut=mt)/en[an])+mt)/2;while(--an);return[_(mt),pt=u(mt),pt/u(mt-ut),mt]}function $t(je,He){if(!He)return je;if(He===1)return m(A(je/2+M));for(var Qe=1,ut=O(1-He),mt=O(He),pt=0;c(mt)>b;pt++){if(je%k){var Ct=i(ut*A(je)/Qe);Ct<0&&(Ct+=k),je+=Ct+~~(je/k)*k}else je+=je;mt=(Qe+ut)/2,ut=O(Qe*ut),mt=((Qe=mt)-ut)/2}return je/(y(2,pt)*Qe)}function Ut(je,He){var Qe=(E-1)/(E+1),ut=O(1-Qe*Qe),mt=$t(w,ut*ut),pt=m(A(k/4+c(He)/2)),Ct=h(-1*pt)/O(Qe),Qt=function(Yt,an){var hn=Yt*Yt,xn=an+1,_n=1-hn-an*an;return[.5*((Yt>=0?w:-w)-s(_n,2*Yt)),-.25*m(_n*_n+4*hn)+.5*m(xn*xn+hn)]}(Ct*u(-1*je),Ct*_(-1*je)),en=function(Yt,an,hn){var xn=c(Yt),_n=N(c(an));if(xn){var On=1/_(xn),sr=1/(A(xn)*A(xn)),mr=-(sr+hn*(_n*_n*On*On)-1+hn),Fr=(-mr+O(mr*mr-4*((hn-1)*sr)))/2;return[$t(i(1/O(Fr)),hn)*x(Yt),$t(i(O((Fr/sr-1)/hn)),1-hn)*x(an)]}return[0,$t(i(_n),1-hn)*x(an)]}(Qt[0],Qt[1],ut*ut);return[-en[1],(He>=0?1:-1)*(.5*mt-en[0])]}function tt(je){var He=_(je),Qe=u(je),ut=bt(je);function mt(pt,Ct){var Qt=ut(pt,Ct);pt=Qt[0],Ct=Qt[1];var en=_(Ct),Yt=u(Ct),an=u(pt),hn=D(He*en+Qe*Yt*an),xn=_(hn),_n=c(xn)>b?hn/xn:1;return[_n*Qe*_(pt),(c(pt)>w?_n:-_n)*(He*Yt-Qe*en*an)]}return ut.invert=bt(-je),mt.invert=function(pt,Ct){var Qt=O(pt*pt+Ct*Ct),en=-_(Qt),Yt=u(Qt),an=Qt*Yt,hn=-Ct*en,xn=Qt*He,_n=O(an*an+hn*hn-xn*xn),On=s(an*xn+hn*_n,hn*xn-an*_n),sr=(Qt>w?-1:1)*s(pt*en,Qt*u(On)*Yt+Ct*_(On)*en);return ut.invert(sr,On)},mt}function bt(je){var He=_(je),Qe=u(je);return function(ut,mt){var pt=u(mt),Ct=u(ut)*pt,Qt=_(ut)*pt,en=_(mt);return[s(Qt,Ct*Qe-en*He),F(en*Qe+Ct*He)]}}At.invert=function(je,He){c(je)>1&&(je=2*x(je)-je),c(He)>1&&(He=2*x(He)-He);var Qe=x(je),ut=x(He),mt=-Qe*je,pt=-ut*He,Ct=pt/mt<1,Qt=function(hn,xn){for(var _n=0,On=1,sr=.5,mr=50;;){var Fr=sr*sr,jr=O(sr),Kr=F(1/O(1+Fr)),Ur=1-Fr+sr*(1+Fr)*Kr,Di=(1-jr)/Ur,qi=O(Di),ha=Di*(1+Fr),ca=qi*(1-Fr),da=O(ha-hn*hn),fo=xn+ca+sr*da;if(c(On-_n)<1e-12||--mr==0||fo===0)break;fo>0?_n=sr:On=sr,sr=.5*(_n+On)}if(!mr)return null;var so=F(jr),za=u(so),Na=1/za,lo=2*jr*za,Fo=(-Ur*za-(-3*sr+Kr*(1+3*Fr))*lo*(1-jr))/(Ur*Ur);return[k/4*(hn*(-2*Na*(.5*Fo/qi*(1-Fr)-2*sr*qi*lo)+-Na*lo*da)+-Na*(sr*(1+Fr)*Fo+Di*(1+3*Fr)*lo)*F(hn/O(ha))),so]}(Ct?pt:mt,Ct?mt:pt),en=Qt[0],Yt=Qt[1],an=u(Yt);return Ct&&(en=-w-en),[Qe*(s(_(en)*an,-_(Yt))+k),ut*F(u(en)*an)]},Ut.invert=function(je,He){var Qe,ut,mt,pt,Ct,Qt,en=(E-1)/(E+1),Yt=O(1-en*en),an=$t(w,Yt*Yt),hn=(ut=-je,mt=Yt*Yt,(Qe=.5*an-He)?(pt=wt(Qe,mt),ut?(Qt=(Ct=wt(ut,1-mt))[1]*Ct[1]+mt*pt[0]*pt[0]*Ct[0]*Ct[0],[[pt[0]*Ct[2]/Qt,pt[1]*pt[2]*Ct[0]*Ct[1]/Qt],[pt[1]*Ct[1]/Qt,-pt[0]*pt[2]*Ct[0]*Ct[2]/Qt],[pt[2]*Ct[1]*Ct[2]/Qt,-mt*pt[0]*pt[1]*Ct[0]/Qt]]):[[pt[0],0],[pt[1],0],[pt[2],0]]):[[0,(Ct=wt(ut,1-mt))[0]/Ct[1]],[1/Ct[1],0],[Ct[2]/Ct[1],0]]),xn=function(_n,On){var sr=On[0]*On[0]+On[1]*On[1];return[(_n[0]*On[0]+_n[1]*On[1])/sr,(_n[1]*On[0]-_n[0]*On[1])/sr]}(hn[0],hn[1]);return[s(xn[1],xn[0])/-1,2*i(h(-.5*m(en*xn[0]*xn[0]+en*xn[1]*xn[1])))-w]};var Ft=F(1-1/3)*L,Et=Re(0);function Pt(je){var He=Ft*R,Qe=Fe(k,He)[0]-Fe(-k,He)[0],ut=Et(0,He)[1],mt=Fe(0,He)[1],pt=S-mt,Ct=P/je,Qt=4/P,en=ut+pt*pt*4/P;function Yt(an,hn){var xn,_n=c(hn);if(_n>He){var On=g(je-1,p(0,d((an+k)/Ct)));(xn=Fe(an+=k*(je-1)/je-On*Ct,_n))[0]=xn[0]*P/Qe-P*(je-1)/(2*je)+On*P/je,xn[1]=ut+4*(xn[1]-mt)*pt/P,hn<0&&(xn[1]=-xn[1])}else xn=Et(an,hn);return xn[0]*=Qt,xn[1]/=en,xn}return Yt.invert=function(an,hn){an/=Qt;var xn=c(hn*=en);if(xn>ut){var _n=g(je-1,p(0,d((an+k)/Ct)));an=(an+k*(je-1)/je-_n*Ct)*Qe/P;var On=Fe.invert(an,.25*(xn-ut)*P/pt+mt);return On[0]-=k*(je-1)/je-_n*Ct,hn<0&&(On[1]=-On[1]),On}return Et.invert(an,hn)},Yt}function De(je,He){return[je,1&He?90-b:Ft]}function Je(je,He){return[je,1&He?-90+b:-Ft]}function st(je){return[je[0]*(1-b),je[1]]}function St(je){var He,Qe=1+je,ut=F(_(1/Qe)),mt=2*O(k/(He=k+4*ut*Qe)),pt=.5*mt*(Qe+O(je*(2+je))),Ct=je*je,Qt=Qe*Qe;function en(Yt,an){var hn,xn,_n=1-_(an);if(_n&&_n<2){var On,sr=w-an,mr=25;do{var Fr=_(sr),jr=u(sr),Kr=ut+s(Fr,Qe-jr),Ur=1+Qt-2*Qe*jr;sr-=On=(sr-Ct*ut-Qe*Fr+Ur*Kr-.5*_n*He)/(2*Qe*Fr*Kr)}while(c(On)>1e-12&&--mr>0);hn=mt*O(Ur),xn=Yt*Kr/k}else hn=mt*(je+_n),xn=Yt*ut/k;return[hn*_(xn),pt-hn*u(xn)]}return en.invert=function(Yt,an){var hn=Yt*Yt+(an-=pt)*an,xn=(1+Qt-hn/(mt*mt))/(2*Qe),_n=D(xn),On=_(_n),sr=ut+s(On,Qe-xn);return[F(Yt/O(hn))*k/sr,F(1-2*(_n-Ct*ut-Qe*On+(1+Qt-2*Qe*xn)*sr)/He)]},en}function It(je,He){return He>-.7109889596207567?((je=ae(je,He))[1]+=.0528035274542,je):me(je,He)}function Zt(je,He){return c(He)>.7109889596207567?((je=ae(je,He))[1]-=He>0?.0528035274542:-.0528035274542,je):me(je,He)}function Kt(je,He,Qe,ut){var mt=O(4*k/(2*Qe+(1+je-He/2)*_(2*Qe)+(je+He)/2*_(4*Qe)+He/2*_(6*Qe))),pt=O(ut*_(Qe)*O((1+je*u(2*Qe)+He*u(4*Qe))/(1+je+He))),Ct=Qe*en(1);function Qt(hn){return O(1+je*u(2*hn)+He*u(4*hn))}function en(hn){var xn=hn*Qe;return(2*xn+(1+je-He/2)*_(2*xn)+(je+He)/2*_(4*xn)+He/2*_(6*xn))/Qe}function Yt(hn){return Qt(hn)*_(hn)}var an=function(hn,xn){var _n=Qe*ne(en,Ct*_(xn)/Qe,xn/k);isNaN(_n)&&(_n=Qe*x(xn));var On=mt*Qt(_n);return[On*pt*hn/k*u(_n),On/pt*_(_n)]};return an.invert=function(hn,xn){var _n=ne(Yt,xn*pt/mt);return[hn*k/(u(_n)*mt*pt*Qt(_n)),F(Qe*en(_n/Qe)/Ct)]},Qe===0&&(mt=O(ut/k),(an=function(hn,xn){return[hn*mt,_(xn)/mt]}).invert=function(hn,xn){return[hn/mt,F(xn*mt)]}),an}function qt(je,He,Qe,ut,mt){ut===void 0&&(ut=1e-8),mt===void 0&&(mt=20);var pt=je(He),Ct=je(.5*(He+Qe)),Qt=je(Qe);return function en(Yt,an,hn,xn,_n,On,sr,mr,Fr,jr,Kr){if(Kr.nanEncountered)return NaN;var Ur,Di,qi,ha,ca,da,fo,so,za,Na;if(Di=Yt(an+.25*(Ur=hn-an)),qi=Yt(hn-.25*Ur),isNaN(Di))Kr.nanEncountered=!0;else{if(!isNaN(qi))return Na=((da=(ha=Ur*(xn+4*Di+_n)/12)+(ca=Ur*(_n+4*qi+On)/12))-sr)/15,jr>Fr?(Kr.maxDepthCount++,da+Na):Math.abs(Na)<mr?da+Na:(so=en(Yt,an,fo=an+.5*Ur,xn,Di,_n,ha,.5*mr,Fr,jr+1,Kr),isNaN(so)?(Kr.nanEncountered=!0,NaN):(za=en(Yt,fo,hn,_n,qi,On,ca,.5*mr,Fr,jr+1,Kr),isNaN(za)?(Kr.nanEncountered=!0,NaN):so+za));Kr.nanEncountered=!0}}(je,He,Qe,pt,Ct,Qt,(pt+4*Ct+Qt)*(Qe-He)/6,ut,mt,1,{maxDepthCount:0,nanEncountered:!1})}function mn(je,He,Qe){function ut(_n){return je+(1-je)*y(1-y(_n,He),1/He)}function mt(_n){return qt(ut,0,_n,1e-4)}for(var pt=1/mt(1),Ct=1e3,Qt=(1+1e-8)*pt,en=[],Yt=0;Yt<=Ct;Yt++)en.push(mt(Yt/Ct)*Qt);function an(_n){var On=0,sr=Ct,mr=500;do en[mr]>_n?sr=mr:On=mr,mr=On+sr>>1;while(mr>On);var Fr=en[mr+1]-en[mr];return Fr&&(Fr=(_n-en[mr+1])/Fr),(mr+1+Fr)/Ct}var hn=2*an(1)/k*pt/Qe,xn=function(_n,On){var sr=an(c(_(On))),mr=ut(sr)*_n;return sr/=hn,[mr,On>=0?sr:-sr]};return xn.invert=function(_n,On){var sr;return c(On*=hn)<1&&(sr=x(On)*F(mt(c(On))*pt)),[_n/ut(c(On)),sr]},xn}function Fn(je,He){return c(je[0]-He[0])<b&&c(je[1]-He[1])<b}function pn(je,He){for(var Qe,ut,mt,pt=-1,Ct=je.length,Qt=je[0],en=[];++pt<Ct;){ut=((Qe=je[pt])[0]-Qt[0])/He,mt=(Qe[1]-Qt[1])/He;for(var Yt=0;Yt<He;++Yt)en.push([Qt[0]+Yt*ut,Qt[1]+Yt*mt]);Qt=Qe}return en.push(Qe),en}function tn(je){var He,Qe,ut,mt,pt,Ct,Qt,en=[],Yt=je[0].length;for(Qt=0;Qt<Yt;++Qt)Qe=(He=je[0][Qt])[0][0],ut=He[0][1],mt=He[1][1],pt=He[2][0],Ct=He[2][1],en.push(pn([[Qe+b,ut+b],[Qe+b,mt-b],[pt-b,mt-b],[pt-b,Ct+b]],30));for(Qt=je[1].length-1;Qt>=0;--Qt)Qe=(He=je[1][Qt])[0][0],ut=He[0][1],mt=He[1][1],pt=He[2][0],Ct=He[2][1],en.push(pn([[pt-b,Ct-b],[pt-b,mt+b],[Qe+b,mt+b],[Qe+b,ut-b]],30));return{type:"Polygon",coordinates:[l.merge(en)]}}function nn(je,He,Qe){var ut,mt;function pt(en,Yt){for(var an=Yt<0?-1:1,hn=He[+(Yt<0)],xn=0,_n=hn.length-1;xn<_n&&en>hn[xn][2][0];++xn);var On=je(en-hn[xn][1][0],Yt);return On[0]+=je(hn[xn][1][0],an*Yt>an*hn[xn][0][1]?hn[xn][0][1]:Yt)[0],On}Qe?pt.invert=Qe(pt):je.invert&&(pt.invert=function(en,Yt){for(var an=mt[+(Yt<0)],hn=He[+(Yt<0)],xn=0,_n=an.length;xn<_n;++xn){var On=an[xn];if(On[0][0]<=en&&en<On[1][0]&&On[0][1]<=Yt&&Yt<On[1][1]){var sr=je.invert(en-je(hn[xn][1][0],0)[0],Yt);return sr[0]+=hn[xn][1][0],Fn(pt(sr[0],sr[1]),[en,Yt])?sr:null}}});var Ct=a.geoProjection(pt),Qt=Ct.stream;return Ct.stream=function(en){var Yt=Ct.rotate(),an=Qt(en),hn=(Ct.rotate([0,0]),Qt(en));return Ct.rotate(Yt),an.sphere=function(){a.geoStream(ut,hn)},an},Ct.lobes=function(en){return arguments.length?(ut=tn(en),He=en.map(function(Yt){return Yt.map(function(an){return[[an[0][0]*R,an[0][1]*R],[an[1][0]*R,an[1][1]*R],[an[2][0]*R,an[2][1]*R]]})}),mt=He.map(function(Yt){return Yt.map(function(an){var hn,xn=je(an[0][0],an[0][1])[0],_n=je(an[2][0],an[2][1])[0],On=je(an[1][0],an[0][1])[1],sr=je(an[1][0],an[1][1])[1];return On>sr&&(hn=On,On=sr,sr=hn),[[xn,On],[_n,sr]]})}),Ct):He.map(function(Yt){return Yt.map(function(an){return[[an[0][0]*L,an[0][1]*L],[an[1][0]*L,an[1][1]*L],[an[2][0]*L,an[2][1]*L]]})})},He!=null&&Ct.lobes(He),Ct}It.invert=function(je,He){return He>-.7109889596207567?ae.invert(je,He-.0528035274542):me.invert(je,He)},Zt.invert=function(je,He){return c(He)>.7109889596207567?ae.invert(je,He+(He>0?.0528035274542:-.0528035274542)):me.invert(je,He)};var sn=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]],gn=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]],bn=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]],In=[[[[-180,0],[-90,90],[0,0]],[[0,0],[90,90],[180,0]]],[[[-180,0],[-90,-90],[0,0]],[[0,0],[90,-90],[180,0]]]],qn=[[[[-180,35],[-30,90],[0,35]],[[0,35],[30,90],[180,35]]],[[[-180,-10],[-102,-90],[-65,-10]],[[-65,-10],[5,-90],[77,-10]],[[77,-10],[103,-90],[180,-10]]]],Wn=[[[[-180,0],[-110,90],[-40,0]],[[-40,0],[0,90],[40,0]],[[40,0],[110,90],[180,0]]],[[[-180,0],[-110,-90],[-40,0]],[[-40,0],[0,-90],[40,0]],[[40,0],[110,-90],[180,0]]]];function ar(je,He){return[3/P*je*O(k*k/3-He*He),He]}function Dr(je){function He(Qe,ut){if(c(c(ut)-w)<b)return[0,ut<0?-2:2];var mt=_(ut),pt=y((1+mt)/(1-mt),je/2),Ct=.5*(pt+1/pt)+u(Qe*=je);return[2*_(Qe)/Ct,(pt-1/pt)/Ct]}return He.invert=function(Qe,ut){var mt=c(ut);if(c(mt-2)<b)return Qe?null:[0,x(ut)*w];if(mt>2)return null;var pt=(Qe/=2)*Qe,Ct=(ut/=2)*ut,Qt=2*ut/(1+pt+Ct);return Qt=y((1+Qt)/(1-Qt),1/je),[s(2*Qe,1-pt-Ct)/je,F((Qt-1)/(Qt+1))]},He}ar.invert=function(je,He){return[P/3*je/O(k*k/3-He*He),He]};var yr=k/E;function Sr(je,He){return[je*(1+O(u(He)))/2,He/(u(He/2)*u(je/6))]}function Kn(je,He){var Qe=je*je,ut=He*He;return[je*(.975534+ut*(-.0143059*Qe-.119161+-.0547009*ut)),He*(1.00384+Qe*(.0802894+-.02855*ut+199025e-9*Qe)+ut*(.0998909+-.0491032*ut))]}function Dn(je,He){return[_(je)/u(He),A(He)*u(je)]}function lr(je){var He=u(je),Qe=A(M+je/2);function ut(mt,pt){var Ct=pt-je,Qt=c(Ct)<b?mt*He:c(Qt=M+pt/2)<b||c(c(Qt)-w)<b?0:mt*Ct/m(A(Qt)/Qe);return[Qt,Ct]}return ut.invert=function(mt,pt){var Ct,Qt=pt+je;return[c(pt)<b?mt/He:c(Ct=M+Qt/2)<b||c(c(Ct)-w)<b?0:mt*m(A(Ct)/Qe)/pt,Qt]},ut}function Yr(je,He){return[je,1.25*m(A(M+.4*He))]}function Mn(je){var He=je.length-1;function Qe(ut,mt){for(var pt,Ct=u(mt),Qt=2/(1+Ct*u(ut)),en=Qt*Ct*_(ut),Yt=Qt*_(mt),an=He,hn=je[an],xn=hn[0],_n=hn[1];--an>=0;)xn=(hn=je[an])[0]+en*(pt=xn)-Yt*_n,_n=hn[1]+en*_n+Yt*pt;return[xn=en*(pt=xn)-Yt*_n,_n=en*_n+Yt*pt]}return Qe.invert=function(ut,mt){var pt=20,Ct=ut,Qt=mt;do{for(var en,Yt=He,an=je[Yt],hn=an[0],xn=an[1],_n=0,On=0;--Yt>=0;)_n=hn+Ct*(en=_n)-Qt*On,On=xn+Ct*On+Qt*en,hn=(an=je[Yt])[0]+Ct*(en=hn)-Qt*xn,xn=an[1]+Ct*xn+Qt*en;var sr,mr,Fr=(_n=hn+Ct*(en=_n)-Qt*On)*_n+(On=xn+Ct*On+Qt*en)*On;Ct-=sr=((hn=Ct*(en=hn)-Qt*xn-ut)*_n+(xn=Ct*xn+Qt*en-mt)*On)/Fr,Qt-=mr=(xn*_n-hn*On)/Fr}while(c(sr)+c(mr)>1e-12&&--pt>0);if(pt){var jr=O(Ct*Ct+Qt*Qt),Kr=2*i(.5*jr),Ur=_(Kr);return[s(Ct*Ur,jr*u(Kr)),jr?F(Qt*Ur/jr):0]}},Qe}Sr.invert=function(je,He){var Qe=c(je),ut=c(He),mt=b,pt=w;ut<yr?pt*=ut/yr:mt+=6*D(yr/ut);for(var Ct=0;Ct<25;Ct++){var Qt=_(pt),en=O(u(pt)),Yt=_(pt/2),an=u(pt/2),hn=_(mt/6),xn=u(mt/6),_n=.5*mt*(1+en)-Qe,On=pt/(an*xn)-ut,sr=en?-.25*mt*Qt/en:0,mr=.5*(1+en),Fr=(1+.5*pt*Yt/an)/(an*xn),jr=pt/an*(hn/6)/(xn*xn),Kr=sr*jr-Fr*mr,Ur=(_n*jr-On*mr)/Kr,Di=(On*sr-_n*Fr)/Kr;if(pt-=Ur,mt-=Di,c(Ur)<b&&c(Di)<b)break}return[je<0?-mt:mt,He<0?-pt:pt]},Kn.invert=function(je,He){var Qe=x(je)*k,ut=He/2,mt=50;do{var pt=Qe*Qe,Ct=ut*ut,Qt=Qe*ut,en=Qe*(.975534+Ct*(-.0143059*pt-.119161+-.0547009*Ct))-je,Yt=ut*(1.00384+pt*(.0802894+-.02855*Ct+199025e-9*pt)+Ct*(.0998909+-.0491032*Ct))-He,an=.975534-Ct*(.119161+3*pt*.0143059+.0547009*Ct),hn=-Qt*(.238322+.2188036*Ct+.0286118*pt),xn=Qt*(.1605788+7961e-7*pt+-.0571*Ct),_n=1.00384+pt*(.0802894+199025e-9*pt)+Ct*(3*(.0998909-.02855*pt)-.245516*Ct),On=hn*xn-_n*an,sr=(Yt*hn-en*_n)/On,mr=(en*xn-Yt*an)/On;Qe-=sr,ut-=mr}while((c(sr)>b||c(mr)>b)&&--mt>0);return mt&&[Qe,ut]},Dn.invert=function(je,He){var Qe=je*je,ut=He*He+1,mt=Qe+ut,pt=je?T*O((mt-O(mt*mt-4*Qe))/Qe):1/O(ut);return[F(je*pt),x(He)*D(pt)]},Yr.invert=function(je,He){return[je,2.5*i(h(.8*He))-.625*k]};var rr=[[.9972523,0],[.0052513,-.0041175],[.0074606,.0048125],[-.0153783,-.1968253],[.0636871,-.1408027],[.3660976,-.2937382]],nr=[[.98879,0],[0,0],[-.050909,0],[0,0],[.075528,0]],Bn=[[.984299,0],[.0211642,.0037608],[-.1036018,-.0575102],[-.0329095,-.0320119],[.0499471,.1223335],[.026046,.0899805],[7388e-7,-.1435792],[.0075848,-.1334108],[-.0216473,.0776645],[-.0225161,.0853673]],Nr=[[.9245,0],[0,0],[.01943,0]],Gr=[[.721316,0],[0,0],[-.00881625,-.00617325]];function pr(je,He){var Qe=a.geoProjection(Mn(je)).rotate(He).clipAngle(90),ut=a.geoRotation(He),mt=Qe.center;return delete Qe.rotate,Qe.center=function(pt){return arguments.length?mt(ut(pt)):ut.invert(mt())},Qe}var qr=O(6),_i=O(7);function cn(je,He){var Qe=F(7*_(He)/(3*qr));return[qr*je*(2*u(2*Qe/3)-1)/_i,9*_(Qe/3)/_i]}function jn(je,He){for(var 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a.geoProjection(yt).scale(173.044)},r.geoEckert5Raw=yt,r.geoEckert6=function(){return a.geoProjection(Ot).scale(173.044)},r.geoEckert6Raw=Ot,r.geoEisenlohr=function(){return a.geoProjection(at).scale(62.5271)},r.geoEisenlohrRaw=at,r.geoFahey=function(){return a.geoProjection(Lt).scale(137.152)},r.geoFaheyRaw=Lt,r.geoFoucaut=function(){return a.geoProjection(Wt).scale(135.264)},r.geoFoucautRaw=Wt,r.geoFoucautSinusoidal=function(){var je=.5,He=a.geoProjectionMutator(Jt),Qe=He(je);return Qe.alpha=function(ut){return arguments.length?He(je=+ut):je},Qe.scale(168.725)},r.geoFoucautSinusoidalRaw=Jt,r.geoGilbert=function(je){je==null&&(je=a.geoOrthographic);var He=je(),Qe=a.geoEquirectangular().scale(L).precision(0).clipAngle(null).translate([0,0]);function ut(pt){return He(Be(pt))}function mt(pt){ut[pt]=function(){return arguments.length?(He[pt].apply(He,arguments),ut):He[pt]()}}return He.invert&&(ut.invert=function(pt){return Ge(He.invert(pt))}),ut.stream=function(pt){var 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pt.rotate(an),hn.sphere=function(){xn.polygonStart(),xn.lineStart();for(var _n=0,On=2*k/je,sr=0;_n<je;++_n,sr-=On)xn.point(s(en*u(sr),Qt)*L,F(en*_(sr))*L),xn.point(s(ut*u(sr-On/2),Qe)*L,F(ut*_(sr-On/2))*L);xn.lineEnd(),xn.polygonEnd()},hn},pt.rotate([90,-40]).scale(91.7095).clipAngle(179.999)},r.geoGingeryRaw=kt,r.geoGinzburg4=function(){return a.geoProjection(Te).scale(149.995)},r.geoGinzburg4Raw=Te,r.geoGinzburg5=function(){return a.geoProjection(Pe).scale(153.93)},r.geoGinzburg5Raw=Pe,r.geoGinzburg6=function(){return a.geoProjection(qe).scale(130.945)},r.geoGinzburg6Raw=qe,r.geoGinzburg8=function(){return a.geoProjection(rt).scale(131.747)},r.geoGinzburg8Raw=rt,r.geoGinzburg9=function(){return a.geoProjection(lt).scale(131.087)},r.geoGinzburg9Raw=lt,r.geoGringorten=function(){return a.geoProjection(ot(At)).scale(239.75)},r.geoGringortenRaw=At,r.geoGuyou=function(){return a.geoProjection(ot(Ut)).scale(151.496)},r.geoGuyouRaw=Ut,r.geoHammer=function(){var 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l=a[0],c=a[1],i=a[2],s=a[3],u=l+l,h=c+c,d=i+i,m=l*u,p=c*u,g=c*h,y=i*u,v=i*h,x=i*d,_=s*u,A=s*h,b=s*d;return r[0]=1-g-x,r[1]=p+b,r[2]=y-A,r[3]=0,r[4]=p-b,r[5]=1-m-x,r[6]=v+_,r[7]=0,r[8]=y+A,r[9]=v-_,r[10]=1-m-g,r[11]=0,r[12]=0,r[13]=0,r[14]=0,r[15]=1,r}},{}],201:[function(t,o,f){o.exports=function(r,a,l){var c,i,s,u=l[0],h=l[1],d=l[2],m=Math.sqrt(u*u+h*h+d*d);return Math.abs(m)<1e-6?null:(u*=m=1/m,h*=m,d*=m,c=Math.sin(a),i=Math.cos(a),s=1-i,r[0]=u*u*s+i,r[1]=h*u*s+d*c,r[2]=d*u*s-h*c,r[3]=0,r[4]=u*h*s-d*c,r[5]=h*h*s+i,r[6]=d*h*s+u*c,r[7]=0,r[8]=u*d*s+h*c,r[9]=h*d*s-u*c,r[10]=d*d*s+i,r[11]=0,r[12]=0,r[13]=0,r[14]=0,r[15]=1,r)}},{}],202:[function(t,o,f){o.exports=function(r,a,l){var c=a[0],i=a[1],s=a[2],u=a[3],h=c+c,d=i+i,m=s+s,p=c*h,g=c*d,y=c*m,v=i*d,x=i*m,_=s*m,A=u*h,b=u*d,k=u*m;return r[0]=1-(v+_),r[1]=g+k,r[2]=y-b,r[3]=0,r[4]=g-k,r[5]=1-(p+_),r[6]=x+A,r[7]=0,r[8]=y+b,r[9]=x-A,r[10]=1-(p+v),r[11]=0,r[12]=l[0],r[13]=l[1],r[14]=l[2],r[15]=1,r}},{}],203:[function(t,o,f){o.exports=function(r,a){return r[0]=a[0],r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=a[1],r[6]=0,r[7]=0,r[8]=0,r[9]=0,r[10]=a[2],r[11]=0,r[12]=0,r[13]=0,r[14]=0,r[15]=1,r}},{}],204:[function(t,o,f){o.exports=function(r,a){return r[0]=1,r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=1,r[6]=0,r[7]=0,r[8]=0,r[9]=0,r[10]=1,r[11]=0,r[12]=a[0],r[13]=a[1],r[14]=a[2],r[15]=1,r}},{}],205:[function(t,o,f){o.exports=function(r,a){var l=Math.sin(a),c=Math.cos(a);return r[0]=1,r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=c,r[6]=l,r[7]=0,r[8]=0,r[9]=-l,r[10]=c,r[11]=0,r[12]=0,r[13]=0,r[14]=0,r[15]=1,r}},{}],206:[function(t,o,f){o.exports=function(r,a){var l=Math.sin(a),c=Math.cos(a);return r[0]=c,r[1]=0,r[2]=-l,r[3]=0,r[4]=0,r[5]=1,r[6]=0,r[7]=0,r[8]=l,r[9]=0,r[10]=c,r[11]=0,r[12]=0,r[13]=0,r[14]=0,r[15]=1,r}},{}],207:[function(t,o,f){o.exports=function(r,a){var l=Math.sin(a),c=Math.cos(a);return r[0]=c,r[1]=l,r[2]=0,r[3]=0,r[4]=-l,r[5]=c,r[6]=0,r[7]=0,r[8]=0,r[9]=0,r[10]=1,r[11]=0,r[12]=0,r[13]=0,r[14]=0,r[15]=1,r}},{}],208:[function(t,o,f){o.exports=function(r,a,l,c,i,s,u){var h=1/(l-a),d=1/(i-c),m=1/(s-u);return r[0]=2*s*h,r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=2*s*d,r[6]=0,r[7]=0,r[8]=(l+a)*h,r[9]=(i+c)*d,r[10]=(u+s)*m,r[11]=-1,r[12]=0,r[13]=0,r[14]=u*s*2*m,r[15]=0,r}},{}],209:[function(t,o,f){o.exports=function(r){return r[0]=1,r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=1,r[6]=0,r[7]=0,r[8]=0,r[9]=0,r[10]=1,r[11]=0,r[12]=0,r[13]=0,r[14]=0,r[15]=1,r}},{}],210:[function(t,o,f){o.exports={create:t("./create"),clone:t("./clone"),copy:t("./copy"),identity:t("./identity"),transpose:t("./transpose"),invert:t("./invert"),adjoint:t("./adjoint"),determinant:t("./determinant"),multiply:t("./multiply"),translate:t("./translate"),scale:t("./scale"),rotate:t("./rotate"),rotateX:t("./rotateX"),rotateY:t("./rotateY"),rotateZ:t("./rotateZ"),fromRotation:t("./fromRotation"),fromRotationTranslation:t("./fromRotationTranslation"),fromScaling:t("./fromScaling"),fromTranslation:t("./fromTranslation"),fromXRotation:t("./fromXRotation"),fromYRotation:t("./fromYRotation"),fromZRotation:t("./fromZRotation"),fromQuat:t("./fromQuat"),frustum:t("./frustum"),perspective:t("./perspective"),perspectiveFromFieldOfView:t("./perspectiveFromFieldOfView"),ortho:t("./ortho"),lookAt:t("./lookAt"),str:t("./str")}},{"./adjoint":195,"./clone":196,"./copy":197,"./create":198,"./determinant":199,"./fromQuat":200,"./fromRotation":201,"./fromRotationTranslation":202,"./fromScaling":203,"./fromTranslation":204,"./fromXRotation":205,"./fromYRotation":206,"./fromZRotation":207,"./frustum":208,"./identity":209,"./invert":211,"./lookAt":212,"./multiply":213,"./ortho":214,"./perspective":215,"./perspectiveFromFieldOfView":216,"./rotate":217,"./rotateX":218,"./rotateY":219,"./rotateZ":220,"./scale":221,"./str":222,"./translate":223,"./transpose":224}],211:[function(t,o,f){o.exports=function(r,a){var l=a[0],c=a[1],i=a[2],s=a[3],u=a[4],h=a[5],d=a[6],m=a[7],p=a[8],g=a[9],y=a[10],v=a[11],x=a[12],_=a[13],A=a[14],b=a[15],k=l*h-c*u,w=l*d-i*u,M=l*m-s*u,T=c*d-i*h,E=c*m-s*h,S=i*m-s*d,P=p*_-g*x,L=p*A-y*x,R=p*b-v*x,F=g*A-y*_,D=g*b-v*_,O=y*b-v*A,N=k*O-w*D+M*F+T*R-E*L+S*P;return N?(N=1/N,r[0]=(h*O-d*D+m*F)*N,r[1]=(i*D-c*O-s*F)*N,r[2]=(_*S-A*E+b*T)*N,r[3]=(y*E-g*S-v*T)*N,r[4]=(d*R-u*O-m*L)*N,r[5]=(l*O-i*R+s*L)*N,r[6]=(A*M-x*S-b*w)*N,r[7]=(p*S-y*M+v*w)*N,r[8]=(u*D-h*R+m*P)*N,r[9]=(c*R-l*D-s*P)*N,r[10]=(x*E-_*M+b*k)*N,r[11]=(g*M-p*E-v*k)*N,r[12]=(h*L-u*F-d*P)*N,r[13]=(l*F-c*L+i*P)*N,r[14]=(_*w-x*T-A*k)*N,r[15]=(p*T-g*w+y*k)*N,r):null}},{}],212:[function(t,o,f){var r=t("./identity");o.exports=function(a,l,c,i){var s,u,h,d,m,p,g,y,v,x,_=l[0],A=l[1],b=l[2],k=i[0],w=i[1],M=i[2],T=c[0],E=c[1],S=c[2];return Math.abs(_-T)<1e-6&&Math.abs(A-E)<1e-6&&Math.abs(b-S)<1e-6?r(a):(g=_-T,y=A-E,v=b-S,x=1/Math.sqrt(g*g+y*y+v*v),s=w*(v*=x)-M*(y*=x),u=M*(g*=x)-k*v,h=k*y-w*g,(x=Math.sqrt(s*s+u*u+h*h))?(s*=x=1/x,u*=x,h*=x):(s=0,u=0,h=0),d=y*h-v*u,m=v*s-g*h,p=g*u-y*s,(x=Math.sqrt(d*d+m*m+p*p))?(d*=x=1/x,m*=x,p*=x):(d=0,m=0,p=0),a[0]=s,a[1]=d,a[2]=g,a[3]=0,a[4]=u,a[5]=m,a[6]=y,a[7]=0,a[8]=h,a[9]=p,a[10]=v,a[11]=0,a[12]=-(s*_+u*A+h*b),a[13]=-(d*_+m*A+p*b),a[14]=-(g*_+y*A+v*b),a[15]=1,a)}},{"./identity":209}],213:[function(t,o,f){o.exports=function(r,a,l){var c=a[0],i=a[1],s=a[2],u=a[3],h=a[4],d=a[5],m=a[6],p=a[7],g=a[8],y=a[9],v=a[10],x=a[11],_=a[12],A=a[13],b=a[14],k=a[15],w=l[0],M=l[1],T=l[2],E=l[3];return r[0]=w*c+M*h+T*g+E*_,r[1]=w*i+M*d+T*y+E*A,r[2]=w*s+M*m+T*v+E*b,r[3]=w*u+M*p+T*x+E*k,w=l[4],M=l[5],T=l[6],E=l[7],r[4]=w*c+M*h+T*g+E*_,r[5]=w*i+M*d+T*y+E*A,r[6]=w*s+M*m+T*v+E*b,r[7]=w*u+M*p+T*x+E*k,w=l[8],M=l[9],T=l[10],E=l[11],r[8]=w*c+M*h+T*g+E*_,r[9]=w*i+M*d+T*y+E*A,r[10]=w*s+M*m+T*v+E*b,r[11]=w*u+M*p+T*x+E*k,w=l[12],M=l[13],T=l[14],E=l[15],r[12]=w*c+M*h+T*g+E*_,r[13]=w*i+M*d+T*y+E*A,r[14]=w*s+M*m+T*v+E*b,r[15]=w*u+M*p+T*x+E*k,r}},{}],214:[function(t,o,f){o.exports=function(r,a,l,c,i,s,u){var h=1/(a-l),d=1/(c-i),m=1/(s-u);return r[0]=-2*h,r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=-2*d,r[6]=0,r[7]=0,r[8]=0,r[9]=0,r[10]=2*m,r[11]=0,r[12]=(a+l)*h,r[13]=(i+c)*d,r[14]=(u+s)*m,r[15]=1,r}},{}],215:[function(t,o,f){o.exports=function(r,a,l,c,i){var s=1/Math.tan(a/2),u=1/(c-i);return r[0]=s/l,r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=s,r[6]=0,r[7]=0,r[8]=0,r[9]=0,r[10]=(i+c)*u,r[11]=-1,r[12]=0,r[13]=0,r[14]=2*i*c*u,r[15]=0,r}},{}],216:[function(t,o,f){o.exports=function(r,a,l,c){var i=Math.tan(a.upDegrees*Math.PI/180),s=Math.tan(a.downDegrees*Math.PI/180),u=Math.tan(a.leftDegrees*Math.PI/180),h=Math.tan(a.rightDegrees*Math.PI/180),d=2/(u+h),m=2/(i+s);return r[0]=d,r[1]=0,r[2]=0,r[3]=0,r[4]=0,r[5]=m,r[6]=0,r[7]=0,r[8]=-(u-h)*d*.5,r[9]=(i-s)*m*.5,r[10]=c/(l-c),r[11]=-1,r[12]=0,r[13]=0,r[14]=c*l/(l-c),r[15]=0,r}},{}],217:[function(t,o,f){o.exports=function(r,a,l,c){var i,s,u,h,d,m,p,g,y,v,x,_,A,b,k,w,M,T,E,S,P,L,R,F,D=c[0],O=c[1],N=c[2],B=Math.sqrt(D*D+O*O+N*N);return Math.abs(B)<1e-6?null:(D*=B=1/B,O*=B,N*=B,i=Math.sin(l),s=Math.cos(l),u=1-s,h=a[0],d=a[1],m=a[2],p=a[3],g=a[4],y=a[5],v=a[6],x=a[7],_=a[8],A=a[9],b=a[10],k=a[11],w=D*D*u+s,M=O*D*u+N*i,T=N*D*u-O*i,E=D*O*u-N*i,S=O*O*u+s,P=N*O*u+D*i,L=D*N*u+O*i,R=O*N*u-D*i,F=N*N*u+s,r[0]=h*w+g*M+_*T,r[1]=d*w+y*M+A*T,r[2]=m*w+v*M+b*T,r[3]=p*w+x*M+k*T,r[4]=h*E+g*S+_*P,r[5]=d*E+y*S+A*P,r[6]=m*E+v*S+b*P,r[7]=p*E+x*S+k*P,r[8]=h*L+g*R+_*F,r[9]=d*L+y*R+A*F,r[10]=m*L+v*R+b*F,r[11]=p*L+x*R+k*F,a!==r&&(r[12]=a[12],r[13]=a[13],r[14]=a[14],r[15]=a[15]),r)}},{}],218:[function(t,o,f){o.exports=function(r,a,l){var c=Math.sin(l),i=Math.cos(l),s=a[4],u=a[5],h=a[6],d=a[7],m=a[8],p=a[9],g=a[10],y=a[11];return a!==r&&(r[0]=a[0],r[1]=a[1],r[2]=a[2],r[3]=a[3],r[12]=a[12],r[13]=a[13],r[14]=a[14],r[15]=a[15]),r[4]=s*i+m*c,r[5]=u*i+p*c,r[6]=h*i+g*c,r[7]=d*i+y*c,r[8]=m*i-s*c,r[9]=p*i-u*c,r[10]=g*i-h*c,r[11]=y*i-d*c,r}},{}],219:[function(t,o,f){o.exports=function(r,a,l){var c=Math.sin(l),i=Math.cos(l),s=a[0],u=a[1],h=a[2],d=a[3],m=a[8],p=a[9],g=a[10],y=a[11];return a!==r&&(r[4]=a[4],r[5]=a[5],r[6]=a[6],r[7]=a[7],r[12]=a[12],r[13]=a[13],r[14]=a[14],r[15]=a[15]),r[0]=s*i-m*c,r[1]=u*i-p*c,r[2]=h*i-g*c,r[3]=d*i-y*c,r[8]=s*c+m*i,r[9]=u*c+p*i,r[10]=h*c+g*i,r[11]=d*c+y*i,r}},{}],220:[function(t,o,f){o.exports=function(r,a,l){var c=Math.sin(l),i=Math.cos(l),s=a[0],u=a[1],h=a[2],d=a[3],m=a[4],p=a[5],g=a[6],y=a[7];return a!==r&&(r[8]=a[8],r[9]=a[9],r[10]=a[10],r[11]=a[11],r[12]=a[12],r[13]=a[13],r[14]=a[14],r[15]=a[15]),r[0]=s*i+m*c,r[1]=u*i+p*c,r[2]=h*i+g*c,r[3]=d*i+y*c,r[4]=m*i-s*c,r[5]=p*i-u*c,r[6]=g*i-h*c,r[7]=y*i-d*c,r}},{}],221:[function(t,o,f){o.exports=function(r,a,l){var c=l[0],i=l[1],s=l[2];return r[0]=a[0]*c,r[1]=a[1]*c,r[2]=a[2]*c,r[3]=a[3]*c,r[4]=a[4]*i,r[5]=a[5]*i,r[6]=a[6]*i,r[7]=a[7]*i,r[8]=a[8]*s,r[9]=a[9]*s,r[10]=a[10]*s,r[11]=a[11]*s,r[12]=a[12],r[13]=a[13],r[14]=a[14],r[15]=a[15],r}},{}],222:[function(t,o,f){o.exports=function(r){return"mat4("+r[0]+", "+r[1]+", "+r[2]+", "+r[3]+", "+r[4]+", "+r[5]+", "+r[6]+", "+r[7]+", "+r[8]+", "+r[9]+", "+r[10]+", "+r[11]+", "+r[12]+", "+r[13]+", "+r[14]+", "+r[15]+")"}},{}],223:[function(t,o,f){o.exports=function(r,a,l){var c,i,s,u,h,d,m,p,g,y,v,x,_=l[0],A=l[1],b=l[2];return a===r?(r[12]=a[0]*_+a[4]*A+a[8]*b+a[12],r[13]=a[1]*_+a[5]*A+a[9]*b+a[13],r[14]=a[2]*_+a[6]*A+a[10]*b+a[14],r[15]=a[3]*_+a[7]*A+a[11]*b+a[15]):(c=a[0],i=a[1],s=a[2],u=a[3],h=a[4],d=a[5],m=a[6],p=a[7],g=a[8],y=a[9],v=a[10],x=a[11],r[0]=c,r[1]=i,r[2]=s,r[3]=u,r[4]=h,r[5]=d,r[6]=m,r[7]=p,r[8]=g,r[9]=y,r[10]=v,r[11]=x,r[12]=c*_+h*A+g*b+a[12],r[13]=i*_+d*A+y*b+a[13],r[14]=s*_+m*A+v*b+a[14],r[15]=u*_+p*A+x*b+a[15]),r}},{}],224:[function(t,o,f){o.exports=function(r,a){if(r===a){var l=a[1],c=a[2],i=a[3],s=a[6],u=a[7],h=a[11];r[1]=a[4],r[2]=a[8],r[3]=a[12],r[4]=l,r[6]=a[9],r[7]=a[13],r[8]=c,r[9]=s,r[11]=a[14],r[12]=i,r[13]=u,r[14]=h}else r[0]=a[0],r[1]=a[4],r[2]=a[8],r[3]=a[12],r[4]=a[1],r[5]=a[5],r[6]=a[9],r[7]=a[13],r[8]=a[2],r[9]=a[6],r[10]=a[10],r[11]=a[14],r[12]=a[3],r[13]=a[7],r[14]=a[11],r[15]=a[15];return r}},{}],225:[function(t,o,f){var r=t("css-font"),a=t("pick-by-alias"),l=t("regl"),c=t("gl-util/context"),i=t("es6-weak-map"),s=t("color-normalize"),u=t("font-atlas"),h=t("typedarray-pool"),d=t("parse-rect"),m=t("is-plain-obj"),p=t("parse-unit"),g=t("to-px"),y=t("detect-kerning"),v=t("object-assign"),x=t("font-measure"),_=t("flatten-vertex-data"),A=t("bit-twiddle").nextPow2,b=new i,k=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(k=!0),document.body.removeChild(w)}var M=function(T){(function(E){return typeof E=="function"&&E._gl&&E.prop&&E.texture&&E.buffer})(T)?(T={regl:T},this.gl=T.regl._gl):this.gl=c(T),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=T.regl||l({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(m(T)?T:{})};M.prototype.createShader=function(){var T=this.regl,E=T({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:T.prop("count"),offset:T.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:T.this("sizeBuffer")},width:{offset:0,stride:8,buffer:T.this("sizeBuffer")},char:T.this("charBuffer"),position:T.this("position")},uniforms:{atlasSize:function(S,P){return[P.atlas.width,P.atlas.height]},atlasDim:function(S,P){return[P.atlas.cols,P.atlas.rows]},atlas:function(S,P){return P.atlas.texture},charStep:function(S,P){return P.atlas.step},em:function(S,P){return P.atlas.em},color:T.prop("color"),opacity:T.prop("opacity"),viewport:T.this("viewportArray"),scale:T.this("scale"),align:T.prop("align"),baseline:T.prop("baseline"),translate:T.this("translate"),positionOffset:T.prop("positionOffset")},primitive:"points",viewport:T.this("viewport"),vert:`
-			precision highp float;
-			attribute float width, charOffset, char;
-			attribute vec2 position;
-			uniform float fontSize, charStep, em, align, baseline;
-			uniform vec4 viewport;
-			uniform vec4 color;
-			uniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;
-			varying vec2 charCoord, charId;
-			varying float charWidth;
-			varying vec4 fontColor;
-			void main () {
-				vec2 offset = floor(em * (vec2(align + charOffset, baseline)
-					+ vec2(positionOffset.x, -positionOffset.y)))
-					/ (viewport.zw * scale.xy);
-
-				vec2 position = (position + translate) * scale;
-				position += offset * scale;
-
-				charCoord = position * viewport.zw + viewport.xy;
-
-				gl_Position = vec4(position * 2. - 1., 0, 1);
-
-				gl_PointSize = charStep;
-
-				charId.x = mod(char, atlasDim.x);
-				charId.y = floor(char / atlasDim.x);
-
-				charWidth = width * em;
-
-				fontColor = color / 255.;
-			}`,frag:`
-			precision highp float;
-			uniform float fontSize, charStep, opacity;
-			uniform vec2 atlasSize;
-			uniform vec4 viewport;
-			uniform sampler2D atlas;
-			varying vec4 fontColor;
-			varying vec2 charCoord, charId;
-			varying float charWidth;
-
-			float lightness(vec4 color) {
-				return color.r * 0.299 + color.g * 0.587 + color.b * 0.114;
-			}
-
-			void main () {
-				vec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;
-				float halfCharStep = floor(charStep * .5 + .5);
-
-				// invert y and shift by 1px (FF expecially needs that)
-				uv.y = charStep - uv.y;
-
-				// ignore points outside of character bounding box
-				float halfCharWidth = ceil(charWidth * .5);
-				if (floor(uv.x) > halfCharStep + halfCharWidth ||
-					floor(uv.x) < halfCharStep - halfCharWidth) return;
-
-				uv += charId * charStep;
-				uv = uv / atlasSize;
-
-				vec4 color = fontColor;
-				vec4 mask = texture2D(atlas, uv);
-
-				float maskY = lightness(mask);
-				// float colorY = lightness(color);
-				color.a *= maskY;
-				color.a *= opacity;
-
-				// color.a += .1;
-
-				// antialiasing, see yiq color space y-channel formula
-				// color.rgb += (1. - color.rgb) * (1. - mask.rgb);
-
-				gl_FragColor = color;
-			}`});return{regl:T,draw:E,atlas:{}}},M.prototype.update=function(T){var E=this;if(typeof T=="string")T={text:T};else if(!T)return;(T=a(T,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity!=null&&(Array.isArray(T.opacity)?this.opacity=T.opacity.map(function(ve){return parseFloat(ve)}):this.opacity=parseFloat(T.opacity)),T.viewport!=null&&(this.viewport=d(T.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),this.viewport==null&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),T.kerning!=null&&(this.kerning=T.kerning),T.offset!=null&&(typeof T.offset=="number"&&(T.offset=[T.offset,0]),this.positionOffset=_(T.offset)),T.direction&&(this.direction=T.direction),T.range&&(this.range=T.range,this.scale=[1/(T.range[2]-T.range[0]),1/(T.range[3]-T.range[1])],this.translate=[-T.range[0],-T.range[1]]),T.scale&&(this.scale=T.scale),T.translate&&(this.translate=T.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||T.font||(T.font=M.baseFontSize+"px sans-serif");var S,P=!1,L=!1;if(T.font&&(Array.isArray(T.font)?T.font:[T.font]).forEach(function(ve,Me){if(typeof ve=="string")try{ve=r.parse(ve)}catch{ve=r.parse(M.baseFontSize+"px "+ve)}else ve=r.parse(r.stringify(ve));var we=r.stringify({size:M.baseFontSize,family:ve.family,stretch:k?ve.stretch:void 0,variant:ve.variant,weight:ve.weight,style:ve.style}),Ce=p(ve.size),Fe=Math.round(Ce[0]*g(Ce[1]));if(Fe!==E.fontSize[Me]&&(L=!0,E.fontSize[Me]=Fe),!(E.font[Me]&&we==E.font[Me].baseString||(P=!0,E.font[Me]=M.fonts[we],E.font[Me]))){var ze=ve.family.join(", "),$e=[ve.style];ve.style!=ve.variant&&$e.push(ve.variant),ve.variant!=ve.weight&&$e.push(ve.weight),k&&ve.weight!=ve.stretch&&$e.push(ve.stretch),E.font[Me]={baseString:we,family:ze,weight:ve.weight,stretch:ve.stretch,style:ve.style,variant:ve.variant,width:{},kerning:{},metrics:x(ze,{origin:"top",fontSize:M.baseFontSize,fontStyle:$e.join(" ")})},M.fonts[we]=E.font[Me]}}),(P||L)&&this.font.forEach(function(ve,Me){var we=r.stringify({size:E.fontSize[Me],family:ve.family,stretch:k?ve.stretch:void 0,variant:ve.variant,weight:ve.weight,style:ve.style});if(E.fontAtlas[Me]=E.shader.atlas[we],!E.fontAtlas[Me]){var Ce=ve.metrics;E.shader.atlas[we]=E.fontAtlas[Me]={fontString:we,step:2*Math.ceil(E.fontSize[Me]*Ce.bottom*.5),em:E.fontSize[Me],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:E.regl.texture()}}T.text==null&&(T.text=E.text)}),typeof T.text=="string"&&T.position&&T.position.length>2){for(var R=Array(.5*T.position.length),F=0;F<R.length;F++)R[F]=T.text;T.text=R}if(T.text!=null||P){if(this.textOffsets=[0],Array.isArray(T.text)){this.count=T.text[0].length,this.counts=[this.count];for(var D=1;D<T.text.length;D++)this.textOffsets[D]=this.textOffsets[D-1]+T.text[D-1].length,this.count+=T.text[D].length,this.counts.push(T.text[D].length);this.text=T.text.join("")}else this.text=T.text,this.count=this.text.length,this.counts=[this.count];S=[],this.font.forEach(function(ve,Me){M.atlasContext.font=ve.baseString;for(var we=E.fontAtlas[Me],Ce=0;Ce<E.text.length;Ce++){var Fe=E.text.charAt(Ce);if(we.ids[Fe]==null&&(we.ids[Fe]=we.chars.length,we.chars.push(Fe),S.push(Fe)),ve.width[Fe]==null&&(ve.width[Fe]=M.atlasContext.measureText(Fe).width/M.baseFontSize,E.kerning)){var ze=[];for(var $e in ve.width)ze.push($e+Fe,Fe+$e);v(ve.kerning,y(ve.family,{pairs:ze}))}}})}if(T.position)if(T.position.length>2){for(var O=!T.position[0].length,N=h.mallocFloat(2*this.count),B=0,W=0;B<this.counts.length;B++){var G=this.counts[B];if(O)for(var K=0;K<G;K++)N[W++]=T.position[2*B],N[W++]=T.position[2*B+1];else for(var te=0;te<G;te++)N[W++]=T.position[B][0],N[W++]=T.position[B][1]}this.position.call?this.position({type:"float",data:N}):this.position=this.regl.buffer({type:"float",data:N}),h.freeFloat(N)}else this.position.destroy&&this.position.destroy(),this.position={constant:T.position};if(T.text||P){var Y=h.mallocUint8(this.count),J=h.mallocFloat(2*this.count);this.textWidth=[];for(var re=0,U=0;re<this.counts.length;re++){for(var V=this.counts[re],H=this.font[re]||this.font[0],ne=this.fontAtlas[re]||this.fontAtlas[0],q=0;q<V;q++){var Q=this.text.charAt(U),ee=this.text.charAt(U-1);if(Y[U]=ne.ids[Q],J[2*U]=H.width[Q],q){var ie=J[2*U-2],ae=J[2*U],ue=J[2*U-1]+.5*ie+.5*ae;if(this.kerning){var le=H.kerning[ee+Q];le&&(ue+=.001*le)}J[2*U+1]=ue}else J[2*U+1]=.5*J[2*U];U++}this.textWidth.push(J.length?.5*J[2*U-2]+J[2*U-1]:0)}T.align||(T.align=this.align),this.charBuffer({data:Y,type:"uint8",usage:"stream"}),this.sizeBuffer({data:J,type:"float",usage:"stream"}),h.freeUint8(Y),h.freeFloat(J),S.length&&this.font.forEach(function(ve,Me){var we=E.fontAtlas[Me],Ce=we.step,Fe=Math.floor(M.maxAtlasSize/Ce),ze=Math.min(Fe,we.chars.length),$e=Math.ceil(we.chars.length/ze),Ke=A(ze*Ce),Re=A($e*Ce);we.width=Ke,we.height=Re,we.rows=$e,we.cols=ze,we.em&&we.texture({data:u({canvas:M.atlasCanvas,font:we.fontString,chars:we.chars,shape:[Ke,Re],step:[Ce,Ce]})})})}if(T.align&&(this.align=T.align,this.alignOffset=this.textWidth.map(function(ve,Me){var we=Array.isArray(E.align)?E.align.length>1?E.align[Me]:E.align[0]:E.align;if(typeof we=="number")return we;switch(we){case"right":case"end":return-ve;case"center":case"centre":case"middle":return .5*-ve}return 0})),this.baseline==null&&T.baseline==null&&(T.baseline=0),T.baseline!=null&&(this.baseline=T.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(ve,Me){var we=(E.font[Me]||E.font[0]).metrics,Ce=0;return Ce+=.5*we.bottom,Ce+=typeof ve=="number"?ve-we.baseline:-we[ve],Ce*=-1})),T.color!=null)if(T.color||(T.color="transparent"),typeof T.color!="string"&&isNaN(T.color)){var ge;if(typeof T.color[0]=="number"&&T.color.length>this.counts.length){var fe=T.color.length;ge=h.mallocUint8(fe);for(var me=(T.color.subarray||T.color.slice).bind(T.color),_e=0;_e<fe;_e+=4)ge.set(s(me(_e,_e+4),"uint8"),_e)}else{var Ae=T.color.length;ge=h.mallocUint8(4*Ae);for(var ke=0;ke<Ae;ke++)ge.set(s(T.color[ke]||0,"uint8"),4*ke)}this.color=ge}else this.color=s(T.color,"uint8");if(T.position||T.text||T.color||T.baseline||T.align||T.font||T.offset||T.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var Le=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(Le);for(var de=0;de<this.batch.length;de++)this.batch[de]={count:this.counts.length>1?this.counts[de]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[de]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*de,4*de+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[de]:this.opacity,baseline:this.baselineOffset[de]!=null?this.baselineOffset[de]:this.baselineOffset[0],align:this.align?this.alignOffset[de]!=null?this.alignOffset[de]:this.alignOffset[0]:0,atlas:this.fontAtlas[de]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*de,2*de+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},M.prototype.destroy=function(){},M.prototype.kerning=!0,M.prototype.position={constant:new Float32Array(2)},M.prototype.translate=null,M.prototype.scale=null,M.prototype.font=null,M.prototype.text="",M.prototype.positionOffset=[0,0],M.prototype.opacity=1,M.prototype.color=new Uint8Array([0,0,0,255]),M.prototype.alignOffset=[0,0],M.maxAtlasSize=1024,M.atlasCanvas=document.createElement("canvas"),M.atlasContext=M.atlasCanvas.getContext("2d",{alpha:!1}),M.baseFontSize=64,M.fonts={},o.exports=M},{"bit-twiddle":81,"color-normalize":89,"css-font":99,"detect-kerning":125,"es6-weak-map":183,"flatten-vertex-data":191,"font-atlas":192,"font-measure":193,"gl-util/context":226,"is-plain-obj":236,"object-assign":247,"parse-rect":249,"parse-unit":251,"pick-by-alias":253,regl:283,"to-px":314,"typedarray-pool":327}],226:[function(t,o,f){(function(r){(function(){var a=t("pick-by-alias");function l(s){if(s.container)if(s.container==document.body)document.body.style.width||(s.canvas.width=s.width||s.pixelRatio*r.innerWidth),document.body.style.height||(s.canvas.height=s.height||s.pixelRatio*r.innerHeight);else{var u=s.container.getBoundingClientRect();s.canvas.width=s.width||u.right-u.left,s.canvas.height=s.height||u.bottom-u.top}}function c(s){return typeof s.getContext=="function"&&"width"in s&&"height"in s}function i(){var s=document.createElement("canvas");return s.style.position="absolute",s.style.top=0,s.style.left=0,s}o.exports=function(s){var u;if(s?typeof s=="string"&&(s={container:s}):s={},c(s)?s={container:s}:s=typeof(u=s).nodeName=="string"&&typeof u.appendChild=="function"&&typeof u.getBoundingClientRect=="function"?{container:s}:function(d){return typeof d.drawArrays=="function"||typeof d.drawElements=="function"}(s)?{gl:s}:a(s,{container:"container target element el canvas holder parent parentNode wrapper use ref root node",gl:"gl context webgl glContext",attrs:"attributes attrs contextAttributes",pixelRatio:"pixelRatio pxRatio px ratio pxratio pixelratio",width:"w width",height:"h height"},!0),s.pixelRatio||(s.pixelRatio=r.pixelRatio||1),s.gl)return s.gl;if(s.canvas&&(s.container=s.canvas.parentNode),s.container){if(typeof s.container=="string"){var h=document.querySelector(s.container);if(!h)throw Error("Element "+s.container+" is not found");s.container=h}c(s.container)?(s.canvas=s.container,s.container=s.canvas.parentNode):s.canvas||(s.canvas=i(),s.container.appendChild(s.canvas),l(s))}else if(!s.canvas){if(typeof document>"u")throw Error("Not DOM environment. 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tu=function(C){this._properties=C,this._values=Object.create(C.defaultPossiblyEvaluatedValues)};tu.prototype.get=function(C){return this._values[C]};var wi=function(C){this.specification=C};wi.prototype.possiblyEvaluate=function(C,z){return C.expression.evaluate(z)},wi.prototype.interpolate=function(C,z,Z){var oe=Ir[this.specification.type];return oe?oe(C,z,Z):C};var Ii=function(C,z){this.specification=C,this.overrides=z};Ii.prototype.possiblyEvaluate=function(C,z,Z,oe){return C.expression.kind==="constant"||C.expression.kind==="camera"?new Do(this,{kind:"constant",value:C.expression.evaluate(z,null,{},Z,oe)},z):new Do(this,C.expression,z)},Ii.prototype.interpolate=function(C,z,Z){if(C.value.kind!=="constant"||z.value.kind!=="constant")return C;if(C.value.value===void 0||z.value.value===void 0)return new Do(this,{kind:"constant",value:void 0},C.parameters);var oe=Ir[this.specification.type];return oe?new Do(this,{kind:"constant",value:oe(C.value.value,z.value.value,Z)},C.parameters):C},Ii.prototype.evaluate=function(C,z,Z,oe,pe,xe){return C.kind==="constant"?C.value:C.evaluate(z,Z,oe,pe,xe)};var Pf=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype.possiblyEvaluate=function(Z,oe,pe,xe){if(Z.value===void 0)return new Do(this,{kind:"constant",value:void 0},oe);if(Z.expression.kind==="constant"){var Se=Z.expression.evaluate(oe,null,{},pe,xe),Ne=Z.property.specification.type==="resolvedImage"&&typeof Se!="string"?Se.name:Se,Ze=this._calculate(Ne,Ne,Ne,oe);return new Do(this,{kind:"constant",value:Ze},oe)}if(Z.expression.kind==="camera"){var ct=this._calculate(Z.expression.evaluate({zoom:oe.zoom-1}),Z.expression.evaluate({zoom:oe.zoom}),Z.expression.evaluate({zoom:oe.zoom+1}),oe);return new Do(this,{kind:"constant",value:ct},oe)}return new Do(this,Z.expression,oe)},z.prototype.evaluate=function(Z,oe,pe,xe,Se,Ne){if(Z.kind==="source"){var Ze=Z.evaluate(oe,pe,xe,Se,Ne);return this._calculate(Ze,Ze,Ze,oe)}return Z.kind==="composite"?this._calculate(Z.evaluate({zoom:Math.floor(oe.zoom)-1},pe,xe),Z.evaluate({zoom:Math.floor(oe.zoom)},pe,xe),Z.evaluate({zoom:Math.floor(oe.zoom)+1},pe,xe),oe):Z.value},z.prototype._calculate=function(Z,oe,pe,xe){return xe.zoom>xe.zoomHistory.lastIntegerZoom?{from:Z,to:oe}:{from:pe,to:oe}},z.prototype.interpolate=function(Z){return Z},z}(Ii),If=function(C){this.specification=C};If.prototype.possiblyEvaluate=function(C,z,Z,oe){if(C.value!==void 0){if(C.expression.kind==="constant"){var pe=C.expression.evaluate(z,null,{},Z,oe);return this._calculate(pe,pe,pe,z)}return this._calculate(C.expression.evaluate(new Ia(Math.floor(z.zoom-1),z)),C.expression.evaluate(new Ia(Math.floor(z.zoom),z)),C.expression.evaluate(new Ia(Math.floor(z.zoom+1),z)),z)}},If.prototype._calculate=function(C,z,Z,oe){return oe.zoom>oe.zoomHistory.lastIntegerZoom?{from:C,to:z}:{from:Z,to:z}},If.prototype.interpolate=function(C){return C};var nu=function(C){this.specification=C};nu.prototype.possiblyEvaluate=function(C,z,Z,oe){return!!C.expression.evaluate(z,null,{},Z,oe)},nu.prototype.interpolate=function(){return!1};var Gs=function(C){for(var z in this.properties=C,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],C){var Z=C[z];Z.specification.overridable&&this.overridableProperties.push(z);var oe=this.defaultPropertyValues[z]=new ls(Z,void 0),pe=this.defaultTransitionablePropertyValues[z]=new 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z.arrayBuffer=C.arrayBuffer,z.length=C.length,z.capacity=C.arrayBuffer.byteLength/z.bytesPerElement,z._refreshViews(),z},Ta.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Ta.prototype.clear=function(){this.length=0},Ta.prototype.resize=function(C){this.reserve(C),this.length=C},Ta.prototype.reserve=function(C){if(C>this.capacity){this.capacity=Math.max(C,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var z=this.uint8;this._refreshViews(),z&&this.uint8.set(z)}},Ta.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var Eu=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new 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z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z,oe,pe,xe,Se,Ne){var Ze=this.length;return this.resize(Ze+1),this.emplace(Ze,Z,oe,pe,xe,Se,Ne)},z.prototype.emplace=function(Z,oe,pe,xe,Se,Ne,Ze){var ct=6*Z;return this.int16[ct+0]=oe,this.int16[ct+1]=pe,this.int16[ct+2]=xe,this.int16[ct+3]=Se,this.int16[ct+4]=Ne,this.int16[ct+5]=Ze,Z},z}(Ta);ec.prototype.bytesPerElement=12,Qn("StructArrayLayout2i4i12",ec);var Uc=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z,oe,pe,xe,Se,Ne){var Ze=this.length;return this.resize(Ze+1),this.emplace(Ze,Z,oe,pe,xe,Se,Ne)},z.prototype.emplace=function(Z,oe,pe,xe,Se,Ne,Ze){var ct=4*Z,gt=8*Z;return this.int16[ct+0]=oe,this.int16[ct+1]=pe,this.uint8[gt+4]=xe,this.uint8[gt+5]=Se,this.uint8[gt+6]=Ne,this.uint8[gt+7]=Ze,Z},z}(Ta);Uc.prototype.bytesPerElement=8,Qn("StructArrayLayout2i4ub8",Uc);var vc=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt){var Xt=this.length;return this.resize(Xt+1),this.emplace(Xt,Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt)},z.prototype.emplace=function(Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt,Xt){var Gt=9*Z,on=18*Z;return this.uint16[Gt+0]=oe,this.uint16[Gt+1]=pe,this.uint16[Gt+2]=xe,this.uint16[Gt+3]=Se,this.uint16[Gt+4]=Ne,this.uint16[Gt+5]=Ze,this.uint16[Gt+6]=ct,this.uint16[Gt+7]=gt,this.uint8[on+16]=Bt,this.uint8[on+17]=Xt,Z},z}(Ta);vc.prototype.bytesPerElement=18,Qn("StructArrayLayout8ui2ub18",vc);var $c=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt,Xt,Gt){var on=this.length;return this.resize(on+1),this.emplace(on,Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt,Xt,Gt)},z.prototype.emplace=function(Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt,Xt,Gt,on){var yn=12*Z;return this.int16[yn+0]=oe,this.int16[yn+1]=pe,this.int16[yn+2]=xe,this.int16[yn+3]=Se,this.uint16[yn+4]=Ne,this.uint16[yn+5]=Ze,this.uint16[yn+6]=ct,this.uint16[yn+7]=gt,this.int16[yn+8]=Bt,this.int16[yn+9]=Xt,this.int16[yn+10]=Gt,this.int16[yn+11]=on,Z},z}(Ta);$c.prototype.bytesPerElement=24,Qn("StructArrayLayout4i4ui4i24",$c);var Su=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z,oe,pe){var xe=this.length;return this.resize(xe+1),this.emplace(xe,Z,oe,pe)},z.prototype.emplace=function(Z,oe,pe,xe){var Se=3*Z;return this.float32[Se+0]=oe,this.float32[Se+1]=pe,this.float32[Se+2]=xe,Z},z}(Ta);Su.prototype.bytesPerElement=12,Qn("StructArrayLayout3f12",Su);var xd=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z){var oe=this.length;return this.resize(oe+1),this.emplace(oe,Z)},z.prototype.emplace=function(Z,oe){var pe=1*Z;return this.uint32[pe+0]=oe,Z},z}(Ta);xd.prototype.bytesPerElement=4,Qn("StructArrayLayout1ul4",xd);var Op=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z,oe,pe,xe,Se,Ne,Ze,ct,gt){var Bt=this.length;return this.resize(Bt+1),this.emplace(Bt,Z,oe,pe,xe,Se,Ne,Ze,ct,gt)},z.prototype.emplace=function(Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt){var Xt=10*Z,Gt=5*Z;return this.int16[Xt+0]=oe,this.int16[Xt+1]=pe,this.int16[Xt+2]=xe,this.int16[Xt+3]=Se,this.int16[Xt+4]=Ne,this.int16[Xt+5]=Ze,this.uint32[Gt+3]=ct,this.uint16[Xt+8]=gt,this.uint16[Xt+9]=Bt,Z},z}(Ta);Op.prototype.bytesPerElement=20,Qn("StructArrayLayout6i1ul2ui20",Op);var tc=function(C){function z(){C.apply(this,arguments)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},z.prototype.emplaceBack=function(Z,oe,pe,xe,Se,Ne){var Ze=this.length;return this.resize(Ze+1),this.emplace(Ze,Z,oe,pe,xe,Se,Ne)},z.prototype.emplace=function(Z,oe,pe,xe,Se,Ne,Ze){var ct=6*Z;return 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Z.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},Z.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},Z.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},Z.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},Z.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},Z.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},Z.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},Z.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},Z.key.get=function(){return this._structArray.uint16[this._pos2+8]},Z.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},Z.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},Z.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},Z.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},Z.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},Z.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},Z.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},Z.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},Z.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},Z.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},Z.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},Z.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},Z.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},Z.useRuntimeCollisionCircles.get=function(){return 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na)&&Se.populatePaintArray(C,z,Z,oe,pe)}},pa.prototype.setConstantPatternPositions=function(C,z){for(var Z in this.binders){var oe=this.binders[Z];oe instanceof Oi&&oe.setConstantPatternPositions(C,z)}},pa.prototype.updatePaintArrays=function(C,z,Z,oe,pe){var xe=!1;for(var Se in C)for(var Ne=0,Ze=z.getPositions(Se);Ne<Ze.length;Ne+=1){var ct=Ze[Ne],gt=Z.feature(ct.index);for(var Bt in this.binders){var Xt=this.binders[Bt];if((Xt instanceof ta||Xt instanceof Ni||Xt instanceof na)&&Xt.expression.isStateDependent===!0){var Gt=oe.paint.get(Bt);Xt.expression=Gt.value,Xt.updatePaintArray(ct.start,ct.end,gt,C[Se],pe),xe=!0}}}return xe},pa.prototype.defines=function(){var C=[];for(var z in this.binders){var Z=this.binders[z];(Z instanceof zi||Z instanceof Oi)&&C.push.apply(C,Z.uniformNames.map(function(oe){return"#define HAS_UNIFORM_"+oe}))}return C},pa.prototype.getPaintVertexBuffers=function(){return this._buffers},pa.prototype.getUniforms=function(C,z){var Z=[];for(var oe in this.binders){var pe=this.binders[oe];if(pe instanceof zi||pe instanceof Oi||pe instanceof Ni)for(var xe=0,Se=pe.uniformNames;xe<Se.length;xe+=1){var Ne=Se[xe];if(z[Ne]){var Ze=pe.getBinding(C,z[Ne],Ne);Z.push({name:Ne,property:oe,binding:Ze})}}}return Z},pa.prototype.setUniforms=function(C,z,Z,oe){for(var pe=0,xe=z;pe<xe.length;pe+=1){var Se=xe[pe],Ne=Se.name,Ze=Se.property,ct=Se.binding;this.binders[Ze].setUniform(ct,oe,Z.get(Ze),Ne)}},pa.prototype.updatePaintBuffers=function(C){for(var z in this._buffers=[],this.binders){var Z=this.binders[z];if(C&&Z instanceof na){var oe=C.fromScale===2?Z.zoomInPaintVertexBuffer:Z.zoomOutPaintVertexBuffer;oe&&this._buffers.push(oe)}else(Z instanceof ta||Z instanceof Ni)&&Z.paintVertexBuffer&&this._buffers.push(Z.paintVertexBuffer)}},pa.prototype.upload=function(C){for(var z in this.binders){var Z=this.binders[z];(Z instanceof ta||Z instanceof Ni||Z instanceof na)&&Z.upload(C)}this.updatePaintBuffers()},pa.prototype.destroy=function(){for(var C in this.binders){var z=this.binders[C];(z instanceof ta||z instanceof Ni||z instanceof na)&&z.destroy()}};var Ga=function(C,z,Z,oe){oe===void 0&&(oe=function(){return!0}),this.programConfigurations={};for(var pe=0,xe=z;pe<xe.length;pe+=1){var Se=xe[pe];this.programConfigurations[Se.id]=new pa(Se,Z,oe,C)}this.needsUpload=!1,this._featureMap=new er,this._bufferOffset=0};function ko(C,z){return{"text-opacity":["opacity"],"icon-opacity":["opacity"],"text-color":["fill_color"],"icon-color":["fill_color"],"text-halo-color":["halo_color"],"icon-halo-color":["halo_color"],"text-halo-blur":["halo_blur"],"icon-halo-blur":["halo_blur"],"text-halo-width":["halo_width"],"icon-halo-width":["halo_width"],"line-gap-width":["gapwidth"],"line-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-extrusion-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"]}[C]||[C.replace(z+"-","").replace(/-/g,"_")]}function To(C,z,Z){var oe={color:{source:se,composite:he},number:{source:ce,composite:se}},pe=function(xe){return{"line-pattern":{source:vc,composite:vc},"fill-pattern":{source:vc,composite:vc},"fill-extrusion-pattern":{source:vc,composite:vc}}[xe]}(C);return pe&&pe[Z]||oe[z][Z]}Ga.prototype.populatePaintArrays=function(C,z,Z,oe,pe,xe){for(var Se in 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z,Z=C.readVarint()+C.pos,oe=1,pe=0,xe=0,Se=0,Ne=[];C.pos<Z;){if(pe<=0){var Ze=C.readVarint();oe=7&Ze,pe=Ze>>3}if(pe--,oe===1||oe===2)xe+=C.readSVarint(),Se+=C.readSVarint(),oe===1&&(z&&Ne.push(z),z=[]),z.push(new d(xe,Se));else{if(oe!==7)throw new Error("unknown command "+oe);z&&z.push(z[0].clone())}}return z&&Ne.push(z),Ne},_g.prototype.bbox=function(){var C=this._pbf;C.pos=this._geometry;for(var z=C.readVarint()+C.pos,Z=1,oe=0,pe=0,xe=0,Se=1/0,Ne=-1/0,Ze=1/0,ct=-1/0;C.pos<z;){if(oe<=0){var gt=C.readVarint();Z=7&gt,oe=gt>>3}if(oe--,Z===1||Z===2)(pe+=C.readSVarint())<Se&&(Se=pe),pe>Ne&&(Ne=pe),(xe+=C.readSVarint())<Ze&&(Ze=xe),xe>ct&&(ct=xe);else if(Z!==7)throw new Error("unknown command "+Z)}return[Se,Ze,Ne,ct]},_g.prototype.toGeoJSON=function(C,z,Z){var oe,pe,xe=this.extent*Math.pow(2,Z),Se=this.extent*C,Ne=this.extent*z,Ze=this.loadGeometry(),ct=_g.types[this.type];function gt(Gt){for(var on=0;on<Gt.length;on++){var yn=Gt[on],Cn=180-360*(yn.y+Ne)/xe;Gt[on]=[360*(yn.x+Se)/xe-180,360/Math.PI*Math.atan(Math.exp(Cn*Math.PI/180))-90]}}switch(this.type){case 1:var Bt=[];for(oe=0;oe<Ze.length;oe++)Bt[oe]=Ze[oe][0];gt(Ze=Bt);break;case 2:for(oe=0;oe<Ze.length;oe++)gt(Ze[oe]);break;case 3:for(Ze=function(Gt){var on=Gt.length;if(on<=1)return[Gt];for(var yn,Cn,Sn=[],$n=0;$n<on;$n++){var Vn=SG(Gt[$n]);Vn!==0&&(Cn===void 0&&(Cn=Vn<0),Cn===Vn<0?(yn&&Sn.push(yn),yn=[Gt[$n]]):yn.push(Gt[$n]))}return yn&&Sn.push(yn),Sn}(Ze),oe=0;oe<Ze.length;oe++)for(pe=0;pe<Ze[oe].length;pe++)gt(Ze[oe][pe])}Ze.length===1?Ze=Ze[0]:ct="Multi"+ct;var Xt={type:"Feature",geometry:{type:ct,coordinates:Ze},properties:this.properties};return"id"in this&&(Xt.id=this.id),Xt};var FC=RC;function RC(C,z){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=C,this._keys=[],this._values=[],this._features=[],C.readFields(CG,this,z),this.length=this._features.length}function CG(C,z,Z){C===15?z.version=Z.readVarint():C===1?z.name=Z.readString():C===5?z.extent=Z.readVarint():C===2?z._features.push(Z.pos):C===3?z._keys.push(Z.readString()):C===4&&z._values.push(function(oe){for(var pe=null,xe=oe.readVarint()+oe.pos;oe.pos<xe;){var Se=oe.readVarint()>>3;pe=Se===1?oe.readString():Se===2?oe.readFloat():Se===3?oe.readDouble():Se===4?oe.readVarint64():Se===5?oe.readVarint():Se===6?oe.readSVarint():Se===7?oe.readBoolean():null}return pe}(Z))}function LG(C,z,Z){if(C===3){var oe=new FC(Z,Z.readVarint()+Z.pos);oe.length&&(z[oe.name]=oe)}}RC.prototype.feature=function(C){if(C<0||C>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[C];var z=this._pbf.readVarint()+this._pbf.pos;return new IC(this._pbf,z,this.extent,this._keys,this._values)};var wg={VectorTile:function(C,z){this.layers=C.readFields(LG,{},z)},VectorTileFeature:IC,VectorTileLayer:FC},DG=wg.VectorTileFeature.types,R5=Math.pow(2,13);function F1(C,z,Z,oe,pe,xe,Se,Ne){C.emplaceBack(z,Z,2*Math.floor(oe*R5)+Se,pe*R5*2,xe*R5*2,Math.round(Ne))}var Hc=function(C){this.zoom=C.zoom,this.overscaling=C.overscaling,this.layers=C.layers,this.layerIds=this.layers.map(function(z){return z.id}),this.index=C.index,this.hasPattern=!1,this.layoutVertexArray=new ec,this.indexArray=new us,this.programConfigurations=new Ga(PC,C.layers,C.zoom),this.segments=new Nt,this.stateDependentLayerIds=this.layers.filter(function(z){return z.isStateDependent()}).map(function(z){return z.id})};function OG(C,z){return C.x===z.x&&(C.x<0||C.x>8192)||C.y===z.y&&(C.y<0||C.y>8192)}function PG(C){return C.every(function(z){return z.x<0})||C.every(function(z){return z.x>8192})||C.every(function(z){return z.y<0})||C.every(function(z){return z.y>8192})}Hc.prototype.populate=function(C,z,Z){this.features=[],this.hasPattern=I5("fill-extrusion",this.layers,z);for(var oe=0,pe=C;oe<pe.length;oe+=1){var xe=pe[oe],Se=xe.feature,Ne=xe.id,Ze=xe.index,ct=xe.sourceLayerIndex,gt=this.layers[0]._featureFilter.needGeometry,Bt={type:Se.type,id:Ne,properties:Se.properties,geometry:gt?ro(Se):[]};if(this.layers[0]._featureFilter.filter(new Ia(this.zoom),Bt,Z)){var Xt={id:Ne,sourceLayerIndex:ct,index:Ze,geometry:gt?Bt.geometry:ro(Se),properties:Se.properties,type:Se.type,patterns:{}};Se.id!==void 0&&(Xt.id=Se.id),this.hasPattern?this.features.push(F5("fill-extrusion",this.layers,Xt,this.zoom,z)):this.addFeature(Xt,Xt.geometry,Ze,Z,{}),z.featureIndex.insert(Se,Xt.geometry,Ze,ct,this.index,!0)}}},Hc.prototype.addFeatures=function(C,z,Z){for(var oe=0,pe=this.features;oe<pe.length;oe+=1){var xe=pe[oe],Se=xe.geometry;this.addFeature(xe,Se,xe.index,z,Z)}},Hc.prototype.update=function(C,z,Z){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(C,z,this.stateDependentLayers,Z)},Hc.prototype.isEmpty=function(){return this.layoutVertexArray.length===0},Hc.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Hc.prototype.upload=function(C){this.uploaded||(this.layoutVertexBuffer=C.createVertexBuffer(this.layoutVertexArray,PC),this.indexBuffer=C.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(C),this.uploaded=!0},Hc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Hc.prototype.addFeature=function(C,z,Z,oe,pe){for(var xe=0,Se=P5(z,500);xe<Se.length;xe+=1){for(var Ne=Se[xe],Ze=0,ct=0,gt=Ne;ct<gt.length;ct+=1)Ze+=gt[ct].length;for(var Bt=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),Xt=0,Gt=Ne;Xt<Gt.length;Xt+=1){var on=Gt[Xt];if(on.length!==0&&!PG(on))for(var yn=0,Cn=0;Cn<on.length;Cn++){var Sn=on[Cn];if(Cn>=1){var $n=on[Cn-1];if(!OG(Sn,$n)){Bt.vertexLength+4>Nt.MAX_VERTEX_ARRAY_LENGTH&&(Bt=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var Vn=Sn.sub($n)._perp()._unit(),Xn=$n.dist(Sn);yn+Xn>32768&&(yn=0),F1(this.layoutVertexArray,Sn.x,Sn.y,Vn.x,Vn.y,0,0,yn),F1(this.layoutVertexArray,Sn.x,Sn.y,Vn.x,Vn.y,0,1,yn),yn+=Xn,F1(this.layoutVertexArray,$n.x,$n.y,Vn.x,Vn.y,0,0,yn),F1(this.layoutVertexArray,$n.x,$n.y,Vn.x,Vn.y,0,1,yn);var dr=Bt.vertexLength;this.indexArray.emplaceBack(dr,dr+2,dr+1),this.indexArray.emplaceBack(dr+1,dr+2,dr+3),Bt.vertexLength+=4,Bt.primitiveLength+=2}}}}if(Bt.vertexLength+Ze>Nt.MAX_VERTEX_ARRAY_LENGTH&&(Bt=this.segments.prepareSegment(Ze,this.layoutVertexArray,this.indexArray)),DG[C.type]==="Polygon"){for(var br=[],Hr=[],Vr=Bt.vertexLength,ti=0,vi=Ne;ti<vi.length;ti+=1){var xi=vi[ti];if(xi.length!==0){xi!==Ne[0]&&Hr.push(br.length/2);for(var ui=0;ui<xi.length;ui++){var Ei=xi[ui];F1(this.layoutVertexArray,Ei.x,Ei.y,0,0,1,1,0),br.push(Ei.x),br.push(Ei.y)}}}for(var ki=C5(br,Hr),bi=0;bi<ki.length;bi+=3)this.indexArray.emplaceBack(Vr+ki[bi],Vr+ki[bi+2],Vr+ki[bi+1]);Bt.primitiveLength+=ki.length/3,Bt.vertexLength+=Ze}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,C,Z,pe,oe)},Qn("FillExtrusionBucket",Hc,{omit:["layers","features"]});var IG={paint:new Gs({"fill-extrusion-opacity":new wi(Pe["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Ii(Pe["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new wi(Pe["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new wi(Pe["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new Pf(Pe["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Ii(Pe["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Ii(Pe["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new wi(Pe["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},FG=function(C){function z(Z){C.call(this,Z,IG)}return C&&(z.__proto__=C),z.prototype=Object.create(C&&C.prototype),z.prototype.constructor=z,z.prototype.createBucket=function(Z){return new Hc(Z)},z.prototype.queryRadius=function(){return Ws(this.paint.get("fill-extrusion-translate"))},z.prototype.is3D=function(){return!0},z.prototype.queryIntersectsFeature=function(Z,oe,pe,xe,Se,Ne,Ze,ct){var gt=Ys(Z,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),Ne.angle,Ze),Bt=this.paint.get("fill-extrusion-height").evaluate(oe,pe),Xt=this.paint.get("fill-extrusion-base").evaluate(oe,pe),Gt=function(yn,Cn,Sn,$n){for(var Vn=[],Xn=0,dr=yn;Xn<dr.length;Xn+=1){var br=dr[Xn],Hr=[br.x,br.y,$n,1];xg(Hr,Hr,Cn),Vn.push(new d(Hr[0]/Hr[3],Hr[1]/Hr[3]))}return Vn}(gt,ct,0,0),on=function(yn,Cn,Sn,$n){for(var Vn=[],Xn=[],dr=$n[8]*Cn,br=$n[9]*Cn,Hr=$n[10]*Cn,Vr=$n[11]*Cn,ti=$n[8]*Sn,vi=$n[9]*Sn,xi=$n[10]*Sn,ui=$n[11]*Sn,Ei=0,ki=yn;Ei<ki.length;Ei+=1){for(var bi=ki[Ei],Ma=[],ma=[],Oa=0,Bi=bi;Oa<Bi.length;Oa+=1){var va=Bi[Oa],ao=va.x,Wa=va.y,yo=$n[0]*ao+$n[4]*Wa+$n[12],Ya=$n[1]*ao+$n[5]*Wa+$n[13],ds=$n[2]*ao+$n[6]*Wa+$n[14],vo=$n[3]*ao+$n[7]*Wa+$n[15],Xs=ds+Hr,Ps=vo+Vr,Bs=yo+ti,kl=Ya+vi,il=ds+xi,ps=vo+ui,js=new d((yo+dr)/Ps,(Ya+br)/Ps);js.z=Xs/Ps,Ma.push(js);var Jo=new d(Bs/ps,kl/ps);Jo.z=il/ps,ma.push(Jo)}Vn.push(Ma),Xn.push(ma)}return[Vn,Xn]}(xe,Xt,Bt,ct);return function(yn,Cn,Sn){var $n=1/0;Ff(Sn,Cn)&&($n=zC(Sn,Cn[0]));for(var Vn=0;Vn<Cn.length;Vn++)for(var Xn=Cn[Vn],dr=yn[Vn],br=0;br<Xn.length-1;br++){var Hr=Xn[br],Vr=Xn[br+1],ti=dr[br],vi=dr[br+1],xi=[Hr,Vr,vi,ti,Hr];nl(Sn,xi)&&($n=Math.min($n,zC(Sn,xi)))}return $n!==1/0&&$n}(on[0],on[1],Gt)},z}(Al);function R1(C,z){return C.x*z.x+C.y*z.y}function zC(C,z){if(C.length===1){for(var Z,oe=0,pe=z[oe++];!Z||pe.equals(Z);)if(!(Z=z[oe++]))return 1/0;for(;oe<z.length;oe++){var xe=z[oe],Se=C[0],Ne=Z.sub(pe),Ze=xe.sub(pe),ct=Se.sub(pe),gt=R1(Ne,Ne),Bt=R1(Ne,Ze),Xt=R1(Ze,Ze),Gt=R1(ct,Ne),on=R1(ct,Ze),yn=gt*Xt-Bt*Bt,Cn=(Xt*Gt-Bt*on)/yn,Sn=(gt*on-Bt*Gt)/yn,$n=1-Cn-Sn,Vn=pe.z*$n+Z.z*Cn+xe.z*Sn;if(isFinite(Vn))return Vn}return 1/0}for(var Xn=1/0,dr=0,br=z;dr<br.length;dr+=1){var Hr=br[dr];Xn=Math.min(Xn,Hr.z)}return Xn}var NC=Fa([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,RG=wg.VectorTileFeature.types,zG=Math.cos(Math.PI/180*37.5),BC=Math.pow(2,14)/.5,zl=function(C){this.zoom=C.zoom,this.overscaling=C.overscaling,this.layers=C.layers,this.layerIds=this.layers.map(function(z){return z.id}),this.index=C.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Uc,this.indexArray=new us,this.programConfigurations=new Ga(NC,C.layers,C.zoom),this.segments=new Nt,this.stateDependentLayerIds=this.layers.filter(function(z){return z.isStateDependent()}).map(function(z){return z.id})};zl.prototype.populate=function(C,z,Z){this.hasPattern=I5("line",this.layers,z);for(var oe=this.layers[0].layout.get("line-sort-key"),pe=[],xe=0,Se=C;xe<Se.length;xe+=1){var Ne=Se[xe],Ze=Ne.feature,ct=Ne.id,gt=Ne.index,Bt=Ne.sourceLayerIndex,Xt=this.layers[0]._featureFilter.needGeometry,Gt={type:Ze.type,id:ct,properties:Ze.properties,geometry:Xt?ro(Ze):[]};if(this.layers[0]._featureFilter.filter(new Ia(this.zoom),Gt,Z)){Xt||(Gt.geometry=ro(Ze));var on=oe?oe.evaluate(Gt,{},Z):void 0,yn={id:ct,properties:Ze.properties,type:Ze.type,sourceLayerIndex:Bt,index:gt,geometry:Gt.geometry,patterns:{},sortKey:on};pe.push(yn)}}oe&&pe.sort(function(ti,vi){return ti.sortKey-vi.sortKey});for(var Cn=0,Sn=pe;Cn<Sn.length;Cn+=1){var $n=Sn[Cn],Vn=$n,Xn=Vn.geometry,dr=Vn.index,br=Vn.sourceLayerIndex;if(this.hasPattern){var Hr=F5("line",this.layers,$n,this.zoom,z);this.patternFeatures.push(Hr)}else this.addFeature($n,Xn,dr,Z,{});var Vr=C[dr].feature;z.featureIndex.insert(Vr,Xn,dr,br,this.index)}},zl.prototype.update=function(C,z,Z){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(C,z,this.stateDependentLayers,Z)},zl.prototype.addFeatures=function(C,z,Z){for(var oe=0,pe=this.patternFeatures;oe<pe.length;oe+=1){var xe=pe[oe];this.addFeature(xe,xe.geometry,xe.index,z,Z)}},zl.prototype.isEmpty=function(){return this.layoutVertexArray.length===0},zl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},zl.prototype.upload=function(C){this.uploaded||(this.layoutVertexBuffer=C.createVertexBuffer(this.layoutVertexArray,NC),this.indexBuffer=C.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(C),this.uploaded=!0},zl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},zl.prototype.addFeature=function(C,z,Z,oe,pe){for(var xe=this.layers[0].layout,Se=xe.get("line-join").evaluate(C,{}),Ne=xe.get("line-cap"),Ze=xe.get("line-miter-limit"),ct=xe.get("line-round-limit"),gt=0,Bt=z;gt<Bt.length;gt+=1){var Xt=Bt[gt];this.addLine(Xt,C,Se,Ne,Ze,ct)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,C,Z,pe,oe)},zl.prototype.addLine=function(C,z,Z,oe,pe,xe){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,z.properties&&z.properties.hasOwnProperty("mapbox_clip_start")&&z.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+z.properties.mapbox_clip_start,this.clipEnd=+z.properties.mapbox_clip_end;for(var Se=0;Se<C.length-1;Se++)this.totalDistance+=C[Se].dist(C[Se+1]);this.updateScaledDistance()}for(var Ne=RG[z.type]==="Polygon",Ze=C.length;Ze>=2&&C[Ze-1].equals(C[Ze-2]);)Ze--;for(var ct=0;ct<Ze-1&&C[ct].equals(C[ct+1]);)ct++;if(!(Ze<(Ne?3:2))){Z==="bevel"&&(pe=1.05);var gt,Bt=this.overscaling<=16?122880/(512*this.overscaling):0,Xt=this.segments.prepareSegment(10*Ze,this.layoutVertexArray,this.indexArray),Gt=void 0,on=void 0,yn=void 0,Cn=void 0;this.e1=this.e2=-1,Ne&&(gt=C[Ze-2],Cn=C[ct].sub(gt)._unit()._perp());for(var Sn=ct;Sn<Ze;Sn++)if(!(on=Sn===Ze-1?Ne?C[ct+1]:void 0:C[Sn+1])||!C[Sn].equals(on)){Cn&&(yn=Cn),gt&&(Gt=gt),gt=C[Sn],Cn=on?on.sub(gt)._unit()._perp():yn;var $n=(yn=yn||Cn).add(Cn);$n.x===0&&$n.y===0||$n._unit();var Vn=yn.x*Cn.x+yn.y*Cn.y,Xn=$n.x*Cn.x+$n.y*Cn.y,dr=Xn!==0?1/Xn:1/0,br=2*Math.sqrt(2-2*Xn),Hr=Xn<zG&&Gt&&on,Vr=yn.x*Cn.y-yn.y*Cn.x>0;if(Hr&&Sn>ct){var ti=gt.dist(Gt);if(ti>2*Bt){var vi=gt.sub(gt.sub(Gt)._mult(Bt/ti)._round());this.updateDistance(Gt,vi),this.addCurrentVertex(vi,yn,0,0,Xt),Gt=vi}}var xi=Gt&&on,ui=xi?Z:Ne?"butt":oe;if(xi&&ui==="round"&&(dr<xe?ui="miter":dr<=2&&(ui="fakeround")),ui==="miter"&&dr>pe&&(ui="bevel"),ui==="bevel"&&(dr>2&&(ui="flipbevel"),dr<pe&&(ui="miter")),Gt&&this.updateDistance(Gt,gt),ui==="miter")$n._mult(dr),this.addCurrentVertex(gt,$n,0,0,Xt);else if(ui==="flipbevel"){if(dr>100)$n=Cn.mult(-1);else{var Ei=dr*yn.add(Cn).mag()/yn.sub(Cn).mag();$n._perp()._mult(Ei*(Vr?-1:1))}this.addCurrentVertex(gt,$n,0,0,Xt),this.addCurrentVertex(gt,$n.mult(-1),0,0,Xt)}else if(ui==="bevel"||ui==="fakeround"){var ki=-Math.sqrt(dr*dr-1),bi=Vr?ki:0,Ma=Vr?0:ki;if(Gt&&this.addCurrentVertex(gt,yn,bi,Ma,Xt),ui==="fakeround")for(var ma=Math.round(180*br/Math.PI/20),Oa=1;Oa<ma;Oa++){var Bi=Oa/ma;if(Bi!==.5){var va=Bi-.5;Bi+=Bi*va*(Bi-1)*((1.0904+Vn*(Vn*(3.55645-1.43519*Vn)-3.2452))*va*va+(.848013+Vn*(.215638*Vn-1.06021)))}var ao=Cn.sub(yn)._mult(Bi)._add(yn)._unit()._mult(Vr?-1:1);this.addHalfVertex(gt,ao.x,ao.y,!1,Vr,0,Xt)}on&&this.addCurrentVertex(gt,Cn,-bi,-Ma,Xt)}else if(ui==="butt")this.addCurrentVertex(gt,$n,0,0,Xt);else if(ui==="square"){var Wa=Gt?1:-1;this.addCurrentVertex(gt,$n,Wa,Wa,Xt)}else ui==="round"&&(Gt&&(this.addCurrentVertex(gt,yn,0,0,Xt),this.addCurrentVertex(gt,yn,1,1,Xt,!0)),on&&(this.addCurrentVertex(gt,Cn,-1,-1,Xt,!0),this.addCurrentVertex(gt,Cn,0,0,Xt)));if(Hr&&Sn<Ze-1){var yo=gt.dist(on);if(yo>2*Bt){var Ya=gt.add(on.sub(gt)._mult(Bt/yo)._round());this.updateDistance(gt,Ya),this.addCurrentVertex(Ya,Cn,0,0,Xt),gt=Ya}}}}},zl.prototype.addCurrentVertex=function(C,z,Z,oe,pe,xe){xe===void 0&&(xe=!1);var Se=z.x+z.y*Z,Ne=z.y-z.x*Z,Ze=-z.x+z.y*oe,ct=-z.y-z.x*oe;this.addHalfVertex(C,Se,Ne,xe,!1,Z,pe),this.addHalfVertex(C,Ze,ct,xe,!0,-oe,pe),this.distance>BC/2&&this.totalDistance===0&&(this.distance=0,this.addCurrentVertex(C,z,Z,oe,pe,xe))},zl.prototype.addHalfVertex=function(C,z,Z,oe,pe,xe,Se){var Ne=C.x,Ze=C.y,ct=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((Ne<<1)+(oe?1:0),(Ze<<1)+(pe?1:0),Math.round(63*z)+128,Math.round(63*Z)+128,1+(xe===0?0:xe<0?-1:1)|(63&ct)<<2,ct>>6);var 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jg=G1[Bg];jg.x+=db,jg.y+=Rg}})(bi.positionedLines,il,Fg,Md,Bs,kl,va,Ig,Bi.length),bi.top+=-Md*Ig,bi.bottom=bi.top+Ig,bi.left+=-Fg*Bs,bi.right=bi.left+Bs}(ki,z,Z,oe,Cn,Se,Ne,Ze,Bt,ct,Xt,yn),!function(bi){for(var Ma=0,ma=bi;Ma<ma.length;Ma+=1)if(ma[Ma].positionedGlyphs.length!==0)return!1;return!0}(Ei)&&ki}rl.fromFeature=function(C,z){for(var Z=new rl,oe=0;oe<C.sections.length;oe++){var pe=C.sections[oe];pe.image?Z.addImageSection(pe):Z.addTextSection(pe,z)}return Z},rl.prototype.length=function(){return this.text.length},rl.prototype.getSection=function(C){return this.sections[this.sectionIndex[C]]},rl.prototype.getSectionIndex=function(C){return this.sectionIndex[C]},rl.prototype.getCharCode=function(C){return this.text.charCodeAt(C)},rl.prototype.verticalizePunctuation=function(){this.text=function(C){for(var z="",Z=0;Z<C.length;Z++){var oe=C.charCodeAt(Z+1)||null,pe=C.charCodeAt(Z-1)||null;(!oe||!oa(oe)||z1[C[Z+1]])&&(!pe||!oa(pe)||z1[C[Z-1]])&&z1[C[Z]]?z+=z1[C[Z]]:z+=C[Z]}return z}(this.text)},rl.prototype.trim=function(){for(var C=0,z=0;z<this.text.length&&Wx[this.text.charCodeAt(z)];z++)C++;for(var Z=this.text.length,oe=this.text.length-1;oe>=0&&oe>=C&&Wx[this.text.charCodeAt(oe)];oe--)Z--;this.text=this.text.substring(C,Z),this.sectionIndex=this.sectionIndex.slice(C,Z)},rl.prototype.substring=function(C,z){var Z=new rl;return Z.text=this.text.substring(C,z),Z.sectionIndex=this.sectionIndex.slice(C,z),Z.sections=this.sections,Z},rl.prototype.toString=function(){return this.text},rl.prototype.getMaxScale=function(){var C=this;return this.sectionIndex.reduce(function(z,Z){return Math.max(z,C.sections[Z].scale)},0)},rl.prototype.addTextSection=function(C,z){this.text+=C.text,this.sections.push(Tg.forText(C.scale,C.fontStack||z));for(var Z=this.sections.length-1,oe=0;oe<C.text.length;++oe)this.sectionIndex.push(Z)},rl.prototype.addImageSection=function(C){var z=C.image?C.image.name:"";if(z.length!==0){var Z=this.getNextImageSectionCharCode();Z?(this.text+=String.fromCharCode(Z),this.sections.push(Tg.forImage(z)),this.sectionIndex.push(this.sections.length-1)):L("Reached maximum number of images 6401")}else L("Can't add FormattedSection with an empty image.")},rl.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var Wx={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},iu={};function ZC(C,z,Z,oe,pe,xe){if(z.imageName){var Se=oe[z.imageName];return Se?Se.displaySize[0]*z.scale*24/xe+pe:0}var Ne=Z[z.fontStack],Ze=Ne&&Ne[C];return Ze?Ze.metrics.advance*z.scale+pe:0}function JC(C,z,Z,oe){var pe=Math.pow(C-z,2);return oe?C<z?pe/2:2*pe:pe+Math.abs(Z)*Z}function nW(C,z,Z){var oe=0;return C===10&&(oe-=1e4),Z&&(oe+=150),C!==40&&C!==65288||(oe+=50),z!==41&&z!==65289||(oe+=50),oe}function KC(C,z,Z,oe,pe,xe){for(var Se=null,Ne=JC(z,Z,pe,xe),Ze=0,ct=oe;Ze<ct.length;Ze+=1){var 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Z=z.expression;if(Z.kind==="constant")return{kind:"constant",layoutSize:Z.evaluate(new Ia(C+1))};if(Z.kind==="source")return{kind:"source"};for(var oe=Z.zoomStops,pe=Z.interpolationType,xe=0;xe<oe.length&&oe[xe]<=C;)xe++;for(var Se=xe=Math.max(0,xe-1);Se<oe.length&&oe[Se]<C+1;)Se++;Se=Math.min(oe.length-1,Se);var Ne=oe[xe],Ze=oe[Se];return Z.kind==="composite"?{kind:"composite",minZoom:Ne,maxZoom:Ze,interpolationType:pe}:{kind:"camera",minZoom:Ne,maxZoom:Ze,minSize:Z.evaluate(new Ia(Ne)),maxSize:Z.evaluate(new Ia(Ze)),interpolationType:pe}}function e9(C,z,Z){var oe=z.uSize,pe=z.uSizeT,xe=Z.lowerSize,Se=Z.upperSize;return C.kind==="source"?xe/128:C.kind==="composite"?Ar(xe/128,Se/128,pe):oe}function t9(C,z){var Z=0,oe=0;if(C.kind==="constant")oe=C.layoutSize;else if(C.kind!=="source"){var pe=C.interpolationType,xe=C.minZoom,Se=C.maxZoom,Ne=pe?y(ua.interpolationFactor(pe,z,xe,Se),0,1):0;C.kind==="camera"?oe=Ar(C.minSize,C.maxSize,Ne):Z=Ne}return{uSizeT:Z,uSize:oe}}var 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ui=Hr.layout.get("text-rotate").evaluate(ti,{})*Math.PI/180,Ei=[],ki=0,bi=dr.positionedLines;ki<bi.length;ki+=1)for(var Ma=bi[ki],ma=0,Oa=Ma.positionedGlyphs;ma<Oa.length;ma+=1){var Bi=Oa[ma];if(Bi.rect){var va=Bi.rect||{},ao=4,Wa=!0,yo=1,Ya=0,ds=(Vr||xi)&&Bi.vertical,vo=Bi.metrics.advance*Bi.scale/2;if(xi&&dr.verticalizable){var Xs=24*(Bi.scale-1),Ps=(24-Bi.metrics.width*Bi.scale)/2;Ya=Ma.lineOffset/2-(Bi.imageName?-Ps:Xs)}if(Bi.imageName){var Bs=vi[Bi.imageName];Wa=Bs.sdf,ao=1/(yo=Bs.pixelRatio)}var kl=Vr?[Bi.x+vo,Bi.y]:[0,0],il=Vr?[0,0]:[Bi.x+vo+br[0],Bi.y+br[1]-Ya],ps=[0,0];ds&&(ps=il,il=[0,0]);var js=(Bi.metrics.left-ao)*Bi.scale-vo+il[0],Jo=(-Bi.metrics.top-ao)*Bi.scale+il[1],bs=js+va.w*Bi.scale/yo,sa=Jo+va.h*Bi.scale/yo,Sa=new d(js,Jo),zo=new d(bs,Jo),No=new d(js,sa),Oo=new d(bs,sa);if(ds){var xo=new d(-vo,vo- -17),Ra=-Math.PI/2,Bo=12-vo,Tl=Bi.imageName?Bo:0,au=new d(22-Bo,-Tl),al=new(Function.prototype.bind.apply(d,[null].concat(ps)));Sa._rotateAround(Ra,xo)._add(au)._add(al),zo._rotateAround(Ra,xo)._add(au)._add(al),No._rotateAround(Ra,xo)._add(au)._add(al),Oo._rotateAround(Ra,xo)._add(au)._add(al)}if(ui){var Gc=Math.sin(ui),Lh=Math.cos(ui),Is=[Lh,-Gc,Gc,Lh];Sa._matMult(Is),zo._matMult(Is),No._matMult(Is),Oo._matMult(Is)}var Wc=new d(0,0),Yc=new d(0,0);Ei.push({tl:Sa,tr:zo,bl:No,br:Oo,tex:va,writingMode:dr.writingMode,glyphOffset:kl,sectionIndex:Bi.sectionIndex,isSDF:Wa,pixelOffsetTL:Wc,pixelOffsetBR:Yc,minFontScaleX:0,minFontScaleY:0})}}return Ei}(0,Z,Ne,pe,xe,Se,oe,C.allowVerticalPlacement),Cn=C.textSizeData,Sn=null;Cn.kind==="source"?(Sn=[128*pe.layout.get("text-size").evaluate(Se,{})])[0]>32640&&L(C.layerIds[0]+': Value for "text-size" is >= 255. Reduce your "text-size".'):Cn.kind==="composite"&&((Sn=[128*Gt.compositeTextSizes[0].evaluate(Se,{},on),128*Gt.compositeTextSizes[1].evaluate(Se,{},on)])[0]>32640||Sn[1]>32640)&&L(C.layerIds[0]+': Value for "text-size" is >= 255. Reduce your "text-size".'),C.addSymbols(C.text,yn,Sn,Ne,xe,Se,ct,z,Ze.lineStartIndex,Ze.lineLength,Xt,on);for(var $n=0,Vn=gt;$n<Vn.length;$n+=1)Bt[Vn[$n]]=C.text.placedSymbolArray.length-1;return 4*yn.length}function f9(C){for(var z in C)return C[z];return null}function cW(C,z,Z,oe){var pe=C.compareText;if(z in pe){for(var xe=pe[z],Se=xe.length-1;Se>=0;Se--)if(oe.dist(xe[Se])<Z)return!0}else pe[z]=[];return pe[z].push(oe),!1}var fW=wg.VectorTileFeature.types,hW=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function Kx(C,z,Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt,Xt){var Gt=Ne?Math.min(32640,Math.round(Ne[0])):0,on=Ne?Math.min(32640,Math.round(Ne[1])):0;C.emplaceBack(z,Z,Math.round(32*oe),Math.round(32*pe),xe,Se,(Gt<<1)+(Ze?1:0),on,16*ct,16*gt,256*Bt,256*Xt)}function V5(C,z,Z){C.emplaceBack(z.x,z.y,Z),C.emplaceBack(z.x,z.y,Z),C.emplaceBack(z.x,z.y,Z),C.emplaceBack(z.x,z.y,Z)}function dW(C){for(var z=0,Z=C.sections;z<Z.length;z+=1)if(gi(Z[z].text))return!0;return!1}var Cg=function(C){this.layoutVertexArray=new $c,this.indexArray=new us,this.programConfigurations=C,this.segments=new Nt,this.dynamicLayoutVertexArray=new Su,this.opacityVertexArray=new xd,this.placedSymbolArray=new Ue};Cg.prototype.isEmpty=function(){return this.layoutVertexArray.length===0&&this.indexArray.length===0&&this.dynamicLayoutVertexArray.length===0&&this.opacityVertexArray.length===0},Cg.prototype.upload=function(C,z,Z,oe){this.isEmpty()||(Z&&(this.layoutVertexBuffer=C.createVertexBuffer(this.layoutVertexArray,z5.members),this.indexBuffer=C.createIndexBuffer(this.indexArray,z),this.dynamicLayoutVertexBuffer=C.createVertexBuffer(this.dynamicLayoutVertexArray,jG.members,!0),this.opacityVertexBuffer=C.createVertexBuffer(this.opacityVertexArray,hW,!0),this.opacityVertexBuffer.itemSize=1),(Z||oe)&&this.programConfigurations.upload(C))},Cg.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Qn("SymbolBuffers",Cg);var j1=function(C,z,Z){this.layoutVertexArray=new C,this.layoutAttributes=z,this.indexArray=new Z,this.segments=new Nt,this.collisionVertexArray=new Vc};j1.prototype.upload=function(C){this.layoutVertexBuffer=C.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=C.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=C.createVertexBuffer(this.collisionVertexArray,UG.members,!0)},j1.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Qn("CollisionBuffers",j1);var Ua=function(C){this.collisionBoxArray=C.collisionBoxArray,this.zoom=C.zoom,this.overscaling=C.overscaling,this.layers=C.layers,this.layerIds=this.layers.map(function(Se){return Se.id}),this.index=C.index,this.pixelRatio=C.pixelRatio,this.sourceLayerIndex=C.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=C1([]),this.placementViewportMatrix=C1([]);var z=this.layers[0]._unevaluatedLayout._values;this.textSizeData=j5(this.zoom,z["text-size"]),this.iconSizeData=j5(this.zoom,z["icon-size"]);var Z=this.layers[0].layout,oe=Z.get("symbol-sort-key"),pe=Z.get("symbol-z-order");this.sortFeaturesByKey=pe!=="viewport-y"&&oe.constantOr(1)!==void 0;var xe=pe==="viewport-y"||pe==="auto"&&!this.sortFeaturesByKey;this.sortFeaturesByY=xe&&(Z.get("text-allow-overlap")||Z.get("icon-allow-overlap")||Z.get("text-ignore-placement")||Z.get("icon-ignore-placement")),Z.get("symbol-placement")==="point"&&(this.writingModes=Z.get("text-writing-mode").map(function(Se){return Cu[Se]})),this.stateDependentLayerIds=this.layers.filter(function(Se){return Se.isStateDependent()}).map(function(Se){return Se.id}),this.sourceID=C.sourceID};Ua.prototype.createArrays=function(){this.text=new Cg(new Ga(z5.members,this.layers,this.zoom,function(C){return/^text/.test(C)})),this.icon=new Cg(new Ga(z5.members,this.layers,this.zoom,function(C){return/^icon/.test(C)})),this.glyphOffsetArray=new xt,this.lineVertexArray=new Dt,this.symbolInstances=new it},Ua.prototype.calculateGlyphDependencies=function(C,z,Z,oe,pe){for(var xe=0;xe<C.length;xe++)if(z[C.charCodeAt(xe)]=!0,(Z||oe)&&pe){var Se=z1[C.charAt(xe)];Se&&(z[Se.charCodeAt(0)]=!0)}},Ua.prototype.populate=function(C,z,Z){var oe=this.layers[0],pe=oe.layout,xe=pe.get("text-font"),Se=pe.get("text-field"),Ne=pe.get("icon-image"),Ze=(Se.value.kind!=="constant"||Se.value.value instanceof gn&&!Se.value.value.isEmpty()||Se.value.value.toString().length>0)&&(xe.value.kind!=="constant"||xe.value.value.length>0),ct=Ne.value.kind!=="constant"||!!Ne.value.value||Object.keys(Ne.parameters).length>0,gt=pe.get("symbol-sort-key");if(this.features=[],Ze||ct){for(var Bt=z.iconDependencies,Xt=z.glyphDependencies,Gt=z.availableImages,on=new Ia(this.zoom),yn=0,Cn=C;yn<Cn.length;yn+=1){var Sn=Cn[yn],$n=Sn.feature,Vn=Sn.id,Xn=Sn.index,dr=Sn.sourceLayerIndex,br=oe._featureFilter.needGeometry,Hr={type:$n.type,id:Vn,properties:$n.properties,geometry:br?ro($n):[]};if(oe._featureFilter.filter(on,Hr,Z)){br||(Hr.geometry=ro($n));var Vr=void 0;if(Ze){var ti=oe.getValueAndResolveTokens("text-field",Hr,Z,Gt),vi=gn.factory(ti);dW(vi)&&(this.hasRTLText=!0),(!this.hasRTLText||qo()==="unavailable"||this.hasRTLText&&xs.isParsed())&&(Vr=VG(vi,oe,Hr))}var xi=void 0;if(ct){var ui=oe.getValueAndResolveTokens("icon-image",Hr,Z,Gt);xi=ui instanceof bn?ui:bn.fromString(ui)}if(Vr||xi){var Ei=this.sortFeaturesByKey?gt.evaluate(Hr,{},Z):void 0,ki={id:Vn,text:Vr,icon:xi,index:Xn,sourceLayerIndex:dr,geometry:ro($n),properties:$n.properties,type:fW[$n.type],sortKey:Ei};if(this.features.push(ki),xi&&(Bt[xi.name]=!0),Vr){var bi=xe.evaluate(Hr,{},Z).join(","),Ma=pe.get("text-rotation-alignment")==="map"&&pe.get("symbol-placement")!=="point";this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cu.vertical)>=0;for(var ma=0,Oa=Vr.sections;ma<Oa.length;ma+=1){var Bi=Oa[ma];if(Bi.image)Bt[Bi.image.name]=!0;else{var va=aa(Vr.toString()),ao=Bi.fontStack||bi,Wa=Xt[ao]=Xt[ao]||{};this.calculateGlyphDependencies(Bi.text,Wa,Ma,this.allowVerticalPlacement,va)}}}}}}pe.get("symbol-placement")==="line"&&(this.features=function(yo){var Ya={},ds={},vo=[],Xs=0;function Ps(Oo){vo.push(yo[Oo]),Xs++}function Bs(Oo,xo,Ra){var Bo=ds[Oo];return delete ds[Oo],ds[xo]=Bo,vo[Bo].geometry[0].pop(),vo[Bo].geometry[0]=vo[Bo].geometry[0].concat(Ra[0]),Bo}function kl(Oo,xo,Ra){var Bo=Ya[xo];return delete Ya[xo],Ya[Oo]=Bo,vo[Bo].geometry[0].shift(),vo[Bo].geometry[0]=Ra[0].concat(vo[Bo].geometry[0]),Bo}function il(Oo,xo,Ra){var Bo=Ra?xo[0][xo[0].length-1]:xo[0][0];return Oo+":"+Bo.x+":"+Bo.y}for(var ps=0;ps<yo.length;ps++){var js=yo[ps],Jo=js.geometry,bs=js.text?js.text.toString():null;if(bs){var sa=il(bs,Jo),Sa=il(bs,Jo,!0);if(sa in ds&&Sa in Ya&&ds[sa]!==Ya[Sa]){var zo=kl(sa,Sa,Jo),No=Bs(sa,Sa,vo[zo].geometry);delete Ya[sa],delete ds[Sa],ds[il(bs,vo[No].geometry,!0)]=No,vo[zo].geometry=null}else sa in ds?Bs(sa,Sa,Jo):Sa in Ya?kl(sa,Sa,Jo):(Ps(ps),Ya[sa]=Xs-1,ds[Sa]=Xs-1)}else Ps(ps)}return vo.filter(function(Oo){return Oo.geometry})}(this.features)),this.sortFeaturesByKey&&this.features.sort(function(yo,Ya){return yo.sortKey-Ya.sortKey})}},Ua.prototype.update=function(C,z,Z){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(C,z,this.layers,Z),this.icon.programConfigurations.updatePaintArrays(C,z,this.layers,Z))},Ua.prototype.isEmpty=function(){return this.symbolInstances.length===0&&!this.hasRTLText},Ua.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Ua.prototype.upload=function(C){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(C),this.iconCollisionBox.upload(C)),this.text.upload(C,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(C,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Ua.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Ua.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Ua.prototype.addToLineVertexArray=function(C,z){var Z=this.lineVertexArray.length;if(C.segment!==void 0){for(var oe=C.dist(z[C.segment+1]),pe=C.dist(z[C.segment]),xe={},Se=C.segment+1;Se<z.length;Se++)xe[Se]={x:z[Se].x,y:z[Se].y,tileUnitDistanceFromAnchor:oe},Se<z.length-1&&(oe+=z[Se+1].dist(z[Se]));for(var Ne=C.segment||0;Ne>=0;Ne--)xe[Ne]={x:z[Ne].x,y:z[Ne].y,tileUnitDistanceFromAnchor:pe},Ne>0&&(pe+=z[Ne-1].dist(z[Ne]));for(var Ze=0;Ze<z.length;Ze++){var ct=xe[Ze];this.lineVertexArray.emplaceBack(ct.x,ct.y,ct.tileUnitDistanceFromAnchor)}}return{lineStartIndex:Z,lineLength:this.lineVertexArray.length-Z}},Ua.prototype.addSymbols=function(C,z,Z,oe,pe,xe,Se,Ne,Ze,ct,gt,Bt){for(var Xt=C.indexArray,Gt=C.layoutVertexArray,on=C.segments.prepareSegment(4*z.length,Gt,Xt,xe.sortKey),yn=this.glyphOffsetArray.length,Cn=on.vertexLength,Sn=this.allowVerticalPlacement&&Se===Cu.vertical?Math.PI/2:0,$n=xe.text&&xe.text.sections,Vn=0;Vn<z.length;Vn++){var Xn=z[Vn],dr=Xn.tl,br=Xn.tr,Hr=Xn.bl,Vr=Xn.br,ti=Xn.tex,vi=Xn.pixelOffsetTL,xi=Xn.pixelOffsetBR,ui=Xn.minFontScaleX,Ei=Xn.minFontScaleY,ki=Xn.glyphOffset,bi=Xn.isSDF,Ma=Xn.sectionIndex,ma=on.vertexLength,Oa=ki[1];Kx(Gt,Ne.x,Ne.y,dr.x,Oa+dr.y,ti.x,ti.y,Z,bi,vi.x,vi.y,ui,Ei),Kx(Gt,Ne.x,Ne.y,br.x,Oa+br.y,ti.x+ti.w,ti.y,Z,bi,xi.x,vi.y,ui,Ei),Kx(Gt,Ne.x,Ne.y,Hr.x,Oa+Hr.y,ti.x,ti.y+ti.h,Z,bi,vi.x,xi.y,ui,Ei),Kx(Gt,Ne.x,Ne.y,Vr.x,Oa+Vr.y,ti.x+ti.w,ti.y+ti.h,Z,bi,xi.x,xi.y,ui,Ei),V5(C.dynamicLayoutVertexArray,Ne,Sn),Xt.emplaceBack(ma,ma+1,ma+2),Xt.emplaceBack(ma+1,ma+2,ma+3),on.vertexLength+=4,on.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(ki[0]),Vn!==z.length-1&&Ma===z[Vn+1].sectionIndex||C.programConfigurations.populatePaintArrays(Gt.length,xe,xe.index,{},Bt,$n&&$n[Ma])}C.placedSymbolArray.emplaceBack(Ne.x,Ne.y,yn,this.glyphOffsetArray.length-yn,Cn,Ze,ct,Ne.segment,Z?Z[0]:0,Z?Z[1]:0,oe[0],oe[1],Se,0,!1,0,gt)},Ua.prototype._addCollisionDebugVertex=function(C,z,Z,oe,pe,xe){return z.emplaceBack(0,0),C.emplaceBack(Z.x,Z.y,oe,pe,Math.round(xe.x),Math.round(xe.y))},Ua.prototype.addCollisionDebugVertices=function(C,z,Z,oe,pe,xe,Se){var Ne=pe.segments.prepareSegment(4,pe.layoutVertexArray,pe.indexArray),Ze=Ne.vertexLength,ct=pe.layoutVertexArray,gt=pe.collisionVertexArray,Bt=Se.anchorX,Xt=Se.anchorY;this._addCollisionDebugVertex(ct,gt,xe,Bt,Xt,new d(C,z)),this._addCollisionDebugVertex(ct,gt,xe,Bt,Xt,new d(Z,z)),this._addCollisionDebugVertex(ct,gt,xe,Bt,Xt,new d(Z,oe)),this._addCollisionDebugVertex(ct,gt,xe,Bt,Xt,new d(C,oe)),Ne.vertexLength+=4;var Gt=pe.indexArray;Gt.emplaceBack(Ze,Ze+1),Gt.emplaceBack(Ze+1,Ze+2),Gt.emplaceBack(Ze+2,Ze+3),Gt.emplaceBack(Ze+3,Ze),Ne.primitiveLength+=4},Ua.prototype.addDebugCollisionBoxes=function(C,z,Z,oe){for(var pe=C;pe<z;pe++){var xe=this.collisionBoxArray.get(pe),Se=xe.x1,Ne=xe.y1,Ze=xe.x2,ct=xe.y2;this.addCollisionDebugVertices(Se,Ne,Ze,ct,oe?this.textCollisionBox:this.iconCollisionBox,xe.anchorPoint,Z)}},Ua.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new j1(tc,VC.members,$),this.iconCollisionBox=new j1(tc,VC.members,$);for(var C=0;C<this.symbolInstances.length;C++){var z=this.symbolInstances.get(C);this.addDebugCollisionBoxes(z.textBoxStartIndex,z.textBoxEndIndex,z,!0),this.addDebugCollisionBoxes(z.verticalTextBoxStartIndex,z.verticalTextBoxEndIndex,z,!0),this.addDebugCollisionBoxes(z.iconBoxStartIndex,z.iconBoxEndIndex,z,!1),this.addDebugCollisionBoxes(z.verticalIconBoxStartIndex,z.verticalIconBoxEndIndex,z,!1)}},Ua.prototype._deserializeCollisionBoxesForSymbol=function(C,z,Z,oe,pe,xe,Se,Ne,Ze){for(var ct={},gt=z;gt<Z;gt++){var Bt=C.get(gt);ct.textBox={x1:Bt.x1,y1:Bt.y1,x2:Bt.x2,y2:Bt.y2,anchorPointX:Bt.anchorPointX,anchorPointY:Bt.anchorPointY},ct.textFeatureIndex=Bt.featureIndex;break}for(var Xt=oe;Xt<pe;Xt++){var Gt=C.get(Xt);ct.verticalTextBox={x1:Gt.x1,y1:Gt.y1,x2:Gt.x2,y2:Gt.y2,anchorPointX:Gt.anchorPointX,anchorPointY:Gt.anchorPointY},ct.verticalTextFeatureIndex=Gt.featureIndex;break}for(var on=xe;on<Se;on++){var yn=C.get(on);ct.iconBox={x1:yn.x1,y1:yn.y1,x2:yn.x2,y2:yn.y2,anchorPointX:yn.anchorPointX,anchorPointY:yn.anchorPointY},ct.iconFeatureIndex=yn.featureIndex;break}for(var Cn=Ne;Cn<Ze;Cn++){var Sn=C.get(Cn);ct.verticalIconBox={x1:Sn.x1,y1:Sn.y1,x2:Sn.x2,y2:Sn.y2,anchorPointX:Sn.anchorPointX,anchorPointY:Sn.anchorPointY},ct.verticalIconFeatureIndex=Sn.featureIndex;break}return ct},Ua.prototype.deserializeCollisionBoxes=function(C){this.collisionArrays=[];for(var z=0;z<this.symbolInstances.length;z++){var Z=this.symbolInstances.get(z);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(C,Z.textBoxStartIndex,Z.textBoxEndIndex,Z.verticalTextBoxStartIndex,Z.verticalTextBoxEndIndex,Z.iconBoxStartIndex,Z.iconBoxEndIndex,Z.verticalIconBoxStartIndex,Z.verticalIconBoxEndIndex))}},Ua.prototype.hasTextData=function(){return this.text.segments.get().length>0},Ua.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Ua.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Ua.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Ua.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Ua.prototype.addIndicesForPlacedSymbol=function(C,z){for(var Z=C.placedSymbolArray.get(z),oe=Z.vertexStartIndex+4*Z.numGlyphs,pe=Z.vertexStartIndex;pe<oe;pe+=4)C.indexArray.emplaceBack(pe,pe+1,pe+2),C.indexArray.emplaceBack(pe+1,pe+2,pe+3)},Ua.prototype.getSortedSymbolIndexes=function(C){if(this.sortedAngle===C&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;for(var z=Math.sin(C),Z=Math.cos(C),oe=[],pe=[],xe=[],Se=0;Se<this.symbolInstances.length;++Se){xe.push(Se);var Ne=this.symbolInstances.get(Se);oe.push(0|Math.round(z*Ne.anchorX+Z*Ne.anchorY)),pe.push(Ne.featureIndex)}return xe.sort(function(Ze,ct){return oe[Ze]-oe[ct]||pe[ct]-pe[Ze]}),xe},Ua.prototype.addToSortKeyRanges=function(C,z){var Z=this.sortKeyRanges[this.sortKeyRanges.length-1];Z&&Z.sortKey===z?Z.symbolInstanceEnd=C+1:this.sortKeyRanges.push({sortKey:z,symbolInstanceStart:C,symbolInstanceEnd:C+1})},Ua.prototype.sortFeatures=function(C){var z=this;if(this.sortFeaturesByY&&this.sortedAngle!==C&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(C),this.sortedAngle=C,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var Z=0,oe=this.symbolInstanceIndexes;Z<oe.length;Z+=1){var pe=oe[Z],xe=this.symbolInstances.get(pe);this.featureSortOrder.push(xe.featureIndex),[xe.rightJustifiedTextSymbolIndex,xe.centerJustifiedTextSymbolIndex,xe.leftJustifiedTextSymbolIndex].forEach(function(Se,Ne,Ze){Se>=0&&Ze.indexOf(Se)===Ne&&z.addIndicesForPlacedSymbol(z.text,Se)}),xe.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,xe.verticalPlacedTextSymbolIndex),xe.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,xe.placedIconSymbolIndex),xe.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,xe.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Qn("SymbolBucket",Ua,{omit:["layers","collisionBoxArray","features","compareText"]}),Ua.MAX_GLYPHS=65535,Ua.addDynamicAttributes=V5;var pW=new Gs({"symbol-placement":new wi(Pe.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Pe.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Pe.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ii(Pe.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Pe.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Pe.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Pe.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Pe.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Pe.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ii(Pe.layout_symbol["icon-size"]),"icon-text-fit":new wi(Pe.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Pe.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ii(Pe.layout_symbol["icon-image"]),"icon-rotate":new Ii(Pe.layout_symbol["icon-rotate"]),"icon-padding":new wi(Pe.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Pe.layout_symbol["icon-keep-upright"]),"icon-offset":new Ii(Pe.layout_symbol["icon-offset"]),"icon-anchor":new Ii(Pe.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Pe.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Pe.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Pe.layout_symbol["text-rotation-alignment"]),"text-field":new Ii(Pe.layout_symbol["text-field"]),"text-font":new Ii(Pe.layout_symbol["text-font"]),"text-size":new Ii(Pe.layout_symbol["text-size"]),"text-max-width":new Ii(Pe.layout_symbol["text-max-width"]),"text-line-height":new wi(Pe.layout_symbol["text-line-height"]),"text-letter-spacing":new Ii(Pe.layout_symbol["text-letter-spacing"]),"text-justify":new Ii(Pe.layout_symbol["text-justify"]),"text-radial-offset":new Ii(Pe.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Pe.layout_symbol["text-variable-anchor"]),"text-anchor":new Ii(Pe.layout_symbol["text-anchor"]),"text-max-angle":new wi(Pe.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Pe.layout_symbol["text-writing-mode"]),"text-rotate":new Ii(Pe.layout_symbol["text-rotate"]),"text-padding":new wi(Pe.layout_symbol["text-padding"]),"text-keep-upright":new wi(Pe.layout_symbol["text-keep-upright"]),"text-transform":new Ii(Pe.layout_symbol["text-transform"]),"text-offset":new Ii(Pe.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Pe.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Pe.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Pe.layout_symbol["text-optional"])}),q5={paint:new Gs({"icon-opacity":new Ii(Pe.paint_symbol["icon-opacity"]),"icon-color":new Ii(Pe.paint_symbol["icon-color"]),"icon-halo-color":new Ii(Pe.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ii(Pe.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ii(Pe.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Pe.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Pe.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ii(Pe.paint_symbol["text-opacity"]),"text-color":new 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hs(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var z=this.canonical.z+1,Z=2*this.canonical.x,oe=2*this.canonical.y;return[new hs(z,this.wrap,z,Z,oe),new hs(z,this.wrap,z,Z+1,oe),new hs(z,this.wrap,z,Z,oe+1),new hs(z,this.wrap,z,Z+1,oe+1)]},hs.prototype.isLessThan=function(C){return this.wrap<C.wrap||!(this.wrap>C.wrap)&&(this.overscaledZ<C.overscaledZ||!(this.overscaledZ>C.overscaledZ)&&(this.canonical.x<C.canonical.x||!(this.canonical.x>C.canonical.x)&&this.canonical.y<C.canonical.y))},hs.prototype.wrapped=function(){return new hs(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},hs.prototype.unwrapTo=function(C){return new hs(this.overscaledZ,C,this.canonical.z,this.canonical.x,this.canonical.y)},hs.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},hs.prototype.toUnwrapped=function(){return new w9(this.wrap,this.canonical)},hs.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},hs.prototype.getTilePoint=function(C){return this.canonical.getTilePoint(new Up(C.x-this.wrap,C.y))},Qn("CanonicalTileID",$p),Qn("OverscaledTileID",hs,{omit:["posMatrix"]});var Sh=function(C,z,Z){if(this.uid=C,z.height!==z.width)throw new RangeError("DEM tiles must be square");if(Z&&Z!=="mapbox"&&Z!=="terrarium")return L('"'+Z+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=z.height;var oe=this.dim=z.height-2;this.data=new Uint32Array(z.data.buffer),this.encoding=Z||"mapbox";for(var pe=0;pe<oe;pe++)this.data[this._idx(-1,pe)]=this.data[this._idx(0,pe)],this.data[this._idx(oe,pe)]=this.data[this._idx(oe-1,pe)],this.data[this._idx(pe,-1)]=this.data[this._idx(pe,0)],this.data[this._idx(pe,oe)]=this.data[this._idx(pe,oe-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(oe,-1)]=this.data[this._idx(oe-1,0)],this.data[this._idx(-1,oe)]=this.data[this._idx(0,oe-1)],this.data[this._idx(oe,oe)]=this.data[this._idx(oe-1,oe-1)]};Sh.prototype.get=function(C,z){var Z=new Uint8Array(this.data.buffer),oe=4*this._idx(C,z);return(this.encoding==="terrarium"?this._unpackTerrarium:this._unpackMapbox)(Z[oe],Z[oe+1],Z[oe+2])},Sh.prototype.getUnpackVector=function(){return this.encoding==="terrarium"?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Sh.prototype._idx=function(C,z){if(C<-1||C>=this.dim+1||z<-1||z>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(z+1)*this.stride+(C+1)},Sh.prototype._unpackMapbox=function(C,z,Z){return(256*C*256+256*z+Z)/10-1e4},Sh.prototype._unpackTerrarium=function(C,z,Z){return 256*C+z+Z/256-32768},Sh.prototype.getPixels=function(){return new Rl({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Sh.prototype.backfillBorder=function(C,z,Z){if(this.dim!==C.dim)throw new Error("dem dimension mismatch");var oe=z*this.dim,pe=z*this.dim+this.dim,xe=Z*this.dim,Se=Z*this.dim+this.dim;switch(z){case-1:oe=pe-1;break;case 1:pe=oe+1}switch(Z){case-1:xe=Se-1;break;case 1:Se=xe+1}for(var Ne=-z*this.dim,Ze=-Z*this.dim,ct=xe;ct<Se;ct++)for(var gt=oe;gt<pe;gt++)this.data[this._idx(gt,ct)]=C.data[this._idx(gt+Ne,ct+Ze)]},Qn("DEMData",Sh);var eb=function(C){this._stringToNumber={},this._numberToString=[];for(var z=0;z<C.length;z++){var Z=C[z];this._stringToNumber[Z]=z,this._numberToString[z]=Z}};eb.prototype.encode=function(C){return this._stringToNumber[C]},eb.prototype.decode=function(C){return this._numberToString[C]};var tb=function(C,z,Z,oe,pe){this.type="Feature",this._vectorTileFeature=C,C._z=z,C._x=Z,C._y=oe,this.properties=C.properties,this.id=pe},W5={geometry:{configurable:!0}};W5.geometry.get=function(){return this._geometry===void 0&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},W5.geometry.set=function(C){this._geometry=C},tb.prototype.toJSON=function(){var C={geometry:this.geometry};for(var z in this)z!=="_geometry"&&z!=="_vectorTileFeature"&&(C[z]=this[z]);return C},Object.defineProperties(tb.prototype,W5);var Og=function(){this.state={},this.stateChanges={},this.deletedStates={}};Og.prototype.updateState=function(C,z,Z){var oe=String(z);if(this.stateChanges[C]=this.stateChanges[C]||{},this.stateChanges[C][oe]=this.stateChanges[C][oe]||{},x(this.stateChanges[C][oe],Z),this.deletedStates[C]===null)for(var pe in this.deletedStates[C]={},this.state[C])pe!==oe&&(this.deletedStates[C][pe]=null);else if(this.deletedStates[C]&&this.deletedStates[C][oe]===null)for(var xe in this.deletedStates[C][oe]={},this.state[C][oe])Z[xe]||(this.deletedStates[C][oe][xe]=null);else for(var Se in Z)this.deletedStates[C]&&this.deletedStates[C][oe]&&this.deletedStates[C][oe][Se]===null&&delete this.deletedStates[C][oe][Se]},Og.prototype.removeFeatureState=function(C,z,Z){if(this.deletedStates[C]!==null){var oe=String(z);if(this.deletedStates[C]=this.deletedStates[C]||{},Z&&z!==void 0)this.deletedStates[C][oe]!==null&&(this.deletedStates[C][oe]=this.deletedStates[C][oe]||{},this.deletedStates[C][oe][Z]=null);else if(z!==void 0)if(this.stateChanges[C]&&this.stateChanges[C][oe])for(Z in this.deletedStates[C][oe]={},this.stateChanges[C][oe])this.deletedStates[C][oe][Z]=null;else this.deletedStates[C][oe]=null;else this.deletedStates[C]=null}},Og.prototype.getState=function(C,z){var Z=String(z),oe=this.state[C]||{},pe=this.stateChanges[C]||{},xe=x({},oe[Z],pe[Z]);if(this.deletedStates[C]===null)return{};if(this.deletedStates[C]){var Se=this.deletedStates[C][z];if(Se===null)return{};for(var Ne in Se)delete xe[Ne]}return xe},Og.prototype.initializeTileState=function(C,z){C.setFeatureState(this.state,z)},Og.prototype.coalesceChanges=function(C,z){var Z={};for(var oe in this.stateChanges){this.state[oe]=this.state[oe]||{};var pe={};for(var xe in this.stateChanges[oe])this.state[oe][xe]||(this.state[oe][xe]={}),x(this.state[oe][xe],this.stateChanges[oe][xe]),pe[xe]=this.state[oe][xe];Z[oe]=pe}for(var Se in this.deletedStates){this.state[Se]=this.state[Se]||{};var Ne={};if(this.deletedStates[Se]===null)for(var Ze in this.state[Se])Ne[Ze]={},this.state[Se][Ze]={};else for(var ct in this.deletedStates[Se]){if(this.deletedStates[Se][ct]===null)this.state[Se][ct]={};else for(var gt=0,Bt=Object.keys(this.deletedStates[Se][ct]);gt<Bt.length;gt+=1){var Xt=Bt[gt];delete this.state[Se][ct][Xt]}Ne[ct]=this.state[Se][ct]}Z[Se]=Z[Se]||{},x(Z[Se],Ne)}if(this.stateChanges={},this.deletedStates={},Object.keys(Z).length!==0)for(var Gt in C)C[Gt].setFeatureState(Z,z)};var Ch=function(C,z){this.tileID=C,this.x=C.canonical.x,this.y=C.canonical.y,this.z=C.canonical.z,this.grid=new hr(8192,16,0),this.grid3D=new hr(8192,16,0),this.featureIndexArray=new Mt,this.promoteId=z};function A9(C,z,Z,oe,pe){return T(C,function(xe,Se){var Ne=z instanceof tu?z.get(Se):null;return Ne&&Ne.evaluate?Ne.evaluate(Z,oe,pe):Ne})}function k9(C){for(var z=1/0,Z=1/0,oe=-1/0,pe=-1/0,xe=0,Se=C;xe<Se.length;xe+=1){var Ne=Se[xe];z=Math.min(z,Ne.x),Z=Math.min(Z,Ne.y),oe=Math.max(oe,Ne.x),pe=Math.max(pe,Ne.y)}return{minX:z,minY:Z,maxX:oe,maxY:pe}}function wW(C,z){return z-C}Ch.prototype.insert=function(C,z,Z,oe,pe,xe){var Se=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(Z,oe,pe);for(var Ne=xe?this.grid3D:this.grid,Ze=0;Ze<z.length;Ze++){for(var ct=z[Ze],gt=[1/0,1/0,-1/0,-1/0],Bt=0;Bt<ct.length;Bt++){var Xt=ct[Bt];gt[0]=Math.min(gt[0],Xt.x),gt[1]=Math.min(gt[1],Xt.y),gt[2]=Math.max(gt[2],Xt.x),gt[3]=Math.max(gt[3],Xt.y)}gt[0]<8192&&gt[1]<8192&&gt[2]>=0&&gt[3]>=0&&Ne.insert(Se,gt[0],gt[1],gt[2],gt[3])}},Ch.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new wg.VectorTile(new Vx(this.rawTileData)).layers,this.sourceLayerCoder=new eb(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},Ch.prototype.query=function(C,z,Z,oe){var pe=this;this.loadVTLayers();for(var xe=C.params||{},Se=8192/C.tileSize/C.scale,Ne=qi(xe.filter),Ze=C.queryGeometry,ct=C.queryPadding*Se,gt=k9(Ze),Bt=this.grid.query(gt.minX-ct,gt.minY-ct,gt.maxX+ct,gt.maxY+ct),Xt=k9(C.cameraQueryGeometry),Gt=this.grid3D.query(Xt.minX-ct,Xt.minY-ct,Xt.maxX+ct,Xt.maxY+ct,function(dr,br,Hr,Vr){return function(ti,vi,xi,ui,Ei){for(var ki=0,bi=ti;ki<bi.length;ki+=1){var Ma=bi[ki];if(vi<=Ma.x&&xi<=Ma.y&&ui>=Ma.x&&Ei>=Ma.y)return!0}var ma=[new d(vi,xi),new d(vi,Ei),new d(ui,Ei),new d(ui,xi)];if(ti.length>2){for(var Oa=0,Bi=ma;Oa<Bi.length;Oa+=1)if(ru(ti,Bi[Oa]))return!0}for(var va=0;va<ti.length-1;va++)if(zp(ti[va],ti[va+1],ma))return!0;return!1}(C.cameraQueryGeometry,dr-ct,br-ct,Hr+ct,Vr+ct)}),on=0,yn=Gt;on<yn.length;on+=1){var Cn=yn[on];Bt.push(Cn)}Bt.sort(wW);for(var Sn,$n={},Vn=function(dr){var br=Bt[dr];if(br!==Sn){Sn=br;var Hr=pe.featureIndexArray.get(br),Vr=null;pe.loadMatchingFeature($n,Hr.bucketIndex,Hr.sourceLayerIndex,Hr.featureIndex,Ne,xe.layers,xe.availableImages,z,Z,oe,function(ti,vi,xi){return Vr||(Vr=ro(ti)),vi.queryIntersectsFeature(Ze,ti,xi,Vr,pe.z,C.transform,Se,C.pixelPosMatrix)})}},Xn=0;Xn<Bt.length;Xn++)Vn(Xn);return $n},Ch.prototype.loadMatchingFeature=function(C,z,Z,oe,pe,xe,Se,Ne,Ze,ct,gt){var Bt=this.bucketLayerIDs[z];if(!xe||function(Hr,Vr){for(var ti=0;ti<Hr.length;ti++)if(Vr.indexOf(Hr[ti])>=0)return!0;return!1}(xe,Bt)){var Xt=this.sourceLayerCoder.decode(Z),Gt=this.vtLayers[Xt].feature(oe);if(pe.filter(new Ia(this.tileID.overscaledZ),Gt))for(var on=this.getId(Gt,Xt),yn=0;yn<Bt.length;yn++){var Cn=Bt[yn];if(!(xe&&xe.indexOf(Cn)<0)){var Sn=Ne[Cn];if(Sn){var $n={};on!==void 0&&ct&&($n=ct.getState(Sn.sourceLayer||"_geojsonTileLayer",on));var Vn=Ze[Cn];Vn.paint=A9(Vn.paint,Sn.paint,Gt,$n,Se),Vn.layout=A9(Vn.layout,Sn.layout,Gt,$n,Se);var Xn=!gt||gt(Gt,Sn,$n);if(Xn){var dr=new tb(Gt,this.z,this.x,this.y,on);dr.layer=Vn;var br=C[Cn];br===void 0&&(br=C[Cn]=[]),br.push({featureIndex:oe,feature:dr,intersectionZ:Xn})}}}}}},Ch.prototype.lookupSymbolFeatures=function(C,z,Z,oe,pe,xe,Se,Ne){var Ze={};this.loadVTLayers();for(var ct=qi(pe),gt=0,Bt=C;gt<Bt.length;gt+=1){var Xt=Bt[gt];this.loadMatchingFeature(Ze,Z,oe,Xt,ct,xe,Se,Ne,z)}return Ze},Ch.prototype.hasLayer=function(C){for(var z=0,Z=this.bucketLayerIDs;z<Z.length;z+=1)for(var oe=0,pe=Z[z];oe<pe.length;oe+=1)if(C===pe[oe])return!0;return!1},Ch.prototype.getId=function(C,z){var Z=C.id;if(this.promoteId){var oe=typeof this.promoteId=="string"?this.promoteId:this.promoteId[z];typeof(Z=C.properties[oe])=="boolean"&&(Z=Number(Z))}return Z},Qn("FeatureIndex",Ch,{omit:["rawTileData","sourceLayerCoder"]});var Os=function(C,z){this.tileID=C,this.uid=A(),this.uses=0,this.tileSize=z,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};Os.prototype.registerFadeDuration=function(C){var z=C+this.timeAdded;z<V.now()||this.fadeEndTime&&z<this.fadeEndTime||(this.fadeEndTime=z)},Os.prototype.wasRequested=function(){return this.state==="errored"||this.state==="loaded"||this.state==="reloading"},Os.prototype.loadVectorData=function(C,z,Z){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",C){for(var oe in C.featureIndex&&(this.latestFeatureIndex=C.featureIndex,C.rawTileData?(this.latestRawTileData=C.rawTileData,this.latestFeatureIndex.rawTileData=C.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=C.collisionBoxArray,this.buckets=function(ct,gt){var Bt={};if(!gt)return Bt;for(var Xt=function(){var yn=on[Gt],Cn=yn.layerIds.map(function(Xn){return gt.getLayer(Xn)}).filter(Boolean);if(Cn.length!==0){yn.layers=Cn,yn.stateDependentLayerIds&&(yn.stateDependentLayers=yn.stateDependentLayerIds.map(function(Xn){return Cn.filter(function(dr){return dr.id===Xn})[0]}));for(var Sn=0,$n=Cn;Sn<$n.length;Sn+=1){var Vn=$n[Sn];Bt[Vn.id]=yn}}},Gt=0,on=ct;Gt<on.length;Gt+=1)Xt();return Bt}(C.buckets,z.style),this.hasSymbolBuckets=!1,this.buckets){var pe=this.buckets[oe];if(pe instanceof Ua){if(this.hasSymbolBuckets=!0,!Z)break;pe.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var xe in this.buckets){var Se=this.buckets[xe];if(Se instanceof Ua&&Se.hasRTLText){this.hasRTLText=!0,xs.isLoading()||xs.isLoaded()||qo()!=="deferred"||Pa();break}}for(var Ne in this.queryPadding=0,this.buckets){var Ze=this.buckets[Ne];this.queryPadding=Math.max(this.queryPadding,z.style.getLayer(Ne).queryRadius(Ze))}C.imageAtlas&&(this.imageAtlas=C.imageAtlas),C.glyphAtlasImage&&(this.glyphAtlasImage=C.glyphAtlasImage)}else this.collisionBoxArray=new be},Os.prototype.unloadVectorData=function(){for(var C in this.buckets)this.buckets[C].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},Os.prototype.getBucket=function(C){return this.buckets[C.id]},Os.prototype.upload=function(C){for(var z in this.buckets){var Z=this.buckets[z];Z.uploadPending()&&Z.upload(C)}var oe=C.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new jp(C,this.imageAtlas.image,oe.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new jp(C,this.glyphAtlasImage,oe.ALPHA),this.glyphAtlasImage=null)},Os.prototype.prepare=function(C){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(C,this.imageAtlasTexture)},Os.prototype.queryRenderedFeatures=function(C,z,Z,oe,pe,xe,Se,Ne,Ze,ct){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:oe,cameraQueryGeometry:pe,scale:xe,tileSize:this.tileSize,pixelPosMatrix:ct,transform:Ne,params:Se,queryPadding:this.queryPadding*Ze},C,z,Z):{}},Os.prototype.querySourceFeatures=function(C,z){var Z=this.latestFeatureIndex;if(Z&&Z.rawTileData){var oe=Z.loadVTLayers(),pe=z?z.sourceLayer:"",xe=oe._geojsonTileLayer||oe[pe];if(xe)for(var Se=qi(z&&z.filter),Ne=this.tileID.canonical,Ze=Ne.z,ct=Ne.x,gt=Ne.y,Bt={z:Ze,x:ct,y:gt},Xt=0;Xt<xe.length;Xt++){var Gt=xe.feature(Xt);if(Se.filter(new Ia(this.tileID.overscaledZ),Gt)){var on=Z.getId(Gt,pe),yn=new tb(Gt,Ze,ct,gt,on);yn.tile=Bt,C.push(yn)}}}},Os.prototype.hasData=function(){return this.state==="loaded"||this.state==="reloading"||this.state==="expired"},Os.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},Os.prototype.setExpiryData=function(C){var z=this.expirationTime;if(C.cacheControl){var Z=O(C.cacheControl);Z["max-age"]&&(this.expirationTime=Date.now()+1e3*Z["max-age"])}else C.expires&&(this.expirationTime=new Date(C.expires).getTime());if(this.expirationTime){var oe=Date.now(),pe=!1;if(this.expirationTime>oe)pe=!1;else if(z)if(this.expirationTime<z)pe=!0;else{var xe=this.expirationTime-z;xe?this.expirationTime=oe+Math.max(xe,3e4):pe=!0}else pe=!0;pe?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},Os.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)},Os.prototype.setFeatureState=function(C,z){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&Object.keys(C).length!==0){var Z=this.latestFeatureIndex.loadVTLayers();for(var oe in this.buckets)if(z.style.hasLayer(oe)){var pe=this.buckets[oe],xe=pe.layers[0].sourceLayer||"_geojsonTileLayer",Se=Z[xe],Ne=C[xe];if(Se&&Ne&&Object.keys(Ne).length!==0){pe.update(Ne,Se,this.imageAtlas&&this.imageAtlas.patternPositions||{});var Ze=z&&z.style&&z.style.getLayer(oe);Ze&&(this.queryPadding=Math.max(this.queryPadding,Ze.queryRadius(pe)))}}}},Os.prototype.holdingForFade=function(){return this.symbolFadeHoldUntil!==void 0},Os.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<V.now()},Os.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},Os.prototype.setHoldDuration=function(C){this.symbolFadeHoldUntil=V.now()+C},Os.prototype.setDependencies=function(C,z){for(var Z={},oe=0,pe=z;oe<pe.length;oe+=1)Z[pe[oe]]=!0;this.dependencies[C]=Z},Os.prototype.hasDependency=function(C,z){for(var Z=0,oe=C;Z<oe.length;Z+=1){var pe=oe[Z],xe=this.dependencies[pe];if(xe){for(var Se=0,Ne=z;Se<Ne.length;Se+=1)if(xe[Ne[Se]])return!0}}return!1};var kd=self.performance,T9=function(C){this._marks={start:[C.url,"start"].join("#"),end:[C.url,"end"].join("#"),measure:C.url.toString()},kd.mark(this._marks.start)};T9.prototype.finish=function(){kd.mark(this._marks.end);var C=kd.getEntriesByName(this._marks.measure);return 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tt={geometry:lt,type:rt==="Polygon"||rt==="MultiPolygon"?3:rt==="LineString"||rt==="MultiLineString"?2:1,tags:$t};Ie.id!==null&&(tt.id=Ie.id),Oe.features.push(tt)}}function Wt(Oe,Ie,Te,Pe,qe,rt){var lt=Pe*Pe;if(Pe>0&&Ie.size<(qe?lt:Pe))Te.numPoints+=Ie.length/3;else{for(var ot=[],At=0;At<Ie.length;At+=3)(Pe===0||Ie[At+2]>lt)&&(Te.numSimplified++,ot.push(Ie[At]),ot.push(Ie[At+1])),Te.numPoints++;qe&&function(wt,$t){for(var Ut=0,tt=0,bt=wt.length,Ft=bt-2;tt<bt;Ft=tt,tt+=2)Ut+=(wt[tt]-wt[Ft])*(wt[tt+1]+wt[Ft+1]);if(Ut>0===$t)for(tt=0,bt=wt.length;tt<bt/2;tt+=2){var Et=wt[tt],Pt=wt[tt+1];wt[tt]=wt[bt-2-tt],wt[tt+1]=wt[bt-1-tt],wt[bt-2-tt]=Et,wt[bt-1-tt]=Pt}}(ot,rt),Oe.push(ot)}}function Jt(Oe,Ie){var Te=(Ie=this.options=function(qe,rt){for(var lt in rt)qe[lt]=rt[lt];return qe}(Object.create(this.options),Ie)).debug;if(Te&&console.time("preprocess data"),Ie.maxZoom<0||Ie.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(Ie.promoteId&&Ie.generateId)throw new Error("promoteId and generateId cannot be used together.");var Pe=function(qe,rt){var lt=[];if(qe.type==="FeatureCollection")for(var ot=0;ot<qe.features.length;ot++)ve(lt,qe.features[ot],rt,ot);else qe.type==="Feature"?ve(lt,qe,rt):ve(lt,{geometry:qe},rt);return lt}(Oe,Ie);this.tiles={},this.tileCoords=[],Te&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",Ie.indexMaxZoom,Ie.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(Pe=function(qe,rt){var lt=rt.buffer/rt.extent,ot=qe,At=$e(qe,1,-1-lt,lt,0,-1,2,rt),wt=$e(qe,1,1-lt,2+lt,0,-1,2,rt);return(At||wt)&&(ot=$e(qe,1,-lt,1+lt,0,-1,2,rt)||[],At&&(ot=yt(At,1).concat(ot)),wt&&(ot=ot.concat(yt(wt,-1)))),ot}(Pe,Ie)).length&&this.splitTile(Pe,0,0,0),Te&&(Pe.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function Be(Oe,Ie,Te){return 32*((1<<Oe)*Te+Ie)+Oe}function Ge(Oe,Ie){var Te=Oe.tileID.canonical;if(!this._geoJSONIndex)return Ie(null,null);var Pe=this._geoJSONIndex.getTile(Te.z,Te.x,Te.y);if(!Pe)return Ie(null,null);var qe=new M(Pe.features),rt=L(qe);rt.byteOffset===0&&rt.byteLength===rt.buffer.byteLength||(rt=new Uint8Array(rt)),Ie(null,{vectorTile:qe,rawData:rt.buffer})}ee.prototype.load=function(Oe){var Ie=this.options,Te=Ie.log,Pe=Ie.minZoom,qe=Ie.maxZoom,rt=Ie.nodeSize;Te&&console.time("total time");var lt="prepare "+Oe.length+" points";Te&&console.time(lt),this.points=Oe;for(var ot=[],At=0;At<Oe.length;At++)Oe[At].geometry&&ot.push(ae(Oe[At],At));this.trees[qe+1]=new q(ot,_e,Ae,rt,Float32Array),Te&&console.timeEnd(lt);for(var wt=qe;wt>=Pe;wt--){var $t=+Date.now();ot=this._cluster(ot,wt),this.trees[wt]=new q(ot,_e,Ae,rt,Float32Array),Te&&console.log("z%d: %d clusters in %dms",wt,ot.length,+Date.now()-$t)}return Te&&console.timeEnd("total time"),this},ee.prototype.getClusters=function(Oe,Ie){var Te=((Oe[0]+180)%360+360)%360-180,Pe=Math.max(-90,Math.min(90,Oe[1])),qe=Oe[2]===180?180:((Oe[2]+180)%360+360)%360-180,rt=Math.max(-90,Math.min(90,Oe[3]));if(Oe[2]-Oe[0]>=360)Te=-180,qe=180;else if(Te>qe){var lt=this.getClusters([Te,Pe,180,rt],Ie),ot=this.getClusters([-180,Pe,qe,rt],Ie);return lt.concat(ot)}for(var At=this.trees[this._limitZoom(Ie)],wt=[],$t=0,Ut=At.range(ge(Te),fe(rt),ge(qe),fe(Pe));$t<Ut.length;$t+=1){var tt=Ut[$t],bt=At.points[tt];wt.push(bt.numPoints?ue(bt):this.points[bt.index])}return wt},ee.prototype.getChildren=function(Oe){var Ie=this._getOriginId(Oe),Te=this._getOriginZoom(Oe),Pe="No cluster with the specified id.",qe=this.trees[Te];if(!qe)throw new Error(Pe);var rt=qe.points[Ie];if(!rt)throw new Error(Pe);for(var lt=this.options.radius/(this.options.extent*Math.pow(2,Te-1)),ot=[],At=0,wt=qe.within(rt.x,rt.y,lt);At<wt.length;At+=1){var $t=wt[At],Ut=qe.points[$t];Ut.parentId===Oe&&ot.push(Ut.numPoints?ue(Ut):this.points[Ut.index])}if(ot.length===0)throw new Error(Pe);return ot},ee.prototype.getLeaves=function(Oe,Ie,Te){Ie=Ie||10,Te=Te||0;var Pe=[];return this._appendLeaves(Pe,Oe,Ie,Te,0),Pe},ee.prototype.getTile=function(Oe,Ie,Te){var Pe=this.trees[this._limitZoom(Oe)],qe=Math.pow(2,Oe),rt=this.options,lt=rt.extent,ot=rt.radius/lt,At=(Te-ot)/qe,wt=(Te+1+ot)/qe,$t={features:[]};return this._addTileFeatures(Pe.range((Ie-ot)/qe,At,(Ie+1+ot)/qe,wt),Pe.points,Ie,Te,qe,$t),Ie===0&&this._addTileFeatures(Pe.range(1-ot/qe,At,1,wt),Pe.points,qe,Te,qe,$t),Ie===qe-1&&this._addTileFeatures(Pe.range(0,At,ot/qe,wt),Pe.points,-1,Te,qe,$t),$t.features.length?$t:null},ee.prototype.getClusterExpansionZoom=function(Oe){for(var Ie=this._getOriginZoom(Oe)-1;Ie<=this.options.maxZoom;){var Te=this.getChildren(Oe);if(Ie++,Te.length!==1)break;Oe=Te[0].properties.cluster_id}return Ie},ee.prototype._appendLeaves=function(Oe,Ie,Te,Pe,qe){for(var rt=0,lt=this.getChildren(Ie);rt<lt.length;rt+=1){var ot=lt[rt],At=ot.properties;if(At&&At.cluster?qe+At.point_count<=Pe?qe+=At.point_count:qe=this._appendLeaves(Oe,At.cluster_id,Te,Pe,qe):qe<Pe?qe++:Oe.push(ot),Oe.length===Te)break}return qe},ee.prototype._addTileFeatures=function(Oe,Ie,Te,Pe,qe,rt){for(var lt=0,ot=Oe;lt<ot.length;lt+=1){var At=Ie[ot[lt]],wt=At.numPoints,$t={type:1,geometry:[[Math.round(this.options.extent*(At.x*qe-Te)),Math.round(this.options.extent*(At.y*qe-Pe))]],tags:wt?le(At):this.points[At.index].properties},Ut=void 0;wt?Ut=At.id:this.options.generateId?Ut=At.index:this.points[At.index].id&&(Ut=this.points[At.index].id),Ut!==void 0&&($t.id=Ut),rt.features.push($t)}},ee.prototype._limitZoom=function(Oe){return Math.max(this.options.minZoom,Math.min(Oe,this.options.maxZoom+1))},ee.prototype._cluster=function(Oe,Ie){for(var Te=[],Pe=this.options,qe=Pe.radius,rt=Pe.extent,lt=Pe.reduce,ot=qe/(rt*Math.pow(2,Ie)),At=0;At<Oe.length;At++){var wt=Oe[At];if(!(wt.zoom<=Ie)){wt.zoom=Ie;for(var $t=this.trees[Ie+1],Ut=$t.within(wt.x,wt.y,ot),tt=wt.numPoints||1,bt=wt.x*tt,Ft=wt.y*tt,Et=lt&&tt>1?this._map(wt,!0):null,Pt=(At<<5)+(Ie+1)+this.points.length,De=0,Je=Ut;De<Je.length;De+=1){var st=Je[De],St=$t.points[st];if(!(St.zoom<=Ie)){St.zoom=Ie;var It=St.numPoints||1;bt+=St.x*It,Ft+=St.y*It,tt+=It,St.parentId=Pt,lt&&(Et||(Et=this._map(wt,!0)),lt(Et,this._map(St)))}}tt===1?Te.push(wt):(wt.parentId=Pt,Te.push(ie(bt/tt,Ft/tt,Pt,tt,Et)))}}return Te},ee.prototype._getOriginId=function(Oe){return Oe-this.points.length>>5},ee.prototype._getOriginZoom=function(Oe){return(Oe-this.points.length)%32},ee.prototype._map=function(Oe,Ie){if(Oe.numPoints)return Ie?me({},Oe.properties):Oe.properties;var Te=this.points[Oe.index].properties,Pe=this.options.map(Te);return Ie&&Pe===Te?me({},Pe):Pe},Jt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Jt.prototype.splitTile=function(Oe,Ie,Te,Pe,qe,rt,lt){for(var ot=[Oe,Ie,Te,Pe],At=this.options,wt=At.debug;ot.length;){Pe=ot.pop(),Te=ot.pop(),Ie=ot.pop(),Oe=ot.pop();var $t=1<<Ie,Ut=Be(Ie,Te,Pe),tt=this.tiles[Ut];if(!tt&&(wt>1&&console.time("creation"),tt=this.tiles[Ut]=et(Oe,Ie,Te,Pe,At),this.tileCoords.push({z:Ie,x:Te,y:Pe}),wt)){wt>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",Ie,Te,Pe,tt.numFeatures,tt.numPoints,tt.numSimplified),console.timeEnd("creation"));var bt="z"+Ie;this.stats[bt]=(this.stats[bt]||0)+1,this.total++}if(tt.source=Oe,qe){if(Ie===At.maxZoom||Ie===qe)continue;var Ft=1<<qe-Ie;if(Te!==Math.floor(rt/Ft)||Pe!==Math.floor(lt/Ft))continue}else if(Ie===At.indexMaxZoom||tt.numPoints<=At.indexMaxPoints)continue;if(tt.source=null,Oe.length!==0){wt>1&&console.time("clipping");var Et,Pt,De,Je,st,St,It=.5*At.buffer/At.extent,Zt=.5-It,Kt=.5+It,qt=1+It;Et=Pt=De=Je=null,st=$e(Oe,$t,Te-It,Te+Kt,0,tt.minX,tt.maxX,At),St=$e(Oe,$t,Te+Zt,Te+qt,0,tt.minX,tt.maxX,At),Oe=null,st&&(Et=$e(st,$t,Pe-It,Pe+Kt,1,tt.minY,tt.maxY,At),Pt=$e(st,$t,Pe+Zt,Pe+qt,1,tt.minY,tt.maxY,At),st=null),St&&(De=$e(St,$t,Pe-It,Pe+Kt,1,tt.minY,tt.maxY,At),Je=$e(St,$t,Pe+Zt,Pe+qt,1,tt.minY,tt.maxY,At),St=null),wt>1&&console.timeEnd("clipping"),ot.push(Et||[],Ie+1,2*Te,2*Pe),ot.push(Pt||[],Ie+1,2*Te,2*Pe+1),ot.push(De||[],Ie+1,2*Te+1,2*Pe),ot.push(Je||[],Ie+1,2*Te+1,2*Pe+1)}}},Jt.prototype.getTile=function(Oe,Ie,Te){var Pe=this.options,qe=Pe.extent,rt=Pe.debug;if(Oe<0||Oe>24)return null;var lt=1<<Oe,ot=Be(Oe,Ie=(Ie%lt+lt)%lt,Te);if(this.tiles[ot])return Tt(this.tiles[ot],qe);rt>1&&console.log("drilling down to z%d-%d-%d",Oe,Ie,Te);for(var At,wt=Oe,$t=Ie,Ut=Te;!At&&wt>0;)wt--,$t=Math.floor($t/2),Ut=Math.floor(Ut/2),At=this.tiles[Be(wt,$t,Ut)];return At&&At.source?(rt>1&&console.log("found parent tile z%d-%d-%d",wt,$t,Ut),rt>1&&console.time("drilling down"),this.splitTile(At.source,wt,$t,Ut,Oe,Ie,Te),rt>1&&console.timeEnd("drilling down"),this.tiles[ot]?Tt(this.tiles[ot],qe):null):null};var kt=function(Oe){function Ie(Te,Pe,qe,rt){Oe.call(this,Te,Pe,qe,Ge),rt&&(this.loadGeoJSON=rt)}return Oe&&(Ie.__proto__=Oe),Ie.prototype=Object.create(Oe&&Oe.prototype),Ie.prototype.constructor=Ie,Ie.prototype.loadData=function(Te,Pe){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=Pe,this._pendingLoadDataParams=Te,this._state&&this._state!=="Idle"?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},Ie.prototype._loadData=function(){var Te=this;if(this._pendingCallback&&this._pendingLoadDataParams){var Pe=this._pendingCallback,qe=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var rt=!!(qe&&qe.request&&qe.request.collectResourceTiming)&&new i.RequestPerformance(qe.request);this.loadGeoJSON(qe,function(lt,ot){if(lt||!ot)return Pe(lt);if(typeof ot!="object")return Pe(new Error("Input data given to '"+qe.source+"' is not a valid GeoJSON object."));_(ot,!0);try{Te._geoJSONIndex=qe.cluster?new ee(function($t){var Ut=$t.superclusterOptions,tt=$t.clusterProperties;if(!tt||!Ut)return Ut;for(var bt={},Ft={},Et={accumulated:null,zoom:0},Pt={properties:null},De=Object.keys(tt),Je=0,st=De;Je<st.length;Je+=1){var St=st[Je],It=tt[St],Zt=It[0],Kt=It[1],qt=i.createExpression(Kt),mn=i.createExpression(typeof Zt=="string"?[Zt,["accumulated"],["get",St]]:Zt);bt[St]=qt.value,Ft[St]=mn.value}return Ut.map=function(Fn){Pt.properties=Fn;for(var pn={},tn=0,nn=De;tn<nn.length;tn+=1){var sn=nn[tn];pn[sn]=bt[sn].evaluate(Et,Pt)}return pn},Ut.reduce=function(Fn,pn){Pt.properties=pn;for(var tn=0,nn=De;tn<nn.length;tn+=1){var sn=nn[tn];Et.accumulated=Fn[sn],Fn[sn]=Ft[sn].evaluate(Et,Pt)}},Ut}(qe)).load(ot.features):function($t,Ut){return new Jt($t,Ut)}(ot,qe.geojsonVtOptions)}catch($t){return Pe($t)}Te.loaded={};var At={};if(rt){var wt=rt.finish();wt&&(At.resourceTiming={},At.resourceTiming[qe.source]=JSON.parse(JSON.stringify(wt)))}Pe(null,At)})}},Ie.prototype.coalesce=function(){this._state==="Coalescing"?this._state="Idle":this._state==="NeedsLoadData"&&(this._state="Coalescing",this._loadData())},Ie.prototype.reloadTile=function(Te,Pe){var qe=this.loaded,rt=Te.uid;return qe&&qe[rt]?Oe.prototype.reloadTile.call(this,Te,Pe):this.loadTile(Te,Pe)},Ie.prototype.loadGeoJSON=function(Te,Pe){if(Te.request)i.getJSON(Te.request,Pe);else{if(typeof Te.data!="string")return Pe(new Error("Input data given to '"+Te.source+"' is not a valid GeoJSON object."));try{return Pe(null,JSON.parse(Te.data))}catch{return Pe(new Error("Input data given to '"+Te.source+"' is not a valid GeoJSON object."))}}},Ie.prototype.removeSource=function(Te,Pe){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),Pe()},Ie.prototype.getClusterExpansionZoom=function(Te,Pe){try{Pe(null,this._geoJSONIndex.getClusterExpansionZoom(Te.clusterId))}catch(qe){Pe(qe)}},Ie.prototype.getClusterChildren=function(Te,Pe){try{Pe(null,this._geoJSONIndex.getChildren(Te.clusterId))}catch(qe){Pe(qe)}},Ie.prototype.getClusterLeaves=function(Te,Pe){try{Pe(null,this._geoJSONIndex.getLeaves(Te.clusterId,Te.limit,Te.offset))}catch(qe){Pe(qe)}},Ie}(y),dt=function(Oe){var Ie=this;this.self=Oe,this.actor=new i.Actor(Oe,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:y,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(Te,Pe){if(Ie.workerSourceTypes[Te])throw new Error('Worker source with name "'+Te+'" already 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qe)qe[rt].availableImages=Ie}Te()},dt.prototype.setLayers=function(Oe,Ie,Te){this.getLayerIndex(Oe).replace(Ie),Te()},dt.prototype.updateLayers=function(Oe,Ie,Te){this.getLayerIndex(Oe).update(Ie.layers,Ie.removedIds),Te()},dt.prototype.loadTile=function(Oe,Ie,Te){this.getWorkerSource(Oe,Ie.type,Ie.source).loadTile(Ie,Te)},dt.prototype.loadDEMTile=function(Oe,Ie,Te){this.getDEMWorkerSource(Oe,Ie.source).loadTile(Ie,Te)},dt.prototype.reloadTile=function(Oe,Ie,Te){this.getWorkerSource(Oe,Ie.type,Ie.source).reloadTile(Ie,Te)},dt.prototype.abortTile=function(Oe,Ie,Te){this.getWorkerSource(Oe,Ie.type,Ie.source).abortTile(Ie,Te)},dt.prototype.removeTile=function(Oe,Ie,Te){this.getWorkerSource(Oe,Ie.type,Ie.source).removeTile(Ie,Te)},dt.prototype.removeDEMTile=function(Oe,Ie){this.getDEMWorkerSource(Oe,Ie.source).removeTile(Ie)},dt.prototype.removeSource=function(Oe,Ie,Te){if(this.workerSources[Oe]&&this.workerSources[Oe][Ie.type]&&this.workerSources[Oe][Ie.type][Ie.source]){var Pe=this.workerSources[Oe][Ie.type][Ie.source];delete this.workerSources[Oe][Ie.type][Ie.source],Pe.removeSource!==void 0?Pe.removeSource(Ie,Te):Te()}},dt.prototype.loadWorkerSource=function(Oe,Ie,Te){try{this.self.importScripts(Ie.url),Te()}catch(Pe){Te(Pe.toString())}},dt.prototype.syncRTLPluginState=function(Oe,Ie,Te){try{i.plugin.setState(Ie);var Pe=i.plugin.getPluginURL();if(i.plugin.isLoaded()&&!i.plugin.isParsed()&&Pe!=null){this.self.importScripts(Pe);var qe=i.plugin.isParsed();Te(qe?void 0:new Error("RTL Text Plugin failed to import scripts from "+Pe),qe)}}catch(rt){Te(rt.toString())}},dt.prototype.getAvailableImages=function(Oe){var Ie=this.availableImages[Oe];return Ie||(Ie=[]),Ie},dt.prototype.getLayerIndex=function(Oe){var Ie=this.layerIndexes[Oe];return Ie||(Ie=this.layerIndexes[Oe]=new h),Ie},dt.prototype.getWorkerSource=function(Oe,Ie,Te){var 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he=1,ye=0;he<se;he++){for(var be=(I[he]+he*he-(I[$[ye]]+$[ye]*$[ye]))/(2*he-2*$[ye]);be<=X[ye];)ye--,be=(I[he]+he*he-(I[$[ye]]+$[ye]*$[ye]))/(2*he-2*$[ye]);$[++ye]=he,X[ye]=be,X[ye+1]=+w}for(he=0,ye=0;he<se;he++){for(;X[ye+1]<he;)ye++;j[he]=(he-$[ye])*(he-$[ye])+I[$[ye]]}}M.prototype.draw=function(I){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(I,this.buffer,this.middle);for(var j=this.ctx.getImageData(0,0,this.size,this.size),$=new Uint8ClampedArray(this.size*this.size),X=0;X<this.size*this.size;X++){var se=j.data[4*X+3]/255;this.gridOuter[X]=se===1?0:se===0?w:Math.pow(Math.max(0,.5-se),2),this.gridInner[X]=se===1?w:se===0?0:Math.pow(Math.max(0,se-.5),2)}for(T(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),T(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),X=0;X<this.size*this.size;X++){var he=this.gridOuter[X]-this.gridInner[X];$[X]=Math.max(0,Math.min(255,Math.round(255-255*(he/this.radius+this.cutoff))))}return $},b.default=k;var S=function(I,j){this.requestManager=I,this.localIdeographFontFamily=j,this.entries={}};S.prototype.setURL=function(I){this.url=I},S.prototype.getGlyphs=function(I,j){var $=this,X=[];for(var se in I)for(var he=0,ye=I[se];he<ye.length;he+=1){var be=ye[he];X.push({stack:se,id:be})}i.asyncAll(X,function(Ee,Ue){var Xe=Ee.stack,it=Ee.id,xt=$.entries[Xe];xt||(xt=$.entries[Xe]={glyphs:{},requests:{},ranges:{}});var Dt=xt.glyphs[it];if(Dt===void 0){if(Dt=$._tinySDF(xt,Xe,it))return xt.glyphs[it]=Dt,void Ue(null,{stack:Xe,id:it,glyph:Dt});var _t=Math.floor(it/256);if(256*_t>65535)Ue(new Error("glyphs > 65535 not supported"));else if(xt.ranges[_t])Ue(null,{stack:Xe,id:it,glyph:Dt});else{var Mt=xt.requests[_t];Mt||(Mt=xt.requests[_t]=[],S.loadGlyphRange(Xe,_t,$.url,$.requestManager,function(vt,Nt){if(Nt){for(var Rt in Nt)$._doesCharSupportLocalGlyph(+Rt)||(xt.glyphs[+Rt]=Nt[+Rt]);xt.ranges[_t]=!0}for(var Vt=0,rn=Mt;Vt<rn.length;Vt+=1)(0,rn[Vt])(vt,Nt);delete xt.requests[_t]})),Mt.push(function(vt,Nt){vt?Ue(vt):Nt&&Ue(null,{stack:Xe,id:it,glyph:Nt[it]||null})})}}else Ue(null,{stack:Xe,id:it,glyph:Dt})},function(Ee,Ue){if(Ee)j(Ee);else if(Ue){for(var Xe={},it=0,xt=Ue;it<xt.length;it+=1){var Dt=xt[it],_t=Dt.stack,Mt=Dt.id,vt=Dt.glyph;(Xe[_t]||(Xe[_t]={}))[Mt]=vt&&{id:vt.id,bitmap:vt.bitmap.clone(),metrics:vt.metrics}}j(null,Xe)}})},S.prototype._doesCharSupportLocalGlyph=function(I){return!!this.localIdeographFontFamily&&(i.isChar["CJK Unified Ideographs"](I)||i.isChar["Hangul Syllables"](I)||i.isChar.Hiragana(I)||i.isChar.Katakana(I))},S.prototype._tinySDF=function(I,j,$){var X=this.localIdeographFontFamily;if(X&&this._doesCharSupportLocalGlyph($)){var se=I.tinySDF;if(!se){var he="400";/bold/i.test(j)?he="900":/medium/i.test(j)?he="500":/light/i.test(j)&&(he="200"),se=I.tinySDF=new S.TinySDF(24,3,8,.25,X,he)}return{id:$,bitmap:new i.AlphaImage({width:30,height:30},se.draw(String.fromCharCode($))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},S.loadGlyphRange=function(I,j,$,X,se){var he=256*j,ye=he+255,be=X.transformRequest(X.normalizeGlyphsURL($).replace("{fontstack}",I).replace("{range}",he+"-"+ye),i.ResourceType.Glyphs);i.getArrayBuffer(be,function(Ee,Ue){if(Ee)se(Ee);else if(Ue){for(var Xe={},it=0,xt=i.parseGlyphPBF(Ue);it<xt.length;it+=1){var Dt=xt[it];Xe[Dt.id]=Dt}se(null,Xe)}})},S.TinySDF=b;var P=function(){this.specification=i.styleSpec.light.position};P.prototype.possiblyEvaluate=function(I,j){return i.sphericalToCartesian(I.expression.evaluate(j))},P.prototype.interpolate=function(I,j,$){return{x:i.number(I.x,j.x,$),y:i.number(I.y,j.y,$),z:i.number(I.z,j.z,$)}};var L=new i.Properties({anchor:new i.DataConstantProperty(i.styleSpec.light.anchor),position:new P,color:new i.DataConstantProperty(i.styleSpec.light.color),intensity:new i.DataConstantProperty(i.styleSpec.light.intensity)}),R=function(I){function j($){I.call(this),this._transitionable=new i.Transitionable(L),this.setLight($),this._transitioning=this._transitionable.untransitioned()}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.getLight=function(){return this._transitionable.serialize()},j.prototype.setLight=function($,X){if(X===void 0&&(X={}),!this._validate(i.validateLight,$,X))for(var se in $){var he=$[se];i.endsWith(se,"-transition")?this._transitionable.setTransition(se.slice(0,-11),he):this._transitionable.setValue(se,he)}},j.prototype.updateTransitions=function($){this._transitioning=this._transitionable.transitioned($,this._transitioning)},j.prototype.hasTransition=function(){return this._transitioning.hasTransition()},j.prototype.recalculate=function($){this.properties=this._transitioning.possiblyEvaluate($)},j.prototype._validate=function($,X,se){return(!se||se.validate!==!1)&&i.emitValidationErrors(this,$.call(i.validateStyle,i.extend({value:X,style:{glyphs:!0,sprite:!0},styleSpec:i.styleSpec})))},j}(i.Evented),F=function(I,j){this.width=I,this.height=j,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};F.prototype.getDash=function(I,j){var $=I.join(",")+String(j);return this.dashEntry[$]||(this.dashEntry[$]=this.addDash(I,j)),this.dashEntry[$]},F.prototype.getDashRanges=function(I,j,$){var X=[],se=I.length%2==1?-I[I.length-1]*$:0,he=I[0]*$,ye=!0;X.push({left:se,right:he,isDash:ye,zeroLength:I[0]===0});for(var be=I[0],Ee=1;Ee<I.length;Ee++){ye=!ye;var Ue=I[Ee];se=be*$,he=(be+=Ue)*$,X.push({left:se,right:he,isDash:ye,zeroLength:Ue===0})}return X},F.prototype.addRoundDash=function(I,j,$){for(var X=j/2,se=-$;se<=$;se++)for(var he=this.nextRow+$+se,ye=this.width*he,be=0,Ee=I[be],Ue=0;Ue<this.width;Ue++){Ue/Ee.right>1&&(Ee=I[++be]);var Xe=Math.abs(Ue-Ee.left),it=Math.abs(Ue-Ee.right),xt=Math.min(Xe,it),Dt=void 0,_t=se/$*(X+1);if(Ee.isDash){var Mt=X-Math.abs(_t);Dt=Math.sqrt(xt*xt+Mt*Mt)}else Dt=X-Math.sqrt(xt*xt+_t*_t);this.data[ye+Ue]=Math.max(0,Math.min(255,Dt+128))}},F.prototype.addRegularDash=function(I){for(var j=I.length-1;j>=0;--j){var $=I[j],X=I[j+1];$.zeroLength?I.splice(j,1):X&&X.isDash===$.isDash&&(X.left=$.left,I.splice(j,1))}var se=I[0],he=I[I.length-1];se.isDash===he.isDash&&(se.left=he.left-this.width,he.right=se.right+this.width);for(var ye=this.width*this.nextRow,be=0,Ee=I[be],Ue=0;Ue<this.width;Ue++){Ue/Ee.right>1&&(Ee=I[++be]);var Xe=Math.abs(Ue-Ee.left),it=Math.abs(Ue-Ee.right),xt=Math.min(Xe,it),Dt=Ee.isDash?xt:-xt;this.data[ye+Ue]=Math.max(0,Math.min(255,Dt+128))}},F.prototype.addDash=function(I,j){var $=j?7:0,X=2*$+1;if(this.nextRow+X>this.height)return i.warnOnce("LineAtlas out of 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I(j,$){this.workerPool=j,this.actors=[],this.currentActor=0,this.id=i.uniqueId();for(var X=this.workerPool.acquire(this.id),se=0;se<X.length;se++){var he=X[se],ye=new I.Actor(he,$,this.id);ye.name="Worker "+se,this.actors.push(ye)}};function O(I,j,$){var X=function(se,he){if(se)return $(se);if(he){var ye=i.pick(i.extend(he,I),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);he.vector_layers&&(ye.vectorLayers=he.vector_layers,ye.vectorLayerIds=ye.vectorLayers.map(function(be){return be.id})),ye.tiles=j.canonicalizeTileset(ye,I.url),$(null,ye)}};return I.url?i.getJSON(j.transformRequest(j.normalizeSourceURL(I.url),i.ResourceType.Source),X):i.browser.frame(function(){return X(null,I)})}D.prototype.broadcast=function(I,j,$){$=$||function(){},i.asyncAll(this.actors,function(X,se){X.send(I,j,se)},$)},D.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},D.prototype.remove=function(){this.actors.forEach(function(I){I.remove()}),this.actors=[],this.workerPool.release(this.id)},D.Actor=i.Actor;var N=function(I,j,$){this.bounds=i.LngLatBounds.convert(this.validateBounds(I)),this.minzoom=j||0,this.maxzoom=$||24};N.prototype.validateBounds=function(I){return Array.isArray(I)&&I.length===4?[Math.max(-180,I[0]),Math.max(-90,I[1]),Math.min(180,I[2]),Math.min(90,I[3])]:[-180,-90,180,90]},N.prototype.contains=function(I){var j=Math.pow(2,I.z),$=Math.floor(i.mercatorXfromLng(this.bounds.getWest())*j),X=Math.floor(i.mercatorYfromLat(this.bounds.getNorth())*j),se=Math.ceil(i.mercatorXfromLng(this.bounds.getEast())*j),he=Math.ceil(i.mercatorYfromLat(this.bounds.getSouth())*j);return I.x>=$&&I.x<se&&I.y>=X&&I.y<he};var B=function(I){function j($,X,se,he){if(I.call(this),this.id=$,this.dispatcher=se,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,i.extend(this,i.pick(X,["url","scheme","tileSize","promoteId"])),this._options=i.extend({type:"vector"},X),this._collectResourceTiming=X.collectResourceTiming,this.tileSize!==512)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(he)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.load=function(){var $=this;this._loaded=!1,this.fire(new i.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=O(this._options,this.map._requestManager,function(X,se){$._tileJSONRequest=null,$._loaded=!0,X?$.fire(new i.ErrorEvent(X)):se&&(i.extend($,se),se.bounds&&($.tileBounds=new N(se.bounds,$.minzoom,$.maxzoom)),i.postTurnstileEvent(se.tiles,$.map._requestManager._customAccessToken),i.postMapLoadEvent(se.tiles,$.map._getMapId(),$.map._requestManager._skuToken,$.map._requestManager._customAccessToken),$.fire(new i.Event("data",{dataType:"source",sourceDataType:"metadata"})),$.fire(new i.Event("data",{dataType:"source",sourceDataType:"content"})))})},j.prototype.loaded=function(){return this._loaded},j.prototype.hasTile=function($){return!this.tileBounds||this.tileBounds.contains($.canonical)},j.prototype.onAdd=function($){this.map=$,this.load()},j.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},j.prototype.serialize=function(){return i.extend({},this._options)},j.prototype.loadTile=function($,X){var se=this.map._requestManager.normalizeTileURL($.tileID.canonical.url(this.tiles,this.scheme)),he={request:this.map._requestManager.transformRequest(se,i.ResourceType.Tile),uid:$.uid,tileID:$.tileID,zoom:$.tileID.overscaledZ,tileSize:this.tileSize*$.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function ye(be,Ee){return delete $.request,$.aborted?X(null):be&&be.status!==404?X(be):(Ee&&Ee.resourceTiming&&($.resourceTiming=Ee.resourceTiming),this.map._refreshExpiredTiles&&Ee&&$.setExpiryData(Ee),$.loadVectorData(Ee,this.map.painter),i.cacheEntryPossiblyAdded(this.dispatcher),X(null),void($.reloadCallback&&(this.loadTile($,$.reloadCallback),$.reloadCallback=null)))}he.request.collectResourceTiming=this._collectResourceTiming,$.actor&&$.state!=="expired"?$.state==="loading"?$.reloadCallback=X:$.request=$.actor.send("reloadTile",he,ye.bind(this)):($.actor=this.dispatcher.getActor(),$.request=$.actor.send("loadTile",he,ye.bind(this)))},j.prototype.abortTile=function($){$.request&&($.request.cancel(),delete $.request),$.actor&&$.actor.send("abortTile",{uid:$.uid,type:this.type,source:this.id},void 0)},j.prototype.unloadTile=function($){$.unloadVectorData(),$.actor&&$.actor.send("removeTile",{uid:$.uid,type:this.type,source:this.id},void 0)},j.prototype.hasTransition=function(){return!1},j}(i.Evented),W=function(I){function j($,X,se,he){I.call(this),this.id=$,this.dispatcher=se,this.setEventedParent(he),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=i.extend({type:"raster"},X),i.extend(this,i.pick(X,["url","scheme","tileSize"]))}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.load=function(){var $=this;this._loaded=!1,this.fire(new i.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=O(this._options,this.map._requestManager,function(X,se){$._tileJSONRequest=null,$._loaded=!0,X?$.fire(new i.ErrorEvent(X)):se&&(i.extend($,se),se.bounds&&($.tileBounds=new N(se.bounds,$.minzoom,$.maxzoom)),i.postTurnstileEvent(se.tiles),i.postMapLoadEvent(se.tiles,$.map._getMapId(),$.map._requestManager._skuToken),$.fire(new i.Event("data",{dataType:"source",sourceDataType:"metadata"})),$.fire(new i.Event("data",{dataType:"source",sourceDataType:"content"})))})},j.prototype.loaded=function(){return this._loaded},j.prototype.onAdd=function($){this.map=$,this.load()},j.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},j.prototype.serialize=function(){return i.extend({},this._options)},j.prototype.hasTile=function($){return!this.tileBounds||this.tileBounds.contains($.canonical)},j.prototype.loadTile=function($,X){var se=this,he=this.map._requestManager.normalizeTileURL($.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);$.request=i.getImage(this.map._requestManager.transformRequest(he,i.ResourceType.Tile),function(ye,be){if(delete $.request,$.aborted)$.state="unloaded",X(null);else if(ye)$.state="errored",X(ye);else if(be){se.map._refreshExpiredTiles&&$.setExpiryData(be),delete be.cacheControl,delete be.expires;var Ee=se.map.painter.context,Ue=Ee.gl;$.texture=se.map.painter.getTileTexture(be.width),$.texture?$.texture.update(be,{useMipmap:!0}):($.texture=new i.Texture(Ee,be,Ue.RGBA,{useMipmap:!0}),$.texture.bind(Ue.LINEAR,Ue.CLAMP_TO_EDGE,Ue.LINEAR_MIPMAP_NEAREST),Ee.extTextureFilterAnisotropic&&Ue.texParameterf(Ue.TEXTURE_2D,Ee.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,Ee.extTextureFilterAnisotropicMax)),$.state="loaded",i.cacheEntryPossiblyAdded(se.dispatcher),X(null)}})},j.prototype.abortTile=function($,X){$.request&&($.request.cancel(),delete $.request),X()},j.prototype.unloadTile=function($,X){$.texture&&this.map.painter.saveTileTexture($.texture),X()},j.prototype.hasTransition=function(){return!1},j}(i.Evented),G=function(I){function j($,X,se,he){I.call(this,$,X,se,he),this.type="raster-dem",this.maxzoom=22,this._options=i.extend({type:"raster-dem"},X),this.encoding=X.encoding||"mapbox"}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},j.prototype.loadTile=function($,X){var se=this.map._requestManager.normalizeTileURL($.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function he(ye,be){ye&&($.state="errored",X(ye)),be&&($.dem=be,$.needsHillshadePrepare=!0,$.state="loaded",X(null))}$.request=i.getImage(this.map._requestManager.transformRequest(se,i.ResourceType.Tile),function(ye,be){if(delete $.request,$.aborted)$.state="unloaded",X(null);else if(ye)$.state="errored",X(ye);else if(be){this.map._refreshExpiredTiles&&$.setExpiryData(be),delete be.cacheControl,delete be.expires;var Ee=i.window.ImageBitmap&&be instanceof i.window.ImageBitmap&&i.offscreenCanvasSupported()?be:i.browser.getImageData(be,1),Ue={uid:$.uid,coord:$.tileID,source:this.id,rawImageData:Ee,encoding:this.encoding};$.actor&&$.state!=="expired"||($.actor=this.dispatcher.getActor(),$.actor.send("loadDEMTile",Ue,he.bind(this)))}}.bind(this)),$.neighboringTiles=this._getNeighboringTiles($.tileID)},j.prototype._getNeighboringTiles=function($){var X=$.canonical,se=Math.pow(2,X.z),he=(X.x-1+se)%se,ye=X.x===0?$.wrap-1:$.wrap,be=(X.x+1+se)%se,Ee=X.x+1===se?$.wrap+1:$.wrap,Ue={};return Ue[new i.OverscaledTileID($.overscaledZ,ye,X.z,he,X.y).key]={backfilled:!1},Ue[new i.OverscaledTileID($.overscaledZ,Ee,X.z,be,X.y).key]={backfilled:!1},X.y>0&&(Ue[new i.OverscaledTileID($.overscaledZ,ye,X.z,he,X.y-1).key]={backfilled:!1},Ue[new i.OverscaledTileID($.overscaledZ,$.wrap,X.z,X.x,X.y-1).key]={backfilled:!1},Ue[new i.OverscaledTileID($.overscaledZ,Ee,X.z,be,X.y-1).key]={backfilled:!1}),X.y+1<se&&(Ue[new i.OverscaledTileID($.overscaledZ,ye,X.z,he,X.y+1).key]={backfilled:!1},Ue[new i.OverscaledTileID($.overscaledZ,$.wrap,X.z,X.x,X.y+1).key]={backfilled:!1},Ue[new i.OverscaledTileID($.overscaledZ,Ee,X.z,be,X.y+1).key]={backfilled:!1}),Ue},j.prototype.unloadTile=function($){$.demTexture&&this.map.painter.saveTileTexture($.demTexture),$.fbo&&($.fbo.destroy(),delete $.fbo),$.dem&&delete $.dem,delete $.neighboringTiles,$.state="unloaded",$.actor&&$.actor.send("removeDEMTile",{uid:$.uid,source:this.id})},j}(W),K=function(I){function j($,X,se,he){I.call(this),this.id=$,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=se.getActor(),this.setEventedParent(he),this._data=X.data,this._options=i.extend({},X),this._collectResourceTiming=X.collectResourceTiming,this._resourceTiming=[],X.maxzoom!==void 0&&(this.maxzoom=X.maxzoom),X.type&&(this.type=X.type),X.attribution&&(this.attribution=X.attribution),this.promoteId=X.promoteId;var ye=i.EXTENT/this.tileSize;this.workerOptions=i.extend({source:this.id,cluster:X.cluster||!1,geojsonVtOptions:{buffer:(X.buffer!==void 0?X.buffer:128)*ye,tolerance:(X.tolerance!==void 0?X.tolerance:.375)*ye,extent:i.EXTENT,maxZoom:this.maxzoom,lineMetrics:X.lineMetrics||!1,generateId:X.generateId||!1},superclusterOptions:{maxZoom:X.clusterMaxZoom!==void 0?Math.min(X.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:i.EXTENT,radius:(X.clusterRadius||50)*ye,log:!1,generateId:X.generateId||!1},clusterProperties:X.clusterProperties},X.workerOptions)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.load=function(){var $=this;this.fire(new i.Event("dataloading",{dataType:"source"})),this._updateWorkerData(function(X){if(X)$.fire(new i.ErrorEvent(X));else{var se={dataType:"source",sourceDataType:"metadata"};$._collectResourceTiming&&$._resourceTiming&&$._resourceTiming.length>0&&(se.resourceTiming=$._resourceTiming,$._resourceTiming=[]),$.fire(new i.Event("data",se))}})},j.prototype.onAdd=function($){this.map=$,this.load()},j.prototype.setData=function($){var X=this;return this._data=$,this.fire(new i.Event("dataloading",{dataType:"source"})),this._updateWorkerData(function(se){if(se)X.fire(new i.ErrorEvent(se));else{var he={dataType:"source",sourceDataType:"content"};X._collectResourceTiming&&X._resourceTiming&&X._resourceTiming.length>0&&(he.resourceTiming=X._resourceTiming,X._resourceTiming=[]),X.fire(new i.Event("data",he))}}),this},j.prototype.getClusterExpansionZoom=function($,X){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:$,source:this.id},X),this},j.prototype.getClusterChildren=function($,X){return this.actor.send("geojson.getClusterChildren",{clusterId:$,source:this.id},X),this},j.prototype.getClusterLeaves=function($,X,se,he){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:$,limit:X,offset:se},he),this},j.prototype._updateWorkerData=function($){var X=this;this._loaded=!1;var se=i.extend({},this.workerOptions),he=this._data;typeof he=="string"?(se.request=this.map._requestManager.transformRequest(i.browser.resolveURL(he),i.ResourceType.Source),se.request.collectResourceTiming=this._collectResourceTiming):se.data=JSON.stringify(he),this.actor.send(this.type+".loadData",se,function(ye,be){X._removed||be&&be.abandoned||(X._loaded=!0,be&&be.resourceTiming&&be.resourceTiming[X.id]&&(X._resourceTiming=be.resourceTiming[X.id].slice(0)),X.actor.send(X.type+".coalesce",{source:se.source},null),$(ye))})},j.prototype.loaded=function(){return this._loaded},j.prototype.loadTile=function($,X){var se=this,he=$.actor?"reloadTile":"loadTile";$.actor=this.actor;var ye={type:this.type,uid:$.uid,tileID:$.tileID,zoom:$.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};$.request=this.actor.send(he,ye,function(be,Ee){return delete $.request,$.unloadVectorData(),$.aborted?X(null):be?X(be):($.loadVectorData(Ee,se.map.painter,he==="reloadTile"),X(null))})},j.prototype.abortTile=function($){$.request&&($.request.cancel(),delete $.request),$.aborted=!0},j.prototype.unloadTile=function($){$.unloadVectorData(),this.actor.send("removeTile",{uid:$.uid,type:this.type,source:this.id})},j.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},j.prototype.serialize=function(){return i.extend({},this._options,{type:this.type,data:this._data})},j.prototype.hasTransition=function(){return!1},j}(i.Evented),te=i.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),Y=function(I){function j($,X,se,he){I.call(this),this.id=$,this.dispatcher=se,this.coordinates=X.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(he),this.options=X}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.load=function($,X){var se=this;this._loaded=!1,this.fire(new i.Event("dataloading",{dataType:"source"})),this.url=this.options.url,i.getImage(this.map._requestManager.transformRequest(this.url,i.ResourceType.Image),function(he,ye){se._loaded=!0,he?se.fire(new i.ErrorEvent(he)):ye&&(se.image=ye,$&&(se.coordinates=$),X&&X(),se._finishLoading())})},j.prototype.loaded=function(){return this._loaded},j.prototype.updateImage=function($){var X=this;return this.image&&$.url?(this.options.url=$.url,this.load($.coordinates,function(){X.texture=null}),this):this},j.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new i.Event("data",{dataType:"source",sourceDataType:"metadata"})))},j.prototype.onAdd=function($){this.map=$,this.load()},j.prototype.setCoordinates=function($){var X=this;this.coordinates=$;var se=$.map(i.MercatorCoordinate.fromLngLat);this.tileID=function(ye){for(var be=1/0,Ee=1/0,Ue=-1/0,Xe=-1/0,it=0,xt=ye;it<xt.length;it+=1){var Dt=xt[it];be=Math.min(be,Dt.x),Ee=Math.min(Ee,Dt.y),Ue=Math.max(Ue,Dt.x),Xe=Math.max(Xe,Dt.y)}var _t=Ue-be,Mt=Xe-Ee,vt=Math.max(_t,Mt),Nt=Math.max(0,Math.floor(-Math.log(vt)/Math.LN2)),Rt=Math.pow(2,Nt);return new i.CanonicalTileID(Nt,Math.floor((be+Ue)/2*Rt),Math.floor((Ee+Xe)/2*Rt))}(se),this.minzoom=this.maxzoom=this.tileID.z;var he=se.map(function(ye){return X.tileID.getTilePoint(ye)._round()});return this._boundsArray=new i.StructArrayLayout4i8,this._boundsArray.emplaceBack(he[0].x,he[0].y,0,0),this._boundsArray.emplaceBack(he[1].x,he[1].y,i.EXTENT,0),this._boundsArray.emplaceBack(he[3].x,he[3].y,0,i.EXTENT),this._boundsArray.emplaceBack(he[2].x,he[2].y,i.EXTENT,i.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new i.Event("data",{dataType:"source",sourceDataType:"content"})),this},j.prototype.prepare=function(){if(Object.keys(this.tiles).length!==0&&this.image){var $=this.map.painter.context,X=$.gl;for(var se in this.boundsBuffer||(this.boundsBuffer=$.createVertexBuffer(this._boundsArray,te.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new i.Texture($,this.image,X.RGBA),this.texture.bind(X.LINEAR,X.CLAMP_TO_EDGE)),this.tiles){var he=this.tiles[se];he.state!=="loaded"&&(he.state="loaded",he.texture=this.texture)}}},j.prototype.loadTile=function($,X){this.tileID&&this.tileID.equals($.tileID.canonical)?(this.tiles[String($.tileID.wrap)]=$,$.buckets={},X(null)):($.state="errored",X(null))},j.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},j.prototype.hasTransition=function(){return!1},j}(i.Evented),J=function(I){function j($,X,se,he){I.call(this,$,X,se,he),this.roundZoom=!0,this.type="video",this.options=X}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.load=function(){var $=this;this._loaded=!1;var X=this.options;this.urls=[];for(var se=0,he=X.urls;se<he.length;se+=1){var ye=he[se];this.urls.push(this.map._requestManager.transformRequest(ye,i.ResourceType.Source).url)}i.getVideo(this.urls,function(be,Ee){$._loaded=!0,be?$.fire(new i.ErrorEvent(be)):Ee&&($.video=Ee,$.video.loop=!0,$.video.addEventListener("playing",function(){$.map.triggerRepaint()}),$.map&&$.video.play(),$._finishLoading())})},j.prototype.pause=function(){this.video&&this.video.pause()},j.prototype.play=function(){this.video&&this.video.play()},j.prototype.seek=function($){if(this.video){var X=this.video.seekable;$<X.start(0)||$>X.end(0)?this.fire(new i.ErrorEvent(new i.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+X.start(0)+" and "+X.end(0)+"-second mark."))):this.video.currentTime=$}},j.prototype.getVideo=function(){return this.video},j.prototype.onAdd=function($){this.map||(this.map=$,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},j.prototype.prepare=function(){if(!(Object.keys(this.tiles).length===0||this.video.readyState<2)){var $=this.map.painter.context,X=$.gl;for(var se in this.boundsBuffer||(this.boundsBuffer=$.createVertexBuffer(this._boundsArray,te.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(X.LINEAR,X.CLAMP_TO_EDGE),X.texSubImage2D(X.TEXTURE_2D,0,0,0,X.RGBA,X.UNSIGNED_BYTE,this.video)):(this.texture=new i.Texture($,this.video,X.RGBA),this.texture.bind(X.LINEAR,X.CLAMP_TO_EDGE)),this.tiles){var he=this.tiles[se];he.state!=="loaded"&&(he.state="loaded",he.texture=this.texture)}}},j.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},j.prototype.hasTransition=function(){return this.video&&!this.video.paused},j}(Y),re=function(I){function j($,X,se,he){I.call(this,$,X,se,he),X.coordinates?Array.isArray(X.coordinates)&&X.coordinates.length===4&&!X.coordinates.some(function(ye){return!Array.isArray(ye)||ye.length!==2||ye.some(function(be){return typeof be!="number"})})||this.fire(new i.ErrorEvent(new i.ValidationError("sources."+$,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new i.ErrorEvent(new i.ValidationError("sources."+$,null,'missing required property "coordinates"'))),X.animate&&typeof X.animate!="boolean"&&this.fire(new i.ErrorEvent(new i.ValidationError("sources."+$,null,'optional "animate" property must be a boolean value'))),X.canvas?typeof X.canvas=="string"||X.canvas instanceof i.window.HTMLCanvasElement||this.fire(new i.ErrorEvent(new i.ValidationError("sources."+$,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new i.ErrorEvent(new i.ValidationError("sources."+$,null,'missing required property "canvas"'))),this.options=X,this.animate=X.animate===void 0||X.animate}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof i.window.HTMLCanvasElement?this.options.canvas:i.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new i.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},j.prototype.getCanvas=function(){return this.canvas},j.prototype.onAdd=function($){this.map=$,this.load(),this.canvas&&this.animate&&this.play()},j.prototype.onRemove=function(){this.pause()},j.prototype.prepare=function(){var $=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,$=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,$=!0),!this._hasInvalidDimensions()&&Object.keys(this.tiles).length!==0){var X=this.map.painter.context,se=X.gl;for(var he in this.boundsBuffer||(this.boundsBuffer=X.createVertexBuffer(this._boundsArray,te.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture?($||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new i.Texture(X,this.canvas,se.RGBA,{premultiply:!0}),this.tiles){var ye=this.tiles[he];ye.state!=="loaded"&&(ye.state="loaded",ye.texture=this.texture)}}},j.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},j.prototype.hasTransition=function(){return this._playing},j.prototype._hasInvalidDimensions=function(){for(var $=0,X=[this.canvas.width,this.canvas.height];$<X.length;$+=1){var 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_e(this),this.stencilFunc=new Ae(this),this.stencilOp=new ke(this),this.stencilTest=new Le(this),this.depthRange=new de(this),this.depthTest=new ve(this),this.depthFunc=new Me(this),this.blend=new we(this),this.blendFunc=new Ce(this),this.blendColor=new Fe(this),this.blendEquation=new ze(this),this.cullFace=new $e(this),this.cullFaceSide=new Ke(this),this.frontFace=new Re(this),this.program=new Ve(this),this.activeTexture=new We(this),this.viewport=new Ye(this),this.bindFramebuffer=new nt(this),this.bindRenderbuffer=new ft(this),this.bindTexture=new yt(this),this.bindVertexBuffer=new Ot(this),this.bindElementBuffer=new Tt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new at(this),this.pixelStoreUnpack=new et(this),this.pixelStoreUnpackPremultiplyAlpha=new Lt(this),this.pixelStoreUnpackFlipY=new Wt(this),this.extTextureFilterAnisotropic=I.getExtension("EXT_texture_filter_anisotropic")||I.getExtension("MOZ_EXT_texture_filter_anisotropic")||I.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=I.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=I.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(I.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=I.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=I.getExtension("EXT_disjoint_timer_query")};Pe.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Pe.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Pe.prototype.createIndexBuffer=function(I,j){return new Q(this,I,j)},Pe.prototype.createVertexBuffer=function(I,j,$){return new ie(this,I,j,$)},Pe.prototype.createRenderbuffer=function(I,j,$){var X=this.gl,se=X.createRenderbuffer();return this.bindRenderbuffer.set(se),X.renderbufferStorage(X.RENDERBUFFER,I,j,$),this.bindRenderbuffer.set(null),se},Pe.prototype.createFramebuffer=function(I,j,$){return new kt(this,I,j,$)},Pe.prototype.clear=function(I){var j=I.color,$=I.depth,X=this.gl,se=0;j&&(se|=X.COLOR_BUFFER_BIT,this.clearColor.set(j),this.colorMask.set([!0,!0,!0,!0])),$!==void 0&&(se|=X.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set($),this.depthMask.set(!0)),X.clear(se)},Pe.prototype.setCullFace=function(I){I.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(I.mode),this.frontFace.set(I.frontFace))},Pe.prototype.setDepthMode=function(I){I.func!==this.gl.ALWAYS||I.mask?(this.depthTest.set(!0),this.depthFunc.set(I.func),this.depthMask.set(I.mask),this.depthRange.set(I.range)):this.depthTest.set(!1)},Pe.prototype.setStencilMode=function(I){I.test.func!==this.gl.ALWAYS||I.mask?(this.stencilTest.set(!0),this.stencilMask.set(I.mask),this.stencilOp.set([I.fail,I.depthFail,I.pass]),this.stencilFunc.set({func:I.test.func,ref:I.ref,mask:I.test.mask})):this.stencilTest.set(!1)},Pe.prototype.setColorMode=function(I){i.deepEqual(I.blendFunction,Ie.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(I.blendFunction),this.blendColor.set(I.blendColor)),this.colorMask.set(I.mask)},Pe.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var qe=function(I){function j($,X,se){var he=this;I.call(this),this.id=$,this.dispatcher=se,this.on("data",function(ye){ye.dataType==="source"&&ye.sourceDataType==="metadata"&&(he._sourceLoaded=!0),he._sourceLoaded&&!he._paused&&ye.dataType==="source"&&ye.sourceDataType==="content"&&(he.reload(),he.transform&&he.update(he.transform))}),this.on("error",function(){he._sourceErrored=!0}),this._source=function(ye,be,Ee,Ue){var Xe=new U[be.type](ye,be,Ee,Ue);if(Xe.id!==ye)throw new Error("Expected Source id to be "+ye+" instead of "+Xe.id);return i.bindAll(["load","abort","unload","serialize","prepare"],Xe),Xe}($,X,se,this),this._tiles={},this._cache=new q(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new i.SourceFeatureState}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.onAdd=function($){this.map=$,this._maxTileCacheSize=$?$._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd($)},j.prototype.onRemove=function($){this._source&&this._source.onRemove&&this._source.onRemove($)},j.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;for(var $ in this._tiles){var X=this._tiles[$];if(X.state!=="loaded"&&X.state!=="errored")return!1}return!0},j.prototype.getSource=function(){return this._source},j.prototype.pause=function(){this._paused=!0},j.prototype.resume=function(){if(this._paused){var $=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,$&&this.reload(),this.transform&&this.update(this.transform)}},j.prototype._loadTile=function($,X){return this._source.loadTile($,X)},j.prototype._unloadTile=function($){if(this._source.unloadTile)return this._source.unloadTile($,function(){})},j.prototype._abortTile=function($){if(this._source.abortTile)return this._source.abortTile($,function(){})},j.prototype.serialize=function(){return this._source.serialize()},j.prototype.prepare=function($){for(var X in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var se=this._tiles[X];se.upload($),se.prepare(this.map.style.imageManager)}},j.prototype.getIds=function(){return i.values(this._tiles).map(function($){return $.tileID}).sort(rt).map(function($){return $.key})},j.prototype.getRenderableIds=function($){var X=this,se=[];for(var he in this._tiles)this._isIdRenderable(he,$)&&se.push(this._tiles[he]);return $?se.sort(function(ye,be){var Ee=ye.tileID,Ue=be.tileID,Xe=new i.Point(Ee.canonical.x,Ee.canonical.y)._rotate(X.transform.angle),it=new i.Point(Ue.canonical.x,Ue.canonical.y)._rotate(X.transform.angle);return Ee.overscaledZ-Ue.overscaledZ||it.y-Xe.y||it.x-Xe.x}).map(function(ye){return ye.tileID.key}):se.map(function(ye){return ye.tileID}).sort(rt).map(function(ye){return ye.key})},j.prototype.hasRenderableParent=function($){var X=this.findLoadedParent($,0);return!!X&&this._isIdRenderable(X.tileID.key)},j.prototype._isIdRenderable=function($,X){return this._tiles[$]&&this._tiles[$].hasData()&&!this._coveredTiles[$]&&(X||!this._tiles[$].holdingForFade())},j.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var $ in this._cache.reset(),this._tiles)this._tiles[$].state!=="errored"&&this._reloadTile($,"reloading")},j.prototype._reloadTile=function($,X){var se=this._tiles[$];se&&(se.state!=="loading"&&(se.state=X),this._loadTile(se,this._tileLoaded.bind(this,se,$,X)))},j.prototype._tileLoaded=function($,X,se,he){if(he)return $.state="errored",void(he.status!==404?this._source.fire(new i.ErrorEvent(he,{tile:$})):this.update(this.transform));$.timeAdded=i.browser.now(),se==="expired"&&($.refreshedUponExpiration=!0),this._setTileReloadTimer(X,$),this.getSource().type==="raster-dem"&&$.dem&&this._backfillDEM($),this._state.initializeTileState($,this.map?this.map.painter:null),this._source.fire(new i.Event("data",{dataType:"source",tile:$,coord:$.tileID}))},j.prototype._backfillDEM=function($){for(var X=this.getRenderableIds(),se=0;se<X.length;se++){var he=X[se];if($.neighboringTiles&&$.neighboringTiles[he]){var ye=this.getTileByID(he);be($,ye),be(ye,$)}}function be(Ee,Ue){Ee.needsHillshadePrepare=!0;var Xe=Ue.tileID.canonical.x-Ee.tileID.canonical.x,it=Ue.tileID.canonical.y-Ee.tileID.canonical.y,xt=Math.pow(2,Ee.tileID.canonical.z),Dt=Ue.tileID.key;Xe===0&&it===0||Math.abs(it)>1||(Math.abs(Xe)>1&&(Math.abs(Xe+xt)===1?Xe+=xt:Math.abs(Xe-xt)===1&&(Xe-=xt)),Ue.dem&&Ee.dem&&(Ee.dem.backfillBorder(Ue.dem,Xe,it),Ee.neighboringTiles&&Ee.neighboringTiles[Dt]&&(Ee.neighboringTiles[Dt].backfilled=!0)))}},j.prototype.getTile=function($){return this.getTileByID($.key)},j.prototype.getTileByID=function($){return this._tiles[$]},j.prototype._retainLoadedChildren=function($,X,se,he){for(var ye in this._tiles){var be=this._tiles[ye];if(!(he[ye]||!be.hasData()||be.tileID.overscaledZ<=X||be.tileID.overscaledZ>se)){for(var Ee=be.tileID;be&&be.tileID.overscaledZ>X+1;){var Ue=be.tileID.scaledTo(be.tileID.overscaledZ-1);(be=this._tiles[Ue.key])&&be.hasData()&&(Ee=Ue)}for(var Xe=Ee;Xe.overscaledZ>X;)if($[(Xe=Xe.scaledTo(Xe.overscaledZ-1)).key]){he[Ee.key]=Ee;break}}}},j.prototype.findLoadedParent=function($,X){if($.key in this._loadedParentTiles){var se=this._loadedParentTiles[$.key];return se&&se.tileID.overscaledZ>=X?se:null}for(var he=$.overscaledZ-1;he>=X;he--){var ye=$.scaledTo(he),be=this._getLoadedTile(ye);if(be)return be}},j.prototype._getLoadedTile=function($){var X=this._tiles[$.key];return X&&X.hasData()?X:this._cache.getByKey($.wrapped().key)},j.prototype.updateCacheSize=function($){var X=(Math.ceil($.width/this._source.tileSize)+1)*(Math.ceil($.height/this._source.tileSize)+1),se=Math.floor(5*X),he=typeof this._maxTileCacheSize=="number"?Math.min(this._maxTileCacheSize,se):se;this._cache.setMaxSize(he)},j.prototype.handleWrapJump=function($){var X=($-(this._prevLng===void 0?$:this._prevLng))/360,se=Math.round(X);if(this._prevLng=$,se){var he={};for(var ye in this._tiles){var be=this._tiles[ye];be.tileID=be.tileID.unwrapTo(be.tileID.wrap+se),he[be.tileID.key]=be}for(var Ee in this._tiles=he,this._timers)clearTimeout(this._timers[Ee]),delete this._timers[Ee];for(var Ue in this._tiles){var Xe=this._tiles[Ue];this._setTileReloadTimer(Ue,Xe)}}},j.prototype.update=function($){var X=this;if(this.transform=$,this._sourceLoaded&&!this._paused){var se;this.updateCacheSize($),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?se=$.getVisibleUnwrappedCoordinates(this._source.tileID).map(function(Tn){return new i.OverscaledTileID(Tn.canonical.z,Tn.wrap,Tn.canonical.z,Tn.canonical.x,Tn.canonical.y)}):(se=$.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(se=se.filter(function(Tn){return X._source.hasTile(Tn)}))):se=[];var he=$.coveringZoomLevel(this._source),ye=Math.max(he-j.maxOverzooming,this._source.minzoom),be=Math.max(he+j.maxUnderzooming,this._source.minzoom),Ee=this._updateRetainedTiles(se,he);if(lt(this._source.type)){for(var Ue={},Xe={},it=0,xt=Object.keys(Ee);it<xt.length;it+=1){var Dt=xt[it],_t=Ee[Dt],Mt=this._tiles[Dt];if(Mt&&!(Mt.fadeEndTime&&Mt.fadeEndTime<=i.browser.now())){var vt=this.findLoadedParent(_t,ye);vt&&(this._addTile(vt.tileID),Ue[vt.tileID.key]=vt.tileID),Xe[Dt]=_t}}for(var Nt in this._retainLoadedChildren(Xe,he,be,Ee),Ue)Ee[Nt]||(this._coveredTiles[Nt]=!0,Ee[Nt]=Ue[Nt])}for(var Rt in Ee)this._tiles[Rt].clearFadeHold();for(var Vt=0,rn=i.keysDifference(this._tiles,Ee);Vt<rn.length;Vt+=1){var dn=rn[Vt],En=this._tiles[dn];En.hasSymbolBuckets&&!En.holdingForFade()?En.setHoldDuration(this.map._fadeDuration):En.hasSymbolBuckets&&!En.symbolFadeFinished()||this._removeTile(dn)}this._updateLoadedParentTileCache()}},j.prototype.releaseSymbolFadeTiles=function(){for(var $ in this._tiles)this._tiles[$].holdingForFade()&&this._removeTile($)},j.prototype._updateRetainedTiles=function($,X){for(var se={},he={},ye=Math.max(X-j.maxOverzooming,this._source.minzoom),be=Math.max(X+j.maxUnderzooming,this._source.minzoom),Ee={},Ue=0,Xe=$;Ue<Xe.length;Ue+=1){var it=Xe[Ue],xt=this._addTile(it);se[it.key]=it,xt.hasData()||X<this._source.maxzoom&&(Ee[it.key]=it)}this._retainLoadedChildren(Ee,X,be,se);for(var Dt=0,_t=$;Dt<_t.length;Dt+=1){var Mt=_t[Dt],vt=this._tiles[Mt.key];if(!vt.hasData()){if(X+1>this._source.maxzoom){var Nt=Mt.children(this._source.maxzoom)[0],Rt=this.getTile(Nt);if(Rt&&Rt.hasData()){se[Nt.key]=Nt;continue}}else{var Vt=Mt.children(this._source.maxzoom);if(se[Vt[0].key]&&se[Vt[1].key]&&se[Vt[2].key]&&se[Vt[3].key])continue}for(var rn=vt.wasRequested(),dn=Mt.overscaledZ-1;dn>=ye;--dn){var En=Mt.scaledTo(dn);if(he[En.key]||(he[En.key]=!0,!(vt=this.getTile(En))&&rn&&(vt=this._addTile(En)),vt&&(se[En.key]=En,rn=vt.wasRequested(),vt.hasData())))break}}}return se},j.prototype._updateLoadedParentTileCache=function(){for(var $ in this._loadedParentTiles={},this._tiles){for(var X=[],se=void 0,he=this._tiles[$].tileID;he.overscaledZ>0;){if(he.key in this._loadedParentTiles){se=this._loadedParentTiles[he.key];break}X.push(he.key);var ye=he.scaledTo(he.overscaledZ-1);if(se=this._getLoadedTile(ye))break;he=ye}for(var be=0,Ee=X;be<Ee.length;be+=1){var Ue=Ee[be];this._loadedParentTiles[Ue]=se}}},j.prototype._addTile=function($){var X=this._tiles[$.key];if(X)return X;(X=this._cache.getAndRemove($))&&(this._setTileReloadTimer($.key,X),X.tileID=$,this._state.initializeTileState(X,this.map?this.map.painter:null),this._cacheTimers[$.key]&&(clearTimeout(this._cacheTimers[$.key]),delete this._cacheTimers[$.key],this._setTileReloadTimer($.key,X)));var se=!!X;return se||(X=new i.Tile($,this._source.tileSize*$.overscaleFactor()),this._loadTile(X,this._tileLoaded.bind(this,X,$.key,X.state))),X?(X.uses++,this._tiles[$.key]=X,se||this._source.fire(new i.Event("dataloading",{tile:X,coord:X.tileID,dataType:"source"})),X):null},j.prototype._setTileReloadTimer=function($,X){var se=this;$ in this._timers&&(clearTimeout(this._timers[$]),delete this._timers[$]);var he=X.getExpiryTimeout();he&&(this._timers[$]=setTimeout(function(){se._reloadTile($,"expired"),delete se._timers[$]},he))},j.prototype._removeTile=function($){var X=this._tiles[$];X&&(X.uses--,delete this._tiles[$],this._timers[$]&&(clearTimeout(this._timers[$]),delete this._timers[$]),X.uses>0||(X.hasData()&&X.state!=="reloading"?this._cache.add(X.tileID,X,X.getExpiryTimeout()):(X.aborted=!0,this._abortTile(X),this._unloadTile(X))))},j.prototype.clearTiles=function(){for(var $ in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile($);this._cache.reset()},j.prototype.tilesIn=function($,X,se){var he=this,ye=[],be=this.transform;if(!be)return ye;for(var Ee=se?be.getCameraQueryGeometry($):$,Ue=$.map(function(dn){return be.pointCoordinate(dn)}),Xe=Ee.map(function(dn){return be.pointCoordinate(dn)}),it=this.getIds(),xt=1/0,Dt=1/0,_t=-1/0,Mt=-1/0,vt=0,Nt=Xe;vt<Nt.length;vt+=1){var Rt=Nt[vt];xt=Math.min(xt,Rt.x),Dt=Math.min(Dt,Rt.y),_t=Math.max(_t,Rt.x),Mt=Math.max(Mt,Rt.y)}for(var Vt=function(dn){var En=he._tiles[it[dn]];if(!En.holdingForFade()){var Tn=En.tileID,tr=Math.pow(2,be.zoom-En.tileID.overscaledZ),er=X*En.queryPadding*i.EXTENT/En.tileSize/tr,gr=[Tn.getTilePoint(new i.MercatorCoordinate(xt,Dt)),Tn.getTilePoint(new i.MercatorCoordinate(_t,Mt))];if(gr[0].x-er<i.EXTENT&&gr[0].y-er<i.EXTENT&&gr[1].x+er>=0&&gr[1].y+er>=0){var cr=Ue.map(function(oi){return Tn.getTilePoint(oi)}),Xr=Xe.map(function(oi){return Tn.getTilePoint(oi)});ye.push({tile:En,tileID:Tn,queryGeometry:cr,cameraQueryGeometry:Xr,scale:tr})}}},rn=0;rn<it.length;rn++)Vt(rn);return ye},j.prototype.getVisibleCoordinates=function($){for(var X=this,se=this.getRenderableIds($).map(function(Ee){return X._tiles[Ee].tileID}),he=0,ye=se;he<ye.length;he+=1){var be=ye[he];be.posMatrix=this.transform.calculatePosMatrix(be.toUnwrapped())}return se},j.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(lt(this._source.type))for(var $ in this._tiles){var X=this._tiles[$];if(X.fadeEndTime!==void 0&&X.fadeEndTime>=i.browser.now())return!0}return!1},j.prototype.setFeatureState=function($,X,se){$=$||"_geojsonTileLayer",this._state.updateState($,X,se)},j.prototype.removeFeatureState=function($,X,se){$=$||"_geojsonTileLayer",this._state.removeFeatureState($,X,se)},j.prototype.getFeatureState=function($,X){return $=$||"_geojsonTileLayer",this._state.getState($,X)},j.prototype.setDependencies=function($,X,se){var he=this._tiles[$];he&&he.setDependencies(X,se)},j.prototype.reloadTilesForDependencies=function($,X){for(var se in this._tiles)this._tiles[se].hasDependency($,X)&&this._reloadTile(se,"reloading");this._cache.filter(function(he){return!he.hasDependency($,X)})},j}(i.Evented);function rt(I,j){var $=Math.abs(2*I.wrap)-+(I.wrap<0),X=Math.abs(2*j.wrap)-+(j.wrap<0);return I.overscaledZ-j.overscaledZ||X-$||j.canonical.y-I.canonical.y||j.canonical.x-I.canonical.x}function lt(I){return I==="raster"||I==="image"||I==="video"}function ot(){return new i.window.Worker(ce.workerUrl)}qe.maxOverzooming=10,qe.maxUnderzooming=3;var At="mapboxgl_preloaded_worker_pool",wt=function(){this.active={}};wt.prototype.acquire=function(I){if(!this.workers)for(this.workers=[];this.workers.length<wt.workerCount;)this.workers.push(new ot);return this.active[I]=!0,this.workers.slice()},wt.prototype.release=function(I){delete 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En=tn(dn.first.point,ye).point,Tn=tn(dn.last.point,ye).point;if(X&&!$){var tr=In(I.writingMode,En,Tn,Dt);if(tr)return tr}_t=[dn.first];for(var er=I.glyphStartIndex+1;er<Rt-1;er++)_t.push(ar(Mt*be.getoffsetX(er),vt,Nt,$,Xe,it,I.segment,Vt,rn,Ee,he,xt));_t.push(dn.last)}else{if(X&&!$){var gr=tn(it,se).point,cr=I.lineStartIndex+I.segment+1,Xr=new i.Point(Ee.getx(cr),Ee.gety(cr)),oi=tn(Xr,se),Ai=oi.signedDistanceFromCamera>0?oi.point:Wn(it,Xr,gr,1,se),Gn=In(I.writingMode,gr,Ai,Dt);if(Gn)return Gn}var Mr=ar(Mt*be.getoffsetX(I.glyphStartIndex),vt,Nt,$,Xe,it,I.segment,I.lineStartIndex,I.lineStartIndex+I.lineLength,Ee,he,xt);if(!Mr)return{notEnoughRoom:!0};_t=[Mr]}for(var si=0,Qr=_t;si<Qr.length;si+=1){var mi=Qr[si];i.addDynamicAttributes(Ue,mi.point,mi.angle)}return{}}function Wn(I,j,$,X,se){var he=tn(I.add(I.sub(j)._unit()),se).point,ye=$.sub(he);return $.add(ye._mult(X/ye.mag()))}function ar(I,j,$,X,se,he,ye,be,Ee,Ue,Xe,it){var xt=X?I-j:I+j,Dt=xt>0?1:-1,_t=0;X&&(Dt*=-1,_t=Math.PI),Dt<0&&(_t+=Math.PI);for(var Mt=Dt>0?be+ye:be+ye+1,vt=se,Nt=se,Rt=0,Vt=0,rn=Math.abs(xt),dn=[];Rt+Vt<=rn;){if((Mt+=Dt)<be||Mt>=Ee)return null;if(Nt=vt,dn.push(vt),(vt=it[Mt])===void 0){var En=new i.Point(Ue.getx(Mt),Ue.gety(Mt)),Tn=tn(En,Xe);if(Tn.signedDistanceFromCamera>0)vt=it[Mt]=Tn.point;else{var tr=Mt-Dt;vt=Wn(Rt===0?he:new i.Point(Ue.getx(tr),Ue.gety(tr)),En,Nt,rn-Rt+1,Xe)}}Rt+=Vt,Vt=Nt.dist(vt)}var er=(rn-Rt)/Vt,gr=vt.sub(Nt),cr=gr.mult(er)._add(Nt);cr._add(gr._unit()._perp()._mult($*Dt));var Xr=_t+Math.atan2(vt.y-Nt.y,vt.x-Nt.x);return dn.push(cr),{point:cr,angle:Xr,path:dn}}mn.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},mn.prototype.insert=function(I,j,$,X,se){this._forEachCell(j,$,X,se,this._insertBoxCell,this.boxUid++),this.boxKeys.push(I),this.bboxes.push(j),this.bboxes.push($),this.bboxes.push(X),this.bboxes.push(se)},mn.prototype.insertCircle=function(I,j,$,X){this._forEachCell(j-X,$-X,j+X,$+X,this._insertCircleCell,this.circleUid++),this.circleKeys.push(I),this.circles.push(j),this.circles.push($),this.circles.push(X)},mn.prototype._insertBoxCell=function(I,j,$,X,se,he){this.boxCells[se].push(he)},mn.prototype._insertCircleCell=function(I,j,$,X,se,he){this.circleCells[se].push(he)},mn.prototype._query=function(I,j,$,X,se,he){if($<0||I>this.width||X<0||j>this.height)return!se&&[];var ye=[];if(I<=0&&j<=0&&this.width<=$&&this.height<=X){if(se)return!0;for(var be=0;be<this.boxKeys.length;be++)ye.push({key:this.boxKeys[be],x1:this.bboxes[4*be],y1:this.bboxes[4*be+1],x2:this.bboxes[4*be+2],y2:this.bboxes[4*be+3]});for(var Ee=0;Ee<this.circleKeys.length;Ee++){var Ue=this.circles[3*Ee],Xe=this.circles[3*Ee+1],it=this.circles[3*Ee+2];ye.push({key:this.circleKeys[Ee],x1:Ue-it,y1:Xe-it,x2:Ue+it,y2:Xe+it})}return he?ye.filter(he):ye}var xt={hitTest:se,seenUids:{box:{},circle:{}}};return this._forEachCell(I,j,$,X,this._queryCell,ye,xt,he),se?ye.length>0:ye},mn.prototype._queryCircle=function(I,j,$,X,se){var he=I-$,ye=I+$,be=j-$,Ee=j+$;if(ye<0||he>this.width||Ee<0||be>this.height)return!X&&[];var Ue=[],Xe={hitTest:X,circle:{x:I,y:j,radius:$},seenUids:{box:{},circle:{}}};return this._forEachCell(he,be,ye,Ee,this._queryCellCircle,Ue,Xe,se),X?Ue.length>0:Ue},mn.prototype.query=function(I,j,$,X,se){return this._query(I,j,$,X,!1,se)},mn.prototype.hitTest=function(I,j,$,X,se){return this._query(I,j,$,X,!0,se)},mn.prototype.hitTestCircle=function(I,j,$,X){return this._queryCircle(I,j,$,!0,X)},mn.prototype._queryCell=function(I,j,$,X,se,he,ye,be){var Ee=ye.seenUids,Ue=this.boxCells[se];if(Ue!==null)for(var Xe=this.bboxes,it=0,xt=Ue;it<xt.length;it+=1){var Dt=xt[it];if(!Ee.box[Dt]){Ee.box[Dt]=!0;var _t=4*Dt;if(I<=Xe[_t+2]&&j<=Xe[_t+3]&&$>=Xe[_t+0]&&X>=Xe[_t+1]&&(!be||be(this.boxKeys[Dt]))){if(ye.hitTest)return he.push(!0),!0;he.push({key:this.boxKeys[Dt],x1:Xe[_t],y1:Xe[_t+1],x2:Xe[_t+2],y2:Xe[_t+3]})}}}var Mt=this.circleCells[se];if(Mt!==null)for(var vt=this.circles,Nt=0,Rt=Mt;Nt<Rt.length;Nt+=1){var Vt=Rt[Nt];if(!Ee.circle[Vt]){Ee.circle[Vt]=!0;var rn=3*Vt;if(this._circleAndRectCollide(vt[rn],vt[rn+1],vt[rn+2],I,j,$,X)&&(!be||be(this.circleKeys[Vt]))){if(ye.hitTest)return he.push(!0),!0;var dn=vt[rn],En=vt[rn+1],Tn=vt[rn+2];he.push({key:this.circleKeys[Vt],x1:dn-Tn,y1:En-Tn,x2:dn+Tn,y2:En+Tn})}}}},mn.prototype._queryCellCircle=function(I,j,$,X,se,he,ye,be){var Ee=ye.circle,Ue=ye.seenUids,Xe=this.boxCells[se];if(Xe!==null)for(var it=this.bboxes,xt=0,Dt=Xe;xt<Dt.length;xt+=1){var _t=Dt[xt];if(!Ue.box[_t]){Ue.box[_t]=!0;var Mt=4*_t;if(this._circleAndRectCollide(Ee.x,Ee.y,Ee.radius,it[Mt+0],it[Mt+1],it[Mt+2],it[Mt+3])&&(!be||be(this.boxKeys[_t])))return he.push(!0),!0}}var vt=this.circleCells[se];if(vt!==null)for(var Nt=this.circles,Rt=0,Vt=vt;Rt<Vt.length;Rt+=1){var rn=Vt[Rt];if(!Ue.circle[rn]){Ue.circle[rn]=!0;var dn=3*rn;if(this._circlesCollide(Nt[dn],Nt[dn+1],Nt[dn+2],Ee.x,Ee.y,Ee.radius)&&(!be||be(this.circleKeys[rn])))return he.push(!0),!0}}},mn.prototype._forEachCell=function(I,j,$,X,se,he,ye,be){for(var Ee=this._convertToXCellCoord(I),Ue=this._convertToYCellCoord(j),Xe=this._convertToXCellCoord($),it=this._convertToYCellCoord(X),xt=Ee;xt<=Xe;xt++)for(var Dt=Ue;Dt<=it;Dt++){var _t=this.xCellCount*Dt+xt;if(se.call(this,I,j,$,X,_t,he,ye,be))return}},mn.prototype._convertToXCellCoord=function(I){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(I*this.xScale)))},mn.prototype._convertToYCellCoord=function(I){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(I*this.yScale)))},mn.prototype._circlesCollide=function(I,j,$,X,se,he){var ye=X-I,be=se-j,Ee=$+he;return Ee*Ee>ye*ye+be*be},mn.prototype._circleAndRectCollide=function(I,j,$,X,se,he,ye){var be=(he-X)/2,Ee=Math.abs(I-(X+be));if(Ee>be+$)return!1;var Ue=(ye-se)/2,Xe=Math.abs(j-(se+Ue));if(Xe>Ue+$)return!1;if(Ee<=be||Xe<=Ue)return!0;var it=Ee-be,xt=Xe-Ue;return it*it+xt*xt<=$*$};var Dr=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function yr(I,j){for(var $=0;$<I;$++){var X=j.length;j.resize(X+4),j.float32.set(Dr,3*X)}}function Sr(I,j,$){var X=j[0],se=j[1];return I[0]=$[0]*X+$[4]*se+$[12],I[1]=$[1]*X+$[5]*se+$[13],I[3]=$[3]*X+$[7]*se+$[15],I}var Kn=function(I,j,$){j===void 0&&(j=new mn(I.width+200,I.height+200,25)),$===void 0&&($=new mn(I.width+200,I.height+200,25)),this.transform=I,this.grid=j,this.ignoredGrid=$,this.pitchfactor=Math.cos(I._pitch)*I.cameraToCenterDistance,this.screenRightBoundary=I.width+100,this.screenBottomBoundary=I.height+100,this.gridRightBoundary=I.width+200,this.gridBottomBoundary=I.height+200};function Dn(I,j,$){return j*(i.EXTENT/(I.tileSize*Math.pow(2,$-I.tileID.overscaledZ)))}Kn.prototype.placeCollisionBox=function(I,j,$,X,se){var he=this.projectAndGetPerspectiveRatio(X,I.anchorPointX,I.anchorPointY),ye=$*he.perspectiveRatio,be=I.x1*ye+he.point.x,Ee=I.y1*ye+he.point.y,Ue=I.x2*ye+he.point.x,Xe=I.y2*ye+he.point.y;return!this.isInsideGrid(be,Ee,Ue,Xe)||!j&&this.grid.hitTest(be,Ee,Ue,Xe,se)?{box:[],offscreen:!1}:{box:[be,Ee,Ue,Xe],offscreen:this.isOffscreen(be,Ee,Ue,Xe)}},Kn.prototype.placeCollisionCircles=function(I,j,$,X,se,he,ye,be,Ee,Ue,Xe,it,xt){var Dt=[],_t=new i.Point(j.anchorX,j.anchorY),Mt=tn(_t,he),vt=nn(this.transform.cameraToCenterDistance,Mt.signedDistanceFromCamera),Nt=(Ue?se/vt:se*vt)/i.ONE_EM,Rt=tn(_t,ye).point,Vt=bn(Nt,X,j.lineOffsetX*Nt,j.lineOffsetY*Nt,!1,Rt,_t,j,$,ye,{}),rn=!1,dn=!1,En=!0;if(Vt){for(var Tn=.5*it*vt+xt,tr=new i.Point(-100,-100),er=new i.Point(this.screenRightBoundary,this.screenBottomBoundary),gr=new qt,cr=Vt.first,Xr=Vt.last,oi=[],Ai=cr.path.length-1;Ai>=1;Ai--)oi.push(cr.path[Ai]);for(var Gn=1;Gn<Xr.path.length;Gn++)oi.push(Xr.path[Gn]);var Mr=2.5*Tn;if(be){var si=oi.map(function(Ls){return tn(Ls,be)});oi=si.some(function(Ls){return Ls.signedDistanceFromCamera<=0})?[]:si.map(function(Ls){return Ls.point})}var Qr=[];if(oi.length>0){for(var mi=oi[0].clone(),Mi=oi[0].clone(),Zi=1;Zi<oi.length;Zi++)mi.x=Math.min(mi.x,oi[Zi].x),mi.y=Math.min(mi.y,oi[Zi].y),Mi.x=Math.max(Mi.x,oi[Zi].x),Mi.y=Math.max(Mi.y,oi[Zi].y);Qr=mi.x>=tr.x&&Mi.x<=er.x&&mi.y>=tr.y&&Mi.y<=er.y?[oi]:Mi.x<tr.x||mi.x>er.x||Mi.y<tr.y||mi.y>er.y?[]:i.clipLine([oi],tr.x,tr.y,er.x,er.y)}for(var fi=0,zi=Qr;fi<zi.length;fi+=1){var Oi=zi[fi];gr.reset(Oi,.25*Tn);var ta=0;ta=gr.length<=.5*Tn?1:Math.ceil(gr.paddedLength/Mr)+1;for(var Ni=0;Ni<ta;Ni++){var na=Ni/Math.max(ta-1,1),pa=gr.lerp(na),Ga=pa.x+100,ko=pa.y+100;Dt.push(Ga,ko,Tn,0);var To=Ga-Tn,Ha=ko-Tn,po=Ga+Tn,ro=ko+Tn;if(En=En&&this.isOffscreen(To,Ha,po,ro),dn=dn||this.isInsideGrid(To,Ha,po,ro),!I&&this.grid.hitTestCircle(Ga,ko,Tn,Xe)&&(rn=!0,!Ee))return{circles:[],offscreen:!1,collisionDetected:rn}}}}return{circles:!Ee&&rn||!dn?[]:Dt,offscreen:En,collisionDetected:rn}},Kn.prototype.queryRenderedSymbols=function(I){if(I.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};for(var j=[],$=1/0,X=1/0,se=-1/0,he=-1/0,ye=0,be=I;ye<be.length;ye+=1){var Ee=be[ye],Ue=new i.Point(Ee.x+100,Ee.y+100);$=Math.min($,Ue.x),X=Math.min(X,Ue.y),se=Math.max(se,Ue.x),he=Math.max(he,Ue.y),j.push(Ue)}for(var Xe={},it={},xt=0,Dt=this.grid.query($,X,se,he).concat(this.ignoredGrid.query($,X,se,he));xt<Dt.length;xt+=1){var _t=Dt[xt],Mt=_t.key;if(Xe[Mt.bucketInstanceId]===void 0&&(Xe[Mt.bucketInstanceId]={}),!Xe[Mt.bucketInstanceId][Mt.featureIndex]){var vt=[new i.Point(_t.x1,_t.y1),new i.Point(_t.x2,_t.y1),new i.Point(_t.x2,_t.y2),new i.Point(_t.x1,_t.y2)];i.polygonIntersectsPolygon(j,vt)&&(Xe[Mt.bucketInstanceId][Mt.featureIndex]=!0,it[Mt.bucketInstanceId]===void 0&&(it[Mt.bucketInstanceId]=[]),it[Mt.bucketInstanceId].push(Mt.featureIndex))}}return it},Kn.prototype.insertCollisionBox=function(I,j,$,X,se){var he={bucketInstanceId:$,featureIndex:X,collisionGroupID:se};(j?this.ignoredGrid:this.grid).insert(he,I[0],I[1],I[2],I[3])},Kn.prototype.insertCollisionCircles=function(I,j,$,X,se){for(var he=j?this.ignoredGrid:this.grid,ye={bucketInstanceId:$,featureIndex:X,collisionGroupID:se},be=0;be<I.length;be+=4)he.insertCircle(ye,I[be],I[be+1],I[be+2])},Kn.prototype.projectAndGetPerspectiveRatio=function(I,j,$){var X=[j,$,0,1];return Sr(X,X,I),{point:new i.Point((X[0]/X[3]+1)/2*this.transform.width+100,(-X[1]/X[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/X[3]*.5}},Kn.prototype.isOffscreen=function(I,j,$,X){return $<100||I>=this.screenRightBoundary||X<100||j>this.screenBottomBoundary},Kn.prototype.isInsideGrid=function(I,j,$,X){return $>=0&&I<this.gridRightBoundary&&X>=0&&j<this.gridBottomBoundary},Kn.prototype.getViewportMatrix=function(){var I=i.identity([]);return i.translate(I,I,[-100,-100,0]),I};var lr=function(I,j,$,X){this.opacity=I?Math.max(0,Math.min(1,I.opacity+(I.placed?j:-j))):X&&$?1:0,this.placed=$};lr.prototype.isHidden=function(){return this.opacity===0&&!this.placed};var Yr=function(I,j,$,X,se){this.text=new lr(I?I.text:null,j,$,se),this.icon=new lr(I?I.icon:null,j,X,se)};Yr.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var Mn=function(I,j,$){this.text=I,this.icon=j,this.skipFade=$},rr=function(){this.invProjMatrix=i.create(),this.viewportMatrix=i.create(),this.circles=[]},nr=function(I,j,$,X,se){this.bucketInstanceId=I,this.featureIndex=j,this.sourceLayerIndex=$,this.bucketIndex=X,this.tileID=se},Bn=function(I){this.crossSourceCollisions=I,this.maxGroupID=0,this.collisionGroups={}};function Nr(I,j,$,X,se){var he=i.getAnchorAlignment(I),ye=-(he.horizontalAlign-.5)*j,be=-(he.verticalAlign-.5)*$,Ee=i.evaluateVariableOffset(I,X);return new i.Point(ye+Ee[0]*se,be+Ee[1]*se)}function Gr(I,j,$,X,se,he){var ye=I.x1,be=I.x2,Ee=I.y1,Ue=I.y2,Xe=I.anchorPointX,it=I.anchorPointY,xt=new i.Point(j,$);return X&&xt._rotate(se?he:-he),{x1:ye+xt.x,y1:Ee+xt.y,x2:be+xt.x,y2:Ue+xt.y,anchorPointX:Xe,anchorPointY:it}}Bn.prototype.get=function(I){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[I]){var j=++this.maxGroupID;this.collisionGroups[I]={ID:j,predicate:function($){return $.collisionGroupID===j}}}return this.collisionGroups[I]};var pr=function(I,j,$,X){this.transform=I.clone(),this.collisionIndex=new Kn(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=j,this.retainedQueryData={},this.collisionGroups=new Bn($),this.collisionCircleArrays={},this.prevPlacement=X,X&&(X.prevPlacement=void 0),this.placedOrientations={}};function qr(I,j,$,X,se){I.emplaceBack(j?1:0,$?1:0,X||0,se||0),I.emplaceBack(j?1:0,$?1:0,X||0,se||0),I.emplaceBack(j?1:0,$?1:0,X||0,se||0),I.emplaceBack(j?1:0,$?1:0,X||0,se||0)}pr.prototype.getBucketParts=function(I,j,$,X){var se=$.getBucket(j),he=$.latestFeatureIndex;if(se&&he&&j.id===se.layerIds[0]){var ye=$.collisionBoxArray,be=se.layers[0].layout,Ee=Math.pow(2,this.transform.zoom-$.tileID.overscaledZ),Ue=$.tileSize/i.EXTENT,Xe=this.transform.calculatePosMatrix($.tileID.toUnwrapped()),it=be.get("text-pitch-alignment")==="map",xt=be.get("text-rotation-alignment")==="map",Dt=Dn($,1,this.transform.zoom),_t=Fn(Xe,it,xt,this.transform,Dt),Mt=null;if(it){var vt=pn(Xe,it,xt,this.transform,Dt);Mt=i.multiply([],this.transform.labelPlaneMatrix,vt)}this.retainedQueryData[se.bucketInstanceId]=new nr(se.bucketInstanceId,he,se.sourceLayerIndex,se.index,$.tileID);var Nt={bucket:se,layout:be,posMatrix:Xe,textLabelPlaneMatrix:_t,labelToScreenMatrix:Mt,scale:Ee,textPixelRatio:Ue,holdingForFade:$.holdingForFade(),collisionBoxArray:ye,partiallyEvaluatedTextSize:i.evaluateSizeForZoom(se.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(se.sourceID)};if(X)for(var Rt=0,Vt=se.sortKeyRanges;Rt<Vt.length;Rt+=1){var rn=Vt[Rt],dn=rn.sortKey,En=rn.symbolInstanceStart,Tn=rn.symbolInstanceEnd;I.push({sortKey:dn,symbolInstanceStart:En,symbolInstanceEnd:Tn,parameters:Nt})}else I.push({symbolInstanceStart:0,symbolInstanceEnd:se.symbolInstances.length,parameters:Nt})}},pr.prototype.attemptAnchorPlacement=function(I,j,$,X,se,he,ye,be,Ee,Ue,Xe,it,xt,Dt,_t){var Mt,vt=[it.textOffset0,it.textOffset1],Nt=Nr(I,$,X,vt,se),Rt=this.collisionIndex.placeCollisionBox(Gr(j,Nt.x,Nt.y,he,ye,this.transform.angle),Xe,be,Ee,Ue.predicate);if(!(_t&&this.collisionIndex.placeCollisionBox(Gr(_t,Nt.x,Nt.y,he,ye,this.transform.angle),Xe,be,Ee,Ue.predicate).box.length===0)&&Rt.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[it.crossTileID]&&this.prevPlacement.placements[it.crossTileID]&&this.prevPlacement.placements[it.crossTileID].text&&(Mt=this.prevPlacement.variableOffsets[it.crossTileID].anchor),this.variableOffsets[it.crossTileID]={textOffset:vt,width:$,height:X,anchor:I,textBoxScale:se,prevAnchor:Mt},this.markUsedJustification(xt,I,it,Dt),xt.allowVerticalPlacement&&(this.markUsedOrientation(xt,Dt,it),this.placedOrientations[it.crossTileID]=Dt),{shift:Nt,placedGlyphBoxes:Rt}},pr.prototype.placeLayerBucketPart=function(I,j,$){var X=this,se=I.parameters,he=se.bucket,ye=se.layout,be=se.posMatrix,Ee=se.textLabelPlaneMatrix,Ue=se.labelToScreenMatrix,Xe=se.textPixelRatio,it=se.holdingForFade,xt=se.collisionBoxArray,Dt=se.partiallyEvaluatedTextSize,_t=se.collisionGroup,Mt=ye.get("text-optional"),vt=ye.get("icon-optional"),Nt=ye.get("text-allow-overlap"),Rt=ye.get("icon-allow-overlap"),Vt=ye.get("text-rotation-alignment")==="map",rn=ye.get("text-pitch-alignment")==="map",dn=ye.get("icon-text-fit")!=="none",En=ye.get("symbol-z-order")==="viewport-y",Tn=Nt&&(Rt||!he.hasIconData()||vt),tr=Rt&&(Nt||!he.hasTextData()||Mt);!he.collisionArrays&&xt&&he.deserializeCollisionBoxes(xt);var er=function(Gn,Mr){if(!j[Gn.crossTileID])if(it)X.placements[Gn.crossTileID]=new Mn(!1,!1,!1);else{var si,Qr=!1,mi=!1,Mi=!0,Zi=null,fi={box:null,offscreen:null},zi={box:null,offscreen:null},Oi=null,ta=null,Ni=0,na=0,pa=0;Mr.textFeatureIndex?Ni=Mr.textFeatureIndex:Gn.useRuntimeCollisionCircles&&(Ni=Gn.featureIndex),Mr.verticalTextFeatureIndex&&(na=Mr.verticalTextFeatureIndex);var Ga=Mr.textBox;if(Ga){var ko=function(go){var Ws=i.WritingMode.horizontal;if(he.allowVerticalPlacement&&!go&&X.prevPlacement){var Ys=X.prevPlacement.placedOrientations[Gn.crossTileID];Ys&&(X.placedOrientations[Gn.crossTileID]=Ys,Ws=Ys,X.markUsedOrientation(he,Ws,Gn))}return Ws},To=function(go,Ws){if(he.allowVerticalPlacement&&Gn.numVerticalGlyphVertices>0&&Mr.verticalTextBox)for(var Ys=0,vg=he.writingModes;Ys<vg.length&&(vg[Ys]===i.WritingMode.vertical?(fi=Ws(),zi=fi):fi=go(),!(fi&&fi.box&&fi.box.length));Ys+=1);else fi=go()};if(ye.get("text-variable-anchor")){var Ha=ye.get("text-variable-anchor");if(X.prevPlacement&&X.prevPlacement.variableOffsets[Gn.crossTileID]){var po=X.prevPlacement.variableOffsets[Gn.crossTileID];Ha.indexOf(po.anchor)>0&&(Ha=Ha.filter(function(go){return go!==po.anchor})).unshift(po.anchor)}var ro=function(go,Ws,Ys){for(var vg=go.x2-go.x1,k5=go.y2-go.y1,Fl=Gn.textBoxScale,C1=dn&&!Rt?Ws:null,Np={box:[],offscreen:!1},T5=Nt?2*Ha.length:Ha.length,Mh=0;Mh<T5;++Mh){var M5=Ha[Mh%Ha.length],xg=Mh>=Ha.length,L1=X.attemptAnchorPlacement(M5,go,vg,k5,Fl,Vt,rn,Xe,be,_t,xg,Gn,he,Ys,C1);if(L1&&(Np=L1.placedGlyphBoxes)&&Np.box&&Np.box.length){Qr=!0,Zi=L1.shift;break}}return Np};To(function(){return ro(Ga,Mr.iconBox,i.WritingMode.horizontal)},function(){var go=Mr.verticalTextBox,Ws=fi&&fi.box&&fi.box.length;return he.allowVerticalPlacement&&!Ws&&Gn.numVerticalGlyphVertices>0&&go?ro(go,Mr.verticalIconBox,i.WritingMode.vertical):{box:null,offscreen:null}}),fi&&(Qr=fi.box,Mi=fi.offscreen);var Ls=ko(fi&&fi.box);if(!Qr&&X.prevPlacement){var Ho=X.prevPlacement.variableOffsets[Gn.crossTileID];Ho&&(X.variableOffsets[Gn.crossTileID]=Ho,X.markUsedJustification(he,Ho.anchor,Gn,Ls))}}else{var nl=function(go,Ws){var Ys=X.collisionIndex.placeCollisionBox(go,Nt,Xe,be,_t.predicate);return Ys&&Ys.box&&Ys.box.length&&(X.markUsedOrientation(he,Ws,Gn),X.placedOrientations[Gn.crossTileID]=Ws),Ys};To(function(){return nl(Ga,i.WritingMode.horizontal)},function(){var go=Mr.verticalTextBox;return he.allowVerticalPlacement&&Gn.numVerticalGlyphVertices>0&&go?nl(go,i.WritingMode.vertical):{box:null,offscreen:null}}),ko(fi&&fi.box&&fi.box.length)}}if(Qr=(si=fi)&&si.box&&si.box.length>0,Mi=si&&si.offscreen,Gn.useRuntimeCollisionCircles){var xc=he.text.placedSymbolArray.get(Gn.centerJustifiedTextSymbolIndex),Ff=i.evaluateSizeForFeature(he.textSizeData,Dt,xc),Fp=ye.get("text-padding"),_d=Gn.collisionCircleDiameter;Oi=X.collisionIndex.placeCollisionCircles(Nt,xc,he.lineVertexArray,he.glyphOffsetArray,Ff,be,Ee,Ue,$,rn,_t.predicate,_d,Fp),Qr=Nt||Oi.circles.length>0&&!Oi.collisionDetected,Mi=Mi&&Oi.offscreen}if(Mr.iconFeatureIndex&&(pa=Mr.iconFeatureIndex),Mr.iconBox){var wd=function(go){var Ws=dn&&Zi?Gr(go,Zi.x,Zi.y,Vt,rn,X.transform.angle):go;return X.collisionIndex.placeCollisionBox(Ws,Rt,Xe,be,_t.predicate)};mi=zi&&zi.box&&zi.box.length&&Mr.verticalIconBox?(ta=wd(Mr.verticalIconBox)).box.length>0:(ta=wd(Mr.iconBox)).box.length>0,Mi=Mi&&ta.offscreen}var Ds=Mt||Gn.numHorizontalGlyphVertices===0&&Gn.numVerticalGlyphVertices===0,Th=vt||Gn.numIconVertices===0;if(Ds||Th?Th?Ds||(mi=mi&&Qr):Qr=mi&&Qr:mi=Qr=mi&&Qr,Qr&&si&&si.box&&(zi&&zi.box&&na?X.collisionIndex.insertCollisionBox(si.box,ye.get("text-ignore-placement"),he.bucketInstanceId,na,_t.ID):X.collisionIndex.insertCollisionBox(si.box,ye.get("text-ignore-placement"),he.bucketInstanceId,Ni,_t.ID)),mi&&ta&&X.collisionIndex.insertCollisionBox(ta.box,ye.get("icon-ignore-placement"),he.bucketInstanceId,pa,_t.ID),Oi&&(Qr&&X.collisionIndex.insertCollisionCircles(Oi.circles,ye.get("text-ignore-placement"),he.bucketInstanceId,Ni,_t.ID),$)){var Rp=he.bucketInstanceId,ru=X.collisionCircleArrays[Rp];ru===void 0&&(ru=X.collisionCircleArrays[Rp]=new rr);for(var zp=0;zp<Oi.circles.length;zp+=4)ru.circles.push(Oi.circles[zp+0]),ru.circles.push(Oi.circles[zp+1]),ru.circles.push(Oi.circles[zp+2]),ru.circles.push(Oi.collisionDetected?1:0)}X.placements[Gn.crossTileID]=new Mn(Qr||Tn,mi||tr,Mi||he.justReloaded),j[Gn.crossTileID]=!0}};if(En)for(var gr=he.getSortedSymbolIndexes(this.transform.angle),cr=gr.length-1;cr>=0;--cr){var Xr=gr[cr];er(he.symbolInstances.get(Xr),he.collisionArrays[Xr])}else for(var oi=I.symbolInstanceStart;oi<I.symbolInstanceEnd;oi++)er(he.symbolInstances.get(oi),he.collisionArrays[oi]);if($&&he.bucketInstanceId in this.collisionCircleArrays){var Ai=this.collisionCircleArrays[he.bucketInstanceId];i.invert(Ai.invProjMatrix,be),Ai.viewportMatrix=this.collisionIndex.getViewportMatrix()}he.justReloaded=!1},pr.prototype.markUsedJustification=function(I,j,$,X){var se,he={left:$.leftJustifiedTextSymbolIndex,center:$.centerJustifiedTextSymbolIndex,right:$.rightJustifiedTextSymbolIndex};se=X===i.WritingMode.vertical?$.verticalPlacedTextSymbolIndex:he[i.getAnchorJustification(j)];for(var ye=0,be=[$.leftJustifiedTextSymbolIndex,$.centerJustifiedTextSymbolIndex,$.rightJustifiedTextSymbolIndex,$.verticalPlacedTextSymbolIndex];ye<be.length;ye+=1){var Ee=be[ye];Ee>=0&&(I.text.placedSymbolArray.get(Ee).crossTileID=se>=0&&Ee!==se?0:$.crossTileID)}},pr.prototype.markUsedOrientation=function(I,j,$){for(var X=j===i.WritingMode.horizontal||j===i.WritingMode.horizontalOnly?j:0,se=j===i.WritingMode.vertical?j:0,he=0,ye=[$.leftJustifiedTextSymbolIndex,$.centerJustifiedTextSymbolIndex,$.rightJustifiedTextSymbolIndex];he<ye.length;he+=1){var be=ye[he];I.text.placedSymbolArray.get(be).placedOrientation=X}$.verticalPlacedTextSymbolIndex&&(I.text.placedSymbolArray.get($.verticalPlacedTextSymbolIndex).placedOrientation=se)},pr.prototype.commit=function(I){this.commitTime=I,this.zoomAtLastRecencyCheck=this.transform.zoom;var j=this.prevPlacement,$=!1;this.prevZoomAdjustment=j?j.zoomAdjustment(this.transform.zoom):0;var X=j?j.symbolFadeChange(I):1,se=j?j.opacities:{},he=j?j.variableOffsets:{},ye=j?j.placedOrientations:{};for(var be in this.placements){var Ee=this.placements[be],Ue=se[be];Ue?(this.opacities[be]=new Yr(Ue,X,Ee.text,Ee.icon),$=$||Ee.text!==Ue.text.placed||Ee.icon!==Ue.icon.placed):(this.opacities[be]=new Yr(null,X,Ee.text,Ee.icon,Ee.skipFade),$=$||Ee.text||Ee.icon)}for(var Xe in se){var it=se[Xe];if(!this.opacities[Xe]){var xt=new Yr(it,X,!1,!1);xt.isHidden()||(this.opacities[Xe]=xt,$=$||it.text.placed||it.icon.placed)}}for(var Dt in he)this.variableOffsets[Dt]||!this.opacities[Dt]||this.opacities[Dt].isHidden()||(this.variableOffsets[Dt]=he[Dt]);for(var _t in ye)this.placedOrientations[_t]||!this.opacities[_t]||this.opacities[_t].isHidden()||(this.placedOrientations[_t]=ye[_t]);$?this.lastPlacementChangeTime=I:typeof this.lastPlacementChangeTime!="number"&&(this.lastPlacementChangeTime=j?j.lastPlacementChangeTime:I)},pr.prototype.updateLayerOpacities=function(I,j){for(var $={},X=0,se=j;X<se.length;X+=1){var he=se[X],ye=he.getBucket(I);ye&&he.latestFeatureIndex&&I.id===ye.layerIds[0]&&this.updateBucketOpacities(ye,$,he.collisionBoxArray)}},pr.prototype.updateBucketOpacities=function(I,j,$){var X=this;I.hasTextData()&&I.text.opacityVertexArray.clear(),I.hasIconData()&&I.icon.opacityVertexArray.clear(),I.hasIconCollisionBoxData()&&I.iconCollisionBox.collisionVertexArray.clear(),I.hasTextCollisionBoxData()&&I.textCollisionBox.collisionVertexArray.clear();var se=I.layers[0].layout,he=new Yr(null,0,!1,!1,!0),ye=se.get("text-allow-overlap"),be=se.get("icon-allow-overlap"),Ee=se.get("text-variable-anchor"),Ue=se.get("text-rotation-alignment")==="map",Xe=se.get("text-pitch-alignment")==="map",it=se.get("icon-text-fit")!=="none",xt=new Yr(null,0,ye&&(be||!I.hasIconData()||se.get("icon-optional")),be&&(ye||!I.hasTextData()||se.get("text-optional")),!0);!I.collisionArrays&&$&&(I.hasIconCollisionBoxData()||I.hasTextCollisionBoxData())&&I.deserializeCollisionBoxes($);for(var Dt=function(Nt,Rt,Vt){for(var rn=0;rn<Rt/4;rn++)Nt.opacityVertexArray.emplaceBack(Vt)},_t=function(Nt){var Rt=I.symbolInstances.get(Nt),Vt=Rt.numHorizontalGlyphVertices,rn=Rt.numVerticalGlyphVertices,dn=Rt.crossTileID,En=j[dn],Tn=X.opacities[dn];En?Tn=he:Tn||(Tn=xt,X.opacities[dn]=Tn),j[dn]=!0;var tr=Vt>0||rn>0,er=Rt.numIconVertices>0,gr=X.placedOrientations[Rt.crossTileID],cr=gr===i.WritingMode.vertical,Xr=gr===i.WritingMode.horizontal||gr===i.WritingMode.horizontalOnly;if(tr){var oi=Un(Tn.text),Ai=cr?Nn:oi;Dt(I.text,Vt,Ai);var Gn=Xr?Nn:oi;Dt(I.text,rn,Gn);var Mr=Tn.text.isHidden();[Rt.rightJustifiedTextSymbolIndex,Rt.centerJustifiedTextSymbolIndex,Rt.leftJustifiedTextSymbolIndex].forEach(function(pa){pa>=0&&(I.text.placedSymbolArray.get(pa).hidden=Mr||cr?1:0)}),Rt.verticalPlacedTextSymbolIndex>=0&&(I.text.placedSymbolArray.get(Rt.verticalPlacedTextSymbolIndex).hidden=Mr||Xr?1:0);var si=X.variableOffsets[Rt.crossTileID];si&&X.markUsedJustification(I,si.anchor,Rt,gr);var Qr=X.placedOrientations[Rt.crossTileID];Qr&&(X.markUsedJustification(I,"left",Rt,Qr),X.markUsedOrientation(I,Qr,Rt))}if(er){var mi=Un(Tn.icon),Mi=!(it&&Rt.verticalPlacedIconSymbolIndex&&cr);if(Rt.placedIconSymbolIndex>=0){var Zi=Mi?mi:Nn;Dt(I.icon,Rt.numIconVertices,Zi),I.icon.placedSymbolArray.get(Rt.placedIconSymbolIndex).hidden=Tn.icon.isHidden()}if(Rt.verticalPlacedIconSymbolIndex>=0){var fi=Mi?Nn:mi;Dt(I.icon,Rt.numVerticalIconVertices,fi),I.icon.placedSymbolArray.get(Rt.verticalPlacedIconSymbolIndex).hidden=Tn.icon.isHidden()}}if(I.hasIconCollisionBoxData()||I.hasTextCollisionBoxData()){var zi=I.collisionArrays[Nt];if(zi){var Oi=new i.Point(0,0);if(zi.textBox||zi.verticalTextBox){var ta=!0;if(Ee){var Ni=X.variableOffsets[dn];Ni?(Oi=Nr(Ni.anchor,Ni.width,Ni.height,Ni.textOffset,Ni.textBoxScale),Ue&&Oi._rotate(Xe?X.transform.angle:-X.transform.angle)):ta=!1}zi.textBox&&qr(I.textCollisionBox.collisionVertexArray,Tn.text.placed,!ta||cr,Oi.x,Oi.y),zi.verticalTextBox&&qr(I.textCollisionBox.collisionVertexArray,Tn.text.placed,!ta||Xr,Oi.x,Oi.y)}var na=!!(!Xr&&zi.verticalIconBox);zi.iconBox&&qr(I.iconCollisionBox.collisionVertexArray,Tn.icon.placed,na,it?Oi.x:0,it?Oi.y:0),zi.verticalIconBox&&qr(I.iconCollisionBox.collisionVertexArray,Tn.icon.placed,!na,it?Oi.x:0,it?Oi.y:0)}}},Mt=0;Mt<I.symbolInstances.length;Mt++)_t(Mt);if(I.sortFeatures(this.transform.angle),this.retainedQueryData[I.bucketInstanceId]&&(this.retainedQueryData[I.bucketInstanceId].featureSortOrder=I.featureSortOrder),I.hasTextData()&&I.text.opacityVertexBuffer&&I.text.opacityVertexBuffer.updateData(I.text.opacityVertexArray),I.hasIconData()&&I.icon.opacityVertexBuffer&&I.icon.opacityVertexBuffer.updateData(I.icon.opacityVertexArray),I.hasIconCollisionBoxData()&&I.iconCollisionBox.collisionVertexBuffer&&I.iconCollisionBox.collisionVertexBuffer.updateData(I.iconCollisionBox.collisionVertexArray),I.hasTextCollisionBoxData()&&I.textCollisionBox.collisionVertexBuffer&&I.textCollisionBox.collisionVertexBuffer.updateData(I.textCollisionBox.collisionVertexArray),I.bucketInstanceId in this.collisionCircleArrays){var vt=this.collisionCircleArrays[I.bucketInstanceId];I.placementInvProjMatrix=vt.invProjMatrix,I.placementViewportMatrix=vt.viewportMatrix,I.collisionCircleArray=vt.circles,delete this.collisionCircleArrays[I.bucketInstanceId]}},pr.prototype.symbolFadeChange=function(I){return this.fadeDuration===0?1:(I-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},pr.prototype.zoomAdjustment=function(I){return Math.max(0,(this.transform.zoom-I)/1.5)},pr.prototype.hasTransitions=function(I){return this.stale||I-this.lastPlacementChangeTime<this.fadeDuration},pr.prototype.stillRecent=function(I,j){var $=this.zoomAtLastRecencyCheck===j?1-this.zoomAdjustment(j):1;return this.zoomAtLastRecencyCheck=j,this.commitTime+this.fadeDuration*$>I},pr.prototype.setStale=function(){this.stale=!0};var _i=Math.pow(2,25),cn=Math.pow(2,24),jn=Math.pow(2,17),jt=Math.pow(2,16),fn=Math.pow(2,9),vn=Math.pow(2,8),Hn=Math.pow(2,1);function Un(I){if(I.opacity===0&&!I.placed)return 0;if(I.opacity===1&&I.placed)return 4294967295;var j=I.placed?1:0,$=Math.floor(127*I.opacity);return $*_i+j*cn+$*jn+j*jt+$*fn+j*vn+$*Hn+j}var Nn=0,Rn=function(I){this._sortAcrossTiles=I.layout.get("symbol-z-order")!=="viewport-y"&&I.layout.get("symbol-sort-key").constantOr(1)!==void 0,this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Rn.prototype.continuePlacement=function(I,j,$,X,se){for(var he=this._bucketParts;this._currentTileIndex<I.length;){var ye=I[this._currentTileIndex];if(j.getBucketParts(he,X,ye,this._sortAcrossTiles),this._currentTileIndex++,se())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,he.sort(function(Ee,Ue){return Ee.sortKey-Ue.sortKey}));this._currentPartIndex<he.length;){var be=he[this._currentPartIndex];if(j.placeLayerBucketPart(be,this._seenCrossTileIDs,$),this._currentPartIndex++,se())return!0}return!1};var wn=function(I,j,$,X,se,he,ye){this.placement=new pr(I,se,he,ye),this._currentPlacementIndex=j.length-1,this._forceFullPlacement=$,this._showCollisionBoxes=X,this._done=!1};wn.prototype.isDone=function(){return this._done},wn.prototype.continuePlacement=function(I,j,$){for(var X=this,se=i.browser.now(),he=function(){var Ee=i.browser.now()-se;return!X._forceFullPlacement&&Ee>2};this._currentPlacementIndex>=0;){var ye=j[I[this._currentPlacementIndex]],be=this.placement.collisionIndex.transform.zoom;if(ye.type==="symbol"&&(!ye.minzoom||ye.minzoom<=be)&&(!ye.maxzoom||ye.maxzoom>be)){if(this._inProgressLayer||(this._inProgressLayer=new Rn(ye)),this._inProgressLayer.continuePlacement($[ye.source],this.placement,this._showCollisionBoxes,ye,he))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},wn.prototype.commit=function(I){return this.placement.commit(I),this.placement};var An=512/i.EXTENT/2,kn=function(I,j,$){this.tileID=I,this.indexedSymbolInstances={},this.bucketInstanceId=$;for(var X=0;X<j.length;X++){var se=j.get(X),he=se.key;this.indexedSymbolInstances[he]||(this.indexedSymbolInstances[he]=[]),this.indexedSymbolInstances[he].push({crossTileID:se.crossTileID,coord:this.getScaledCoordinates(se,I)})}};kn.prototype.getScaledCoordinates=function(I,j){var $=j.canonical.z-this.tileID.canonical.z,X=An/Math.pow(2,$);return{x:Math.floor((j.canonical.x*i.EXTENT+I.anchorX)*X),y:Math.floor((j.canonical.y*i.EXTENT+I.anchorY)*X)}},kn.prototype.findMatches=function(I,j,$){for(var X=this.tileID.canonical.z<j.canonical.z?1:Math.pow(2,this.tileID.canonical.z-j.canonical.z),se=0;se<I.length;se++){var he=I.get(se);if(!he.crossTileID){var ye=this.indexedSymbolInstances[he.key];if(ye)for(var be=this.getScaledCoordinates(he,j),Ee=0,Ue=ye;Ee<Ue.length;Ee+=1){var Xe=Ue[Ee];if(Math.abs(Xe.coord.x-be.x)<=X&&Math.abs(Xe.coord.y-be.y)<=X&&!$[Xe.crossTileID]){$[Xe.crossTileID]=!0,he.crossTileID=Xe.crossTileID;break}}}}};var Pn=function(){this.maxCrossTileID=0};Pn.prototype.generate=function(){return++this.maxCrossTileID};var Zn=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Zn.prototype.handleWrapJump=function(I){var j=Math.round((I-this.lng)/360);if(j!==0)for(var $ in this.indexes){var X=this.indexes[$],se={};for(var he in X){var ye=X[he];ye.tileID=ye.tileID.unwrapTo(ye.tileID.wrap+j),se[ye.tileID.key]=ye}this.indexes[$]=se}this.lng=I},Zn.prototype.addBucket=function(I,j,$){if(this.indexes[I.overscaledZ]&&this.indexes[I.overscaledZ][I.key]){if(this.indexes[I.overscaledZ][I.key].bucketInstanceId===j.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(I.overscaledZ,this.indexes[I.overscaledZ][I.key])}for(var X=0;X<j.symbolInstances.length;X++)j.symbolInstances.get(X).crossTileID=0;this.usedCrossTileIDs[I.overscaledZ]||(this.usedCrossTileIDs[I.overscaledZ]={});var se=this.usedCrossTileIDs[I.overscaledZ];for(var he in this.indexes){var ye=this.indexes[he];if(Number(he)>I.overscaledZ)for(var be in ye){var Ee=ye[be];Ee.tileID.isChildOf(I)&&Ee.findMatches(j.symbolInstances,I,se)}else{var Ue=ye[I.scaledTo(Number(he)).key];Ue&&Ue.findMatches(j.symbolInstances,I,se)}}for(var Xe=0;Xe<j.symbolInstances.length;Xe++){var it=j.symbolInstances.get(Xe);it.crossTileID||(it.crossTileID=$.generate(),se[it.crossTileID]=!0)}return this.indexes[I.overscaledZ]===void 0&&(this.indexes[I.overscaledZ]={}),this.indexes[I.overscaledZ][I.key]=new kn(I,j.symbolInstances,j.bucketInstanceId),!0},Zn.prototype.removeBucketCrossTileIDs=function(I,j){for(var $ in j.indexedSymbolInstances)for(var X=0,se=j.indexedSymbolInstances[$];X<se.length;X+=1){var he=se[X];delete this.usedCrossTileIDs[I][he.crossTileID]}},Zn.prototype.removeStaleBuckets=function(I){var j=!1;for(var $ in this.indexes){var X=this.indexes[$];for(var se in X)I[X[se].bucketInstanceId]||(this.removeBucketCrossTileIDs($,X[se]),delete X[se],j=!0)}return j};var Yn=function(){this.layerIndexes={},this.crossTileIDs=new Pn,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Yn.prototype.addLayer=function(I,j,$){var X=this.layerIndexes[I.id];X===void 0&&(X=this.layerIndexes[I.id]=new Zn);var se=!1,he={};X.handleWrapJump($);for(var ye=0,be=j;ye<be.length;ye+=1){var Ee=be[ye],Ue=Ee.getBucket(I);Ue&&I.id===Ue.layerIds[0]&&(Ue.bucketInstanceId||(Ue.bucketInstanceId=++this.maxBucketInstanceId),X.addBucket(Ee.tileID,Ue,this.crossTileIDs)&&(se=!0),he[Ue.bucketInstanceId]=!0)}return X.removeStaleBuckets(he)&&(se=!0),se},Yn.prototype.pruneUnusedLayers=function(I){var j={};for(var $ in I.forEach(function(X){j[X]=!0}),this.layerIndexes)j[$]||delete this.layerIndexes[$]};var ir=function(I,j){return i.emitValidationErrors(I,j&&j.filter(function($){return $.identifier!=="source.canvas"}))},or=i.pick(Et,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),xr=i.pick(Et,["setCenter","setZoom","setBearing","setPitch"]),wr=function(){var I={},j=i.styleSpec.$version;for(var $ in i.styleSpec.$root){var X=i.styleSpec.$root[$];if(X.required){var se=null;(se=$==="version"?j:X.type==="array"?[]:{})!=null&&(I[$]=se)}}return I}(),Ar=function(I){function j($,X){var se=this;X===void 0&&(X={}),I.call(this),this.map=$,this.dispatcher=new D(tt(),this),this.imageManager=new A,this.imageManager.setEventedParent(this),this.glyphManager=new S($._requestManager,X.localIdeographFontFamily),this.lineAtlas=new F(256,512),this.crossTileSymbolIndex=new Yn,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new i.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",i.getReferrer());var he=this;this._rtlTextPluginCallback=j.registerForPluginStateChange(function(ye){var be={pluginStatus:ye.pluginStatus,pluginURL:ye.pluginURL};he.dispatcher.broadcast("syncRTLPluginState",be,function(Ee,Ue){if(i.triggerPluginCompletionEvent(Ee),Ue&&Ue.every(function(it){return it}))for(var Xe in he.sourceCaches)he.sourceCaches[Xe].reload()})}),this.on("data",function(ye){if(ye.dataType==="source"&&ye.sourceDataType==="metadata"){var be=se.sourceCaches[ye.sourceId];if(be){var Ee=be.getSource();if(Ee&&Ee.vectorLayerIds)for(var Ue in se._layers){var Xe=se._layers[Ue];Xe.source===Ee.id&&se._validateLayer(Xe)}}}})}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.loadURL=function($,X){var se=this;X===void 0&&(X={}),this.fire(new i.Event("dataloading",{dataType:"style"}));var he=typeof X.validate=="boolean"?X.validate:!i.isMapboxURL($);$=this.map._requestManager.normalizeStyleURL($,X.accessToken);var ye=this.map._requestManager.transformRequest($,i.ResourceType.Style);this._request=i.getJSON(ye,function(be,Ee){se._request=null,be?se.fire(new i.ErrorEvent(be)):Ee&&se._load(Ee,he)})},j.prototype.loadJSON=function($,X){var se=this;X===void 0&&(X={}),this.fire(new i.Event("dataloading",{dataType:"style"})),this._request=i.browser.frame(function(){se._request=null,se._load($,X.validate!==!1)})},j.prototype.loadEmpty=function(){this.fire(new i.Event("dataloading",{dataType:"style"})),this._load(wr,!1)},j.prototype._load=function($,X){if(!X||!ir(this,i.validateStyle($))){for(var se in this._loaded=!0,this.stylesheet=$,$.sources)this.addSource(se,$.sources[se],{validate:!1});$.sprite?this._loadSprite($.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL($.glyphs);var he=Ft(this.stylesheet.layers);this._order=he.map(function(Ue){return Ue.id}),this._layers={},this._serializedLayers={};for(var ye=0,be=he;ye<be.length;ye+=1){var Ee=be[ye];(Ee=i.createStyleLayer(Ee)).setEventedParent(this,{layer:{id:Ee.id}}),this._layers[Ee.id]=Ee,this._serializedLayers[Ee.id]=Ee.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new R(this.stylesheet.light),this.fire(new i.Event("data",{dataType:"style"})),this.fire(new i.Event("style.load"))}},j.prototype._loadSprite=function($){var X=this;this._spriteRequest=function(se,he,ye){var be,Ee,Ue,Xe=i.browser.devicePixelRatio>1?"@2x":"",it=i.getJSON(he.transformRequest(he.normalizeSpriteURL(se,Xe,".json"),i.ResourceType.SpriteJSON),function(_t,Mt){it=null,Ue||(Ue=_t,be=Mt,Dt())}),xt=i.getImage(he.transformRequest(he.normalizeSpriteURL(se,Xe,".png"),i.ResourceType.SpriteImage),function(_t,Mt){xt=null,Ue||(Ue=_t,Ee=Mt,Dt())});function Dt(){if(Ue)ye(Ue);else if(be&&Ee){var _t=i.browser.getImageData(Ee),Mt={};for(var vt in be){var Nt=be[vt],Rt=Nt.width,Vt=Nt.height,rn=Nt.x,dn=Nt.y,En=Nt.sdf,Tn=Nt.pixelRatio,tr=Nt.stretchX,er=Nt.stretchY,gr=Nt.content,cr=new i.RGBAImage({width:Rt,height:Vt});i.RGBAImage.copy(_t,cr,{x:rn,y:dn},{x:0,y:0},{width:Rt,height:Vt}),Mt[vt]={data:cr,pixelRatio:Tn,sdf:En,stretchX:tr,stretchY:er,content:gr}}ye(null,Mt)}}return{cancel:function(){it&&(it.cancel(),it=null),xt&&(xt.cancel(),xt=null)}}}($,this.map._requestManager,function(se,he){if(X._spriteRequest=null,se)X.fire(new i.ErrorEvent(se));else if(he)for(var ye in he)X.imageManager.addImage(ye,he[ye]);X.imageManager.setLoaded(!0),X._availableImages=X.imageManager.listImages(),X.dispatcher.broadcast("setImages",X._availableImages),X.fire(new i.Event("data",{dataType:"style"}))})},j.prototype._validateLayer=function($){var X=this.sourceCaches[$.source];if(X){var se=$.sourceLayer;if(se){var he=X.getSource();(he.type==="geojson"||he.vectorLayerIds&&he.vectorLayerIds.indexOf(se)===-1)&&this.fire(new i.ErrorEvent(new Error('Source layer "'+se+'" does not exist on source "'+he.id+'" as specified by style layer "'+$.id+'"')))}}},j.prototype.loaded=function(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(var $ in this.sourceCaches)if(!this.sourceCaches[$].loaded())return!1;return!!this.imageManager.isLoaded()},j.prototype._serializeLayers=function($){for(var X=[],se=0,he=$;se<he.length;se+=1){var ye=he[se],be=this._layers[ye];be.type!=="custom"&&X.push(be.serialize())}return X},j.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var $ in this.sourceCaches)if(this.sourceCaches[$].hasTransition())return!0;for(var X in this._layers)if(this._layers[X].hasTransition())return!0;return!1},j.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},j.prototype.update=function($){if(this._loaded){var X=this._changed;if(this._changed){var se=Object.keys(this._updatedLayers),he=Object.keys(this._removedLayers);for(var ye in(se.length||he.length)&&this._updateWorkerLayers(se,he),this._updatedSources){var be=this._updatedSources[ye];be==="reload"?this._reloadSource(ye):be==="clear"&&this._clearSource(ye)}for(var Ee in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[Ee].updateTransitions($);this.light.updateTransitions($),this._resetUpdates()}for(var Ue in this.sourceCaches)this.sourceCaches[Ue].used=!1;for(var Xe=0,it=this._order;Xe<it.length;Xe+=1){var xt=it[Xe],Dt=this._layers[xt];Dt.recalculate($,this._availableImages),!Dt.isHidden($.zoom)&&Dt.source&&(this.sourceCaches[Dt.source].used=!0)}this.light.recalculate($),this.z=$.zoom,X&&this.fire(new i.Event("data",{dataType:"style"}))}},j.prototype._updateTilesForChangedImages=function(){var $=Object.keys(this._changedImages);if($.length){for(var X in this.sourceCaches)this.sourceCaches[X].reloadTilesForDependencies(["icons","patterns"],$);this._changedImages={}}},j.prototype._updateWorkerLayers=function($,X){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers($),removedIds:X})},j.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},j.prototype.setState=function($){var X=this;if(this._checkLoaded(),ir(this,i.validateStyle($)))return!1;($=i.clone$1($)).layers=Ft($.layers);var se=Kt(this.serialize(),$).filter(function(ye){return!(ye.command in xr)});if(se.length===0)return!1;var he=se.filter(function(ye){return!(ye.command in or)});if(he.length>0)throw new Error("Unimplemented: "+he.map(function(ye){return ye.command}).join(", ")+".");return se.forEach(function(ye){ye.command!=="setTransition"&&X[ye.command].apply(X,ye.args)}),this.stylesheet=$,!0},j.prototype.addImage=function($,X){if(this.getImage($))return this.fire(new i.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage($,X),this._availableImages=this.imageManager.listImages(),this._changedImages[$]=!0,this._changed=!0,this.fire(new i.Event("data",{dataType:"style"}))},j.prototype.updateImage=function($,X){this.imageManager.updateImage($,X)},j.prototype.getImage=function($){return this.imageManager.getImage($)},j.prototype.removeImage=function($){if(!this.getImage($))return this.fire(new i.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage($),this._availableImages=this.imageManager.listImages(),this._changedImages[$]=!0,this._changed=!0,this.fire(new i.Event("data",{dataType:"style"}))},j.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},j.prototype.addSource=function($,X,se){var he=this;if(se===void 0&&(se={}),this._checkLoaded(),this.sourceCaches[$]!==void 0)throw new Error("There is already a source with this ID");if(!X.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(X).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(X.type)>=0)||!this._validate(i.validateStyle.source,"sources."+$,X,null,se)){this.map&&this.map._collectResourceTiming&&(X.collectResourceTiming=!0);var ye=this.sourceCaches[$]=new qe($,X,this.dispatcher);ye.style=this,ye.setEventedParent(this,function(){return{isSourceLoaded:he.loaded(),source:ye.serialize(),sourceId:$}}),ye.onAdd(this.map),this._changed=!0}},j.prototype.removeSource=function($){if(this._checkLoaded(),this.sourceCaches[$]===void 0)throw new Error("There is no source with this ID");for(var X in this._layers)if(this._layers[X].source===$)return this.fire(new i.ErrorEvent(new Error('Source "'+$+'" cannot be removed while layer "'+X+'" is using it.')));var se=this.sourceCaches[$];delete this.sourceCaches[$],delete this._updatedSources[$],se.fire(new i.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:$})),se.setEventedParent(null),se.clearTiles(),se.onRemove&&se.onRemove(this.map),this._changed=!0},j.prototype.setGeoJSONSourceData=function($,X){this._checkLoaded(),this.sourceCaches[$].getSource().setData(X),this._changed=!0},j.prototype.getSource=function($){return this.sourceCaches[$]&&this.sourceCaches[$].getSource()},j.prototype.addLayer=function($,X,se){se===void 0&&(se={}),this._checkLoaded();var he=$.id;if(this.getLayer(he))this.fire(new i.ErrorEvent(new Error('Layer with id "'+he+'" already exists on this map')));else{var ye;if($.type==="custom"){if(ir(this,i.validateCustomStyleLayer($)))return;ye=i.createStyleLayer($)}else{if(typeof $.source=="object"&&(this.addSource(he,$.source),$=i.clone$1($),$=i.extend($,{source:he})),this._validate(i.validateStyle.layer,"layers."+he,$,{arrayIndex:-1},se))return;ye=i.createStyleLayer($),this._validateLayer(ye),ye.setEventedParent(this,{layer:{id:he}}),this._serializedLayers[ye.id]=ye.serialize()}var be=X?this._order.indexOf(X):this._order.length;if(X&&be===-1)this.fire(new i.ErrorEvent(new Error('Layer with id "'+X+'" does not exist on this map.')));else{if(this._order.splice(be,0,he),this._layerOrderChanged=!0,this._layers[he]=ye,this._removedLayers[he]&&ye.source&&ye.type!=="custom"){var Ee=this._removedLayers[he];delete this._removedLayers[he],Ee.type!==ye.type?this._updatedSources[ye.source]="clear":(this._updatedSources[ye.source]="reload",this.sourceCaches[ye.source].pause())}this._updateLayer(ye),ye.onAdd&&ye.onAdd(this.map)}}},j.prototype.moveLayer=function($,X){if(this._checkLoaded(),this._changed=!0,this._layers[$]){if($!==X){var se=this._order.indexOf($);this._order.splice(se,1);var he=X?this._order.indexOf(X):this._order.length;X&&he===-1?this.fire(new i.ErrorEvent(new Error('Layer with id "'+X+'" does not exist on this map.'))):(this._order.splice(he,0,$),this._layerOrderChanged=!0)}}else this.fire(new i.ErrorEvent(new Error("The layer '"+$+"' does not exist in the map's style and cannot be moved.")))},j.prototype.removeLayer=function($){this._checkLoaded();var X=this._layers[$];if(X){X.setEventedParent(null);var se=this._order.indexOf($);this._order.splice(se,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[$]=X,delete this._layers[$],delete this._serializedLayers[$],delete this._updatedLayers[$],delete this._updatedPaintProps[$],X.onRemove&&X.onRemove(this.map)}else this.fire(new i.ErrorEvent(new Error("The layer '"+$+"' does not exist in the map's style and cannot be removed.")))},j.prototype.getLayer=function($){return this._layers[$]},j.prototype.hasLayer=function($){return $ in this._layers},j.prototype.setLayerZoomRange=function($,X,se){this._checkLoaded();var he=this.getLayer($);he?he.minzoom===X&&he.maxzoom===se||(X!=null&&(he.minzoom=X),se!=null&&(he.maxzoom=se),this._updateLayer(he)):this.fire(new i.ErrorEvent(new Error("The layer '"+$+"' does not exist in the map's style and cannot have zoom extent.")))},j.prototype.setFilter=function($,X,se){se===void 0&&(se={}),this._checkLoaded();var he=this.getLayer($);if(he){if(!i.deepEqual(he.filter,X))return X==null?(he.filter=void 0,void this._updateLayer(he)):void(this._validate(i.validateStyle.filter,"layers."+he.id+".filter",X,null,se)||(he.filter=i.clone$1(X),this._updateLayer(he)))}else this.fire(new i.ErrorEvent(new Error("The layer '"+$+"' does not exist in the map's style and cannot be filtered.")))},j.prototype.getFilter=function($){return i.clone$1(this.getLayer($).filter)},j.prototype.setLayoutProperty=function($,X,se,he){he===void 0&&(he={}),this._checkLoaded();var ye=this.getLayer($);ye?i.deepEqual(ye.getLayoutProperty(X),se)||(ye.setLayoutProperty(X,se,he),this._updateLayer(ye)):this.fire(new i.ErrorEvent(new Error("The layer '"+$+"' does not exist in the map's style and cannot be styled.")))},j.prototype.getLayoutProperty=function($,X){var se=this.getLayer($);if(se)return se.getLayoutProperty(X);this.fire(new i.ErrorEvent(new Error("The layer '"+$+"' does not exist in the map's style.")))},j.prototype.setPaintProperty=function($,X,se,he){he===void 0&&(he={}),this._checkLoaded();var ye=this.getLayer($);ye?i.deepEqual(ye.getPaintProperty(X),se)||(ye.setPaintProperty(X,se,he)&&this._updateLayer(ye),this._changed=!0,this._updatedPaintProps[$]=!0):this.fire(new i.ErrorEvent(new Error("The layer '"+$+"' does not exist in the map's style and cannot be styled.")))},j.prototype.getPaintProperty=function($,X){return this.getLayer($).getPaintProperty(X)},j.prototype.setFeatureState=function($,X){this._checkLoaded();var se=$.source,he=$.sourceLayer,ye=this.sourceCaches[se];if(ye!==void 0){var be=ye.getSource().type;be==="geojson"&&he?this.fire(new i.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):be!=="vector"||he?($.id===void 0&&this.fire(new i.ErrorEvent(new Error("The feature id parameter must be provided."))),ye.setFeatureState(he,$.id,X)):this.fire(new i.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new i.ErrorEvent(new Error("The source '"+se+"' does not exist in the map's style.")))},j.prototype.removeFeatureState=function($,X){this._checkLoaded();var se=$.source,he=this.sourceCaches[se];if(he!==void 0){var ye=he.getSource().type,be=ye==="vector"?$.sourceLayer:void 0;ye!=="vector"||be?X&&typeof $.id!="string"&&typeof $.id!="number"?this.fire(new i.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):he.removeFeatureState(be,$.id,X):this.fire(new i.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new i.ErrorEvent(new Error("The source '"+se+"' does not exist in the map's style.")))},j.prototype.getFeatureState=function($){this._checkLoaded();var X=$.source,se=$.sourceLayer,he=this.sourceCaches[X];if(he!==void 0){if(he.getSource().type!=="vector"||se)return $.id===void 0&&this.fire(new i.ErrorEvent(new Error("The feature id parameter must be provided."))),he.getFeatureState(se,$.id);this.fire(new i.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new i.ErrorEvent(new Error("The source '"+X+"' does not exist in the map's style.")))},j.prototype.getTransition=function(){return i.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},j.prototype.serialize=function(){return i.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:i.mapObject(this.sourceCaches,function($){return $.serialize()}),layers:this._serializeLayers(this._order)},function($){return $!==void 0})},j.prototype._updateLayer=function($){this._updatedLayers[$.id]=!0,$.source&&!this._updatedSources[$.source]&&this.sourceCaches[$.source].getSource().type!=="raster"&&(this._updatedSources[$.source]="reload",this.sourceCaches[$.source].pause()),this._changed=!0},j.prototype._flattenAndSortRenderedFeatures=function($){for(var X=this,se=function(gr){return X._layers[gr].type==="fill-extrusion"},he={},ye=[],be=this._order.length-1;be>=0;be--){var Ee=this._order[be];if(se(Ee)){he[Ee]=be;for(var Ue=0,Xe=$;Ue<Xe.length;Ue+=1){var it=Xe[Ue][Ee];if(it)for(var xt=0,Dt=it;xt<Dt.length;xt+=1){var _t=Dt[xt];ye.push(_t)}}}}ye.sort(function(gr,cr){return cr.intersectionZ-gr.intersectionZ});for(var Mt=[],vt=this._order.length-1;vt>=0;vt--){var Nt=this._order[vt];if(se(Nt))for(var Rt=ye.length-1;Rt>=0;Rt--){var Vt=ye[Rt].feature;if(he[Vt.layer.id]<vt)break;Mt.push(Vt),ye.pop()}else for(var rn=0,dn=$;rn<dn.length;rn+=1){var En=dn[rn][Nt];if(En)for(var Tn=0,tr=En;Tn<tr.length;Tn+=1){var er=tr[Tn];Mt.push(er.feature)}}}return Mt},j.prototype.queryRenderedFeatures=function($,X,se){X&&X.filter&&this._validate(i.validateStyle.filter,"queryRenderedFeatures.filter",X.filter,null,X);var he={};if(X&&X.layers){if(!Array.isArray(X.layers))return this.fire(new i.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var ye=0,be=X.layers;ye<be.length;ye+=1){var Ee=be[ye],Ue=this._layers[Ee];if(!Ue)return this.fire(new i.ErrorEvent(new Error("The layer '"+Ee+"' does not exist in the map's style and cannot be queried for features."))),[];he[Ue.source]=!0}}var Xe=[];for(var it in X.availableImages=this._availableImages,this.sourceCaches)X.layers&&!he[it]||Xe.push(H(this.sourceCaches[it],this._layers,this._serializedLayers,$,X,se));return this.placement&&Xe.push(function(xt,Dt,_t,Mt,vt,Nt,Rt){for(var Vt={},rn=Nt.queryRenderedSymbols(Mt),dn=[],En=0,Tn=Object.keys(rn).map(Number);En<Tn.length;En+=1){var tr=Tn[En];dn.push(Rt[tr])}dn.sort(ne);for(var er=function(){var Ai=cr[gr],Gn=Ai.featureIndex.lookupSymbolFeatures(rn[Ai.bucketInstanceId],Dt,Ai.bucketIndex,Ai.sourceLayerIndex,vt.filter,vt.layers,vt.availableImages,xt);for(var Mr in Gn){var si=Vt[Mr]=Vt[Mr]||[],Qr=Gn[Mr];Qr.sort(function(fi,zi){var Oi=Ai.featureSortOrder;if(Oi){var ta=Oi.indexOf(fi.featureIndex);return Oi.indexOf(zi.featureIndex)-ta}return zi.featureIndex-fi.featureIndex});for(var mi=0,Mi=Qr;mi<Mi.length;mi+=1){var Zi=Mi[mi];si.push(Zi)}}},gr=0,cr=dn;gr<cr.length;gr+=1)er();var Xr=function(Ai){Vt[Ai].forEach(function(Gn){var Mr=Gn.feature,si=xt[Ai],Qr=_t[si.source].getFeatureState(Mr.layer["source-layer"],Mr.id);Mr.source=Mr.layer.source,Mr.layer["source-layer"]&&(Mr.sourceLayer=Mr.layer["source-layer"]),Mr.state=Qr})};for(var oi in Vt)Xr(oi);return Vt}(this._layers,this._serializedLayers,this.sourceCaches,$,X,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(Xe)},j.prototype.querySourceFeatures=function($,X){X&&X.filter&&this._validate(i.validateStyle.filter,"querySourceFeatures.filter",X.filter,null,X);var se=this.sourceCaches[$];return se?function(he,ye){for(var be=he.getRenderableIds().map(function(Dt){return he.getTileByID(Dt)}),Ee=[],Ue={},Xe=0;Xe<be.length;Xe++){var it=be[Xe],xt=it.tileID.canonical.key;Ue[xt]||(Ue[xt]=!0,it.querySourceFeatures(Ee,ye))}return Ee}(se,X):[]},j.prototype.addSourceType=function($,X,se){return j.getSourceType($)?se(new Error('A source type called "'+$+'" already exists.')):(j.setSourceType($,X),X.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:$,url:X.workerSourceURL},se):se(null,null))},j.prototype.getLight=function(){return this.light.getLight()},j.prototype.setLight=function($,X){X===void 0&&(X={}),this._checkLoaded();var se=this.light.getLight(),he=!1;for(var ye in $)if(!i.deepEqual($[ye],se[ye])){he=!0;break}if(he){var be={now:i.browser.now(),transition:i.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight($,X),this.light.updateTransitions(be)}},j.prototype._validate=function($,X,se,he,ye){return ye===void 0&&(ye={}),(!ye||ye.validate!==!1)&&ir(this,$.call(i.validateStyle,i.extend({key:X,style:this.serialize(),value:se,styleSpec:i.styleSpec},he)))},j.prototype._remove=function(){for(var $ in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),i.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers)this._layers[$].setEventedParent(null);for(var X in this.sourceCaches)this.sourceCaches[X].clearTiles(),this.sourceCaches[X].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},j.prototype._clearSource=function($){this.sourceCaches[$].clearTiles()},j.prototype._reloadSource=function($){this.sourceCaches[$].resume(),this.sourceCaches[$].reload()},j.prototype._updateSources=function($){for(var X in this.sourceCaches)this.sourceCaches[X].update($)},j.prototype._generateCollisionBoxes=function(){for(var $ in this.sourceCaches)this._reloadSource($)},j.prototype._updatePlacement=function($,X,se,he,ye){ye===void 0&&(ye=!1);for(var be=!1,Ee=!1,Ue={},Xe=0,it=this._order;Xe<it.length;Xe+=1){var xt=it[Xe],Dt=this._layers[xt];if(Dt.type==="symbol"){if(!Ue[Dt.source]){var _t=this.sourceCaches[Dt.source];Ue[Dt.source]=_t.getRenderableIds(!0).map(function(rn){return _t.getTileByID(rn)}).sort(function(rn,dn){return dn.tileID.overscaledZ-rn.tileID.overscaledZ||(rn.tileID.isLessThan(dn.tileID)?-1:1)})}var Mt=this.crossTileSymbolIndex.addLayer(Dt,Ue[Dt.source],$.center.lng);be=be||Mt}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((ye=ye||this._layerOrderChanged||se===0)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(i.browser.now(),$.zoom))&&(this.pauseablePlacement=new wn($,this._order,ye,X,se,he,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,Ue),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(i.browser.now()),Ee=!0),be&&this.pauseablePlacement.placement.setStale()),Ee||be)for(var vt=0,Nt=this._order;vt<Nt.length;vt+=1){var Rt=Nt[vt],Vt=this._layers[Rt];Vt.type==="symbol"&&this.placement.updateLayerOpacities(Vt,Ue[Vt.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(i.browser.now())},j.prototype._releaseSymbolFadeTiles=function(){for(var $ in this.sourceCaches)this.sourceCaches[$].releaseSymbolFadeTiles()},j.prototype.getImages=function($,X,se){this.imageManager.getImages(X.icons,se),this._updateTilesForChangedImages();var he=this.sourceCaches[X.source];he&&he.setDependencies(X.tileID.key,X.type,X.icons)},j.prototype.getGlyphs=function($,X,se){this.glyphManager.getGlyphs(X.stacks,se)},j.prototype.getResource=function($,X,se){return i.makeRequest(X,se)},j}(i.Evented);Ar.getSourceType=function(I){return U[I]},Ar.setSourceType=function(I,j){U[I]=j},Ar.registerForPluginStateChange=i.registerForPluginStateChange;var Ir=i.createLayout([{name:"a_pos",type:"Int16",components:2}]),Br=wa(`#ifdef GL_ES
-precision mediump float;
-#else
-#if !defined(lowp)
-#define lowp
-#endif
-#if !defined(mediump)
-#define mediump
-#endif
-#if !defined(highp)
-#define highp
-#endif
-#endif`,`#ifdef GL_ES
-precision highp float;
-#else
-#if !defined(lowp)
-#define lowp
-#endif
-#if !defined(mediump)
-#define mediump
-#endif
-#if !defined(highp)
-#define highp
-#endif
-#endif
-vec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0
-);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}`),ai=wa(`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Vi=wa(`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),$i=wa(`varying vec3 v_data;
-#pragma mapbox: define highp vec4 color
-#pragma mapbox: define mediump float radius
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define highp vec4 stroke_color
-#pragma mapbox: define mediump float stroke_width
-#pragma mapbox: define lowp float stroke_opacity
-void main() {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize mediump float radius
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize highp vec4 stroke_color
-#pragma mapbox: initialize mediump float stroke_width
-#pragma mapbox: initialize lowp float stroke_opacity
-vec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;
-#pragma mapbox: define highp vec4 color
-#pragma mapbox: define mediump float radius
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define highp vec4 stroke_color
-#pragma mapbox: define mediump float stroke_width
-#pragma mapbox: define lowp float stroke_opacity
-void main(void) {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize mediump float radius
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize highp vec4 stroke_color
-#pragma mapbox: initialize mediump float stroke_width
-#pragma mapbox: initialize lowp float stroke_opacity
-vec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`),Er=wa("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),ci=wa(`uniform highp float u_intensity;varying vec2 v_extrude;
-#pragma mapbox: define highp float weight
-#define GAUSS_COEF 0.3989422804014327
-void main() {
-#pragma mapbox: initialize highp float weight
-float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;
-#pragma mapbox: define highp float weight
-#pragma mapbox: define mediump float radius
-const highp float ZERO=1.0/255.0/16.0;
-#define GAUSS_COEF 0.3989422804014327
-void main(void) {
-#pragma mapbox: initialize highp float weight
-#pragma mapbox: initialize mediump float radius
-vec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}`),li=wa(`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(0.0);
-#endif
-}`,"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),ra=wa("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),eo=wa("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),Co=wa("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),ms=wa(`#pragma mapbox: define highp vec4 color
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize lowp float opacity
-gl_FragColor=color*opacity;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`attribute vec2 a_pos;uniform mat4 u_matrix;
-#pragma mapbox: define highp vec4 color
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize lowp float opacity
-gl_Position=u_matrix*vec4(a_pos,0,1);}`),ba=wa(`varying vec2 v_pos;
-#pragma mapbox: define highp vec4 outline_color
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize highp vec4 outline_color
-#pragma mapbox: initialize lowp float opacity
-float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;
-#pragma mapbox: define highp vec4 outline_color
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize highp vec4 outline_color
-#pragma mapbox: initialize lowp float opacity
-gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),_a=wa(`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-void main() {
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-#pragma mapbox: define lowp float pixel_ratio_from
-#pragma mapbox: define lowp float pixel_ratio_to
-void main() {
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-#pragma mapbox: initialize lowp float pixel_ratio_from
-#pragma mapbox: initialize lowp float pixel_ratio_to
-vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),ns=wa(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-void main() {
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-#pragma mapbox: define lowp float pixel_ratio_from
-#pragma mapbox: define lowp float pixel_ratio_to
-void main() {
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-#pragma mapbox: initialize lowp float pixel_ratio_from
-#pragma mapbox: initialize lowp float pixel_ratio_to
-vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`),ua=wa(`varying vec4 v_color;void main() {gl_FragColor=v_color;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;
-#pragma mapbox: define highp float base
-#pragma mapbox: define highp float height
-#pragma mapbox: define highp vec4 color
-void main() {
-#pragma mapbox: initialize highp float base
-#pragma mapbox: initialize highp float height
-#pragma mapbox: initialize highp vec4 color
-vec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`),ys=wa(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;
-#pragma mapbox: define lowp float base
-#pragma mapbox: define lowp float height
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-#pragma mapbox: define lowp float pixel_ratio_from
-#pragma mapbox: define lowp float pixel_ratio_to
-void main() {
-#pragma mapbox: initialize lowp float base
-#pragma mapbox: initialize lowp float height
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-#pragma mapbox: initialize lowp float pixel_ratio_from
-#pragma mapbox: initialize lowp float pixel_ratio_to
-vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;
-#pragma mapbox: define lowp float base
-#pragma mapbox: define lowp float height
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-#pragma mapbox: define lowp float pixel_ratio_from
-#pragma mapbox: define lowp float pixel_ratio_to
-void main() {
-#pragma mapbox: initialize lowp float base
-#pragma mapbox: initialize lowp float height
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-#pragma mapbox: initialize lowp float pixel_ratio_from
-#pragma mapbox: initialize lowp float pixel_ratio_to
-vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0
-? a_pos
-: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`),Ts=wa(`#ifdef GL_ES
-precision highp float;
-#endif
-uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),co=wa(`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;
-#define PI 3.141592653589793
-void main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),rs=wa(`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;
-#pragma mapbox: define highp vec4 color
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`
-#define scale 0.015873016
-attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;
-#pragma mapbox: define highp vec4 color
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define mediump float gapwidth
-#pragma mapbox: define lowp float offset
-#pragma mapbox: define mediump float width
-void main() {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump float gapwidth
-#pragma mapbox: initialize lowp float offset
-#pragma mapbox: initialize mediump float width
-float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`),Ms=wa(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`
-#define MAX_LINE_DISTANCE 32767.0
-#define scale 0.015873016
-attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define mediump float gapwidth
-#pragma mapbox: define lowp float offset
-#pragma mapbox: define mediump float width
-void main() {
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump float gapwidth
-#pragma mapbox: initialize lowp float offset
-#pragma mapbox: initialize mediump float width
-float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`),Ns=wa(`uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-#pragma mapbox: define lowp float pixel_ratio_from
-#pragma mapbox: define lowp float pixel_ratio_to
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-#pragma mapbox: initialize lowp float pixel_ratio_from
-#pragma mapbox: initialize lowp float pixel_ratio_to
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`
-#define scale 0.015873016
-#define LINE_DISTANCE_SCALE 2.0
-attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp float offset
-#pragma mapbox: define mediump float gapwidth
-#pragma mapbox: define mediump float width
-#pragma mapbox: define lowp float floorwidth
-#pragma mapbox: define lowp vec4 pattern_from
-#pragma mapbox: define lowp vec4 pattern_to
-#pragma mapbox: define lowp float pixel_ratio_from
-#pragma mapbox: define lowp float pixel_ratio_to
-void main() {
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize lowp float offset
-#pragma mapbox: initialize mediump float gapwidth
-#pragma mapbox: initialize mediump float width
-#pragma mapbox: initialize lowp float floorwidth
-#pragma mapbox: initialize mediump vec4 pattern_from
-#pragma mapbox: initialize mediump vec4 pattern_to
-#pragma mapbox: initialize lowp float pixel_ratio_from
-#pragma mapbox: initialize lowp float pixel_ratio_to
-float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`),Io=wa(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;
-#pragma mapbox: define highp vec4 color
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define mediump float width
-#pragma mapbox: define lowp float floorwidth
-void main() {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump float width
-#pragma mapbox: initialize lowp float floorwidth
-float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`
-#define scale 0.015873016
-#define LINE_DISTANCE_SCALE 2.0
-attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;
-#pragma mapbox: define highp vec4 color
-#pragma mapbox: define lowp float blur
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define mediump float gapwidth
-#pragma mapbox: define lowp float offset
-#pragma mapbox: define mediump float width
-#pragma mapbox: define lowp float floorwidth
-void main() {
-#pragma mapbox: initialize highp vec4 color
-#pragma mapbox: initialize lowp float blur
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize mediump float gapwidth
-#pragma mapbox: initialize lowp float offset
-#pragma mapbox: initialize mediump float width
-#pragma mapbox: initialize lowp float floorwidth
-float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`),to=wa(`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),Zo=wa(`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize lowp float opacity
-lowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;
-#pragma mapbox: define lowp float opacity
-void main() {
-#pragma mapbox: initialize lowp float opacity
-vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
-camera_to_anchor_distance/u_camera_to_center_distance :
-u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}`),mc=wa(`#define SDF_PX 8.0
-uniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;
-#pragma mapbox: define highp vec4 fill_color
-#pragma mapbox: define highp vec4 halo_color
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp float halo_width
-#pragma mapbox: define lowp float halo_blur
-void main() {
-#pragma mapbox: initialize highp vec4 fill_color
-#pragma mapbox: initialize highp vec4 halo_color
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize lowp float halo_width
-#pragma mapbox: initialize lowp float halo_blur
-float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;
-#pragma mapbox: define highp vec4 fill_color
-#pragma mapbox: define highp vec4 halo_color
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp float halo_width
-#pragma mapbox: define lowp float halo_blur
-void main() {
-#pragma mapbox: initialize highp vec4 fill_color
-#pragma mapbox: initialize highp vec4 halo_color
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize lowp float halo_width
-#pragma mapbox: initialize lowp float halo_blur
-vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
-camera_to_anchor_distance/u_camera_to_center_distance :
-u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`),Rc=wa(`#define SDF_PX 8.0
-#define SDF 1.0
-#define ICON 0.0
-uniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;
-#pragma mapbox: define highp vec4 fill_color
-#pragma mapbox: define highp vec4 halo_color
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp float halo_width
-#pragma mapbox: define lowp float halo_blur
-void main() {
-#pragma mapbox: initialize highp vec4 fill_color
-#pragma mapbox: initialize highp vec4 halo_color
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize lowp float halo_width
-#pragma mapbox: initialize lowp float halo_blur
-float fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);
-#ifdef OVERDRAW_INSPECTOR
-gl_FragColor=vec4(1.0);
-#endif
-}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;
-#pragma mapbox: define highp vec4 fill_color
-#pragma mapbox: define highp vec4 halo_color
-#pragma mapbox: define lowp float opacity
-#pragma mapbox: define lowp float halo_width
-#pragma mapbox: define lowp float halo_blur
-void main() {
-#pragma mapbox: initialize highp vec4 fill_color
-#pragma mapbox: initialize highp vec4 halo_color
-#pragma mapbox: initialize lowp float opacity
-#pragma mapbox: initialize lowp float halo_width
-#pragma mapbox: initialize lowp float halo_blur
-vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?
-camera_to_anchor_distance/u_camera_to_center_distance :
-u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`);function wa(I,j){var $=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,X={};return{fragmentSource:I=I.replace($,function(se,he,ye,be,Ee){return X[Ee]=!0,he==="define"?`
-#ifndef HAS_UNIFORM_u_`+Ee+`
-varying `+ye+" "+be+" "+Ee+`;
-#else
-uniform `+ye+" "+be+" u_"+Ee+`;
-#endif
-`:`
-#ifdef HAS_UNIFORM_u_`+Ee+`
-    `+ye+" "+be+" "+Ee+" = u_"+Ee+`;
-#endif
-`}),vertexSource:j=j.replace($,function(se,he,ye,be,Ee){var Ue=be==="float"?"vec2":"vec4",Xe=Ee.match(/color/)?"color":Ue;return X[Ee]?he==="define"?`
-#ifndef HAS_UNIFORM_u_`+Ee+`
-uniform lowp float u_`+Ee+`_t;
-attribute `+ye+" "+Ue+" a_"+Ee+`;
-varying `+ye+" "+be+" "+Ee+`;
-#else
-uniform `+ye+" "+be+" u_"+Ee+`;
-#endif
-`:Xe==="vec4"?`
-#ifndef HAS_UNIFORM_u_`+Ee+`
-    `+Ee+" = a_"+Ee+`;
-#else
-    `+ye+" "+be+" "+Ee+" = u_"+Ee+`;
-#endif
-`:`
-#ifndef HAS_UNIFORM_u_`+Ee+`
-    `+Ee+" = unpack_mix_"+Xe+"(a_"+Ee+", u_"+Ee+`_t);
-#else
-    `+ye+" "+be+" "+Ee+" = u_"+Ee+`;
-#endif
-`:he==="define"?`
-#ifndef HAS_UNIFORM_u_`+Ee+`
-uniform lowp float u_`+Ee+`_t;
-attribute `+ye+" "+Ue+" a_"+Ee+`;
-#else
-uniform `+ye+" "+be+" u_"+Ee+`;
-#endif
-`:Xe==="vec4"?`
-#ifndef HAS_UNIFORM_u_`+Ee+`
-    `+ye+" "+be+" "+Ee+" = a_"+Ee+`;
-#else
-    `+ye+" "+be+" "+Ee+" = u_"+Ee+`;
-#endif
-`:`
-#ifndef HAS_UNIFORM_u_`+Ee+`
-    `+ye+" "+be+" "+Ee+" = unpack_mix_"+Xe+"(a_"+Ee+", u_"+Ee+`_t);
-#else
-    `+ye+" "+be+" "+Ee+" = u_"+Ee+`;
-#endif
-`})}}var Zu=Object.freeze({__proto__:null,prelude:Br,background:ai,backgroundPattern:Vi,circle:$i,clippingMask:Er,heatmap:ci,heatmapTexture:li,collisionBox:ra,collisionCircle:eo,debug:Co,fill:ms,fillOutline:ba,fillOutlinePattern:_a,fillPattern:ns,fillExtrusion:ua,fillExtrusionPattern:ys,hillshadePrepare:Ts,hillshade:co,line:rs,lineGradient:Ms,linePattern:Ns,lineSDF:Io,raster:to,symbolIcon:Zo,symbolSDF:mc,symbolTextAndIcon:Rc}),Kl=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};Kl.prototype.bind=function(I,j,$,X,se,he,ye,be){this.context=I;for(var Ee=this.boundPaintVertexBuffers.length!==X.length,Ue=0;!Ee&&Ue<X.length;Ue++)this.boundPaintVertexBuffers[Ue]!==X[Ue]&&(Ee=!0);var Xe=!this.vao||this.boundProgram!==j||this.boundLayoutVertexBuffer!==$||Ee||this.boundIndexBuffer!==se||this.boundVertexOffset!==he||this.boundDynamicVertexBuffer!==ye||this.boundDynamicVertexBuffer2!==be;!I.extVertexArrayObject||Xe?this.freshBind(j,$,X,se,he,ye,be):(I.bindVertexArrayOES.set(this.vao),ye&&ye.bind(),se&&se.dynamicDraw&&se.bind(),be&&be.bind())},Kl.prototype.freshBind=function(I,j,$,X,se,he,ye){var be,Ee=I.numAttributes,Ue=this.context,Xe=Ue.gl;if(Ue.extVertexArrayObject)this.vao&&this.destroy(),this.vao=Ue.extVertexArrayObject.createVertexArrayOES(),Ue.bindVertexArrayOES.set(this.vao),be=0,this.boundProgram=I,this.boundLayoutVertexBuffer=j,this.boundPaintVertexBuffers=$,this.boundIndexBuffer=X,this.boundVertexOffset=se,this.boundDynamicVertexBuffer=he,this.boundDynamicVertexBuffer2=ye;else{be=Ue.currentNumAttributes||0;for(var it=Ee;it<be;it++)Xe.disableVertexAttribArray(it)}j.enableAttributes(Xe,I);for(var xt=0,Dt=$;xt<Dt.length;xt+=1)Dt[xt].enableAttributes(Xe,I);he&&he.enableAttributes(Xe,I),ye&&ye.enableAttributes(Xe,I),j.bind(),j.setVertexAttribPointers(Xe,I,se);for(var _t=0,Mt=$;_t<Mt.length;_t+=1){var vt=Mt[_t];vt.bind(),vt.setVertexAttribPointers(Xe,I,se)}he&&(he.bind(),he.setVertexAttribPointers(Xe,I,se)),X&&X.bind(),ye&&(ye.bind(),ye.setVertexAttribPointers(Xe,I,se)),Ue.currentNumAttributes=Ee},Kl.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var zc=function(I,j,$,X,se){var he=I.gl;this.program=he.createProgram();var ye=$?$.defines():[];se&&ye.push("#define OVERDRAW_INSPECTOR;");var be=ye.concat(Br.fragmentSource,j.fragmentSource).join(`
-`),Ee=ye.concat(Br.vertexSource,j.vertexSource).join(`
-`),Ue=he.createShader(he.FRAGMENT_SHADER);if(he.isContextLost())this.failedToCreate=!0;else{he.shaderSource(Ue,be),he.compileShader(Ue),he.attachShader(this.program,Ue);var Xe=he.createShader(he.VERTEX_SHADER);if(he.isContextLost())this.failedToCreate=!0;else{he.shaderSource(Xe,Ee),he.compileShader(Xe),he.attachShader(this.program,Xe);for(var it=$?$.layoutAttributes:[],xt=0;xt<it.length;xt++)he.bindAttribLocation(this.program,xt,it[xt].name);he.linkProgram(this.program),he.deleteShader(Xe),he.deleteShader(Ue),this.numAttributes=he.getProgramParameter(this.program,he.ACTIVE_ATTRIBUTES),this.attributes={};for(var Dt={},_t=0;_t<this.numAttributes;_t++){var Mt=he.getActiveAttrib(this.program,_t);Mt&&(this.attributes[Mt.name]=he.getAttribLocation(this.program,Mt.name))}for(var vt=he.getProgramParameter(this.program,he.ACTIVE_UNIFORMS),Nt=0;Nt<vt;Nt++){var Rt=he.getActiveUniform(this.program,Nt);Rt&&(Dt[Rt.name]=he.getUniformLocation(this.program,Rt.name))}this.fixedUniforms=X(I,Dt),this.binderUniforms=$?$.getUniforms(I,Dt):[]}}};function Nc(I,j,$){var X=1/Dn($,1,j.transform.tileZoom),se=Math.pow(2,$.tileID.overscaledZ),he=$.tileSize*Math.pow(2,j.transform.tileZoom)/se,ye=he*($.tileID.canonical.x+$.tileID.wrap*se),be=he*$.tileID.canonical.y;return{u_image:0,u_texsize:$.imageAtlasTexture.size,u_scale:[X,I.fromScale,I.toScale],u_fade:I.t,u_pixel_coord_upper:[ye>>16,be>>16],u_pixel_coord_lower:[65535&ye,65535&be]}}zc.prototype.draw=function(I,j,$,X,se,he,ye,be,Ee,Ue,Xe,it,xt,Dt,_t,Mt){var vt,Nt=I.gl;if(!this.failedToCreate){for(var Rt in I.program.set(this.program),I.setDepthMode($),I.setStencilMode(X),I.setColorMode(se),I.setCullFace(he),this.fixedUniforms)this.fixedUniforms[Rt].set(ye[Rt]);Dt&&Dt.setUniforms(I,this.binderUniforms,it,{zoom:xt});for(var Vt=(vt={},vt[Nt.LINES]=2,vt[Nt.TRIANGLES]=3,vt[Nt.LINE_STRIP]=1,vt)[j],rn=0,dn=Xe.get();rn<dn.length;rn+=1){var En=dn[rn],Tn=En.vaos||(En.vaos={});(Tn[be]||(Tn[be]=new Kl)).bind(I,this,Ee,Dt?Dt.getPaintVertexBuffers():[],Ue,En.vertexOffset,_t,Mt),Nt.drawElements(j,En.primitiveLength*Vt,Nt.UNSIGNED_SHORT,En.primitiveOffset*Vt*2)}}};var yc=function(I,j,$,X){var se=j.style.light,he=se.properties.get("position"),ye=[he.x,he.y,he.z],be=i.create$1();se.properties.get("anchor")==="viewport"&&i.fromRotation(be,-j.transform.angle),i.transformMat3(ye,ye,be);var Ee=se.properties.get("color");return{u_matrix:I,u_lightpos:ye,u_lightintensity:se.properties.get("intensity"),u_lightcolor:[Ee.r,Ee.g,Ee.b],u_vertical_gradient:+$,u_opacity:X}},Bc=function(I,j,$,X,se,he,ye){return i.extend(yc(I,j,$,X),Nc(he,j,ye),{u_height_factor:-Math.pow(2,se.overscaledZ)/ye.tileSize/8})},Vs=function(I){return{u_matrix:I}},qs=function(I,j,$,X){return i.extend(Vs(I),Nc($,j,X))},xl=function(I,j){return{u_matrix:I,u_world:j}},bl=function(I,j,$,X,se){return i.extend(qs(I,j,$,X),{u_world:se})},_l=function(I,j,$,X){var se,he,ye=I.transform;if(X.paint.get("circle-pitch-alignment")==="map"){var be=Dn($,1,ye.zoom);se=!0,he=[be,be]}else se=!1,he=ye.pixelsToGLUnits;return{u_camera_to_center_distance:ye.cameraToCenterDistance,u_scale_with_map:+(X.paint.get("circle-pitch-scale")==="map"),u_matrix:I.translatePosMatrix(j.posMatrix,$,X.paint.get("circle-translate"),X.paint.get("circle-translate-anchor")),u_pitch_with_map:+se,u_device_pixel_ratio:i.browser.devicePixelRatio,u_extrude_scale:he}},Ql=function(I,j,$){var X=Dn($,1,j.zoom),se=Math.pow(2,j.zoom-$.tileID.overscaledZ),he=$.tileID.overscaleFactor();return{u_matrix:I,u_camera_to_center_distance:j.cameraToCenterDistance,u_pixels_to_tile_units:X,u_extrude_scale:[j.pixelsToGLUnits[0]/(X*se),j.pixelsToGLUnits[1]/(X*se)],u_overscale_factor:he}},Es=function(I,j,$){return{u_matrix:I,u_inv_matrix:j,u_camera_to_center_distance:$.cameraToCenterDistance,u_viewport_size:[$.width,$.height]}},Ju=function(I,j,$){return $===void 0&&($=1),{u_matrix:I,u_color:j,u_overlay:0,u_overlay_scale:$}},vs=function(I){return{u_matrix:I}},wl=function(I,j,$,X){return{u_matrix:I,u_extrude_scale:Dn(j,1,$),u_intensity:X}};function Ku(I,j){var $=Math.pow(2,j.canonical.z),X=j.canonical.y;return[new i.MercatorCoordinate(0,X/$).toLngLat().lat,new i.MercatorCoordinate(0,(X+1)/$).toLngLat().lat]}var Hs=function(I,j,$){var X=I.transform;return{u_matrix:ut(I,j,$),u_ratio:1/Dn(j,1,X.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_units_to_pixels:[1/X.pixelsToGLUnits[0],1/X.pixelsToGLUnits[1]]}},ya=function(I,j,$){return i.extend(Hs(I,j,$),{u_image:0})},je=function(I,j,$,X){var se=I.transform,he=Qe(j,se);return{u_matrix:ut(I,j,$),u_texsize:j.imageAtlasTexture.size,u_ratio:1/Dn(j,1,se.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_image:0,u_scale:[he,X.fromScale,X.toScale],u_fade:X.t,u_units_to_pixels:[1/se.pixelsToGLUnits[0],1/se.pixelsToGLUnits[1]]}},He=function(I,j,$,X,se){var he=I.transform,ye=I.lineAtlas,be=Qe(j,he),Ee=$.layout.get("line-cap")==="round",Ue=ye.getDash(X.from,Ee),Xe=ye.getDash(X.to,Ee),it=Ue.width*se.fromScale,xt=Xe.width*se.toScale;return i.extend(Hs(I,j,$),{u_patternscale_a:[be/it,-Ue.height/2],u_patternscale_b:[be/xt,-Xe.height/2],u_sdfgamma:ye.width/(256*Math.min(it,xt)*i.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:Ue.y,u_tex_y_b:Xe.y,u_mix:se.t})};function Qe(I,j){return 1/Dn(I,1,j.tileZoom)}function ut(I,j,$){return I.translatePosMatrix(j.tileID.posMatrix,j,$.paint.get("line-translate"),$.paint.get("line-translate-anchor"))}var mt=function(I,j,$,X,se){return{u_matrix:I,u_tl_parent:j,u_scale_parent:$,u_buffer_scale:1,u_fade_t:X.mix,u_opacity:X.opacity*se.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:se.paint.get("raster-brightness-min"),u_brightness_high:se.paint.get("raster-brightness-max"),u_saturation_factor:(ye=se.paint.get("raster-saturation"),ye>0?1-1/(1.001-ye):-ye),u_contrast_factor:(he=se.paint.get("raster-contrast"),he>0?1/(1-he):1+he),u_spin_weights:pt(se.paint.get("raster-hue-rotate"))};var he,ye};function pt(I){I*=Math.PI/180;var j=Math.sin(I),$=Math.cos(I);return[(2*$+1)/3,(-Math.sqrt(3)*j-$+1)/3,(Math.sqrt(3)*j-$+1)/3]}var Ct,Qt=function(I,j,$,X,se,he,ye,be,Ee,Ue){var Xe=se.transform;return{u_is_size_zoom_constant:+(I==="constant"||I==="source"),u_is_size_feature_constant:+(I==="constant"||I==="camera"),u_size_t:j?j.uSizeT:0,u_size:j?j.uSize:0,u_camera_to_center_distance:Xe.cameraToCenterDistance,u_pitch:Xe.pitch/360*2*Math.PI,u_rotate_symbol:+$,u_aspect_ratio:Xe.width/Xe.height,u_fade_change:se.options.fadeDuration?se.symbolFadeChange:1,u_matrix:he,u_label_plane_matrix:ye,u_coord_matrix:be,u_is_text:+Ee,u_pitch_with_map:+X,u_texsize:Ue,u_texture:0}},en=function(I,j,$,X,se,he,ye,be,Ee,Ue,Xe){var it=se.transform;return i.extend(Qt(I,j,$,X,se,he,ye,be,Ee,Ue),{u_gamma_scale:X?Math.cos(it._pitch)*it.cameraToCenterDistance:1,u_device_pixel_ratio:i.browser.devicePixelRatio,u_is_halo:+Xe})},Yt=function(I,j,$,X,se,he,ye,be,Ee,Ue){return i.extend(en(I,j,$,X,se,he,ye,be,!0,Ee,!0),{u_texsize_icon:Ue,u_texture_icon:1})},an=function(I,j,$){return{u_matrix:I,u_opacity:j,u_color:$}},hn=function(I,j,$,X,se,he){return i.extend(function(ye,be,Ee,Ue){var Xe=Ee.imageManager.getPattern(ye.from.toString()),it=Ee.imageManager.getPattern(ye.to.toString()),xt=Ee.imageManager.getPixelSize(),Dt=xt.width,_t=xt.height,Mt=Math.pow(2,Ue.tileID.overscaledZ),vt=Ue.tileSize*Math.pow(2,Ee.transform.tileZoom)/Mt,Nt=vt*(Ue.tileID.canonical.x+Ue.tileID.wrap*Mt),Rt=vt*Ue.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:Xe.tl,u_pattern_br_a:Xe.br,u_pattern_tl_b:it.tl,u_pattern_br_b:it.br,u_texsize:[Dt,_t],u_mix:be.t,u_pattern_size_a:Xe.displaySize,u_pattern_size_b:it.displaySize,u_scale_a:be.fromScale,u_scale_b:be.toScale,u_tile_units_to_pixels:1/Dn(Ue,1,Ee.transform.tileZoom),u_pixel_coord_upper:[Nt>>16,Rt>>16],u_pixel_coord_lower:[65535&Nt,65535&Rt]}}(X,he,$,se),{u_matrix:I,u_opacity:j})},xn={fillExtrusion:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_lightpos:new i.Uniform3f(I,j.u_lightpos),u_lightintensity:new i.Uniform1f(I,j.u_lightintensity),u_lightcolor:new i.Uniform3f(I,j.u_lightcolor),u_vertical_gradient:new i.Uniform1f(I,j.u_vertical_gradient),u_opacity:new i.Uniform1f(I,j.u_opacity)}},fillExtrusionPattern:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_lightpos:new i.Uniform3f(I,j.u_lightpos),u_lightintensity:new i.Uniform1f(I,j.u_lightintensity),u_lightcolor:new i.Uniform3f(I,j.u_lightcolor),u_vertical_gradient:new i.Uniform1f(I,j.u_vertical_gradient),u_height_factor:new i.Uniform1f(I,j.u_height_factor),u_image:new i.Uniform1i(I,j.u_image),u_texsize:new i.Uniform2f(I,j.u_texsize),u_pixel_coord_upper:new i.Uniform2f(I,j.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(I,j.u_pixel_coord_lower),u_scale:new i.Uniform3f(I,j.u_scale),u_fade:new i.Uniform1f(I,j.u_fade),u_opacity:new i.Uniform1f(I,j.u_opacity)}},fill:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix)}},fillPattern:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_image:new i.Uniform1i(I,j.u_image),u_texsize:new i.Uniform2f(I,j.u_texsize),u_pixel_coord_upper:new i.Uniform2f(I,j.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(I,j.u_pixel_coord_lower),u_scale:new i.Uniform3f(I,j.u_scale),u_fade:new i.Uniform1f(I,j.u_fade)}},fillOutline:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_world:new i.Uniform2f(I,j.u_world)}},fillOutlinePattern:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_world:new i.Uniform2f(I,j.u_world),u_image:new i.Uniform1i(I,j.u_image),u_texsize:new i.Uniform2f(I,j.u_texsize),u_pixel_coord_upper:new i.Uniform2f(I,j.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(I,j.u_pixel_coord_lower),u_scale:new i.Uniform3f(I,j.u_scale),u_fade:new i.Uniform1f(I,j.u_fade)}},circle:function(I,j){return{u_camera_to_center_distance:new i.Uniform1f(I,j.u_camera_to_center_distance),u_scale_with_map:new i.Uniform1i(I,j.u_scale_with_map),u_pitch_with_map:new i.Uniform1i(I,j.u_pitch_with_map),u_extrude_scale:new i.Uniform2f(I,j.u_extrude_scale),u_device_pixel_ratio:new i.Uniform1f(I,j.u_device_pixel_ratio),u_matrix:new i.UniformMatrix4f(I,j.u_matrix)}},collisionBox:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_camera_to_center_distance:new i.Uniform1f(I,j.u_camera_to_center_distance),u_pixels_to_tile_units:new i.Uniform1f(I,j.u_pixels_to_tile_units),u_extrude_scale:new i.Uniform2f(I,j.u_extrude_scale),u_overscale_factor:new i.Uniform1f(I,j.u_overscale_factor)}},collisionCircle:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_inv_matrix:new i.UniformMatrix4f(I,j.u_inv_matrix),u_camera_to_center_distance:new i.Uniform1f(I,j.u_camera_to_center_distance),u_viewport_size:new i.Uniform2f(I,j.u_viewport_size)}},debug:function(I,j){return{u_color:new i.UniformColor(I,j.u_color),u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_overlay:new i.Uniform1i(I,j.u_overlay),u_overlay_scale:new i.Uniform1f(I,j.u_overlay_scale)}},clippingMask:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix)}},heatmap:function(I,j){return{u_extrude_scale:new i.Uniform1f(I,j.u_extrude_scale),u_intensity:new i.Uniform1f(I,j.u_intensity),u_matrix:new i.UniformMatrix4f(I,j.u_matrix)}},heatmapTexture:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_world:new i.Uniform2f(I,j.u_world),u_image:new i.Uniform1i(I,j.u_image),u_color_ramp:new i.Uniform1i(I,j.u_color_ramp),u_opacity:new i.Uniform1f(I,j.u_opacity)}},hillshade:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_image:new i.Uniform1i(I,j.u_image),u_latrange:new i.Uniform2f(I,j.u_latrange),u_light:new i.Uniform2f(I,j.u_light),u_shadow:new i.UniformColor(I,j.u_shadow),u_highlight:new i.UniformColor(I,j.u_highlight),u_accent:new i.UniformColor(I,j.u_accent)}},hillshadePrepare:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_image:new i.Uniform1i(I,j.u_image),u_dimension:new i.Uniform2f(I,j.u_dimension),u_zoom:new i.Uniform1f(I,j.u_zoom),u_maxzoom:new i.Uniform1f(I,j.u_maxzoom),u_unpack:new i.Uniform4f(I,j.u_unpack)}},line:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_ratio:new i.Uniform1f(I,j.u_ratio),u_device_pixel_ratio:new i.Uniform1f(I,j.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(I,j.u_units_to_pixels)}},lineGradient:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_ratio:new i.Uniform1f(I,j.u_ratio),u_device_pixel_ratio:new i.Uniform1f(I,j.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(I,j.u_units_to_pixels),u_image:new i.Uniform1i(I,j.u_image)}},linePattern:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_texsize:new i.Uniform2f(I,j.u_texsize),u_ratio:new i.Uniform1f(I,j.u_ratio),u_device_pixel_ratio:new i.Uniform1f(I,j.u_device_pixel_ratio),u_image:new i.Uniform1i(I,j.u_image),u_units_to_pixels:new i.Uniform2f(I,j.u_units_to_pixels),u_scale:new i.Uniform3f(I,j.u_scale),u_fade:new i.Uniform1f(I,j.u_fade)}},lineSDF:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_ratio:new i.Uniform1f(I,j.u_ratio),u_device_pixel_ratio:new i.Uniform1f(I,j.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(I,j.u_units_to_pixels),u_patternscale_a:new i.Uniform2f(I,j.u_patternscale_a),u_patternscale_b:new i.Uniform2f(I,j.u_patternscale_b),u_sdfgamma:new i.Uniform1f(I,j.u_sdfgamma),u_image:new i.Uniform1i(I,j.u_image),u_tex_y_a:new i.Uniform1f(I,j.u_tex_y_a),u_tex_y_b:new i.Uniform1f(I,j.u_tex_y_b),u_mix:new i.Uniform1f(I,j.u_mix)}},raster:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_tl_parent:new i.Uniform2f(I,j.u_tl_parent),u_scale_parent:new i.Uniform1f(I,j.u_scale_parent),u_buffer_scale:new i.Uniform1f(I,j.u_buffer_scale),u_fade_t:new i.Uniform1f(I,j.u_fade_t),u_opacity:new i.Uniform1f(I,j.u_opacity),u_image0:new i.Uniform1i(I,j.u_image0),u_image1:new i.Uniform1i(I,j.u_image1),u_brightness_low:new i.Uniform1f(I,j.u_brightness_low),u_brightness_high:new i.Uniform1f(I,j.u_brightness_high),u_saturation_factor:new i.Uniform1f(I,j.u_saturation_factor),u_contrast_factor:new i.Uniform1f(I,j.u_contrast_factor),u_spin_weights:new i.Uniform3f(I,j.u_spin_weights)}},symbolIcon:function(I,j){return{u_is_size_zoom_constant:new i.Uniform1i(I,j.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(I,j.u_is_size_feature_constant),u_size_t:new i.Uniform1f(I,j.u_size_t),u_size:new i.Uniform1f(I,j.u_size),u_camera_to_center_distance:new i.Uniform1f(I,j.u_camera_to_center_distance),u_pitch:new i.Uniform1f(I,j.u_pitch),u_rotate_symbol:new i.Uniform1i(I,j.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(I,j.u_aspect_ratio),u_fade_change:new i.Uniform1f(I,j.u_fade_change),u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(I,j.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(I,j.u_coord_matrix),u_is_text:new i.Uniform1i(I,j.u_is_text),u_pitch_with_map:new i.Uniform1i(I,j.u_pitch_with_map),u_texsize:new i.Uniform2f(I,j.u_texsize),u_texture:new i.Uniform1i(I,j.u_texture)}},symbolSDF:function(I,j){return{u_is_size_zoom_constant:new i.Uniform1i(I,j.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(I,j.u_is_size_feature_constant),u_size_t:new i.Uniform1f(I,j.u_size_t),u_size:new i.Uniform1f(I,j.u_size),u_camera_to_center_distance:new i.Uniform1f(I,j.u_camera_to_center_distance),u_pitch:new i.Uniform1f(I,j.u_pitch),u_rotate_symbol:new i.Uniform1i(I,j.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(I,j.u_aspect_ratio),u_fade_change:new i.Uniform1f(I,j.u_fade_change),u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(I,j.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(I,j.u_coord_matrix),u_is_text:new i.Uniform1i(I,j.u_is_text),u_pitch_with_map:new i.Uniform1i(I,j.u_pitch_with_map),u_texsize:new i.Uniform2f(I,j.u_texsize),u_texture:new i.Uniform1i(I,j.u_texture),u_gamma_scale:new i.Uniform1f(I,j.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(I,j.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(I,j.u_is_halo)}},symbolTextAndIcon:function(I,j){return{u_is_size_zoom_constant:new i.Uniform1i(I,j.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(I,j.u_is_size_feature_constant),u_size_t:new i.Uniform1f(I,j.u_size_t),u_size:new i.Uniform1f(I,j.u_size),u_camera_to_center_distance:new i.Uniform1f(I,j.u_camera_to_center_distance),u_pitch:new i.Uniform1f(I,j.u_pitch),u_rotate_symbol:new i.Uniform1i(I,j.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(I,j.u_aspect_ratio),u_fade_change:new i.Uniform1f(I,j.u_fade_change),u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(I,j.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(I,j.u_coord_matrix),u_is_text:new i.Uniform1i(I,j.u_is_text),u_pitch_with_map:new i.Uniform1i(I,j.u_pitch_with_map),u_texsize:new i.Uniform2f(I,j.u_texsize),u_texsize_icon:new i.Uniform2f(I,j.u_texsize_icon),u_texture:new i.Uniform1i(I,j.u_texture),u_texture_icon:new i.Uniform1i(I,j.u_texture_icon),u_gamma_scale:new i.Uniform1f(I,j.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(I,j.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(I,j.u_is_halo)}},background:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_opacity:new i.Uniform1f(I,j.u_opacity),u_color:new i.UniformColor(I,j.u_color)}},backgroundPattern:function(I,j){return{u_matrix:new i.UniformMatrix4f(I,j.u_matrix),u_opacity:new i.Uniform1f(I,j.u_opacity),u_image:new i.Uniform1i(I,j.u_image),u_pattern_tl_a:new i.Uniform2f(I,j.u_pattern_tl_a),u_pattern_br_a:new i.Uniform2f(I,j.u_pattern_br_a),u_pattern_tl_b:new i.Uniform2f(I,j.u_pattern_tl_b),u_pattern_br_b:new i.Uniform2f(I,j.u_pattern_br_b),u_texsize:new i.Uniform2f(I,j.u_texsize),u_mix:new i.Uniform1f(I,j.u_mix),u_pattern_size_a:new i.Uniform2f(I,j.u_pattern_size_a),u_pattern_size_b:new i.Uniform2f(I,j.u_pattern_size_b),u_scale_a:new i.Uniform1f(I,j.u_scale_a),u_scale_b:new i.Uniform1f(I,j.u_scale_b),u_pixel_coord_upper:new i.Uniform2f(I,j.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(I,j.u_pixel_coord_lower),u_tile_units_to_pixels:new i.Uniform1f(I,j.u_tile_units_to_pixels)}}};function _n(I,j,$,X,se,he,ye){for(var be=I.context,Ee=be.gl,Ue=I.useProgram("collisionBox"),Xe=[],it=0,xt=0,Dt=0;Dt<X.length;Dt++){var _t=X[Dt],Mt=j.getTile(_t),vt=Mt.getBucket($);if(vt){var Nt=_t.posMatrix;se[0]===0&&se[1]===0||(Nt=I.translatePosMatrix(_t.posMatrix,Mt,se,he));var Rt=ye?vt.textCollisionBox:vt.iconCollisionBox,Vt=vt.collisionCircleArray;if(Vt.length>0){var rn=i.create(),dn=Nt;i.mul(rn,vt.placementInvProjMatrix,I.transform.glCoordMatrix),i.mul(rn,rn,vt.placementViewportMatrix),Xe.push({circleArray:Vt,circleOffset:xt,transform:dn,invTransform:rn}),xt=it+=Vt.length/4}Rt&&Ue.draw(be,Ee.LINES,dt.disabled,Oe.disabled,I.colorModeForRenderPass(),Te.disabled,Ql(Nt,I.transform,Mt),$.id,Rt.layoutVertexBuffer,Rt.indexBuffer,Rt.segments,null,I.transform.zoom,null,null,Rt.collisionVertexBuffer)}}if(ye&&Xe.length){var En=I.useProgram("collisionCircle"),Tn=new i.StructArrayLayout2f1f2i16;Tn.resize(4*it),Tn._trim();for(var tr=0,er=0,gr=Xe;er<gr.length;er+=1)for(var cr=gr[er],Xr=0;Xr<cr.circleArray.length/4;Xr++){var oi=4*Xr,Ai=cr.circleArray[oi+0],Gn=cr.circleArray[oi+1],Mr=cr.circleArray[oi+2],si=cr.circleArray[oi+3];Tn.emplace(tr++,Ai,Gn,Mr,si,0),Tn.emplace(tr++,Ai,Gn,Mr,si,1),Tn.emplace(tr++,Ai,Gn,Mr,si,2),Tn.emplace(tr++,Ai,Gn,Mr,si,3)}(!Ct||Ct.length<2*it)&&(Ct=function(Oi){var ta=2*Oi,Ni=new i.StructArrayLayout3ui6;Ni.resize(ta),Ni._trim();for(var na=0;na<ta;na++){var pa=6*na;Ni.uint16[pa+0]=4*na+0,Ni.uint16[pa+1]=4*na+1,Ni.uint16[pa+2]=4*na+2,Ni.uint16[pa+3]=4*na+2,Ni.uint16[pa+4]=4*na+3,Ni.uint16[pa+5]=4*na+0}return Ni}(it));for(var Qr=be.createIndexBuffer(Ct,!0),mi=be.createVertexBuffer(Tn,i.collisionCircleLayout.members,!0),Mi=0,Zi=Xe;Mi<Zi.length;Mi+=1){var fi=Zi[Mi],zi=Es(fi.transform,fi.invTransform,I.transform);En.draw(be,Ee.TRIANGLES,dt.disabled,Oe.disabled,I.colorModeForRenderPass(),Te.disabled,zi,$.id,mi,Qr,i.SegmentVector.simpleSegment(0,2*fi.circleOffset,fi.circleArray.length,fi.circleArray.length/2),null,I.transform.zoom,null,null,null)}mi.destroy(),Qr.destroy()}}var On=i.identity(new Float32Array(16));function sr(I,j,$,X,se,he){var ye=i.getAnchorAlignment(I),be=-(ye.horizontalAlign-.5)*j,Ee=-(ye.verticalAlign-.5)*$,Ue=i.evaluateVariableOffset(I,X);return new i.Point((be/se+Ue[0])*he,(Ee/se+Ue[1])*he)}function mr(I,j,$,X,se,he,ye,be,Ee,Ue,Xe){var it=I.text.placedSymbolArray,xt=I.text.dynamicLayoutVertexArray,Dt=I.icon.dynamicLayoutVertexArray,_t={};xt.clear();for(var Mt=0;Mt<it.length;Mt++){var vt=it.get(Mt),Nt=I.allowVerticalPlacement&&!vt.placedOrientation,Rt=vt.hidden||!vt.crossTileID||Nt?null:X[vt.crossTileID];if(Rt){var Vt=new i.Point(vt.anchorX,vt.anchorY),rn=tn(Vt,$?be:ye),dn=nn(he.cameraToCenterDistance,rn.signedDistanceFromCamera),En=se.evaluateSizeForFeature(I.textSizeData,Ue,vt)*dn/i.ONE_EM;$&&(En*=I.tilePixelRatio/Ee);for(var Tn=Rt.width,tr=Rt.height,er=sr(Rt.anchor,Tn,tr,Rt.textOffset,Rt.textBoxScale,En),gr=$?tn(Vt.add(er),ye).point:rn.point.add(j?er.rotate(-he.angle):er),cr=I.allowVerticalPlacement&&vt.placedOrientation===i.WritingMode.vertical?Math.PI/2:0,Xr=0;Xr<vt.numGlyphs;Xr++)i.addDynamicAttributes(xt,gr,cr);Xe&&vt.associatedIconIndex>=0&&(_t[vt.associatedIconIndex]={shiftedAnchor:gr,angle:cr})}else yr(vt.numGlyphs,xt)}if(Xe){Dt.clear();for(var oi=I.icon.placedSymbolArray,Ai=0;Ai<oi.length;Ai++){var Gn=oi.get(Ai);if(Gn.hidden)yr(Gn.numGlyphs,Dt);else{var Mr=_t[Ai];if(Mr)for(var si=0;si<Gn.numGlyphs;si++)i.addDynamicAttributes(Dt,Mr.shiftedAnchor,Mr.angle);else yr(Gn.numGlyphs,Dt)}}I.icon.dynamicLayoutVertexBuffer.updateData(Dt)}I.text.dynamicLayoutVertexBuffer.updateData(xt)}function Fr(I,j,$){return $.iconsInText&&j?"symbolTextAndIcon":I?"symbolSDF":"symbolIcon"}function jr(I,j,$,X,se,he,ye,be,Ee,Ue,Xe,it){for(var xt=I.context,Dt=xt.gl,_t=I.transform,Mt=be==="map",vt=Ee==="map",Nt=Mt&&$.layout.get("symbol-placement")!=="point",Rt=Mt&&!vt&&!Nt,Vt=$.layout.get("symbol-sort-key").constantOr(1)!==void 0,rn=I.depthModeForSublayer(0,dt.ReadOnly),dn=$.layout.get("text-variable-anchor"),En=[],Tn=0,tr=X;Tn<tr.length;Tn+=1){var er=tr[Tn],gr=j.getTile(er),cr=gr.getBucket($);if(cr){var Xr=se?cr.text:cr.icon;if(Xr&&Xr.segments.get().length){var oi=Xr.programConfigurations.get($.id),Ai=se||cr.sdfIcons,Gn=se?cr.textSizeData:cr.iconSizeData,Mr=vt||_t.pitch!==0,si=I.useProgram(Fr(Ai,se,cr),oi),Qr=i.evaluateSizeForZoom(Gn,_t.zoom),mi=void 0,Mi=[0,0],Zi=void 0,fi=void 0,zi=null,Oi=void 0;if(se){if(Zi=gr.glyphAtlasTexture,fi=Dt.LINEAR,mi=gr.glyphAtlasTexture.size,cr.iconsInText){Mi=gr.imageAtlasTexture.size,zi=gr.imageAtlasTexture;var ta=Gn.kind==="composite"||Gn.kind==="camera";Oi=Mr||I.options.rotating||I.options.zooming||ta?Dt.LINEAR:Dt.NEAREST}}else{var Ni=$.layout.get("icon-size").constantOr(0)!==1||cr.iconsNeedLinear;Zi=gr.imageAtlasTexture,fi=Ai||I.options.rotating||I.options.zooming||Ni||Mr?Dt.LINEAR:Dt.NEAREST,mi=gr.imageAtlasTexture.size}var na=Dn(gr,1,I.transform.zoom),pa=Fn(er.posMatrix,vt,Mt,I.transform,na),Ga=pn(er.posMatrix,vt,Mt,I.transform,na),ko=dn&&cr.hasTextData(),To=$.layout.get("icon-text-fit")!=="none"&&ko&&cr.hasIconData();Nt&&gn(cr,er.posMatrix,I,se,pa,Ga,vt,Ue);var 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be,Ee,Ue,Xe,it,xt=I.context.gl,Dt=$.paint.get("fill-pattern"),_t=Dt&&Dt.constantOr(1),Mt=$.getCrossfadeParameters();ye?(Ee=_t&&!$.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",be=xt.LINES):(Ee=_t?"fillPattern":"fill",be=xt.TRIANGLES);for(var vt=0,Nt=X;vt<Nt.length;vt+=1){var Rt=Nt[vt],Vt=j.getTile(Rt);if(!_t||Vt.patternsLoaded()){var rn=Vt.getBucket($);if(rn){var dn=rn.programConfigurations.get($.id),En=I.useProgram(Ee,dn);_t&&(I.context.activeTexture.set(xt.TEXTURE0),Vt.imageAtlasTexture.bind(xt.LINEAR,xt.CLAMP_TO_EDGE),dn.updatePaintBuffers(Mt));var Tn=Dt.constantOr(null);if(Tn&&Vt.imageAtlas){var tr=Vt.imageAtlas,er=tr.patternPositions[Tn.to.toString()],gr=tr.patternPositions[Tn.from.toString()];er&&gr&&dn.setConstantPatternPositions(er,gr)}var cr=I.translatePosMatrix(Rt.posMatrix,Vt,$.paint.get("fill-translate"),$.paint.get("fill-translate-anchor"));if(ye){Xe=rn.indexBuffer2,it=rn.segments2;var 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dn=vt.imageAtlas,En=dn.patternPositions[rn.to.toString()],Tn=dn.patternPositions[rn.from.toString()];En&&Tn&&Rt.setConstantPatternPositions(En,Tn)}var tr=I.translatePosMatrix(Mt.posMatrix,vt,$.paint.get("fill-extrusion-translate"),$.paint.get("fill-extrusion-translate-anchor")),er=$.paint.get("fill-extrusion-vertical-gradient"),gr=Xe?Bc(tr,I,er,xt,Mt,it,vt):yc(tr,I,er,xt);Vt.draw(be,be.gl.TRIANGLES,se,he,ye,Te.backCCW,gr,$.id,Nt.layoutVertexBuffer,Nt.indexBuffer,Nt.segments,$.paint,I.transform.zoom,Rt)}}}function qi(I,j,$,X,se,he){var ye=I.context,be=ye.gl,Ee=j.fbo;if(Ee){var Ue=I.useProgram("hillshade");ye.activeTexture.set(be.TEXTURE0),be.bindTexture(be.TEXTURE_2D,Ee.colorAttachment.get());var Xe=function(it,xt,Dt){var 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he=X.paint.get("raster-fade-duration");if(he>0){var ye=i.browser.now(),be=(ye-I.timeAdded)/he,Ee=j?(ye-j.timeAdded)/he:-1,Ue=$.getSource(),Xe=se.coveringZoomLevel({tileSize:Ue.tileSize,roundZoom:Ue.roundZoom}),it=!j||Math.abs(j.tileID.overscaledZ-Xe)>Math.abs(I.tileID.overscaledZ-Xe),xt=it&&I.refreshedUponExpiration?1:i.clamp(it?be:1-Ee,0,1);return I.refreshedUponExpiration&&be>=1&&(I.refreshedUponExpiration=!1),j?{opacity:1,mix:1-xt}:{opacity:xt,mix:0}}return{opacity:1,mix:0}}var da=new i.Color(1,0,0,1),fo=new i.Color(0,1,0,1),so=new i.Color(0,0,1,1),za=new i.Color(1,0,1,1),Na=new i.Color(0,1,1,1);function lo(I){var j=I.transform.padding;Fo(I,I.transform.height-(j.top||0),3,da),Fo(I,j.bottom||0,3,fo),is(I,j.left||0,3,so),is(I,I.transform.width-(j.right||0),3,za);var $=I.transform.centerPoint;(function(X,se,he,ye){as(X,se-1,he-10,2,20,ye),as(X,se-10,he-1,20,2,ye)})(I,$.x,I.transform.height-$.y,Na)}function Fo(I,j,$,X){as(I,0,j+$/2,I.transform.width,$,X)}function 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gr=dn.context.gl;return{u_matrix:er,u_world:[gr.drawingBufferWidth,gr.drawingBufferHeight],u_image:Tn,u_color_ramp:tr,u_opacity:En.paint.get("heatmap-opacity")}}(Mt,vt,0,1),vt.id,Mt.viewportBuffer,Mt.quadTriangleIndexBuffer,Mt.viewportSegments,vt.paint,Mt.transform.zoom)}}(I,$))},line:function(I,j,$,X){if(I.renderPass==="translucent"){var se=$.paint.get("line-opacity"),he=$.paint.get("line-width");if(se.constantOr(1)!==0&&he.constantOr(1)!==0){var ye=I.depthModeForSublayer(0,dt.ReadOnly),be=I.colorModeForRenderPass(),Ee=$.paint.get("line-dasharray"),Ue=$.paint.get("line-pattern"),Xe=Ue.constantOr(1),it=$.paint.get("line-gradient"),xt=$.getCrossfadeParameters(),Dt=Xe?"linePattern":Ee?"lineSDF":it?"lineGradient":"line",_t=I.context,Mt=_t.gl,vt=!0;if(it){_t.activeTexture.set(Mt.TEXTURE0);var Nt=$.gradientTexture;if(!$.gradient)return;Nt||(Nt=$.gradientTexture=new i.Texture(_t,$.gradient,Mt.RGBA)),Nt.bind(Mt.LINEAR,Mt.CLAMP_TO_EDGE)}for(var Rt=0,Vt=X;Rt<Vt.length;Rt+=1){var rn=Vt[Rt],dn=j.getTile(rn);if(!Xe||dn.patternsLoaded()){var En=dn.getBucket($);if(En){var Tn=En.programConfigurations.get($.id),tr=I.context.program.get(),er=I.useProgram(Dt,Tn),gr=vt||er.program!==tr,cr=Ue.constantOr(null);if(cr&&dn.imageAtlas){var Xr=dn.imageAtlas,oi=Xr.patternPositions[cr.to.toString()],Ai=Xr.patternPositions[cr.from.toString()];oi&&Ai&&Tn.setConstantPatternPositions(oi,Ai)}var Gn=Xe?je(I,dn,$,xt):Ee?He(I,dn,$,Ee,xt):it?ya(I,dn,$):Hs(I,dn,$);Xe?(_t.activeTexture.set(Mt.TEXTURE0),dn.imageAtlasTexture.bind(Mt.LINEAR,Mt.CLAMP_TO_EDGE),Tn.updatePaintBuffers(xt)):Ee&&(gr||I.lineAtlas.dirty)&&(_t.activeTexture.set(Mt.TEXTURE0),I.lineAtlas.bind(_t)),er.draw(_t,Mt.TRIANGLES,ye,I.stencilModeForClipping(rn),be,Te.disabled,Gn,$.id,En.layoutVertexBuffer,En.indexBuffer,En.segments,$.paint,I.transform.zoom,Tn),vt=!1}}}}}},fill:function(I,j,$,X){var se=$.paint.get("fill-color"),he=$.paint.get("fill-opacity");if(he.constantOr(1)!==0){var ye=I.colorModeForRenderPass(),be=$.paint.get("fill-pattern"),Ee=I.opaquePassEnabledForLayer()&&!be.constantOr(1)&&se.constantOr(i.Color.transparent).a===1&&he.constantOr(0)===1?"opaque":"translucent";if(I.renderPass===Ee){var Ue=I.depthModeForSublayer(1,I.renderPass==="opaque"?dt.ReadWrite:dt.ReadOnly);Ur(I,j,$,X,Ue,ye,!1)}if(I.renderPass==="translucent"&&$.paint.get("fill-antialias")){var Xe=I.depthModeForSublayer($.getPaintProperty("fill-outline-color")?2:0,dt.ReadOnly);Ur(I,j,$,X,Xe,ye,!0)}}},"fill-extrusion":function(I,j,$,X){var se=$.paint.get("fill-extrusion-opacity");if(se!==0&&I.renderPass==="translucent"){var he=new dt(I.context.gl.LEQUAL,dt.ReadWrite,I.depthRangeFor3D);if(se!==1||$.paint.get("fill-extrusion-pattern").constantOr(1))Di(I,j,$,X,he,Oe.disabled,Ie.disabled),Di(I,j,$,X,he,I.stencilModeFor3D(),I.colorModeForRenderPass());else{var ye=I.colorModeForRenderPass();Di(I,j,$,X,he,Oe.disabled,ye)}}},hillshade:function(I,j,$,X){if(I.renderPass==="offscreen"||I.renderPass==="translucent"){for(var se=I.context,he=j.getSource().maxzoom,ye=I.depthModeForSublayer(0,dt.ReadOnly),be=I.colorModeForRenderPass(),Ee=I.renderPass==="translucent"?I.stencilConfigForOverlap(X):[{},X],Ue=Ee[0],Xe=0,it=Ee[1];Xe<it.length;Xe+=1){var xt=it[Xe],Dt=j.getTile(xt);Dt.needsHillshadePrepare&&I.renderPass==="offscreen"?ha(I,Dt,$,he,ye,Oe.disabled,be):I.renderPass==="translucent"&&qi(I,Dt,$,ye,Ue[xt.overscaledZ],be)}se.viewport.set([0,0,I.width,I.height])}},raster:function(I,j,$,X){if(I.renderPass==="translucent"&&$.paint.get("raster-opacity")!==0&&X.length)for(var se=I.context,he=se.gl,ye=j.getSource(),be=I.useProgram("raster"),Ee=I.colorModeForRenderPass(),Ue=ye instanceof Y?[{},X]:I.stencilConfigForOverlap(X),Xe=Ue[0],it=Ue[1],xt=it[it.length-1].overscaledZ,Dt=!I.options.moving,_t=0,Mt=it;_t<Mt.length;_t+=1){var vt=Mt[_t],Nt=I.depthModeForSublayer(vt.overscaledZ-xt,$.paint.get("raster-opacity")===1?dt.ReadWrite:dt.ReadOnly,he.LESS),Rt=j.getTile(vt),Vt=I.transform.calculatePosMatrix(vt.toUnwrapped(),Dt);Rt.registerFadeDuration($.paint.get("raster-fade-duration"));var rn=j.findLoadedParent(vt,0),dn=ca(Rt,rn,j,$,I.transform),En=void 0,Tn=void 0,tr=$.paint.get("raster-resampling")==="nearest"?he.NEAREST:he.LINEAR;se.activeTexture.set(he.TEXTURE0),Rt.texture.bind(tr,he.CLAMP_TO_EDGE,he.LINEAR_MIPMAP_NEAREST),se.activeTexture.set(he.TEXTURE1),rn?(rn.texture.bind(tr,he.CLAMP_TO_EDGE,he.LINEAR_MIPMAP_NEAREST),En=Math.pow(2,rn.tileID.overscaledZ-Rt.tileID.overscaledZ),Tn=[Rt.tileID.canonical.x*En%1,Rt.tileID.canonical.y*En%1]):Rt.texture.bind(tr,he.CLAMP_TO_EDGE,he.LINEAR_MIPMAP_NEAREST);var er=mt(Vt,Tn||[0,0],En||1,dn,$);ye instanceof Y?be.draw(se,he.TRIANGLES,Nt,Oe.disabled,Ee,Te.disabled,er,$.id,ye.boundsBuffer,I.quadTriangleIndexBuffer,ye.boundsSegments):be.draw(se,he.TRIANGLES,Nt,Xe[vt.overscaledZ],Ee,Te.disabled,er,$.id,I.rasterBoundsBuffer,I.quadTriangleIndexBuffer,I.rasterBoundsSegments)}},background:function(I,j,$){var X=$.paint.get("background-color"),se=$.paint.get("background-opacity");if(se!==0){var he=I.context,ye=he.gl,be=I.transform,Ee=be.tileSize,Ue=$.paint.get("background-pattern");if(!I.isPatternMissing(Ue)){var Xe=!Ue&&X.a===1&&se===1&&I.opaquePassEnabledForLayer()?"opaque":"translucent";if(I.renderPass===Xe){var it=Oe.disabled,xt=I.depthModeForSublayer(0,Xe==="opaque"?dt.ReadWrite:dt.ReadOnly),Dt=I.colorModeForRenderPass(),_t=I.useProgram(Ue?"backgroundPattern":"background"),Mt=be.coveringTiles({tileSize:Ee});Ue&&(he.activeTexture.set(ye.TEXTURE0),I.imageManager.bind(I.context));for(var vt=$.getCrossfadeParameters(),Nt=0,Rt=Mt;Nt<Rt.length;Nt+=1){var Vt=Rt[Nt],rn=I.transform.calculatePosMatrix(Vt.toUnwrapped()),dn=Ue?hn(rn,se,I,Ue,{tileID:Vt,tileSize:Ee},vt):an(rn,se,X);_t.draw(he,ye.TRIANGLES,xt,it,Dt,Te.disabled,dn,$.id,I.tileExtentBuffer,I.quadTriangleIndexBuffer,I.tileExtentSegments)}}}}},debug:function(I,j,$){for(var X=0;X<$.length;X++)os(I,j,$[X])},custom:function(I,j,$){var X=I.context,se=$.implementation;if(I.renderPass==="offscreen"){var he=se.prerender;he&&(I.setCustomLayerDefaults(),X.setColorMode(I.colorModeForRenderPass()),he.call(se,X.gl,I.transform.customLayerMatrix()),X.setDirty(),I.setBaseState())}else if(I.renderPass==="translucent"){I.setCustomLayerDefaults(),X.setColorMode(I.colorModeForRenderPass()),X.setStencilMode(Oe.disabled);var ye=se.renderingMode==="3d"?new dt(I.context.gl.LEQUAL,dt.ReadWrite,I.depthRangeFor3D):I.depthModeForSublayer(0,dt.ReadOnly);X.setDepthMode(ye),se.render(X.gl,I.transform.customLayerMatrix()),X.setDirty(),I.setBaseState(),X.bindFramebuffer.set(null)}}},ia=function(I,j){this.context=new Pe(I),this.transform=j,this._tileTextures={},this.setup(),this.numSublayers=qe.maxUnderzooming+qe.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Yn,this.gpuTimers={}};ia.prototype.resize=function(I,j){if(this.width=I*i.browser.devicePixelRatio,this.height=j*i.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var $=0,X=this.style._order;$<X.length;$+=1){var se=X[$];this.style._layers[se].resize()}},ia.prototype.setup=function(){var I=this.context,j=new i.StructArrayLayout2i4;j.emplaceBack(0,0),j.emplaceBack(i.EXTENT,0),j.emplaceBack(0,i.EXTENT),j.emplaceBack(i.EXTENT,i.EXTENT),this.tileExtentBuffer=I.createVertexBuffer(j,Ir.members),this.tileExtentSegments=i.SegmentVector.simpleSegment(0,0,4,2);var $=new i.StructArrayLayout2i4;$.emplaceBack(0,0),$.emplaceBack(i.EXTENT,0),$.emplaceBack(0,i.EXTENT),$.emplaceBack(i.EXTENT,i.EXTENT),this.debugBuffer=I.createVertexBuffer($,Ir.members),this.debugSegments=i.SegmentVector.simpleSegment(0,0,4,5);var X=new i.StructArrayLayout4i8;X.emplaceBack(0,0,0,0),X.emplaceBack(i.EXTENT,0,i.EXTENT,0),X.emplaceBack(0,i.EXTENT,0,i.EXTENT),X.emplaceBack(i.EXTENT,i.EXTENT,i.EXTENT,i.EXTENT),this.rasterBoundsBuffer=I.createVertexBuffer(X,te.members),this.rasterBoundsSegments=i.SegmentVector.simpleSegment(0,0,4,2);var se=new i.StructArrayLayout2i4;se.emplaceBack(0,0),se.emplaceBack(1,0),se.emplaceBack(0,1),se.emplaceBack(1,1),this.viewportBuffer=I.createVertexBuffer(se,Ir.members),this.viewportSegments=i.SegmentVector.simpleSegment(0,0,4,2);var he=new i.StructArrayLayout1ui2;he.emplaceBack(0),he.emplaceBack(1),he.emplaceBack(3),he.emplaceBack(2),he.emplaceBack(0),this.tileBorderIndexBuffer=I.createIndexBuffer(he);var ye=new i.StructArrayLayout3ui6;ye.emplaceBack(0,1,2),ye.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=I.createIndexBuffer(ye),this.emptyTexture=new i.Texture(I,{width:1,height:1,data:new Uint8Array([0,0,0,0])},I.gl.RGBA);var be=this.context.gl;this.stencilClearMode=new Oe({func:be.ALWAYS,mask:0},0,255,be.ZERO,be.ZERO,be.ZERO)},ia.prototype.clearStencil=function(){var I=this.context,j=I.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var $=i.create();i.ortho($,0,this.width,this.height,0,0,1),i.scale($,$,[j.drawingBufferWidth,j.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(I,j.TRIANGLES,dt.disabled,this.stencilClearMode,Ie.disabled,Te.disabled,vs($),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},ia.prototype._renderTileClippingMasks=function(I,j){if(this.currentStencilSource!==I.source&&I.isTileClipped()&&j&&j.length){this.currentStencilSource=I.source;var $=this.context,X=$.gl;this.nextStencilID+j.length>256&&this.clearStencil(),$.setColorMode(Ie.disabled),$.setDepthMode(dt.disabled);var se=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var he=0,ye=j;he<ye.length;he+=1){var be=ye[he],Ee=this._tileClippingMaskIDs[be.key]=this.nextStencilID++;se.draw($,X.TRIANGLES,dt.disabled,new Oe({func:X.ALWAYS,mask:0},Ee,255,X.KEEP,X.KEEP,X.REPLACE),Ie.disabled,Te.disabled,vs(be.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},ia.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var I=this.nextStencilID++,j=this.context.gl;return new Oe({func:j.NOTEQUAL,mask:255},I,255,j.KEEP,j.KEEP,j.REPLACE)},ia.prototype.stencilModeForClipping=function(I){var j=this.context.gl;return new Oe({func:j.EQUAL,mask:255},this._tileClippingMaskIDs[I.key],0,j.KEEP,j.KEEP,j.REPLACE)},ia.prototype.stencilConfigForOverlap=function(I){var j,$=this.context.gl,X=I.sort(function(Ee,Ue){return Ue.overscaledZ-Ee.overscaledZ}),se=X[X.length-1].overscaledZ,he=X[0].overscaledZ-se+1;if(he>1){this.currentStencilSource=void 0,this.nextStencilID+he>256&&this.clearStencil();for(var ye={},be=0;be<he;be++)ye[be+se]=new Oe({func:$.GEQUAL,mask:255},be+this.nextStencilID,255,$.KEEP,$.KEEP,$.REPLACE);return this.nextStencilID+=he,[ye,X]}return[(j={},j[se]=Oe.disabled,j),X]},ia.prototype.colorModeForRenderPass=function(){var I=this.context.gl;return this._showOverdrawInspector?new Ie([I.CONSTANT_COLOR,I.ONE],new i.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0]):this.renderPass==="opaque"?Ie.unblended:Ie.alphaBlended},ia.prototype.depthModeForSublayer=function(I,j,$){if(!this.opaquePassEnabledForLayer())return dt.disabled;var X=1-((1+this.currentLayer)*this.numSublayers+I)*this.depthEpsilon;return new dt($||this.context.gl.LEQUAL,j,[X,X])},ia.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},ia.prototype.render=function(I,j){var $=this;this.style=I,this.options=j,this.lineAtlas=I.lineAtlas,this.imageManager=I.imageManager,this.glyphManager=I.glyphManager,this.symbolFadeChange=I.placement.symbolFadeChange(i.browser.now()),this.imageManager.beginFrame();var X=this.style._order,se=this.style.sourceCaches;for(var he in se){var ye=se[he];ye.used&&ye.prepare(this.context)}var be,Ee,Ue={},Xe={},it={};for(var xt in se){var Dt=se[xt];Ue[xt]=Dt.getVisibleCoordinates(),Xe[xt]=Ue[xt].slice().reverse(),it[xt]=Dt.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var _t=0;_t<X.length;_t++){var Mt=X[_t];if(this.style._layers[Mt].is3D()){this.opaquePassCutoff=_t;break}}this.renderPass="offscreen";for(var vt=0,Nt=X;vt<Nt.length;vt+=1){var Rt=Nt[vt],Vt=this.style._layers[Rt];if(Vt.hasOffscreenPass()&&!Vt.isHidden(this.transform.zoom)){var rn=Xe[Vt.source];(Vt.type==="custom"||rn.length)&&this.renderLayer(this,se[Vt.source],Vt,rn)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:j.showOverdrawInspector?i.Color.black:i.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=j.showOverdrawInspector,this.depthRangeFor3D=[0,1-(I._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=X.length-1;this.currentLayer>=0;this.currentLayer--){var dn=this.style._layers[X[this.currentLayer]],En=se[dn.source],Tn=Ue[dn.source];this._renderTileClippingMasks(dn,Tn),this.renderLayer(this,En,dn,Tn)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<X.length;this.currentLayer++){var tr=this.style._layers[X[this.currentLayer]],er=se[tr.source],gr=(tr.type==="symbol"?it:Xe)[tr.source];this._renderTileClippingMasks(tr,Ue[tr.source]),this.renderLayer(this,er,tr,gr)}this.options.showTileBoundaries&&(i.values(this.style._layers).forEach(function(cr){cr.source&&!cr.isHidden($.transform.zoom)&&(cr.source!==(Ee&&Ee.id)&&(Ee=$.style.sourceCaches[cr.source]),(!be||be.getSource().maxzoom<Ee.getSource().maxzoom)&&(be=Ee))}),be&&ss.debug(this,be,be.getVisibleCoordinates())),this.options.showPadding&&lo(this),this.context.setDefault()},ia.prototype.renderLayer=function(I,j,$,X){$.isHidden(this.transform.zoom)||($.type==="background"||$.type==="custom"||X.length)&&(this.id=$.id,this.gpuTimingStart($),ss[$.type](I,j,$,X,this.style.placement.variableOffsets),this.gpuTimingEnd())},ia.prototype.gpuTimingStart=function(I){if(this.options.gpuTiming){var j=this.context.extTimerQuery,$=this.gpuTimers[I.id];$||($=this.gpuTimers[I.id]={calls:0,cpuTime:0,query:j.createQueryEXT()}),$.calls++,j.beginQueryEXT(j.TIME_ELAPSED_EXT,$.query)}},ia.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var I=this.context.extTimerQuery;I.endQueryEXT(I.TIME_ELAPSED_EXT)}},ia.prototype.collectGpuTimers=function(){var I=this.gpuTimers;return this.gpuTimers={},I},ia.prototype.queryGpuTimers=function(I){var j={};for(var $ in I){var X=I[$],se=this.context.extTimerQuery,he=se.getQueryObjectEXT(X.query,se.QUERY_RESULT_EXT)/1e6;se.deleteQueryEXT(X.query),j[$]=he}return j},ia.prototype.translatePosMatrix=function(I,j,$,X,se){if(!$[0]&&!$[1])return I;var he=se?X==="map"?this.transform.angle:0:X==="viewport"?-this.transform.angle:0;if(he){var ye=Math.sin(he),be=Math.cos(he);$=[$[0]*be-$[1]*ye,$[0]*ye+$[1]*be]}var Ee=[se?$[0]:Dn(j,$[0],this.transform.zoom),se?$[1]:Dn(j,$[1],this.transform.zoom),0],Ue=new Float32Array(16);return i.translate(Ue,I,Ee),Ue},ia.prototype.saveTileTexture=function(I){var j=this._tileTextures[I.size[0]];j?j.push(I):this._tileTextures[I.size[0]]=[I]},ia.prototype.getTileTexture=function(I){var j=this._tileTextures[I];return j&&j.length>0?j.pop():null},ia.prototype.isPatternMissing=function(I){if(!I)return!1;if(!I.from||!I.to)return!0;var j=this.imageManager.getPattern(I.from.toString()),$=this.imageManager.getPattern(I.to.toString());return!j||!$},ia.prototype.useProgram=function(I,j){this.cache=this.cache||{};var $=""+I+(j?j.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[$]||(this.cache[$]=new zc(this.context,Zu[I],j,xn[I],this._showOverdrawInspector)),this.cache[$]},ia.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},ia.prototype.setBaseState=function(){var I=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(I.FUNC_ADD)},ia.prototype.initDebugOverlayCanvas=function(){if(this.debugOverlayCanvas==null){this.debugOverlayCanvas=i.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var I=this.context.gl;this.debugOverlayTexture=new i.Texture(this.context,this.debugOverlayCanvas,I.RGBA)}},ia.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var ht=function(I,j){this.points=I,this.planes=j};ht.fromInvProjectionMatrix=function(I,j,$){var X=Math.pow(2,$),se=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map(function(ye){return i.transformMat4([],ye,I)}).map(function(ye){return i.scale$1([],ye,1/ye[3]/j*X)}),he=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map(function(ye){var be=i.sub([],se[ye[0]],se[ye[1]]),Ee=i.sub([],se[ye[2]],se[ye[1]]),Ue=i.normalize([],i.cross([],be,Ee)),Xe=-i.dot(Ue,se[ye[1]]);return Ue.concat(Xe)});return new ht(se,he)};var zt=function(I,j){this.min=I,this.max=j,this.center=i.scale$2([],i.add([],this.min,this.max),.5)};zt.prototype.quadrant=function(I){for(var j=[I%2==0,I<2],$=i.clone$2(this.min),X=i.clone$2(this.max),se=0;se<j.length;se++)$[se]=j[se]?this.min[se]:this.center[se],X[se]=j[se]?this.center[se]:this.max[se];return X[2]=this.max[2],new zt($,X)},zt.prototype.distanceX=function(I){return Math.max(Math.min(this.max[0],I[0]),this.min[0])-I[0]},zt.prototype.distanceY=function(I){return Math.max(Math.min(this.max[1],I[1]),this.min[1])-I[1]},zt.prototype.intersects=function(I){for(var j=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],$=!0,X=0;X<I.planes.length;X++){for(var se=I.planes[X],he=0,ye=0;ye<j.length;ye++)he+=i.dot$1(se,j[ye])>=0;if(he===0)return 0;he!==j.length&&($=!1)}if($)return 2;for(var be=0;be<3;be++){for(var Ee=Number.MAX_VALUE,Ue=-Number.MAX_VALUE,Xe=0;Xe<I.points.length;Xe++){var it=I.points[Xe][be]-this.min[be];Ee=Math.min(Ee,it),Ue=Math.max(Ue,it)}if(Ue<0||Ee>this.max[be]-this.min[be])return 0}return 1};var ln=function(I,j,$,X){if(I===void 0&&(I=0),j===void 0&&(j=0),$===void 0&&($=0),X===void 0&&(X=0),isNaN(I)||I<0||isNaN(j)||j<0||isNaN($)||$<0||isNaN(X)||X<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=I,this.bottom=j,this.left=$,this.right=X};ln.prototype.interpolate=function(I,j,$){return j.top!=null&&I.top!=null&&(this.top=i.number(I.top,j.top,$)),j.bottom!=null&&I.bottom!=null&&(this.bottom=i.number(I.bottom,j.bottom,$)),j.left!=null&&I.left!=null&&(this.left=i.number(I.left,j.left,$)),j.right!=null&&I.right!=null&&(this.right=i.number(I.right,j.right,$)),this},ln.prototype.getCenter=function(I,j){var $=i.clamp((this.left+I-this.right)/2,0,I),X=i.clamp((this.top+j-this.bottom)/2,0,j);return new i.Point($,X)},ln.prototype.equals=function(I){return this.top===I.top&&this.bottom===I.bottom&&this.left===I.left&&this.right===I.right},ln.prototype.clone=function(){return new ln(this.top,this.bottom,this.left,this.right)},ln.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Ht=function(I,j,$,X,se){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=se===void 0||se,this._minZoom=I||0,this._maxZoom=j||22,this._minPitch=$??0,this._maxPitch=X??60,this.setMaxBounds(),this.width=0,this.height=0,this._center=new i.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new ln,this._posMatrixCache={},this._alignedPosMatrixCache={}},un={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Ht.prototype.clone=function(){var I=new Ht(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return I.tileSize=this.tileSize,I.latRange=this.latRange,I.width=this.width,I.height=this.height,I._center=this._center,I.zoom=this.zoom,I.angle=this.angle,I._fov=this._fov,I._pitch=this._pitch,I._unmodified=this._unmodified,I._edgeInsets=this._edgeInsets.clone(),I._calcMatrices(),I},un.minZoom.get=function(){return this._minZoom},un.minZoom.set=function(I){this._minZoom!==I&&(this._minZoom=I,this.zoom=Math.max(this.zoom,I))},un.maxZoom.get=function(){return this._maxZoom},un.maxZoom.set=function(I){this._maxZoom!==I&&(this._maxZoom=I,this.zoom=Math.min(this.zoom,I))},un.minPitch.get=function(){return this._minPitch},un.minPitch.set=function(I){this._minPitch!==I&&(this._minPitch=I,this.pitch=Math.max(this.pitch,I))},un.maxPitch.get=function(){return this._maxPitch},un.maxPitch.set=function(I){this._maxPitch!==I&&(this._maxPitch=I,this.pitch=Math.min(this.pitch,I))},un.renderWorldCopies.get=function(){return this._renderWorldCopies},un.renderWorldCopies.set=function(I){I===void 0?I=!0:I===null&&(I=!1),this._renderWorldCopies=I},un.worldSize.get=function(){return this.tileSize*this.scale},un.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},un.size.get=function(){return new i.Point(this.width,this.height)},un.bearing.get=function(){return-this.angle/Math.PI*180},un.bearing.set=function(I){var j=-i.wrap(I,-180,180)*Math.PI/180;this.angle!==j&&(this._unmodified=!1,this.angle=j,this._calcMatrices(),this.rotationMatrix=i.create$2(),i.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},un.pitch.get=function(){return this._pitch/Math.PI*180},un.pitch.set=function(I){var j=i.clamp(I,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==j&&(this._unmodified=!1,this._pitch=j,this._calcMatrices())},un.fov.get=function(){return this._fov/Math.PI*180},un.fov.set=function(I){I=Math.max(.01,Math.min(60,I)),this._fov!==I&&(this._unmodified=!1,this._fov=I/180*Math.PI,this._calcMatrices())},un.zoom.get=function(){return this._zoom},un.zoom.set=function(I){var j=Math.min(Math.max(I,this.minZoom),this.maxZoom);this._zoom!==j&&(this._unmodified=!1,this._zoom=j,this.scale=this.zoomScale(j),this.tileZoom=Math.floor(j),this.zoomFraction=j-this.tileZoom,this._constrain(),this._calcMatrices())},un.center.get=function(){return this._center},un.center.set=function(I){I.lat===this._center.lat&&I.lng===this._center.lng||(this._unmodified=!1,this._center=I,this._constrain(),this._calcMatrices())},un.padding.get=function(){return this._edgeInsets.toJSON()},un.padding.set=function(I){this._edgeInsets.equals(I)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,I,1),this._calcMatrices())},un.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Ht.prototype.isPaddingEqual=function(I){return this._edgeInsets.equals(I)},Ht.prototype.interpolatePadding=function(I,j,$){this._unmodified=!1,this._edgeInsets.interpolate(I,j,$),this._constrain(),this._calcMatrices()},Ht.prototype.coveringZoomLevel=function(I){var j=(I.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/I.tileSize));return Math.max(0,j)},Ht.prototype.getVisibleUnwrappedCoordinates=function(I){var j=[new i.UnwrappedTileID(0,I)];if(this._renderWorldCopies)for(var $=this.pointCoordinate(new i.Point(0,0)),X=this.pointCoordinate(new i.Point(this.width,0)),se=this.pointCoordinate(new i.Point(this.width,this.height)),he=this.pointCoordinate(new i.Point(0,this.height)),ye=Math.floor(Math.min($.x,X.x,se.x,he.x)),be=Math.floor(Math.max($.x,X.x,se.x,he.x)),Ee=ye-1;Ee<=be+1;Ee++)Ee!==0&&j.push(new i.UnwrappedTileID(Ee,I));return j},Ht.prototype.coveringTiles=function(I){var j=this.coveringZoomLevel(I),$=j;if(I.minzoom!==void 0&&j<I.minzoom)return[];I.maxzoom!==void 0&&j>I.maxzoom&&(j=I.maxzoom);var X=i.MercatorCoordinate.fromLngLat(this.center),se=Math.pow(2,j),he=[se*X.x,se*X.y,0],ye=ht.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,j),be=I.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(be=j);var Ee=function(gr){return{aabb:new zt([gr*se,0,0],[(gr+1)*se,se,0]),zoom:0,x:0,y:0,wrap:gr,fullyVisible:!1}},Ue=[],Xe=[],it=j,xt=I.reparseOverscaled?$:j;if(this._renderWorldCopies)for(var Dt=1;Dt<=3;Dt++)Ue.push(Ee(-Dt)),Ue.push(Ee(Dt));for(Ue.push(Ee(0));Ue.length>0;){var _t=Ue.pop(),Mt=_t.x,vt=_t.y,Nt=_t.fullyVisible;if(!Nt){var Rt=_t.aabb.intersects(ye);if(Rt===0)continue;Nt=Rt===2}var Vt=_t.aabb.distanceX(he),rn=_t.aabb.distanceY(he),dn=Math.max(Math.abs(Vt),Math.abs(rn)),En=3+(1<<it-_t.zoom)-2;if(_t.zoom===it||dn>En&&_t.zoom>=be)Xe.push({tileID:new i.OverscaledTileID(_t.zoom===it?xt:_t.zoom,_t.wrap,_t.zoom,Mt,vt),distanceSq:i.sqrLen([he[0]-.5-Mt,he[1]-.5-vt])});else for(var Tn=0;Tn<4;Tn++){var tr=(Mt<<1)+Tn%2,er=(vt<<1)+(Tn>>1);Ue.push({aabb:_t.aabb.quadrant(Tn),zoom:_t.zoom+1,x:tr,y:er,wrap:_t.wrap,fullyVisible:Nt})}}return Xe.sort(function(gr,cr){return gr.distanceSq-cr.distanceSq}).map(function(gr){return gr.tileID})},Ht.prototype.resize=function(I,j){this.width=I,this.height=j,this.pixelsToGLUnits=[2/I,-2/j],this._constrain(),this._calcMatrices()},un.unmodified.get=function(){return this._unmodified},Ht.prototype.zoomScale=function(I){return Math.pow(2,I)},Ht.prototype.scaleZoom=function(I){return Math.log(I)/Math.LN2},Ht.prototype.project=function(I){var j=i.clamp(I.lat,-this.maxValidLatitude,this.maxValidLatitude);return new i.Point(i.mercatorXfromLng(I.lng)*this.worldSize,i.mercatorYfromLat(j)*this.worldSize)},Ht.prototype.unproject=function(I){return new i.MercatorCoordinate(I.x/this.worldSize,I.y/this.worldSize).toLngLat()},un.point.get=function(){return this.project(this.center)},Ht.prototype.setLocationAtPoint=function(I,j){var $=this.pointCoordinate(j),X=this.pointCoordinate(this.centerPoint),se=this.locationCoordinate(I),he=new i.MercatorCoordinate(se.x-($.x-X.x),se.y-($.y-X.y));this.center=this.coordinateLocation(he),this._renderWorldCopies&&(this.center=this.center.wrap())},Ht.prototype.locationPoint=function(I){return this.coordinatePoint(this.locationCoordinate(I))},Ht.prototype.pointLocation=function(I){return this.coordinateLocation(this.pointCoordinate(I))},Ht.prototype.locationCoordinate=function(I){return i.MercatorCoordinate.fromLngLat(I)},Ht.prototype.coordinateLocation=function(I){return I.toLngLat()},Ht.prototype.pointCoordinate=function(I){var j=[I.x,I.y,0,1],$=[I.x,I.y,1,1];i.transformMat4(j,j,this.pixelMatrixInverse),i.transformMat4($,$,this.pixelMatrixInverse);var X=j[3],se=$[3],he=j[0]/X,ye=$[0]/se,be=j[1]/X,Ee=$[1]/se,Ue=j[2]/X,Xe=$[2]/se,it=Ue===Xe?0:(0-Ue)/(Xe-Ue);return new i.MercatorCoordinate(i.number(he,ye,it)/this.worldSize,i.number(be,Ee,it)/this.worldSize)},Ht.prototype.coordinatePoint=function(I){var j=[I.x*this.worldSize,I.y*this.worldSize,0,1];return i.transformMat4(j,j,this.pixelMatrix),new i.Point(j[0]/j[3],j[1]/j[3])},Ht.prototype.getBounds=function(){return new i.LngLatBounds().extend(this.pointLocation(new i.Point(0,0))).extend(this.pointLocation(new i.Point(this.width,0))).extend(this.pointLocation(new i.Point(this.width,this.height))).extend(this.pointLocation(new i.Point(0,this.height)))},Ht.prototype.getMaxBounds=function(){return this.latRange&&this.latRange.length===2&&this.lngRange&&this.lngRange.length===2?new i.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Ht.prototype.setMaxBounds=function(I){I?(this.lngRange=[I.getWest(),I.getEast()],this.latRange=[I.getSouth(),I.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Ht.prototype.calculatePosMatrix=function(I,j){j===void 0&&(j=!1);var $=I.key,X=j?this._alignedPosMatrixCache:this._posMatrixCache;if(X[$])return X[$];var se=I.canonical,he=this.worldSize/this.zoomScale(se.z),ye=se.x+Math.pow(2,se.z)*I.wrap,be=i.identity(new Float64Array(16));return i.translate(be,be,[ye*he,se.y*he,0]),i.scale(be,be,[he/i.EXTENT,he/i.EXTENT,1]),i.multiply(be,j?this.alignedProjMatrix:this.projMatrix,be),X[$]=new Float32Array(be),X[$]},Ht.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Ht.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var I,j,$,X,se=-90,he=90,ye=-180,be=180,Ee=this.size,Ue=this._unmodified;if(this.latRange){var Xe=this.latRange;se=i.mercatorYfromLat(Xe[1])*this.worldSize,I=(he=i.mercatorYfromLat(Xe[0])*this.worldSize)-se<Ee.y?Ee.y/(he-se):0}if(this.lngRange){var it=this.lngRange;ye=i.mercatorXfromLng(it[0])*this.worldSize,j=(be=i.mercatorXfromLng(it[1])*this.worldSize)-ye<Ee.x?Ee.x/(be-ye):0}var xt=this.point,Dt=Math.max(j||0,I||0);if(Dt)return this.center=this.unproject(new i.Point(j?(be+ye)/2:xt.x,I?(he+se)/2:xt.y)),this.zoom+=this.scaleZoom(Dt),this._unmodified=Ue,void(this._constraining=!1);if(this.latRange){var _t=xt.y,Mt=Ee.y/2;_t-Mt<se&&(X=se+Mt),_t+Mt>he&&(X=he-Mt)}if(this.lngRange){var vt=xt.x,Nt=Ee.x/2;vt-Nt<ye&&($=ye+Nt),vt+Nt>be&&($=be-Nt)}$===void 0&&X===void 0||(this.center=this.unproject(new i.Point($!==void 0?$:xt.x,X!==void 0?X:xt.y))),this._unmodified=Ue,this._constraining=!1}},Ht.prototype._calcMatrices=function(){if(this.height){var I=this._fov/2,j=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(I)*this.height;var $=Math.PI/2+this._pitch,X=this._fov*(.5+j.y/this.height),se=Math.sin(X)*this.cameraToCenterDistance/Math.sin(i.clamp(Math.PI-$-X,.01,Math.PI-.01)),he=this.point,ye=he.x,be=he.y,Ee=1.01*(Math.cos(Math.PI/2-this._pitch)*se+this.cameraToCenterDistance),Ue=this.height/50,Xe=new Float64Array(16);i.perspective(Xe,this._fov,this.width/this.height,Ue,Ee),Xe[8]=2*-j.x/this.width,Xe[9]=2*j.y/this.height,i.scale(Xe,Xe,[1,-1,1]),i.translate(Xe,Xe,[0,0,-this.cameraToCenterDistance]),i.rotateX(Xe,Xe,this._pitch),i.rotateZ(Xe,Xe,this.angle),i.translate(Xe,Xe,[-ye,-be,0]),this.mercatorMatrix=i.scale([],Xe,[this.worldSize,this.worldSize,this.worldSize]),i.scale(Xe,Xe,[1,1,i.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=Xe,this.invProjMatrix=i.invert([],this.projMatrix);var it=this.width%2/2,xt=this.height%2/2,Dt=Math.cos(this.angle),_t=Math.sin(this.angle),Mt=ye-Math.round(ye)+Dt*it+_t*xt,vt=be-Math.round(be)+Dt*xt+_t*it,Nt=new Float64Array(Xe);if(i.translate(Nt,Nt,[Mt>.5?Mt-1:Mt,vt>.5?vt-1:vt,0]),this.alignedProjMatrix=Nt,Xe=i.create(),i.scale(Xe,Xe,[this.width/2,-this.height/2,1]),i.translate(Xe,Xe,[1,-1,0]),this.labelPlaneMatrix=Xe,Xe=i.create(),i.scale(Xe,Xe,[1,-1,1]),i.translate(Xe,Xe,[-1,-1,0]),i.scale(Xe,Xe,[2/this.width,2/this.height,1]),this.glCoordMatrix=Xe,this.pixelMatrix=i.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(Xe=i.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=Xe,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Ht.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var I=this.pointCoordinate(new i.Point(0,0)),j=[I.x*this.worldSize,I.y*this.worldSize,0,1];return i.transformMat4(j,j,this.pixelMatrix)[3]/this.cameraToCenterDistance},Ht.prototype.getCameraPoint=function(){var I=this._pitch,j=Math.tan(I)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new i.Point(0,j))},Ht.prototype.getCameraQueryGeometry=function(I){var j=this.getCameraPoint();if(I.length===1)return[I[0],j];for(var $=j.x,X=j.y,se=j.x,he=j.y,ye=0,be=I;ye<be.length;ye+=1){var Ee=be[ye];$=Math.min($,Ee.x),X=Math.min(X,Ee.y),se=Math.max(se,Ee.x),he=Math.max(he,Ee.y)}return[new i.Point($,X),new i.Point(se,X),new i.Point(se,he),new i.Point($,he),new i.Point($,X)]},Object.defineProperties(Ht.prototype,un);var Ln=function(I){var j,$,X,se,he;this._hashName=I&&encodeURIComponent(I),i.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(j=this._updateHashUnthrottled.bind(this),$=300,X=!1,se=null,he=function(){se=null,X&&(j(),se=setTimeout(he,$),X=!1)},function(){return X=!0,se||he(),se})};Ln.prototype.addTo=function(I){return this._map=I,i.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Ln.prototype.remove=function(){return i.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Ln.prototype.getHashString=function(I){var j=this._map.getCenter(),$=Math.round(100*this._map.getZoom())/100,X=Math.ceil(($*Math.LN2+Math.log(512/360/.5))/Math.LN10),se=Math.pow(10,X),he=Math.round(j.lng*se)/se,ye=Math.round(j.lat*se)/se,be=this._map.getBearing(),Ee=this._map.getPitch(),Ue="";if(Ue+=I?"/"+he+"/"+ye+"/"+$:$+"/"+ye+"/"+he,(be||Ee)&&(Ue+="/"+Math.round(10*be)/10),Ee&&(Ue+="/"+Math.round(Ee)),this._hashName){var Xe=this._hashName,it=!1,xt=i.window.location.hash.slice(1).split("&").map(function(Dt){var _t=Dt.split("=")[0];return _t===Xe?(it=!0,_t+"="+Ue):Dt}).filter(function(Dt){return Dt});return it||xt.push(Xe+"="+Ue),"#"+xt.join("&")}return"#"+Ue},Ln.prototype._getCurrentHash=function(){var I,j=this,$=i.window.location.hash.replace("#","");return this._hashName?($.split("&").map(function(X){return X.split("=")}).forEach(function(X){X[0]===j._hashName&&(I=X)}),(I&&I[1]||"").split("/")):$.split("/")},Ln.prototype._onHashChange=function(){var I=this._getCurrentHash();if(I.length>=3&&!I.some(function($){return isNaN($)})){var j=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(I[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+I[2],+I[1]],zoom:+I[0],bearing:j,pitch:+(I[4]||0)}),!0}return!1},Ln.prototype._updateHashUnthrottled=function(){var I=this.getHashString();try{i.window.history.replaceState(i.window.history.state,"",I)}catch{}};var zn={linearity:.3,easing:i.bezier(0,0,.3,1)},Jn=i.extend({deceleration:2500,maxSpeed:1400},zn),fr=i.extend({deceleration:20,maxSpeed:1400},zn),ur=i.extend({deceleration:1e3,maxSpeed:360},zn),vr=i.extend({deceleration:1e3,maxSpeed:90},zn),kr=function(I){this._map=I,this.clear()};function hr(I,j){(!I.duration||I.duration<j.duration)&&(I.duration=j.duration,I.easing=j.easing)}function Tr(I,j,$){var X=$.maxSpeed,se=$.linearity,he=$.deceleration,ye=i.clamp(I*se/(j/1e3),-X,X),be=Math.abs(ye)/(he*se);return{easing:$.easing,duration:1e3*be,amount:ye*(be/2)}}kr.prototype.clear=function(){this._inertiaBuffer=[]},kr.prototype.record=function(I){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:i.browser.now(),settings:I})},kr.prototype._drainInertiaBuffer=function(){for(var I=this._inertiaBuffer,j=i.browser.now();I.length>0&&j-I[0].time>160;)I.shift()},kr.prototype._onMoveEnd=function(I){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var j={zoom:0,bearing:0,pitch:0,pan:new i.Point(0,0),pinchAround:void 0,around:void 0},$=0,X=this._inertiaBuffer;$<X.length;$+=1){var se=X[$].settings;j.zoom+=se.zoomDelta||0,j.bearing+=se.bearingDelta||0,j.pitch+=se.pitchDelta||0,se.panDelta&&j.pan._add(se.panDelta),se.around&&(j.around=se.around),se.pinchAround&&(j.pinchAround=se.pinchAround)}var he=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,ye={};if(j.pan.mag()){var be=Tr(j.pan.mag(),he,i.extend({},Jn,I||{}));ye.offset=j.pan.mult(be.amount/j.pan.mag()),ye.center=this._map.transform.center,hr(ye,be)}if(j.zoom){var Ee=Tr(j.zoom,he,fr);ye.zoom=this._map.transform.zoom+Ee.amount,hr(ye,Ee)}if(j.bearing){var Ue=Tr(j.bearing,he,ur);ye.bearing=this._map.transform.bearing+i.clamp(Ue.amount,-179,179),hr(ye,Ue)}if(j.pitch){var Xe=Tr(j.pitch,he,vr);ye.pitch=this._map.transform.pitch+Xe.amount,hr(ye,Xe)}if(ye.zoom||ye.bearing){var it=j.pinchAround===void 0?j.around:j.pinchAround;ye.around=it?this._map.unproject(it):this._map.getCenter()}return this.clear(),i.extend(ye,{noMoveStart:!0})}};var $r=function(I){function j(X,se,he,ye){ye===void 0&&(ye={});var be=u.mousePos(se.getCanvasContainer(),he),Ee=se.unproject(be);I.call(this,X,i.extend({point:be,lngLat:Ee,originalEvent:he},ye)),this._defaultPrevented=!1,this.target=se}I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j;var $={defaultPrevented:{configurable:!0}};return j.prototype.preventDefault=function(){this._defaultPrevented=!0},$.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(j.prototype,$),j}(i.Event),Jr=function(I){function j(X,se,he){var ye=X==="touchend"?he.changedTouches:he.touches,be=u.touchPos(se.getCanvasContainer(),ye),Ee=be.map(function(it){return se.unproject(it)}),Ue=be.reduce(function(it,xt,Dt,_t){return it.add(xt.div(_t.length))},new i.Point(0,0)),Xe=se.unproject(Ue);I.call(this,X,{points:be,point:Ue,lngLats:Ee,lngLat:Xe,originalEvent:he}),this._defaultPrevented=!1}I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j;var $={defaultPrevented:{configurable:!0}};return j.prototype.preventDefault=function(){this._defaultPrevented=!0},$.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(j.prototype,$),j}(i.Event),yi=function(I){function j(X,se,he){I.call(this,X,{originalEvent:he}),this._defaultPrevented=!1}I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j;var $={defaultPrevented:{configurable:!0}};return j.prototype.preventDefault=function(){this._defaultPrevented=!0},$.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(j.prototype,$),j}(i.Event),Qn=function(I,j){this._map=I,this._clickTolerance=j.clickTolerance};Qn.prototype.reset=function(){delete this._mousedownPos},Qn.prototype.wheel=function(I){return this._firePreventable(new yi(I.type,this._map,I))},Qn.prototype.mousedown=function(I,j){return this._mousedownPos=j,this._firePreventable(new $r(I.type,this._map,I))},Qn.prototype.mouseup=function(I){this._map.fire(new $r(I.type,this._map,I))},Qn.prototype.click=function(I,j){this._mousedownPos&&this._mousedownPos.dist(j)>=this._clickTolerance||this._map.fire(new $r(I.type,this._map,I))},Qn.prototype.dblclick=function(I){return this._firePreventable(new $r(I.type,this._map,I))},Qn.prototype.mouseover=function(I){this._map.fire(new $r(I.type,this._map,I))},Qn.prototype.mouseout=function(I){this._map.fire(new $r(I.type,this._map,I))},Qn.prototype.touchstart=function(I){return this._firePreventable(new Jr(I.type,this._map,I))},Qn.prototype.touchmove=function(I){this._map.fire(new Jr(I.type,this._map,I))},Qn.prototype.touchend=function(I){this._map.fire(new Jr(I.type,this._map,I))},Qn.prototype.touchcancel=function(I){this._map.fire(new Jr(I.type,this._map,I))},Qn.prototype._firePreventable=function(I){if(this._map.fire(I),I.defaultPrevented)return{}},Qn.prototype.isEnabled=function(){return!0},Qn.prototype.isActive=function(){return!1},Qn.prototype.enable=function(){},Qn.prototype.disable=function(){};var pi=function(I){this._map=I};pi.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},pi.prototype.mousemove=function(I){this._map.fire(new $r(I.type,this._map,I))},pi.prototype.mousedown=function(){this._delayContextMenu=!0},pi.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new $r("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},pi.prototype.contextmenu=function(I){this._delayContextMenu?this._contextMenuEvent=I:this._map.fire(new $r(I.type,this._map,I)),this._map.listens("contextmenu")&&I.preventDefault()},pi.prototype.isEnabled=function(){return!0},pi.prototype.isActive=function(){return!1},pi.prototype.enable=function(){},pi.prototype.disable=function(){};var Rr=function(I,j){this._map=I,this._el=I.getCanvasContainer(),this._container=I.getContainer(),this._clickTolerance=j.clickTolerance||1};function Wr(I,j){for(var $={},X=0;X<I.length;X++)$[I[X].identifier]=j[X];return $}Rr.prototype.isEnabled=function(){return!!this._enabled},Rr.prototype.isActive=function(){return!!this._active},Rr.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Rr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Rr.prototype.mousedown=function(I,j){this.isEnabled()&&I.shiftKey&&I.button===0&&(u.disableDrag(),this._startPos=this._lastPos=j,this._active=!0)},Rr.prototype.mousemoveWindow=function(I,j){if(this._active){var $=j;if(!(this._lastPos.equals($)||!this._box&&$.dist(this._startPos)<this._clickTolerance)){var X=this._startPos;this._lastPos=$,this._box||(this._box=u.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",I));var se=Math.min(X.x,$.x),he=Math.max(X.x,$.x),ye=Math.min(X.y,$.y),be=Math.max(X.y,$.y);u.setTransform(this._box,"translate("+se+"px,"+ye+"px)"),this._box.style.width=he-se+"px",this._box.style.height=be-ye+"px"}}},Rr.prototype.mouseupWindow=function(I,j){var $=this;if(this._active&&I.button===0){var X=this._startPos,se=j;if(this.reset(),u.suppressClick(),X.x!==se.x||X.y!==se.y)return this._map.fire(new i.Event("boxzoomend",{originalEvent:I})),{cameraAnimation:function(he){return he.fitScreenCoordinates(X,se,$._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",I)}},Rr.prototype.keydown=function(I){this._active&&I.keyCode===27&&(this.reset(),this._fireEvent("boxzoomcancel",I))},Rr.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(u.remove(this._box),this._box=null),u.enableDrag(),delete this._startPos,delete this._lastPos},Rr.prototype._fireEvent=function(I,j){return this._map.fire(new i.Event(I,{originalEvent:j}))};var di=function(I){this.reset(),this.numTouches=I.numTouches};di.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},di.prototype.touchstart=function(I,j,$){(this.centroid||$.length>this.numTouches)&&(this.aborted=!0),this.aborted||(this.startTime===void 0&&(this.startTime=I.timeStamp),$.length===this.numTouches&&(this.centroid=function(X){for(var se=new i.Point(0,0),he=0,ye=X;he<ye.length;he+=1){var be=ye[he];se._add(be)}return se.div(X.length)}(j),this.touches=Wr($,j)))},di.prototype.touchmove=function(I,j,$){if(!this.aborted&&this.centroid){var X=Wr($,j);for(var se in this.touches){var he=this.touches[se],ye=X[se];(!ye||ye.dist(he)>30)&&(this.aborted=!0)}}},di.prototype.touchend=function(I,j,$){if((!this.centroid||I.timeStamp-this.startTime>500)&&(this.aborted=!0),$.length===0){var X=!this.aborted&&this.centroid;if(this.reset(),X)return X}};var Ui=function(I){this.singleTap=new di(I),this.numTaps=I.numTaps,this.reset()};Ui.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Ui.prototype.touchstart=function(I,j,$){this.singleTap.touchstart(I,j,$)},Ui.prototype.touchmove=function(I,j,$){this.singleTap.touchmove(I,j,$)},Ui.prototype.touchend=function(I,j,$){var X=this.singleTap.touchend(I,j,$);if(X){var se=I.timeStamp-this.lastTime<500,he=!this.lastTap||this.lastTap.dist(X)<30;if(se&&he||this.reset(),this.count++,this.lastTime=I.timeStamp,this.lastTap=X,this.count===this.numTaps)return this.reset(),X}};var ea=function(){this._zoomIn=new Ui({numTouches:1,numTaps:2}),this._zoomOut=new Ui({numTouches:2,numTaps:1}),this.reset()};ea.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},ea.prototype.touchstart=function(I,j,$){this._zoomIn.touchstart(I,j,$),this._zoomOut.touchstart(I,j,$)},ea.prototype.touchmove=function(I,j,$){this._zoomIn.touchmove(I,j,$),this._zoomOut.touchmove(I,j,$)},ea.prototype.touchend=function(I,j,$){var X=this,se=this._zoomIn.touchend(I,j,$),he=this._zoomOut.touchend(I,j,$);return se?(this._active=!0,I.preventDefault(),setTimeout(function(){return X.reset()},0),{cameraAnimation:function(ye){return ye.easeTo({duration:300,zoom:ye.getZoom()+1,around:ye.unproject(se)},{originalEvent:I})}}):he?(this._active=!0,I.preventDefault(),setTimeout(function(){return X.reset()},0),{cameraAnimation:function(ye){return ye.easeTo({duration:300,zoom:ye.getZoom()-1,around:ye.unproject(he)},{originalEvent:I})}}):void 0},ea.prototype.touchcancel=function(){this.reset()},ea.prototype.enable=function(){this._enabled=!0},ea.prototype.disable=function(){this._enabled=!1,this.reset()},ea.prototype.isEnabled=function(){return this._enabled},ea.prototype.isActive=function(){return this._active};var Or=function(I){this.reset(),this._clickTolerance=I.clickTolerance||1};Or.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Or.prototype._correctButton=function(I,j){return!1},Or.prototype._move=function(I,j){return{}},Or.prototype.mousedown=function(I,j){if(!this._lastPoint){var $=u.mouseButton(I);this._correctButton(I,$)&&(this._lastPoint=j,this._eventButton=$)}},Or.prototype.mousemoveWindow=function(I,j){var $=this._lastPoint;if($&&(I.preventDefault(),this._moved||!(j.dist($)<this._clickTolerance)))return this._moved=!0,this._lastPoint=j,this._move($,j)},Or.prototype.mouseupWindow=function(I){u.mouseButton(I)===this._eventButton&&(this._moved&&u.suppressClick(),this.reset())},Or.prototype.enable=function(){this._enabled=!0},Or.prototype.disable=function(){this._enabled=!1,this.reset()},Or.prototype.isEnabled=function(){return this._enabled},Or.prototype.isActive=function(){return this._active};var aa=function(I){function j(){I.apply(this,arguments)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.mousedown=function($,X){I.prototype.mousedown.call(this,$,X),this._lastPoint&&(this._active=!0)},j.prototype._correctButton=function($,X){return X===0&&!$.ctrlKey},j.prototype._move=function($,X){return{around:X,panDelta:X.sub($)}},j}(Or),Qi=function(I){function j(){I.apply(this,arguments)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype._correctButton=function($,X){return X===0&&$.ctrlKey||X===2},j.prototype._move=function($,X){var se=.8*(X.x-$.x);if(se)return this._active=!0,{bearingDelta:se}},j.prototype.contextmenu=function($){$.preventDefault()},j}(Or),Ci=function(I){function j(){I.apply(this,arguments)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype._correctButton=function($,X){return X===0&&$.ctrlKey||X===2},j.prototype._move=function($,X){var se=-.5*(X.y-$.y);if(se)return this._active=!0,{pitchDelta:se}},j.prototype.contextmenu=function($){$.preventDefault()},j}(Or),oa=function(I){this._minTouches=1,this._clickTolerance=I.clickTolerance||1,this.reset()};oa.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new i.Point(0,0)},oa.prototype.touchstart=function(I,j,$){return this._calculateTransform(I,j,$)},oa.prototype.touchmove=function(I,j,$){if(this._active)return I.preventDefault(),this._calculateTransform(I,j,$)},oa.prototype.touchend=function(I,j,$){this._calculateTransform(I,j,$),this._active&&$.length<this._minTouches&&this.reset()},oa.prototype.touchcancel=function(){this.reset()},oa.prototype._calculateTransform=function(I,j,$){$.length>0&&(this._active=!0);var X=Wr($,j),se=new i.Point(0,0),he=new i.Point(0,0),ye=0;for(var be in X){var Ee=X[be],Ue=this._touches[be];Ue&&(se._add(Ee),he._add(Ee.sub(Ue)),ye++,X[be]=Ee)}if(this._touches=X,!(ye<this._minTouches)&&he.mag()){var Xe=he.div(ye);if(this._sum._add(Xe),!(this._sum.mag()<this._clickTolerance))return{around:se.div(ye),panDelta:Xe}}},oa.prototype.enable=function(){this._enabled=!0},oa.prototype.disable=function(){this._enabled=!1,this.reset()},oa.prototype.isEnabled=function(){return this._enabled},oa.prototype.isActive=function(){return this._active};var Xi=function(){this.reset()};function Da(I,j,$){for(var X=0;X<I.length;X++)if(I[X].identifier===$)return j[X]}Xi.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Xi.prototype._start=function(I){},Xi.prototype._move=function(I,j,$){return{}},Xi.prototype.touchstart=function(I,j,$){this._firstTwoTouches||$.length<2||(this._firstTwoTouches=[$[0].identifier,$[1].identifier],this._start([j[0],j[1]]))},Xi.prototype.touchmove=function(I,j,$){if(this._firstTwoTouches){I.preventDefault();var X=this._firstTwoTouches,se=X[0],he=X[1],ye=Da($,j,se),be=Da($,j,he);if(ye&&be){var Ee=this._aroundCenter?null:ye.add(be).div(2);return this._move([ye,be],Ee,I)}}},Xi.prototype.touchend=function(I,j,$){if(this._firstTwoTouches){var X=this._firstTwoTouches,se=X[0],he=X[1],ye=Da($,j,se),be=Da($,j,he);ye&&be||(this._active&&u.suppressClick(),this.reset())}},Xi.prototype.touchcancel=function(){this.reset()},Xi.prototype.enable=function(I){this._enabled=!0,this._aroundCenter=!!I&&I.around==="center"},Xi.prototype.disable=function(){this._enabled=!1,this.reset()},Xi.prototype.isEnabled=function(){return this._enabled},Xi.prototype.isActive=function(){return this._active};function gi(I,j){return Math.log(I/j)/Math.LN2}var Aa=function(I){function j(){I.apply(this,arguments)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.reset=function(){I.prototype.reset.call(this),delete this._distance,delete this._startDistance},j.prototype._start=function($){this._startDistance=this._distance=$[0].dist($[1])},j.prototype._move=function($,X){var se=this._distance;if(this._distance=$[0].dist($[1]),this._active||!(Math.abs(gi(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:gi(this._distance,se),pinchAround:X}},j}(Xi);function Ro(I,j){return 180*I.angleWith(j)/Math.PI}var Il=function(I){function j(){I.apply(this,arguments)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.reset=function(){I.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},j.prototype._start=function($){this._startVector=this._vector=$[0].sub($[1]),this._minDiameter=$[0].dist($[1])},j.prototype._move=function($,X){var se=this._vector;if(this._vector=$[0].sub($[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:Ro(this._vector,se),pinchAround:X}},j.prototype._isBelowThreshold=function($){this._minDiameter=Math.min(this._minDiameter,$.mag());var X=25/(Math.PI*this._minDiameter)*360,se=Ro($,this._startVector);return Math.abs(se)<X},j}(Xi);function tl(I){return Math.abs(I.y)>Math.abs(I.x)}var Ss=function(I){function j(){I.apply(this,arguments)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.reset=function(){I.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},j.prototype._start=function($){this._lastPoints=$,tl($[0].sub($[1]))&&(this._valid=!1)},j.prototype._move=function($,X,se){var he=$[0].sub(this._lastPoints[0]),ye=$[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(he,ye,se.timeStamp),this._valid)return this._lastPoints=$,this._active=!0,{pitchDelta:-.5*((he.y+ye.y)/2)}},j.prototype.gestureBeginsVertically=function($,X,se){if(this._valid!==void 0)return this._valid;var he=$.mag()>=2,ye=X.mag()>=2;if(he||ye){if(!he||!ye)return this._firstMove===void 0&&(this._firstMove=se),se-this._firstMove<100&&void 0;var be=$.y>0==X.y>0;return tl($)&&tl(X)&&be}},j}(Xi),Pi={panStep:100,bearingStep:15,pitchStep:10},no=function(){var I=Pi;this._panStep=I.panStep,this._bearingStep=I.bearingStep,this._pitchStep=I.pitchStep};function Cs(I){return I*(2-I)}no.prototype.reset=function(){this._active=!1},no.prototype.keydown=function(I){var j=this;if(!(I.altKey||I.ctrlKey||I.metaKey)){var $=0,X=0,se=0,he=0,ye=0;switch(I.keyCode){case 61:case 107:case 171:case 187:$=1;break;case 189:case 109:case 173:$=-1;break;case 37:I.shiftKey?X=-1:(I.preventDefault(),he=-1);break;case 39:I.shiftKey?X=1:(I.preventDefault(),he=1);break;case 38:I.shiftKey?se=1:(I.preventDefault(),ye=-1);break;case 40:I.shiftKey?se=-1:(I.preventDefault(),ye=1);break;default:return}return{cameraAnimation:function(be){var Ee=be.getZoom();be.easeTo({duration:300,easeId:"keyboardHandler",easing:Cs,zoom:$?Math.round(Ee)+$*(I.shiftKey?2:1):Ee,bearing:be.getBearing()+X*j._bearingStep,pitch:be.getPitch()+se*j._pitchStep,offset:[-he*j._panStep,-ye*j._panStep],center:be.getCenter()},{originalEvent:I})}}}},no.prototype.enable=function(){this._enabled=!0},no.prototype.disable=function(){this._enabled=!1,this.reset()},no.prototype.isEnabled=function(){return this._enabled},no.prototype.isActive=function(){return this._active};var ka=function(I,j){this._map=I,this._el=I.getCanvasContainer(),this._handler=j,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,i.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};ka.prototype.setZoomRate=function(I){this._defaultZoomRate=I},ka.prototype.setWheelZoomRate=function(I){this._wheelZoomRate=I},ka.prototype.isEnabled=function(){return!!this._enabled},ka.prototype.isActive=function(){return!!this._active||this._finishTimeout!==void 0},ka.prototype.isZooming=function(){return!!this._zooming},ka.prototype.enable=function(I){this.isEnabled()||(this._enabled=!0,this._aroundCenter=I&&I.around==="center")},ka.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},ka.prototype.wheel=function(I){if(this.isEnabled()){var j=I.deltaMode===i.window.WheelEvent.DOM_DELTA_LINE?40*I.deltaY:I.deltaY,$=i.browser.now(),X=$-(this._lastWheelEventTime||0);this._lastWheelEventTime=$,j!==0&&j%4.000244140625==0?this._type="wheel":j!==0&&Math.abs(j)<4?this._type="trackpad":X>400?(this._type=null,this._lastValue=j,this._timeout=setTimeout(this._onTimeout,40,I)):this._type||(this._type=Math.abs(X*j)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,j+=this._lastValue)),I.shiftKey&&j&&(j/=4),this._type&&(this._lastWheelEvent=I,this._delta-=j,this._active||this._start(I)),I.preventDefault()}},ka.prototype._onTimeout=function(I){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(I)},ka.prototype._start=function(I){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var j=u.mousePos(this._el,I);this._around=i.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(j)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},ka.prototype.renderFrame=function(){return this._onScrollFrame()},ka.prototype._onScrollFrame=function(){var I=this;if(this._frameId&&(this._frameId=null,this.isActive())){var j=this._map.transform;if(this._delta!==0){var $=this._type==="wheel"&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,X=2/(1+Math.exp(-Math.abs(this._delta*$)));this._delta<0&&X!==0&&(X=1/X);var se=typeof this._targetZoom=="number"?j.zoomScale(this._targetZoom):j.scale;this._targetZoom=Math.min(j.maxZoom,Math.max(j.minZoom,j.scaleZoom(se*X))),this._type==="wheel"&&(this._startZoom=j.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var he,ye=typeof this._targetZoom=="number"?this._targetZoom:j.zoom,be=this._startZoom,Ee=this._easing,Ue=!1;if(this._type==="wheel"&&be&&Ee){var Xe=Math.min((i.browser.now()-this._lastWheelEventTime)/200,1),it=Ee(Xe);he=i.number(be,ye,it),Xe<1?this._frameId||(this._frameId=!0):Ue=!0}else he=ye,Ue=!0;return this._active=!0,Ue&&(this._active=!1,this._finishTimeout=setTimeout(function(){I._zooming=!1,I._handler._triggerRenderFrame(),delete I._targetZoom,delete I._finishTimeout},200)),{noInertia:!0,needsRenderFrame:!Ue,zoomDelta:he-j.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},ka.prototype._smoothOutEasing=function(I){var j=i.ease;if(this._prevEase){var $=this._prevEase,X=(i.browser.now()-$.start)/$.duration,se=$.easing(X+.01)-$.easing(X),he=.27/Math.sqrt(se*se+1e-4)*.01,ye=Math.sqrt(.0729-he*he);j=i.bezier(he,ye,.25,1)}return this._prevEase={start:i.browser.now(),duration:I,easing:j},j},ka.prototype.reset=function(){this._active=!1};var ho=function(I,j){this._clickZoom=I,this._tapZoom=j};ho.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ho.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ho.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ho.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var qo=function(){this.reset()};qo.prototype.reset=function(){this._active=!1},qo.prototype.dblclick=function(I,j){return I.preventDefault(),{cameraAnimation:function($){$.easeTo({duration:300,zoom:$.getZoom()+(I.shiftKey?-1:1),around:$.unproject(j)},{originalEvent:I})}}},qo.prototype.enable=function(){this._enabled=!0},qo.prototype.disable=function(){this._enabled=!1,this.reset()},qo.prototype.isEnabled=function(){return this._enabled},qo.prototype.isActive=function(){return this._active};var Pa=function(){this._tap=new Ui({numTouches:1,numTaps:1}),this.reset()};Pa.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},Pa.prototype.touchstart=function(I,j,$){this._swipePoint||(this._tapTime&&I.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?$.length>0&&(this._swipePoint=j[0],this._swipeTouch=$[0].identifier):this._tap.touchstart(I,j,$))},Pa.prototype.touchmove=function(I,j,$){if(this._tapTime){if(this._swipePoint){if($[0].identifier!==this._swipeTouch)return;var X=j[0],se=X.y-this._swipePoint.y;return this._swipePoint=X,I.preventDefault(),this._active=!0,{zoomDelta:se/128}}}else this._tap.touchmove(I,j,$)},Pa.prototype.touchend=function(I,j,$){this._tapTime?this._swipePoint&&$.length===0&&this.reset():this._tap.touchend(I,j,$)&&(this._tapTime=I.timeStamp)},Pa.prototype.touchcancel=function(){this.reset()},Pa.prototype.enable=function(){this._enabled=!0},Pa.prototype.disable=function(){this._enabled=!1,this.reset()},Pa.prototype.isEnabled=function(){return this._enabled},Pa.prototype.isActive=function(){return this._active};var xs=function(I,j,$){this._el=I,this._mousePan=j,this._touchPan=$};xs.prototype.enable=function(I){this._inertiaOptions=I||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},xs.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},xs.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},xs.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var Ia=function(I,j,$){this._pitchWithRotate=I.pitchWithRotate,this._mouseRotate=j,this._mousePitch=$};Ia.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},Ia.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},Ia.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},Ia.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var ls=function(I,j,$,X){this._el=I,this._touchZoom=j,this._touchRotate=$,this._tapDragZoom=X,this._rotationDisabled=!1,this._enabled=!0};ls.prototype.enable=function(I){this._touchZoom.enable(I),this._rotationDisabled||this._touchRotate.enable(I),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},ls.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},ls.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},ls.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},ls.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},ls.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Mu=function(I){return I.zoom||I.drag||I.pitch||I.rotate},eu=function(I){function j(){I.apply(this,arguments)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j}(i.Event);function Df(I){return I.panDelta&&I.panDelta.mag()||I.zoomDelta||I.bearingDelta||I.pitchDelta}var Lo=function(I,j){this._map=I,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new kr(I),this._bearingSnap=j.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(j),i.bindAll(["handleEvent","handleWindowEvent"],this);var $=this._el;this._listeners=[[$,"touchstart",{passive:!1}],[$,"touchmove",{passive:!1}],[$,"touchend",void 0],[$,"touchcancel",void 0],[$,"mousedown",void 0],[$,"mousemove",void 0],[$,"mouseup",void 0],[i.window.document,"mousemove",{capture:!0}],[i.window.document,"mouseup",void 0],[$,"mouseover",void 0],[$,"mouseout",void 0],[$,"dblclick",void 0],[$,"click",void 0],[$,"keydown",{capture:!1}],[$,"keyup",void 0],[$,"wheel",{passive:!1}],[$,"contextmenu",void 0],[i.window,"blur",void 0]];for(var X=0,se=this._listeners;X<se.length;X+=1){var he=se[X],ye=he[0],be=he[1],Ee=he[2];u.addEventListener(ye,be,ye===i.window.document?this.handleWindowEvent:this.handleEvent,Ee)}};Lo.prototype.destroy=function(){for(var I=0,j=this._listeners;I<j.length;I+=1){var $=j[I],X=$[0],se=$[1],he=$[2];u.removeEventListener(X,se,X===i.window.document?this.handleWindowEvent:this.handleEvent,he)}},Lo.prototype._addDefaultHandlers=function(I){var j=this._map,$=j.getCanvasContainer();this._add("mapEvent",new Qn(j,I));var X=j.boxZoom=new Rr(j,I);this._add("boxZoom",X);var se=new ea,he=new qo;j.doubleClickZoom=new ho(he,se),this._add("tapZoom",se),this._add("clickZoom",he);var ye=new Pa;this._add("tapDragZoom",ye);var be=j.touchPitch=new Ss;this._add("touchPitch",be);var Ee=new Qi(I),Ue=new Ci(I);j.dragRotate=new Ia(I,Ee,Ue),this._add("mouseRotate",Ee,["mousePitch"]),this._add("mousePitch",Ue,["mouseRotate"]);var Xe=new aa(I),it=new oa(I);j.dragPan=new xs($,Xe,it),this._add("mousePan",Xe),this._add("touchPan",it,["touchZoom","touchRotate"]);var xt=new Il,Dt=new Aa;j.touchZoomRotate=new ls($,Dt,xt,ye),this._add("touchRotate",xt,["touchPan","touchZoom"]),this._add("touchZoom",Dt,["touchPan","touchRotate"]);var _t=j.scrollZoom=new ka(j,this);this._add("scrollZoom",_t,["mousePan"]);var Mt=j.keyboard=new no;this._add("keyboard",Mt),this._add("blockableMapEvent",new pi(j));for(var vt=0,Nt=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];vt<Nt.length;vt+=1){var Rt=Nt[vt];I.interactive&&I[Rt]&&j[Rt].enable(I[Rt])}},Lo.prototype._add=function(I,j,$){this._handlers.push({handlerName:I,handler:j,allowed:$}),this._handlersById[I]=j},Lo.prototype.stop=function(){if(!this._updatingCamera){for(var I=0,j=this._handlers;I<j.length;I+=1)j[I].handler.reset();this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},Lo.prototype.isActive=function(){for(var I=0,j=this._handlers;I<j.length;I+=1)if(j[I].handler.isActive())return!0;return!1},Lo.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Lo.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Lo.prototype.isMoving=function(){return!!Mu(this._eventsInProgress)||this.isZooming()},Lo.prototype._blockedByActive=function(I,j,$){for(var X in I)if(X!==$&&(!j||j.indexOf(X)<0))return!0;return!1},Lo.prototype.handleWindowEvent=function(I){this.handleEvent(I,I.type+"Window")},Lo.prototype._getMapTouches=function(I){for(var j=[],$=0,X=I;$<X.length;$+=1){var se=X[$],he=se.target;this._el.contains(he)&&j.push(se)}return j},Lo.prototype.handleEvent=function(I,j){if(I.type!=="blur"){this._updatingCamera=!0;for(var $=I.type==="renderFrame"?void 0:I,X={needsRenderFrame:!1},se={},he={},ye=I.touches?this._getMapTouches(I.touches):void 0,be=ye?u.touchPos(this._el,ye):u.mousePos(this._el,I),Ee=0,Ue=this._handlers;Ee<Ue.length;Ee+=1){var Xe=Ue[Ee],it=Xe.handlerName,xt=Xe.handler,Dt=Xe.allowed;if(xt.isEnabled()){var _t=void 0;this._blockedByActive(he,Dt,it)?xt.reset():xt[j||I.type]&&(_t=xt[j||I.type](I,be,ye),this.mergeHandlerResult(X,se,_t,it,$),_t&&_t.needsRenderFrame&&this._triggerRenderFrame()),(_t||xt.isActive())&&(he[it]=xt)}}var Mt={};for(var vt in this._previousActiveHandlers)he[vt]||(Mt[vt]=$);this._previousActiveHandlers=he,(Object.keys(Mt).length||Df(X))&&(this._changes.push([X,se,Mt]),this._triggerRenderFrame()),(Object.keys(he).length||Df(X))&&this._map._stop(!0),this._updatingCamera=!1;var Nt=X.cameraAnimation;Nt&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],Nt(this._map))}else this.stop()},Lo.prototype.mergeHandlerResult=function(I,j,$,X,se){if($){i.extend(I,$);var he={handlerName:X,originalEvent:$.originalEvent||se};$.zoomDelta!==void 0&&(j.zoom=he),$.panDelta!==void 0&&(j.drag=he),$.pitchDelta!==void 0&&(j.pitch=he),$.bearingDelta!==void 0&&(j.rotate=he)}},Lo.prototype._applyChanges=function(){for(var I={},j={},$={},X=0,se=this._changes;X<se.length;X+=1){var he=se[X],ye=he[0],be=he[1],Ee=he[2];ye.panDelta&&(I.panDelta=(I.panDelta||new i.Point(0,0))._add(ye.panDelta)),ye.zoomDelta&&(I.zoomDelta=(I.zoomDelta||0)+ye.zoomDelta),ye.bearingDelta&&(I.bearingDelta=(I.bearingDelta||0)+ye.bearingDelta),ye.pitchDelta&&(I.pitchDelta=(I.pitchDelta||0)+ye.pitchDelta),ye.around!==void 0&&(I.around=ye.around),ye.pinchAround!==void 0&&(I.pinchAround=ye.pinchAround),ye.noInertia&&(I.noInertia=ye.noInertia),i.extend(j,be),i.extend($,Ee)}this._updateMapTransform(I,j,$),this._changes=[]},Lo.prototype._updateMapTransform=function(I,j,$){var X=this._map,se=X.transform;if(!Df(I))return this._fireEvents(j,$);var he=I.panDelta,ye=I.zoomDelta,be=I.bearingDelta,Ee=I.pitchDelta,Ue=I.around,Xe=I.pinchAround;Xe!==void 0&&(Ue=Xe),X._stop(!0),Ue=Ue||X.transform.centerPoint;var it=se.pointLocation(he?Ue.sub(he):Ue);be&&(se.bearing+=be),Ee&&(se.pitch+=Ee),ye&&(se.zoom+=ye),se.setLocationAtPoint(it,Ue),this._map._update(),I.noInertia||this._inertia.record(I),this._fireEvents(j,$)},Lo.prototype._fireEvents=function(I,j){var $=this,X=Mu(this._eventsInProgress),se=Mu(I),he={};for(var ye in I){var be=I[ye].originalEvent;this._eventsInProgress[ye]||(he[ye+"start"]=be),this._eventsInProgress[ye]=I[ye]}for(var Ee in!X&&se&&this._fireEvent("movestart",se.originalEvent),he)this._fireEvent(Ee,he[Ee]);for(var Ue in I.rotate&&(this._bearingChanged=!0),se&&this._fireEvent("move",se.originalEvent),I){var Xe=I[Ue].originalEvent;this._fireEvent(Ue,Xe)}var it,xt={};for(var Dt in this._eventsInProgress){var _t=this._eventsInProgress[Dt],Mt=_t.handlerName,vt=_t.originalEvent;this._handlersById[Mt].isActive()||(delete this._eventsInProgress[Dt],it=j[Mt]||vt,xt[Dt+"end"]=it)}for(var Nt in xt)this._fireEvent(Nt,xt[Nt]);var Rt=Mu(this._eventsInProgress);if((X||se)&&!Rt){this._updatingCamera=!0;var Vt=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),rn=function(dn){return dn!==0&&-$._bearingSnap<dn&&dn<$._bearingSnap};Vt?(rn(Vt.bearing||this._map.getBearing())&&(Vt.bearing=0),this._map.easeTo(Vt,{originalEvent:it})):(this._map.fire(new i.Event("moveend",{originalEvent:it})),rn(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},Lo.prototype._fireEvent=function(I,j){this._map.fire(new i.Event(I,j?{originalEvent:j}:{}))},Lo.prototype._triggerRenderFrame=function(){var I=this;this._frameId===void 0&&(this._frameId=this._map._requestRenderFrame(function(j){delete I._frameId,I.handleEvent(new eu("renderFrame",{timeStamp:j})),I._applyChanges()}))};var Of=function(I){function j($,X){I.call(this),this._moving=!1,this._zooming=!1,this.transform=$,this._bearingSnap=X.bearingSnap,i.bindAll(["_renderFrameCallback"],this)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.getCenter=function(){return new i.LngLat(this.transform.center.lng,this.transform.center.lat)},j.prototype.setCenter=function($,X){return this.jumpTo({center:$},X)},j.prototype.panBy=function($,X,se){return $=i.Point.convert($).mult(-1),this.panTo(this.transform.center,i.extend({offset:$},X),se)},j.prototype.panTo=function($,X,se){return this.easeTo(i.extend({center:$},X),se)},j.prototype.getZoom=function(){return this.transform.zoom},j.prototype.setZoom=function($,X){return this.jumpTo({zoom:$},X),this},j.prototype.zoomTo=function($,X,se){return this.easeTo(i.extend({zoom:$},X),se)},j.prototype.zoomIn=function($,X){return this.zoomTo(this.getZoom()+1,$,X),this},j.prototype.zoomOut=function($,X){return this.zoomTo(this.getZoom()-1,$,X),this},j.prototype.getBearing=function(){return this.transform.bearing},j.prototype.setBearing=function($,X){return this.jumpTo({bearing:$},X),this},j.prototype.getPadding=function(){return this.transform.padding},j.prototype.setPadding=function($,X){return this.jumpTo({padding:$},X),this},j.prototype.rotateTo=function($,X,se){return this.easeTo(i.extend({bearing:$},X),se)},j.prototype.resetNorth=function($,X){return this.rotateTo(0,i.extend({duration:1e3},$),X),this},j.prototype.resetNorthPitch=function($,X){return this.easeTo(i.extend({bearing:0,pitch:0,duration:1e3},$),X),this},j.prototype.snapToNorth=function($,X){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth($,X):this},j.prototype.getPitch=function(){return this.transform.pitch},j.prototype.setPitch=function($,X){return this.jumpTo({pitch:$},X),this},j.prototype.cameraForBounds=function($,X){return $=i.LngLatBounds.convert($),this._cameraForBoxAndBearing($.getNorthWest(),$.getSouthEast(),0,X)},j.prototype._cameraForBoxAndBearing=function($,X,se,he){var ye={top:0,bottom:0,right:0,left:0};if(typeof(he=i.extend({padding:ye,offset:[0,0],maxZoom:this.transform.maxZoom},he)).padding=="number"){var be=he.padding;he.padding={top:be,bottom:be,right:be,left:be}}he.padding=i.extend(ye,he.padding);var Ee=this.transform,Ue=Ee.padding,Xe=Ee.project(i.LngLat.convert($)),it=Ee.project(i.LngLat.convert(X)),xt=Xe.rotate(-se*Math.PI/180),Dt=it.rotate(-se*Math.PI/180),_t=new i.Point(Math.max(xt.x,Dt.x),Math.max(xt.y,Dt.y)),Mt=new i.Point(Math.min(xt.x,Dt.x),Math.min(xt.y,Dt.y)),vt=_t.sub(Mt),Nt=(Ee.width-(Ue.left+Ue.right+he.padding.left+he.padding.right))/vt.x,Rt=(Ee.height-(Ue.top+Ue.bottom+he.padding.top+he.padding.bottom))/vt.y;if(!(Rt<0||Nt<0)){var Vt=Math.min(Ee.scaleZoom(Ee.scale*Math.min(Nt,Rt)),he.maxZoom),rn=i.Point.convert(he.offset),dn=(he.padding.left-he.padding.right)/2,En=(he.padding.top-he.padding.bottom)/2,Tn=new i.Point(rn.x+dn,rn.y+En).mult(Ee.scale/Ee.zoomScale(Vt));return{center:Ee.unproject(Xe.add(it).div(2).sub(Tn)),zoom:Vt,bearing:se}}i.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},j.prototype.fitBounds=function($,X,se){return this._fitInternal(this.cameraForBounds($,X),X,se)},j.prototype.fitScreenCoordinates=function($,X,se,he,ye){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(i.Point.convert($)),this.transform.pointLocation(i.Point.convert(X)),se,he),he,ye)},j.prototype._fitInternal=function($,X,se){return $?(delete(X=i.extend($,X)).padding,X.linear?this.easeTo(X,se):this.flyTo(X,se)):this},j.prototype.jumpTo=function($,X){this.stop();var se=this.transform,he=!1,ye=!1,be=!1;return"zoom"in $&&se.zoom!==+$.zoom&&(he=!0,se.zoom=+$.zoom),$.center!==void 0&&(se.center=i.LngLat.convert($.center)),"bearing"in $&&se.bearing!==+$.bearing&&(ye=!0,se.bearing=+$.bearing),"pitch"in $&&se.pitch!==+$.pitch&&(be=!0,se.pitch=+$.pitch),$.padding==null||se.isPaddingEqual($.padding)||(se.padding=$.padding),this.fire(new i.Event("movestart",X)).fire(new i.Event("move",X)),he&&this.fire(new i.Event("zoomstart",X)).fire(new i.Event("zoom",X)).fire(new i.Event("zoomend",X)),ye&&this.fire(new i.Event("rotatestart",X)).fire(new i.Event("rotate",X)).fire(new i.Event("rotateend",X)),be&&this.fire(new i.Event("pitchstart",X)).fire(new i.Event("pitch",X)).fire(new i.Event("pitchend",X)),this.fire(new i.Event("moveend",X))},j.prototype.easeTo=function($,X){var se=this;this._stop(!1,$.easeId),(($=i.extend({offset:[0,0],duration:500,easing:i.ease},$)).animate===!1||!$.essential&&i.browser.prefersReducedMotion)&&($.duration=0);var he=this.transform,ye=this.getZoom(),be=this.getBearing(),Ee=this.getPitch(),Ue=this.getPadding(),Xe="zoom"in $?+$.zoom:ye,it="bearing"in $?this._normalizeBearing($.bearing,be):be,xt="pitch"in $?+$.pitch:Ee,Dt="padding"in $?$.padding:he.padding,_t=i.Point.convert($.offset),Mt=he.centerPoint.add(_t),vt=he.pointLocation(Mt),Nt=i.LngLat.convert($.center||vt);this._normalizeCenter(Nt);var Rt,Vt,rn=he.project(vt),dn=he.project(Nt).sub(rn),En=he.zoomScale(Xe-ye);$.around&&(Rt=i.LngLat.convert($.around),Vt=he.locationPoint(Rt));var Tn={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||Xe!==ye,this._rotating=this._rotating||be!==it,this._pitching=this._pitching||xt!==Ee,this._padding=!he.isPaddingEqual(Dt),this._easeId=$.easeId,this._prepareEase(X,$.noMoveStart,Tn),clearTimeout(this._easeEndTimeoutID),this._ease(function(tr){if(se._zooming&&(he.zoom=i.number(ye,Xe,tr)),se._rotating&&(he.bearing=i.number(be,it,tr)),se._pitching&&(he.pitch=i.number(Ee,xt,tr)),se._padding&&(he.interpolatePadding(Ue,Dt,tr),Mt=he.centerPoint.add(_t)),Rt)he.setLocationAtPoint(Rt,Vt);else{var er=he.zoomScale(he.zoom-ye),gr=Xe>ye?Math.min(2,En):Math.max(.5,En),cr=Math.pow(gr,1-tr),Xr=he.unproject(rn.add(dn.mult(tr*cr)).mult(er));he.setLocationAtPoint(he.renderWorldCopies?Xr.wrap():Xr,Mt)}se._fireMoveEvents(X)},function(tr){se._afterEase(X,tr)},$),this},j.prototype._prepareEase=function($,X,se){se===void 0&&(se={}),this._moving=!0,X||se.moving||this.fire(new i.Event("movestart",$)),this._zooming&&!se.zooming&&this.fire(new i.Event("zoomstart",$)),this._rotating&&!se.rotating&&this.fire(new i.Event("rotatestart",$)),this._pitching&&!se.pitching&&this.fire(new i.Event("pitchstart",$))},j.prototype._fireMoveEvents=function($){this.fire(new i.Event("move",$)),this._zooming&&this.fire(new i.Event("zoom",$)),this._rotating&&this.fire(new i.Event("rotate",$)),this._pitching&&this.fire(new i.Event("pitch",$))},j.prototype._afterEase=function($,X){if(!this._easeId||!X||this._easeId!==X){delete this._easeId;var se=this._zooming,he=this._rotating,ye=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,se&&this.fire(new i.Event("zoomend",$)),he&&this.fire(new i.Event("rotateend",$)),ye&&this.fire(new i.Event("pitchend",$)),this.fire(new i.Event("moveend",$))}},j.prototype.flyTo=function($,X){var se=this;if(!$.essential&&i.browser.prefersReducedMotion){var he=i.pick($,["center","zoom","bearing","pitch","around"]);return this.jumpTo(he,X)}this.stop(),$=i.extend({offset:[0,0],speed:1.2,curve:1.42,easing:i.ease},$);var ye=this.transform,be=this.getZoom(),Ee=this.getBearing(),Ue=this.getPitch(),Xe=this.getPadding(),it="zoom"in $?i.clamp(+$.zoom,ye.minZoom,ye.maxZoom):be,xt="bearing"in $?this._normalizeBearing($.bearing,Ee):Ee,Dt="pitch"in $?+$.pitch:Ue,_t="padding"in $?$.padding:ye.padding,Mt=ye.zoomScale(it-be),vt=i.Point.convert($.offset),Nt=ye.centerPoint.add(vt),Rt=ye.pointLocation(Nt),Vt=i.LngLat.convert($.center||Rt);this._normalizeCenter(Vt);var rn=ye.project(Rt),dn=ye.project(Vt).sub(rn),En=$.curve,Tn=Math.max(ye.width,ye.height),tr=Tn/Mt,er=dn.mag();if("minZoom"in $){var gr=i.clamp(Math.min($.minZoom,be,it),ye.minZoom,ye.maxZoom),cr=Tn/ye.zoomScale(gr-be);En=Math.sqrt(cr/er*2)}var Xr=En*En;function oi(fi){var zi=(tr*tr-Tn*Tn+(fi?-1:1)*Xr*Xr*er*er)/(2*(fi?tr:Tn)*Xr*er);return Math.log(Math.sqrt(zi*zi+1)-zi)}function Ai(fi){return(Math.exp(fi)-Math.exp(-fi))/2}function Gn(fi){return(Math.exp(fi)+Math.exp(-fi))/2}var Mr=oi(0),si=function(fi){return Gn(Mr)/Gn(Mr+En*fi)},Qr=function(fi){return Tn*((Gn(Mr)*(Ai(zi=Mr+En*fi)/Gn(zi))-Ai(Mr))/Xr)/er;var zi},mi=(oi(1)-Mr)/En;if(Math.abs(er)<1e-6||!isFinite(mi)){if(Math.abs(Tn-tr)<1e-6)return this.easeTo($,X);var Mi=tr<Tn?-1:1;mi=Math.abs(Math.log(tr/Tn))/En,Qr=function(){return 0},si=function(fi){return Math.exp(Mi*En*fi)}}if("duration"in $)$.duration=+$.duration;else{var Zi="screenSpeed"in $?+$.screenSpeed/En:+$.speed;$.duration=1e3*mi/Zi}return $.maxDuration&&$.duration>$.maxDuration&&($.duration=0),this._zooming=!0,this._rotating=Ee!==xt,this._pitching=Dt!==Ue,this._padding=!ye.isPaddingEqual(_t),this._prepareEase(X,!1),this._ease(function(fi){var zi=fi*mi,Oi=1/si(zi);ye.zoom=fi===1?it:be+ye.scaleZoom(Oi),se._rotating&&(ye.bearing=i.number(Ee,xt,fi)),se._pitching&&(ye.pitch=i.number(Ue,Dt,fi)),se._padding&&(ye.interpolatePadding(Xe,_t,fi),Nt=ye.centerPoint.add(vt));var ta=fi===1?Vt:ye.unproject(rn.add(dn.mult(Qr(zi))).mult(Oi));ye.setLocationAtPoint(ye.renderWorldCopies?ta.wrap():ta,Nt),se._fireMoveEvents(X)},function(){return se._afterEase(X)},$),this},j.prototype.isEasing=function(){return!!this._easeFrameId},j.prototype.stop=function(){return this._stop()},j.prototype._stop=function($,X){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var se=this._onEaseEnd;delete this._onEaseEnd,se.call(this,X)}if(!$){var he=this.handlers;he&&he.stop()}return this},j.prototype._ease=function($,X,se){se.animate===!1||se.duration===0?($(1),X()):(this._easeStart=i.browser.now(),this._easeOptions=se,this._onEaseFrame=$,this._onEaseEnd=X,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},j.prototype._renderFrameCallback=function(){var $=Math.min((i.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing($)),$<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},j.prototype._normalizeBearing=function($,X){$=i.wrap($,-180,180);var se=Math.abs($-X);return Math.abs($-360-X)<se&&($-=360),Math.abs($+360-X)<se&&($+=360),$},j.prototype._normalizeCenter=function($){var X=this.transform;if(X.renderWorldCopies&&!X.lngRange){var se=$.lng-X.center.lng;$.lng+=se>180?-360:se<-180?360:0}},j}(i.Evented),Do=function(I){I===void 0&&(I={}),this.options=I,i.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};Do.prototype.getDefaultPosition=function(){return"bottom-right"},Do.prototype.onAdd=function(I){var j=this.options&&this.options.compact;return this._map=I,this._container=u.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=u.create("div","mapboxgl-ctrl-attrib-inner",this._container),j&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),j===void 0&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},Do.prototype.onRemove=function(){u.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Do.prototype._updateEditLink=function(){var I=this._editLink;I||(I=this._editLink=this._container.querySelector(".mapbox-improve-map"));var j=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||i.config.ACCESS_TOKEN}];if(I){var $=j.reduce(function(X,se,he){return se.value&&(X+=se.key+"="+se.value+(he<j.length-1?"&":"")),X},"?");I.href=i.config.FEEDBACK_URL+"/"+$+(this._map._hash?this._map._hash.getHashString(!0):""),I.rel="noopener nofollow"}},Do.prototype._updateData=function(I){!I||I.sourceDataType!=="metadata"&&I.dataType!=="style"||(this._updateAttributions(),this._updateEditLink())},Do.prototype._updateAttributions=function(){if(this._map.style){var I=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?I=I.concat(this.options.customAttribution.map(function(be){return typeof be!="string"?"":be})):typeof this.options.customAttribution=="string"&&I.push(this.options.customAttribution)),this._map.style.stylesheet){var j=this._map.style.stylesheet;this.styleOwner=j.owner,this.styleId=j.id}var $=this._map.style.sourceCaches;for(var X in $){var se=$[X];if(se.used){var he=se.getSource();he.attribution&&I.indexOf(he.attribution)<0&&I.push(he.attribution)}}I.sort(function(be,Ee){return be.length-Ee.length});var ye=(I=I.filter(function(be,Ee){for(var Ue=Ee+1;Ue<I.length;Ue++)if(I[Ue].indexOf(be)>=0)return!1;return!0})).join(" | ");ye!==this._attribHTML&&(this._attribHTML=ye,I.length?(this._innerContainer.innerHTML=ye,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},Do.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var tu=function(){i.bindAll(["_updateLogo"],this),i.bindAll(["_updateCompact"],this)};tu.prototype.onAdd=function(I){this._map=I,this._container=u.create("div","mapboxgl-ctrl");var j=u.create("a","mapboxgl-ctrl-logo");return j.target="_blank",j.rel="noopener nofollow",j.href="https://www.mapbox.com/",j.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),j.setAttribute("rel","noopener nofollow"),this._container.appendChild(j),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},tu.prototype.onRemove=function(){u.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},tu.prototype.getDefaultPosition=function(){return"bottom-left"},tu.prototype._updateLogo=function(I){I&&I.sourceDataType!=="metadata"||(this._container.style.display=this._logoRequired()?"block":"none")},tu.prototype._logoRequired=function(){if(this._map.style){var I=this._map.style.sourceCaches;for(var j in I)if(I[j].getSource().mapbox_logo)return!0;return!1}},tu.prototype._updateCompact=function(){var I=this._container.children;if(I.length){var j=I[0];this._map.getCanvasContainer().offsetWidth<250?j.classList.add("mapboxgl-compact"):j.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(I){var j=++this._id;return this._queue.push({callback:I,id:j,cancelled:!1}),j},wi.prototype.remove=function(I){for(var j=this._currentlyRunning,$=0,X=j?this._queue.concat(j):this._queue;$<X.length;$+=1){var se=X[$];if(se.id===I)return void(se.cancelled=!0)}},wi.prototype.run=function(I){I===void 0&&(I=0);var j=this._currentlyRunning=this._queue;this._queue=[];for(var $=0,X=j;$<X.length;$+=1){var se=X[$];if(!se.cancelled&&(se.callback(I),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ii={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},Pf=i.window.HTMLImageElement,If=i.window.HTMLElement,nu=i.window.ImageBitmap,Gs={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Al=function(I){function j(X){var se=this;if((X=i.extend({},Gs,X)).minZoom!=null&&X.maxZoom!=null&&X.minZoom>X.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(X.minPitch!=null&&X.maxPitch!=null&&X.minPitch>X.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(X.minPitch!=null&&X.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(X.maxPitch!=null&&X.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var he=new Ht(X.minZoom,X.maxZoom,X.minPitch,X.maxPitch,X.renderWorldCopies);if(I.call(this,he,X),this._interactive=X.interactive,this._maxTileCacheSize=X.maxTileCacheSize,this._failIfMajorPerformanceCaveat=X.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=X.preserveDrawingBuffer,this._antialias=X.antialias,this._trackResize=X.trackResize,this._bearingSnap=X.bearingSnap,this._refreshExpiredTiles=X.refreshExpiredTiles,this._fadeDuration=X.fadeDuration,this._crossSourceCollisions=X.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=X.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=i.uniqueId(),this._locale=i.extend({},Ii,X.locale),this._requestManager=new i.RequestManager(X.transformRequest,X.accessToken),typeof X.container=="string"){if(this._container=i.window.document.getElementById(X.container),!this._container)throw new Error("Container '"+X.container+"' not found.")}else{if(!(X.container instanceof If))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=X.container}if(X.maxBounds&&this.setMaxBounds(X.maxBounds),i.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),this.painter===void 0)throw new Error("Failed to initialize WebGL.");this.on("move",function(){return se._update(!1)}),this.on("moveend",function(){return se._update(!1)}),this.on("zoom",function(){return se._update(!0)}),i.window!==void 0&&(i.window.addEventListener("online",this._onWindowOnline,!1),i.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new Lo(this,X);var ye=typeof X.hash=="string"&&X.hash||void 0;this._hash=X.hash&&new Ln(ye).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:X.center,zoom:X.zoom,bearing:X.bearing,pitch:X.pitch}),X.bounds&&(this.resize(),this.fitBounds(X.bounds,i.extend({},X.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=X.localIdeographFontFamily,X.style&&this.setStyle(X.style,{localIdeographFontFamily:X.localIdeographFontFamily}),X.attributionControl&&this.addControl(new Do({customAttribution:X.customAttribution})),this.addControl(new tu,X.logoPosition),this.on("style.load",function(){se.transform.unmodified&&se.jumpTo(se.style.stylesheet)}),this.on("data",function(be){se._update(be.dataType==="style"),se.fire(new i.Event(be.dataType+"data",be))}),this.on("dataloading",function(be){se.fire(new i.Event(be.dataType+"dataloading",be))})}I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j;var $={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return j.prototype._getMapId=function(){return this._mapId},j.prototype.addControl=function(X,se){if(se===void 0&&X.getDefaultPosition&&(se=X.getDefaultPosition()),se===void 0&&(se="top-right"),!X||!X.onAdd)return this.fire(new i.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var he=X.onAdd(this);this._controls.push(X);var ye=this._controlPositions[se];return se.indexOf("bottom")!==-1?ye.insertBefore(he,ye.firstChild):ye.appendChild(he),this},j.prototype.removeControl=function(X){if(!X||!X.onRemove)return this.fire(new i.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var se=this._controls.indexOf(X);return se>-1&&this._controls.splice(se,1),X.onRemove(this),this},j.prototype.resize=function(X){var se=this._containerDimensions(),he=se[0],ye=se[1];this._resizeCanvas(he,ye),this.transform.resize(he,ye),this.painter.resize(he,ye);var be=!this._moving;return be&&(this.stop(),this.fire(new i.Event("movestart",X)).fire(new i.Event("move",X))),this.fire(new i.Event("resize",X)),be&&this.fire(new i.Event("moveend",X)),this},j.prototype.getBounds=function(){return this.transform.getBounds()},j.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},j.prototype.setMaxBounds=function(X){return this.transform.setMaxBounds(i.LngLatBounds.convert(X)),this._update()},j.prototype.setMinZoom=function(X){if((X=X??-2)>=-2&&X<=this.transform.maxZoom)return this.transform.minZoom=X,this._update(),this.getZoom()<X&&this.setZoom(X),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},j.prototype.getMinZoom=function(){return this.transform.minZoom},j.prototype.setMaxZoom=function(X){if((X=X??22)>=this.transform.minZoom)return this.transform.maxZoom=X,this._update(),this.getZoom()>X&&this.setZoom(X),this;throw new Error("maxZoom must be greater than the current minZoom")},j.prototype.getMaxZoom=function(){return this.transform.maxZoom},j.prototype.setMinPitch=function(X){if((X=X??0)<0)throw new Error("minPitch must be greater than or equal to 0");if(X>=0&&X<=this.transform.maxPitch)return this.transform.minPitch=X,this._update(),this.getPitch()<X&&this.setPitch(X),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},j.prototype.getMinPitch=function(){return this.transform.minPitch},j.prototype.setMaxPitch=function(X){if((X=X??60)>60)throw new Error("maxPitch must be less than or equal to 60");if(X>=this.transform.minPitch)return this.transform.maxPitch=X,this._update(),this.getPitch()>X&&this.setPitch(X),this;throw new Error("maxPitch must be greater than the current minPitch")},j.prototype.getMaxPitch=function(){return this.transform.maxPitch},j.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},j.prototype.setRenderWorldCopies=function(X){return this.transform.renderWorldCopies=X,this._update()},j.prototype.project=function(X){return this.transform.locationPoint(i.LngLat.convert(X))},j.prototype.unproject=function(X){return this.transform.pointLocation(i.Point.convert(X))},j.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},j.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},j.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},j.prototype._createDelegatedListener=function(X,se,he){var ye,be=this;if(X==="mouseenter"||X==="mouseover"){var Ee=!1;return{layer:se,listener:he,delegates:{mousemove:function(Xe){var it=be.getLayer(se)?be.queryRenderedFeatures(Xe.point,{layers:[se]}):[];it.length?Ee||(Ee=!0,he.call(be,new $r(X,be,Xe.originalEvent,{features:it}))):Ee=!1},mouseout:function(){Ee=!1}}}}if(X==="mouseleave"||X==="mouseout"){var Ue=!1;return{layer:se,listener:he,delegates:{mousemove:function(Xe){(be.getLayer(se)?be.queryRenderedFeatures(Xe.point,{layers:[se]}):[]).length?Ue=!0:Ue&&(Ue=!1,he.call(be,new $r(X,be,Xe.originalEvent)))},mouseout:function(Xe){Ue&&(Ue=!1,he.call(be,new $r(X,be,Xe.originalEvent)))}}}}return{layer:se,listener:he,delegates:(ye={},ye[X]=function(Xe){var it=be.getLayer(se)?be.queryRenderedFeatures(Xe.point,{layers:[se]}):[];it.length&&(Xe.features=it,he.call(be,Xe),delete Xe.features)},ye)}},j.prototype.on=function(X,se,he){if(he===void 0)return I.prototype.on.call(this,X,se);var ye=this._createDelegatedListener(X,se,he);for(var be in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[X]=this._delegatedListeners[X]||[],this._delegatedListeners[X].push(ye),ye.delegates)this.on(be,ye.delegates[be]);return this},j.prototype.once=function(X,se,he){if(he===void 0)return I.prototype.once.call(this,X,se);var ye=this._createDelegatedListener(X,se,he);for(var be in ye.delegates)this.once(be,ye.delegates[be]);return this},j.prototype.off=function(X,se,he){var ye=this;return he===void 0?I.prototype.off.call(this,X,se):(this._delegatedListeners&&this._delegatedListeners[X]&&function(be){for(var Ee=be[X],Ue=0;Ue<Ee.length;Ue++){var Xe=Ee[Ue];if(Xe.layer===se&&Xe.listener===he){for(var it in Xe.delegates)ye.off(it,Xe.delegates[it]);return Ee.splice(Ue,1),ye}}}(this._delegatedListeners),this)},j.prototype.queryRenderedFeatures=function(X,se){if(!this.style)return[];var he;if(se!==void 0||X===void 0||X instanceof i.Point||Array.isArray(X)||(se=X,X=void 0),se=se||{},(X=X||[[0,0],[this.transform.width,this.transform.height]])instanceof i.Point||typeof X[0]=="number")he=[i.Point.convert(X)];else{var ye=i.Point.convert(X[0]),be=i.Point.convert(X[1]);he=[ye,new i.Point(be.x,ye.y),be,new i.Point(ye.x,be.y),ye]}return this.style.queryRenderedFeatures(he,se,this.transform)},j.prototype.querySourceFeatures=function(X,se){return this.style.querySourceFeatures(X,se)},j.prototype.setStyle=function(X,se){return(se=i.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},se)).diff!==!1&&se.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&X?(this._diffStyle(X,se),this):(this._localIdeographFontFamily=se.localIdeographFontFamily,this._updateStyle(X,se))},j.prototype._getUIString=function(X){var se=this._locale[X];if(se==null)throw new Error("Missing UI string '"+X+"'");return se},j.prototype._updateStyle=function(X,se){return this.style&&(this.style.setEventedParent(null),this.style._remove()),X?(this.style=new Ar(this,se||{}),this.style.setEventedParent(this,{style:this.style}),typeof X=="string"?this.style.loadURL(X):this.style.loadJSON(X),this):(delete this.style,this)},j.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new Ar(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},j.prototype._diffStyle=function(X,se){var he=this;if(typeof X=="string"){var ye=this._requestManager.normalizeStyleURL(X),be=this._requestManager.transformRequest(ye,i.ResourceType.Style);i.getJSON(be,function(Ee,Ue){Ee?he.fire(new i.ErrorEvent(Ee)):Ue&&he._updateDiff(Ue,se)})}else typeof X=="object"&&this._updateDiff(X,se)},j.prototype._updateDiff=function(X,se){try{this.style.setState(X)&&this._update(!0)}catch(he){i.warnOnce("Unable to perform style diff: "+(he.message||he.error||he)+".  Rebuilding the style from scratch."),this._updateStyle(X,se)}},j.prototype.getStyle=function(){if(this.style)return this.style.serialize()},j.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():i.warnOnce("There is no style added to the map.")},j.prototype.addSource=function(X,se){return this._lazyInitEmptyStyle(),this.style.addSource(X,se),this._update(!0)},j.prototype.isSourceLoaded=function(X){var se=this.style&&this.style.sourceCaches[X];if(se!==void 0)return se.loaded();this.fire(new i.ErrorEvent(new Error("There is no source with ID '"+X+"'")))},j.prototype.areTilesLoaded=function(){var X=this.style&&this.style.sourceCaches;for(var se in X){var he=X[se]._tiles;for(var ye in he){var be=he[ye];if(be.state!=="loaded"&&be.state!=="errored")return!1}}return!0},j.prototype.addSourceType=function(X,se,he){return this._lazyInitEmptyStyle(),this.style.addSourceType(X,se,he)},j.prototype.removeSource=function(X){return this.style.removeSource(X),this._update(!0)},j.prototype.getSource=function(X){return this.style.getSource(X)},j.prototype.addImage=function(X,se,he){he===void 0&&(he={});var ye=he.pixelRatio;ye===void 0&&(ye=1);var be=he.sdf;be===void 0&&(be=!1);var Ee=he.stretchX,Ue=he.stretchY,Xe=he.content;if(this._lazyInitEmptyStyle(),se instanceof Pf||nu&&se instanceof nu){var it=i.browser.getImageData(se),xt=it.width,Dt=it.height,_t=it.data;this.style.addImage(X,{data:new i.RGBAImage({width:xt,height:Dt},_t),pixelRatio:ye,stretchX:Ee,stretchY:Ue,content:Xe,sdf:be,version:0})}else{if(se.width===void 0||se.height===void 0)return this.fire(new i.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var Mt=se.width,vt=se.height,Nt=se.data,Rt=se;this.style.addImage(X,{data:new i.RGBAImage({width:Mt,height:vt},new Uint8Array(Nt)),pixelRatio:ye,stretchX:Ee,stretchY:Ue,content:Xe,sdf:be,version:0,userImage:Rt}),Rt.onAdd&&Rt.onAdd(this,X)}},j.prototype.updateImage=function(X,se){var he=this.style.getImage(X);if(!he)return this.fire(new i.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var ye=se instanceof Pf||nu&&se instanceof nu?i.browser.getImageData(se):se,be=ye.width,Ee=ye.height,Ue=ye.data;if(be===void 0||Ee===void 0)return this.fire(new i.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(be!==he.data.width||Ee!==he.data.height)return this.fire(new i.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var Xe=!(se instanceof Pf||nu&&se instanceof nu);he.data.replace(Ue,Xe),this.style.updateImage(X,he)},j.prototype.hasImage=function(X){return X?!!this.style.getImage(X):(this.fire(new i.ErrorEvent(new Error("Missing required image id"))),!1)},j.prototype.removeImage=function(X){this.style.removeImage(X)},j.prototype.loadImage=function(X,se){i.getImage(this._requestManager.transformRequest(X,i.ResourceType.Image),se)},j.prototype.listImages=function(){return this.style.listImages()},j.prototype.addLayer=function(X,se){return this._lazyInitEmptyStyle(),this.style.addLayer(X,se),this._update(!0)},j.prototype.moveLayer=function(X,se){return this.style.moveLayer(X,se),this._update(!0)},j.prototype.removeLayer=function(X){return this.style.removeLayer(X),this._update(!0)},j.prototype.getLayer=function(X){return this.style.getLayer(X)},j.prototype.setLayerZoomRange=function(X,se,he){return this.style.setLayerZoomRange(X,se,he),this._update(!0)},j.prototype.setFilter=function(X,se,he){return he===void 0&&(he={}),this.style.setFilter(X,se,he),this._update(!0)},j.prototype.getFilter=function(X){return this.style.getFilter(X)},j.prototype.setPaintProperty=function(X,se,he,ye){return ye===void 0&&(ye={}),this.style.setPaintProperty(X,se,he,ye),this._update(!0)},j.prototype.getPaintProperty=function(X,se){return this.style.getPaintProperty(X,se)},j.prototype.setLayoutProperty=function(X,se,he,ye){return ye===void 0&&(ye={}),this.style.setLayoutProperty(X,se,he,ye),this._update(!0)},j.prototype.getLayoutProperty=function(X,se){return this.style.getLayoutProperty(X,se)},j.prototype.setLight=function(X,se){return se===void 0&&(se={}),this._lazyInitEmptyStyle(),this.style.setLight(X,se),this._update(!0)},j.prototype.getLight=function(){return this.style.getLight()},j.prototype.setFeatureState=function(X,se){return this.style.setFeatureState(X,se),this._update()},j.prototype.removeFeatureState=function(X,se){return this.style.removeFeatureState(X,se),this._update()},j.prototype.getFeatureState=function(X){return this.style.getFeatureState(X)},j.prototype.getContainer=function(){return this._container},j.prototype.getCanvasContainer=function(){return this._canvasContainer},j.prototype.getCanvas=function(){return this._canvas},j.prototype._containerDimensions=function(){var X=0,se=0;return this._container&&(X=this._container.clientWidth||400,se=this._container.clientHeight||300),[X,se]},j.prototype._detectMissingCSS=function(){i.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")!=="rgb(250, 128, 114)"&&i.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},j.prototype._setupContainer=function(){var X=this._container;X.classList.add("mapboxgl-map"),(this._missingCSSCanary=u.create("div","mapboxgl-canary",X)).style.visibility="hidden",this._detectMissingCSS();var se=this._canvasContainer=u.create("div","mapboxgl-canvas-container",X);this._interactive&&se.classList.add("mapboxgl-interactive"),this._canvas=u.create("canvas","mapboxgl-canvas",se),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var he=this._containerDimensions();this._resizeCanvas(he[0],he[1]);var ye=this._controlContainer=u.create("div","mapboxgl-control-container",X),be=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach(function(Ee){be[Ee]=u.create("div","mapboxgl-ctrl-"+Ee,ye)})},j.prototype._resizeCanvas=function(X,se){var he=i.browser.devicePixelRatio||1;this._canvas.width=he*X,this._canvas.height=he*se,this._canvas.style.width=X+"px",this._canvas.style.height=se+"px"},j.prototype._setupPainter=function(){var X=i.extend({},s.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),se=this._canvas.getContext("webgl",X)||this._canvas.getContext("experimental-webgl",X);se?(this.painter=new ia(se,this.transform),i.webpSupported.testSupport(se)):this.fire(new i.ErrorEvent(new Error("Failed to initialize WebGL")))},j.prototype._contextLost=function(X){X.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new i.Event("webglcontextlost",{originalEvent:X}))},j.prototype._contextRestored=function(X){this._setupPainter(),this.resize(),this._update(),this.fire(new i.Event("webglcontextrestored",{originalEvent:X}))},j.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},j.prototype._update=function(X){return this.style?(this._styleDirty=this._styleDirty||X,this._sourcesDirty=!0,this.triggerRepaint(),this):this},j.prototype._requestRenderFrame=function(X){return this._update(),this._renderTaskQueue.add(X)},j.prototype._cancelRenderFrame=function(X){this._renderTaskQueue.remove(X)},j.prototype._render=function(X){var se,he=this,ye=0,be=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(se=be.createQueryEXT(),be.beginQueryEXT(be.TIME_ELAPSED_EXT,se),ye=i.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(X),!this._removed){var Ee=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var Ue=this.transform.zoom,Xe=i.browser.now();this.style.zoomHistory.update(Ue,Xe);var it=new i.EvaluationParameters(Ue,{now:Xe,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),xt=it.crossFadingFactor();xt===1&&xt===this._crossFadingFactor||(Ee=!0,this._crossFadingFactor=xt),this.style.update(it)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new i.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new i.Event("load"))),this.style&&(this.style.hasTransitions()||Ee)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var Dt=i.browser.now()-ye;be.endQueryEXT(be.TIME_ELAPSED_EXT,se),setTimeout(function(){var Mt=be.getQueryObjectEXT(se,be.QUERY_RESULT_EXT)/1e6;be.deleteQueryEXT(se),he.fire(new i.Event("gpu-timing-frame",{cpuTime:Dt,gpuTime:Mt}))},50)}if(this.listens("gpu-timing-layer")){var _t=this.painter.collectGpuTimers();setTimeout(function(){var Mt=he.painter.queryGpuTimers(_t);he.fire(new i.Event("gpu-timing-layer",{layerTimes:Mt}))},50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new i.Event("idle"))),this}},j.prototype.remove=function(){this._hash&&this._hash.remove();for(var X=0,se=this._controls;X<se.length;X+=1)se[X].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),i.window!==void 0&&(i.window.removeEventListener("resize",this._onWindowResize,!1),i.window.removeEventListener("online",this._onWindowOnline,!1));var he=this.painter.context.gl.getExtension("WEBGL_lose_context");he&&he.loseContext(),vd(this._canvasContainer),vd(this._controlContainer),vd(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new i.Event("remove"))},j.prototype.triggerRepaint=function(){var X=this;this.style&&!this._frame&&(this._frame=i.browser.frame(function(se){X._frame=null,X._render(se)}))},j.prototype._onWindowOnline=function(){this._update()},j.prototype._onWindowResize=function(X){this._trackResize&&this.resize({originalEvent:X})._update()},$.showTileBoundaries.get=function(){return!!this._showTileBoundaries},$.showTileBoundaries.set=function(X){this._showTileBoundaries!==X&&(this._showTileBoundaries=X,this._update())},$.showPadding.get=function(){return!!this._showPadding},$.showPadding.set=function(X){this._showPadding!==X&&(this._showPadding=X,this._update())},$.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},$.showCollisionBoxes.set=function(X){this._showCollisionBoxes!==X&&(this._showCollisionBoxes=X,X?this.style._generateCollisionBoxes():this._update())},$.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},$.showOverdrawInspector.set=function(X){this._showOverdrawInspector!==X&&(this._showOverdrawInspector=X,this._update())},$.repaint.get=function(){return!!this._repaint},$.repaint.set=function(X){this._repaint!==X&&(this._repaint=X,this.triggerRepaint())},$.vertices.get=function(){return!!this._vertices},$.vertices.set=function(X){this._vertices=X,this._update()},j.prototype._setCacheLimits=function(X,se){i.setCacheLimits(X,se)},$.version.get=function(){return i.version},Object.defineProperties(j.prototype,$),j}(Of);function vd(I){I.parentNode&&I.parentNode.removeChild(I)}var jc={showCompass:!0,showZoom:!0,visualizePitch:!1},Ta=function(I){var j=this;this.options=i.extend({},jc,I),this._container=u.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",function($){return $.preventDefault()}),this.options.showZoom&&(i.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",function($){return j._map.zoomIn({},{originalEvent:$})}),u.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",function($){return j._map.zoomOut({},{originalEvent:$})}),u.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(i.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",function($){j.options.visualizePitch?j._map.resetNorthPitch({},{originalEvent:$}):j._map.resetNorth({},{originalEvent:$})}),this._compassIcon=u.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Ta.prototype._updateZoomButtons=function(){var I=this._map.getZoom();this._zoomInButton.disabled=I===this._map.getMaxZoom(),this._zoomOutButton.disabled=I===this._map.getMinZoom()},Ta.prototype._rotateCompassArrow=function(){var I=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=I},Ta.prototype.onAdd=function(I){return this._map=I,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Fa(this._map,this._compass,this.options.visualizePitch)),this._container},Ta.prototype.onRemove=function(){u.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Ta.prototype._createButton=function(I,j){var $=u.create("button",I,this._container);return $.type="button",$.addEventListener("click",j),$},Ta.prototype._setButtonTitle=function(I,j){var $=this._map._getUIString("NavigationControl."+j);I.title=$,I.setAttribute("aria-label",$)};var Fa=function(I,j,$){$===void 0&&($=!1),this._clickTolerance=10,this.element=j,this.mouseRotate=new Qi({clickTolerance:I.dragRotate._mouseRotate._clickTolerance}),this.map=I,$&&(this.mousePitch=new Ci({clickTolerance:I.dragRotate._mousePitch._clickTolerance})),i.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),u.addEventListener(j,"mousedown",this.mousedown),u.addEventListener(j,"touchstart",this.touchstart,{passive:!1}),u.addEventListener(j,"touchmove",this.touchmove),u.addEventListener(j,"touchend",this.touchend),u.addEventListener(j,"touchcancel",this.reset)};function Qu(I,j,$){if(I=new i.LngLat(I.lng,I.lat),j){var X=new i.LngLat(I.lng-360,I.lat),se=new i.LngLat(I.lng+360,I.lat),he=$.locationPoint(I).distSqr(j);$.locationPoint(X).distSqr(j)<he?I=X:$.locationPoint(se).distSqr(j)<he&&(I=se)}for(;Math.abs(I.lng-$.center.lng)>180;){var ye=$.locationPoint(I);if(ye.x>=0&&ye.y>=0&&ye.x<=$.width&&ye.y<=$.height)break;I.lng>$.center.lng?I.lng-=360:I.lng+=360}return I}Fa.prototype.down=function(I,j){this.mouseRotate.mousedown(I,j),this.mousePitch&&this.mousePitch.mousedown(I,j),u.disableDrag()},Fa.prototype.move=function(I,j){var $=this.map,X=this.mouseRotate.mousemoveWindow(I,j);if(X&&X.bearingDelta&&$.setBearing($.getBearing()+X.bearingDelta),this.mousePitch){var se=this.mousePitch.mousemoveWindow(I,j);se&&se.pitchDelta&&$.setPitch($.getPitch()+se.pitchDelta)}},Fa.prototype.off=function(){var I=this.element;u.removeEventListener(I,"mousedown",this.mousedown),u.removeEventListener(I,"touchstart",this.touchstart,{passive:!1}),u.removeEventListener(I,"touchmove",this.touchmove),u.removeEventListener(I,"touchend",this.touchend),u.removeEventListener(I,"touchcancel",this.reset),this.offTemp()},Fa.prototype.offTemp=function(){u.enableDrag(),u.removeEventListener(i.window,"mousemove",this.mousemove),u.removeEventListener(i.window,"mouseup",this.mouseup)},Fa.prototype.mousedown=function(I){this.down(i.extend({},I,{ctrlKey:!0,preventDefault:function(){return I.preventDefault()}}),u.mousePos(this.element,I)),u.addEventListener(i.window,"mousemove",this.mousemove),u.addEventListener(i.window,"mouseup",this.mouseup)},Fa.prototype.mousemove=function(I){this.move(I,u.mousePos(this.element,I))},Fa.prototype.mouseup=function(I){this.mouseRotate.mouseupWindow(I),this.mousePitch&&this.mousePitch.mouseupWindow(I),this.offTemp()},Fa.prototype.touchstart=function(I){I.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=u.touchPos(this.element,I.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return I.preventDefault()}},this._startPos))},Fa.prototype.touchmove=function(I){I.targetTouches.length!==1?this.reset():(this._lastPos=u.touchPos(this.element,I.targetTouches)[0],this.move({preventDefault:function(){return I.preventDefault()}},this._lastPos))},Fa.prototype.touchend=function(I){I.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Fa.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Eu={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Ao(I,j,$){var X=I.classList;for(var se in Eu)X.remove("mapboxgl-"+$+"-anchor-"+se);X.add("mapboxgl-"+$+"-anchor-"+j)}var ec,Uc=function(I){function j($,X){var se=this;if(I.call(this),($ instanceof i.window.HTMLElement||X)&&($=i.extend({element:$},X)),i.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=$&&$.anchor||"center",this._color=$&&$.color||"#3FB1CE",this._draggable=$&&$.draggable||!1,this._state="inactive",this._rotation=$&&$.rotation||0,this._rotationAlignment=$&&$.rotationAlignment||"auto",this._pitchAlignment=$&&$.pitchAlignment&&$.pitchAlignment!=="auto"?$.pitchAlignment:this._rotationAlignment,$&&$.element)this._element=$.element,this._offset=i.Point.convert($&&$.offset||[0,0]);else{this._defaultMarker=!0,this._element=u.create("div"),this._element.setAttribute("aria-label","Map marker");var he=u.createNS("http://www.w3.org/2000/svg","svg");he.setAttributeNS(null,"display","block"),he.setAttributeNS(null,"height","41px"),he.setAttributeNS(null,"width","27px"),he.setAttributeNS(null,"viewBox","0 0 27 41");var ye=u.createNS("http://www.w3.org/2000/svg","g");ye.setAttributeNS(null,"stroke","none"),ye.setAttributeNS(null,"stroke-width","1"),ye.setAttributeNS(null,"fill","none"),ye.setAttributeNS(null,"fill-rule","evenodd");var be=u.createNS("http://www.w3.org/2000/svg","g");be.setAttributeNS(null,"fill-rule","nonzero");var Ee=u.createNS("http://www.w3.org/2000/svg","g");Ee.setAttributeNS(null,"transform","translate(3.0, 29.0)"),Ee.setAttributeNS(null,"fill","#000000");for(var Ue=0,Xe=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];Ue<Xe.length;Ue+=1){var it=Xe[Ue],xt=u.createNS("http://www.w3.org/2000/svg","ellipse");xt.setAttributeNS(null,"opacity","0.04"),xt.setAttributeNS(null,"cx","10.5"),xt.setAttributeNS(null,"cy","5.80029008"),xt.setAttributeNS(null,"rx",it.rx),xt.setAttributeNS(null,"ry",it.ry),Ee.appendChild(xt)}var Dt=u.createNS("http://www.w3.org/2000/svg","g");Dt.setAttributeNS(null,"fill",this._color);var _t=u.createNS("http://www.w3.org/2000/svg","path");_t.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),Dt.appendChild(_t);var Mt=u.createNS("http://www.w3.org/2000/svg","g");Mt.setAttributeNS(null,"opacity","0.25"),Mt.setAttributeNS(null,"fill","#000000");var vt=u.createNS("http://www.w3.org/2000/svg","path");vt.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),Mt.appendChild(vt);var Nt=u.createNS("http://www.w3.org/2000/svg","g");Nt.setAttributeNS(null,"transform","translate(6.0, 7.0)"),Nt.setAttributeNS(null,"fill","#FFFFFF");var Rt=u.createNS("http://www.w3.org/2000/svg","g");Rt.setAttributeNS(null,"transform","translate(8.0, 8.0)");var Vt=u.createNS("http://www.w3.org/2000/svg","circle");Vt.setAttributeNS(null,"fill","#000000"),Vt.setAttributeNS(null,"opacity","0.25"),Vt.setAttributeNS(null,"cx","5.5"),Vt.setAttributeNS(null,"cy","5.5"),Vt.setAttributeNS(null,"r","5.4999962");var rn=u.createNS("http://www.w3.org/2000/svg","circle");rn.setAttributeNS(null,"fill","#FFFFFF"),rn.setAttributeNS(null,"cx","5.5"),rn.setAttributeNS(null,"cy","5.5"),rn.setAttributeNS(null,"r","5.4999962"),Rt.appendChild(Vt),Rt.appendChild(rn),be.appendChild(Ee),be.appendChild(Dt),be.appendChild(Mt),be.appendChild(Nt),be.appendChild(Rt),he.appendChild(be),this._element.appendChild(he),this._offset=i.Point.convert($&&$.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",function(dn){dn.preventDefault()}),this._element.addEventListener("mousedown",function(dn){dn.preventDefault()}),this._element.addEventListener("focus",function(){var dn=se._map.getContainer();dn.scrollTop=0,dn.scrollLeft=0}),Ao(this._element,this._anchor,"marker"),this._popup=null}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.addTo=function($){return this.remove(),this._map=$,$.getCanvasContainer().appendChild(this._element),$.on("move",this._update),$.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},j.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),u.remove(this._element),this._popup&&this._popup.remove(),this},j.prototype.getLngLat=function(){return this._lngLat},j.prototype.setLngLat=function($){return this._lngLat=i.LngLat.convert($),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},j.prototype.getElement=function(){return this._element},j.prototype.setPopup=function($){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),$){if(!("offset"in $.options)){var X=Math.sqrt(Math.pow(13.5,2)/2);$.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[X,-1*(24.6+X)],"bottom-right":[-X,-1*(24.6+X)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=$,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},j.prototype._onKeyPress=function($){var X=$.code,se=$.charCode||$.keyCode;X!=="Space"&&X!=="Enter"&&se!==32&&se!==13||this.togglePopup()},j.prototype._onMapClick=function($){var X=$.originalEvent.target,se=this._element;this._popup&&(X===se||se.contains(X))&&this.togglePopup()},j.prototype.getPopup=function(){return this._popup},j.prototype.togglePopup=function(){var $=this._popup;return $?($.isOpen()?$.remove():$.addTo(this._map),this):this},j.prototype._update=function($){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Qu(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var X="";this._rotationAlignment==="viewport"||this._rotationAlignment==="auto"?X="rotateZ("+this._rotation+"deg)":this._rotationAlignment==="map"&&(X="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var se="";this._pitchAlignment==="viewport"||this._pitchAlignment==="auto"?se="rotateX(0deg)":this._pitchAlignment==="map"&&(se="rotateX("+this._map.getPitch()+"deg)"),$&&$.type!=="moveend"||(this._pos=this._pos.round()),u.setTransform(this._element,Eu[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+se+" "+X)}},j.prototype.getOffset=function(){return this._offset},j.prototype.setOffset=function($){return this._offset=i.Point.convert($),this._update(),this},j.prototype._onMove=function($){this._pos=$.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none",this._state==="pending"&&(this._state="active",this.fire(new i.Event("dragstart"))),this.fire(new i.Event("drag"))},j.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),this._state==="active"&&this.fire(new i.Event("dragend")),this._state="inactive"},j.prototype._addDragHandler=function($){this._element.contains($.originalEvent.target)&&($.preventDefault(),this._positionDelta=$.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},j.prototype.setDraggable=function($){return this._draggable=!!$,this._map&&($?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},j.prototype.isDraggable=function(){return this._draggable},j.prototype.setRotation=function($){return this._rotation=$||0,this._update(),this},j.prototype.getRotation=function(){return this._rotation},j.prototype.setRotationAlignment=function($){return this._rotationAlignment=$||"auto",this._update(),this},j.prototype.getRotationAlignment=function(){return this._rotationAlignment},j.prototype.setPitchAlignment=function($){return this._pitchAlignment=$&&$!=="auto"?$:this._rotationAlignment,this._update(),this},j.prototype.getPitchAlignment=function(){return this._pitchAlignment},j}(i.Evented),vc={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},$c=0,Su=!1,xd=function(I){function j($){I.call(this),this.options=i.extend({},vc,$),i.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.onAdd=function($){var X;return this._map=$,this._container=u.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),X=this._setupUI,ec!==void 0?X(ec):i.window.navigator.permissions!==void 0?i.window.navigator.permissions.query({name:"geolocation"}).then(function(se){ec=se.state!=="denied",X(ec)}):(ec=!!i.window.navigator.geolocation,X(ec)),this._container},j.prototype.onRemove=function(){this._geolocationWatchID!==void 0&&(i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),u.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,$c=0,Su=!1},j.prototype._isOutOfMapMaxBounds=function($){var X=this._map.getMaxBounds(),se=$.coords;return X&&(se.longitude<X.getWest()||se.longitude>X.getEast()||se.latitude<X.getSouth()||se.latitude>X.getNorth())},j.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},j.prototype._onSuccess=function($){if(this._map){if(this._isOutOfMapMaxBounds($))return this._setErrorState(),this.fire(new i.Event("outofmaxbounds",$)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=$,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&this._watchState!=="OFF"&&this._updateMarker($),this.options.trackUserLocation&&this._watchState!=="ACTIVE_LOCK"||this._updateCamera($),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new i.Event("geolocate",$)),this._finish()}},j.prototype._updateCamera=function($){var X=new i.LngLat($.coords.longitude,$.coords.latitude),se=$.coords.accuracy,he=this._map.getBearing(),ye=i.extend({bearing:he},this.options.fitBoundsOptions);this._map.fitBounds(X.toBounds(se),ye,{geolocateSource:!0})},j.prototype._updateMarker=function($){if($){var X=new i.LngLat($.coords.longitude,$.coords.latitude);this._accuracyCircleMarker.setLngLat(X).addTo(this._map),this._userLocationDotMarker.setLngLat(X).addTo(this._map),this._accuracy=$.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},j.prototype._updateCircleRadius=function(){var $=this._map._container.clientHeight/2,X=this._map.unproject([0,$]),se=this._map.unproject([1,$]),he=X.distanceTo(se),ye=Math.ceil(2*this._accuracy/he);this._circleElement.style.width=ye+"px",this._circleElement.style.height=ye+"px"},j.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},j.prototype._onError=function($){if(this._map){if(this.options.trackUserLocation)if($.code===1){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var X=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=X,this._geolocateButton.setAttribute("aria-label",X),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if($.code===3&&Su)return;this._setErrorState()}this._watchState!=="OFF"&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new i.Event("error",$)),this._finish()}},j.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},j.prototype._setupUI=function($){var X=this;if(this._container.addEventListener("contextmenu",function(ye){return ye.preventDefault()}),this._geolocateButton=u.create("button","mapboxgl-ctrl-geolocate",this._container),u.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",$===!1){i.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var se=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=se,this._geolocateButton.setAttribute("aria-label",se)}else{var he=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=he,this._geolocateButton.setAttribute("aria-label",he)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=u.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Uc(this._dotElement),this._circleElement=u.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Uc({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",function(ye){var be=ye.originalEvent&&ye.originalEvent.type==="resize";ye.geolocateSource||X._watchState!=="ACTIVE_LOCK"||be||(X._watchState="BACKGROUND",X._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),X._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),X.fire(new i.Event("trackuserlocationend")))})},j.prototype.trigger=function(){if(!this._setup)return i.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new i.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":$c--,Su=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new i.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new i.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if(this._watchState==="OFF"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){var $;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++$c>1?($={maximumAge:6e5,timeout:0},Su=!0):($=this.options.positionOptions,Su=!1),this._geolocationWatchID=i.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,$)}}else i.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},j.prototype._clearWatch=function(){i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},j}(i.Evented),Op={maxWidth:100,unit:"metric"},tc=function(I){this.options=i.extend({},Op,I),i.bindAll(["_onMove","setUnit"],this)};function bd(I,j,$){var X=$&&$.maxWidth||100,se=I._container.clientHeight/2,he=I.unproject([0,se]),ye=I.unproject([X,se]),be=he.distanceTo(ye);if($&&$.unit==="imperial"){var Ee=3.2808*be;Ee>5280?Vc(j,X,Ee/5280,I._getUIString("ScaleControl.Miles")):Vc(j,X,Ee,I._getUIString("ScaleControl.Feet"))}else $&&$.unit==="nautical"?Vc(j,X,be/1852,I._getUIString("ScaleControl.NauticalMiles")):be>=1e3?Vc(j,X,be/1e3,I._getUIString("ScaleControl.Kilometers")):Vc(j,X,be,I._getUIString("ScaleControl.Meters"))}function Vc(I,j,$,X){var se,he,ye,be=(se=$,he=Math.pow(10,(""+Math.floor(se)).length-1),ye=(ye=se/he)>=10?10:ye>=5?5:ye>=3?3:ye>=2?2:ye>=1?1:function(Ue){var Xe=Math.pow(10,Math.ceil(-Math.log(Ue)/Math.LN10));return Math.round(Ue*Xe)/Xe}(ye),he*ye),Ee=be/$;I.style.width=j*Ee+"px",I.innerHTML=be+"&nbsp;"+X}tc.prototype.getDefaultPosition=function(){return"bottom-left"},tc.prototype._onMove=function(){bd(this._map,this._container,this.options)},tc.prototype.onAdd=function(I){return 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i.window.document&&(this._fullscreenchange="MSFullscreenChange")};us.prototype.onAdd=function(I){return this._map=I,this._container||(this._container=this._map.getContainer()),this._controlContainer=u.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",i.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},us.prototype.onRemove=function(){u.remove(this._controlContainer),this._map=null,i.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},us.prototype._checkFullscreenSupport=function(){return!!(i.window.document.fullscreenEnabled||i.window.document.mozFullScreenEnabled||i.window.document.msFullscreenEnabled||i.window.document.webkitFullscreenEnabled)},us.prototype._setupUI=function(){var I=this._fullscreenButton=u.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);u.create("span","mapboxgl-ctrl-icon",I).setAttribute("aria-hidden",!0),I.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),i.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},us.prototype._updateTitle=function(){var I=this._getTitle();this._fullscreenButton.setAttribute("aria-label",I),this._fullscreenButton.title=I},us.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},us.prototype._isFullscreen=function(){return this._fullscreen},us.prototype._changeIcon=function(){(i.window.document.fullscreenElement||i.window.document.mozFullScreenElement||i.window.document.webkitFullscreenElement||i.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},us.prototype._onClickFullscreen=function(){this._isFullscreen()?i.window.document.exitFullscreen?i.window.document.exitFullscreen():i.window.document.mozCancelFullScreen?i.window.document.mozCancelFullScreen():i.window.document.msExitFullscreen?i.window.document.msExitFullscreen():i.window.document.webkitCancelFullScreen&&i.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Pp={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Ip=function(I){function j($){I.call(this),this.options=i.extend(Object.create(Pp),$),i.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return I&&(j.__proto__=I),j.prototype=Object.create(I&&I.prototype),j.prototype.constructor=j,j.prototype.addTo=function($){return this._map&&this.remove(),this._map=$,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new i.Event("open")),this},j.prototype.isOpen=function(){return!!this._map},j.prototype.remove=function(){return this._content&&u.remove(this._content),this._container&&(u.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new i.Event("close")),this},j.prototype.getLngLat=function(){return this._lngLat},j.prototype.setLngLat=function($){return this._lngLat=i.LngLat.convert($),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},j.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},j.prototype.getElement=function(){return this._container},j.prototype.setText=function($){return this.setDOMContent(i.window.document.createTextNode($))},j.prototype.setHTML=function($){var X,se=i.window.document.createDocumentFragment(),he=i.window.document.createElement("body");for(he.innerHTML=$;X=he.firstChild;)se.appendChild(X);return this.setDOMContent(se)},j.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},j.prototype.setMaxWidth=function($){return this.options.maxWidth=$,this._update(),this},j.prototype.setDOMContent=function($){return this._createContent(),this._content.appendChild($),this._update(),this},j.prototype.addClassName=function($){this._container&&this._container.classList.add($)},j.prototype.removeClassName=function($){this._container&&this._container.classList.remove($)},j.prototype.toggleClassName=function($){if(this._container)return this._container.classList.toggle($)},j.prototype._createContent=function(){this._content&&u.remove(this._content),this._content=u.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=u.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},j.prototype._onMouseUp=function($){this._update($.point)},j.prototype._onMouseMove=function($){this._update($.point)},j.prototype._onDrag=function($){this._update($.point)},j.prototype._update=function($){var X=this,se=this._lngLat||this._trackPointer;if(this._map&&se&&this._content&&(this._container||(this._container=u.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=u.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach(function(xt){return X._container.classList.add(xt)}),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Qu(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||$)){var he=this._pos=this._trackPointer&&$?$:this._map.project(this._lngLat),ye=this.options.anchor,be=function xt(Dt){if(Dt){if(typeof Dt=="number"){var _t=Math.round(Math.sqrt(.5*Math.pow(Dt,2)));return{center:new i.Point(0,0),top:new i.Point(0,Dt),"top-left":new i.Point(_t,_t),"top-right":new i.Point(-_t,_t),bottom:new i.Point(0,-Dt),"bottom-left":new i.Point(_t,-_t),"bottom-right":new i.Point(-_t,-_t),left:new i.Point(Dt,0),right:new i.Point(-Dt,0)}}if(Dt instanceof i.Point||Array.isArray(Dt)){var Mt=i.Point.convert(Dt);return{center:Mt,top:Mt,"top-left":Mt,"top-right":Mt,bottom:Mt,"bottom-left":Mt,"bottom-right":Mt,left:Mt,right:Mt}}return{center:i.Point.convert(Dt.center||[0,0]),top:i.Point.convert(Dt.top||[0,0]),"top-left":i.Point.convert(Dt["top-left"]||[0,0]),"top-right":i.Point.convert(Dt["top-right"]||[0,0]),bottom:i.Point.convert(Dt.bottom||[0,0]),"bottom-left":i.Point.convert(Dt["bottom-left"]||[0,0]),"bottom-right":i.Point.convert(Dt["bottom-right"]||[0,0]),left:i.Point.convert(Dt.left||[0,0]),right:i.Point.convert(Dt.right||[0,0])}}return xt(new i.Point(0,0))}(this.options.offset);if(!ye){var Ee,Ue=this._container.offsetWidth,Xe=this._container.offsetHeight;Ee=he.y+be.bottom.y<Xe?["top"]:he.y>this._map.transform.height-Xe?["bottom"]:[],he.x<Ue/2?Ee.push("left"):he.x>this._map.transform.width-Ue/2&&Ee.push("right"),ye=Ee.length===0?"bottom":Ee.join("-")}var it=he.add(be[ye]).round();u.setTransform(this._container,Eu[ye]+" 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Y=!K&&l.pointBetween(O,B,W),J=!te&&l.pointBetween(N,B,W);if(K)return J?d(R,N):d(L,W),R;Y&&(te||(J?d(R,N):d(L,W)),d(R,O))}else G.alongA===0&&(G.alongB===-1?d(L,B):G.alongB===0?d(L,G.pt):G.alongB===1&&d(L,W)),G.alongB===0&&(G.alongA===-1?d(R,O):G.alongA===0?d(R,G.pt):G.alongA===1&&d(R,N));return!1}for(var _=[];!s.isEmpty();){var A=s.getHead();if(c&&c.vert(A.pt[0]),A.isStart){let L=function(){if(k){var R=x(A,k);if(R)return R}return!!w&&x(A,w)};c&&c.segmentNew(A.seg,A.primary);var b=v(A),k=b.before?b.before.ev:null,w=b.after?b.after.ev:null;c&&c.tempStatus(A.seg,!!k&&k.seg,!!w&&w.seg);var M,T=L();if(T){var E;a?(E=A.seg.myFill.below===null||A.seg.myFill.above!==A.seg.myFill.below)&&(T.seg.myFill.above=!T.seg.myFill.above):T.seg.otherFill=A.seg.myFill,c&&c.segmentUpdate(T.seg),A.other.remove(),A.remove()}if(s.getHead()!==A){c&&c.rewind(A.seg);continue}a?(E=A.seg.myFill.below===null||A.seg.myFill.above!==A.seg.myFill.below,A.seg.myFill.below=w?w.seg.myFill.above:p,A.seg.myFill.above=E?!A.seg.myFill.below:A.seg.myFill.below):A.seg.otherFill===null&&(M=w?A.primary===w.primary?w.seg.otherFill.above:w.seg.myFill.above:A.primary?g:p,A.seg.otherFill={above:M,below:M}),c&&c.status(A.seg,!!k&&k.seg,!!w&&w.seg),A.other.status=b.insert(r.node({ev:A}))}else{var S=A.status;if(S===null)throw new Error("PolyBool: Zero-length segment detected; your epsilon is probably too small or too large");if(y.exists(S.prev)&&y.exists(S.next)&&x(S.prev.ev,S.next.ev),c&&c.statusRemove(S.ev.seg),S.remove(),!A.primary){var P=A.seg.myFill;A.seg.myFill=A.seg.otherFill,A.seg.otherFill=P}_.push(A.seg)}s.getHead().remove()}return c&&c.done(),_}return a?{addRegion:function(p){for(var g,y,v,x=p[p.length-1],_=0;_<p.length;_++){g=x,x=p[_];var A=l.pointsCompare(g,x);A!==0&&h((y=A<0?g:x,v=A<0?x:g,{id:c?c.segmentId():-1,start:y,end:v,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(p){return m(p,!1)}}:{calculate:function(p,g,y,v){return p.forEach(function(x){h(i(x.start,x.end,x),!0)}),y.forEach(function(x){h(i(x.start,x.end,x),!1)}),m(g,v)}}}},{"./linked-list":259}],259:[function(t,o,f){o.exports={create:function(){var r={root:{root:!0,next:null},exists:function(a){return a!==null&&a!==r.root},isEmpty:function(){return r.root.next===null},getHead:function(){return r.root.next},insertBefore:function(a,l){for(var c=r.root,i=r.root.next;i!==null;){if(l(i))return a.prev=i.prev,a.next=i,i.prev.next=a,void(i.prev=a);c=i,i=i.next}c.next=a,a.prev=c,a.next=null},findTransition:function(a){for(var l=r.root,c=r.root.next;c!==null&&!a(c);)l=c,c=c.next;return{before:l===r.root?null:l,after:c,insert:function(i){return i.prev=l,i.next=c,l.next=i,c!==null&&(c.prev=i),i}}}};return r},node:function(r){return r.prev=null,r.next=null,r.remove=function(){r.prev.next=r.next,r.next&&(r.next.prev=r.prev),r.prev=null,r.next=null},r}}},{}],260:[function(t,o,f){o.exports=function(r,a,l){var c=[],i=[];return r.forEach(function(s){var u=s.start,h=s.end;if(a.pointsSame(u,h))console.warn("PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large");else{l&&l.chainStart(s);for(var d={index:0,matches_head:!1,matches_pt1:!1},m={index:0,matches_head:!1,matches_pt1:!1},p=d,g=0;g<c.length;g++){var y=(b=c[g])[0],v=(b[1],b[b.length-1]);if(b[b.length-2],a.pointsSame(y,u)){if(L(g,!0,!0))break}else if(a.pointsSame(y,h)){if(L(g,!0,!1))break}else if(a.pointsSame(v,u)){if(L(g,!1,!0))break}else if(a.pointsSame(v,h)&&L(g,!1,!1))break}if(p===d)return c.push([u,h]),void(l&&l.chainNew(u,h));if(p===m){l&&l.chainMatch(d.index);var x=d.index,_=d.matches_pt1?h:u,A=d.matches_head,b=c[x],k=A?b[0]:b[b.length-1],w=A?b[1]:b[b.length-2],M=A?b[b.length-1]:b[0],T=A?b[b.length-2]:b[1];return a.pointsCollinear(w,k,_)&&(A?(l&&l.chainRemoveHead(d.index,_),b.shift()):(l&&l.chainRemoveTail(d.index,_),b.pop()),k=w),a.pointsSame(M,_)?(c.splice(x,1),a.pointsCollinear(T,M,k)&&(A?(l&&l.chainRemoveTail(d.index,k),b.pop()):(l&&l.chainRemoveHead(d.index,k),b.shift())),l&&l.chainClose(d.index),void i.push(b)):void(A?(l&&l.chainAddHead(d.index,_),b.unshift(_)):(l&&l.chainAddTail(d.index,_),b.push(_)))}var E=d.index,S=m.index;l&&l.chainConnect(E,S);var P=c[E].length<c[S].length;d.matches_head?m.matches_head?P?(R(E),F(E,S)):(R(S),F(S,E)):F(S,E):m.matches_head?F(E,S):P?(R(E),F(S,E)):(R(S),F(E,S))}function L(D,O,N){return p.index=D,p.matches_head=O,p.matches_pt1=N,p===d?(p=m,!1):(p=null,!0)}function R(D){l&&l.chainReverse(D),c[D].reverse()}function F(D,O){var N=c[D],B=c[O],W=N[N.length-1],G=N[N.length-2],K=B[0],te=B[1];a.pointsCollinear(G,W,K)&&(l&&l.chainRemoveTail(D,W),N.pop(),W=G),a.pointsCollinear(W,K,te)&&(l&&l.chainRemoveHead(O,K),B.shift()),l&&l.chainJoin(D,O),c[D]=N.concat(B),c.splice(O,1)}}),i}},{}],261:[function(t,o,f){function r(l,c,i){var s=[];return l.forEach(function(u){var h=(u.myFill.above?8:0)+(u.myFill.below?4:0)+(u.otherFill&&u.otherFill.above?2:0)+(u.otherFill&&u.otherFill.below?1:0);c[h]!==0&&s.push({id:i?i.segmentId():-1,start:u.start,end:u.end,myFill:{above:c[h]===1,below:c[h]===2},otherFill:null})}),i&&i.selected(s),s}var a={union:function(l,c){return r(l,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],c)},intersect:function(l,c){return r(l,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],c)},difference:function(l,c){return r(l,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],c)},differenceRev:function(l,c){return r(l,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],c)},xor:function(l,c){return r(l,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],c)}};o.exports=a},{}],262:[function(t,o,f){var r=t("stream").Transform,a=t("stream-parser");function l(){r.call(this,{readableObjectMode:!0})}function c(i,s,u){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=new Error().stack||"",this.name=this.constructor.name,this.message=i,s&&(this.code=s),u&&(this.statusCode=u)}l.prototype=Object.create(r.prototype),l.prototype.constructor=l,a(l.prototype),f.ParserStream=l,f.sliceEq=function(i,s,u){for(var h=s,d=0;d<u.length;)if(i[h++]!==u[d++])return!1;return!0},f.str2arr=function(i,s){var u=[],h=0;if(s&&s==="hex")for(;h<i.length;)u.push(parseInt(i.slice(h,h+2),16)),h+=2;else for(;h<i.length;h++)u.push(255&i.charCodeAt(h));return u},f.readUInt16LE=function(i,s){return i[s]|i[s+1]<<8},f.readUInt16BE=function(i,s){return i[s+1]|i[s]<<8},f.readUInt32LE=function(i,s){return i[s]|i[s+1]<<8|i[s+2]<<16|16777216*i[s+3]},f.readUInt32BE=function(i,s){return i[s+3]|i[s+2]<<8|i[s+1]<<16|16777216*i[s]},c.prototype=Object.create(Error.prototype),c.prototype.constructor=c,f.ProbeError=c},{stream:285,"stream-parser":301}],263:[function(t,o,f){function r(c,i){var s=new Error(c);return s.code=i,s}function a(c){try{return decodeURIComponent(escape(c))}catch{return c}}function l(c,i,s){this.input=c.subarray(i,s),this.start=i;var u=String.fromCharCode.apply(null,this.input.subarray(0,4));if(u!=="II*\0"&&u!=="MM\0*")throw r("invalid TIFF signature","EBADDATA");this.big_endian=u[0]==="M"}l.prototype.each=function(c){this.aborted=!1;var i=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:i}];this.ifds_to_read.length>0&&!this.aborted;){var s=this.ifds_to_read.shift();s.offset&&this.scan_ifd(s.id,s.offset,c)}},l.prototype.read_uint16=function(c){var i=this.input;if(c+2>i.length)throw r("unexpected EOF","EBADDATA");return this.big_endian?256*i[c]+i[c+1]:i[c]+256*i[c+1]},l.prototype.read_uint32=function(c){var i=this.input;if(c+4>i.length)throw r("unexpected EOF","EBADDATA");return this.big_endian?16777216*i[c]+65536*i[c+1]+256*i[c+2]+i[c+3]:i[c]+256*i[c+1]+65536*i[c+2]+16777216*i[c+3]},l.prototype.is_subifd_link=function(c,i){return c===0&&i===34665||c===0&&i===34853||c===34665&&i===40965},l.prototype.exif_format_length=function(c){switch(c){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},l.prototype.exif_format_read=function(c,i){var s;switch(c){case 1:case 2:return s=this.input[i];case 6:return(s=this.input[i])|33554430*(128&s);case 3:return s=this.read_uint16(i);case 8:return(s=this.read_uint16(i))|131070*(32768&s);case 4:return s=this.read_uint32(i);case 9:return 0|(s=this.read_uint32(i));case 5:case 10:case 11:case 12:case 7:default:return null}},l.prototype.scan_ifd=function(c,i,s){var u=this.read_uint16(i);i+=2;for(var h=0;h<u;h++){var d=this.read_uint16(i),m=this.read_uint16(i+2),p=this.read_uint32(i+4),g=this.exif_format_length(m),y=p*g,v=y<=4?i+8:this.read_uint32(i+8),x=!1;if(v+y>this.input.length)throw r("unexpected EOF","EBADDATA");for(var _=[],A=v,b=0;b<p;b++,A+=g){var k=this.exif_format_read(m,A);if(k===null){_=null;break}_.push(k)}if(Array.isArray(_)&&m===2&&(_=a(String.fromCharCode.apply(null,_)))&&_[_.length-1]==="\0"&&(_=_.slice(0,-1)),this.is_subifd_link(c,d)&&Array.isArray(_)&&Number.isInteger(_[0])&&_[0]>0&&(this.ifds_to_read.push({id:d,offset:_[0]}),x=!0),s({is_big_endian:this.big_endian,ifd:c,tag:d,format:m,count:p,entry_offset:i+this.start,data_length:y,data_offset:v+this.start,value:_,is_subifd_link:x})===!1)return void(this.aborted=!0);i+=12}c===0&&this.ifds_to_read.push({id:1,offset:this.read_uint32(i)})},o.exports.ExifParser=l,o.exports.get_orientation=function(c){var i=0;try{return new l(c,0,c.length).each(function(s){if(s.ifd===0&&s.tag===274&&Array.isArray(s.value))return i=s.value[0],!1}),i}catch{return-1}}},{}],264:[function(t,o,f){var r=t("./common").readUInt16BE,a=t("./common").readUInt32BE;function l(d,m){if(d.length<4+m)return null;var p=a(d,m);return d.length<p+m||p<8?null:{boxtype:String.fromCharCode.apply(null,d.slice(m+4,m+8)),data:d.slice(m+8,m+p),end:m+p}}function c(d,m){for(var p=0;;){var g=l(d,p);if(!g)break;switch(g.boxtype){case"ispe":m.sizes.push({width:a(g.data,4),height:a(g.data,8)});break;case"irot":m.transforms.push({type:"irot",value:3&g.data[0]});break;case"imir":m.transforms.push({type:"imir",value:1&g.data[0]})}p=g.end}}function i(d,m,p){for(var g=0,y=0;y<p;y++)g=256*g+(d[m+y]||0);return g}function s(d,m){for(var p=d[4]>>4&15,g=15&d[4],y=d[5]>>4&15,v=r(d,6),x=8,_=0;_<v;_++){var A=r(d,x),b=r(d,x+=2),k=i(d,x+=2,y),w=r(d,x+=y);if(x+=2,b===0&&w===1){var M=i(d,x,p),T=i(d,x+p,g);m.item_loc[A]={length:T,offset:M+k}}x+=w*(p+g)}}function u(d,m){for(var p=r(d,4),g=6,y=0;y<p;y++){var v=l(d,g);if(!v)break;if(v.boxtype==="infe"){for(var x=r(v.data,4),_="",A=8;A<v.data.length&&v.data[A];A++)_+=String.fromCharCode(v.data[A]);m.item_inf[_]=x}g=v.end}}function h(d,m){for(var p=0;;){var g=l(d,p);if(!g)break;g.boxtype==="ipco"&&c(g.data,m),p=g.end}}o.exports.unbox=l,o.exports.readSizeFromMeta=function(d){var m={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(A,b){for(var k=4;;){var w=l(A,k);if(!w)break;w.boxtype==="iprp"&&h(w.data,b),w.boxtype==="iloc"&&s(w.data,b),w.boxtype==="iinf"&&u(w.data,b),k=w.end}}(d,m),m.sizes.length){var p,g,y,v=(p=m.sizes,g=p.reduce(function(A,b){return A.width>b.width||A.width===b.width&&A.height>b.height?A:b}),y=p.reduce(function(A,b){return A.height>b.height||A.height===b.height&&A.width>b.width?A:b}),g.width>y.height||g.width===y.height&&g.height>y.width?g:y),x=1;m.transforms.forEach(function(A){var b={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},k={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(A.type==="imir"&&(x=A.value===0?k[x]:b[x=b[x=k[x]]]),A.type==="irot")for(var w=0;w<A.value;w++)x=b[x]});var _=null;return m.item_inf.Exif&&(_=m.item_loc[m.item_inf.Exif]),{width:v.width,height:v.height,orientation:m.transforms.length?x:null,variants:m.sizes,exif_location:_}}},o.exports.getMimeType=function(d){var m=String.fromCharCode.apply(null,d.slice(0,4)),p={};p[m]=!0;for(var g=8;g<d.length;g+=4)p[String.fromCharCode.apply(null,d.slice(g,g+4))]=!0;if(p.mif1||p.msf1||p.miaf)return m==="avif"||m==="avis"||m==="avio"?{type:"avif",mime:"image/avif"}:m==="heic"||m==="heix"?{type:"heic",mime:"image/heic"}:m==="hevc"||m==="hevx"?{type:"heic",mime:"image/heic-sequence"}:p.avif||p.avis?{type:"avif",mime:"image/avif"}:p.heic||p.heix||p.hevc||p.hevx||p.heis?p.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":262}],265:[function(t,o,f){var r=t("../common").str2arr,a=t("../common").sliceEq,l=t("../common").readUInt32BE,c=t("../miaf_utils"),i=t("../exif_utils"),s=r("ftyp");o.exports=function(u){if(a(u,4,s)){var h=c.unbox(u,0);if(h){var d=c.getMimeType(h.data);if(d){for(var m,p=h.end;;){var g=c.unbox(u,p);if(!g)break;if(p=g.end,g.boxtype==="mdat")return;if(g.boxtype==="meta"){m=g.data;break}}if(m){var y=c.readSizeFromMeta(m);if(y){var v={width:y.width,height:y.height,type:d.type,mime:d.mime,wUnits:"px",hUnits:"px"};if(y.variants.length>1&&(v.variants=y.variants),y.orientation&&(v.orientation=y.orientation),y.exif_location&&y.exif_location.offset+y.exif_location.length<=u.length){var x=l(u,y.exif_location.offset),_=u.slice(y.exif_location.offset+x+4,y.exif_location.offset+y.exif_location.length),A=i.get_orientation(_);A>0&&(v.orientation=A)}return v}}}}}}},{"../common":262,"../exif_utils":263,"../miaf_utils":264}],266:[function(t,o,f){var r=t("../common").str2arr,a=t("../common").sliceEq,l=t("../common").readUInt16LE,c=r("BM");o.exports=function(i){if(!(i.length<26)&&a(i,0,c))return{width:l(i,18),height:l(i,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":262}],267:[function(t,o,f){var r=t("../common").str2arr,a=t("../common").sliceEq,l=t("../common").readUInt16LE,c=r("GIF87a"),i=r("GIF89a");o.exports=function(s){if(!(s.length<10)&&(a(s,0,c)||a(s,0,i)))return{width:l(s,6),height:l(s,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":262}],268:[function(t,o,f){var r=t("../common").readUInt16LE;o.exports=function(a){var l=r(a,0),c=r(a,2),i=r(a,4);if(l===0&&c===1&&i){for(var s=[],u={width:0,height:0},h=0;h<i;h++){var d=a[6+16*h]||256,m=a[6+16*h+1]||256,p={width:d,height:m};s.push(p),(d>u.width||m>u.height)&&(u=p)}return{width:u.width,height:u.height,variants:s,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":262}],269:[function(t,o,f){var r=t("../common").readUInt16BE,a=t("../common").str2arr,l=t("../common").sliceEq,c=t("../exif_utils"),i=a("Exif\0\0");o.exports=function(s){if(!(s.length<2)&&s[0]===255&&s[1]===216&&s[2]===255)for(var u=2;;){for(;;){if(s.length-u<2)return;if(s[u++]===255)break}for(var h,d,m=s[u++];m===255;)m=s[u++];if(208<=m&&m<=217||m===1)h=0;else{if(!(192<=m&&m<=254)||s.length-u<2)return;h=r(s,u)-2,u+=2}if(m===217||m===218)return;if(m===225&&h>=10&&l(s,u,i)&&(d=c.get_orientation(s.slice(u+6,u+h))),h>=5&&192<=m&&m<=207&&m!==196&&m!==200&&m!==204){if(s.length-u<h)return;var p={width:r(s,u+3),height:r(s,u+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return d>0&&(p.orientation=d),p}u+=h}}},{"../common":262,"../exif_utils":263}],270:[function(t,o,f){var r=t("../common").str2arr,a=t("../common").sliceEq,l=t("../common").readUInt32BE,c=r(`‰PNG\r
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v>0&&(y.orientation=v),y}}}},{"../common":262,"../exif_utils":263}],275:[function(t,o,f){o.exports={avif:t("./parse_sync/avif"),bmp:t("./parse_sync/bmp"),gif:t("./parse_sync/gif"),ico:t("./parse_sync/ico"),jpeg:t("./parse_sync/jpeg"),png:t("./parse_sync/png"),psd:t("./parse_sync/psd"),svg:t("./parse_sync/svg"),tiff:t("./parse_sync/tiff"),webp:t("./parse_sync/webp")}},{"./parse_sync/avif":265,"./parse_sync/bmp":266,"./parse_sync/gif":267,"./parse_sync/ico":268,"./parse_sync/jpeg":269,"./parse_sync/png":270,"./parse_sync/psd":271,"./parse_sync/svg":272,"./parse_sync/tiff":273,"./parse_sync/webp":274}],276:[function(t,o,f){var r=t("./lib/parsers_sync");o.exports=function(a){return function(l){for(var c=Object.keys(r),i=0;i<c.length;i++){var s=r[c[i]](l);if(s)return s}return null}(a)},o.exports.parsers=r},{"./lib/parsers_sync":275}],277:[function(t,o,f){var r,a,l=o.exports={};function c(){throw new Error("setTimeout has not been defined")}function i(){throw new Error("clearTimeout has not 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y,v,x,_,A,b,k=p._gl,w={color:"black",capSize:5,lineWidth:1,opacity:1,viewport:null,range:null,offset:0,count:0,bounds:null,positions:[],errors:[]},M=[];return _=p.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array(0)}),v=p.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)}),x=p.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)}),A=p.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)}),b=p.buffer({usage:"static",type:"float",data:m}),P(g),y=p({vert:`
-		precision highp float;
-
-		attribute vec2 position, positionFract;
-		attribute vec4 error;
-		attribute vec4 color;
-
-		attribute vec2 direction, lineOffset, capOffset;
-
-		uniform vec4 viewport;
-		uniform float lineWidth, capSize;
-		uniform vec2 scale, scaleFract, translate, translateFract;
-
-		varying vec4 fragColor;
-
-		void main() {
-			fragColor = color / 255.;
-
-			vec2 pixelOffset = lineWidth * lineOffset + (capSize + lineWidth) * capOffset;
-
-			vec2 dxy = -step(.5, direction.xy) * error.xz + step(direction.xy, vec2(-.5)) * error.yw;
-
-			vec2 position = position + dxy;
-
-			vec2 pos = (position + translate) * scale
-				+ (positionFract + translateFract) * scale
-				+ (position + translate) * scaleFract
-				+ (positionFract + translateFract) * scaleFract;
-
-			pos += pixelOffset / viewport.zw;
-
-			gl_Position = vec4(pos * 2. - 1., 0, 1);
-		}
-		`,frag:`
-		precision highp float;
-
-		varying vec4 fragColor;
-
-		uniform float opacity;
-
-		void main() {
-			gl_FragColor = fragColor;
-			gl_FragColor.a *= opacity;
-		}
-		`,uniforms:{range:p.prop("range"),lineWidth:p.prop("lineWidth"),capSize:p.prop("capSize"),opacity:p.prop("opacity"),scale:p.prop("scale"),translate:p.prop("translate"),scaleFract:p.prop("scaleFract"),translateFract:p.prop("translateFract"),viewport:function(R,F){return[F.viewport.x,F.viewport.y,R.viewportWidth,R.viewportHeight]}},attributes:{color:{buffer:_,offset:function(R,F){return 4*F.offset},divisor:1},position:{buffer:v,offset:function(R,F){return 8*F.offset},divisor:1},positionFract:{buffer:x,offset:function(R,F){return 8*F.offset},divisor:1},error:{buffer:A,offset:function(R,F){return 16*F.offset},divisor:1},direction:{buffer:b,stride:24,offset:0},lineOffset:{buffer:b,stride:24,offset:8},capOffset:{buffer:b,stride:24,offset:16}},primitive:"triangles",blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst 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m=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{"array-bounds":71,"color-normalize":89,"flatten-vertex-data":191,"object-assign":247,"pick-by-alias":253,"to-float32":313,"update-diff":329}],280:[function(t,o,f){var r=t("color-normalize"),a=t("array-bounds"),l=t("object-assign"),c=t("glslify"),i=t("pick-by-alias"),s=t("flatten-vertex-data"),u=t("earcut"),h=t("array-normalize"),d=t("to-float32"),m=d.float32,p=d.fract32,g=t("es6-weak-map"),y=t("parse-rect"),v=t("array-find-index");function x(_,A){if(!(this instanceof x))return new x(_,A);if(typeof _=="function"?(A||(A={}),A.regl=_):A=_,A.length&&(A.positions=A),!(_=A.regl).hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=_._gl,this.regl=_,this.passes=[],this.shaders=x.shaders.has(_)?x.shaders.get(_):x.shaders.set(_,x.createShaders(_)).get(_),this.update(A)}o.exports=x,x.dashMult=2,x.maxPatternLength=256,x.precisionThreshold=3e6,x.maxPoints=1e4,x.maxLines=2048,x.shaders=new g,x.createShaders=function(_){var A,b=_.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),k={primitive:"triangle strip",instances:_.prop("count"),count:4,offset:0,uniforms:{miterMode:function(M,T){return T.join==="round"?2:1},miterLimit:_.prop("miterLimit"),scale:_.prop("scale"),scaleFract:_.prop("scaleFract"),translateFract:_.prop("translateFract"),translate:_.prop("translate"),thickness:_.prop("thickness"),dashTexture:_.prop("dashTexture"),opacity:_.prop("opacity"),pixelRatio:_.context("pixelRatio"),id:_.prop("id"),dashLength:_.prop("dashLength"),viewport:function(M,T){return[T.viewport.x,T.viewport.y,M.viewportWidth,M.viewportHeight]},depth:_.prop("depth")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},depth:{enable:function(M,T){return!T.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:_.prop("viewport")},viewport:_.prop("viewport")},w=_(l({vert:c([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;
-attribute vec4 color;
-attribute float lineEnd, lineTop;
-
-uniform vec2 scale, scaleFract, translate, translateFract;
-uniform float thickness, pixelRatio, id, depth;
-uniform vec4 viewport;
-
-varying vec4 fragColor;
-varying vec2 tangent;
-
-vec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {
-	// the order is important
-	return position * scale + translate
-       + positionFract * scale + translateFract
-       + position * scaleFract
-       + positionFract * scaleFract;
-}
-
-void main() {
-	float lineStart = 1. - lineEnd;
-	float lineOffset = lineTop * 2. - 1.;
-
-	vec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);
-	tangent = normalize(diff * scale * viewport.zw);
-	vec2 normal = vec2(-tangent.y, tangent.x);
-
-	vec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart
-		+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd
-
-		+ thickness * normal * .5 * lineOffset / viewport.zw;
-
-	gl_Position = vec4(position * 2.0 - 1.0, depth, 1);
-
-	fragColor = color / 255.;
-}
-`]),frag:c([`precision highp float;
-#define GLSLIFY 1
-
-uniform float dashLength, pixelRatio, thickness, opacity, id;
-uniform sampler2D dashTexture;
-
-varying vec4 fragColor;
-varying vec2 tangent;
-
-void main() {
-	float alpha = 1.;
-
-	float t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;
-	float dash = texture2D(dashTexture, vec2(t, .5)).r;
-
-	gl_FragColor = fragColor;
-	gl_FragColor.a *= alpha * opacity * dash;
-}
-`]),attributes:{lineEnd:{buffer:b,divisor:0,stride:8,offset:0},lineTop:{buffer:b,divisor:0,stride:8,offset:4},aCoord:{buffer:_.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:_.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:_.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:_.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:_.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},k));try{A=_(l({cull:{enable:!0,face:"back"},vert:c([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec2 aCoord, bCoord, nextCoord, prevCoord;
-attribute vec4 aColor, bColor;
-attribute float lineEnd, lineTop;
-
-uniform vec2 scale, translate;
-uniform float thickness, pixelRatio, id, depth;
-uniform vec4 viewport;
-uniform float miterLimit, miterMode;
-
-varying vec4 fragColor;
-varying vec4 startCutoff, endCutoff;
-varying vec2 tangent;
-varying vec2 startCoord, endCoord;
-varying float enableStartMiter, enableEndMiter;
-
-const float REVERSE_THRESHOLD = -.875;
-const float MIN_DIFF = 1e-6;
-
-// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead
-// TODO: precalculate dot products, normalize things beforehead etc.
-// TODO: refactor to rectangular algorithm
-
-float distToLine(vec2 p, vec2 a, vec2 b) {
-	vec2 diff = b - a;
-	vec2 perp = normalize(vec2(-diff.y, diff.x));
-	return dot(p - a, perp);
-}
-
-bool isNaN( float val ){
-  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;
-}
-
-void main() {
-	vec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;
-
-  vec2 adjustedScale;
-  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;
-  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;
-
-  vec2 scaleRatio = adjustedScale * viewport.zw;
-	vec2 normalWidth = thickness / scaleRatio;
-
-	float lineStart = 1. - lineEnd;
-	float lineBot = 1. - lineTop;
-
-	fragColor = (lineStart * aColor + lineEnd * bColor) / 255.;
-
-	if (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;
-
-	if (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);
-	if (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);
-
-	vec2 prevDiff = aCoord - prevCoord;
-	vec2 currDiff = bCoord - aCoord;
-	vec2 nextDiff = nextCoord - bCoord;
-
-	vec2 prevTangent = normalize(prevDiff * scaleRatio);
-	vec2 currTangent = normalize(currDiff * scaleRatio);
-	vec2 nextTangent = normalize(nextDiff * scaleRatio);
-
-	vec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);
-	vec2 currNormal = vec2(-currTangent.y, currTangent.x);
-	vec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);
-
-	vec2 startJoinDirection = normalize(prevTangent - currTangent);
-	vec2 endJoinDirection = normalize(currTangent - nextTangent);
-
-	// collapsed/unidirectional segment cases
-	// FIXME: there should be more elegant solution
-	vec2 prevTanDiff = abs(prevTangent - currTangent);
-	vec2 nextTanDiff = abs(nextTangent - currTangent);
-	if (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {
-		startJoinDirection = currNormal;
-	}
-	if (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {
-		endJoinDirection = currNormal;
-	}
-	if (aCoord == bCoord) {
-		endJoinDirection = startJoinDirection;
-		currNormal = prevNormal;
-		currTangent = prevTangent;
-	}
-
-	tangent = currTangent;
-
-	//calculate join shifts relative to normals
-	float startJoinShift = dot(currNormal, startJoinDirection);
-	float endJoinShift = dot(currNormal, endJoinDirection);
-
-	float startMiterRatio = abs(1. / startJoinShift);
-	float endMiterRatio = abs(1. / endJoinShift);
-
-	vec2 startJoin = startJoinDirection * startMiterRatio;
-	vec2 endJoin = endJoinDirection * endMiterRatio;
-
-	vec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;
-	startTopJoin = sign(startJoinShift) * startJoin * .5;
-	startBotJoin = -startTopJoin;
-
-	endTopJoin = sign(endJoinShift) * endJoin * .5;
-	endBotJoin = -endTopJoin;
-
-	vec2 aTopCoord = aCoord + normalWidth * startTopJoin;
-	vec2 bTopCoord = bCoord + normalWidth * endTopJoin;
-	vec2 aBotCoord = aCoord + normalWidth * startBotJoin;
-	vec2 bBotCoord = bCoord + normalWidth * endBotJoin;
-
-	//miter anti-clipping
-	float baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));
-	float abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));
-
-	//prevent close to reverse direction switch
-	bool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);
-	bool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);
-
-	if (prevReverse) {
-		//make join rectangular
-		vec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;
-		float normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);
-		aBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;
-		aTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;
-	}
-	else if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {
-		//handle miter clipping
-		bTopCoord -= normalWidth * endTopJoin;
-		bTopCoord += normalize(endTopJoin * normalWidth) * baClipping;
-	}
-
-	if (nextReverse) {
-		//make join rectangular
-		vec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;
-		float normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);
-		bBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;
-		bTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;
-	}
-	else if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {
-		//handle miter clipping
-		aBotCoord -= normalWidth * startBotJoin;
-		aBotCoord += normalize(startBotJoin * normalWidth) * abClipping;
-	}
-
-	vec2 aTopPosition = (aTopCoord) * adjustedScale + translate;
-	vec2 aBotPosition = (aBotCoord) * adjustedScale + translate;
-
-	vec2 bTopPosition = (bTopCoord) * adjustedScale + translate;
-	vec2 bBotPosition = (bBotCoord) * adjustedScale + translate;
-
-	//position is normalized 0..1 coord on the screen
-	vec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;
-
-	startCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;
-	endCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;
-
-	gl_Position = vec4(position  * 2.0 - 1.0, depth, 1);
-
-	enableStartMiter = step(dot(currTangent, prevTangent), .5);
-	enableEndMiter = step(dot(currTangent, nextTangent), .5);
-
-	//bevel miter cutoffs
-	if (miterMode == 1.) {
-		if (enableStartMiter == 1.) {
-			vec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;
-			startCutoff = vec4(aCoord, aCoord);
-			startCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;
-			startCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
-			startCutoff += viewport.xyxy;
-			startCutoff += startMiterWidth.xyxy;
-		}
-
-		if (enableEndMiter == 1.) {
-			vec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;
-			endCutoff = vec4(bCoord, bCoord);
-			endCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;
-			endCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
-			endCutoff += viewport.xyxy;
-			endCutoff += endMiterWidth.xyxy;
-		}
-	}
-
-	//round miter cutoffs
-	else if (miterMode == 2.) {
-		if (enableStartMiter == 1.) {
-			vec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;
-			startCutoff = vec4(aCoord, aCoord);
-			startCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;
-			startCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
-			startCutoff += viewport.xyxy;
-			startCutoff += startMiterWidth.xyxy;
-		}
-
-		if (enableEndMiter == 1.) {
-			vec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;
-			endCutoff = vec4(bCoord, bCoord);
-			endCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;
-			endCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;
-			endCutoff += viewport.xyxy;
-			endCutoff += endMiterWidth.xyxy;
-		}
-	}
-}
-`]),frag:c([`precision highp float;
-#define GLSLIFY 1
-
-uniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;
-uniform sampler2D dashTexture;
-
-varying vec4 fragColor;
-varying vec2 tangent;
-varying vec4 startCutoff, endCutoff;
-varying vec2 startCoord, endCoord;
-varying float enableStartMiter, enableEndMiter;
-
-float distToLine(vec2 p, vec2 a, vec2 b) {
-	vec2 diff = b - a;
-	vec2 perp = normalize(vec2(-diff.y, diff.x));
-	return dot(p - a, perp);
-}
-
-void main() {
-	float alpha = 1., distToStart, distToEnd;
-	float cutoff = thickness * .5;
-
-	//bevel miter
-	if (miterMode == 1.) {
-		if (enableStartMiter == 1.) {
-			distToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);
-			if (distToStart < -1.) {
-				discard;
-				return;
-			}
-			alpha *= min(max(distToStart + 1., 0.), 1.);
-		}
-
-		if (enableEndMiter == 1.) {
-			distToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);
-			if (distToEnd < -1.) {
-				discard;
-				return;
-			}
-			alpha *= min(max(distToEnd + 1., 0.), 1.);
-		}
-	}
-
-	// round miter
-	else if (miterMode == 2.) {
-		if (enableStartMiter == 1.) {
-			distToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);
-			if (distToStart < 0.) {
-				float radius = length(gl_FragCoord.xy - startCoord);
-
-				if(radius > cutoff + .5) {
-					discard;
-					return;
-				}
-
-				alpha -= smoothstep(cutoff - .5, cutoff + .5, radius);
-			}
-		}
-
-		if (enableEndMiter == 1.) {
-			distToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);
-			if (distToEnd < 0.) {
-				float radius = length(gl_FragCoord.xy - endCoord);
-
-				if(radius > cutoff + .5) {
-					discard;
-					return;
-				}
-
-				alpha -= smoothstep(cutoff - .5, cutoff + .5, radius);
-			}
-		}
-	}
-
-	float t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;
-	float dash = texture2D(dashTexture, vec2(t, .5)).r;
-
-	gl_FragColor = fragColor;
-	gl_FragColor.a *= alpha * opacity * dash;
-}
-`]),attributes:{lineEnd:{buffer:b,divisor:0,stride:8,offset:0},lineTop:{buffer:b,divisor:0,stride:8,offset:4},aColor:{buffer:_.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:_.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:_.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:_.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:_.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:_.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},k))}catch{A=w}return{fill:_({primitive:"triangle",elements:function(M,T){return T.triangles},offset:0,vert:c([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec2 position, positionFract;
-
-uniform vec4 color;
-uniform vec2 scale, scaleFract, translate, translateFract;
-uniform float pixelRatio, id;
-uniform vec4 viewport;
-uniform float opacity;
-
-varying vec4 fragColor;
-
-const float MAX_LINES = 256.;
-
-void main() {
-	float depth = (MAX_LINES - 4. - id) / (MAX_LINES);
-
-	vec2 position = position * scale + translate
-       + positionFract * scale + translateFract
-       + position * scaleFract
-       + positionFract * scaleFract;
-
-	gl_Position = vec4(position * 2.0 - 1.0, depth, 1);
-
-	fragColor = color / 255.;
-	fragColor.a *= opacity;
-}
-`]),frag:c([`precision highp float;
-#define GLSLIFY 1
-
-varying vec4 fragColor;
-
-void main() {
-	gl_FragColor = fragColor;
-}
-`]),uniforms:{scale:_.prop("scale"),color:_.prop("fill"),scaleFract:_.prop("scaleFract"),translateFract:_.prop("translateFract"),translate:_.prop("translate"),opacity:_.prop("opacity"),pixelRatio:_.context("pixelRatio"),id:_.prop("id"),viewport:function(M,T){return[T.viewport.x,T.viewport.y,M.viewportWidth,M.viewportHeight]}},attributes:{position:{buffer:_.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:_.prop("positionFractBuffer"),stride:8,offset:8}},blend:k.blend,depth:{enable:!1},scissor:k.scissor,stencil:k.stencil,viewport:k.viewport}),rect:w,miter:A}},x.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},x.prototype.render=function(){for(var _,A=[],b=arguments.length;b--;)A[b]=arguments[b];A.length&&(_=this).update.apply(_,A),this.draw()},x.prototype.draw=function(){for(var _=this,A=[],b=arguments.length;b--;)A[b]=arguments[b];return(A.length?A:this.passes).forEach(function(k,w){var M;if(k&&Array.isArray(k))return(M=_).draw.apply(M,k);typeof k=="number"&&(k=_.passes[k]),k&&k.count>1&&k.opacity&&(_.regl._refresh(),k.fill&&k.triangles&&k.triangles.length>2&&_.shaders.fill(k),k.thickness&&(k.scale[0]*k.viewport.width>x.precisionThreshold||k.scale[1]*k.viewport.height>x.precisionThreshold||k.join==="rect"||!k.join&&(k.thickness<=2||k.count>=x.maxPoints)?_.shaders.rect(k):_.shaders.miter(k)))}),this},x.prototype.update=function(_){var A=this;if(_){_.length!=null?typeof _[0]=="number"&&(_=[{positions:_}]):Array.isArray(_)||(_=[_]);var b=this.regl,k=this.gl;if(_.forEach(function(S,P){var L=A.passes[P];if(S!==void 0)if(S!==null){if(typeof S[0]=="number"&&(S={positions:S}),S=i(S,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),L||(A.passes[P]=L={id:P,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:b.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:b.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:b.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:b.buffer({usage:"dynamic",type:"float",data:new 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Uint8Array(Fe*x.dashMult);for(var $e=0,Ke=255,Re=0;Re<2;Re++)for(var Ve=0;Ve<S.dashes.length;++Ve){for(var We=0,Ye=S.dashes[Ve]*x.dashMult*.5;We<Ye;++We)Ce[$e++]=Ke;Ke^=255}}L.dashLength=Fe,L.dashTexture({channels:1,data:Ce,width:Ce.length,height:1,mag:"linear",min:"linear"},0,0)}if(S.color){var nt=L.count,ft=S.color;ft||(ft="transparent");var yt=new Uint8Array(4*nt+4);if(Array.isArray(ft)&&typeof ft[0]!="number"){for(var Ot=0;Ot<nt;Ot++){var Tt=r(ft[Ot],"uint8");yt.set(Tt,4*Ot)}yt.set(r(ft[0],"uint8"),4*nt)}else for(var at=r(ft,"uint8"),et=0;et<nt+1;et++)yt.set(at,4*et);L.colorBuffer({usage:"dynamic",type:"uint8",data:yt})}}else A.passes[P]=null}),_.length<this.passes.length){for(var w=_.length;w<this.passes.length;w++){var M=this.passes[w];M&&(M.colorBuffer.destroy(),M.positionBuffer.destroy(),M.dashTexture.destroy())}this.passes.length=_.length}for(var T=[],E=0;E<this.passes.length;E++)this.passes[E]!==null&&T.push(this.passes[E]);return 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-#define GLSLIFY 1
-
-uniform float opacity;
-uniform sampler2D markerTexture;
-
-varying vec4 fragColor, fragBorderColor;
-varying float fragWidth, fragBorderColorLevel, fragColorLevel;
-
-float smoothStep(float x, float y) {
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-}
-
-void main() {
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-
-  // max-distance alpha
-  if (dist < 0.003) discard;
-
-  // null-border case
-  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {
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-  else {
-    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);
-    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);
-
-    vec4 color = fragBorderColor;
-    color.a *= borderColorAmt;
-    color = mix(color, fragColor, colorAmt);
-    color.a *= opacity;
-
-    gl_FragColor = color;
-  }
-
-}
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-#define GLSLIFY 1
-
-attribute float x, y, xFract, yFract;
-attribute float size, borderSize;
-attribute vec4 colorId, borderColorId;
-attribute float isActive;
-
-uniform bool constPointSize;
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-uniform vec2 scale, scaleFract, translate, translateFract, paletteSize;
-uniform sampler2D paletteTexture;
-
-const float maxSize = 100.;
-const float borderLevel = .5;
-
-varying vec4 fragColor, fragBorderColor;
-varying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;
-
-float pointSizeScale = (constPointSize) ? 2. : pixelRatio;
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-bool isDirect = (paletteSize.x < 1.);
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-vec4 getColor(vec4 id) {
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-      (id.y + .5) / paletteSize.y
-    )
-  );
-}
-
-void main() {
-  // ignore inactive points
-  if (isActive == 0.) return;
-
-  vec2 position = vec2(x, y);
-  vec2 positionFract = vec2(xFract, yFract);
-
-  vec4 color = getColor(colorId);
-  vec4 borderColor = getColor(borderColorId);
-
-  float size = size * maxSize / 255.;
-  float borderSize = borderSize * maxSize / 255.;
-
-  gl_PointSize = 2. * size * pointSizeScale;
-  fragPointSize = size * pixelRatio;
-
-  vec2 pos = (position + translate) * scale
-      + (positionFract + translateFract) * scale
-      + (position + translate) * scaleFract
-      + (positionFract + translateFract) * scaleFract;
-
-  gl_Position = vec4(pos * 2. - 1., 0., 1.);
-
-  fragColor = color;
-  fragBorderColor = borderColor;
-  fragWidth = 1. / gl_PointSize;
-
-  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);
-  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);
-}`]),this.drawMarker=w(R);var F=d({},L);F.frag=m([`precision highp float;
-#define GLSLIFY 1
-
-varying vec4 fragColor, fragBorderColor;
-varying float fragBorderRadius, fragWidth;
-
-uniform float opacity;
-
-float smoothStep(float edge0, float edge1, float x) {
-	float t;
-	t = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);
-	return t * t * (3.0 - 2.0 * t);
-}
-
-void main() {
-	float radius, alpha = 1.0, delta = fragWidth;
-
-	radius = length(2.0 * gl_PointCoord.xy - 1.0);
-
-	if (radius > 1.0 + delta) {
-		discard;
-	}
-
-	alpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);
-
-	float borderRadius = fragBorderRadius;
-	float ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);
-	vec4 color = mix(fragColor, fragBorderColor, ratio);
-	color.a *= alpha * opacity;
-	gl_FragColor = color;
-}
-`]),F.vert=m([`precision highp float;
-#define GLSLIFY 1
-
-attribute float x, y, xFract, yFract;
-attribute float size, borderSize;
-attribute vec4 colorId, borderColorId;
-attribute float isActive;
-
-uniform bool constPointSize;
-uniform float pixelRatio;
-uniform vec2 paletteSize, scale, scaleFract, translate, translateFract;
-uniform sampler2D paletteTexture;
-
-const float maxSize = 100.;
-
-varying vec4 fragColor, fragBorderColor;
-varying float fragBorderRadius, fragWidth;
-
-float pointSizeScale = (constPointSize) ? 2. : pixelRatio;
-
-bool isDirect = (paletteSize.x < 1.);
-
-vec4 getColor(vec4 id) {
-  return isDirect ? id / 255. : texture2D(paletteTexture,
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-      (id.y + .5) / paletteSize.y
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-}
-
-void main() {
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-  if (isActive == 0.) return;
-
-  vec2 position = vec2(x, y);
-  vec2 positionFract = vec2(xFract, yFract);
-
-  vec4 color = getColor(colorId);
-  vec4 borderColor = getColor(borderColorId);
-
-  float size = size * maxSize / 255.;
-  float borderSize = borderSize * maxSize / 255.;
-
-  gl_PointSize = (size + borderSize) * pointSizeScale;
-
-  vec2 pos = (position + translate) * scale
-      + (positionFract + translateFract) * scale
-      + (position + translate) * scaleFract
-      + (positionFract + translateFract) * scaleFract;
-
-  gl_Position = vec4(pos * 2. - 1., 0., 1.);
-
-  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);
-  fragColor = color;
-  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;
-  fragWidth = 1. / gl_PointSize;
-}
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S=[],P=1;P<E.activation.length;P++)E.activation[P]&&(E.activation[P]===!0||E.activation[P].data.length)&&S.push.apply(S,a(this.getMarkerDrawOptions(P,E,M)));S.length&&this.drawMarker(S)}},b.prototype.getMarkerDrawOptions=function(w,M,T){var E=M.range,S=M.tree,P=M.viewport,L=M.activation,R=M.selectionBuffer,F=M.count;if(this.regl,!S)return T?[d({},M,{markerTexture:this.markerTextures[w],activation:L[w],count:T.length,elements:T,offset:0})]:[d({},M,{markerTexture:this.markerTextures[w],activation:L[w],offset:0})];var D=[],O=S.range(E,{lod:!0,px:[(E[2]-E[0])/P.width,(E[3]-E[1])/P.height]});if(T){for(var N=L[w].data,B=new Uint8Array(F),W=0;W<T.length;W++){var G=T[W];B[G]=N?N[G]:1}R.subdata(B)}for(var K=O.length;K--;){var te=r(O[K],2),Y=te[0],J=te[1];D.push(d({},M,{markerTexture:this.markerTextures[w],activation:T?R:L[w],offset:Y,count:J-Y}))}return D},b.prototype.update=function(){for(var w=this,M=arguments.length,T=new Array(M),E=0;E<M;E++)T[E]=arguments[E];if(T.length){T.length===1&&Array.isArray(T[0])&&(T=T[0]);var S=this.groups,P=this.gl,L=this.regl,R=this.maxSize,F=this.maxColors,D=this.palette;this.groups=S=T.map(function(O,N){var B=S[N];if(O===void 0)return B;O===null?O={positions:null}:typeof O=="function"?O={ondraw:O}:typeof O[0]=="number"&&(O={positions:O}),(O=p(O,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette 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O=u(D,!1);if(M[O]==null){var N=T.length;M[O]=Math.floor(N/4),T[N]=D[0],T[N+1]=D[1],T[N+2]=D[2],T[N+3]=D[3]}S[F]=M[O]}return!this.tooManyColors&&T.length>4*E&&(this.tooManyColors=!0),this.updatePalette(T),S.length===1?S[0]:S},b.prototype.updatePalette=function(w){if(!this.tooManyColors){var M=this.maxColors,T=this.paletteTexture,E=Math.ceil(.25*w.length/M);if(E>1)for(var S=.25*(w=w.slice()).length%M;S<E*M;S++)w.push(0,0,0,0);T.height<E&&T.resize(M,E),T.subimage({width:Math.min(.25*w.length,M),height:E,data:w},0,0)}},b.prototype.destroy=function(){return this.groups.forEach(function(w){w.sizeBuffer.destroy(),w.positionBuffer.destroy(),w.positionFractBuffer.destroy(),w.colorBuffer.destroy(),w.activation.forEach(function(M){return M&&M.destroy&&M.destroy()}),w.selectionBuffer.destroy(),w.elements&&w.elements.destroy()}),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach(function(w){return w&&w.destroy&&w.destroy()}),this};var k=t("object-assign");o.exports=function(w,M){var T=new A(w,M),E=T.render.bind(T);return k(E,{render:E,update:T.update.bind(T),draw:T.draw.bind(T),destroy:T.destroy.bind(T),regl:T.regl,gl:T.gl,canvas:T.gl.canvas,groups:T.groups,markers:T.markerCache,palette:T.palette}),E}},{"@plotly/point-cluster":59,"array-bounds":71,"color-id":87,"color-normalize":89,"flatten-vertex-data":191,glslify:227,"is-iexplorer":233,"object-assign":247,"parse-rect":249,"pick-by-alias":253,"to-float32":313,"update-diff":329}],282:[function(t,o,f){var r=t("regl-scatter2d"),a=t("pick-by-alias"),l=t("array-bounds"),c=t("raf"),i=t("array-range"),s=t("parse-rect"),u=t("flatten-vertex-data");function h(g,y){if(!(this instanceof h))return new h(g);this.traces=[],this.passes={},this.regl=g,this.scatter=r(g),this.canvas=this.scatter.canvas}function d(g,y,v){return(g.id!=null?g.id:g)<<16|(255&y)<<8|255&v}function m(g,y,v){var x,_,A,b,k=g[y],w=g[v];return 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Hn=="number"?0|Hn:0|vn):vn?(mn(Un,vn),St(Nn,vn)):st(Nn,1,1),Un.genMipmaps&&(Nn.mipmask=(Nn.width<<1)-1),fn.mipmask=Nn.mipmask,$t(fn,Nn),fn.internalformat=Nn.internalformat,jt.width=Nn.width,jt.height=Nn.height,tn(fn),It(Nn,3553),Fn(Un,3553),nn(),Kt(Nn),At.profile&&(fn.stats.size=S(fn.internalformat,fn.type,Nn.width,Nn.height,Un.genMipmaps,!1)),jt.format=Dn[fn.internalformat],jt.type=lr[fn.type],jt.mag=Yr[Un.magFilter],jt.min=Mn[Un.minFilter],jt.wrapS=rr[Un.wrapS],jt.wrapT=rr[Un.wrapT],jt}var fn=new pn(3553);return pr[fn.id]=fn,ot.textureCount++,jt(cn,jn),jt.subimage=function(vn,Hn,Un,Nn){Hn|=0,Un|=0,Nn|=0;var Rn=Pt();return $t(Rn,fn),Rn.width=0,Rn.height=0,Ft(Rn,vn),Rn.width=Rn.width||(fn.width>>Nn)-Hn,Rn.height=Rn.height||(fn.height>>Nn)-Un,tn(fn),Et(Rn,3553,Hn,Un,Nn),nn(),De(Rn),jt},jt.resize=function(vn,Hn){var Un=0|vn,Nn=0|Hn||Un;if(Un===fn.width&&Nn===fn.height)return jt;jt.width=fn.width=Un,jt.height=fn.height=Nn,tn(fn);for(var Rn=0;fn.mipmask>>Rn;++Rn){var wn=Un>>Rn,An=Nn>>Rn;if(!wn||!An)break;Te.texImage2D(3553,Rn,fn.format,wn,An,0,fn.format,fn.type,null)}return nn(),At.profile&&(fn.stats.size=S(fn.internalformat,fn.type,Un,Nn,!1,!1)),jt},jt._reglType="texture2d",jt._texture=fn,At.profile&&(jt.stats=fn.stats),jt.destroy=function(){fn.decRef()},jt},createCube:function(cn,jn,jt,fn,vn,Hn){function Un(wn,An,kn,Pn,Zn,Yn){var ir,or=Nn.texInfo;for(qt.call(or),ir=0;6>ir;++ir)Rn[ir]=Zt();if(typeof wn!="number"&&wn){if(typeof wn=="object")if(An)St(Rn[0],wn),St(Rn[1],An),St(Rn[2],kn),St(Rn[3],Pn),St(Rn[4],Zn),St(Rn[5],Yn);else if(mn(or,wn),Ut(Nn,wn),"faces"in wn)for(wn=wn.faces,ir=0;6>ir;++ir)$t(Rn[ir],Nn),St(Rn[ir],wn[ir]);else for(ir=0;6>ir;++ir)St(Rn[ir],wn)}else for(wn=0|wn||1,ir=0;6>ir;++ir)st(Rn[ir],wn,wn);for($t(Nn,Rn[0]),Nn.mipmask=or.genMipmaps?(Rn[0].width<<1)-1:Rn[0].mipmask,Nn.internalformat=Rn[0].internalformat,Un.width=Rn[0].width,Un.height=Rn[0].height,tn(Nn),ir=0;6>ir;++ir)It(Rn[ir],34069+ir);for(Fn(or,34067),nn(),At.profile&&(Nn.stats.size=S(Nn.internalformat,Nn.type,Un.width,Un.height,or.genMipmaps,!0)),Un.format=Dn[Nn.internalformat],Un.type=lr[Nn.type],Un.mag=Yr[or.magFilter],Un.min=Mn[or.minFilter],Un.wrapS=rr[or.wrapS],Un.wrapT=rr[or.wrapT],ir=0;6>ir;++ir)Kt(Rn[ir]);return Un}var Nn=new pn(34067);pr[Nn.id]=Nn,ot.cubeCount++;var Rn=Array(6);return Un(cn,jn,jt,fn,vn,Hn),Un.subimage=function(wn,An,kn,Pn,Zn){kn|=0,Pn|=0,Zn|=0;var Yn=Pt();return $t(Yn,Nn),Yn.width=0,Yn.height=0,Ft(Yn,An),Yn.width=Yn.width||(Nn.width>>Zn)-kn,Yn.height=Yn.height||(Nn.height>>Zn)-Pn,tn(Nn),Et(Yn,34069+wn,kn,Pn,Zn),nn(),De(Yn),Un},Un.resize=function(wn){if((wn|=0)!==Nn.width){Un.width=Nn.width=wn,Un.height=Nn.height=wn,tn(Nn);for(var An=0;6>An;++An)for(var kn=0;Nn.mipmask>>kn;++kn)Te.texImage2D(34069+An,kn,Nn.format,wn>>kn,wn>>kn,0,Nn.format,Nn.type,null);return nn(),At.profile&&(Nn.stats.size=S(Nn.internalformat,Nn.type,Un.width,Un.height,!1,!0)),Un}},Un._reglType="textureCube",Un._texture=Nn,At.profile&&(Un.stats=Nn.stats),Un.destroy=function(){Nn.decRef()},Un},clear:function(){for(var cn=0;cn<qr;++cn)Te.activeTexture(33984+cn),Te.bindTexture(3553,null),_i[cn]=null;fe(pr).forEach(sn),ot.cubeCount=0,ot.textureCount=0},getTexture:function(cn){return null},restore:function(){for(var cn=0;cn<qr;++cn){var jn=_i[cn];jn&&(jn.bindCount=0,jn.unit=-1,_i[cn]=null)}fe(pr).forEach(function(jt){jt.texture=Te.createTexture(),Te.bindTexture(jt.target,jt.texture);for(var fn=0;32>fn;++fn)if(jt.mipmask&1<<fn)if(jt.target===3553)Te.texImage2D(3553,fn,jt.internalformat,jt.width>>fn,jt.height>>fn,0,jt.internalformat,jt.type,null);else for(var vn=0;6>vn;++vn)Te.texImage2D(34069+vn,fn,jt.internalformat,jt.width>>fn,jt.height>>fn,0,jt.internalformat,jt.type,null);Fn(jt.texInfo,jt.target)})},refresh:function(){for(var cn=0;cn<qr;++cn){var jn=_i[cn];jn&&(jn.bindCount=0,jn.unit=-1,_i[cn]=null),Te.activeTexture(33984+cn),Te.bindTexture(3553,null),Te.bindTexture(34067,null)}}}}function L(Te,Pe,qe,rt,lt,ot){function At(pn,tn,nn){this.target=pn,this.texture=tn,this.renderbuffer=nn;var sn=pn=0;tn?(pn=tn.width,sn=tn.height):nn&&(pn=nn.width,sn=nn.height),this.width=pn,this.height=sn}function wt(pn){pn&&(pn.texture&&pn.texture._texture.decRef(),pn.renderbuffer&&pn.renderbuffer._renderbuffer.decRef())}function $t(pn,tn,nn){pn&&(pn.texture?pn.texture._texture.refCount+=1:pn.renderbuffer._renderbuffer.refCount+=1)}function Ut(pn,tn){tn&&(tn.texture?Te.framebufferTexture2D(36160,pn,tn.target,tn.texture._texture.texture,0):Te.framebufferRenderbuffer(36160,pn,36161,tn.renderbuffer._renderbuffer.renderbuffer))}function tt(pn){var tn=3553,nn=null,sn=null,gn=pn;return typeof pn=="object"&&(gn=pn.data,"target"in pn&&(tn=0|pn.target)),(pn=gn._reglType)==="texture2d"||pn==="textureCube"?nn=gn:pn==="renderbuffer"&&(sn=gn,tn=36161),new At(tn,nn,sn)}function bt(pn,tn,nn,sn,gn){return nn?((pn=rt.create2D({width:pn,height:tn,format:sn,type:gn}))._texture.refCount=0,new At(3553,pn,null)):((pn=lt.create({width:pn,height:tn,format:sn}))._renderbuffer.refCount=0,new At(36161,null,pn))}function Ft(pn){return pn&&(pn.texture||pn.renderbuffer)}function Et(pn,tn,nn){pn&&(pn.texture?pn.texture.resize(tn,nn):pn.renderbuffer&&pn.renderbuffer.resize(tn,nn),pn.width=tn,pn.height=nn)}function Pt(){this.id=mn++,Fn[this.id]=this,this.framebuffer=Te.createFramebuffer(),this.height=this.width=0,this.colorAttachments=[],this.depthStencilAttachment=this.stencilAttachment=this.depthAttachment=null}function De(pn){pn.colorAttachments.forEach(wt),wt(pn.depthAttachment),wt(pn.stencilAttachment),wt(pn.depthStencilAttachment)}function Je(pn){Te.deleteFramebuffer(pn.framebuffer),pn.framebuffer=null,ot.framebufferCount--,delete Fn[pn.id]}function st(pn){var tn;Te.bindFramebuffer(36160,pn.framebuffer);var nn=pn.colorAttachments;for(tn=0;tn<nn.length;++tn)Ut(36064+tn,nn[tn]);for(tn=nn.length;tn<qe.maxColorAttachments;++tn)Te.framebufferTexture2D(36160,36064+tn,3553,null,0);Te.framebufferTexture2D(36160,33306,3553,null,0),Te.framebufferTexture2D(36160,36096,3553,null,0),Te.framebufferTexture2D(36160,36128,3553,null,0),Ut(36096,pn.depthAttachment),Ut(36128,pn.stencilAttachment),Ut(33306,pn.depthStencilAttachment),Te.checkFramebufferStatus(36160),Te.isContextLost(),Te.bindFramebuffer(36160,It.next?It.next.framebuffer:null),It.cur=It.next,Te.getError()}function St(pn,tn){function nn(gn,bn){var In,qn=0,Wn=0,ar=!0,Dr=!0;In=null;var yr=!0,Sr="rgba",Kn="uint8",Dn=1,lr=null,Yr=null,Mn=null,rr=!1;typeof gn=="number"?(qn=0|gn,Wn=0|bn||qn):gn?("shape"in gn?(qn=(Wn=gn.shape)[0],Wn=Wn[1]):("radius"in gn&&(qn=Wn=gn.radius),"width"in gn&&(qn=gn.width),"height"in gn&&(Wn=gn.height)),("color"in gn||"colors"in gn)&&(In=gn.color||gn.colors,Array.isArray(In)),In||("colorCount"in gn&&(Dn=0|gn.colorCount),"colorTexture"in gn&&(yr=!!gn.colorTexture,Sr="rgba4"),"colorType"in gn&&(Kn=gn.colorType,!yr)&&(Kn==="half float"||Kn==="float16"?Sr="rgba16f":Kn!=="float"&&Kn!=="float32"||(Sr="rgba32f")),"colorFormat"in gn&&(Sr=gn.colorFormat,0<=Zt.indexOf(Sr)?yr=!0:0<=Kt.indexOf(Sr)&&(yr=!1))),("depthTexture"in gn||"depthStencilTexture"in gn)&&(rr=!(!gn.depthTexture&&!gn.depthStencilTexture)),"depth"in gn&&(typeof gn.depth=="boolean"?ar=gn.depth:(lr=gn.depth,Dr=!1)),"stencil"in gn&&(typeof gn.stencil=="boolean"?Dr=gn.stencil:(Yr=gn.stencil,ar=!1)),"depthStencil"in gn&&(typeof gn.depthStencil=="boolean"?ar=Dr=gn.depthStencil:(Mn=gn.depthStencil,Dr=ar=!1))):qn=Wn=1;var nr=null,Bn=null,Nr=null,Gr=null;if(Array.isArray(In))nr=In.map(tt);else if(In)nr=[tt(In)];else for(nr=Array(Dn),In=0;In<Dn;++In)nr[In]=bt(qn,Wn,yr,Sr,Kn);for(qn=qn||nr[0].width,Wn=Wn||nr[0].height,lr?Bn=tt(lr):ar&&!Dr&&(Bn=bt(qn,Wn,rr,"depth","uint32")),Yr?Nr=tt(Yr):Dr&&!ar&&(Nr=bt(qn,Wn,!1,"stencil","uint8")),Mn?Gr=tt(Mn):!lr&&!Yr&&Dr&&ar&&(Gr=bt(qn,Wn,rr,"depth stencil","depth stencil")),ar=null,In=0;In<nr.length;++In)$t(nr[In]),nr[In]&&nr[In].texture&&(Dr=et[nr[In].texture._texture.format]*Lt[nr[In].texture._texture.type],ar===null&&(ar=Dr));return $t(Bn),$t(Nr),$t(Gr),De(sn),sn.width=qn,sn.height=Wn,sn.colorAttachments=nr,sn.depthAttachment=Bn,sn.stencilAttachment=Nr,sn.depthStencilAttachment=Gr,nn.color=nr.map(Ft),nn.depth=Ft(Bn),nn.stencil=Ft(Nr),nn.depthStencil=Ft(Gr),nn.width=sn.width,nn.height=sn.height,st(sn),nn}var sn=new Pt;return ot.framebufferCount++,nn(pn,tn),q(nn,{resize:function(gn,bn){var In=Math.max(0|gn,1),qn=Math.max(0|bn||In,1);if(In===sn.width&&qn===sn.height)return nn;for(var Wn=sn.colorAttachments,ar=0;ar<Wn.length;++ar)Et(Wn[ar],In,qn);return Et(sn.depthAttachment,In,qn),Et(sn.stencilAttachment,In,qn),Et(sn.depthStencilAttachment,In,qn),sn.width=nn.width=In,sn.height=nn.height=qn,st(sn),nn},_reglType:"framebuffer",_framebuffer:sn,destroy:function(){Je(sn),De(sn)},use:function(gn){It.setFBO({framebuffer:nn},gn)}})}var It={cur:null,next:null,dirty:!1,setFBO:null},Zt=["rgba"],Kt=["rgba4","rgb565","rgb5 a1"];Pe.ext_srgb&&Kt.push("srgba"),Pe.ext_color_buffer_half_float&&Kt.push("rgba16f","rgb16f"),Pe.webgl_color_buffer_float&&Kt.push("rgba32f");var qt=["uint8"];Pe.oes_texture_half_float&&qt.push("half float","float16"),Pe.oes_texture_float&&qt.push("float","float32");var mn=0,Fn={};return q(It,{getFramebuffer:function(pn){return typeof pn=="function"&&pn._reglType==="framebuffer"&&(pn=pn._framebuffer)instanceof Pt?pn:null},create:St,createCube:function(pn){function tn(sn){var gn,bn={color:null},In=0,qn=null;gn="rgba";var Wn="uint8",ar=1;if(typeof sn=="number"?In=0|sn:sn?("shape"in sn?In=sn.shape[0]:("radius"in sn&&(In=0|sn.radius),"width"in sn?In=0|sn.width:"height"in sn&&(In=0|sn.height)),("color"in sn||"colors"in sn)&&(qn=sn.color||sn.colors,Array.isArray(qn)),qn||("colorCount"in sn&&(ar=0|sn.colorCount),"colorType"in sn&&(Wn=sn.colorType),"colorFormat"in sn&&(gn=sn.colorFormat)),"depth"in sn&&(bn.depth=sn.depth),"stencil"in sn&&(bn.stencil=sn.stencil),"depthStencil"in sn&&(bn.depthStencil=sn.depthStencil)):In=1,qn)if(Array.isArray(qn))for(sn=[],gn=0;gn<qn.length;++gn)sn[gn]=qn[gn];else sn=[qn];else for(sn=Array(ar),qn={radius:In,format:gn,type:Wn},gn=0;gn<ar;++gn)sn[gn]=rt.createCube(qn);for(bn.color=Array(sn.length),gn=0;gn<sn.length;++gn)ar=sn[gn],In=In||ar.width,bn.color[gn]={target:34069,data:sn[gn]};for(gn=0;6>gn;++gn){for(ar=0;ar<sn.length;++ar)bn.color[ar].target=34069+gn;0<gn&&(bn.depth=nn[0].depth,bn.stencil=nn[0].stencil,bn.depthStencil=nn[0].depthStencil),nn[gn]?nn[gn](bn):nn[gn]=St(bn)}return q(tn,{width:In,height:In,color:sn})}var nn=Array(6);return tn(pn),q(tn,{faces:nn,resize:function(sn){var gn=0|sn;if(gn===tn.width)return tn;var bn=tn.color;for(sn=0;sn<bn.length;++sn)bn[sn].resize(gn);for(sn=0;6>sn;++sn)nn[sn].resize(gn);return tn.width=tn.height=gn,tn},_reglType:"framebufferCube",destroy:function(){nn.forEach(function(sn){sn.destroy()})}})},clear:function(){fe(Fn).forEach(Je)},restore:function(){It.cur=null,It.next=null,It.dirty=!0,fe(Fn).forEach(function(pn){pn.framebuffer=Te.createFramebuffer(),st(pn)})}})}function R(){this.w=this.z=this.y=this.x=this.state=0,this.buffer=null,this.size=0,this.normalized=!1,this.type=5126,this.divisor=this.stride=this.offset=0}function F(Te,Pe,qe,rt,lt,ot,At){function wt(){this.id=++tt,this.attributes=[],this.elements=null,this.ownsElements=!1,this.offset=this.count=0,this.instances=-1,this.primitive=4;var Et=Pe.oes_vertex_array_object;this.vao=Et?Et.createVertexArrayOES():null,bt[this.id]=this,this.buffers=[]}var $t=qe.maxAttributes,Ut=Array($t);for(qe=0;qe<$t;++qe)Ut[qe]=new R;var tt=0,bt={},Ft={Record:R,scope:{},state:Ut,currentVAO:null,targetVAO:null,restore:Pe.oes_vertex_array_object?function(){Pe.oes_vertex_array_object&&fe(bt).forEach(function(Et){Et.refresh()})}:function(){},createVAO:function(Et){function Pt(Je){var st;Array.isArray(Je)?(st=Je,De.elements&&De.ownsElements&&De.elements.destroy(),De.elements=null,De.ownsElements=!1,De.offset=0,De.count=0,De.instances=-1,De.primitive=4):(Je.elements?(st=Je.elements,De.ownsElements?(typeof st=="function"&&st._reglType==="elements"?De.elements.destroy():De.elements(st),De.ownsElements=!1):ot.getElements(Je.elements)?(De.elements=Je.elements,De.ownsElements=!1):(De.elements=ot.create(Je.elements),De.ownsElements=!0)):(De.elements=null,De.ownsElements=!1),st=Je.attributes,De.offset=0,De.count=-1,De.instances=-1,De.primitive=4,De.elements&&(De.count=De.elements._elements.vertCount,De.primitive=De.elements._elements.primType),"offset"in Je&&(De.offset=0|Je.offset),"count"in Je&&(De.count=0|Je.count),"instances"in Je&&(De.instances=0|Je.instances),"primitive"in Je&&(De.primitive=Me[Je.primitive])),Je={};var St=De.attributes;St.length=st.length;for(var It=0;It<st.length;++It){var Zt,Kt=st[It],qt=St[It]=new R,mn=Kt.data||Kt;Array.isArray(mn)||ge(mn)||d(mn)?(De.buffers[It]&&(Zt=De.buffers[It],ge(mn)&&Zt._buffer.byteLength>=mn.byteLength?Zt.subdata(mn):(Zt.destroy(),De.buffers[It]=null)),De.buffers[It]||(Zt=De.buffers[It]=lt.create(Kt,34962,!1,!0)),qt.buffer=lt.getBuffer(Zt),qt.size=0|qt.buffer.dimension,qt.normalized=!1,qt.type=qt.buffer.dtype,qt.offset=0,qt.stride=0,qt.divisor=0,qt.state=1,Je[It]=1):lt.getBuffer(Kt)?(qt.buffer=lt.getBuffer(Kt),qt.size=0|qt.buffer.dimension,qt.normalized=!1,qt.type=qt.buffer.dtype,qt.offset=0,qt.stride=0,qt.divisor=0,qt.state=1):lt.getBuffer(Kt.buffer)?(qt.buffer=lt.getBuffer(Kt.buffer),qt.size=0|(+Kt.size||qt.buffer.dimension),qt.normalized=!!Kt.normalized||!1,qt.type="type"in Kt?Ae[Kt.type]:qt.buffer.dtype,qt.offset=0|(Kt.offset||0),qt.stride=0|(Kt.stride||0),qt.divisor=0|(Kt.divisor||0),qt.state=1):"x"in Kt&&(qt.x=+Kt.x||0,qt.y=+Kt.y||0,qt.z=+Kt.z||0,qt.w=+Kt.w||0,qt.state=2)}for(Zt=0;Zt<De.buffers.length;++Zt)!Je[Zt]&&De.buffers[Zt]&&(De.buffers[Zt].destroy(),De.buffers[Zt]=null);return De.refresh(),Pt}var De=new wt;return rt.vaoCount+=1,Pt.destroy=function(){for(var Je=0;Je<De.buffers.length;++Je)De.buffers[Je]&&De.buffers[Je].destroy();De.buffers.length=0,De.ownsElements&&(De.elements.destroy(),De.elements=null,De.ownsElements=!1),De.destroy()},Pt._vao=De,Pt._reglType="vao",Pt(Et)},getVAO:function(Et){return typeof Et=="function"&&Et._vao?Et._vao:null},destroyBuffer:function(Et){for(var Pt=0;Pt<Ut.length;++Pt){var De=Ut[Pt];De.buffer===Et&&(Te.disableVertexAttribArray(Pt),De.buffer=null)}},setVAO:Pe.oes_vertex_array_object?function(Et){if(Et!==Ft.currentVAO){var Pt=Pe.oes_vertex_array_object;Et?Pt.bindVertexArrayOES(Et.vao):Pt.bindVertexArrayOES(null),Ft.currentVAO=Et}}:function(Et){if(Et!==Ft.currentVAO){if(Et)Et.bindAttrs();else{for(var Pt=Pe.angle_instanced_arrays,De=0;De<Ut.length;++De){var Je=Ut[De];Je.buffer?(Te.enableVertexAttribArray(De),Je.buffer.bind(),Te.vertexAttribPointer(De,Je.size,Je.type,Je.normalized,Je.stride,Je.offfset),Pt&&Je.divisor&&Pt.vertexAttribDivisorANGLE(De,Je.divisor)):(Te.disableVertexAttribArray(De),Te.vertexAttrib4f(De,Je.x,Je.y,Je.z,Je.w))}At.elements?Te.bindBuffer(34963,At.elements.buffer.buffer):Te.bindBuffer(34963,null)}Ft.currentVAO=Et}},clear:Pe.oes_vertex_array_object?function(){fe(bt).forEach(function(Et){Et.destroy()})}:function(){}};return wt.prototype.bindAttrs=function(){for(var Et=Pe.angle_instanced_arrays,Pt=this.attributes,De=0;De<Pt.length;++De){var Je=Pt[De];Je.buffer?(Te.enableVertexAttribArray(De),Te.bindBuffer(34962,Je.buffer.buffer),Te.vertexAttribPointer(De,Je.size,Je.type,Je.normalized,Je.stride,Je.offset),Et&&Je.divisor&&Et.vertexAttribDivisorANGLE(De,Je.divisor)):(Te.disableVertexAttribArray(De),Te.vertexAttrib4f(De,Je.x,Je.y,Je.z,Je.w))}for(Et=Pt.length;Et<$t;++Et)Te.disableVertexAttribArray(Et);(Et=ot.getElements(this.elements))?Te.bindBuffer(34963,Et.buffer.buffer):Te.bindBuffer(34963,null)},wt.prototype.refresh=function(){var Et=Pe.oes_vertex_array_object;Et&&(Et.bindVertexArrayOES(this.vao),this.bindAttrs(),Ft.currentVAO=null,Et.bindVertexArrayOES(null))},wt.prototype.destroy=function(){if(this.vao){var Et=Pe.oes_vertex_array_object;this===Ft.currentVAO&&(Ft.currentVAO=null,Et.bindVertexArrayOES(null)),Et.deleteVertexArrayOES(this.vao),this.vao=null}this.ownsElements&&(this.elements.destroy(),this.elements=null,this.ownsElements=!1),bt[this.id]&&(delete bt[this.id],--rt.vaoCount)},Ft}function D(Te,Pe,qe,rt){function 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ee=re(V.pipes,U);return ee===-1||(V.pipes.splice(ee,1),V.pipesCount-=1,V.pipesCount===1&&(V.pipes=V.pipes[0]),U.emit("unpipe",this,H)),this},S.prototype.on=function(U,V){var H=i.prototype.on.call(this,U,V),ne=this._readableState;return U==="data"?(ne.readableListening=this.listenerCount("readable")>0,ne.flowing!==!1&&this.resume()):U==="readable"&&(ne.endEmitted||ne.readableListening||(ne.readableListening=ne.needReadable=!0,ne.flowing=!1,ne.emittedReadable=!1,h("on readable",ne.length,ne.reading),ne.length?F(this):ne.reading||r.nextTick(W,this))),H},S.prototype.addListener=S.prototype.on,S.prototype.removeListener=function(U,V){var H=i.prototype.removeListener.call(this,U,V);return U==="readable"&&r.nextTick(B,this),H},S.prototype.removeAllListeners=function(U){var V=i.prototype.removeAllListeners.apply(this,arguments);return U!=="readable"&&U!==void 0||r.nextTick(B,this),V},S.prototype.resume=function(){var U=this._readableState;return 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u=this.newDate(c,i,s);return u.add(-u.dayOfWeek(),"d"),Math.floor((u.dayOfYear()-1)/7)+1},daysInMonth:function(c,i){var s=this._validate(c,i,this.minDay,r.local.invalidMonth);return this.daysPerMonth[s.month()-1]+(s.month()===13&&this.leapYear(s.year())?1:0)},weekDay:function(c,i,s){return(this.dayOfWeek(c,i,s)||7)<6},toJD:function(c,i,s){var u=this._validate(c,i,s,r.local.invalidDate);return(c=u.year())<0&&c++,u.day()+30*(u.month()-1)+365*(c-1)+Math.floor(c/4)+this.jdEpoch-1},fromJD:function(c){var i=Math.floor(c)+.5-this.jdEpoch,s=Math.floor((i-Math.floor((i+366)/1461))/365)+1;s<=0&&s--,i=Math.floor(c)+.5-this.newDate(s,1,1).toJD();var u=Math.floor(i/30)+1,h=i-30*(u-1)+1;return this.newDate(s,u,h)}}),r.calendars.ethiopian=l},{"../main":346,"object-assign":247}],336:[function(t,o,f){var r=t("../main"),a=t("object-assign");function l(i){this.local=this.regionalOptions[i||""]||this.regionalOptions[""]}function c(i,s){return i-s*Math.floor(i/s)}l.prototype=new 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d=i<=0?i+1:i,m=this.jdEpoch+this._delay1(d)+this._delay2(d)+u+1;if(s<7){for(var p=7;p<=this.monthsInYear(i);p++)m+=this.daysInMonth(i,p);for(p=1;p<s;p++)m+=this.daysInMonth(i,p)}else for(p=7;p<s;p++)m+=this.daysInMonth(i,p);return m},_delay1:function(i){var s=Math.floor((235*i-234)/19),u=12084+13753*s,h=29*s+Math.floor(u/25920);return c(3*(h+1),7)<3&&h++,h},_delay2:function(i){var s=this._delay1(i-1),u=this._delay1(i);return this._delay1(i+1)-u==356?2:u-s==382?1:0},fromJD:function(i){i=Math.floor(i)+.5;for(var s=Math.floor(98496*(i-this.jdEpoch)/35975351)-1;i>=this.toJD(s===-1?1:s+1,7,1);)s++;for(var u=i<this.toJD(s,1,1)?7:1;i>this.toJD(s,u,this.daysInMonth(s,u));)u++;var h=i-this.toJD(s,u,1)+1;return this.newDate(s,u,h)}}),r.calendars.hebrew=l},{"../main":346,"object-assign":247}],337:[function(t,o,f){var r=t("../main"),a=t("object-assign");function l(c){this.local=this.regionalOptions[c||""]||this.regionalOptions[""]}l.prototype=new 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u.add(-u.dayOfWeek(),"d"),Math.floor((u.dayOfYear()-1)/7)+1},daysInYear:function(c){return this.leapYear(c)?355:354},daysInMonth:function(c,i){var s=this._validate(c,i,this.minDay,r.local.invalidMonth);return this.daysPerMonth[s.month()-1]+(s.month()===12&&this.leapYear(s.year())?1:0)},weekDay:function(c,i,s){return this.dayOfWeek(c,i,s)!==5},toJD:function(c,i,s){var u=this._validate(c,i,s,r.local.invalidDate);return c=u.year(),i=u.month(),c=c<=0?c+1:c,(s=u.day())+Math.ceil(29.5*(i-1))+354*(c-1)+Math.floor((3+11*c)/30)+this.jdEpoch-1},fromJD:function(c){c=Math.floor(c)+.5;var i=Math.floor((30*(c-this.jdEpoch)+10646)/10631);i=i<=0?i-1:i;var s=Math.min(12,Math.ceil((c-29-this.toJD(i,1,1))/29.5)+1),u=c-this.toJD(i,s,1)+1;return this.newDate(i,s,u)}}),r.calendars.islamic=l},{"../main":346,"object-assign":247}],338:[function(t,o,f){var r=t("../main"),a=t("object-assign");function l(c){this.local=this.regionalOptions[c||""]||this.regionalOptions[""]}l.prototype=new 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this._validate(s,u,h,r.local.invalidDate),!0},extraInfo:function(s,u,h){var d=this._validate(s,u,h,r.local.invalidDate).toJD(),m=this._toHaab(d),p=this._toTzolkin(d);return{haabMonthName:this.local.haabMonths[m[0]-1],haabMonth:m[0],haabDay:m[1],tzolkinDayName:this.local.tzolkinMonths[p[0]-1],tzolkinDay:p[0],tzolkinTrecena:p[1]}},_toHaab:function(s){var u=c((s-=this.jdEpoch)+8+340,365);return[Math.floor(u/20)+1,c(u,20)]},_toTzolkin:function(s){return[i((s-=this.jdEpoch)+20,20),i(s+4,13)]},toJD:function(s,u,h){var d=this._validate(s,u,h,r.local.invalidDate);return d.day()+20*d.month()+360*d.year()+this.jdEpoch},fromJD:function(s){s=Math.floor(s)+.5-this.jdEpoch;var u=Math.floor(s/360);s%=360,s+=s<0?360:0;var h=Math.floor(s/20),d=s%20;return this.newDate(u,h,d)}}),r.calendars.mayan=l},{"../main":346,"object-assign":247}],340:[function(t,o,f){var r=t("../main"),a=t("object-assign");function l(i){this.local=this.regionalOptions[i||""]||this.regionalOptions[""]}l.prototype=new 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h.add(1-(h.dayOfWeek()||7),"d"),Math.floor((h.dayOfYear()-1)/7)+1},daysInMonth:function(i,s){var u=this._validate(i,s,this.minDay,r.local.invalidMonth);return this.daysPerMonth[u.month()-1]+(u.month()===12&&this.leapYear(u.year())?1:0)},weekDay:function(i,s,u){return(this.dayOfWeek(i,s,u)||7)<6},toJD:function(i,s,u){var h=this._validate(i,s,u,r.local.invalidMonth);(i=h.year())<0&&i++;for(var d=h.day(),m=1;m<h.month();m++)d+=this.daysPerMonth[m-1];return d+c.toJD(i+1468,3,13)},fromJD:function(i){i=Math.floor(i+.5);for(var s=Math.floor((i-(this.jdEpoch-1))/366);i>=this.toJD(s+1,1,1);)s++;for(var u=i-Math.floor(this.toJD(s,1,1)+.5)+1,h=1;u>this.daysInMonth(s,h);)u-=this.daysInMonth(s,h),h++;return this.newDate(s,h,u)}}),r.calendars.nanakshahi=l},{"../main":346,"object-assign":247}],341:[function(t,o,f){var r=t("../main"),a=t("object-assign");function l(c){this.local=this.regionalOptions[c||""]||this.regionalOptions[""]}l.prototype=new 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u.add(-u.dayOfWeek(),"d"),Math.floor((u.dayOfYear()-1)/7)+1},daysInYear:function(c){if(c=this._validate(c,this.minMonth,this.minDay,r.local.invalidYear).year(),this.NEPALI_CALENDAR_DATA[c]===void 0)return this.daysPerYear;for(var i=0,s=this.minMonth;s<=12;s++)i+=this.NEPALI_CALENDAR_DATA[c][s];return i},daysInMonth:function(c,i){return c.year&&(i=c.month(),c=c.year()),this._validate(c,i,this.minDay,r.local.invalidMonth),this.NEPALI_CALENDAR_DATA[c]===void 0?this.daysPerMonth[i-1]:this.NEPALI_CALENDAR_DATA[c][i]},weekDay:function(c,i,s){return this.dayOfWeek(c,i,s)!==6},toJD:function(c,i,s){var u=this._validate(c,i,s,r.local.invalidDate);c=u.year(),i=u.month(),s=u.day();var h=r.instance(),d=0,m=i,p=c;this._createMissingCalendarData(c);var g=c-(m>9||m===9&&s>=this.NEPALI_CALENDAR_DATA[p][0]?56:57);for(i!==9&&(d=s,m--);m!==9;)m<=0&&(m=12,p--),d+=this.NEPALI_CALENDAR_DATA[p][m],m--;return i===9?(d+=s-this.NEPALI_CALENDAR_DATA[p][0])<0&&(d+=h.daysInYear(g)):d+=this.NEPALI_CALENDAR_DATA[p][9]-this.NEPALI_CALENDAR_DATA[p][0],h.newDate(g,1,1).add(d,"d").toJD()},fromJD:function(c){var i=r.instance().fromJD(c),s=i.year(),u=i.dayOfYear(),h=s+56;this._createMissingCalendarData(h);for(var d=9,m=this.NEPALI_CALENDAR_DATA[h][0],p=this.NEPALI_CALENDAR_DATA[h][d]-m+1;u>p;)++d>12&&(d=1,h++),p+=this.NEPALI_CALENDAR_DATA[h][d];var g=this.NEPALI_CALENDAR_DATA[h][d]-(p-u);return this.newDate(h,d,g)},_createMissingCalendarData:function(c){var i=this.daysPerMonth.slice(0);i.unshift(17);for(var s=c-1;s<c+2;s++)this.NEPALI_CALENDAR_DATA[s]===void 0&&(this.NEPALI_CALENDAR_DATA[s]=i)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29,29,30,30],2077:[17,31,32,31,32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r=t("../main"),a=t("object-assign");function l(i){this.local=this.regionalOptions[i||""]||this.regionalOptions[""]}function c(i,s){return i-s*Math.floor(i/s)}l.prototype=new r.baseCalendar,a(l.prototype,{name:"Persian",jdEpoch:19483205e-1,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Persian",epochs:["BP","AP"],monthNames:["Farvardin","Ordibehesht","Khordad","Tir","Mordad","Shahrivar","Mehr","Aban","Azar","Day","Bahman","Esfand"],monthNamesShort:["Far","Ord","Kho","Tir","Mor","Sha","Meh","Aba","Aza","Day","Bah","Esf"],dayNames:["Yekshambe","Doshambe","Seshambe","Chæharshambe","Panjshambe","Jom'e","Shambe"],dayNamesShort:["Yek","Do","Se","Chæ","Panj","Jom","Sha"],dayNamesMin:["Ye","Do","Se","Ch","Pa","Jo","Sh"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(i){var s=this._validate(i,this.minMonth,this.minDay,r.local.invalidYear);return 682*((s.year()-(s.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(i,s,u){var h=this.newDate(i,s,u);return h.add(-(h.dayOfWeek()+1)%7,"d"),Math.floor((h.dayOfYear()-1)/7)+1},daysInMonth:function(i,s){var u=this._validate(i,s,this.minDay,r.local.invalidMonth);return this.daysPerMonth[u.month()-1]+(u.month()===12&&this.leapYear(u.year())?1:0)},weekDay:function(i,s,u){return this.dayOfWeek(i,s,u)!==5},toJD:function(i,s,u){var h=this._validate(i,s,u,r.local.invalidDate);i=h.year(),s=h.month(),u=h.day();var d=i-(i>=0?474:473),m=474+c(d,2820);return u+(s<=7?31*(s-1):30*(s-1)+6)+Math.floor((682*m-110)/2816)+365*(m-1)+1029983*Math.floor(d/2820)+this.jdEpoch-1},fromJD:function(i){var s=(i=Math.floor(i)+.5)-this.toJD(475,1,1),u=Math.floor(s/1029983),h=c(s,1029983),d=2820;if(h!==1029982){var m=Math.floor(h/366),p=c(h,366);d=Math.floor((2134*m+2816*p+2815)/1028522)+m+1}var g=d+2820*u+474;g=g<=0?g-1:g;var y=i-this.toJD(g,1,1)+1,v=y<=186?Math.ceil(y/31):Math.ceil((y-6)/30),x=i-this.toJD(g,v,1)+1;return this.newDate(g,v,x)}}),r.calendars.persian=l,r.calendars.jalali=l},{"../main":346,"object-assign":247}],343:[function(t,o,f){var r=t("../main"),a=t("object-assign"),l=r.instance();function c(i){this.local=this.regionalOptions[i||""]||this.regionalOptions[""]}c.prototype=new 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r.baseCalendar,a(c.prototype,{name:"Thai",jdEpoch:15230985e-1,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Thai",epochs:["BBE","BE"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(i){var s=this._validate(i,this.minMonth,this.minDay,r.local.invalidYear);return i=this._t2gYear(s.year()),l.leapYear(i)},weekOfYear:function(i,s,u){var h=this._validate(i,this.minMonth,this.minDay,r.local.invalidYear);return i=this._t2gYear(h.year()),l.weekOfYear(i,h.month(),h.day())},daysInMonth:function(i,s){var u=this._validate(i,s,this.minDay,r.local.invalidMonth);return this.daysPerMonth[u.month()-1]+(u.month()===2&&this.leapYear(u.year())?1:0)},weekDay:function(i,s,u){return(this.dayOfWeek(i,s,u)||7)<6},toJD:function(i,s,u){var h=this._validate(i,s,u,r.local.invalidDate);return i=this._t2gYear(h.year()),l.toJD(i,h.month(),h.day())},fromJD:function(i){var s=l.fromJD(i),u=this._g2tYear(s.year());return this.newDate(u,s.month(),s.day())},_t2gYear:function(i){return i-this.yearsOffset-(i>=1&&i<=this.yearsOffset?1:0)},_g2tYear:function(i){return i+this.yearsOffset+(i>=-this.yearsOffset&&i<=-1?1:0)}}),r.calendars.thai=c},{"../main":346,"object-assign":247}],345:[function(t,o,f){var r=t("../main"),a=t("object-assign");function l(i){this.local=this.regionalOptions[i||""]||this.regionalOptions[""]}l.prototype=new r.baseCalendar,a(l.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thalāthā’","Yawm al-Arba‘ā’","Yawm al-Khamīs","Yawm al-Jum‘a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(i){var s=this._validate(i,this.minMonth,this.minDay,r.local.invalidYear);return this.daysInYear(s.year())===355},weekOfYear:function(i,s,u){var h=this.newDate(i,s,u);return h.add(-h.dayOfWeek(),"d"),Math.floor((h.dayOfYear()-1)/7)+1},daysInYear:function(i){for(var s=0,u=1;u<=12;u++)s+=this.daysInMonth(i,u);return s},daysInMonth:function(i,s){for(var u=this._validate(i,s,this.minDay,r.local.invalidMonth).toJD()-24e5+.5,h=0,d=0;d<c.length;d++){if(c[d]>u)return c[h]-c[h-1];h++}return 30},weekDay:function(i,s,u){return this.dayOfWeek(i,s,u)!==5},toJD:function(i,s,u){var h=this._validate(i,s,u,r.local.invalidDate),d=12*(h.year()-1)+h.month()-15292;return h.day()+c[d-1]-1+24e5-.5},fromJD:function(i){for(var s=i-24e5+.5,u=0,h=0;h<c.length&&!(c[h]>s);h++)u++;var d=u+15292,m=Math.floor((d-1)/12),p=m+1,g=d-12*m,y=s-c[u-1]+1;return this.newDate(p,g,y)},isValid:function(i,s,u){var h=r.baseCalendar.prototype.isValid.apply(this,arguments);return h&&(h=(i=i.year!=null?i.year:i)>=1276&&i<=1500),h},_validate:function(i,s,u,h){var d=r.baseCalendar.prototype._validate.apply(this,arguments);if(d.year<1276||d.year>1500)throw h.replace(/\{0\}/,this.local.name);return d}}),r.calendars.ummalqura=l;var 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r=t("object-assign");function a(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function l(h,d,m,p){if(this._calendar=h,this._year=d,this._month=m,this._day=p,this._calendar._validateLevel===0&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function c(h,d){return"000000".substring(0,d-(h=""+h).length)+h}function i(){this.shortYearCutoff="+10"}function s(h){this.local=this.regionalOptions[h]||this.regionalOptions[""]}r(a.prototype,{instance:function(h,d){h=(h||"gregorian").toLowerCase(),d=d||"";var m=this._localCals[h+"-"+d];if(!m&&this.calendars[h]&&(m=new this.calendars[h](d),this._localCals[h+"-"+d]=m),!m)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,h);return m},newDate:function(h,d,m,p,g){return(p=(h!=null&&h.year?h.calendar():typeof p=="string"?this.instance(p,g):p)||this.instance()).newDate(h,d,m)},substituteDigits:function(h){return function(d){return(d+"").replace(/[0-9]/g,function(m){return h[m]})}},substituteChineseDigits:function(h,d){return function(m){for(var p="",g=0;m>0;){var y=m%10;p=(y===0?"":h[y]+d[g])+p,g++,m=Math.floor(m/10)}return p.indexOf(h[1]+d[1])===0&&(p=p.substr(1)),p||h[0]}}}),r(l.prototype,{newDate:function(h,d,m){return this._calendar.newDate(h??this,d,m)},year:function(h){return arguments.length===0?this._year:this.set(h,"y")},month:function(h){return arguments.length===0?this._month:this.set(h,"m")},day:function(h){return arguments.length===0?this._day:this.set(h,"d")},date:function(h,d,m){if(!this._calendar.isValid(h,d,m))throw(u.local.invalidDate||u.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=h,this._month=d,this._day=m,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(h,d){return this._calendar.add(this,h,d)},set:function(h,d){return this._calendar.set(this,h,d)},compareTo:function(h){if(this._calendar.name!==h._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,h._calendar.local.name);var d=this._year!==h._year?this._year-h._year:this._month!==h._month?this.monthOfYear()-h.monthOfYear():this._day-h._day;return d===0?0:d<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(h){return this._calendar.fromJD(h)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(h){return this._calendar.fromJSDate(h)},toString:function(){return(this.year()<0?"-":"")+c(Math.abs(this.year()),4)+"-"+c(this.month(),2)+"-"+c(this.day(),2)}}),r(i.prototype,{_validateLevel:0,newDate:function(h,d,m){return h==null?this.today():(h.year&&(this._validate(h,d,m,u.local.invalidDate||u.regionalOptions[""].invalidDate),m=h.day(),d=h.month(),h=h.year()),new l(this,h,d,m))},today:function(){return this.fromJSDate(new Date)},epoch:function(h){return this._validate(h,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(h){var d=this._validate(h,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear);return(d.year()<0?"-":"")+c(Math.abs(d.year()),4)},monthsInYear:function(h){return this._validate(h,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear),12},monthOfYear:function(h,d){var m=this._validate(h,d,this.minDay,u.local.invalidMonth||u.regionalOptions[""].invalidMonth);return(m.month()+this.monthsInYear(m)-this.firstMonth)%this.monthsInYear(m)+this.minMonth},fromMonthOfYear:function(h,d){var m=(d+this.firstMonth-2*this.minMonth)%this.monthsInYear(h)+this.minMonth;return this._validate(h,m,this.minDay,u.local.invalidMonth||u.regionalOptions[""].invalidMonth),m},daysInYear:function(h){var d=this._validate(h,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear);return this.leapYear(d)?366:365},dayOfYear:function(h,d,m){var p=this._validate(h,d,m,u.local.invalidDate||u.regionalOptions[""].invalidDate);return p.toJD()-this.newDate(p.year(),this.fromMonthOfYear(p.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(h,d,m){var p=this._validate(h,d,m,u.local.invalidDate||u.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(p))+2)%this.daysInWeek()},extraInfo:function(h,d,m){return this._validate(h,d,m,u.local.invalidDate||u.regionalOptions[""].invalidDate),{}},add:function(h,d,m){return this._validate(h,this.minMonth,this.minDay,u.local.invalidDate||u.regionalOptions[""].invalidDate),this._correctAdd(h,this._add(h,d,m),d,m)},_add:function(h,d,m){if(this._validateLevel++,m==="d"||m==="w"){var p=h.toJD()+d*(m==="w"?this.daysInWeek():1),g=h.calendar().fromJD(p);return this._validateLevel--,[g.year(),g.month(),g.day()]}try{var y=h.year()+(m==="y"?d:0),v=h.monthOfYear()+(m==="m"?d:0);g=h.day(),m==="y"?(h.month()!==this.fromMonthOfYear(y,v)&&(v=this.newDate(y,h.month(),this.minDay).monthOfYear()),v=Math.min(v,this.monthsInYear(y)),g=Math.min(g,this.daysInMonth(y,this.fromMonthOfYear(y,v)))):m==="m"&&(function(_){for(;v<_.minMonth;)y--,v+=_.monthsInYear(y);for(var A=_.monthsInYear(y);v>A-1+_.minMonth;)y++,v-=A,A=_.monthsInYear(y)}(this),g=Math.min(g,this.daysInMonth(y,this.fromMonthOfYear(y,v))));var x=[y,this.fromMonthOfYear(y,v),g];return this._validateLevel--,x}catch(_){throw 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p=this._validate(h,d,m,u.local.invalidDate||u.regionalOptions[""].invalidDate),g=new Date(p.year(),p.month()-1,p.day());return g.setHours(0),g.setMinutes(0),g.setSeconds(0),g.setMilliseconds(0),g.setHours(g.getHours()>12?g.getHours()+2:0),g},fromJSDate:function(h){return this.newDate(h.getFullYear(),h.getMonth()+1,h.getDate())}});var u=o.exports=new a;u.cdate=l,u.baseCalendar=i,u.calendars.gregorian=s},{"object-assign":247}],347:[function(t,o,f){var r=t("object-assign"),a=t("./main");r(a.regionalOptions[""],{invalidArguments:"Invalid arguments",invalidFormat:"Cannot format a date from another calendar",missingNumberAt:"Missing number at position {0}",unknownNameAt:"Unknown name at position {0}",unexpectedLiteralAt:"Unexpected literal at position {0}",unexpectedText:"Additional text found at end"}),a.local=a.regionalOptions[""],r(a.cdate.prototype,{formatDate:function(l,c){return typeof 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u=i.dayNamesShort||this.local.dayNamesShort,h=i.dayNames||this.local.dayNames,d=i.parseMonth||this.local.parseMonth,m=i.monthNumbers||this.local.monthNumbers,p=i.monthNamesShort||this.local.monthNamesShort,g=i.monthNames||this.local.monthNames,y=-1,v=-1,x=-1,_=-1,A=-1,b=!1,k=!1,w=function(N,B){for(var W=1;F+W<l.length&&l.charAt(F+W)===N;)W++;return F+=W-1,Math.floor(W/(B||1))>1},M=function(N,B){var W=w(N,B),G=[2,3,W?4:2,W?4:2,10,11,20]["oyYJ@!".indexOf(N)+1],K=new RegExp("^-?\\d{1,"+G+"}"),te=c.substring(R).match(K);if(!te)throw(a.local.missingNumberAt||a.regionalOptions[""].missingNumberAt).replace(/\{0\}/,R);return R+=te[0].length,parseInt(te[0],10)},T=this,E=function(){if(typeof m=="function"){w("m");var N=m.call(T,c.substring(R));return R+=N.length,N}return M("m")},S=function(N,B,W,G){for(var K=w(N,G)?W:B,te=0;te<K.length;te++)if(c.substr(R,K[te].length).toLowerCase()===K[te].toLowerCase())return 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a.local.unexpectedText||a.regionalOptions[""].unexpectedText;if(v===-1?v=this.today().year():v<100&&b&&(v+=s===-1?1900:this.today().year()-this.today().year()%100-(v<=s?0:100)),typeof x=="string"&&(x=d.call(this,v,x)),A>-1){x=1,_=A;for(var O=this.daysInMonth(v,x);_>O;O=this.daysInMonth(v,x))x++,_-=O}return y>-1?this.fromJD(y):this.newDate(v,x,_)},determineDate:function(l,c,i,s,u){i&&typeof i!="object"&&(u=s,s=i,i=null),typeof s!="string"&&(u=s,s="");var h=this;return c=c?c.newDate():null,l=l==null?c:typeof l=="string"?function(d){try{return h.parseDate(s,d,u)}catch{}for(var m=((d=d.toLowerCase()).match(/^c/)&&i?i.newDate():null)||h.today(),p=/([+-]?[0-9]+)\s*(d|w|m|y)?/g,g=p.exec(d);g;)m.add(parseInt(g[1],10),g[2]||"d"),g=p.exec(d);return m}(l):typeof 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h,d,m=c(s,u),p=m.on,g=m.off.concat(m.explicitOff),y={},v=s._fullLayout.annotations;if(!(!p.length&&!g.length)){for(h=0;h<p.length;h++)(d=l(s.layout,"annotations",v[p[h]])).modifyItem("visible",!0),r.extendFlat(y,d.getUpdateObj());for(h=0;h<g.length;h++)(d=l(s.layout,"annotations",v[g[h]])).modifyItem("visible",!1),r.extendFlat(y,d.getUpdateObj());return a.call("update",s,{},y)}}}},{"../../lib":503,"../../plot_api/plot_template":543,"../../registry":638}],352:[function(t,o,f){var r=t("../../lib"),a=t("../color");o.exports=function(l,c,i,s){s("opacity");var u=s("bgcolor"),h=s("bordercolor"),d=a.opacity(h);s("borderpad");var m=s("borderwidth"),p=s("showarrow");if(s("text",p?" 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d,m,p=l._fullLayout.annotations,g=c._id.charAt(0),y=0;y<p.length;y++)d=p[y],m="annotations["+y+"].",d[g+"ref"]===c._id&&v(g),d["a"+g+"ref"]===c._id&&v("a"+g);function v(x){var _=d[x],A=null;A=u?a(_,c.range):Math.pow(10,_),r(A)||(A=null),s(m+x,A)}}},{"../../lib/to_log_range":531,"fast-isnumeric":190}],354:[function(t,o,f){var r=t("../../lib"),a=t("../../plots/cartesian/axes"),l=t("../../plots/array_container_defaults"),c=t("./common_defaults"),i=t("./attributes");function s(u,h,d){function m(P,L){return r.coerce(u,h,i,P,L)}var p=m("visible"),g=m("clicktoshow");if(p||g){c(u,h,d,m);for(var y=h.showarrow,v=["x","y"],x=[-10,-30],_={_fullLayout:d},A=0;A<2;A++){var b=v[A],k=a.coerceRef(u,h,_,b,"","paper");if(k!=="paper"&&a.getFromId(_,k)._annIndices.push(h._index),a.coercePosition(h,_,m,k,b,.5),y){var w="a"+b,M=a.coerceRef(u,h,_,w,"pixel",["pixel","paper"]);M!=="pixel"&&M!==k&&(M=h[w]="pixel");var T=M==="pixel"?x[A]:.4;a.coercePosition(h,_,m,M,w,T)}m(b+"anchor"),m(b+"shift")}if(r.noneOrAll(u,h,["x","y"]),y&&r.noneOrAll(u,h,["ax","ay"]),g){var E=m("xclick"),S=m("yclick");h._xclick=E===void 0?h.x:a.cleanPosition(E,_,h.xref),h._yclick=S===void 0?h.y:a.cleanPosition(S,_,h.yref)}}}o.exports=function(u,h){l(u,h,{name:"annotations",handleItemDefaults:s})}},{"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/cartesian/axes":554,"./attributes":349,"./common_defaults":352}],355:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../registry"),l=t("../../plots/plots"),c=t("../../lib"),i=c.strTranslate,s=t("../../plots/cartesian/axes"),u=t("../color"),h=t("../drawing"),d=t("../fx"),m=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),g=t("../dragelement"),y=t("../../plot_api/plot_template").arrayEditor,v=t("./draw_arrow_head");function x(b,k){var w=b._fullLayout.annotations[k]||{},M=s.getFromId(b,w.xref),T=s.getFromId(b,w.yref);M&&M.setScale(),T&&T.setScale(),A(b,w,k,!1,M,T)}function _(b,k,w,M,T){var E=T[w],S=T[w+"ref"],P=w.indexOf("y")!==-1,L=s.getRefType(S)==="domain",R=P?M.h:M.w;return b?L?E+(P?-k:k)/b._length:b.p2r(b.r2p(E)+k):E+(P?-k:k)/R}function A(b,k,w,M,T,E){var S,P,L=b._fullLayout,R=b._fullLayout._size,F=b._context.edits;M?(S="annotation-"+M,P=M+".annotations"):(S="annotation",P="annotations");var D=y(b.layout,P,k),O=D.modifyBase,N=D.modifyItem,B=D.getUpdateObj;L._infolayer.selectAll("."+S+'[data-index="'+w+'"]').remove();var W="clip"+L._uid+"_ann"+w;if(k._input&&k.visible!==!1){var G={x:{},y:{}},K=+k.textangle||0,te=L._infolayer.append("g").classed(S,!0).attr("data-index",String(w)).style("opacity",k.opacity),Y=te.append("g").classed("annotation-text-g",!0),J=F[k.showarrow?"annotationTail":"annotationPosition"],re=k.captureevents||F.annotationText||J,U=Y.append("g").style("pointer-events",re?"all":null).call(p,"pointer").on("click",function(){b._dragging=!1,b.emit("plotly_clickannotation",le(r.event))});k.hovertext&&U.on("mouseover",function(){var me=k.hoverlabel,_e=me.font,Ae=this.getBoundingClientRect(),ke=b.getBoundingClientRect();d.loneHover({x0:Ae.left-ke.left,x1:Ae.right-ke.left,y:(Ae.top+Ae.bottom)/2-ke.top,text:k.hovertext,color:me.bgcolor,borderColor:me.bordercolor,fontFamily:_e.family,fontSize:_e.size,fontColor:_e.color},{container:L._hoverlayer.node(),outerContainer:L._paper.node(),gd:b})}).on("mouseout",function(){d.loneUnhover(L._hoverlayer.node())});var V=k.borderwidth,H=k.borderpad,ne=V+H,q=U.append("rect").attr("class","bg").style("stroke-width",V+"px").call(u.stroke,k.bordercolor).call(u.fill,k.bgcolor),Q=k.width||k.height,ee=L._topclips.selectAll("#"+W).data(Q?[0]:[]);ee.enter().append("clipPath").classed("annclip",!0).attr("id",W).append("rect"),ee.exit().remove();var ie=k.font,ae=L._meta?c.templateString(k.text,L._meta):k.text,ue=U.append("text").classed("annotation-text",!0).text(ae);F.annotationText?ue.call(m.makeEditable,{delegate:U,gd:b}).call(ge).on("edit",function(me){k.text=me,this.call(ge),N("text",me),T&&T.autorange&&O(T._name+".autorange",!0),E&&E.autorange&&O(E._name+".autorange",!0),a.call("_guiRelayout",b,B())}):ue.call(ge)}else r.selectAll("#"+W).remove();function le(me){var _e={index:w,annotation:k._input,fullAnnotation:k,event:me};return M&&(_e.subplotId=M),_e}function ge(me){return me.call(h.font,ie).attr({"text-anchor":{left:"start",right:"end"}[k.align]||"middle"}),m.convertToTspans(me,b,fe),me}function fe(){var me=ue.selectAll("a");me.size()===1&&me.text()===ue.text()&&U.insert("a",":first-child").attr({"xlink:xlink:href":me.attr("xlink:href"),"xlink:xlink:show":me.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(q.node());var _e=U.select(".annotation-text-math-group"),Ae=!_e.empty(),ke=h.bBox((Ae?_e:ue).node()),Le=ke.width,de=ke.height,ve=k.width||Le,Me=k.height||de,we=Math.round(ve+2*ne),Ce=Math.round(Me+2*ne);function Fe(tt,bt){return bt==="auto"&&(bt=tt<1/3?"left":tt>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[bt]}for(var ze=!1,$e=["x","y"],Ke=0;Ke<$e.length;Ke++){var Re,Ve,We,Ye,nt,ft=$e[Ke],yt=k[ft+"ref"]||ft,Ot=k["a"+ft+"ref"],Tt={x:T,y:E}[ft],at=(K+(ft==="x"?0:-90))*Math.PI/180,et=we*Math.cos(at),Lt=Ce*Math.sin(at),Wt=Math.abs(et)+Math.abs(Lt),Jt=k[ft+"anchor"],Be=k[ft+"shift"]*(ft==="x"?1:-1),Ge=G[ft],kt=s.getRefType(yt);if(Tt&&kt!=="domain"){var dt=Tt.r2fraction(k[ft]);(dt<0||dt>1)&&(Ot===yt?((dt=Tt.r2fraction(k["a"+ft]))<0||dt>1)&&(ze=!0):ze=!0),Re=Tt._offset+Tt.r2p(k[ft]),Ye=.5}else{var Oe=kt==="domain";ft==="x"?(We=k[ft],Re=Oe?Tt._offset+Tt._length*We:Re=R.l+R.w*We):(We=1-k[ft],Re=Oe?Tt._offset+Tt._length*We:Re=R.t+R.h*We),Ye=k.showarrow?.5:We}if(k.showarrow){Ge.head=Re;var Ie=k["a"+ft];if(nt=et*Fe(.5,k.xanchor)-Lt*Fe(.5,k.yanchor),Ot===yt){var Te=s.getRefType(Ot);Te==="domain"?(ft==="y"&&(Ie=1-Ie),Ge.tail=Tt._offset+Tt._length*Ie):Te==="paper"?ft==="y"?(Ie=1-Ie,Ge.tail=R.t+R.h*Ie):Ge.tail=R.l+R.w*Ie:Ge.tail=Tt._offset+Tt.r2p(Ie),Ve=nt}else Ge.tail=Re+Ie,Ve=nt+Ie;Ge.text=Ge.tail+nt;var Pe=L[ft==="x"?"width":"height"];if(yt==="paper"&&(Ge.head=c.constrain(Ge.head,1,Pe-1)),Ot==="pixel"){var qe=-Math.max(Ge.tail-3,Ge.text),rt=Math.min(Ge.tail+3,Ge.text)-Pe;qe>0?(Ge.tail+=qe,Ge.text+=qe):rt>0&&(Ge.tail-=rt,Ge.text-=rt)}Ge.tail+=Be,Ge.head+=Be}else Ve=nt=Wt*Fe(Ye,Jt),Ge.text=Re+nt;Ge.text+=Be,nt+=Be,Ve+=Be,k["_"+ft+"padplus"]=Wt/2+Ve,k["_"+ft+"padminus"]=Wt/2-Ve,k["_"+ft+"size"]=Wt,k["_"+ft+"shift"]=nt}if(ze)U.remove();else{var lt=0,ot=0;if(k.align!=="left"&&(lt=(ve-Le)*(k.align==="center"?.5:1)),k.valign!=="top"&&(ot=(Me-de)*(k.valign==="middle"?.5:1)),Ae)_e.select("svg").attr({x:ne+lt-1,y:ne+ot}).call(h.setClipUrl,Q?W:null,b);else{var At=ne+ot-ke.top,wt=ne+lt-ke.left;ue.call(m.positionText,wt,At).call(h.setClipUrl,Q?W:null,b)}ee.select("rect").call(h.setRect,ne,ne,ve,Me),q.call(h.setRect,V/2,V/2,we-V,Ce-V),U.call(h.setTranslate,Math.round(G.x.text-we/2),Math.round(G.y.text-Ce/2)),Y.attr({transform:"rotate("+K+","+G.x.text+","+G.y.text+")"});var $t,Ut=function(tt,bt){te.selectAll(".annotation-arrow-g").remove();var Ft=G.x.head,Et=G.y.head,Pt=G.x.tail+tt,De=G.y.tail+bt,Je=G.x.text+tt,st=G.y.text+bt,St=c.rotationXYMatrix(K,Je,st),It=c.apply2DTransform(St),Zt=c.apply2DTransform2(St),Kt=+q.attr("width"),qt=+q.attr("height"),mn=Je-.5*Kt,Fn=mn+Kt,pn=st-.5*qt,tn=pn+qt,nn=[[mn,pn,mn,tn],[mn,tn,Fn,tn],[Fn,tn,Fn,pn],[Fn,pn,mn,pn]].map(Zt);if(!nn.reduce(function(Dn,lr){return Dn^!!c.segmentsIntersect(Ft,Et,Ft+1e6,Et+1e6,lr[0],lr[1],lr[2],lr[3])},!1)){nn.forEach(function(Dn){var lr=c.segmentsIntersect(Pt,De,Ft,Et,Dn[0],Dn[1],Dn[2],Dn[3]);lr&&(Pt=lr.x,De=lr.y)});var sn=k.arrowwidth,gn=k.arrowcolor,bn=k.arrowside,In=te.append("g").style({opacity:u.opacity(gn)}).classed("annotation-arrow-g",!0),qn=In.append("path").attr("d","M"+Pt+","+De+"L"+Ft+","+Et).style("stroke-width",sn+"px").call(u.stroke,u.rgb(gn));if(v(qn,bn,k),F.annotationPosition&&qn.node().parentNode&&!M){var Wn=Ft,ar=Et;if(k.standoff){var Dr=Math.sqrt(Math.pow(Ft-Pt,2)+Math.pow(Et-De,2));Wn+=k.standoff*(Pt-Ft)/Dr,ar+=k.standoff*(De-Et)/Dr}var yr,Sr,Kn=In.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(Pt-Wn)+","+(De-ar),transform:i(Wn,ar)}).style("stroke-width",sn+6+"px").call(u.stroke,"rgba(0,0,0,0)").call(u.fill,"rgba(0,0,0,0)");g.init({element:Kn.node(),gd:b,prepFn:function(){var Dn=h.getTranslate(U);yr=Dn.x,Sr=Dn.y,T&&T.autorange&&O(T._name+".autorange",!0),E&&E.autorange&&O(E._name+".autorange",!0)},moveFn:function(Dn,lr){var Yr=It(yr,Sr),Mn=Yr[0]+Dn,rr=Yr[1]+lr;U.call(h.setTranslate,Mn,rr),N("x",_(T,Dn,"x",R,k)),N("y",_(E,lr,"y",R,k)),k.axref===k.xref&&N("ax",_(T,Dn,"ax",R,k)),k.ayref===k.yref&&N("ay",_(E,lr,"ay",R,k)),In.attr("transform",i(Dn,lr)),Y.attr({transform:"rotate("+K+","+Mn+","+rr+")"})},doneFn:function(){a.call("_guiRelayout",b,B());var Dn=document.querySelector(".js-notes-box-panel");Dn&&Dn.redraw(Dn.selectedObj)}})}}};k.showarrow&&Ut(0,0),J&&g.init({element:U.node(),gd:b,prepFn:function(){$t=Y.attr("transform")},moveFn:function(tt,bt){var Ft="pointer";if(k.showarrow)k.axref===k.xref?N("ax",_(T,tt,"ax",R,k)):N("ax",k.ax+tt),k.ayref===k.yref?N("ay",_(E,bt,"ay",R.w,k)):N("ay",k.ay+bt),Ut(tt,bt);else{if(M)return;var Et,Pt;if(T)Et=_(T,tt,"x",R,k);else{var De=k._xsize/R.w,Je=k.x+(k._xshift-k.xshift)/R.w-De/2;Et=g.align(Je+tt/R.w,De,0,1,k.xanchor)}if(E)Pt=_(E,bt,"y",R,k);else{var st=k._ysize/R.h,St=k.y-(k._yshift+k.yshift)/R.h-st/2;Pt=g.align(St-bt/R.h,st,0,1,k.yanchor)}N("x",Et),N("y",Pt),T&&E||(Ft=g.getCursor(T?.5:Et,E?.5:Pt,k.xanchor,k.yanchor))}Y.attr({transform:i(tt,bt)+$t}),p(U,Ft)},clickFn:function(tt,bt){k.captureevents&&b.emit("plotly_clickannotation",le(bt))},doneFn:function(){p(U),a.call("_guiRelayout",b,B());var tt=document.querySelector(".js-notes-box-panel");tt&&tt.redraw(tt.selectedObj)}})}}}o.exports={draw:function(b){var k=b._fullLayout;k._infolayer.selectAll(".annotation").remove();for(var w=0;w<k.annotations.length;w++)k.annotations[w].visible&&x(b,w);return l.previousPromises(b)},drawOne:x,drawRaw:A}},{"../../lib":503,"../../lib/setcursor":524,"../../lib/svg_text_utils":529,"../../plot_api/plot_template":543,"../../plots/cartesian/axes":554,"../../plots/plots":619,"../../registry":638,"../color":366,"../dragelement":385,"../drawing":388,"../fx":406,"./draw_arrow_head":356,"@plotly/d3":58}],356:[function(t,o,f){var r=t("@plotly/d3"),a=t("../color"),l=t("./arrow_paths"),c=t("../../lib"),i=c.strScale,s=c.strRotate,u=c.strTranslate;o.exports=function(h,d,m){var p,g,y,v,x=h.node(),_=l[m.arrowhead||0],A=l[m.startarrowhead||0],b=(m.arrowwidth||1)*(m.arrowsize||1),k=(m.arrowwidth||1)*(m.startarrowsize||1),w=d.indexOf("start")>=0,M=d.indexOf("end")>=0,T=_.backoff*b+m.standoff,E=A.backoff*k+m.startstandoff;if(x.nodeName==="line"){p={x:+h.attr("x1"),y:+h.attr("y1")},g={x:+h.attr("x2"),y:+h.attr("y2")};var S=p.x-g.x,P=p.y-g.y;if(v=(y=Math.atan2(P,S))+Math.PI,T&&E&&T+E>Math.sqrt(S*S+P*P))return void te();if(T){if(T*T>S*S+P*P)return void te();var L=T*Math.cos(y),R=T*Math.sin(y);g.x+=L,g.y+=R,h.attr({x2:g.x,y2:g.y})}if(E){if(E*E>S*S+P*P)return void te();var F=E*Math.cos(y),D=E*Math.sin(y);p.x-=F,p.y-=D,h.attr({x1:p.x,y1:p.y})}}else if(x.nodeName==="path"){var O=x.getTotalLength(),N="";if(O<T+E)return void te();var B=x.getPointAtLength(0),W=x.getPointAtLength(.1);y=Math.atan2(B.y-W.y,B.x-W.x),p=x.getPointAtLength(Math.min(E,O)),N="0px,"+E+"px,";var G=x.getPointAtLength(O),K=x.getPointAtLength(O-.1);v=Math.atan2(G.y-K.y,G.x-K.x),g=x.getPointAtLength(Math.max(0,O-T)),N+=O-(N?E+T:T)+"px,"+O+"px",h.style("stroke-dasharray",N)}function te(){h.style("stroke-dasharray","0px,100px")}function Y(J,re,U,V){J.path&&(J.noRotate&&(U=0),r.select(x.parentNode).append("path").attr({class:h.attr("class"),d:J.path,transform:u(re.x,re.y)+s(180*U/Math.PI)+i(V)}).style({fill:a.rgb(m.arrowcolor),"stroke-width":0}))}w&&Y(A,p,y,k),M&&Y(_,g,v,b)}},{"../../lib":503,"../color":366,"./arrow_paths":348,"@plotly/d3":58}],357:[function(t,o,f){var r=t("./draw"),a=t("./click");o.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:r.draw,drawOne:r.drawOne,drawRaw:r.drawRaw,hasClickToShow:a.hasClickToShow,onClick:a.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":567,"./attributes":349,"./calc_autorange":350,"./click":351,"./convert_coords":353,"./defaults":354,"./draw":355}],358:[function(t,o,f){var r=t("../annotations/attributes"),a=t("../../plot_api/edit_types").overrideAll,l=t("../../plot_api/plot_template").templatedArray;o.exports=a(l("annotation",{visible:r.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:r.xanchor,xshift:r.xshift,yanchor:r.yanchor,yshift:r.yshift,text:r.text,textangle:r.textangle,font:r.font,width:r.width,height:r.height,opacity:r.opacity,align:r.align,valign:r.valign,bgcolor:r.bgcolor,bordercolor:r.bordercolor,borderpad:r.borderpad,borderwidth:r.borderwidth,showarrow:r.showarrow,arrowcolor:r.arrowcolor,arrowhead:r.arrowhead,startarrowhead:r.startarrowhead,arrowside:r.arrowside,arrowsize:r.arrowsize,startarrowsize:r.startarrowsize,arrowwidth:r.arrowwidth,standoff:r.standoff,startstandoff:r.startstandoff,hovertext:r.hovertext,hoverlabel:r.hoverlabel,captureevents:r.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../annotations/attributes":349}],359:[function(t,o,f){var r=t("../../lib"),a=t("../../plots/cartesian/axes");function l(c,i){var s=i.fullSceneLayout.domain,u=i.fullLayout._size,h={pdata:null,type:"linear",autorange:!1,range:[-1/0,1/0]};c._xa={},r.extendFlat(c._xa,h),a.setConvert(c._xa),c._xa._offset=u.l+s.x[0]*u.w,c._xa.l2p=function(){return .5*(1+c._pdata[0]/c._pdata[3])*u.w*(s.x[1]-s.x[0])},c._ya={},r.extendFlat(c._ya,h),a.setConvert(c._ya),c._ya._offset=u.t+(1-s.y[1])*u.h,c._ya.l2p=function(){return .5*(1-c._pdata[1]/c._pdata[3])*u.h*(s.y[1]-s.y[0])}}o.exports=function(c){for(var i=c.fullSceneLayout.annotations,s=0;s<i.length;s++)l(i[s],c);c.fullLayout._infolayer.selectAll(".annotation-"+c.id).remove()}},{"../../lib":503,"../../plots/cartesian/axes":554}],360:[function(t,o,f){var r=t("../../lib"),a=t("../../plots/cartesian/axes"),l=t("../../plots/array_container_defaults"),c=t("../annotations/common_defaults"),i=t("./attributes");function s(u,h,d,m){function p(y,v){return r.coerce(u,h,i,y,v)}function g(y){var v=y+"axis",x={_fullLayout:{}};return x._fullLayout[v]=d[v],a.coercePosition(h,x,p,y,y,.5)}p("visible")&&(c(u,h,m.fullLayout,p),g("x"),g("y"),g("z"),r.noneOrAll(u,h,["x","y","z"]),h.xref="x",h.yref="y",h.zref="z",p("xanchor"),p("yanchor"),p("xshift"),p("yshift"),h.showarrow&&(h.axref="pixel",h.ayref="pixel",p("ax",-10),p("ay",-30),r.noneOrAll(u,h,["ax","ay"])))}o.exports=function(u,h,d){l(u,h,{name:"annotations",handleItemDefaults:s,fullLayout:d.fullLayout})}},{"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/cartesian/axes":554,"../annotations/common_defaults":352,"./attributes":358}],361:[function(t,o,f){var r=t("../annotations/draw").drawRaw,a=t("../../plots/gl3d/project"),l=["x","y","z"];o.exports=function(c){for(var i=c.fullSceneLayout,s=c.dataScale,u=i.annotations,h=0;h<u.length;h++){for(var d=u[h],m=!1,p=0;p<3;p++){var 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r=t("./calendars"),a=t("../../lib"),l=t("../../constants/numerical"),c=l.EPOCHJD,i=l.ONEDAY,s={valType:"enumerated",values:a.sortObjectKeys(r.calendars),editType:"calc",dflt:"gregorian"},u=function(A,b,k,w){var M={};return M[k]=s,a.coerce(A,b,M,k,w)},h={d:{0:"dd","-":"d"},e:{0:"d","-":"d"},a:{0:"D","-":"D"},A:{0:"DD","-":"DD"},j:{0:"oo","-":"o"},W:{0:"ww","-":"w"},m:{0:"mm","-":"m"},b:{0:"M","-":"M"},B:{0:"MM","-":"MM"},y:{0:"yy","-":"yy"},Y:{0:"yyyy","-":"yyyy"},U:"##",w:"##",c:{0:"D M d %X yyyy","-":"D M d %X yyyy"},x:{0:"mm/dd/yyyy","-":"mm/dd/yyyy"}},d={};function m(A){var b=d[A];return b||(b=d[A]=r.instance(A))}function p(A){return a.extendFlat({},s,{description:A})}function g(A){return"Sets the calendar system to use with `"+A+"` date data."}var y={xcalendar:p(g("x"))},v=a.extendFlat({},y,{ycalendar:p(g("y"))}),x=a.extendFlat({},v,{zcalendar:p(g("z"))}),_=p(["Sets the calendar system to use for `range` and `tick0`","if this is a date axis. 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r=t("../../lib"),a=t("../../plot_api/plot_template"),l=t("../../plots/cartesian/tick_value_defaults"),c=t("../../plots/cartesian/tick_mark_defaults"),i=t("../../plots/cartesian/tick_label_defaults"),s=t("../../plots/cartesian/prefix_suffix_defaults"),u=t("./attributes");o.exports=function(h,d,m){var p=a.newContainer(d,"colorbar"),g=h.colorbar||{};function y(P,L){return r.coerce(g,p,u,P,L)}var v=m.margin||{t:0,b:0,l:0,r:0},x=m.width-v.l-v.r,_=m.height-v.t-v.b,A=y("orientation")==="v",b=y("thicknessmode");y("thickness",b==="fraction"?30/(A?x:_):30);var k=y("lenmode");y("len",k==="fraction"?1:A?_:x),y("x",A?1.02:.5),y("xanchor",A?"left":"center"),y("xpad"),y("y",A?.5:1.02),y("yanchor",A?"middle":"bottom"),y("ypad"),r.noneOrAll(g,p,["x","y"]),y("outlinecolor"),y("outlinewidth"),y("bordercolor"),y("borderwidth"),y("bgcolor");var w=r.coerce(g,p,{ticklabelposition:{valType:"enumerated",dflt:"outside",values:A?["outside","inside","outside top","inside top","outside bottom","inside 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r=t("@plotly/d3"),a=t("tinycolor2"),l=t("../../plots/plots"),c=t("../../registry"),i=t("../../plots/cartesian/axes"),s=t("../dragelement"),u=t("../../lib"),h=u.strTranslate,d=t("../../lib/extend").extendFlat,m=t("../../lib/setcursor"),p=t("../drawing"),g=t("../color"),y=t("../titles"),v=t("../../lib/svg_text_utils"),x=t("../colorscale/helpers").flipScale,_=t("../../plots/cartesian/axis_defaults"),A=t("../../plots/cartesian/position_defaults"),b=t("../../plots/cartesian/layout_attributes"),k=t("../../constants/alignment"),w=k.LINE_SPACING,M=k.FROM_TL,T=k.FROM_BR,E=t("./constants").cn;o.exports={draw:function(S){var P=S._fullLayout._infolayer.selectAll("g."+E.colorbar).data(function(L){var R,F,D,O,N=L._fullLayout,B=L.calcdata,W=[];function G(Q){return d(Q,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function K(){typeof 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Oe={propContainer:We,propName:O._propPrefix+"title",traceIndex:O._traceIndex,_meta:O._meta,placeholder:ee._dfltTitle.colorbar,containerGroup:D.select("."+E.cbtitle)},Ie=kt.charAt(0)==="h"?kt.substr(1):"h"+kt;D.selectAll("."+Ie+",."+Ie+"-math-group").remove(),y.draw(N,kt,d(Oe,dt||{}))}return u.syncOrAsync([l.previousPromises,function(){var kt,dt;(B&&Ye||!B&&!Ye)&&(ge==="top"&&(kt=H+ie.l+ie.w*q,dt=ne+ie.t+ie.h*(1-Re-Ce)+3+.75*Lt),ge==="bottom"&&(kt=H+ie.l+ie.w*q,dt=ne+ie.t+ie.h*(1-Re)-3-.25*Lt),ge==="right"&&(dt=ne+ie.t+ie.h*Q+3+.75*Lt,kt=H+ie.l+ie.w*Re),Ge(We._id+"title",{attributes:{x:kt,y:dt,"text-anchor":B?"start":"middle"}}))},function(){if(!B&&!Ye||B&&Ye){var 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rt=D.select("."+E.cbfills).selectAll("rect."+E.cbfill).attr("style","").data(Le);rt.enter().append("rect").classed(E.cbfill,!0).style("stroke","none"),rt.exit().remove();var lt=fe.map(We.c2p).map(Math.round).sort(function(Ut,tt){return Ut-tt});rt.each(function(Ut,tt){var bt=[tt===0?fe[0]:(Le[tt]+Le[tt-1])/2,tt===Le.length-1?fe[1]:(Le[tt]+Le[tt+1])/2].map(We.c2p).map(Math.round);B&&(bt[1]=u.constrain(bt[1]+(bt[1]>bt[0])?1:-1,lt[0],lt[1]));var Ft=r.select(this).attr(B?"x":"y",Fe).attr(B?"y":"x",r.min(bt)).attr(B?"width":"height",Math.max(ve,2)).attr(B?"height":"width",Math.max(r.max(bt)-r.min(bt),2));if(O._fillgradient)p.gradient(Ft,N,O._id,B?"vertical":"horizontalreversed",O._fillgradient,"fill");else{var Et=_e(Ut).replace("e-","");Ft.attr("fill",a(Et).toHexString())}});var ot=D.select("."+E.cblines).selectAll("path."+E.cbline).data(ue.color&&ue.width?de:[]);ot.enter().append("path").classed(E.cbline,!0),ot.exit().remove(),ot.each(function(Ut){var 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lt=2*(B?H:ne)+dt+J+Y/2,ot=0;!B&&le.text&&V==="bottom"&&Q<=0&&(lt+=ot=lt/2,Pe+=ot),ee._hColorbarMoveTitle=ot,ee._hColorbarMoveCBTitle=Pe;var At=J+Y;D.select("."+E.cbbg).attr("x",(B?Fe:Ve)-At/2-(B?H:0)).attr("y",(B?Ve:Fe)-(B?we:ne+Pe-ot)).attr(B?"width":"height",Math.max(lt-ot,2)).attr(B?"height":"width",Math.max(we+At,2)).call(g.fill,re).call(g.stroke,O.bordercolor).style("stroke-width",J);var wt=Te?Math.max(Oe-10,0):0;if(D.selectAll("."+E.cboutline).attr("x",(B?Fe:Ve+H)+wt).attr("y",(B?Ve+ne-we:Fe)+(Ie?Jt:0)).attr(B?"width":"height",Math.max(ve,2)).attr(B?"height":"width",Math.max(we-(B?2*ne+Jt:2*H+wt),2)).call(g.stroke,O.outlinecolor).style({fill:"none","stroke-width":Y}),D.attr("transform",h(ie.l-(B?ze*lt:0),ie.t-(B?0:(1-$e)*lt-Pe))),!B&&(J||a(re).getAlpha()&&!a.equals(ee.paper_bgcolor,re))){var $t=Wt.selectAll("text"),Ut=$t[0].length,tt=D.select("."+E.cbbg).node(),bt=p.bBox(tt),Ft=p.getTranslate(D);$t.each(function(It,Zt){var Kt=Ut-1;if(Zt===0||Zt===Kt){var qt,mn=p.bBox(this),Fn=p.getTranslate(this);if(Zt===Kt){var pn=mn.right+Fn.x;(qt=bt.right+Ft.x+Ve-J-2+q-pn)>0&&(qt=0)}else if(Zt===0){var tn=mn.left+Fn.x;(qt=bt.left+Ft.x+Ve+J+2-tn)<0&&(qt=0)}qt&&(Ut<3?this.setAttribute("transform","translate("+qt+",0) "+this.getAttribute("transform")):this.setAttribute("visibility","hidden"))}})}var Et={},Pt=M[U],De=T[U],Je=M[V],st=T[V],St=lt-ve;B?(G==="pixels"?(Et.y=Q,Et.t=we*Je,Et.b=we*st):(Et.t=Et.b=0,Et.yt=Q+W*Je,Et.yb=Q-W*st),te==="pixels"?(Et.x=q,Et.l=lt*Pt,Et.r=lt*De):(Et.l=St*Pt,Et.r=St*De,Et.xl=q-K*Pt,Et.xr=q+K*De)):(G==="pixels"?(Et.x=q,Et.l=we*Pt,Et.r=we*De):(Et.l=Et.r=0,Et.xl=q+W*Pt,Et.xr=q-W*De),te==="pixels"?(Et.y=1-Q,Et.t=lt*Je,Et.b=lt*st):(Et.t=St*Je,Et.b=St*st,Et.yt=Q-K*Je,Et.yb=Q+K*st)),l.autoMargin(N,O._id,Et)}],N)}(R,L,S);F&&F.then&&(S._promises||[]).push(F),S._context.edits.colorbarPosition&&function(D,O,N){var 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r=t("fast-isnumeric"),a=t("../../lib"),l=t("./helpers").extractOpts;o.exports=function(c,i,s){var u,h=c._fullLayout,d=s.vals,m=s.containerStr,p=m?a.nestedProperty(i,m).get():i,g=l(p),y=g.auto!==!1,v=g.min,x=g.max,_=g.mid,A=function(){return a.aggNums(Math.min,null,d)},b=function(){return a.aggNums(Math.max,null,d)};v===void 0?v=A():y&&(v=p._colorAx&&r(v)?Math.min(v,A()):A()),x===void 0?x=b():y&&(x=p._colorAx&&r(x)?Math.max(x,b()):b()),y&&_!==void 0&&(x-_>_-v?v=_-(x-_):x-_<_-v&&(x=_+(_-v))),v===x&&(v-=.5,x+=.5),g._sync("min",v),g._sync("max",x),g.autocolorscale&&(u=v*x<0?h.colorscale.diverging:v>=0?h.colorscale.sequential:h.colorscale.sequentialminus,g._sync("colorscale",u))}},{"../../lib":503,"./helpers":377,"fast-isnumeric":190}],375:[function(t,o,f){var r=t("../../lib"),a=t("./helpers").hasColorscale,l=t("./helpers").extractOpts;o.exports=function(c,i){function s(y,v){var x=y["_"+v];x!==void 0&&(y[v]=x)}function u(y,v){var 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M=p._colorAxes||{},T=g(v+"coloraxis");if(T){var E=s(m,"contour")&&a.nestedProperty(m,"contours.coloring").get()||"heatmap",S=M[T];return void(S?(S[2].push(w),S[0]!==E&&(S[0]=!1,a.warn(["Ignoring coloraxis:",T,"setting","as it is linked to incompatible colorscales."].join(" ")))):M[T]=[E,m,[w]])}}var P=A[x+"min"],L=A[x+"max"],R=r(P)&&r(L)&&P<L;g(v+x+"auto",!R)?g(v+x+"mid"):(g(v+x+"min"),g(v+x+"max"));var F,D,O=A.colorscale,N=k.colorscale;O!==void 0&&(F=!i(O)),N!==void 0&&(F=!i(N)),g(v+"autocolorscale",F),g(v+"colorscale"),g(v+"reversescale"),v!=="marker.line."&&(v&&_&&(D=l(A)),g(v+"showscale",D)&&(v&&k&&(b._template=k),c(A,b,p)))}},{"../../lib":503,"../../registry":638,"../colorbar/defaults":369,"../colorbar/has_colorbar":371,"./scales":381,"fast-isnumeric":190}],377:[function(t,o,f){var 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i=c[0].trace,s=i.error_y||{},u=i.error_x||{},h=r.select(this);h.selectAll("path.yerror").style("stroke-width",s.thickness+"px").call(a.stroke,s.color),u.copy_ystyle&&(u=s),h.selectAll("path.xerror").style("stroke-width",u.thickness+"px").call(a.stroke,u.color)})}},{"../color":366,"@plotly/d3":58}],397:[function(t,o,f){var r=t("../../plots/font_attributes"),a=t("./layout_attributes").hoverlabel,l=t("../../lib/extend").extendFlat;o.exports={hoverlabel:{bgcolor:l({},a.bgcolor,{arrayOk:!0}),bordercolor:l({},a.bordercolor,{arrayOk:!0}),font:r({arrayOk:!0,editType:"none"}),align:l({},a.align,{arrayOk:!0}),namelength:l({},a.namelength,{arrayOk:!0}),editType:"none"}}},{"../../lib/extend":493,"../../plots/font_attributes":585,"./layout_attributes":407}],398:[function(t,o,f){var r=t("../../lib"),a=t("../../registry");function l(c,i,s,u){u=u||r.identity,Array.isArray(c)&&(i[0][s]=u(c))}o.exports=function(c){var i=c.calcdata,s=c._fullLayout;function u(g){return function(y){return r.coerceHoverinfo({hoverinfo:y},{_module:g._module},s)}}for(var h=0;h<i.length;h++){var d=i[h],m=d[0].trace;if(!a.traceIs(m,"pie-like")){var p=a.traceIs(m,"2dMap")?l:r.fillArray;p(m.hoverinfo,d,"hi",u(m)),m.hovertemplate&&p(m.hovertemplate,d,"ht"),m.hoverlabel&&(p(m.hoverlabel.bgcolor,d,"hbg"),p(m.hoverlabel.bordercolor,d,"hbc"),p(m.hoverlabel.font.size,d,"hts"),p(m.hoverlabel.font.color,d,"htc"),p(m.hoverlabel.font.family,d,"htf"),p(m.hoverlabel.namelength,d,"hnl"),p(m.hoverlabel.align,d,"hta"))}}}},{"../../lib":503,"../../registry":638}],399:[function(t,o,f){var r=t("../../registry"),a=t("./hover").hover;o.exports=function(l,c,i){var s=r.getComponentMethod("annotations","onClick")(l,l._hoverdata);function u(){l.emit("plotly_click",{points:l._hoverdata,event:c})}i!==void 0&&a(l,c,i,!0),l._hoverdata&&c&&c.target&&(s&&s.then?s.then(u):u(),c.stopImmediatePropagation&&c.stopImmediatePropagation())}},{"../../registry":638,"./hover":403}],400:[function(t,o,f){o.exports={YANGLE:60,HOVERARROWSIZE:6,HOVERTEXTPAD:3,HOVERFONTSIZE:13,HOVERFONT:"Arial, sans-serif",HOVERMINTIME:50,HOVERID:"-hover"}},{}],401:[function(t,o,f){var r=t("../../lib"),a=t("./attributes"),l=t("./hoverlabel_defaults");o.exports=function(c,i,s,u){var h=r.extendFlat({},u.hoverlabel);i.hovertemplate&&(h.namelength=-1),l(c,i,function(d,m){return r.coerce(c,i,a,d,m)},h)}},{"../../lib":503,"./attributes":397,"./hoverlabel_defaults":404}],402:[function(t,o,f){var r=t("../../lib");f.getSubplot=function(u){return u.subplot||u.xaxis+u.yaxis||u.geo},f.isTraceInSubplots=function(u,h){if(u.type==="splom"){for(var d=u.xaxes||[],m=u.yaxes||[],p=0;p<d.length;p++)for(var g=0;g<m.length;g++)if(h.indexOf(d[p]+m[g])!==-1)return!0;return!1}return h.indexOf(f.getSubplot(u))!==-1},f.flat=function(u,h){for(var d=new Array(u.length),m=0;m<u.length;m++)d[m]=h;return d},f.p2c=function(u,h){for(var d=new Array(u.length),m=0;m<u.length;m++)d[m]=u[m].p2c(h);return d},f.getDistanceFunction=function(u,h,d,m){return u==="closest"?m||f.quadrature(h,d):u.charAt(0)==="x"?h:d},f.getClosest=function(u,h,d){if(d.index!==!1)d.index>=0&&d.index<u.length?d.distance=0:d.index=!1;else for(var m=0;m<u.length;m++){var p=h(u[m]);p<=d.distance&&(d.index=m,d.distance=p)}return d},f.inbox=function(u,h,d){return u*h<0||u===0?d:1/0},f.quadrature=function(u,h){return function(d){var m=u(d),p=h(d);return Math.sqrt(m*m+p*p)}},f.makeEventData=function(u,h,d){var m="index"in u?u.index:u.pointNumber,p={data:h._input,fullData:h,curveNumber:h.index,pointNumber:m};if(h._indexToPoints){var g=h._indexToPoints[m];g.length===1?p.pointIndex=g[0]:p.pointIndices=g}else p.pointIndex=m;return h._module.eventData?p=h._module.eventData(p,u,h,d,m):("xVal"in u?p.x=u.xVal:"x"in u&&(p.x=u.x),"yVal"in u?p.y=u.yVal:"y"in 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Re,Ve,We,Ye,nt,ft,yt,Ot,Tt,at,et,Lt,Wt,Jt=[],Be=[],Ge={hLinePoint:null,vLinePoint:null},kt=!1;if(Array.isArray(ie))for(ze="array",We=0;We<ie.length;We++)(nt=ee.calcdata[ie[We].curveNumber||0])&&(ft=nt[0].trace,nt[0].trace.hoverinfo!=="skip"&&(Be.push(nt),ft.orientation==="h"&&(kt=!0)));else{for(Ye=0;Ye<ee.calcdata.length;Ye++)nt=ee.calcdata[Ye],(ft=nt[0].trace).hoverinfo!=="skip"&&x.isTraceInSubplots(ft,ge)&&(Be.push(nt),ft.orientation==="h"&&(kt=!0));var dt,Oe;if(!le)dt="xpx"in ie?ie.xpx:de[0]._length/2,Oe="ypx"in ie?ie.ypx:ve[0]._length/2;else{if(u.triggerHandler(ee,"plotly_beforehover",ie)===!1)return;var Ie=le.getBoundingClientRect();dt=ie.clientX-Ie.left,Oe=ie.clientY-Ie.top,fe._calcInverseTransform(ee);var Te=c.apply3DTransform(fe._invTransform)(dt,Oe);if(dt=Te[0],Oe=Te[1],dt<0||dt>de[0]._length||Oe<0||Oe>ve[0]._length)return g.unhoverRaw(ee,ie)}if(ie.pointerX=dt+de[0]._offset,ie.pointerY=Oe+ve[0]._offset,Re="xval"in ie?x.flat(ge,ie.xval):x.p2c(de,dt),Ve="yval"in 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nr&&(et=nr.yval,Tt=Tt?"closest":"y"))}else Mn!==void 0&&rr!==void 0?(at=Mn,et=rr):(at=Re[Ot],et=Ve[Ot]);if($e!==0)if(ft._module&&ft._module.hoverPoints){var Bn=ft._module.hoverPoints(Lt,at,et,Tt,{finiteRange:!0,hoverLayer:fe._hoverlayer});if(Bn)for(var Nr,Gr=0;Gr<Bn.length;Gr++)Nr=Bn[Gr],a(Nr.x0)&&a(Nr.y0)&&Jt.push(W(Nr,ze))}else c.log("Unrecognized trace type in hover:",ft);if(ze==="closest"&&Jt.length>Wt&&(Jt.splice(0,Wt),Pe=Jt[0].distance),Ae&&Ke!==0&&Jt.length===0){Lt.distance=Ke,Lt.index=!1;var pr=ft._module.hoverPoints(Lt,at,et,"closest",{hoverLayer:fe._hoverlayer});if(pr&&(pr=pr.filter(function(fn){return fn.spikeDistance<=Ke})),pr&&pr.length){var qr,_i=pr.filter(function(fn){return fn.xa.showspikes&&fn.xa.spikesnap!=="hovered data"});if(_i.length){var cn=_i[0];a(cn.x0)&&a(cn.y0)&&(qr=lt(cn),(!Ge.vLinePoint||Ge.vLinePoint.spikeDistance>qr.spikeDistance)&&(Ge.vLinePoint=qr))}var jn=pr.filter(function(fn){return fn.ya.showspikes&&fn.ya.spikesnap!=="hovered 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qr=Mn[pr];v.traceIs(qr.trace,"bar-like")||v.traceIs(qr.trace,"box-violin")?Gr.push(qr):qr.trace[nr+"period"]?Nr.push(qr):Bn.push(qr)}return Bn.concat(Nr).concat(Gr)}(Jt,ze)};$t();var Ut=ze.charAt(0),tt=(Ut==="x"||Ut==="y")&&Jt[0]&&R[Jt[0].trace.type];if(Ae&&Ke!==0&&Jt.length!==0){var bt=rt(Jt.filter(function(Mn){return Mn.ya.showspikes}),Ke,tt);Ge.hLinePoint=lt(bt);var Ft=rt(Jt.filter(function(Mn){return Mn.xa.showspikes}),Ke,tt);Ge.vLinePoint=lt(Ft)}if(Jt.length===0){var Et=g.unhoverRaw(ee,ie);return!Ae||Ge.hLinePoint===null&&Ge.vLinePoint===null||K(At)&&G(ee,Ge,ot),Et}if(Ae&&K(At)&&G(ee,Ge,ot),x.isXYhover(Tt)&&Jt[0].length!==0&&Jt[0].trace.type!=="splom"){var Pt=Jt[0],De=(Jt=L[Pt.trace.type]?Jt.filter(function(Mn){return Mn.trace.index===Pt.trace.index}):[Pt]).length,Je=Y("x",Pt,fe),st=Y("y",Pt,fe);qe(Je,st);var St,It=[],Zt={},Kt=0,qt=function(Mn){var rr=L[Mn.trace.type]?F(Mn):Mn.trace.index;if(Zt[rr]){var nr=Zt[rr]-1,Bn=It[nr];nr>0&&Math.abs(Mn.distance)<Math.abs(Bn.distance)&&(It[nr]=Mn)}else Kt++,Zt[rr]=Kt,It.push(Mn)};for(St=0;St<De;St++)qt(Jt[St]);for(St=Jt.length-1;St>De-1;St--)qt(Jt[St]);Jt=It,$t()}var mn=ee._hoverdata,Fn=[],pn=J(ee),tn=re(ee);for(We=0;We<Jt.length;We++){var nn=Jt[We],sn=x.makeEventData(nn,nn.trace,nn.cd);if(nn.hovertemplate!==!1){var gn=!1;nn.cd[nn.index]&&nn.cd[nn.index].ht&&(gn=nn.cd[nn.index].ht),nn.hovertemplate=gn||nn.trace.hovertemplate||!1}if(nn.xa&&nn.ya){var bn=nn.x0+nn.xa._offset,In=nn.x1+nn.xa._offset,qn=nn.y0+nn.ya._offset,Wn=nn.y1+nn.ya._offset,ar=Math.min(bn,In),Dr=Math.max(bn,In),yr=Math.min(qn,Wn),Sr=Math.max(qn,Wn);sn.bbox={x0:ar+tn,x1:Dr+tn,y0:yr+pn,y1:Sr+pn}}nn.eventData=[sn],Fn.push(sn)}ee._hoverdata=Fn;var Kn=ze==="y"&&(Be.length>1||Jt.length>1)||ze==="closest"&&kt&&Jt.length>1,Dn=p.combine(fe.plot_bgcolor||p.background,fe.paper_bgcolor),lr=O(Jt,{gd:ee,hovermode:ze,rotateLabels:Kn,bgColor:Dn,container:fe._hoverlayer,outerContainer:fe._paper.node(),commonLabelOpts:fe.hoverlabel,hoverdistance:fe.hoverdistance});if(x.isUnifiedHover(ze)||(function(Mn,rr,nr){var Bn,Nr,Gr,pr,qr,_i,cn,jn=0,jt=1,fn=Mn.size(),vn=new Array(fn),Hn=0;function Un(Yn){var ir=Yn[0],or=Yn[Yn.length-1];if(Nr=ir.pmin-ir.pos-ir.dp+ir.size,Gr=or.pos+or.dp+or.size-ir.pmax,Nr>.01){for(qr=Yn.length-1;qr>=0;qr--)Yn[qr].dp+=Nr;Bn=!1}if(!(Gr<.01)){if(Nr<-.01){for(qr=Yn.length-1;qr>=0;qr--)Yn[qr].dp-=Gr;Bn=!1}if(Bn){var 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Nn=vn[pr],Rn=vn[pr+1],wn=Nn[Nn.length-1],An=Rn[0];if((Nr=wn.pos+wn.dp+wn.size-An.pos-An.dp+An.size)>.01&&wn.pmin===An.pmin&&wn.pmax===An.pmax){for(qr=Rn.length-1;qr>=0;qr--)Rn[qr].dp+=Nr;for(Nn.push.apply(Nn,Rn),vn.splice(pr+1,1),cn=0,qr=Nn.length-1;qr>=0;qr--)cn+=Nn[qr].dp;for(Gr=cn/Nn.length,qr=Nn.length-1;qr>=0;qr--)Nn[qr].dp-=Gr;Bn=!1}else pr++}vn.forEach(Un)}for(pr=vn.length-1;pr>=0;pr--){var kn=vn[pr];for(qr=kn.length-1;qr>=0;qr--){var Pn=kn[qr],Zn=Pn.datum;Zn.offset=Pn.dp,Zn.del=Pn.del}}}(lr,Kn?"xa":"ya",fe),B(lr,Kn,fe._invScaleX,fe._invScaleY)),le&&le.tagName){var Yr=v.getComponentMethod("annotations","hasClickToShow")(ee,Fn);d(r.select(le),Yr?"pointer":"")}!le||ue||!function(Mn,rr,nr){if(!nr||nr.length!==Mn._hoverdata.length)return!0;for(var Bn=nr.length-1;Bn>=0;Bn--){var 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x={left:{sizing:"xMin",offset:0},center:{sizing:"xMid",offset:-.5},right:{sizing:"xMax",offset:-1}},_={top:{sizing:"YMin",offset:0},middle:{sizing:"YMid",offset:-.5},bottom:{sizing:"YMax",offset:-1}};function A(S){var P=r.select(this);if(this._imgSrc!==S.source)if(P.attr("xmlns",i.svg),S.source&&S.source.slice(0,5)==="data:")P.attr("xlink:href",S.source),this._imgSrc=S.source;else{var L=new Promise(function(R){var F=new Image;function D(){P.remove(),R()}this.img=F,F.setAttribute("crossOrigin","anonymous"),F.onerror=D,F.onload=function(){var O=document.createElement("canvas");O.width=this.width,O.height=this.height,O.getContext("2d").drawImage(this,0,0);var N=O.toDataURL("image/png");P.attr("xlink:href",N),R()},P.on("error",D),F.src=S.source,this._imgSrc=S.source}.bind(this));s._promises.push(L)}}function b(S){var P,L,R=r.select(this),F=l.getFromId(s,S.xref),D=l.getFromId(s,S.yref),O=l.getRefType(S.xref)==="domain",N=l.getRefType(S.yref)==="domain",B=d._size;P=F!==void 0?typeof S.xref=="string"&&O?F._length*S.sizex:Math.abs(F.l2p(S.sizex)-F.l2p(0)):S.sizex*B.w,L=D!==void 0?typeof S.yref=="string"&&N?D._length*S.sizey:Math.abs(D.l2p(S.sizey)-D.l2p(0)):S.sizey*B.h;var W,G,K=P*x[S.xanchor].offset,te=L*_[S.yanchor].offset,Y=x[S.xanchor].sizing+_[S.yanchor].sizing;switch(W=F!==void 0?typeof S.xref=="string"&&O?F._length*S.x+F._offset:F.r2p(S.x)+F._offset:S.x*B.w+B.l,W+=K,G=D!==void 0?typeof S.yref=="string"&&N?D._length*(1-S.y)+D._offset:D.r2p(S.y)+D._offset:B.h-S.y*B.h+B.t,G+=te,S.sizing){case"fill":Y+=" slice";break;case"stretch":Y="none"}R.attr({x:W,y:G,width:P,height:L,preserveAspectRatio:Y,opacity:S.opacity});var J=(F&&l.getRefType(S.xref)!=="domain"?F._id:"")+(D&&l.getRefType(S.yref)!=="domain"?D._id:"");a.setClipUrl(R,J?"clip"+d._uid+J:null,s)}var k=d._imageLowerLayer.selectAll("image").data(g),w=d._imageUpperLayer.selectAll("image").data(m);k.enter().append("image"),w.enter().append("image"),k.exit().remove(),w.exit().remove(),k.each(function(S){A.bind(this)(S),b.bind(this)(S)}),w.each(function(S){A.bind(this)(S),b.bind(this)(S)});var M=Object.keys(d._plots);for(h=0;h<M.length;h++){u=M[h];var T=d._plots[u];if(T.imagelayer){var E=T.imagelayer.selectAll("image").data(p[u]||[]);E.enter().append("image"),E.exit().remove(),E.each(function(S){A.bind(this)(S),b.bind(this)(S)})}}}},{"../../constants/xmlns_namespaces":480,"../../plots/cartesian/axes":554,"../../plots/cartesian/axis_ids":558,"../drawing":388,"@plotly/d3":58}],415:[function(t,o,f){o.exports={moduleType:"component",name:"images",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("images"),draw:t("./draw"),convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":567,"./attributes":411,"./convert_coords":412,"./defaults":413,"./draw":414}],416:[function(t,o,f){var 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r=t("@plotly/d3"),a=t("../../lib"),l=t("../../plots/plots"),c=t("../../registry"),i=t("../../lib/events"),s=t("../dragelement"),u=t("../drawing"),h=t("../color"),d=t("../../lib/svg_text_utils"),m=t("./handle_click"),p=t("./constants"),g=t("../../constants/alignment"),y=g.LINE_SPACING,v=g.FROM_TL,x=g.FROM_BR,_=t("./get_legend_data"),A=t("./style"),b=t("./helpers");function k(L,R,F,D,O){var 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Date().getTime())-R._legendMouseDownTime<D?O+=1:(O=1,R._legendMouseDownTime=F)}),N.on("mouseup",function(){if(!R._dragged&&!R._editing){var B=R._fullLayout.legend;new Date().getTime()-R._legendMouseDownTime>D&&(O=Math.max(O-1,1)),k(R,B,L,O,r.event)}}))}function E(L,R,F,D,O){D._inHover&&L.attr("data-notex",!0),d.convertToTspans(L,F,function(){(function(N,B,W,G){var K=N.data()[0][0];if(!W._inHover&&K&&!K.trace.showlegend)return void N.remove();var te=N.select("g[class*=math-group]"),Y=te.node();W||(W=B._fullLayout.legend);var J,re=W.borderwidth;J=G===1?W.title.font:K.groupTitle?K.groupTitle.font:W.font;var U,V,H=J.size*y;if(Y){var ne=u.bBox(Y);U=ne.height,V=ne.width,G===1?u.setTranslate(te,re,re+.75*U):u.setTranslate(te,0,.25*U)}else{var q=N.select(G===1?".legendtitletext":".legendtext"),Q=d.lineCount(q),ee=q.node();if(U=H*Q,V=ee?u.bBox(ee).width:0,G===1)W.title.side==="left"&&(V+=2*p.itemGap),d.positionText(q,re+p.titlePad,re+H);else{var 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We=ie+Re,Ye=ae+Ve;u.setTranslate(te,We,Ye),Q=s.align(We,0,ue.l,ue.l+ue.w,D.xanchor),ee=s.align(Ye,0,ue.t+ue.h,ue.t,D.yanchor)},doneFn:function(){Q!==void 0&&ee!==void 0&&c.call("_guiRelayout",F,{"legend.x":Q,"legend.y":ee})},clickFn:function(Re,Ve){var We=O.selectAll("g.traces").filter(function(){var Ye=this.getBoundingClientRect();return Ve.clientX>=Ye.left&&Ve.clientX<=Ye.right&&Ve.clientY>=Ye.top&&Ve.clientY<=Ye.bottom});We.size()>0&&k(F,te,We,Re,Ve)}}))}],F)}}(L,R)}},{"../../constants/alignment":471,"../../lib":503,"../../lib/events":492,"../../lib/svg_text_utils":529,"../../plots/plots":619,"../../registry":638,"../color":366,"../dragelement":385,"../drawing":388,"./constants":417,"./get_legend_data":420,"./handle_click":421,"./helpers":422,"./style":424,"@plotly/d3":58}],420:[function(t,o,f){var r=t("../../registry"),a=t("./helpers");o.exports=function(l,c){var i,s,u=c._inHover,h=a.isGrouped(c),d=a.isReversed(c),m={},p=[],g=!1,y={},v=0,x=0;function 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R,F,D=y(L),O=D.showFill,N=D.showLine,B=D.showGradientLine,W=D.showGradientFill,G=D.anyFill,K=D.anyLine,te=L[0],Y=te.trace,J=u(Y),re=J.colorscale,U=J.reversescale,V=h.hasMarkers(Y)||!G?"M5,0":K?"M5,-2":"M5,-3",H=r.select(this),ne=H.select(".legendfill").selectAll("path").data(O||W?[L]:[]);if(ne.enter().append("path").classed("js-fill",!0),ne.exit().remove(),ne.attr("d",V+"h"+w+"v6h-"+w+"z").call(function(ee){if(ee.size())if(O)i.fillGroupStyle(ee,_);else{var ie="legendfill-"+Y.uid;i.gradient(ee,_,ie,g(U),re,"fill")}}),N||B){var q=E(void 0,Y.line,10,5);F=l.minExtend(Y,{line:{width:q}}),R=[l.minExtend(te,{trace:F})]}var Q=H.select(".legendlines").selectAll("path").data(N||B?[R]:[]);Q.enter().append("path").classed("js-line",!0),Q.exit().remove(),Q.attr("d",V+(B?"l"+w+",0.0001":"h"+w)).call(N?i.lineGroupStyle:function(ee){if(ee.size()){var ie="legendline-"+Y.uid;i.lineGroupStyle(ee),i.gradient(ee,_,ie,g(U),re,"stroke")}})}).each(function(L){var R,F,D=y(L),O=D.anyFill,N=D.anyLine,B=D.showLine,W=D.showMarker,G=L[0],K=G.trace,te=!W&&!N&&!O&&h.hasText(K);function Y(Q,ee,ie,ae){var ue=l.nestedProperty(K,Q).get(),le=l.isArrayOrTypedArray(ue)&&ee?ee(ue):ue;if(k&&le&&ae!==void 0&&(le=ae),ie){if(le<ie[0])return ie[0];if(le>ie[1])return ie[1]}return le}function J(Q){return G._distinct&&G.index&&Q[G.index]?Q[G.index]:Q[0]}if(W||te||B){var re={},U={};if(W){re.mc=Y("marker.color",J),re.mx=Y("marker.symbol",J),re.mo=Y("marker.opacity",l.mean,[.2,1]),re.mlc=Y("marker.line.color",J),re.mlw=Y("marker.line.width",l.mean,[0,5],2),U.marker={sizeref:1,sizemin:1,sizemode:"diameter"};var V=Y("marker.size",l.mean,[2,16],12);re.ms=V,U.marker.size=V}B&&(U.line={width:Y("line.width",J,[0,10],5)}),te&&(re.tx="Aa",re.tp=Y("textposition",J),re.ts=10,re.tc=Y("textfont.color",J),re.tf=Y("textfont.family",J)),R=[l.minExtend(G,re)],(F=l.minExtend(K,U)).selectedpoints=null,F.texttemplate=null}var H=r.select(this).select("g.legendpoints"),ne=H.selectAll("path.scatterpts").data(W?R:[]);ne.enter().insert("path",":first-child").classed("scatterpts",!0).attr("transform",T),ne.exit().remove(),ne.call(i.pointStyle,F,_),W&&(R[0].mrc=3);var q=H.selectAll("g.pointtext").data(te?R:[]);q.enter().append("g").classed("pointtext",!0).append("text").attr("transform",T),q.exit().remove(),q.selectAll("text").call(i.textPointStyle,F,_)}).each(function(L){var R=L[0].trace,F=r.select(this).select("g.legendpoints").selectAll("path.legendcandle").data(R.visible&&R.type==="candlestick"?[L,L]:[]);F.enter().append("path").classed("legendcandle",!0).attr("d",function(D,O){return O?"M-15,0H-8M-8,6V-6H8Z":"M15,0H8M8,-6V6H-8Z"}).attr("transform",T).style("stroke-miterlimit",1),F.exit().remove(),F.each(function(D,O){var N=r.select(this),B=R[O?"increasing":"decreasing"],W=E(void 0,B.line,5,2);N.style("stroke-width",W+"px").call(s.fill,B.fillcolor),W&&s.stroke(N,B.line.color)})}).each(function(L){var R=L[0].trace,F=r.select(this).select("g.legendpoints").selectAll("path.legendohlc").data(R.visible&&R.type==="ohlc"?[L,L]:[]);F.enter().append("path").classed("legendohlc",!0).attr("d",function(D,O){return O?"M-15,0H0M-8,-6V0":"M15,0H0M8,6V0"}).attr("transform",T).style("stroke-miterlimit",1),F.exit().remove(),F.each(function(D,O){var N=r.select(this),B=R[O?"increasing":"decreasing"],W=E(void 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r=t("../../registry"),a=t("../../plots/plots"),l=t("../../plots/cartesian/axis_ids"),c=t("../../fonts/ploticon"),i=t("../shapes/draw").eraseActiveShape,s=t("../../lib"),u=s._,h=o.exports={};function d(b,k){var w,M,T=k.currentTarget,E=T.getAttribute("data-attr"),S=T.getAttribute("data-val")||!0,P=b._fullLayout,L={},R=l.list(b,null,!0),F=P._cartesianSpikesEnabled;if(E==="zoom"){var D,O=S==="in"?.5:2,N=(1+O)/2,B=(1-O)/2;for(M=0;M<R.length;M++)if(!(w=R[M]).fixedrange)if(D=w._name,S==="auto")L[D+".autorange"]=!0;else if(S==="reset"){if(w._rangeInitial===void 0)L[D+".autorange"]=!0;else{var W=w._rangeInitial.slice();L[D+".range[0]"]=W[0],L[D+".range[1]"]=W[1]}w._showSpikeInitial!==void 0&&(L[D+".showspikes"]=w._showSpikeInitial,F!=="on"||w._showSpikeInitial||(F="off"))}else{var G=[w.r2l(w.range[0]),w.r2l(w.range[1])],K=[N*G[0]+B*G[1],N*G[1]+B*G[0]];L[D+".range[0]"]=w.l2r(K[0]),L[D+".range[1]"]=w.l2r(K[1])}}else E!=="hovermode"||S!=="x"&&S!=="y"||(S=P._isHoriz?"y":"x",T.setAttribute("data-val",S)),L[E]=S;P._cartesianSpikesEnabled=F,r.call("_guiRelayout",b,L)}function m(b,k){for(var w=k.currentTarget,M=w.getAttribute("data-attr"),T=w.getAttribute("data-val")||!0,E=b._fullLayout._subplots.gl3d||[],S={},P=M.split("."),L=0;L<E.length;L++)S[E[L]+"."+P[1]]=T;var R=T==="pan"?T:"zoom";S.dragmode=R,r.call("_guiRelayout",b,S)}function p(b,k){for(var w=k.currentTarget.getAttribute("data-attr"),M=w==="resetLastSave",T=w==="resetDefault",E=b._fullLayout,S=E._subplots.gl3d||[],P={},L=0;L<S.length;L++){var R,F=S[L],D=F+".camera",O=F+".aspectratio",N=F+".aspectmode",B=E[F]._scene;M?(P[D+".up"]=B.viewInitial.up,P[D+".eye"]=B.viewInitial.eye,P[D+".center"]=B.viewInitial.center,R=!0):T&&(P[D+".up"]=null,P[D+".eye"]=null,P[D+".center"]=null,R=!0),R&&(P[O+".x"]=B.viewInitial.aspectratio.x,P[O+".y"]=B.viewInitial.aspectratio.y,P[O+".z"]=B.viewInitial.aspectratio.z,P[N]=B.viewInitial.aspectmode)}r.call("_guiRelayout",b,P)}function g(b,k){var w=k.currentTarget,M=w._previousVal,T=b._fullLayout,E=T._subplots.gl3d||[],S=["xaxis","yaxis","zaxis"],P={},L={};if(M)L=M,w._previousVal=null;else{for(var R=0;R<E.length;R++){var F=E[R],D=T[F],O=F+".hovermode";P[O]=D.hovermode,L[O]=!1;for(var N=0;N<3;N++){var B=S[N],W=F+"."+B+".showspikes";L[W]=!1,P[W]=D[B].showspikes}}w._previousVal=P}return L}function y(b,k){for(var w=k.currentTarget,M=w.getAttribute("data-attr"),T=w.getAttribute("data-val")||!0,E=b._fullLayout,S=E._subplots.geo||[],P=0;P<S.length;P++){var L=S[P],R=E[L];if(M==="zoom"){var F=R.projection.scale,D=T==="in"?2*F:.5*F;r.call("_guiRelayout",b,L+".projection.scale",D)}}M==="reset"&&A(b,"geo")}function v(b){var k=b._fullLayout;return!k.hovermode&&(k._has("cartesian")?k._isHoriz?"y":"x":"closest")}function x(b){var k=v(b);r.call("_guiRelayout",b,"hovermode",k)}function _(b,k){for(var w=k.currentTarget.getAttribute("data-val"),M=b._fullLayout,T=M._subplots.mapbox||[],E={},S=0;S<T.length;S++){var P=T[S],L=M[P].zoom,R=w==="in"?1.05*L:L/1.05;E[P+".zoom"]=R}r.call("_guiRelayout",b,E)}function A(b,k){for(var w=b._fullLayout,M=w._subplots[k]||[],T={},E=0;E<M.length;E++)for(var S=M[E],P=w[S]._subplot.viewInitial,L=Object.keys(P),R=0;R<L.length;R++){var F=L[R];T[S+"."+F]=P[F]}r.call("_guiRelayout",b,T)}h.toImage={name:"toImage",title:function(b){var k=(b._context.toImageButtonOptions||{}).format||"png";return u(b,k==="png"?"Download plot as a png":"Download plot")},icon:c.camera,click:function(b){var k=b._context.toImageButtonOptions,w={format:k.format||"png"};s.notifier(u(b,"Taking snapshot - this may take a few seconds"),"long"),w.format!=="svg"&&s.isIE()&&(s.notifier(u(b,"IE only supports svg.  Changing format to svg."),"long"),w.format="svg"),["filename","width","height","scale"].forEach(function(M){M in k&&(w[M]=k[M])}),r.call("downloadImage",b,w).then(function(M){s.notifier(u(b,"Snapshot succeeded")+" - "+M,"long")}).catch(function(){s.notifier(u(b,"Sorry, there was a problem downloading your snapshot!"),"long")})}},h.sendDataToCloud={name:"sendDataToCloud",title:function(b){return u(b,"Edit in Chart Studio")},icon:c.disk,click:function(b){a.sendDataToCloud(b)}},h.editInChartStudio={name:"editInChartStudio",title:function(b){return u(b,"Edit in Chart Studio")},icon:c.pencil,click:function(b){a.sendDataToCloud(b)}},h.zoom2d={name:"zoom2d",_cat:"zoom",title:function(b){return u(b,"Zoom")},attr:"dragmode",val:"zoom",icon:c.zoombox,click:d},h.pan2d={name:"pan2d",_cat:"pan",title:function(b){return u(b,"Pan")},attr:"dragmode",val:"pan",icon:c.pan,click:d},h.select2d={name:"select2d",_cat:"select",title:function(b){return u(b,"Box Select")},attr:"dragmode",val:"select",icon:c.selectbox,click:d},h.lasso2d={name:"lasso2d",_cat:"lasso",title:function(b){return u(b,"Lasso Select")},attr:"dragmode",val:"lasso",icon:c.lasso,click:d},h.drawclosedpath={name:"drawclosedpath",title:function(b){return u(b,"Draw closed freeform")},attr:"dragmode",val:"drawclosedpath",icon:c.drawclosedpath,click:d},h.drawopenpath={name:"drawopenpath",title:function(b){return u(b,"Draw open freeform")},attr:"dragmode",val:"drawopenpath",icon:c.drawopenpath,click:d},h.drawline={name:"drawline",title:function(b){return u(b,"Draw line")},attr:"dragmode",val:"drawline",icon:c.drawline,click:d},h.drawrect={name:"drawrect",title:function(b){return u(b,"Draw rectangle")},attr:"dragmode",val:"drawrect",icon:c.drawrect,click:d},h.drawcircle={name:"drawcircle",title:function(b){return u(b,"Draw circle")},attr:"dragmode",val:"drawcircle",icon:c.drawcircle,click:d},h.eraseshape={name:"eraseshape",title:function(b){return u(b,"Erase active shape")},icon:c.eraseshape,click:i},h.zoomIn2d={name:"zoomIn2d",_cat:"zoomin",title:function(b){return u(b,"Zoom in")},attr:"zoom",val:"in",icon:c.zoom_plus,click:d},h.zoomOut2d={name:"zoomOut2d",_cat:"zoomout",title:function(b){return u(b,"Zoom out")},attr:"zoom",val:"out",icon:c.zoom_minus,click:d},h.autoScale2d={name:"autoScale2d",_cat:"autoscale",title:function(b){return u(b,"Autoscale")},attr:"zoom",val:"auto",icon:c.autoscale,click:d},h.resetScale2d={name:"resetScale2d",_cat:"resetscale",title:function(b){return u(b,"Reset axes")},attr:"zoom",val:"reset",icon:c.home,click:d},h.hoverClosestCartesian={name:"hoverClosestCartesian",_cat:"hoverclosest",title:function(b){return u(b,"Show closest data on hover")},attr:"hovermode",val:"closest",icon:c.tooltip_basic,gravity:"ne",click:d},h.hoverCompareCartesian={name:"hoverCompareCartesian",_cat:"hoverCompare",title:function(b){return u(b,"Compare data on hover")},attr:"hovermode",val:function(b){return b._fullLayout._isHoriz?"y":"x"},icon:c.tooltip_compare,gravity:"ne",click:d},h.zoom3d={name:"zoom3d",_cat:"zoom",title:function(b){return u(b,"Zoom")},attr:"scene.dragmode",val:"zoom",icon:c.zoombox,click:m},h.pan3d={name:"pan3d",_cat:"pan",title:function(b){return u(b,"Pan")},attr:"scene.dragmode",val:"pan",icon:c.pan,click:m},h.orbitRotation={name:"orbitRotation",title:function(b){return u(b,"Orbital rotation")},attr:"scene.dragmode",val:"orbit",icon:c["3d_rotate"],click:m},h.tableRotation={name:"tableRotation",title:function(b){return u(b,"Turntable rotation")},attr:"scene.dragmode",val:"turntable",icon:c["z-axis"],click:m},h.resetCameraDefault3d={name:"resetCameraDefault3d",_cat:"resetCameraDefault",title:function(b){return u(b,"Reset camera to default")},attr:"resetDefault",icon:c.home,click:p},h.resetCameraLastSave3d={name:"resetCameraLastSave3d",_cat:"resetCameraLastSave",title:function(b){return u(b,"Reset camera to last save")},attr:"resetLastSave",icon:c.movie,click:p},h.hoverClosest3d={name:"hoverClosest3d",_cat:"hoverclosest",title:function(b){return u(b,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:c.tooltip_basic,gravity:"ne",click:function(b,k){var w=g(b,k);r.call("_guiRelayout",b,w)}},h.zoomInGeo={name:"zoomInGeo",_cat:"zoomin",title:function(b){return u(b,"Zoom in")},attr:"zoom",val:"in",icon:c.zoom_plus,click:y},h.zoomOutGeo={name:"zoomOutGeo",_cat:"zoomout",title:function(b){return u(b,"Zoom out")},attr:"zoom",val:"out",icon:c.zoom_minus,click:y},h.resetGeo={name:"resetGeo",_cat:"reset",title:function(b){return u(b,"Reset")},attr:"reset",val:null,icon:c.autoscale,click:y},h.hoverClosestGeo={name:"hoverClosestGeo",_cat:"hoverclosest",title:function(b){return u(b,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:c.tooltip_basic,gravity:"ne",click:x},h.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(b){return u(b,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:c.tooltip_basic,gravity:"ne",click:x},h.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(b){return u(b,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:c.tooltip_basic,gravity:"ne",click:x},h.resetViewSankey={name:"resetSankeyGroup",title:function(b){return u(b,"Reset view")},icon:c.home,click:function(b){for(var k={"node.groups":[],"node.x":[],"node.y":[]},w=0;w<b._fullData.length;w++){var M=b._fullData[w]._viewInitial;k["node.groups"].push(M.node.groups.slice()),k["node.x"].push(M.node.x.slice()),k["node.y"].push(M.node.y.slice())}r.call("restyle",b,k)}},h.toggleHover={name:"toggleHover",title:function(b){return u(b,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:c.tooltip_basic,gravity:"ne",click:function(b,k){var w=g(b,k);w.hovermode=v(b),r.call("_guiRelayout",b,w)}},h.resetViews={name:"resetViews",title:function(b){return u(b,"Reset views")},icon:c.home,click:function(b,k){var w=k.currentTarget;w.setAttribute("data-attr","zoom"),w.setAttribute("data-val","reset"),d(b,k),w.setAttribute("data-attr","resetLastSave"),p(b,k),A(b,"geo"),A(b,"mapbox")}},h.toggleSpikelines={name:"toggleSpikelines",title:function(b){return u(b,"Toggle Spike Lines")},icon:c.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(b){var k=b._fullLayout,w=k._cartesianSpikesEnabled;k._cartesianSpikesEnabled=w==="on"?"off":"on",r.call("_guiRelayout",b,function(M){for(var T=M._fullLayout._cartesianSpikesEnabled==="on",E=l.list(M,null,!0),S={},P=0;P<E.length;P++){var L=E[P];S[L._name+".showspikes"]=!!T||L._showSpikeInitial}return S}(b))}},h.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(b){return u(b,"Reset view")},attr:"reset",icon:c.home,click:function(b){A(b,"mapbox")}},h.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(b){return u(b,"Zoom in")},attr:"zoom",val:"in",icon:c.zoom_plus,click:_},h.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(b){return u(b,"Zoom out")},attr:"zoom",val:"out",icon:c.zoom_minus,click:_}},{"../../fonts/ploticon":482,"../../lib":503,"../../plots/cartesian/axis_ids":558,"../../plots/plots":619,"../../registry":638,"../shapes/draw":450}],427:[function(t,o,f){var r=t("./buttons"),a=Object.keys(r),l=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],c=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(l),i=[];a.forEach(function(s){(function(u){if(c.indexOf(u._cat||u.name)===-1){var h=u.name,d=(u._cat||u.name).toLowerCase();i.indexOf(h)===-1&&i.push(h),i.indexOf(d)===-1&&i.push(d)}})(r[s])}),i.sort(),o.exports={DRAW_MODES:l,backButtons:c,foreButtons:i}},{"./buttons":426}],428:[function(t,o,f){var r=t("../../lib"),a=t("../color"),l=t("../../plot_api/plot_template"),c=t("./attributes");o.exports=function(i,s){var u=i.modebar||{},h=l.newContainer(s,"modebar");function d(p,g){return r.coerce(u,h,c,p,g)}d("orientation"),d("bgcolor",a.addOpacity(s.paper_bgcolor,.5));var m=a.contrast(a.rgb(s.modebar.bgcolor));d("color",a.addOpacity(m,.3)),d("activecolor",a.addOpacity(m,.7)),d("uirevision",s.uirevision),d("add"),d("remove")}},{"../../lib":503,"../../plot_api/plot_template":543,"../color":366,"./attributes":425}],429:[function(t,o,f){o.exports={moduleType:"component",name:"modebar",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),manage:t("./manage")}},{"./attributes":425,"./defaults":428,"./manage":430}],430:[function(t,o,f){var r=t("../../plots/cartesian/axis_ids"),a=t("../../traces/scatter/subtypes"),l=t("../../registry"),c=t("../fx/helpers").isUnifiedHover,i=t("./modebar"),s=t("./buttons"),u=t("./constants").DRAW_MODES;o.exports=function(h){var d=h._fullLayout,m=h._context,p=d._modeBar;if(m.displayModeBar||m.watermark){if(!Array.isArray(m.modeBarButtonsToRemove))throw new Error(["*modeBarButtonsToRemove* configuration options","must be an array."].join(" "));if(!Array.isArray(m.modeBarButtonsToAdd))throw new Error(["*modeBarButtonsToAdd* configuration options","must be an array."].join(" "));var g,y=m.modeBarButtons;g=Array.isArray(y)&&y.length?function(v){for(var x=0;x<v.length;x++)for(var _=v[x],A=0;A<_.length;A++){var b=_[A];if(typeof b=="string"){if(s[b]===void 0)throw new Error(["*modeBarButtons* configuration options","invalid button name"].join(" "));v[x][A]=s[b]}}return v}(y):!m.displayModeBar&&m.watermark?[]:function(v){var x=v._fullLayout,_=v._fullData,A=v._context;function b(ae,ue){if(typeof ue=="string"){if(ue.toLowerCase()===ae.toLowerCase())return!0}else{var le=ue.name,ge=ue._cat||ue.name;if(le===ae||ge===ae.toLowerCase())return!0}return!1}var k=x.modebar.add;typeof k=="string"&&(k=[k]);var w=x.modebar.remove;typeof w=="string"&&(w=[w]);var M=A.modeBarButtonsToAdd.concat(k.filter(function(ae){for(var ue=0;ue<A.modeBarButtonsToRemove.length;ue++)if(b(ae,A.modeBarButtonsToRemove[ue]))return!1;return!0})),T=A.modeBarButtonsToRemove.concat(w.filter(function(ae){for(var ue=0;ue<A.modeBarButtonsToAdd.length;ue++)if(b(ae,A.modeBarButtonsToAdd[ue]))return!1;return!0})),E=x._has("cartesian"),S=x._has("gl3d"),P=x._has("geo"),L=x._has("pie"),R=x._has("funnelarea"),F=x._has("gl2d"),D=x._has("ternary"),O=x._has("mapbox"),N=x._has("polar"),B=x._has("smith"),W=x._has("sankey"),G=function(ae){for(var ue=r.list({_fullLayout:ae},null,!0),le=0;le<ue.length;le++)if(!ue[le].fixedrange)return!1;return!0}(x),K=c(x.hovermode),te=[];function Y(ae){if(ae.length){for(var ue=[],le=0;le<ae.length;le++){for(var ge=ae[le],fe=s[ge],me=fe.name.toLowerCase(),_e=(fe._cat||fe.name).toLowerCase(),Ae=!1,ke=0;ke<T.length;ke++){var Le=T[ke].toLowerCase();if(Le===me||Le===_e){Ae=!0;break}}Ae||ue.push(s[ge])}te.push(ue)}}var J=["toImage"];A.showEditInChartStudio?J.push("editInChartStudio"):A.showSendToCloud&&J.push("sendDataToCloud"),Y(J);var re=[],U=[],V=[],H=[];(E||F||L||R||D)+P+S+O+N+B>1?(U=["toggleHover"],V=["resetViews"]):P?(re=["zoomInGeo","zoomOutGeo"],U=["hoverClosestGeo"],V=["resetGeo"]):S?(U=["hoverClosest3d"],V=["resetCameraDefault3d","resetCameraLastSave3d"]):O?(re=["zoomInMapbox","zoomOutMapbox"],U=["toggleHover"],V=["resetViewMapbox"]):F?U=["hoverClosestGl2d"]:L?U=["hoverClosestPie"]:W?(U=["hoverClosestCartesian","hoverCompareCartesian"],V=["resetViewSankey"]):U=["toggleHover"],E&&(U=["toggleSpikelines","hoverClosestCartesian","hoverCompareCartesian"]),(function(ae){for(var ue=0;ue<ae.length;ue++)if(!l.traceIs(ae[ue],"noHover"))return!1;return!0}(_)||K)&&(U=[]),!E&&!F||G||(re=["zoomIn2d","zoomOut2d","autoScale2d"],V[0]!=="resetViews"&&(V=["resetScale2d"])),S?H=["zoom3d","pan3d","orbitRotation","tableRotation"]:(E||F)&&!G||D?H=["zoom2d","pan2d"]:O||P?H=["pan2d"]:N&&(H=["zoom2d"]),function(ae){for(var ue=!1,le=0;le<ae.length&&!ue;le++){var ge=ae[le];ge._module&&ge._module.selectPoints&&(l.traceIs(ge,"scatter-like")?(a.hasMarkers(ge)||a.hasText(ge))&&(ue=!0):l.traceIs(ge,"box-violin")&&ge.boxpoints!=="all"&&ge.points!=="all"||(ue=!0))}return ue}(_)&&H.push("select2d","lasso2d");var ne=[],q=function(ae){ne.indexOf(ae)===-1&&U.indexOf(ae)!==-1&&ne.push(ae)};if(Array.isArray(M)){for(var Q=[],ee=0;ee<M.length;ee++){var ie=M[ee];typeof ie=="string"?(ie=ie.toLowerCase(),u.indexOf(ie)!==-1?(x._has("mapbox")||x._has("cartesian"))&&H.push(ie):ie==="togglespikelines"?q("toggleSpikelines"):ie==="togglehover"?q("toggleHover"):ie==="hovercompare"?q("hoverCompareCartesian"):ie==="hoverclosest"?(q("hoverClosestCartesian"),q("hoverClosestGeo"),q("hoverClosest3d"),q("hoverClosestGl2d"),q("hoverClosestPie")):ie==="v1hovermode"&&(q("toggleHover"),q("hoverClosestCartesian"),q("hoverCompareCartesian"),q("hoverClosestGeo"),q("hoverClosest3d"),q("hoverClosestGl2d"),q("hoverClosestPie"))):Q.push(ie)}M=Q}return Y(H),Y(re.concat(V)),Y(ne),function(ae,ue){if(ue.length)if(Array.isArray(ue[0]))for(var le=0;le<ue.length;le++)ae.push(ue[le]);else ae.push(ue);return ae}(te,M)}(h),p?p.update(h,g):d._modeBar=i(h,g)}else p&&(p.destroy(),delete d._modeBar)}},{"../../plots/cartesian/axis_ids":558,"../../registry":638,"../../traces/scatter/subtypes":952,"../fx/helpers":402,"./buttons":426,"./constants":427,"./modebar":431}],431:[function(t,o,f){var r=t("@plotly/d3"),a=t("fast-isnumeric"),l=t("../../lib"),c=t("../../fonts/ploticon"),i=t("../../version").version,s=new DOMParser;function u(d){this.container=d.container,this.element=document.createElement("div"),this.update(d.graphInfo,d.buttons),this.container.appendChild(this.element)}var h=u.prototype;h.update=function(d,m){this.graphInfo=d;var p=this.graphInfo._context,g=this.graphInfo._fullLayout,y="modebar-"+g._uid;this.element.setAttribute("id",y),this._uid=y,this.element.className="modebar",p.displayModeBar==="hover"&&(this.element.className+=" modebar--hover ease-bg"),g.modebar.orientation==="v"&&(this.element.className+=" vertical",m=m.reverse());var v=g.modebar,x=p.displayModeBar==="hover"?".js-plotly-plot .plotly:hover ":"";l.deleteRelatedStyleRule(y),l.addRelatedStyleRule(y,x+"#"+y+" .modebar-group","background-color: "+v.bgcolor),l.addRelatedStyleRule(y,"#"+y+" .modebar-btn .icon path","fill: "+v.color),l.addRelatedStyleRule(y,"#"+y+" .modebar-btn:hover .icon 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x=m.createButton(y);m.buttonElements.push(x),g.appendChild(x)}),m.element.appendChild(g)})},h.createGroup=function(){var d=document.createElement("div");return d.className="modebar-group",d},h.createButton=function(d){var m=this,p=document.createElement("a");p.setAttribute("rel","tooltip"),p.className="modebar-btn";var g=d.title;g===void 0?g=d.name:typeof g=="function"&&(g=g(this.graphInfo)),(g||g===0)&&p.setAttribute("data-title",g),d.attr!==void 0&&p.setAttribute("data-attr",d.attr);var y=d.val;if(y!==void 0&&(typeof y=="function"&&(y=y(this.graphInfo)),p.setAttribute("data-val",y)),typeof d.click!="function")throw new Error("must provide button 'click' function in button config");p.addEventListener("click",function(x){d.click(m.graphInfo,x),m.updateActiveButton(x.currentTarget)}),p.setAttribute("data-toggle",d.toggle||!1),d.toggle&&r.select(p).classed("active",!0);var v=d.icon;return typeof 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y=g.getAttribute("data-val")||!0,v=g.getAttribute("data-attr"),x=g.getAttribute("data-toggle")==="true",_=r.select(g);if(x)v===p&&_.classed("active",!_.classed("active"));else{var A=v===null?v:l.nestedProperty(m,v).get();_.classed("active",A===y)}})},h.hasButtons=function(d){var m=this.buttons;if(!m||d.length!==m.length)return!1;for(var p=0;p<d.length;++p){if(d[p].length!==m[p].length)return!1;for(var g=0;g<d[p].length;g++)if(d[p][g].name!==m[p][g].name)return!1}return!0},h.getLogo=function(){var d=this.createGroup(),m=document.createElement("a");return m.href="https://plotly.com/",m.target="_blank",m.setAttribute("data-title",l._(this.graphInfo,"Produced with Plotly.js")+" (v"+i+")"),m.className="modebar-btn plotlyjsicon 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r=t("../../plots/font_attributes"),a=t("../color/attributes"),l=(0,t("../../plot_api/plot_template").templatedArray)("button",{visible:{valType:"boolean",dflt:!0,editType:"plot"},step:{valType:"enumerated",values:["month","year","day","hour","minute","second","all"],dflt:"month",editType:"plot"},stepmode:{valType:"enumerated",values:["backward","todate"],dflt:"backward",editType:"plot"},count:{valType:"number",min:0,dflt:1,editType:"plot"},label:{valType:"string",editType:"plot"},editType:"plot"});o.exports={visible:{valType:"boolean",editType:"plot"},buttons:l,x:{valType:"number",min:-2,max:3,editType:"plot"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"plot"},y:{valType:"number",min:-2,max:3,editType:"plot"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"bottom",editType:"plot"},font:r({editType:"plot"}),bgcolor:{valType:"color",dflt:a.lightLine,editType:"plot"},activecolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:a.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"}},{"../../plot_api/plot_template":543,"../../plots/font_attributes":585,"../color/attributes":365}],433:[function(t,o,f){o.exports={yPad:.02,minButtonWidth:30,rx:3,ry:3,lightAmount:25,darkAmount:10}},{}],434:[function(t,o,f){var r=t("../../lib"),a=t("../color"),l=t("../../plot_api/plot_template"),c=t("../../plots/array_container_defaults"),i=t("./attributes"),s=t("./constants");function u(h,d,m,p){var g=p.calendar;function y(x,_){return r.coerce(h,d,i.buttons,x,_)}if(y("visible")){var v=y("step");v!=="all"&&(!g||g==="gregorian"||v!=="month"&&v!=="year"?y("stepmode"):d.stepmode="backward",y("count")),y("label")}}o.exports=function(h,d,m,p,g){var y=h.rangeselector||{},v=l.newContainer(d,"rangeselector");function x(b,k){return r.coerce(y,v,i,b,k)}if(x("visible",c(y,v,{name:"buttons",handleItemDefaults:u,calendar:g}).length>0)){var _=function(b,k,w){for(var M=w.filter(function(P){return k[P].anchor===b._id}),T=0,E=0;E<M.length;E++){var S=k[M[E]].domain;S&&(T=Math.max(S[1],T))}return[b.domain[0],T+s.yPad]}(d,m,p);x("x",_[0]),x("y",_[1]),r.noneOrAll(h,d,["x","y"]),x("xanchor"),x("yanchor"),r.coerceFont(x,"font",m.font);var A=x("bgcolor");x("activecolor",a.contrast(A,s.lightAmount,s.darkAmount)),x("bordercolor"),x("borderwidth")}}},{"../../lib":503,"../../plot_api/plot_template":543,"../../plots/array_container_defaults":549,"../color":366,"./attributes":432,"./constants":433}],435:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../registry"),l=t("../../plots/plots"),c=t("../color"),i=t("../drawing"),s=t("../../lib"),u=s.strTranslate,h=t("../../lib/svg_text_utils"),d=t("../../plots/cartesian/axis_ids"),m=t("../../constants/alignment"),p=m.LINE_SPACING,g=m.FROM_TL,y=m.FROM_BR,v=t("./constants"),x=t("./get_update_object");function _(k){return k._id}function A(k,w,M){var T=s.ensureSingle(k,"rect","selector-rect",function(E){E.attr("shape-rendering","crispEdges")});T.attr({rx:v.rx,ry:v.ry}),T.call(c.stroke,w.bordercolor).call(c.fill,function(E,S){return S._isActive||S._isHovered?E.activecolor:E.bgcolor}(w,M)).style("stroke-width",w.borderwidth+"px")}function b(k,w,M,T){s.ensureSingle(k,"text","selector-text",function(E){E.attr("text-anchor","middle")}).call(i.font,w.font).text(function(E,S){return E.label?S?s.templateString(E.label,S):E.label:E.step==="all"?"all":E.count+E.step.charAt(0)}(M,T._fullLayout._meta)).call(function(E){h.convertToTspans(E,T)})}o.exports=function(k){var w=k._fullLayout._infolayer.selectAll(".rangeselector").data(function(M){for(var T=d.list(M,"x",!0),E=[],S=0;S<T.length;S++){var P=T[S];P.rangeselector&&P.rangeselector.visible&&E.push(P)}return E}(k),_);w.enter().append("g").classed("rangeselector",!0),w.exit().remove(),w.style({cursor:"pointer","pointer-events":"all"}),w.each(function(M){var T=r.select(this),E=M,S=E.rangeselector,P=T.selectAll("g.button").data(s.filterVisible(S.buttons));P.enter().append("g").classed("button",!0),P.exit().remove(),P.each(function(L){var R=r.select(this),F=x(E,L);L._isActive=function(D,O,N){if(O.step==="all")return D.autorange===!0;var B=Object.keys(N);return D.range[0]===N[B[0]]&&D.range[1]===N[B[1]]}(E,L,F),R.call(A,S,L),R.call(b,S,L,k),R.on("click",function(){k._dragged||a.call("_guiRelayout",k,F)}),R.on("mouseover",function(){L._isHovered=!0,R.call(A,S,L)}),R.on("mouseout",function(){L._isHovered=!1,R.call(A,S,L)})}),function(L,R,F,D,O){var N=0,B=0,W=F.borderwidth;R.each(function(){var re=r.select(this).select(".selector-text"),U=F.font.size*p,V=Math.max(U*h.lineCount(re),16)+3;B=Math.max(B,V)}),R.each(function(){var re=r.select(this),U=re.select(".selector-rect"),V=re.select(".selector-text"),H=V.node()&&i.bBox(V.node()).width,ne=F.font.size*p,q=h.lineCount(V),Q=Math.max(H+10,v.minButtonWidth);re.attr("transform",u(W+N,W)),U.attr({x:0,y:0,width:Q,height:B}),h.positionText(V,Q/2,B/2-(q-1)*ne/2+3),N+=Q+5});var G=L._fullLayout._size,K=G.l+G.w*F.x,te=G.t+G.h*(1-F.y),Y="left";s.isRightAnchor(F)&&(K-=N,Y="right"),s.isCenterAnchor(F)&&(K-=N/2,Y="center");var J="top";s.isBottomAnchor(F)&&(te-=B,J="bottom"),s.isMiddleAnchor(F)&&(te-=B/2,J="middle"),N=Math.ceil(N),B=Math.ceil(B),K=Math.round(K),te=Math.round(te),l.autoMargin(L,D+"-range-selector",{x:F.x,y:F.y,l:N*g[Y],r:N*y[Y],b:B*y[J],t:B*g[J]}),O.attr("transform",u(K,te))}(k,P,S,E._name,T)})}},{"../../constants/alignment":471,"../../lib":503,"../../lib/svg_text_utils":529,"../../plots/cartesian/axis_ids":558,"../../plots/plots":619,"../../registry":638,"../color":366,"../drawing":388,"./constants":433,"./get_update_object":436,"@plotly/d3":58}],436:[function(t,o,f){var r=t("d3-time"),a=t("../../lib").titleCase;o.exports=function(l,c){var i=l._name,s={};if(c.step==="all")s[i+".autorange"]=!0;else{var u=function(h,d){var m,p=h.range,g=new Date(h.r2l(p[1])),y=d.step,v=r["utc"+a(y)],x=d.count;switch(d.stepmode){case"backward":m=h.l2r(+v.offset(g,-x));break;case"todate":var _=v.offset(g,-x);m=h.l2r(+v.ceil(_))}var A=p[1];return[m,A]}(l,c);s[i+".range[0]"]=u[0],s[i+".range[1]"]=u[1]}return s}},{"../../lib":503,"d3-time":122}],437:[function(t,o,f){o.exports={moduleType:"component",name:"rangeselector",schema:{subplots:{xaxis:{rangeselector:t("./attributes")}}},layoutAttributes:t("./attributes"),handleDefaults:t("./defaults"),draw:t("./draw")}},{"./attributes":432,"./defaults":434,"./draw":435}],438:[function(t,o,f){var r=t("../color/attributes");o.exports={bgcolor:{valType:"color",dflt:r.background,editType:"plot"},bordercolor:{valType:"color",dflt:r.defaultLine,editType:"plot"},borderwidth:{valType:"integer",dflt:0,min:0,editType:"plot"},autorange:{valType:"boolean",dflt:!0,editType:"calc",impliedEdits:{"range[0]":void 0,"range[1]":void 0}},range:{valType:"info_array",items:[{valType:"any",editType:"calc",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"calc",impliedEdits:{"^autorange":!1}}],editType:"calc",impliedEdits:{autorange:!1}},thickness:{valType:"number",dflt:.15,min:0,max:1,editType:"plot"},visible:{valType:"boolean",dflt:!0,editType:"calc"},editType:"calc"}},{"../color/attributes":365}],439:[function(t,o,f){var r=t("../../plots/cartesian/axis_ids").list,a=t("../../plots/cartesian/autorange").getAutoRange,l=t("./constants");o.exports=function(c){for(var i=r(c,"x",!0),s=0;s<i.length;s++){var 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r=t("../../lib"),a=t("../../plot_api/plot_template"),l=t("../../plots/cartesian/axis_ids"),c=t("./attributes"),i=t("./oppaxis_attributes");o.exports=function(s,u,h){var d=s[h],m=u[h];if(d.rangeslider||u._requestRangeslider[m._id]){r.isPlainObject(d.rangeslider)||(d.rangeslider={});var p,g,y=d.rangeslider,v=a.newContainer(m,"rangeslider");if(T("visible")){T("bgcolor",u.plot_bgcolor),T("bordercolor"),T("borderwidth"),T("thickness"),T("autorange",!m.isValidRange(y.range)),T("range");var x=u._subplots;if(x)for(var _=x.cartesian.filter(function(S){return S.substr(0,S.indexOf("y"))===l.name2id(h)}).map(function(S){return S.substr(S.indexOf("y"),S.length)}),A=r.simpleMap(_,l.id2name),b=0;b<A.length;b++){var k=A[b];p=y[k]||{},g=a.newContainer(v,k,"yaxis");var w,M=u[k];p.range&&M.isValidRange(p.range)&&(w="fixed"),E("rangemode",w)!=="match"&&E("range",M.range.slice())}v._input=y}}function T(S,P){return r.coerce(y,v,c,S,P)}function E(S,P){return r.coerce(p,g,i,S,P)}}},{"../../lib":503,"../../plot_api/plot_template":543,"../../plots/cartesian/axis_ids":558,"./attributes":438,"./oppaxis_attributes":445}],442:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../registry"),l=t("../../plots/plots"),c=t("../../lib"),i=c.strTranslate,s=t("../drawing"),u=t("../color"),h=t("../titles"),d=t("../../plots/cartesian"),m=t("../../plots/cartesian/axis_ids"),p=t("../dragelement"),g=t("../../lib/setcursor"),y=t("./constants");function v(w,M,T,E){var S=c.ensureSingle(w,"rect",y.bgClassName,function(F){F.attr({x:0,y:0,"shape-rendering":"crispEdges"})}),P=E.borderwidth%2==0?E.borderwidth:E.borderwidth-1,L=-E._offsetShift,R=s.crispRound(M,E.borderwidth);S.attr({width:E._width+P,height:E._height+P,transform:i(L,L),fill:E.bgcolor,stroke:E.bordercolor,"stroke-width":R})}function x(w,M,T,E){var S=M._fullLayout;c.ensureSingleById(S._topdefs,"clipPath",E._clipId,function(P){P.append("rect").attr({x:0,y:0})}).select("rect").attr({width:E._width,height:E._height})}function _(w,M,T,E){var S,P=M.calcdata,L=w.selectAll("g."+y.rangePlotClassName).data(T._subplotsWith,c.identity);L.enter().append("g").attr("class",function(R){return y.rangePlotClassName+" "+R}).call(s.setClipUrl,E._clipId,M),L.order(),L.exit().remove(),L.each(function(R,F){var D=r.select(this),O=F===0,N=m.getFromId(M,R,"y"),B=N._name,W=E[B],G={data:[],layout:{xaxis:{type:T.type,domain:[0,1],range:E.range.slice(),calendar:T.calendar},width:E._width,height:E._height,margin:{t:0,b:0,l:0,r:0}},_context:M._context};T.rangebreaks&&(G.layout.xaxis.rangebreaks=T.rangebreaks),G.layout[B]={type:N.type,domain:[0,1],range:W.rangemode!=="match"?W.range.slice():N.range.slice(),calendar:N.calendar},N.rangebreaks&&(G.layout[B].rangebreaks=N.rangebreaks),l.supplyDefaults(G);var K=G._fullLayout.xaxis,te=G._fullLayout[B];K.clearCalc(),K.setScale(),te.clearCalc(),te.setScale();var Y={id:R,plotgroup:D,xaxis:K,yaxis:te,isRangePlot:!0};O?S=Y:(Y.mainplot="xy",Y.mainplotinfo=S),d.rangePlot(M,Y,function(J,re){for(var U=[],V=0;V<J.length;V++){var H=J[V],ne=H[0].trace;ne.xaxis+ne.yaxis===re&&U.push(H)}return U}(P,R))})}function A(w,M,T,E,S){c.ensureSingle(w,"rect",y.maskMinClassName,function(P){P.attr({x:0,y:0,"shape-rendering":"crispEdges"})}).attr("height",E._height).call(u.fill,y.maskColor),c.ensureSingle(w,"rect",y.maskMaxClassName,function(P){P.attr({y:0,"shape-rendering":"crispEdges"})}).attr("height",E._height).call(u.fill,y.maskColor),S.rangemode!=="match"&&(c.ensureSingle(w,"rect",y.maskMinOppAxisClassName,function(P){P.attr({y:0,"shape-rendering":"crispEdges"})}).attr("width",E._width).call(u.fill,y.maskOppAxisColor),c.ensureSingle(w,"rect",y.maskMaxOppAxisClassName,function(P){P.attr({y:0,"shape-rendering":"crispEdges"})}).attr("width",E._width).style("border-top",y.maskOppBorder).call(u.fill,y.maskOppAxisColor))}function b(w,M,T,E){M._context.staticPlot||c.ensureSingle(w,"rect",y.slideBoxClassName,function(S){S.attr({y:0,cursor:y.slideBoxCursor,"shape-rendering":"crispEdges"})}).attr({height:E._height,fill:y.slideBoxFill})}function k(w,M,T,E){var S=c.ensureSingle(w,"g",y.grabberMinClassName),P=c.ensureSingle(w,"g",y.grabberMaxClassName),L={x:0,width:y.handleWidth,rx:y.handleRadius,fill:u.background,stroke:u.defaultLine,"stroke-width":y.handleStrokeWidth,"shape-rendering":"crispEdges"},R={y:Math.round(E._height/4),height:Math.round(E._height/2)};c.ensureSingle(S,"rect",y.handleMinClassName,function(D){D.attr(L)}).attr(R),c.ensureSingle(P,"rect",y.handleMaxClassName,function(D){D.attr(L)}).attr(R);var F={width:y.grabAreaWidth,x:0,y:0,fill:y.grabAreaFill,cursor:M._context.staticPlot?void 0:y.grabAreaCursor};c.ensureSingle(S,"rect",y.grabAreaMinClassName,function(D){D.attr(F)}).attr("height",E._height),c.ensureSingle(P,"rect",y.grabAreaMaxClassName,function(D){D.attr(F)}).attr("height",E._height)}o.exports=function(w){for(var M=w._fullLayout,T=M._rangeSliderData,E=0;E<T.length;E++){var S=T[E][y.name];S._clipId=S._id+"-"+M._uid}var P=M._infolayer.selectAll("g."+y.containerClassName).data(T,function(L){return 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W=E._fullLayout._activeShapeIndex;if(W<B.length)switch(E._fullLayout.shapes[W].type){case"rect":N="drawrect";break;case"circle":N="drawcircle";break;case"line":N="drawline";break;case"path":var G=B[W].path||"";N=G[G.length-1]==="Z"?"drawclosedpath":"drawopenpath"}}var K,te=l(N),Y=_(T,E,P,O),J={editable:!0,xref:F?"paper":L._id,yref:D?"paper":R._id,layer:S.layer,opacity:S.opacity,line:{color:S.line.color,width:S.line.width,dash:S.line.dash}};if(te||(J.fillcolor=S.fillcolor,J.fillrule=S.fillrule),Y.length===1&&(K=Y[0]),K&&N==="drawrect")J.type="rect",J.x0=K[0][1],J.y0=K[0][2],J.x1=K[2][1],J.y1=K[2][2];else if(K&&N==="drawline")J.type="line",J.x0=K[0][1],J.y0=K[0][2],J.x1=K[1][1],J.y1=K[1][2];else if(K&&N==="drawcircle"){J.type="circle";var re=K[i][1],U=K[s][1],V=K[u][1],H=K[h][1],ne=K[i][2],q=K[s][2],Q=K[u][2],ee=K[h][2],ie=P.xaxis&&(P.xaxis.type==="date"||P.xaxis.type==="log"),ae=P.yaxis&&(P.yaxis.type==="date"||P.yaxis.type==="log");ie&&(re=y(P.xaxis,re),U=y(P.xaxis,U),V=y(P.xaxis,V),H=y(P.xaxis,H)),ae&&(ne=y(P.yaxis,ne),q=y(P.yaxis,q),Q=y(P.yaxis,Q),ee=y(P.yaxis,ee));var ue=(U+H)/2,le=(ne+Q)/2,ge=b({x0:ue,y0:le,x1:ue+(H-U+V-re)/2*d,y1:le+(ee-q+Q-ne)/2*m});ie&&(ge.x0=g(P.xaxis,ge.x0),ge.x1=g(P.xaxis,ge.x1)),ae&&(ge.y0=g(P.yaxis,ge.y0),ge.y1=g(P.yaxis,ge.y1)),J.x0=ge.x0,J.y0=ge.y0,J.x1=ge.x1,J.y1=ge.y1}else J.type="path",L&&R&&function(de,ve,Me){var we=ve.type==="date",Ce=Me.type==="date";if(!we&&!Ce)return de;for(var Fe=0;Fe<de.length;Fe++)for(var ze=0;ze<de[Fe].length;ze++)for(var $e=0;$e+2<de[Fe][ze].length;$e+=2)we&&(de[Fe][ze][$e+1]=de[Fe][ze][$e+1].replace(" ","_")),Ce&&(de[Fe][ze][$e+2]=de[Fe][ze][$e+2].replace(" ","_"))}(Y,L,R),J.path=A(Y),K=null;v(E);for(var fe=w.editHelpers,me=(fe||{}).modifyItem,_e=[],Ae=0;Ae<B.length;Ae++){var ke=E._fullLayout.shapes[Ae];if(_e[Ae]=ke._input,O!==void 0&&Ae===E._fullLayout._activeShapeIndex){var Le=J;switch(ke.type){case"line":case"rect":case"circle":me("x0",Le.x0),me("x1",Le.x1),me("y0",Le.y0),me("y1",Le.y1);break;case"path":me("path",Le.path)}}}return O===void 0?(_e.push(J),_e):fe?fe.getUpdateObj():{}}}}},{"../../../plots/cartesian/handle_outline":565,"../../../plots/cartesian/helpers":566,"../../dragelement/helpers":384,"./constants":452,"./helpers":455}],457:[function(t,o,f){var r=t("./constants"),a=t("../../lib");f.rangeToShapePosition=function(l){return l.type==="log"?l.r2d:function(c){return c}},f.shapePositionToRange=function(l){return l.type==="log"?l.d2r:function(c){return c}},f.decodeDate=function(l){return function(c){return c.replace&&(c=c.replace("_"," ")),l(c)}},f.encodeDate=function(l){return function(c){return l(c).replace(" ","_")}},f.extractPathCoords=function(l,c){var i=[];return l.match(r.segmentRE).forEach(function(s){var u=c[s.charAt(0)].drawn;if(u!==void 0){var h=s.substr(1).match(r.paramRE);!h||h.length<u||i.push(a.cleanNumber(h[u]))}}),i},f.getDataToPixel=function(l,c,i,s){var u,h=l._fullLayout._size;if(c)if(s==="domain")u=function(m){return c._length*(i?1-m:m)+c._offset};else{var d=f.shapePositionToRange(c);u=function(m){return c._offset+c.r2p(d(m,!0))},c.type==="date"&&(u=f.decodeDate(u))}else u=i?function(m){return h.t+h.h*(1-m)}:function(m){return h.l+h.w*m};return u},f.getPixelToData=function(l,c,i,s){var u,h=l._fullLayout._size;if(c)if(s==="domain")u=function(m){var p=(m-c._offset)/c._length;return i?1-p:p};else{var d=f.rangeToShapePosition(c);u=function(m){return d(c.p2r(m-c._offset))}}else u=i?function(m){return 1-(m-h.t)/h.h}:function(m){return(m-h.l)/h.w};return u},f.roundPositionForSharpStrokeRendering=function(l,c){var i=Math.round(c%2)===1,s=Math.round(l);return i?s+.5:s},f.makeOptionsAndPlotinfo=function(l,c){var i=l._fullLayout.shapes[c]||{},s=l._fullLayout._plots[i.xref+i.yref];return s?s._hadPlotinfo=!0:(s={},i.xref&&i.xref!=="paper"&&(s.xaxis=l._fullLayout[i.xref+"axis"]),i.yref&&i.yref!=="paper"&&(s.yaxis=l._fullLayout[i.yref+"axis"])),s.xsizemode=i.xsizemode,s.ysizemode=i.ysizemode,s.xanchor=i.xanchor,s.yanchor=i.yanchor,{options:i,plotinfo:s}}},{"../../lib":503,"./constants":448}],458:[function(t,o,f){var r=t("./draw");o.exports={moduleType:"component",name:"shapes",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),supplyDrawNewShapeDefaults:t("./draw_newshape/defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("shapes"),calcAutorange:t("./calc_autorange"),draw:r.draw,drawOne:r.drawOne}},{"../../plots/cartesian/include_components":567,"./attributes":446,"./calc_autorange":447,"./defaults":449,"./draw":450,"./draw_newshape/defaults":453}],459:[function(t,o,f){var r=t("../../plots/font_attributes"),a=t("../../plots/pad_attributes"),l=t("../../lib/extend").extendDeepAll,c=t("../../plot_api/edit_types").overrideAll,i=t("../../plots/animation_attributes"),s=t("../../plot_api/plot_template").templatedArray,u=t("./constants"),h=s("step",{visible:{valType:"boolean",dflt:!0},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string"},value:{valType:"string"},execute:{valType:"boolean",dflt:!0}});o.exports=c(s("slider",{visible:{valType:"boolean",dflt:!0},active:{valType:"number",min:0,dflt:0},steps:h,lenmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"fraction"},len:{valType:"number",min:0,dflt:1},x:{valType:"number",min:-2,max:3,dflt:0},pad:l(a({editType:"arraydraw"}),{},{t:{dflt:20}}),xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left"},y:{valType:"number",min:-2,max:3,dflt:0},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},transition:{duration:{valType:"number",min:0,dflt:150},easing:{valType:"enumerated",values:i.transition.easing.values,dflt:"cubic-in-out"}},currentvalue:{visible:{valType:"boolean",dflt:!0},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left"},offset:{valType:"number",dflt:10},prefix:{valType:"string"},suffix:{valType:"string"},font:r({})},font:r({}),activebgcolor:{valType:"color",dflt:u.gripBgActiveColor},bgcolor:{valType:"color",dflt:u.railBgColor},bordercolor:{valType:"color",dflt:u.railBorderColor},borderwidth:{valType:"number",min:0,dflt:u.railBorderWidth},ticklen:{valType:"number",min:0,dflt:u.tickLength},tickcolor:{valType:"color",dflt:u.tickColor},tickwidth:{valType:"number",min:0,dflt:1},minorticklen:{valType:"number",min:0,dflt:u.minorTickLength}}),"arraydraw","from-root")},{"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../../plots/animation_attributes":548,"../../plots/font_attributes":585,"../../plots/pad_attributes":618,"./constants":460}],460:[function(t,o,f){o.exports={name:"sliders",containerClassName:"slider-container",groupClassName:"slider-group",inputAreaClass:"slider-input-area",railRectClass:"slider-rail-rect",railTouchRectClass:"slider-rail-touch-rect",gripRectClass:"slider-grip-rect",tickRectClass:"slider-tick-rect",inputProxyClass:"slider-input-proxy",labelsClass:"slider-labels",labelGroupClass:"slider-label-group",labelClass:"slider-label",currentValueClass:"slider-current-value",railHeight:5,menuIndexAttrName:"slider-active-index",autoMarginIdRoot:"slider-",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:"#bec8d9",railBgColor:"#f8fafc",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:"#bec8d9",gripBgColor:"#f6f8fa",gripBgActiveColor:"#dbdde0",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:"#333",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:"#333",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},{}],461:[function(t,o,f){var r=t("../../lib"),a=t("../../plots/array_container_defaults"),l=t("./attributes"),c=t("./constants").name,i=l.steps;function s(h,d,m){function p(_,A){return r.coerce(h,d,l,_,A)}for(var g=a(h,d,{name:"steps",handleItemDefaults:u}),y=0,v=0;v<g.length;v++)g[v].visible&&y++;if(y<2?d.visible=!1:p("visible")){d._stepCount=y;var x=d._visibleSteps=r.filterVisible(g);(g[p("active")]||{}).visible||(d.active=x[0]._index),p("x"),p("y"),r.noneOrAll(h,d,["x","y"]),p("xanchor"),p("yanchor"),p("len"),p("lenmode"),p("pad.t"),p("pad.r"),p("pad.b"),p("pad.l"),r.coerceFont(p,"font",m.font),p("currentvalue.visible")&&(p("currentvalue.xanchor"),p("currentvalue.prefix"),p("currentvalue.suffix"),p("currentvalue.offset"),r.coerceFont(p,"currentvalue.font",d.font)),p("transition.duration"),p("transition.easing"),p("bgcolor"),p("activebgcolor"),p("bordercolor"),p("borderwidth"),p("ticklen"),p("tickwidth"),p("tickcolor"),p("minorticklen")}}function u(h,d){function m(g,y){return r.coerce(h,d,i,g,y)}if(h.method==="skip"||Array.isArray(h.args)?m("visible"):d.visible=!1){m("method"),m("args");var p=m("label","step-"+d._index);m("value",p),m("execute")}}o.exports=function(h,d){a(h,d,{name:c,handleItemDefaults:s})}},{"../../lib":503,"../../plots/array_container_defaults":549,"./attributes":459,"./constants":460}],462:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../plots/plots"),l=t("../color"),c=t("../drawing"),i=t("../../lib"),s=i.strTranslate,u=t("../../lib/svg_text_utils"),h=t("../../plot_api/plot_template").arrayEditor,d=t("./constants"),m=t("../../constants/alignment"),p=m.LINE_SPACING,g=m.FROM_TL,y=m.FROM_BR;function v(O){return d.autoMarginIdRoot+O._index}function x(O){return O._index}function _(O,N){var B=c.tester.selectAll("g."+d.labelGroupClass).data(N._visibleSteps);B.enter().append("g").classed(d.labelGroupClass,!0);var W=0,G=0;B.each(function(ne){var q=k(r.select(this),{step:ne},N).node();if(q){var Q=c.bBox(q);G=Math.max(G,Q.height),W=Math.max(W,Q.width)}}),B.remove();var K=N._dims={};K.inputAreaWidth=Math.max(d.railWidth,d.gripHeight);var te=O._fullLayout._size;K.lx=te.l+te.w*N.x,K.ly=te.t+te.h*(1-N.y),N.lenmode==="fraction"?K.outerLength=Math.round(te.w*N.len):K.outerLength=N.len,K.inputAreaStart=0,K.inputAreaLength=Math.round(K.outerLength-N.pad.l-N.pad.r);var Y=(K.inputAreaLength-2*d.stepInset)/(N._stepCount-1),J=W+d.labelPadding;if(K.labelStride=Math.max(1,Math.ceil(J/Y)),K.labelHeight=G,K.currentValueMaxWidth=0,K.currentValueHeight=0,K.currentValueTotalHeight=0,K.currentValueMaxLines=1,N.currentvalue.visible){var re=c.tester.append("g");B.each(function(ne){var q=A(re,N,ne.label),Q=q.node()&&c.bBox(q.node())||{width:0,height:0},ee=u.lineCount(q);K.currentValueMaxWidth=Math.max(K.currentValueMaxWidth,Math.ceil(Q.width)),K.currentValueHeight=Math.max(K.currentValueHeight,Math.ceil(Q.height)),K.currentValueMaxLines=Math.max(K.currentValueMaxLines,ee)}),K.currentValueTotalHeight=K.currentValueHeight+N.currentvalue.offset,re.remove()}K.height=K.currentValueTotalHeight+d.tickOffset+N.ticklen+d.labelOffset+K.labelHeight+N.pad.t+N.pad.b;var U="left";i.isRightAnchor(N)&&(K.lx-=K.outerLength,U="right"),i.isCenterAnchor(N)&&(K.lx-=K.outerLength/2,U="center");var V="top";i.isBottomAnchor(N)&&(K.ly-=K.height,V="bottom"),i.isMiddleAnchor(N)&&(K.ly-=K.height/2,V="middle"),K.outerLength=Math.ceil(K.outerLength),K.height=Math.ceil(K.height),K.lx=Math.round(K.lx),K.ly=Math.round(K.ly);var H={y:N.y,b:K.height*y[V],t:K.height*g[V]};N.lenmode==="fraction"?(H.l=0,H.xl=N.x-N.len*g[U],H.r=0,H.xr=N.x+N.len*y[U]):(H.x=N.x,H.l=K.outerLength*g[U],H.r=K.outerLength*y[U]),a.autoMargin(O,v(N),H)}function A(O,N,B){if(N.currentvalue.visible){var W,G,K=N._dims;switch(N.currentvalue.xanchor){case"right":W=K.inputAreaLength-d.currentValueInset-K.currentValueMaxWidth,G="left";break;case"center":W=.5*K.inputAreaLength,G="middle";break;default:W=d.currentValueInset,G="left"}var te=i.ensureSingle(O,"text",d.labelClass,function(H){H.attr({"text-anchor":G,"data-notex":1})}),Y=N.currentvalue.prefix?N.currentvalue.prefix:"";if(typeof B=="string")Y+=B;else{var J=N.steps[N.active].label,re=N._gd._fullLayout._meta;re&&(J=i.templateString(J,re)),Y+=J}N.currentvalue.suffix&&(Y+=N.currentvalue.suffix),te.call(c.font,N.currentvalue.font).text(Y).call(u.convertToTspans,N._gd);var U=u.lineCount(te),V=(K.currentValueMaxLines+1-U)*N.currentvalue.font.size*p;return u.positionText(te,W,V),te}}function b(O,N,B){i.ensureSingle(O,"rect",d.gripRectClass,function(W){W.call(E,N,O,B).style("pointer-events","all")}).attr({width:d.gripWidth,height:d.gripHeight,rx:d.gripRadius,ry:d.gripRadius}).call(l.stroke,B.bordercolor).call(l.fill,B.bgcolor).style("stroke-width",B.borderwidth+"px")}function k(O,N,B){var W=i.ensureSingle(O,"text",d.labelClass,function(te){te.attr({"text-anchor":"middle","data-notex":1})}),G=N.step.label,K=B._gd._fullLayout._meta;return K&&(G=i.templateString(G,K)),W.call(c.font,B.font).text(G).call(u.convertToTspans,B._gd),W}function w(O,N){var B=i.ensureSingle(O,"g",d.labelsClass),W=N._dims,G=B.selectAll("g."+d.labelGroupClass).data(W.labelSteps);G.enter().append("g").classed(d.labelGroupClass,!0),G.exit().remove(),G.each(function(K){var te=r.select(this);te.call(k,K,N),c.setTranslate(te,L(N,K.fraction),d.tickOffset+N.ticklen+N.font.size*p+d.labelOffset+W.currentValueTotalHeight)})}function M(O,N,B,W,G){var K=Math.round(W*(B._stepCount-1)),te=B._visibleSteps[K]._index;te!==B.active&&T(O,N,B,te,!0,G)}function T(O,N,B,W,G,K){var te=B.active;B.active=W,h(O.layout,d.name,B).applyUpdate("active",W);var Y=B.steps[B.active];N.call(P,B,K),N.call(A,B),O.emit("plotly_sliderchange",{slider:B,step:B.steps[B.active],interaction:G,previousActive:te}),Y&&Y.method&&G&&(N._nextMethod?(N._nextMethod.step=Y,N._nextMethod.doCallback=G,N._nextMethod.doTransition=K):(N._nextMethod={step:Y,doCallback:G,doTransition:K},N._nextMethodRaf=window.requestAnimationFrame(function(){var J=N._nextMethod.step;J.method&&(J.execute&&a.executeAPICommand(O,J.method,J.args),N._nextMethod=null,N._nextMethodRaf=null)})))}function E(O,N,B){var W=B.node(),G=r.select(N);function K(){return B.data()[0]}function te(){var Y=K();N.emit("plotly_sliderstart",{slider:Y});var J=B.select("."+d.gripRectClass);r.event.stopPropagation(),r.event.preventDefault(),J.call(l.fill,Y.activebgcolor);var re=R(Y,r.mouse(W)[0]);function U(){var H=K(),ne=R(H,r.mouse(W)[0]);M(N,B,H,ne,!1)}function V(){var H=K();H._dragging=!1,J.call(l.fill,H.bgcolor),G.on("mouseup",null),G.on("mousemove",null),G.on("touchend",null),G.on("touchmove",null),N.emit("plotly_sliderend",{slider:H,step:H.steps[H.active]})}M(N,B,Y,re,!0),Y._dragging=!0,G.on("mousemove",U),G.on("touchmove",U),G.on("mouseup",V),G.on("touchend",V)}O.on("mousedown",te),O.on("touchstart",te)}function S(O,N){var B=O.selectAll("rect."+d.tickRectClass).data(N._visibleSteps),W=N._dims;B.enter().append("rect").classed(d.tickRectClass,!0),B.exit().remove(),B.attr({width:N.tickwidth+"px","shape-rendering":"crispEdges"}),B.each(function(G,K){var te=K%W.labelStride==0,Y=r.select(this);Y.attr({height:te?N.ticklen:N.minorticklen}).call(l.fill,N.tickcolor),c.setTranslate(Y,L(N,K/(N._stepCount-1))-.5*N.tickwidth,(te?d.tickOffset:d.minorTickOffset)+W.currentValueTotalHeight)})}function P(O,N,B){for(var W=O.select("rect."+d.gripRectClass),G=0,K=0;K<N._stepCount;K++)if(N._visibleSteps[K]._index===N.active){G=K;break}var te=L(N,G/(N._stepCount-1));if(!N._invokingCommand){var Y=W;B&&N.transition.duration>0&&(Y=Y.transition().duration(N.transition.duration).ease(N.transition.easing)),Y.attr("transform",s(te-.5*d.gripWidth,N._dims.currentValueTotalHeight))}}function L(O,N){var B=O._dims;return B.inputAreaStart+d.stepInset+(B.inputAreaLength-2*d.stepInset)*Math.min(1,Math.max(0,N))}function R(O,N){var B=O._dims;return Math.min(1,Math.max(0,(N-d.stepInset-B.inputAreaStart)/(B.inputAreaLength-2*d.stepInset-2*B.inputAreaStart)))}function F(O,N,B){var W=B._dims,G=i.ensureSingle(O,"rect",d.railTouchRectClass,function(K){K.call(E,N,O,B).style("pointer-events","all")});G.attr({width:W.inputAreaLength,height:Math.max(W.inputAreaWidth,d.tickOffset+B.ticklen+W.labelHeight)}).call(l.fill,B.bgcolor).attr("opacity",0),c.setTranslate(G,0,W.currentValueTotalHeight)}function D(O,N){var B=N._dims,W=B.inputAreaLength-2*d.railInset,G=i.ensureSingle(O,"rect",d.railRectClass);G.attr({width:W,height:d.railWidth,rx:d.railRadius,ry:d.railRadius,"shape-rendering":"crispEdges"}).call(l.stroke,N.bordercolor).call(l.fill,N.bgcolor).style("stroke-width",N.borderwidth+"px"),c.setTranslate(G,d.railInset,.5*(B.inputAreaWidth-d.railWidth)+B.currentValueTotalHeight)}o.exports=function(O){var N=O._fullLayout,B=function(J,re){for(var U=J[d.name],V=[],H=0;H<U.length;H++){var ne=U[H];ne.visible&&(ne._gd=re,V.push(ne))}return V}(N,O),W=N._infolayer.selectAll("g."+d.containerClassName).data(B.length>0?[0]:[]);function G(J){J._commandObserver&&(J._commandObserver.remove(),delete J._commandObserver),a.autoMargin(O,v(J))}if(W.enter().append("g").classed(d.containerClassName,!0).style("cursor","ew-resize"),W.exit().each(function(){r.select(this).selectAll("g."+d.groupClassName).each(G)}).remove(),B.length!==0){var K=W.selectAll("g."+d.groupClassName).data(B,x);K.enter().append("g").classed(d.groupClassName,!0),K.exit().each(G).remove();for(var te=0;te<B.length;te++){var Y=B[te];_(O,Y)}K.each(function(J){var re=r.select(this);(function(U){var V=U._dims;V.labelSteps=[];for(var H=U._stepCount,ne=0;ne<H;ne+=V.labelStride)V.labelSteps.push({fraction:ne/(H-1),step:U._visibleSteps[ne]})})(J),a.manageCommandObserver(O,J,J._visibleSteps,function(U){var V=re.data()[0];V.active!==U.index&&(V._dragging||T(O,re,V,U.index,!1,!0))}),function(U,V,H){(H.steps[H.active]||{}).visible||(H.active=H._visibleSteps[0]._index),V.call(A,H).call(D,H).call(w,H).call(S,H).call(F,U,H).call(b,U,H);var ne=H._dims;c.setTranslate(V,ne.lx+H.pad.l,ne.ly+H.pad.t),V.call(P,H,!1),V.call(A,H)}(O,r.select(this),J)})}}},{"../../constants/alignment":471,"../../lib":503,"../../lib/svg_text_utils":529,"../../plot_api/plot_template":543,"../../plots/plots":619,"../color":366,"../drawing":388,"./constants":460,"@plotly/d3":58}],463:[function(t,o,f){var r=t("./constants");o.exports={moduleType:"component",name:r.name,layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),draw:t("./draw")}},{"./attributes":459,"./constants":460,"./defaults":461,"./draw":462}],464:[function(t,o,f){var r=t("@plotly/d3"),a=t("fast-isnumeric"),l=t("../../plots/plots"),c=t("../../registry"),i=t("../../lib"),s=i.strTranslate,u=t("../drawing"),h=t("../color"),d=t("../../lib/svg_text_utils"),m=t("../../constants/interactions"),p=t("../../constants/alignment").OPPOSITE_SIDE,g=/ [XY][0-9]* /;o.exports={draw:function(y,v,x){var 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W=h(w,B),G=y(w,W,!0),K=M._length/W._length;for(S=0;S<G.min.length;S++)P=G.min[S],v(D,P.val,P.pad*K,{extrapad:P.extrapad});for(S=0;S<G.max.length;S++)P=G.max[S],x(O,P.val,P.pad*K,{extrapad:P.extrapad})}}return{min:D,max:O}}function v(w,M,T,E){_(w,M,T,E,b)}function x(w,M,T,E){_(w,M,T,E,k)}function _(w,M,T,E,S){for(var P=E.tozero,L=E.extrapad,R=!0,F=0;F<w.length&&R;F++){var D=w[F];if(S(D.val,M)&&D.pad>=T&&(D.extrapad||!L)){R=!1;break}S(M,D.val)&&D.pad<=T&&(L||!D.extrapad)&&(w.splice(F,1),F--)}if(R){var O=P&&M===0;w.push({val:M,pad:O?0:T,extrapad:!O&&L})}}function A(w){return a(w)&&Math.abs(w)<c}function b(w,M){return w<=M}function k(w,M){return w>=M}},{"../../components/drawing":388,"../../constants/numerical":479,"../../lib":503,"../../registry":638,"./axis_ids":558,"@plotly/d3":58,"fast-isnumeric":190}],554:[function(t,o,f){var r=t("@plotly/d3"),a=t("fast-isnumeric"),l=t("../../plots/plots"),c=t("../../registry"),i=t("../../lib"),s=i.strTranslate,u=t("../../lib/svg_text_utils"),h=t("../../components/titles"),d=t("../../components/color"),m=t("../../components/drawing"),p=t("./layout_attributes"),g=t("./clean_ticks"),y=t("../../constants/numerical"),v=y.ONEMAXYEAR,x=y.ONEAVGYEAR,_=y.ONEMINYEAR,A=y.ONEMAXQUARTER,b=y.ONEAVGQUARTER,k=y.ONEMINQUARTER,w=y.ONEMAXMONTH,M=y.ONEAVGMONTH,T=y.ONEMINMONTH,E=y.ONEWEEK,S=y.ONEDAY,P=S/2,L=y.ONEHOUR,R=y.ONEMIN,F=y.ONESEC,D=y.MINUS_SIGN,O=y.BADNUM,N={K:"zeroline"},B={K:"gridline",L:"path"},W={K:"tick",L:"path"},G={K:"tick",L:"text"},K=t("../../constants/alignment"),te=K.MID_SHIFT,Y=K.CAP_SHIFT,J=K.LINE_SPACING,re=K.OPPOSITE_SIDE,U=o.exports={};U.setConvert=t("./set_convert");var V=t("./axis_autotype"),H=t("./axis_ids"),ne=H.idSort,q=H.isLinked;U.id2name=H.id2name,U.name2id=H.name2id,U.cleanId=H.cleanId,U.list=H.list,U.listIds=H.listIds,U.getFromId=H.getFromId,U.getFromTrace=H.getFromTrace;var Q=t("./autorange");U.getAutoRange=Q.getAutoRange,U.findExtremes=Q.findExtremes;function ee(Be){var Ge=1e-4*(Be[1]-Be[0]);return[Be[0]-Ge,Be[1]+Ge]}U.coerceRef=function(Be,Ge,kt,dt,Oe,Ie){var Te=dt.charAt(dt.length-1),Pe=kt._fullLayout._subplots[Te+"axis"],qe=dt+"ref",rt={};return Oe||(Oe=Pe[0]||(typeof Ie=="string"?Ie:Ie[0])),Ie||(Ie=Oe),Pe=Pe.concat(Pe.map(function(lt){return lt+" domain"})),rt[qe]={valType:"enumerated",values:Pe.concat(Ie?typeof Ie=="string"?[Ie]:Ie:[]),dflt:Oe},i.coerce(Be,Ge,rt,qe)},U.getRefType=function(Be){return Be===void 0?Be:Be==="paper"?"paper":Be==="pixel"?"pixel":/( domain)$/.test(Be)?"domain":"range"},U.coercePosition=function(Be,Ge,kt,dt,Oe,Ie){var Te,Pe;if(U.getRefType(dt)!=="range")Te=i.ensureNumber,Pe=kt(Oe,Ie);else{var 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rt;if(kt)rt=(qe-Pe)/kt;else{var lt=i.distinctVals(Be),ot=Math.pow(10,Math.floor(Math.log(lt.minDiff)/Math.LN10)),At=ot*i.roundUp(lt.minDiff/ot,[.9,1.9,4.9,9.9],!0);rt=Math.max(At,2*i.stdev(Be)/Math.pow(Be.length,dt?.25:.4)),a(rt)||(rt=1)}U.autoTicks(Te,rt)}var wt,$t=Te.dtick,Ut=U.tickIncrement(U.tickFirst(Te),$t,"reverse",Oe);if(typeof $t=="number")wt=(Ut=function(tt,bt,Ft,Et,Pt){var De=0,Je=0,st=0,St=0;function It(mn){return(1+100*(mn-tt)/Ft.dtick)%100<2}for(var Zt=0;Zt<bt.length;Zt++)bt[Zt]%1==0?st++:a(bt[Zt])||St++,It(bt[Zt])&&De++,It(bt[Zt]+Ft.dtick/2)&&Je++;var Kt=bt.length-St;if(st===Kt&&Ft.type!=="date")Ft.dtick<1?tt=Et-.5*Ft.dtick:(tt-=.5)+Ft.dtick<Et&&(tt+=Ft.dtick);else if(Je<.1*Kt&&(De>.3*Kt||It(Et)||It(Pt))){var qt=Ft.dtick/2;tt+=tt+qt<Et?qt:-qt}return tt}(Ut,Be,Te,Pe,qe))+(1+Math.floor((qe-Ut)/$t))*$t;else for(Te.dtick.charAt(0)==="M"&&(Ut=function(tt,bt,Ft,Et,Pt){var De=i.findExactDates(bt,Pt);if(De.exactDays>.8){var 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At=Be._tmin;Be.rangebreaks&&Be._tick0Init!==Be.tick0&&(At=Lt(At,Be),Te||(At=U.tickIncrement(At,Be.dtick,!Te,Be.calendar))),ot&&(At=U.tickIncrement(At,Be.dtick,!Te,Be.calendar));var wt,$t=Be.ticklabelstep,Ut=Math.max(1e3,Be._length||0),tt=[],bt=null;rt?wt=Be.dtick:Be.type==="date"?typeof Be.dtick=="string"&&Be.dtick.charAt(0)==="M"&&(wt=M*Be.dtick.substring(1)):wt=Be._roughDTick;for(var Ft,Et,Pt=Math.round((Be.r2l(At)-Be.r2l(Be.tick0))/wt)-1;Te?At>=Ie:At<=Ie;At=U.tickIncrement(At,Be.dtick,Te,Be.calendar)){if(Pt++,Be.rangebreaks&&!Te){if(At<Oe)continue;if(Be.maskBreaks(At)===O&&Lt(At,Be)>=qe)break}if(tt.length>Ut||At===bt)break;bt=At;var De=!1;lt&&At!==(0|At)&&(De=!0);var Je={minor:De,value:At};$t>1&&Pt%$t&&(Je.skipLabel=!0),tt.push(Je)}if(ot&&function(nn,sn,gn){for(var bn=0;bn<nn.length;bn++){var In=nn[bn].value,qn=bn,Wn=bn+1;bn<nn.length-1?(qn=bn,Wn=bn+1):bn>0?(qn=bn-1,Wn=bn):(qn=bn,Wn=bn);var 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Ft=Te[Oe][ot];ot==="type"&&lt.indexOf(wt)!==-1&&lt.indexOf(Ft)!==-1&&wt!==Ft?tt=!0:Te[Oe][ot]!==wt&&($t=!1)}$t&&(Ut&&(Pe[Ie[0]._name].type="linear"),tt&&(Pe[Te[0]._name].type="linear"),at(Pe,ot,Ie,Te,Be._fullLayout._dfltTitle))}}for(dt=0;dt<Be._fullLayout.annotations.length;dt++){var Et=Be._fullLayout.annotations[dt];Ge.indexOf(Et.xref)!==-1&&kt.indexOf(Et.yref)!==-1&&i.swapAttrs(Pe.annotations[dt],["?"])}}function at(Be,Ge,kt,dt,Oe){var Ie,Te=i.nestedProperty,Pe=Te(Be[kt[0]._name],Ge).get(),qe=Te(Be[dt[0]._name],Ge).get();for(Ge==="title"&&(Pe&&Pe.text===Oe.x&&(Pe.text=Oe.y),qe&&qe.text===Oe.y&&(qe.text=Oe.x)),Ie=0;Ie<kt.length;Ie++)Te(Be,kt[Ie]._name+"."+Ge).set(qe);for(Ie=0;Ie<dt.length;Ie++)Te(Be,dt[Ie]._name+"."+Ge).set(Pe)}function et(Be){return Be._id==="angularaxis"}function Lt(Be,Ge){for(var kt=Ge._rangebreaks.length,dt=0;dt<kt;dt++){var Oe=Ge._rangebreaks[dt];if(Be>=Oe.min&&Be<Oe.max)return Oe.max}return Be}function 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qe=Pe.xaxis,rt=Pe.yaxis;Pe.xaxislayer.selectAll("."+qe._id+"tick").remove(),Pe.yaxislayer.selectAll("."+rt._id+"tick").remove(),Pe.xaxislayer.selectAll("."+qe._id+"tick2").remove(),Pe.yaxislayer.selectAll("."+rt._id+"tick2").remove(),Pe.xaxislayer.selectAll("."+qe._id+"divider").remove(),Pe.yaxislayer.selectAll("."+rt._id+"divider").remove(),Pe.gridlayer&&Pe.gridlayer.selectAll("path").remove(),Pe.zerolinelayer&&Pe.zerolinelayer.selectAll("path").remove(),dt._infolayer.select(".g-"+qe._id+"title").remove(),dt._infolayer.select(".g-"+rt._id+"title").remove()}});var Oe=Ge&&Ge!=="redraw"?Ge:U.listIds(Be);return i.syncOrAsync(Oe.map(function(Ie){return function(){if(Ie){var Te=U.getFromId(Be,Ie),Pe=U.drawOne(Be,Te,kt);return Te._r=Te.range.slice(),Te._rl=i.simpleMap(Te._r,Te.r2l),Pe}}}))},U.drawOne=function(Be,Ge,kt){var dt,Oe,Ie;kt=kt||{},Ge.setScale();var Te=Be._fullLayout,Pe=Ge._id,qe=Pe.charAt(0),rt=U.counterLetter(Pe),lt=Te._plots[Ge._mainSubplot];if(lt){var ot=lt[qe+"axislayer"],At=Ge._mainLinePosition,wt=Ge._mainMirrorPosition,$t=Ge._vals=U.calcTicks(Ge),Ut=[Ge.mirror,At,wt].join("_");for(dt=0;dt<$t.length;dt++)$t[dt].axInfo=Ut;Ge._selections={},Ge._tickAngles&&(Ge._prevTickAngles=Ge._tickAngles),Ge._tickAngles={},Ge._depth=null;var tt={};if(Ge.visible){var bt,Ft,Et=U.makeTransTickFn(Ge),Pt=U.makeTransTickLabelFn(Ge),De=Ge.ticks==="inside",Je=Ge.ticks==="outside";if(Ge.tickson==="boundaries"){var st=function(Kn,Dn){var lr,Yr=[],Mn=function(rr,nr){var Bn=rr.xbnd[nr];Bn!==null&&Yr.push(i.extendFlat({},rr,{x:Bn}))};if(Dn.length){for(lr=0;lr<Dn.length;lr++)Mn(Dn[lr],0);Mn(Dn[lr-1],1)}return Yr}(0,$t);Ft=U.clipEnds(Ge,st),bt=De?Ft:st}else Ft=U.clipEnds(Ge,$t),bt=De&&Ge.ticklabelmode!=="period"?Ft:$t;var St=Ge._gridVals=Ft,It=function(Kn,Dn){var lr,Yr,Mn=[],rr=Dn.length&&Dn[Dn.length-1].x<Dn[0].x,nr=function(Nr,Gr){var pr=Nr.xbnd[Gr];pr!==null&&Mn.push(i.extendFlat({},Nr,{x:pr}))};if(Kn.showdividers&&Dn.length){for(lr=0;lr<Dn.length;lr++){var Bn=Dn[lr];Bn.text2!==Yr&&nr(Bn,rr?1:0),Yr=Bn.text2}nr(Dn[lr-1],rr?0:1)}return Mn}(Ge,$t);if(!Te._hasOnlyLargeSploms){var Zt=Ge._subplotsWith,Kt={};for(dt=0;dt<Zt.length;dt++){Oe=Zt[dt];var qt=(Ie=Te._plots[Oe])[rt+"axis"],mn=qt._mainAxis._id;if(!Kt[mn]){Kt[mn]=1;var Fn=qe==="x"?"M0,"+qt._offset+"v"+qt._length:"M"+qt._offset+",0h"+qt._length;U.drawGrid(Be,Ge,{vals:St,counterAxis:qt,layer:Ie.gridlayer.select("."+Pe),path:Fn,transFn:Et}),U.drawZeroLine(Be,Ge,{counterAxis:qt,layer:Ie.zerolinelayer,path:Fn,transFn:Et})}}}var pn=U.getTickSigns(Ge),tn=[];if(Ge.ticks){var nn,sn,gn,bn=U.makeTickPath(Ge,At,pn[2]);if(Ge._anchorAxis&&Ge.mirror&&Ge.mirror!==!0?(nn=U.makeTickPath(Ge,wt,pn[3]),sn=bn+nn):(nn="",sn=bn),Ge.showdividers&&Je&&Ge.tickson==="boundaries"){var In={};for(dt=0;dt<It.length;dt++)In[It[dt].x]=1;gn=function(Kn){return In[Kn.x]?nn:sn}}else gn=sn;U.drawTicks(Be,Ge,{vals:bt,layer:ot,path:gn,transFn:Et}),Ge.mirror==="allticks"&&(tn=Object.keys(Ge._linepositions||{}))}for(dt=0;dt<tn.length;dt++){Oe=tn[dt],Ie=Te._plots[Oe];var qn=Ge._linepositions[Oe]||[],Wn=U.makeTickPath(Ge,qn[0],pn[0])+U.makeTickPath(Ge,qn[1],pn[1]);U.drawTicks(Be,Ge,{vals:bt,layer:Ie[qe+"axislayer"],path:Wn,transFn:Et})}var ar=[];if(ar.push(function(){return U.drawLabels(Be,Ge,{vals:$t,layer:ot,plotinfo:Ie,transFn:Pt,labelFns:U.makeLabelFns(Ge,At)})}),Ge.type==="multicategory"){var Dr={x:2,y:10}[qe];ar.push(function(){var Kn={x:"height",y:"width"}[qe],Dn=Sr()[Kn]+Dr+(Ge._tickAngles[Pe+"tick"]?Ge.tickfont.size*J:0);return U.drawLabels(Be,Ge,{vals:$e(Ge,$t),layer:ot,cls:Pe+"tick2",repositionOnUpdate:!0,secondary:!0,transFn:Et,labelFns:U.makeLabelFns(Ge,At+Dn*pn[4])})}),ar.push(function(){return Ge._depth=pn[4]*(Sr("tick2")[Ge.side]-At),function(Kn,Dn,lr){var Yr=Dn._id+"divider",Mn=lr.vals,rr=lr.layer.selectAll("path."+Yr).data(Mn,Re);rr.exit().remove(),rr.enter().insert("path",":first-child").classed(Yr,1).classed("crisp",1).call(d.stroke,Dn.dividercolor).style("stroke-width",m.crispRound(Kn,Dn.dividerwidth,1)+"px"),rr.attr("transform",lr.transFn).attr("d",lr.path)}(Be,Ge,{vals:It,layer:ot,path:U.makeTickPath(Ge,At,pn[4],Ge._depth),transFn:Et})})}else Ge.title.hasOwnProperty("standoff")&&ar.push(function(){Ge._depth=pn[4]*(Sr()[Ge.side]-At)});var yr=c.getComponentMethod("rangeslider","isVisible")(Ge);return ar.push(function(){var Kn,Dn,lr,Yr,Mn=Ge.side.charAt(0),rr=re[Ge.side].charAt(0),nr=U.getPxPosition(Be,Ge),Bn=Je?Ge.ticklen:0;if((Ge.automargin||yr)&&(Ge.type==="multicategory"?Kn=Sr("tick2"):(Kn=Sr(),qe==="x"&&Mn==="b"&&(Ge._depth=Math.max(Kn.width>0?Kn.bottom-nr:0,Bn)))),Ge.automargin){Dn={x:0,y:0,r:0,l:0,t:0,b:0};var Nr=[0,1];if(qe==="x"){if(Mn==="b"?Dn[Mn]=Ge._depth:(Dn[Mn]=Ge._depth=Math.max(Kn.width>0?nr-Kn.top:0,Bn),Nr.reverse()),Kn.width>0){var Gr=Kn.right-(Ge._offset+Ge._length);Gr>0&&(Dn.xr=1,Dn.r=Gr);var pr=Ge._offset-Kn.left;pr>0&&(Dn.xl=0,Dn.l=pr)}}else if(Mn==="l"?Dn[Mn]=Ge._depth=Math.max(Kn.height>0?nr-Kn.left:0,Bn):(Dn[Mn]=Ge._depth=Math.max(Kn.height>0?Kn.right-nr:0,Bn),Nr.reverse()),Kn.height>0){var qr=Kn.bottom-(Ge._offset+Ge._length);qr>0&&(Dn.yb=0,Dn.b=qr);var _i=Ge._offset-Kn.top;_i>0&&(Dn.yt=1,Dn.t=_i)}Dn[rt]=Ge.anchor==="free"?Ge.position:Ge._anchorAxis.domain[Nr[0]],Ge.title.text!==Te._dfltTitle[qe]&&(Dn[Mn]+=Ve(Ge)+(Ge.title.standoff||0)),Ge.mirror&&Ge.anchor!=="free"&&((lr={x:0,y:0,r:0,l:0,t:0,b:0})[rr]=Ge.linewidth,Ge.mirror&&Ge.mirror!==!0&&(lr[rr]+=Bn),Ge.mirror===!0||Ge.mirror==="ticks"?lr[rt]=Ge._anchorAxis.domain[Nr[1]]:Ge.mirror!=="all"&&Ge.mirror!=="allticks"||(lr[rt]=[Ge._counterDomainMin,Ge._counterDomainMax][Nr[1]]))}yr&&(Yr=c.getComponentMethod("rangeslider","autoMarginOpts")(Be,Ge)),l.autoMargin(Be,nt(Ge),Dn),l.autoMargin(Be,ft(Ge),lr),l.autoMargin(Be,yt(Ge),Yr)}),kt.skipTitle||yr&&Ge.side==="bottom"||ar.push(function(){return function(Kn,Dn){var lr,Yr=Kn._fullLayout,Mn=Dn._id,rr=Mn.charAt(0),nr=Dn.title.font.size;if(Dn.title.hasOwnProperty("standoff"))lr=Dn._depth+Dn.title.standoff+Ve(Dn);else{var Bn=Wt(Dn);if(Dn.type==="multicategory")lr=Dn._depth;else{var Nr=1.5*nr;Bn&&(Nr=.5*nr,Dn.ticks==="outside"&&(Nr+=Dn.ticklen)),lr=10+Nr+(Dn.linewidth?Dn.linewidth-1:0)}Bn||(lr+=rr==="x"?Dn.side==="top"?nr*(Dn.showticklabels?1:0):nr*(Dn.showticklabels?1.5:.5):Dn.side==="right"?nr*(Dn.showticklabels?1:.5):nr*(Dn.showticklabels?.5:0))}var Gr,pr,qr,_i,cn=U.getPxPosition(Kn,Dn);if(rr==="x"?(pr=Dn._offset+Dn._length/2,qr=Dn.side==="top"?cn-lr:cn+lr):(qr=Dn._offset+Dn._length/2,pr=Dn.side==="right"?cn+lr:cn-lr,Gr={rotate:"-90",offset:0}),Dn.type!=="multicategory"){var jn=Dn._selections[Dn._id+"tick"];if(_i={selection:jn,side:Dn.side},jn&&jn.node()&&jn.node().parentNode){var jt=m.getTranslate(jn.node().parentNode);_i.offsetLeft=jt.x,_i.offsetTop=jt.y}Dn.title.hasOwnProperty("standoff")&&(_i.pad=0)}return h.draw(Kn,Mn+"title",{propContainer:Dn,propName:Dn._name+".title.text",placeholder:Yr._dfltTitle[rr],avoid:_i,transform:Gr,attributes:{x:pr,y:qr,"text-anchor":"middle"}})}(Be,Ge)}),i.syncOrAsync(ar)}}function Sr(Kn){var Dn=Pe+(Kn||"tick");return tt[Dn]||(tt[Dn]=function(lr,Yr){var Mn,rr,nr,Bn;return lr._selections[Yr].size()?(Mn=1/0,rr=-1/0,nr=1/0,Bn=-1/0,lr._selections[Yr].each(function(){var Nr=Ye(this),Gr=m.bBox(Nr.node().parentNode);Mn=Math.min(Mn,Gr.top),rr=Math.max(rr,Gr.bottom),nr=Math.min(nr,Gr.left),Bn=Math.max(Bn,Gr.right)})):(Mn=0,rr=0,nr=0,Bn=0),{top:Mn,bottom:rr,left:nr,right:Bn,height:rr-Mn,width:Bn-nr}}(Ge,Dn)),tt[Dn]}},U.getTickSigns=function(Be){var Ge=Be._id.charAt(0),kt={x:"top",y:"right"}[Ge],dt=Be.side===kt?1:-1,Oe=[-1,1,dt,-dt];return Be.ticks!=="inside"==(Ge==="x")&&(Oe=Oe.map(function(Ie){return-Ie})),Be.side&&Oe.push({l:-1,t:-1,r:1,b:1}[Be.side.charAt(0)]),Oe},U.makeTransTickFn=function(Be){return Be._id.charAt(0)==="x"?function(Ge){return s(Be._offset+Be.l2p(Ge.x),0)}:function(Ge){return s(0,Be._offset+Be.l2p(Ge.x))}},U.makeTransTickLabelFn=function(Be){var Ge=function(Oe){var Ie=Oe.ticklabelposition||"",Te=function(bt){return Ie.indexOf(bt)!==-1},Pe=Te("top"),qe=Te("left"),rt=Te("right"),lt=Te("bottom"),ot=Te("inside"),At=lt||qe||Pe||rt;if(!At&&!ot)return[0,0];var wt=Oe.side,$t=At?(Oe.tickwidth||0)/2:0,Ut=3,tt=Oe.tickfont?Oe.tickfont.size:12;return(lt||Pe)&&($t+=tt*Y,Ut+=(Oe.linewidth||0)/2),(qe||rt)&&($t+=(Oe.linewidth||0)/2,Ut+=3),ot&&wt==="top"&&(Ut-=tt*(1-Y)),(qe||Pe)&&($t=-$t),wt!=="bottom"&&wt!=="right"||(Ut=-Ut),[At?$t:0,ot?Ut:0]}(Be),kt=Ge[0],dt=Ge[1];return Be._id.charAt(0)==="x"?function(Oe){return s(kt+Be._offset+Be.l2p(Ke(Oe)),dt)}:function(Oe){return s(dt,kt+Be._offset+Be.l2p(Ke(Oe)))}},U.makeTickPath=function(Be,Ge,kt,dt){dt=dt!==void 0?dt:Be.ticklen;var Oe=Be._id.charAt(0),Ie=(Be.linewidth||1)/2;return Oe==="x"?"M0,"+(Ge+Ie*kt)+"v"+dt*kt:"M"+(Ge+Ie*kt)+",0h"+dt*kt},U.makeLabelFns=function(Be,Ge,kt){var dt=Be.ticklabelposition||"",Oe=function(Kt){return dt.indexOf(Kt)!==-1},Ie=Oe("top"),Te=Oe("left"),Pe=Oe("right"),qe=Oe("bottom")||Te||Ie||Pe,rt=Oe("inside"),lt=dt==="inside"&&Be.ticks==="inside"||!rt&&Be.ticks==="outside"&&Be.tickson!=="boundaries",ot=0,At=0,wt=lt?Be.ticklen:0;if(rt?wt*=-1:qe&&(wt=0),lt&&(ot+=wt,kt)){var $t=i.deg2rad(kt);ot=wt*Math.cos($t)+1,At=wt*Math.sin($t)}Be.showticklabels&&(lt||Be.showline)&&(ot+=.2*Be.tickfont.size);var Ut,tt,bt,Ft,Et,Pt={labelStandoff:ot+=(Be.linewidth||1)/2*(rt?-1:1),labelShift:At},De=0,Je=Be.side,st=Be._id.charAt(0),St=Be.tickangle;if(st==="x")Ft=(Et=!rt&&Je==="bottom"||rt&&Je==="top")?1:-1,rt&&(Ft*=-1),Ut=At*Ft,tt=Ge+ot*Ft,bt=Et?1:-.2,Math.abs(St)===90&&(rt?bt+=te:bt=St===-90&&Je==="bottom"?Y:St===90&&Je==="top"?te:.5,De=te/2*(St/90)),Pt.xFn=function(Kt){return Kt.dx+Ut+De*Kt.fontSize},Pt.yFn=function(Kt){return Kt.dy+tt+Kt.fontSize*bt},Pt.anchorFn=function(Kt,qt){if(qe){if(Te)return"end";if(Pe)return"start"}return a(qt)&&qt!==0&&qt!==180?qt*Ft<0!==rt?"end":"start":"middle"},Pt.heightFn=function(Kt,qt,mn){return qt<-60||qt>60?-.5*mn:Be.side==="top"!==rt?-mn:0};else if(st==="y"){if(Ft=(Et=!rt&&Je==="left"||rt&&Je==="right")?1:-1,rt&&(Ft*=-1),Ut=ot,tt=At*Ft,bt=0,rt||Math.abs(St)!==90||(bt=St===-90&&Je==="left"||St===90&&Je==="right"?Y:.5),rt){var It=a(St)?+St:0;if(It!==0){var Zt=i.deg2rad(It);De=Math.abs(Math.sin(Zt))*Y*Ft,bt=0}}Pt.xFn=function(Kt){return Kt.dx+Ge-(Ut+Kt.fontSize*bt)*Ft+De*Kt.fontSize},Pt.yFn=function(Kt){return Kt.dy+tt+Kt.fontSize*te},Pt.anchorFn=function(Kt,qt){return a(qt)&&Math.abs(qt)===90?"middle":Et?"end":"start"},Pt.heightFn=function(Kt,qt,mn){return Be.side==="right"&&(qt*=-1),qt<-30?-mn:qt<30?-.5*mn:0}}return Pt},U.drawTicks=function(Be,Ge,kt){kt=kt||{};var dt=Ge._id+"tick",Oe=kt.vals;Ge.ticklabelmode==="period"&&(Oe=Oe.slice()).shift();var Ie=kt.layer.selectAll("path."+dt).data(Ge.ticks?Oe:[],Re);Ie.exit().remove(),Ie.enter().append("path").classed(dt,1).classed("ticks",1).classed("crisp",kt.crisp!==!1).call(d.stroke,Ge.tickcolor).style("stroke-width",m.crispRound(Be,Ge.tickwidth,1)+"px").attr("d",kt.path).style("display",null),Jt(Ge,[W]),Ie.attr("transform",kt.transFn)},U.drawGrid=function(Be,Ge,kt){kt=kt||{};var dt=Ge._id+"grid",Oe=kt.vals,Ie=kt.counterAxis;if(Ge.showgrid===!1)Oe=[];else if(Ie&&U.shouldShowZeroLine(Be,Ge,Ie))for(var Te=Ge.tickmode==="array",Pe=0;Pe<Oe.length;Pe++){var qe=Oe[Pe].x;if(Te?!qe:Math.abs(qe)<Ge.dtick/100){if(Oe=Oe.slice(0,Pe).concat(Oe.slice(Pe+1)),!Te)break;Pe--}}var rt=kt.layer.selectAll("path."+dt).data(Oe,Re);rt.exit().remove(),rt.enter().append("path").classed(dt,1).classed("crisp",kt.crisp!==!1),Ge._gw=m.crispRound(Be,Ge.gridwidth,1),rt.attr("transform",kt.transFn).attr("d",kt.path).call(d.stroke,Ge.gridcolor||"#ddd").style("stroke-width",Ge._gw+"px").style("display",null),Jt(Ge,[B]),typeof kt.path=="function"&&rt.attr("d",kt.path)},U.drawZeroLine=function(Be,Ge,kt){kt=kt||kt;var dt=Ge._id+"zl",Oe=U.shouldShowZeroLine(Be,Ge,kt.counterAxis),Ie=kt.layer.selectAll("path."+dt).data(Oe?[{x:0,id:Ge._id}]:[]);Ie.exit().remove(),Ie.enter().append("path").classed(dt,1).classed("zl",1).classed("crisp",kt.crisp!==!1).each(function(){kt.layer.selectAll("path").sort(function(Te,Pe){return ne(Te.id,Pe.id)})}),Ie.attr("transform",kt.transFn).attr("d",kt.path).call(d.stroke,Ge.zerolinecolor||d.defaultLine).style("stroke-width",m.crispRound(Be,Ge.zerolinewidth,Ge._gw||1)+"px").style("display",null),Jt(Ge,[N])},U.drawLabels=function(Be,Ge,kt){kt=kt||{};var dt=Be._fullLayout,Oe=Ge._id,Ie=Oe.charAt(0),Te=kt.cls||Oe+"tick",Pe=kt.vals,qe=kt.labelFns,rt=kt.secondary?0:Ge.tickangle,lt=(Ge._prevTickAngles||{})[Te],ot=kt.layer.selectAll("g."+Te).data(Ge.showticklabels?Pe:[],Re),At=[];function wt(Ft,Et){Ft.each(function(Pt){var De=r.select(this),Je=De.select(".text-math-group"),st=qe.anchorFn(Pt,Et),St=kt.transFn.call(De.node(),Pt)+(a(Et)&&+Et!=0?" rotate("+Et+","+qe.xFn(Pt)+","+(qe.yFn(Pt)-Pt.fontSize/2)+")":""),It=u.lineCount(De),Zt=J*Pt.fontSize,Kt=qe.heightFn(Pt,a(Et)?+Et:0,(It-1)*Zt);if(Kt&&(St+=s(0,Kt)),Je.empty()){var qt=De.select("text");qt.attr({transform:St,"text-anchor":st}),qt.style("opacity",1),Ge._adjustTickLabelsOverflow&&Ge._adjustTickLabelsOverflow()}else{var mn=m.bBox(Je.node()).width*{end:-.5,start:.5}[st];Je.attr("transform",St+s(mn,0))}})}ot.enter().append("g").classed(Te,1).append("text").attr("text-anchor","middle").each(function(Ft){var Et=r.select(this),Pt=Be._promises.length;Et.call(u.positionText,qe.xFn(Ft),qe.yFn(Ft)).call(m.font,Ft.font,Ft.fontSize,Ft.fontColor).text(Ft.text).call(u.convertToTspans,Be),Be._promises[Pt]?At.push(Be._promises.pop().then(function(){wt(Et,rt)})):wt(Et,rt)}),Jt(Ge,[G]),ot.exit().remove(),kt.repositionOnUpdate&&ot.each(function(Ft){r.select(this).select("text").call(u.positionText,qe.xFn(Ft),qe.yFn(Ft))}),Ge._adjustTickLabelsOverflow=function(){var Ft=Ge.ticklabeloverflow;if(Ft&&Ft!=="allow"){var Et=Ft.indexOf("hide")!==-1,Pt=Ge._id.charAt(0)==="x",De=0,Je=Pt?Be._fullLayout.width:Be._fullLayout.height;if(Ft.indexOf("domain")!==-1){var st=i.simpleMap(Ge.range,Ge.r2l);De=Ge.l2p(st[0])+Ge._offset,Je=Ge.l2p(st[1])+Ge._offset}var St=Math.min(De,Je),It=Math.max(De,Je),Zt=Ge.side,Kt=1/0,qt=-1/0;for(var mn in ot.each(function(tn){var nn=r.select(this);if(nn.select(".text-math-group").empty()){var sn=m.bBox(nn.node()),gn=0;Pt?(sn.right>It||sn.left<St)&&(gn=1):(sn.bottom>It||sn.top+(Ge.tickangle?0:tn.fontSize/4)<St)&&(gn=1);var bn=nn.select("text");gn?Et&&bn.style("opacity",0):(bn.style("opacity",1),Kt=Zt==="bottom"||Zt==="right"?Math.min(Kt,Pt?sn.top:sn.left):-1/0,qt=Zt==="top"||Zt==="left"?Math.max(qt,Pt?sn.bottom:sn.right):1/0)}}),dt._plots){var Fn=dt._plots[mn];if(Ge._id===Fn.xaxis._id||Ge._id===Fn.yaxis._id){var pn=Pt?Fn.yaxis:Fn.xaxis;pn&&(pn["_visibleLabelMin_"+Ge._id]=Kt,pn["_visibleLabelMax_"+Ge._id]=qt)}}}},Ge._hideCounterAxisInsideTickLabels=function(Ft){var Et=Ge._id.charAt(0)==="x",Pt=[];for(var De in dt._plots){var Je=dt._plots[De];Ge._id!==Je.xaxis._id&&Ge._id!==Je.yaxis._id||Pt.push(Et?Je.yaxis:Je.xaxis)}Pt.forEach(function(st,St){st&&Wt(st)&&(Ft||[N,B,W,G]).forEach(function(It){var Zt=It.K==="tick"&&It.L==="text"&&Ge.ticklabelmode==="period",Kt=dt._plots[Ge._mainSubplot];(It.K===N.K?Kt.zerolinelayer.selectAll("."+Ge._id+"zl"):It.K===B.K?Kt.gridlayer.selectAll("."+Ge._id):Kt[Ge._id.charAt(0)+"axislayer"]).each(function(){var qt=r.select(this);It.L&&(qt=qt.selectAll(It.L)),qt.each(function(mn){var Fn=Ge.l2p(Zt?Ke(mn):mn.x)+Ge._offset,pn=r.select(this);Fn<Ge["_visibleLabelMax_"+st._id]&&Fn>Ge["_visibleLabelMin_"+st._id]?pn.style("display","none"):It.K!=="tick"||St||pn.style("display",null)})})})})},wt(ot,lt+1?lt:rt);var $t=null;Ge._selections&&(Ge._selections[Te]=ot);var Ut=[function(){return At.length&&Promise.all(At)}];Ge.automargin&&dt._redrawFromAutoMarginCount&&lt===90?($t=90,Ut.push(function(){wt(ot,lt)})):Ut.push(function(){if(wt(ot,rt),Pe.length&&Ie==="x"&&!a(rt)&&(Ge.type!=="log"||String(Ge.dtick).charAt(0)!=="D")){$t=0;var Ft,Et=0,Pt=[];if(ot.each(function(nn){Et=Math.max(Et,nn.fontSize);var sn=Ge.l2p(nn.x),gn=Ye(this),bn=m.bBox(gn.node());Pt.push({top:0,bottom:10,height:10,left:sn-bn.width/2,right:sn+bn.width/2+2,width:bn.width+2})}),Ge.tickson!=="boundaries"&&!Ge.showdividers||kt.secondary){var De=Pe.length,Je=Math.abs((Pe[De-1].x-Pe[0].x)*Ge._m)/(De-1),st=Ge.ticklabelposition||"",St=function(nn){return st.indexOf(nn)!==-1},It=St("top"),Zt=St("left"),Kt=St("right"),qt=St("bottom")||Zt||It||Kt?(Ge.tickwidth||0)+6:0,mn=Je<2.5*Et||Ge.type==="multicategory"||Ge._name==="realaxis";for(Ft=0;Ft<Pt.length-1;Ft++)if(i.bBoxIntersect(Pt[Ft],Pt[Ft+1],qt)){$t=mn?90:30;break}}else{var Fn=2;for(Ge.ticks&&(Fn+=Ge.tickwidth/2),Ft=0;Ft<Pt.length;Ft++){var pn=Pe[Ft].xbnd,tn=Pt[Ft];if(pn[0]!==null&&tn.left-Ge.l2p(pn[0])<Fn||pn[1]!==null&&Ge.l2p(pn[1])-tn.right<Fn){$t=90;break}}}$t&&wt(ot,$t)}}),Ge._tickAngles&&Ut.push(function(){Ge._tickAngles[Te]=$t===null?a(rt)?rt:0:$t});var 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r=t("fast-isnumeric"),a=t("../../registry"),l=t("../../lib"),c=t("../array_container_defaults"),i=t("./layout_attributes"),s=t("./tick_value_defaults"),u=t("./tick_mark_defaults"),h=t("./tick_label_defaults"),d=t("./prefix_suffix_defaults"),m=t("./category_order_defaults"),p=t("./line_grid_defaults"),g=t("./set_convert"),y=t("./constants").WEEKDAY_PATTERN,v=t("./constants").HOUR_PATTERN;function x(b,k,w){function M(D,O){return l.coerce(b,k,i.rangebreaks,D,O)}if(M("enabled")){var T=M("bounds");if(T&&T.length>=2){var E,S,P="";if(T.length===2){for(E=0;E<2;E++)if(S=A(T[E])){P=y;break}}var L=M("pattern",P);if(L===y)for(E=0;E<2;E++)(S=A(T[E]))&&(k.bounds[E]=T[E]=S-1);if(L)for(E=0;E<2;E++)switch(S=T[E],L){case y:if(!r(S)||(S=+S)!==Math.floor(S)||S<0||S>=7)return void(k.enabled=!1);k.bounds[E]=T[E]=S;break;case v:if(!r(S)||(S=+S)<0||S>24)return void(k.enabled=!1);k.bounds[E]=T[E]=S}if(w.autorange===!1){var R=w.range;if(R[0]<R[1]){if(T[0]<R[0]&&T[1]>R[1])return void(k.enabled=!1)}else 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In=sn[bn],qn=In._id,Wn=Ke.xLinks[qn]||Ke.yLinks[qn],ar=gn[0]||Le[Wn]||de[Wn];ar&&(nn?(nn[In._name+".range[0]"]=nn[ar._name+".range[0]"],nn[In._name+".range[1]"]=nn[ar._name+".range[1]"]):In.range=ar.range.slice())}}function It(){var tn,nn=[];function sn(In){for(tn=0;tn<In.length;tn++)In[tn].fixedrange||nn.push(In[tn]._id)}for(Ye&&(sn(ve),sn($e.xaxes),sn(Ke.xaxes)),nt&&(sn(Me),sn($e.yaxes),sn(Ke.yaxes)),Tt={},tn=0;tn<nn.length;tn++){var gn=nn[tn],bn=T(ie,gn);g.drawOne(ie,bn,{skipTitle:!0}),Tt[bn._name+".range[0]"]=bn.range[0],Tt[bn._name+".range[1]"]=bn.range[1]}g.redrawComponents(ie,nn)}function Zt(){if(!ie._transitioningWithDuration){var tn=ie._context.doubleClick,nn=[];Re&&(nn=nn.concat(ve)),Ve&&(nn=nn.concat(Me)),Ke.xaxes&&(nn=nn.concat(Ke.xaxes)),Ke.yaxes&&(nn=nn.concat(Ke.yaxes));var sn,gn,bn,In={};if(tn==="reset+autosize"){for(tn="autosize",gn=0;gn<nn.length;gn++)if((sn=nn[gn])._rangeInitial&&(sn.range[0]!==sn._rangeInitial[0]||sn.range[1]!==sn._rangeInitial[1])||!sn._rangeInitial&&!sn.autorange){tn="reset";break}}if(tn==="autosize")for(gn=0;gn<nn.length;gn++)(sn=nn[gn]).fixedrange||(In[sn._name+".autorange"]=!0);else if(tn==="reset")for((Re||$e.isSubplotConstrained)&&(nn=nn.concat($e.xaxes)),Ve&&!$e.isSubplotConstrained&&(nn=nn.concat($e.yaxes)),$e.isSubplotConstrained&&(Re?Ve||(nn=nn.concat(Me)):nn=nn.concat(ve)),gn=0;gn<nn.length;gn++)(sn=nn[gn]).fixedrange||(sn._rangeInitial?(bn=sn._rangeInitial,In[sn._name+".range[0]"]=bn[0],In[sn._name+".range[1]"]=bn[1]):In[sn._name+".autorange"]=!0);ie.emit("plotly_doubleclick",null),s.call("_guiRelayout",ie,In)}}function Kt(){qt([0,0,Fe,ze]),a.syncOrAsync([M.previousPromises,function(){ie._fullLayout._replotting=!1,s.call("_guiRelayout",ie,Tt)}],ie)}function qt(tn){var nn,sn,gn,bn,In=ie._fullLayout,qn=In._plots,Wn=In._subplots.cartesian;if(yt&&s.subplotsRegistry.splom.drag(ie),ft){for(nn=0;nn<Wn.length;nn++)if(gn=(sn=qn[Wn[nn]]).xaxis,bn=sn.yaxis,sn._scene){var ar=a.simpleMap(gn.range,gn.r2l),Dr=a.simpleMap(bn.range,bn.r2l);sn._scene.update({range:[ar[0],Dr[0],ar[1],Dr[1]]})}}if((yt||ft)&&(k(ie),w(ie)),Ot){var yr=tn[2]/Ae._length,Sr=tn[3]/ke._length;for(nn=0;nn<Wn.length;nn++){gn=(sn=qn[Wn[nn]]).xaxis,bn=sn.yaxis;var Kn,Dn,lr,Yr,Mn=(Ye||Ke.isSubplotConstrained)&&!gn.fixedrange&&Le[gn._id],rr=(nt||Ke.isSubplotConstrained)&&!bn.fixedrange&&de[bn._id];if(Mn?(Kn=yr,lr=_e||Ke.isSubplotConstrained?tn[0]:pn(gn,Kn)):Ke.xaHash[gn._id]?(Kn=yr,lr=tn[0]*gn._length/Ae._length):Ke.yaHash[gn._id]?(Kn=Sr,lr=Ve==="ns"?-tn[1]*gn._length/ke._length:pn(gn,Kn,{n:"top",s:"bottom"}[Ve])):lr=Fn(gn,Kn=mn(gn,yr,Sr)),rr?(Dn=Sr,Yr=me||Ke.isSubplotConstrained?tn[1]:pn(bn,Dn)):Ke.yaHash[bn._id]?(Dn=Sr,Yr=tn[1]*bn._length/ke._length):Ke.xaHash[bn._id]?(Dn=yr,Yr=Re==="ew"?-tn[0]*bn._length/Ae._length:pn(bn,Dn,{e:"right",w:"left"}[Re])):Yr=Fn(bn,Dn=mn(bn,yr,Sr)),Kn||Dn){Kn||(Kn=1),Dn||(Dn=1);var nr=gn._offset-lr/Kn,Bn=bn._offset-Yr/Dn;sn.clipRect.call(m.setTranslate,lr,Yr).call(m.setScale,Kn,Dn),sn.plot.call(m.setTranslate,nr,Bn).call(m.setScale,1/Kn,1/Dn),Kn===sn.xScaleFactor&&Dn===sn.yScaleFactor||(m.setPointGroupScale(sn.zoomScalePts,Kn,Dn),m.setTextPointsScale(sn.zoomScaleTxt,Kn,Dn)),m.hideOutsideRangePoints(sn.clipOnAxisFalseTraces,sn),sn.xScaleFactor=Kn,sn.yScaleFactor=Dn}}}}function mn(tn,nn,sn){return tn.fixedrange?0:Ye&&$e.xaHash[tn._id]?nn:nt&&($e.isSubplotConstrained?$e.xaHash:$e.yaHash)[tn._id]?sn:0}function Fn(tn,nn){return nn?(tn.range=tn._r.slice(),L(tn,nn),pn(tn,nn)):0}function pn(tn,nn,sn){return tn._length*(1-nn)*b[sn||tn.constraintoward||"middle"]}return me.length*_e.length!=1&&Q(kt,function(tn){if(ie._context._scrollZoom.cartesian||ie._fullLayout._enablescrollzoom){if($t(),ie._transitioningWithDuration)return tn.preventDefault(),void tn.stopPropagation();Be(),clearTimeout(Pt);var nn=-tn.deltaY;if(isFinite(nn)||(nn=tn.wheelDelta/10),isFinite(nn)){var sn,gn=Math.exp(-Math.min(Math.max(nn,-20),20)/200),bn=Je.draglayer.select(".nsewdrag").node().getBoundingClientRect(),In=(tn.clientX-bn.left)/bn.width,qn=(bn.bottom-tn.clientY)/bn.height;if(Ye){for(_e||(In=.5),sn=0;sn<ve.length;sn++)Wn(ve[sn],In,gn);St("x"),Et[2]*=gn,Et[0]+=Et[2]*In*(1/gn-1)}if(nt){for(me||(qn=.5),sn=0;sn<Me.length;sn++)Wn(Me[sn],qn,gn);St("y"),Et[3]*=gn,Et[1]+=Et[3]*(1-qn)*(1/gn-1)}qt(Et),It(),ie.emit("plotly_relayouting",Tt),Pt=setTimeout(function(){ie._fullLayout&&(Et=[0,0,Fe,ze],Kt())},De),tn.preventDefault()}else a.log("Did not find wheel motion attributes: ",tn)}function Wn(ar,Dr,yr){if(!ar.fixedrange){var Sr=a.simpleMap(ar.range,ar.r2l),Kn=Sr[0]+(Sr[1]-Sr[0])*Dr;ar.range=Sr.map(function(Dn){return ar.l2r(Kn+(Dn-Kn)*yr)})}}}),kt},makeDragger:N,makeRectDragger:B,makeZoombox:Y,makeCorners:J,updateZoombox:re,xyCorners:ne,transitionZoombox:U,removeZoombox:V,showDoubleClickNotifier:H,attachWheelEventHandler:Q}},{"../../components/color":366,"../../components/dragelement":385,"../../components/dragelement/helpers":384,"../../components/drawing":388,"../../components/fx":406,"../../constants/alignment":471,"../../lib":503,"../../lib/clear_gl_canvases":487,"../../lib/setcursor":524,"../../lib/svg_text_utils":529,"../../plot_api/subroutines":544,"../../registry":638,"../plots":619,"./axes":554,"./axis_ids":558,"./constants":561,"./scale_zoom":574,"./select":575,"@plotly/d3":58,"has-passive-events":229,tinycolor2:312}],564:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/fx"),l=t("../../components/dragelement"),c=t("../../lib/setcursor"),i=t("./dragbox").makeDragBox,s=t("./constants").DRAGGERSIZE;f.initInteractions=function(u){var h=u._fullLayout;if(u._context.staticPlot)r.select(u).selectAll(".drag").remove();else if(h._has("cartesian")||h._has("splom")){Object.keys(h._plots||{}).sort(function(m,p){if((h._plots[m].mainplot&&!0)===(h._plots[p].mainplot&&!0)){var g=m.split("y"),y=p.split("y");return g[0]===y[0]?Number(g[1]||1)-Number(y[1]||1):Number(g[0]||1)-Number(y[0]||1)}return h._plots[m].mainplot?1:-1}).forEach(function(m){var p=h._plots[m],g=p.xaxis,y=p.yaxis;if(!p.mainplot){var v=i(u,p,g._offset,y._offset,g._length,y._length,"ns","ew");v.onmousemove=function(A){u._fullLayout._rehover=function(){u._fullLayout._hoversubplot===m&&u._fullLayout._plots[m]&&a.hover(u,A,m)},a.hover(u,A,m),u._fullLayout._lasthover=v,u._fullLayout._hoversubplot=m},v.onmouseout=function(A){u._dragging||(u._fullLayout._hoversubplot=null,l.unhover(u,A))},u._context.showAxisDragHandles&&(i(u,p,g._offset-s,y._offset-s,s,s,"n","w"),i(u,p,g._offset+g._length,y._offset-s,s,s,"n","e"),i(u,p,g._offset-s,y._offset+y._length,s,s,"s","w"),i(u,p,g._offset+g._length,y._offset+y._length,s,s,"s","e"))}if(u._context.showAxisDragHandles){if(m===g._mainSubplot){var x=g._mainLinePosition;g.side==="top"&&(x-=s),i(u,p,g._offset+.1*g._length,x,.8*g._length,s,"","ew"),i(u,p,g._offset,x,.1*g._length,s,"","w"),i(u,p,g._offset+.9*g._length,x,.1*g._length,s,"","e")}if(m===y._mainSubplot){var _=y._mainLinePosition;y.side!=="right"&&(_-=s),i(u,p,_,y._offset+.1*y._length,s,.8*y._length,"ns",""),i(u,p,_,y._offset+.9*y._length,s,.1*y._length,"s",""),i(u,p,_,y._offset,s,.1*y._length,"n","")}}});var d=h._hoverlayer.node();d.onmousemove=function(m){m.target=u._fullLayout._lasthover,a.hover(u,m,h._hoversubplot)},d.onclick=function(m){m.target=u._fullLayout._lasthover,a.click(u,m)},d.onmousedown=function(m){u._fullLayout._lasthover.onmousedown(m)},f.updateFx(u)}},f.updateFx=function(u){var h=u._fullLayout,d=h.dragmode==="pan"?"move":"crosshair";c(h._draggers,d)}},{"../../components/dragelement":385,"../../components/fx":406,"../../lib/setcursor":524,"./constants":561,"./dragbox":563,"@plotly/d3":58}],565:[function(t,o,f){o.exports={clearOutlineControllers:function(r){var a=r._fullLayout._zoomlayer;a&&a.selectAll(".outline-controllers").remove()},clearSelect:function(r){var a=r._fullLayout._zoomlayer;a&&a.selectAll(".select-outline").remove(),r._fullLayout._drawing=!1}}},{}],566:[function(t,o,f){var r=t("../../lib").strTranslate;function a(l,c){switch(l.type){case"log":return l.p2d(c);case"date":return l.p2r(c,0,l.calendar);default:return l.p2r(c)}}o.exports={p2r:a,r2p:function(l,c){switch(l.type){case"log":return l.d2p(c);case"date":return l.r2p(c,0,l.calendar);default:return l.r2p(c)}},axValue:function(l){var c=l._id.charAt(0)==="y"?1:0;return function(i){return a(l,i[c])}},getTransform:function(l){return r(l.xaxis._offset,l.yaxis._offset)}}},{"../../lib":503}],567:[function(t,o,f){var r=t("../../registry"),a=t("../../lib"),l=t("./axis_ids");o.exports=function(c){return function(i,s){var u=i[c];if(Array.isArray(u))for(var h=r.subplotsRegistry.cartesian,d=h.idRegex,m=s._subplots,p=m.xaxis,g=m.yaxis,y=m.cartesian,v=s._has("cartesian")||s._has("gl2d"),x=0;x<u.length;x++){var _=u[x];if(a.isPlainObject(_)){var A=l.cleanId(_.xref,"x",!1),b=l.cleanId(_.yref,"y",!1),k=d.x.test(A),w=d.y.test(b);if(k||w){v||a.pushUnique(s._basePlotModules,h);var M=!1;k&&p.indexOf(A)===-1&&(p.push(A),M=!0),w&&g.indexOf(b)===-1&&(g.push(b),M=!0),M&&k&&w&&y.push(A+b)}}}}}},{"../../lib":503,"../../registry":638,"./axis_ids":558}],568:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../registry"),l=t("../../lib"),c=t("../plots"),i=t("../../components/drawing"),s=t("../get_data").getModuleCalcData,u=t("./axis_ids"),h=t("./constants"),d=t("../../constants/xmlns_namespaces"),m=l.ensureSingle;function p(_,A,b){return l.ensureSingle(_,A,b,function(k){k.datum(b)})}function g(_,A,b,k,w){for(var M,T,E,S=h.traceLayerClasses,P=_._fullLayout,L=P._modules,R=[],F=[],D=0;D<L.length;D++){var O=(M=L[D]).name,N=a.modules[O].categories;if(N.svg){var B=M.layerName||O+"layer",W=M.plot;E=(T=s(b,W))[0],b=T[1],E.length&&R.push({i:S.indexOf(B),className:B,plotMethod:W,cdModule:E}),N.zoomScale&&F.push("."+B)}}R.sort(function(te,Y){return te.i-Y.i});var G=A.plot.selectAll("g.mlayer").data(R,function(te){return te.className});if(G.enter().append("g").attr("class",function(te){return te.className}).classed("mlayer",!0).classed("rangeplot",A.isRangePlot),G.exit().remove(),G.order(),G.each(function(te){var Y=r.select(this),J=te.className;te.plotMethod(_,A,te.cdModule,Y,k,w),h.clipOnAxisFalseQuery.indexOf("."+J)===-1&&i.setClipUrl(Y,A.layerClipId,_)}),P._has("scattergl")&&(M=a.getModule("scattergl"),E=s(b,M)[0],M.plot(_,A,E)),!_._context.staticPlot&&(A._hasClipOnAxisFalse&&(A.clipOnAxisFalseTraces=A.plot.selectAll(h.clipOnAxisFalseQuery.join(",")).selectAll(".trace")),F.length)){var K=A.plot.selectAll(F.join(",")).selectAll(".trace");A.zoomScalePts=K.selectAll("path.point"),A.zoomScaleTxt=K.selectAll(".textpoint")}}function y(_,A){var b=A.plotgroup,k=A.id,w=h.layerValue2layerClass[A.xaxis.layer],M=h.layerValue2layerClass[A.yaxis.layer],T=_._fullLayout._hasOnlyLargeSploms;if(A.mainplot){var E=A.mainplotinfo,S=E.plotgroup,P=k+"-x",L=k+"-y";A.gridlayer=E.gridlayer,A.zerolinelayer=E.zerolinelayer,m(E.overlinesBelow,"path",P),m(E.overlinesBelow,"path",L),m(E.overaxesBelow,"g",P),m(E.overaxesBelow,"g",L),A.plot=m(E.overplot,"g",k),m(E.overlinesAbove,"path",P),m(E.overlinesAbove,"path",L),m(E.overaxesAbove,"g",P),m(E.overaxesAbove,"g",L),A.xlines=S.select(".overlines-"+w).select("."+P),A.ylines=S.select(".overlines-"+M).select("."+L),A.xaxislayer=S.select(".overaxes-"+w).select("."+P),A.yaxislayer=S.select(".overaxes-"+M).select("."+L)}else if(T)A.xlines=m(b,"path","xlines-above"),A.ylines=m(b,"path","ylines-above"),A.xaxislayer=m(b,"g","xaxislayer-above"),A.yaxislayer=m(b,"g","yaxislayer-above");else{var R=m(b,"g","layer-subplot");A.shapelayer=m(R,"g","shapelayer"),A.imagelayer=m(R,"g","imagelayer"),A.gridlayer=m(b,"g","gridlayer"),A.zerolinelayer=m(b,"g","zerolinelayer"),m(b,"path","xlines-below"),m(b,"path","ylines-below"),A.overlinesBelow=m(b,"g","overlines-below"),m(b,"g","xaxislayer-below"),m(b,"g","yaxislayer-below"),A.overaxesBelow=m(b,"g","overaxes-below"),A.plot=m(b,"g","plot"),A.overplot=m(b,"g","overplot"),A.xlines=m(b,"path","xlines-above"),A.ylines=m(b,"path","ylines-above"),A.overlinesAbove=m(b,"g","overlines-above"),m(b,"g","xaxislayer-above"),m(b,"g","yaxislayer-above"),A.overaxesAbove=m(b,"g","overaxes-above"),A.xlines=b.select(".xlines-"+w),A.ylines=b.select(".ylines-"+M),A.xaxislayer=b.select(".xaxislayer-"+w),A.yaxislayer=b.select(".yaxislayer-"+M)}T||(p(A.gridlayer,"g",A.xaxis._id),p(A.gridlayer,"g",A.yaxis._id),A.gridlayer.selectAll("g").map(function(F){return 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Ke,Re,Ve,We=0;for(Ve=0;Ve<$e.length;Ve++)if(Ke=$e[Ve],(Re=Ke.cd[0].trace).selectedpoints&&(Re.selectedpoints.length>1||(We+=Re.selectedpoints.length)>1))return!1;return We===1}(ue)&&(_e=J(we))){for(ae&&ae.remove(),Le=0;Le<ue.length;Le++)(le=ue[Le])._module.selectPoints(le,!1);re(ne,ue),te(ie),ve&&ne.emit("plotly_deselect",null)}else{for(Ae=H.shiftKey&&(_e!==void 0?_e:J(we)),ge=function($e,Ke,Re){return{pointNumber:$e,searchInfo:Ke,subtract:Re}}(we.pointNumber,we.searchInfo,Ae),fe=G(ie.selectionDefs.concat([ge])),Le=0;Le<ue.length;Le++)if(me=U(ue[Le]._module.selectPoints(ue[Le],fe),ue[Le]),Me.length)for(var Ce=0;Ce<me.length;Ce++)Me.push(me[Ce]);else Me=me;if(re(ne,ue,ke={points:Me}),ge&&ie&&ie.selectionDefs.push(ge),ae){var Fe=ie.mergedPolygons,ze=p(ie.dragmode);y(V(Fe,ze),ae,ie)}ve&&ne.emit("plotly_selected",ke)}}}function B(H){return"pointNumber"in H&&"searchInfo"in H}function W(H){return{xmin:0,xmax:0,ymin:0,ymax:0,pts:[],contains:function(ne,q,Q,ee){var 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Q=ne._fullLayout,ee=q.plotinfo,ie=q.dragmode,ae=Q._lastSelectedSubplot&&Q._lastSelectedSubplot===ee.id,ue=(H.shiftKey||H.altKey)&&!(m(ie)&&p(ie));ae&&ue&&ee.selection&&ee.selection.selectionDefs&&!q.selectionDefs?(q.selectionDefs=ee.selection.selectionDefs,q.mergedPolygons=ee.selection.mergedPolygons):ue&&ee.selection||te(q),ae||(L(ne),Q._lastSelectedSubplot=ee.id)}function te(H){var ne=H.dragmode,q=H.plotinfo,Q=H.gd;if(Q._fullLayout._activeShapeIndex>=0&&Q._fullLayout._deactivateShape(Q),m(ne)){var ee=Q._fullLayout._zoomlayer.selectAll(".select-outline-"+q.id);if(ee&&Q._fullLayout._drawing){var ie=x(ee,H);ie&&a.call("_guiRelayout",Q,{shapes:ie}),Q._fullLayout._drawing=!1}}q.selection={},q.selection.selectionDefs=H.selectionDefs=[],q.selection.mergedPolygons=H.mergedPolygons=[]}function Y(H,ne,q,Q){var ee,ie,ae,ue=[],le=ne.map(function(Ae){return Ae._id}),ge=q.map(function(Ae){return 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D=this,O=D.topojson,N=D.layers,B=D.basePaths;function W(J){return J==="lonaxis"||J==="lataxis"}function G(J){return!!w.lineLayers[J]}function K(J){return!!w.fillLayers[J]}var te=(this.hasChoropleth?w.layersForChoropleth:w.layers).filter(function(J){return G(J)||K(J)?F["show"+J]:!W(J)||F[J].showgrid}),Y=D.framework.selectAll(".layer").data(te,String);Y.exit().each(function(J){delete N[J],delete B[J],r.select(this).remove()}),Y.enter().append("g").attr("class",function(J){return"layer "+J}).each(function(J){var re=N[J]=r.select(this);J==="bg"?D.bgRect=re.append("rect").style("pointer-events","all"):W(J)?B[J]=re.append("path").style("fill","none"):J==="backplot"?re.append("g").classed("choroplethlayer",!0):J==="frontplot"?re.append("g").classed("scatterlayer",!0):G(J)?B[J]=re.append("path").style("fill","none").style("stroke-miterlimit",2):K(J)&&(B[J]=re.append("path").style("stroke","none"))}),Y.order(),Y.each(function(J){var 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M(E){return[Math.atan2(2*(E[0]*E[1]+E[2]*E[3]),1-2*(E[1]*E[1]+E[2]*E[2]))*i,Math.asin(Math.max(-1,Math.min(1,2*(E[0]*E[2]-E[3]*E[1]))))*i,Math.atan2(2*(E[0]*E[3]+E[1]*E[2]),1-2*(E[2]*E[2]+E[3]*E[3]))*i]}function T(E,S){for(var P=0,L=0,R=E.length;L<R;++L)P+=E[L]*S[L];return P}o.exports=function(E,S){var P=E.projection;return(S._isScoped?m:S._isClipped?g:p)(E,P)}},{"../../lib":503,"../../registry":638,"@plotly/d3":58}],593:[function(t,o,f){var r=t("../registry"),a=t("./cartesian/constants").SUBPLOT_PATTERN;f.getSubplotCalcData=function(l,c,i){var s=r.subplotsRegistry[c];if(!s)return[];for(var u=s.attr,h=[],d=0;d<l.length;d++){var m=l[d];m[0].trace[u]===i&&h.push(m)}return h},f.getModuleCalcData=function(l,c){var i,s=[],u=[];if(!(i=typeof c=="string"?r.getModule(c).plot:typeof c=="function"?c:c.plot))return[s,l];for(var h=0;h<l.length;h++){var d=l[h],m=d[0].trace;m.visible===!0&&m._length!==0&&(m._module.plot===i?s.push(d):u.push(d))}return[s,u]},f.getSubplotData=function(l,c,i){if(!r.subplotsRegistry[c])return[];var s,u,h,d=r.subplotsRegistry[c].attr,m=[];if(c==="gl2d"){var p=i.match(a);u="x"+p[1],h="y"+p[2]}for(var g=0;g<l.length;g++)s=l[g],c==="gl2d"&&r.traceIs(s,"gl2d")?s[d[0]]===u&&s[d[1]]===h&&m.push(s):s[d]===i&&m.push(s);return m}},{"../registry":638,"./cartesian/constants":561}],594:[function(t,o,f){var r=t("mouse-change"),a=t("mouse-wheel"),l=t("mouse-event-offset"),c=t("../cartesian/constants"),i=t("has-passive-events");function s(u,h){this.element=u,this.plot=h,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}o.exports=function(u){var h=u.mouseContainer,d=u.glplot,m=new s(h,d);function p(){u.xaxis.autorange=!1,u.yaxis.autorange=!1}function g(y,v,x){var _,A,b=u.calcDataBox(),k=d.viewBox,w=m.lastPos[0],M=m.lastPos[1],T=c.MINDRAG*d.pixelRatio,E=c.MINZOOM*d.pixelRatio;function S(O,N,B){var W=Math.min(N,B),G=Math.max(N,B);W!==G?(b[O]=W,b[O+2]=G,m.dataBox=b,u.setRanges(b)):(u.selectBox.selectBox=[0,0,1,1],u.glplot.setDirty())}switch(v*=d.pixelRatio,x*=d.pixelRatio,x=k[3]-k[1]-x,u.fullLayout.dragmode){case"zoom":if(y){var P=v/(k[2]-k[0])*(b[2]-b[0])+b[0],L=x/(k[3]-k[1])*(b[3]-b[1])+b[1];m.boxInited||(m.boxStart[0]=P,m.boxStart[1]=L,m.dragStart[0]=v,m.dragStart[1]=x),m.boxEnd[0]=P,m.boxEnd[1]=L,m.boxInited=!0,m.boxEnabled||m.boxStart[0]===m.boxEnd[0]&&m.boxStart[1]===m.boxEnd[1]||(m.boxEnabled=!0);var R=Math.abs(m.dragStart[0]-v)<E,F=Math.abs(m.dragStart[1]-x)<E;if(!function(){for(var O=u.graphDiv._fullLayout._axisConstraintGroups,N=u.xaxis._id,B=u.yaxis._id,W=0;W<O.length;W++)if(O[W][N]!==-1){if(O[W][B]!==-1)return!0;break}return!1}()||R&&F)R&&(m.boxEnd[0]=m.boxStart[0]),F&&(m.boxEnd[1]=m.boxStart[1]);else{_=m.boxEnd[0]-m.boxStart[0],A=m.boxEnd[1]-m.boxStart[1];var D=(b[3]-b[1])/(b[2]-b[0]);Math.abs(_*D)>Math.abs(A)?(m.boxEnd[1]=m.boxStart[1]+Math.abs(_)*D*(A>=0?1:-1),m.boxEnd[1]<b[1]?(m.boxEnd[1]=b[1],m.boxEnd[0]=m.boxStart[0]+(b[1]-m.boxStart[1])/Math.abs(D)):m.boxEnd[1]>b[3]&&(m.boxEnd[1]=b[3],m.boxEnd[0]=m.boxStart[0]+(b[3]-m.boxStart[1])/Math.abs(D))):(m.boxEnd[0]=m.boxStart[0]+Math.abs(A)/D*(_>=0?1:-1),m.boxEnd[0]<b[0]?(m.boxEnd[0]=b[0],m.boxEnd[1]=m.boxStart[1]+(b[0]-m.boxStart[0])*Math.abs(D)):m.boxEnd[0]>b[2]&&(m.boxEnd[0]=b[2],m.boxEnd[1]=m.boxStart[1]+(b[2]-m.boxStart[0])*Math.abs(D)))}}else m.boxEnabled?(_=m.boxStart[0]!==m.boxEnd[0],A=m.boxStart[1]!==m.boxEnd[1],_||A?(_&&(S(0,m.boxStart[0],m.boxEnd[0]),u.xaxis.autorange=!1),A&&(S(1,m.boxStart[1],m.boxEnd[1]),u.yaxis.autorange=!1),u.relayoutCallback()):u.glplot.setDirty(),m.boxEnabled=!1,m.boxInited=!1):m.boxInited&&(m.boxInited=!1);break;case"pan":m.boxEnabled=!1,m.boxInited=!1,y?(m.panning||(m.dragStart[0]=v,m.dragStart[1]=x),Math.abs(m.dragStart[0]-v)<T&&(v=m.dragStart[0]),Math.abs(m.dragStart[1]-x)<T&&(x=m.dragStart[1]),_=(w-v)*(b[2]-b[0])/(d.viewBox[2]-d.viewBox[0]),A=(M-x)*(b[3]-b[1])/(d.viewBox[3]-d.viewBox[1]),b[0]+=_,b[2]+=_,b[1]+=A,b[3]+=A,u.setRanges(b),m.panning=!0,m.lastInputTime=Date.now(),p(),u.cameraChanged(),u.handleAnnotations()):m.panning&&(m.panning=!1,u.relayoutCallback())}m.lastPos[0]=v,m.lastPos[1]=x}return m.mouseListener=r(h,g),h.addEventListener("touchstart",function(y){var v=l(y.changedTouches[0],h);g(0,v[0],v[1]),g(1,v[0],v[1]),y.preventDefault()},!!i&&{passive:!1}),h.addEventListener("touchmove",function(y){y.preventDefault();var v=l(y.changedTouches[0],h);g(1,v[0],v[1]),y.preventDefault()},!!i&&{passive:!1}),h.addEventListener("touchend",function(y){g(0,m.lastPos[0],m.lastPos[1]),y.preventDefault()},!!i&&{passive:!1}),m.wheelListener=a(h,function(y,v){if(!u.scrollZoom)return!1;var x=u.calcDataBox(),_=d.viewBox,A=m.lastPos[0],b=m.lastPos[1],k=Math.exp(5*v/(_[3]-_[1])),w=A/(_[2]-_[0])*(x[2]-x[0])+x[0],M=b/(_[3]-_[1])*(x[3]-x[1])+x[1];return x[0]=(x[0]-w)*k+w,x[2]=(x[2]-w)*k+w,x[1]=(x[1]-M)*k+M,x[3]=(x[3]-M)*k+M,u.setRanges(x),m.lastInputTime=Date.now(),p(),u.cameraChanged(),u.handleAnnotations(),u.relayoutCallback(),!0},!0),m}},{"../cartesian/constants":561,"has-passive-events":229,"mouse-change":241,"mouse-event-offset":242,"mouse-wheel":244}],595:[function(t,o,f){var r=t("../cartesian/axes"),a=t("../../lib/str2rgbarray");function l(s){this.scene=s,this.gl=s.gl,this.pixelRatio=s.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var c=l.prototype,i=["xaxis","yaxis"];c.merge=function(s){var u,h,d,m,p,g,y,v,x,_,A;for(this.titleEnable=!1,this.backgroundColor=a(s.plot_bgcolor),_=0;_<2;++_){var b=(u=i[_]).charAt(0);for(d=(h=s[this.scene[u]._name]).title.text===this.scene.fullLayout._dfltTitle[b]?"":h.title.text,A=0;A<=2;A+=2)this.labelEnable[_+A]=!1,this.labels[_+A]=d,this.labelColor[_+A]=a(h.title.font.color),this.labelFont[_+A]=h.title.font.family,this.labelSize[_+A]=h.title.font.size,this.labelPad[_+A]=this.getLabelPad(u,h),this.tickEnable[_+A]=!1,this.tickColor[_+A]=a((h.tickfont||{}).color),this.tickAngle[_+A]=h.tickangle==="auto"?0:Math.PI*-h.tickangle/180,this.tickPad[_+A]=this.getTickPad(h),this.tickMarkLength[_+A]=0,this.tickMarkWidth[_+A]=h.tickwidth||0,this.tickMarkColor[_+A]=a(h.tickcolor),this.borderLineEnable[_+A]=!1,this.borderLineColor[_+A]=a(h.linecolor),this.borderLineWidth[_+A]=h.linewidth||0;y=this.hasSharedAxis(h),p=this.hasAxisInDfltPos(u,h)&&!y,g=this.hasAxisInAltrPos(u,h)&&!y,m=h.mirror||!1,v=y?String(m).indexOf("all")!==-1:!!m,x=y?m==="allticks":String(m).indexOf("ticks")!==-1,p?this.labelEnable[_]=!0:g&&(this.labelEnable[_+2]=!0),p?this.tickEnable[_]=h.showticklabels:g&&(this.tickEnable[_+2]=h.showticklabels),(p||v)&&(this.borderLineEnable[_]=h.showline),(g||v)&&(this.borderLineEnable[_+2]=h.showline),(p||x)&&(this.tickMarkLength[_]=this.getTickMarkLength(h)),(g||x)&&(this.tickMarkLength[_+2]=this.getTickMarkLength(h)),this.gridLineEnable[_]=h.showgrid,this.gridLineColor[_]=a(h.gridcolor),this.gridLineWidth[_]=h.gridwidth,this.zeroLineEnable[_]=h.zeroline,this.zeroLineColor[_]=a(h.zerolinecolor),this.zeroLineWidth[_]=h.zerolinewidth}},c.hasSharedAxis=function(s){var u=this.scene,h=u.fullLayout._subplots.gl2d;return r.findSubplotsWithAxis(h,s).indexOf(u.id)!==0},c.hasAxisInDfltPos=function(s,u){var h=u.side;return s==="xaxis"?h==="bottom":s==="yaxis"?h==="left":void 0},c.hasAxisInAltrPos=function(s,u){var h=u.side;return s==="xaxis"?h==="top":s==="yaxis"?h==="right":void 0},c.getLabelPad=function(s,u){var h=u.title.font.size,d=u.showticklabels;return s==="xaxis"?u.side==="top"?h*(1.5+(d?1:0))-10:h*(1.5+(d?.5:0))-10:s==="yaxis"?u.side==="right"?10+h*(1.5+(d?1:.5)):10+h*(1.5+(d?.5:0)):void 0},c.getTickPad=function(s){return s.ticks==="outside"?10+s.ticklen:15},c.getTickMarkLength=function(s){if(!s.ticks)return 0;var u=s.ticklen;return s.ticks==="inside"?-u:u},o.exports=function(s){return new l(s)}},{"../../lib/str2rgbarray":528,"../cartesian/axes":554}],596:[function(t,o,f){var r=t("../../plot_api/edit_types").overrideAll,a=t("./scene2d"),l=t("../layout_attributes"),c=t("../../constants/xmlns_namespaces"),i=t("../cartesian/constants"),s=t("../cartesian"),u=t("../../components/fx/layout_attributes"),h=t("../get_data").getSubplotData;f.name="gl2d",f.attr=["xaxis","yaxis"],f.idRoot=["x","y"],f.idRegex=i.idRegex,f.attrRegex=i.attrRegex,f.attributes=t("../cartesian/attributes"),f.supplyLayoutDefaults=function(d,m,p){m._has("cartesian")||s.supplyLayoutDefaults(d,m,p)},f.layoutAttrOverrides=r(s.layoutAttributes,"plot","from-root"),f.baseLayoutAttrOverrides=r({plot_bgcolor:l.plot_bgcolor,hoverlabel:u.hoverlabel},"plot","nested"),f.plot=function(d){for(var m=d._fullLayout,p=d._fullData,g=m._subplots.gl2d,y=0;y<g.length;y++){var v=g[y],x=m._plots[v],_=h(p,"gl2d",v),A=x._scene2d;A===void 0&&(A=new a({id:v,graphDiv:d,container:d.querySelector(".gl-container"),staticPlot:d._context.staticPlot,plotGlPixelRatio:d._context.plotGlPixelRatio},m),x._scene2d=A),A.plot(_,d.calcdata,m,d.layout)}},f.clean=function(d,m,p,g){for(var y=g._subplots.gl2d||[],v=0;v<y.length;v++){var x=y[v],_=g._plots[x];if(_._scene2d){var A=h(d,"gl2d",x);A.length===0&&(_._scene2d.destroy(),delete g._plots[x])}}s.clean.apply(this,arguments)},f.drawFramework=function(d){d._context.staticPlot||s.drawFramework(d)},f.toSVG=function(d){for(var m=d._fullLayout,p=m._subplots.gl2d,g=0;g<p.length;g++){var y=m._plots[p[g]]._scene2d,v=y.toImage("png");m._glimages.append("svg:image").attr({xmlns:c.svg,"xlink:href":v,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),y.destroy()}},f.updateFx=function(d){for(var m=d._fullLayout,p=m._subplots.gl2d,g=0;g<p.length;g++)m._plots[p[g]]._scene2d.updateFx(m.dragmode)}},{"../../components/fx/layout_attributes":407,"../../constants/xmlns_namespaces":480,"../../plot_api/edit_types":536,"../cartesian":568,"../cartesian/attributes":552,"../cartesian/constants":561,"../get_data":593,"../layout_attributes":610,"./scene2d":597}],597:[function(t,o,f){var r,a,l=t("../../registry"),c=t("../../plots/cartesian/axes"),i=t("../../components/fx"),s=t("../../../stackgl_modules").gl_plot2d,u=t("../../../stackgl_modules").gl_spikes2d,h=t("../../../stackgl_modules").gl_select_box,d=t("webgl-context"),m=t("./convert"),p=t("./camera"),g=t("../../lib/show_no_webgl_msg"),y=t("../cartesian/constraints"),v=y.enforce,x=y.clean,_=t("../cartesian/autorange").doAutoRange,A=t("../../components/dragelement/helpers"),b=A.drawMode,k=A.selectMode,w=["xaxis","yaxis"],M=t("../cartesian/constants").SUBPLOT_PATTERN;function T(S,P){this.container=S.container,this.graphDiv=S.graphDiv,this.pixelRatio=S.plotGlPixelRatio||window.devicePixelRatio,this.id=S.id,this.staticPlot=!!S.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(P),this.makeFramework(),this.stopped||(this.glplotOptions=m(this),this.glplotOptions.merge(P),this.glplot=s(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=h(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}o.exports=T;var E=T.prototype;E.makeFramework=function(){if(this.staticPlot){if(!(a||(r=document.createElement("canvas"),a=d({canvas:r,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=r,this.gl=a}else{var S=this.container.querySelector(".gl-canvas-focus"),P=d({canvas:S,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!P)return g(this),void(this.stopped=!0);this.canvas=S,this.gl=P}var L=this.canvas;L.style.width="100%",L.style.height="100%",L.style.position="absolute",L.style.top="0px",L.style.left="0px",L.style["pointer-events"]="none",this.updateSize(L);var R=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");R.style.position="absolute",R.style.top=R.style.left="0px",R.style.width=R.style.height="100%",R.style["z-index"]=20,R.style["pointer-events"]="none";var F=this.mouseContainer=document.createElement("div");F.style.position="absolute",F.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var D=this.container;D.appendChild(R),D.appendChild(F);var O=this;F.addEventListener("mouseout",function(){O.isMouseOver=!1,O.unhover()}),F.addEventListener("mouseover",function(){O.isMouseOver=!0})},E.toImage=function(S){S||(S="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(r),this.updateSize(this.canvas);var P=this.glplot.gl,L=P.drawingBufferWidth,R=P.drawingBufferHeight;P.clearColor(1,1,1,0),P.clear(P.COLOR_BUFFER_BIT|P.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),P.bindFramebuffer(P.FRAMEBUFFER,null);var F=new Uint8Array(L*R*4);P.readPixels(0,0,L,R,P.RGBA,P.UNSIGNED_BYTE,F);for(var D=0,O=R-1;D<O;++D,--O)for(var N=0;N<L;++N)for(var B=0;B<4;++B){var W=F[4*(L*D+N)+B];F[4*(L*D+N)+B]=F[4*(L*O+N)+B],F[4*(L*O+N)+B]=W}var G=document.createElement("canvas");G.width=L,G.height=R;var K,te=G.getContext("2d"),Y=te.createImageData(L,R);switch(Y.data.set(F),te.putImageData(Y,0,0),S){case"jpeg":K=G.toDataURL("image/jpeg");break;case"webp":K=G.toDataURL("image/webp");break;default:K=G.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(r),K},E.updateSize=function(S){S||(S=this.canvas);var P=this.pixelRatio,L=this.fullLayout,R=L.width,F=L.height,D=0|Math.ceil(P*R),O=0|Math.ceil(P*F);return S.width===D&&S.height===O||(S.width=D,S.height=O),S},E.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var S=[c.calcTicks(this.xaxis),c.calcTicks(this.yaxis)],P=0;P<2;++P)for(var L=0;L<S[P].length;++L)S[P][L].text=S[P][L].text+"";return S},E.updateRefs=function(S){this.fullLayout=S;var P=this.id.match(M),L="xaxis"+P[1],R="yaxis"+P[2];this.xaxis=this.fullLayout[L],this.yaxis=this.fullLayout[R]},E.relayoutCallback=function(){var S=this.graphDiv,P=this.xaxis,L=this.yaxis,R=S.layout,F={},D=F[P._name+".range"]=P.range.slice(),O=F[L._name+".range"]=L.range.slice();F[P._name+".autorange"]=P.autorange,F[L._name+".autorange"]=L.autorange,l.call("_storeDirectGUIEdit",S.layout,S._fullLayout._preGUI,F);var N=R[P._name];N.range=D,N.autorange=P.autorange;var B=R[L._name];B.range=O,B.autorange=L.autorange,F.lastInputTime=this.camera.lastInputTime,S.emit("plotly_relayout",F)},E.cameraChanged=function(){var S=this.camera;this.glplot.setDataBox(this.calcDataBox());var P=this.computeTickMarks();(function(L,R){for(var F=0;F<2;++F){var D=L[F],O=R[F];if(D.length!==O.length)return!0;for(var N=0;N<D.length;++N)if(D[N].x!==O[N].x)return!0}return!1})(P,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=P,this.glplotOptions.dataBox=S.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},E.handleAnnotations=function(){for(var S=this.graphDiv,P=this.fullLayout.annotations,L=0;L<P.length;L++){var R=P[L];R.xref===this.xaxis._id&&R.yref===this.yaxis._id&&l.getComponentMethod("annotations","drawOne")(S,L)}},E.destroy=function(){if(this.glplot){var S=this.traces;S&&Object.keys(S).map(function(P){S[P].dispose(),delete S[P]}),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},E.plot=function(S,P,L){var R=this.glplot;this.updateRefs(L),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(S,P),this.updateFx(L.dragmode);var F=L.width,D=L.height;this.updateSize(this.canvas);var O=this.glplotOptions;O.merge(L),O.screenBox=[0,0,F,D];var N={_fullLayout:{_axisConstraintGroups:L._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:L._size}};x(N,this.xaxis),x(N,this.yaxis);var B,W,G=L._size,K=this.xaxis.domain,te=this.yaxis.domain;for(O.viewBox=[G.l+K[0]*G.w,G.b+te[0]*G.h,F-G.r-(1-K[1])*G.w,D-G.t-(1-te[1])*G.h],this.mouseContainer.style.width=G.w*(K[1]-K[0])+"px",this.mouseContainer.style.height=G.h*(te[1]-te[0])+"px",this.mouseContainer.height=G.h*(te[1]-te[0]),this.mouseContainer.style.left=G.l+K[0]*G.w+"px",this.mouseContainer.style.top=G.t+(1-te[1])*G.h+"px",W=0;W<2;++W)(B=this[w[W]])._length=O.viewBox[W+2]-O.viewBox[W],_(this.graphDiv,B),B.setScale();v(N),O.ticks=this.computeTickMarks(),O.dataBox=this.calcDataBox(),O.merge(L),R.update(O),this.glplot.draw()},E.calcDataBox=function(){var S=this.xaxis,P=this.yaxis,L=S.range,R=P.range,F=S.r2l,D=P.r2l;return[F(L[0]),D(R[0]),F(L[1]),D(R[1])]},E.setRanges=function(S){var P=this.xaxis,L=this.yaxis,R=P.l2r,F=L.l2r;P.range=[R(S[0]),R(S[2])],L.range=[F(S[1]),F(S[3])]},E.updateTraces=function(S,P){var L,R,F,D=Object.keys(this.traces);this.fullData=S;e:for(L=0;L<D.length;L++){var O=D[L],N=this.traces[O];for(R=0;R<S.length;R++)if((F=S[R]).uid===O&&F.type===N.type)continue e;N.dispose(),delete this.traces[O]}for(L=0;L<S.length;L++){F=S[L];var B=P[L],W=this.traces[F.uid];W?W.update(F,B):(W=F._module.plot(this,F,B),this.traces[F.uid]=W)}this.glplot.objects.sort(function(G,K){return G._trace.index-K._trace.index})},E.updateFx=function(S){k(S)||b(S)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor=S==="pan"?"move":S==="zoom"?"crosshair":null},E.emitPointAction=function(S,P){for(var L,R=S.trace.uid,F=S.pointIndex,D=0;D<this.fullData.length;D++)this.fullData[D].uid===R&&(L=this.fullData[D]);var O={x:S.traceCoord[0],y:S.traceCoord[1],curveNumber:L.index,pointNumber:F,data:L._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};i.appendArrayPointValue(O,L,F),this.graphDiv.emit(P,{points:[O]})},E.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var S=this.glplot,P=this.camera,L=P.mouseListener,R=this.lastButtonState===1&&L.buttons===0,F=this.fullLayout;this.lastButtonState=L.buttons,this.cameraChanged();var D,O=L.x*S.pixelRatio,N=this.canvas.height-S.pixelRatio*L.y;if(P.boxEnabled&&F.dragmode==="zoom"){this.selectBox.enabled=!0;for(var B=this.selectBox.selectBox=[Math.min(P.boxStart[0],P.boxEnd[0]),Math.min(P.boxStart[1],P.boxEnd[1]),Math.max(P.boxStart[0],P.boxEnd[0]),Math.max(P.boxStart[1],P.boxEnd[1])],W=0;W<2;W++)P.boxStart[W]===P.boxEnd[W]&&(B[W]=S.dataBox[W],B[W+2]=S.dataBox[W+2]);S.setDirty()}else if(!P.panning&&this.isMouseOver){this.selectBox.enabled=!1;var G=F._size,K=this.xaxis.domain,te=this.yaxis.domain,Y=(D=S.pick(O/S.pixelRatio+G.l+K[0]*G.w,N/S.pixelRatio-(G.t+(1-te[1])*G.h)))&&D.object._trace.handlePick(D);if(Y&&R&&this.emitPointAction(Y,"plotly_click"),D&&D.object._trace.hoverinfo!=="skip"&&F.hovermode&&Y&&(!this.lastPickResult||this.lastPickResult.traceUid!==Y.trace.uid||this.lastPickResult.dataCoord[0]!==Y.dataCoord[0]||this.lastPickResult.dataCoord[1]!==Y.dataCoord[1])){var J=Y;this.lastPickResult={traceUid:Y.trace?Y.trace.uid:null,dataCoord:Y.dataCoord.slice()},this.spikes.update({center:D.dataCoord}),J.screenCoord=[((S.viewBox[2]-S.viewBox[0])*(D.dataCoord[0]-S.dataBox[0])/(S.dataBox[2]-S.dataBox[0])+S.viewBox[0])/S.pixelRatio,(this.canvas.height-(S.viewBox[3]-S.viewBox[1])*(D.dataCoord[1]-S.dataBox[1])/(S.dataBox[3]-S.dataBox[1])-S.viewBox[1])/S.pixelRatio],this.emitPointAction(Y,"plotly_hover");var re=this.fullData[J.trace.index]||{},U=J.pointIndex,V=i.castHoverinfo(re,F,U);if(V&&V!=="all"){var H=V.split("+");H.indexOf("x")===-1&&(J.traceCoord[0]=void 0),H.indexOf("y")===-1&&(J.traceCoord[1]=void 0),H.indexOf("z")===-1&&(J.traceCoord[2]=void 0),H.indexOf("text")===-1&&(J.textLabel=void 0),H.indexOf("name")===-1&&(J.name=void 0)}i.loneHover({x:J.screenCoord[0],y:J.screenCoord[1],xLabel:this.hoverFormatter("xaxis",J.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",J.traceCoord[1]),zLabel:J.traceCoord[2],text:J.textLabel,name:J.name,color:i.castHoverOption(re,U,"bgcolor")||J.color,borderColor:i.castHoverOption(re,U,"bordercolor"),fontFamily:i.castHoverOption(re,U,"font.family"),fontSize:i.castHoverOption(re,U,"font.size"),fontColor:i.castHoverOption(re,U,"font.color"),nameLength:i.castHoverOption(re,U,"namelength"),textAlign:i.castHoverOption(re,U,"align")},{container:this.svgContainer,gd:this.graphDiv})}}D||this.unhover(),S.draw()}},E.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),i.loneUnhover(this.svgContainer))},E.hoverFormatter=function(S,P){if(P!==void 0){var L=this[S];return c.tickText(L,L.c2l(P),"hover").text}}},{"../../../stackgl_modules":1124,"../../components/dragelement/helpers":384,"../../components/fx":406,"../../lib/show_no_webgl_msg":525,"../../plots/cartesian/axes":554,"../../registry":638,"../cartesian/autorange":553,"../cartesian/constants":561,"../cartesian/constraints":562,"./camera":594,"./convert":595,"webgl-context":331}],598:[function(t,o,f){var r=t("../../plot_api/edit_types").overrideAll,a=t("../../components/fx/layout_attributes"),l=t("./scene"),c=t("../get_data").getSubplotData,i=t("../../lib"),s=t("../../constants/xmlns_namespaces");f.name="gl3d",f.attr="scene",f.idRoot="scene",f.idRegex=f.attrRegex=i.counterRegex("scene"),f.attributes=t("./layout/attributes"),f.layoutAttributes=t("./layout/layout_attributes"),f.baseLayoutAttrOverrides=r({hoverlabel:a.hoverlabel},"plot","nested"),f.supplyLayoutDefaults=t("./layout/defaults"),f.plot=function(u){for(var h=u._fullLayout,d=u._fullData,m=h._subplots.gl3d,p=0;p<m.length;p++){var g=m[p],y=c(d,"gl3d",g),v=h[g],x=v.camera,_=v._scene;_||(_=new l({id:g,graphDiv:u,container:u.querySelector(".gl-container"),staticPlot:u._context.staticPlot,plotGlPixelRatio:u._context.plotGlPixelRatio,camera:x},h),v._scene=_),_.viewInitial||(_.viewInitial={up:{x:x.up.x,y:x.up.y,z:x.up.z},eye:{x:x.eye.x,y:x.eye.y,z:x.eye.z},center:{x:x.center.x,y:x.center.y,z:x.center.z}}),_.plot(y,h,u.layout)}},f.clean=function(u,h,d,m){for(var p=m._subplots.gl3d||[],g=0;g<p.length;g++){var y=p[g];!h[y]&&m[y]._scene&&(m[y]._scene.destroy(),m._infolayer&&m._infolayer.selectAll(".annotation-"+y).remove())}},f.toSVG=function(u){for(var h=u._fullLayout,d=h._subplots.gl3d,m=h._size,p=0;p<d.length;p++){var g=h[d[p]],y=g.domain,v=g._scene,x=v.toImage("png");h._glimages.append("svg:image").attr({xmlns:s.svg,"xlink:href":x,x:m.l+m.w*y.x[0],y:m.t+m.h*(1-y.y[1]),width:m.w*(y.x[1]-y.x[0]),height:m.h*(y.y[1]-y.y[0]),preserveAspectRatio:"none"}),v.destroy()}},f.cleanId=function(u){if(u.match(/^scene[0-9]*$/)){var h=u.substr(5);return h==="1"&&(h=""),"scene"+h}},f.updateFx=function(u){for(var h=u._fullLayout,d=h._subplots.gl3d,m=0;m<d.length;m++)h[d[m]]._scene.updateFx(h.dragmode,h.hovermode)}},{"../../components/fx/layout_attributes":407,"../../constants/xmlns_namespaces":480,"../../lib":503,"../../plot_api/edit_types":536,"../get_data":593,"./layout/attributes":599,"./layout/defaults":603,"./layout/layout_attributes":604,"./scene":608}],599:[function(t,o,f){o.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],600:[function(t,o,f){var r=t("../../../components/color"),a=t("../../cartesian/layout_attributes"),l=t("../../../lib/extend").extendFlat,c=t("../../../plot_api/edit_types").overrideAll;o.exports=c({visible:a.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:r.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:a.color,categoryorder:a.categoryorder,categoryarray:a.categoryarray,title:{text:a.title.text,font:a.title.font},type:l({},a.type,{values:["-","linear","log","date","category"]}),autotypenumbers:a.autotypenumbers,autorange:a.autorange,rangemode:a.rangemode,range:l({},a.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:a.tickmode,nticks:a.nticks,tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,mirror:a.mirror,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,tickfont:a.tickfont,tickangle:a.tickangle,tickprefix:a.tickprefix,showtickprefix:a.showtickprefix,ticksuffix:a.ticksuffix,showticksuffix:a.showticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:a.showline,linecolor:a.linecolor,linewidth:a.linewidth,showgrid:a.showgrid,gridcolor:l({},a.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:a.gridwidth,zeroline:a.zeroline,zerolinecolor:a.zerolinecolor,zerolinewidth:a.zerolinewidth,_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":366,"../../../lib/extend":493,"../../../plot_api/edit_types":536,"../../cartesian/layout_attributes":569}],601:[function(t,o,f){var r=t("tinycolor2").mix,a=t("../../../lib"),l=t("../../../plot_api/plot_template"),c=t("./axis_attributes"),i=t("../../cartesian/type_defaults"),s=t("../../cartesian/axis_defaults"),u=["xaxis","yaxis","zaxis"];o.exports=function(h,d,m){var p,g;function y(_,A){return a.coerce(p,g,c,_,A)}for(var v=0;v<u.length;v++){var x=u[v];p=h[x]||{},(g=l.newContainer(d,x))._id=x[0]+m.scene,g._name=x,i(p,g,y,m),s(p,g,y,{font:m.font,letter:x[0],data:m.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:m.bgColor,calendar:m.calendar},m.fullLayout),y("gridcolor",r(g.color,m.bgColor,13600/187).toRgbString()),y("title.text",x[0]),g.setScale=a.noop,y("showspikes")&&(y("spikesides"),y("spikethickness"),y("spikecolor",g.color)),y("showaxeslabels"),y("showbackground")&&y("backgroundcolor")}}},{"../../../lib":503,"../../../plot_api/plot_template":543,"../../cartesian/axis_defaults":556,"../../cartesian/type_defaults":582,"./axis_attributes":600,tinycolor2:312}],602:[function(t,o,f){var r=t("../../../lib/str2rgbarray"),a=t("../../../lib"),l=["xaxis","yaxis","zaxis"];function c(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}c.prototype.merge=function(i,s){for(var u=0;u<3;++u){var h=s[l[u]];h.visible?(this.labels[u]=i._meta?a.templateString(h.title.text,i._meta):h.title.text,"font"in h.title&&(h.title.font.color&&(this.labelColor[u]=r(h.title.font.color)),h.title.font.family&&(this.labelFont[u]=h.title.font.family),h.title.font.size&&(this.labelSize[u]=h.title.font.size)),"showline"in h&&(this.lineEnable[u]=h.showline),"linecolor"in h&&(this.lineColor[u]=r(h.linecolor)),"linewidth"in h&&(this.lineWidth[u]=h.linewidth),"showgrid"in h&&(this.gridEnable[u]=h.showgrid),"gridcolor"in h&&(this.gridColor[u]=r(h.gridcolor)),"gridwidth"in h&&(this.gridWidth[u]=h.gridwidth),h.type==="log"?this.zeroEnable[u]=!1:"zeroline"in h&&(this.zeroEnable[u]=h.zeroline),"zerolinecolor"in h&&(this.zeroLineColor[u]=r(h.zerolinecolor)),"zerolinewidth"in h&&(this.zeroLineWidth[u]=h.zerolinewidth),"ticks"in h&&h.ticks?this.lineTickEnable[u]=!0:this.lineTickEnable[u]=!1,"ticklen"in h&&(this.lineTickLength[u]=this._defaultLineTickLength[u]=h.ticklen),"tickcolor"in h&&(this.lineTickColor[u]=r(h.tickcolor)),"tickwidth"in h&&(this.lineTickWidth[u]=h.tickwidth),"tickangle"in h&&(this.tickAngle[u]=h.tickangle==="auto"?-3600:Math.PI*-h.tickangle/180),"showticklabels"in h&&(this.tickEnable[u]=h.showticklabels),"tickfont"in h&&(h.tickfont.color&&(this.tickColor[u]=r(h.tickfont.color)),h.tickfont.family&&(this.tickFont[u]=h.tickfont.family),h.tickfont.size&&(this.tickSize[u]=h.tickfont.size)),"mirror"in h?["ticks","all","allticks"].indexOf(h.mirror)!==-1?(this.lineTickMirror[u]=!0,this.lineMirror[u]=!0):h.mirror===!0?(this.lineTickMirror[u]=!1,this.lineMirror[u]=!0):(this.lineTickMirror[u]=!1,this.lineMirror[u]=!1):this.lineMirror[u]=!1,"showbackground"in h&&h.showbackground!==!1?(this.backgroundEnable[u]=!0,this.backgroundColor[u]=r(h.backgroundcolor)):this.backgroundEnable[u]=!1):(this.tickEnable[u]=!1,this.labelEnable[u]=!1,this.lineEnable[u]=!1,this.lineTickEnable[u]=!1,this.gridEnable[u]=!1,this.zeroEnable[u]=!1,this.backgroundEnable[u]=!1)}},o.exports=function(i,s){var u=new c;return u.merge(i,s),u}},{"../../../lib":503,"../../../lib/str2rgbarray":528}],603:[function(t,o,f){var r=t("../../../lib"),a=t("../../../components/color"),l=t("../../../registry"),c=t("../../subplot_defaults"),i=t("./axis_defaults"),s=t("./layout_attributes"),u=t("../../get_data").getSubplotData;function h(d,m,p,g){for(var y=p("bgcolor"),v=a.combine(y,g.paper_bgcolor),x=["up","center","eye"],_=0;_<x.length;_++)p("camera."+x[_]+".x"),p("camera."+x[_]+".y"),p("camera."+x[_]+".z");p("camera.projection.type");var A=!!p("aspectratio.x")&&!!p("aspectratio.y")&&!!p("aspectratio.z"),b=p("aspectmode",A?"manual":"auto");A||(d.aspectratio=m.aspectratio={x:1,y:1,z:1},b==="manual"&&(m.aspectmode="auto"),d.aspectmode=m.aspectmode);var k=u(g.fullData,"gl3d",g.id);i(d,m,{font:g.font,scene:g.id,data:k,bgColor:v,calendar:g.calendar,autotypenumbersDflt:g.autotypenumbersDflt,fullLayout:g.fullLayout}),l.getComponentMethod("annotations3d","handleDefaults")(d,m,g);var w=g.getDfltFromLayout("dragmode");if(w!==!1&&!w)if(w="orbit",d.camera&&d.camera.up){var M=d.camera.up.x,T=d.camera.up.y,E=d.camera.up.z;E!==0&&(M&&T&&E?E/Math.sqrt(M*M+T*T+E*E)>.999&&(w="turntable"):w="turntable")}else w="turntable";p("dragmode",w),p("hovermode",g.getDfltFromLayout("hovermode"))}o.exports=function(d,m,p){var g=m._basePlotModules.length>1;c(d,m,p,{type:"gl3d",attributes:s,handleDefaults:h,fullLayout:m,font:m.font,fullData:p,getDfltFromLayout:function(y){if(!g)return r.validate(d[y],s[y])?d[y]:void 0},autotypenumbersDflt:m.autotypenumbers,paper_bgcolor:m.paper_bgcolor,calendar:m.calendar})}},{"../../../components/color":366,"../../../lib":503,"../../../registry":638,"../../get_data":593,"../../subplot_defaults":632,"./axis_defaults":601,"./layout_attributes":604}],604:[function(t,o,f){var r=t("./axis_attributes"),a=t("../../domain").attributes,l=t("../../../lib/extend").extendFlat,c=t("../../../lib").counterRegex;function i(s,u,h){return{x:{valType:"number",dflt:s,editType:"camera"},y:{valType:"number",dflt:u,editType:"camera"},z:{valType:"number",dflt:h,editType:"camera"},editType:"camera"}}o.exports={_arrayAttrRegexps:[c("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:l(i(0,0,1),{}),center:l(i(0,0,0),{}),eye:l(i(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:a({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:r,yaxis:r,zaxis:r,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":503,"../../../lib/extend":493,"../../domain":584,"./axis_attributes":600}],605:[function(t,o,f){var r=t("../../../lib/str2rgbarray"),a=["xaxis","yaxis","zaxis"];function l(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}l.prototype.merge=function(c){for(var i=0;i<3;++i){var s=c[a[i]];s.visible?(this.enabled[i]=s.showspikes,this.colors[i]=r(s.spikecolor),this.drawSides[i]=s.spikesides,this.lineWidth[i]=s.spikethickness):(this.enabled[i]=!1,this.drawSides[i]=!1)}},o.exports=function(c){var i=new l;return i.merge(c),i}},{"../../../lib/str2rgbarray":528}],606:[function(t,o,f){o.exports=function(i){for(var s=i.axesOptions,u=i.glplot.axesPixels,h=i.fullSceneLayout,d=[[],[],[]],m=0;m<3;++m){var p=h[l[m]];if(p._length=(u[m].hi-u[m].lo)*u[m].pixelsPerDataUnit/i.dataScale[m],Math.abs(p._length)===1/0||isNaN(p._length))d[m]=[];else{p._input_range=p.range.slice(),p.range[0]=u[m].lo/i.dataScale[m],p.range[1]=u[m].hi/i.dataScale[m],p._m=1/(i.dataScale[m]*u[m].pixelsPerDataUnit),p.range[0]===p.range[1]&&(p.range[0]-=1,p.range[1]+=1);var g=p.tickmode;if(p.tickmode==="auto"){p.tickmode="linear";var y=p.nticks||a.constrain(p._length/40,4,9);r.autoTicks(p,Math.abs(p.range[1]-p.range[0])/y)}for(var v=r.calcTicks(p,{msUTC:!0}),x=0;x<v.length;++x)v[x].x=v[x].x*i.dataScale[m],p.type==="date"&&(v[x].text=v[x].text.replace(/\<br\>/g," "));d[m]=v,p.tickmode=g}}for(s.ticks=d,m=0;m<3;++m)for(c[m]=.5*(i.glplot.bounds[0][m]+i.glplot.bounds[1][m]),x=0;x<2;++x)s.bounds[x][m]=i.glplot.bounds[x][m];i.contourLevels=function(_){for(var A=new Array(3),b=0;b<3;++b){for(var k=_[b],w=new Array(k.length),M=0;M<k.length;++M)w[M]=k[M].x;A[b]=w}return A}(d)};var r=t("../../cartesian/axes"),a=t("../../../lib"),l=["xaxis","yaxis","zaxis"],c=[0,0,0]},{"../../../lib":503,"../../cartesian/axes":554}],607:[function(t,o,f){function r(a,l){var c,i,s=[0,0,0,0];for(c=0;c<4;++c)for(i=0;i<4;++i)s[i]+=a[4*c+i]*l[c];return s}o.exports=function(a,l){return r(a.projection,r(a.view,r(a.model,[l[0],l[1],l[2],1])))}},{}],608:[function(t,o,f){var r,a,l=t("../../../stackgl_modules").gl_plot3d,c=l.createCamera,i=l.createScene,s=t("webgl-context"),u=t("has-passive-events"),h=t("../../registry"),d=t("../../lib"),m=d.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),g=t("../../components/fx"),y=t("../../lib/str2rgbarray"),v=t("../../lib/show_no_webgl_msg"),x=t("./project"),_=t("./layout/convert"),A=t("./layout/spikes"),b=t("./layout/tick_marks");function k(S,P){var L=document.createElement("div"),R=S.container;this.graphDiv=S.graphDiv;var F=document.createElementNS("http://www.w3.org/2000/svg","svg");F.style.position="absolute",F.style.top=F.style.left="0px",F.style.width=F.style.height="100%",F.style["z-index"]=20,F.style["pointer-events"]="none",L.appendChild(F),this.svgContainer=F,L.id=S.id,L.style.position="absolute",L.style.top=L.style.left="0px",L.style.width=L.style.height="100%",R.appendChild(L),this.fullLayout=P,this.id=S.id||"scene",this.fullSceneLayout=P[this.id],this.plotArgs=[[],{},{}],this.axesOptions=_(P,P[this.id]),this.spikeOptions=A(P[this.id]),this.container=L,this.staticMode=!!S.staticPlot,this.pixelRatio=this.pixelRatio||S.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=h.getComponentMethod("annotations3d","convert"),this.drawAnnotations=h.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=k.prototype;w.prepareOptions=function(){var S={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:m,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(a||(r=document.createElement("canvas"),a=s({canvas:r,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");S.gl=a,S.canvas=r}return S};var M=!0;w.tryCreatePlot=function(){var S=this.prepareOptions(),P=!0;try{this.glplot=i(S)}catch{if(this.staticMode||!M||m)P=!1;else{d.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{m=S.glOptions.preserveDrawingBuffer=!0,this.glplot=i(S)}catch{m=S.glOptions.preserveDrawingBuffer=!1,P=!1}}}return M=!1,P},w.initializeGLCamera=function(){var S=this.fullSceneLayout.camera,P=S.projection.type==="orthographic";this.camera=c(this.container,{center:[S.center.x,S.center.y,S.center.z],eye:[S.eye.x,S.eye.y,S.eye.z],up:[S.up.x,S.up.y,S.up.z],_ortho:P,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var S=this;if(S.initializeGLCamera(),!S.tryCreatePlot())return v(S);S.traces={},S.make4thDimension();var P=S.graphDiv,L=P.layout,R=function(){var D={};return S.isCameraChanged(L)&&(D[S.id+".camera"]=S.getCamera()),S.isAspectChanged(L)&&(D[S.id+".aspectratio"]=S.glplot.getAspectratio(),L[S.id].aspectmode!=="manual"&&(S.fullSceneLayout.aspectmode=L[S.id].aspectmode=D[S.id+".aspectmode"]="manual")),D},F=function(D){if(D.fullSceneLayout.dragmode!==!1){var O=R();D.saveLayout(L),D.graphDiv.emit("plotly_relayout",O)}};return S.glplot.canvas&&(S.glplot.canvas.addEventListener("mouseup",function(){F(S)}),S.glplot.canvas.addEventListener("wheel",function(D){if(P._context._scrollZoom.gl3d){if(S.camera._ortho){var O=D.deltaX>D.deltaY?1.1:.9090909090909091,N=S.glplot.getAspectratio();S.glplot.setAspectratio({x:O*N.x,y:O*N.y,z:O*N.z})}F(S)}},!!u&&{passive:!1}),S.glplot.canvas.addEventListener("mousemove",function(){if(S.fullSceneLayout.dragmode!==!1&&S.camera.mouseListener.buttons!==0){var D=R();S.graphDiv.emit("plotly_relayouting",D)}}),S.staticMode||S.glplot.canvas.addEventListener("webglcontextlost",function(D){P&&P.emit&&P.emit("plotly_webglcontextlost",{event:D,layer:S.id})},!1)),S.glplot.oncontextloss=function(){S.recoverContext()},S.glplot.onrender=function(){S.render()},!0},w.render=function(){var S,P=this,L=P.graphDiv,R=P.svgContainer,F=P.container.getBoundingClientRect();L._fullLayout._calcInverseTransform(L);var D=L._fullLayout._invScaleX,O=L._fullLayout._invScaleY,N=F.width*D,B=F.height*O;R.setAttributeNS(null,"viewBox","0 0 "+N+" "+B),R.setAttributeNS(null,"width",N),R.setAttributeNS(null,"height",B),b(P),P.glplot.axes.update(P.axesOptions);for(var W=Object.keys(P.traces),G=null,K=P.glplot.selection,te=0;te<W.length;++te)(S=P.traces[W[te]]).data.hoverinfo!=="skip"&&S.handlePick(K)&&(G=S),S.setContourLevels&&S.setContourLevels();function Y(le,ge,fe){var me=P.fullSceneLayout[le+"axis"];return me.type!=="log"&&(ge=me.d2l(ge)),p.hoverLabelText(me,ge,fe)}if(G!==null){var J=x(P.glplot.cameraParams,K.dataCoordinate);S=G.data;var re,U=L._fullData[S.index],V=K.index,H={xLabel:Y("x",K.traceCoordinate[0],S.xhoverformat),yLabel:Y("y",K.traceCoordinate[1],S.yhoverformat),zLabel:Y("z",K.traceCoordinate[2],S.zhoverformat)},ne=g.castHoverinfo(U,P.fullLayout,V),q=(ne||"").split("+"),Q=ne&&ne==="all";U.hovertemplate||Q||(q.indexOf("x")===-1&&(H.xLabel=void 0),q.indexOf("y")===-1&&(H.yLabel=void 0),q.indexOf("z")===-1&&(H.zLabel=void 0),q.indexOf("text")===-1&&(K.textLabel=void 0),q.indexOf("name")===-1&&(G.name=void 0));var ee=[];S.type==="cone"||S.type==="streamtube"?(H.uLabel=Y("x",K.traceCoordinate[3],S.uhoverformat),(Q||q.indexOf("u")!==-1)&&ee.push("u: "+H.uLabel),H.vLabel=Y("y",K.traceCoordinate[4],S.vhoverformat),(Q||q.indexOf("v")!==-1)&&ee.push("v: "+H.vLabel),H.wLabel=Y("z",K.traceCoordinate[5],S.whoverformat),(Q||q.indexOf("w")!==-1)&&ee.push("w: "+H.wLabel),H.normLabel=K.traceCoordinate[6].toPrecision(3),(Q||q.indexOf("norm")!==-1)&&ee.push("norm: "+H.normLabel),S.type==="streamtube"&&(H.divergenceLabel=K.traceCoordinate[7].toPrecision(3),(Q||q.indexOf("divergence")!==-1)&&ee.push("divergence: "+H.divergenceLabel)),K.textLabel&&ee.push(K.textLabel),re=ee.join("<br>")):S.type==="isosurface"||S.type==="volume"?(H.valueLabel=p.hoverLabelText(P._mockAxis,P._mockAxis.d2l(K.traceCoordinate[3]),S.valuehoverformat),ee.push("value: "+H.valueLabel),K.textLabel&&ee.push(K.textLabel),re=ee.join("<br>")):re=K.textLabel;var ie={x:K.traceCoordinate[0],y:K.traceCoordinate[1],z:K.traceCoordinate[2],data:U._input,fullData:U,curveNumber:U.index,pointNumber:V};g.appendArrayPointValue(ie,U,V),S._module.eventData&&(ie=U._module.eventData(ie,K,U,{},V));var ae={points:[ie]};if(P.fullSceneLayout.hovermode){var ue=[];g.loneHover({trace:U,x:(.5+.5*J[0]/J[3])*N,y:(.5-.5*J[1]/J[3])*B,xLabel:H.xLabel,yLabel:H.yLabel,zLabel:H.zLabel,text:re,name:G.name,color:g.castHoverOption(U,V,"bgcolor")||G.color,borderColor:g.castHoverOption(U,V,"bordercolor"),fontFamily:g.castHoverOption(U,V,"font.family"),fontSize:g.castHoverOption(U,V,"font.size"),fontColor:g.castHoverOption(U,V,"font.color"),nameLength:g.castHoverOption(U,V,"namelength"),textAlign:g.castHoverOption(U,V,"align"),hovertemplate:d.castOption(U,V,"hovertemplate"),hovertemplateLabels:d.extendFlat({},ie,H),eventData:[ie]},{container:R,gd:L,inOut_bbox:ue}),ie.bbox=ue[0]}K.buttons&&K.distance<5?L.emit("plotly_click",ae):L.emit("plotly_hover",ae),this.oldEventData=ae}else g.loneUnhover(R),this.oldEventData&&L.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;P.drawAnnotations(P)},w.recoverContext=function(){var S=this;S.glplot.dispose();var P=function(){S.glplot.gl.isContextLost()?requestAnimationFrame(P):S.initializeGLPlot()?S.plot.apply(S,S.plotArgs):d.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(P)};var T=["xaxis","yaxis","zaxis"];function E(S,P,L){for(var R=S.fullSceneLayout,F=0;F<3;F++){var D=T[F],O=D.charAt(0),N=R[D],B=P[O],W=P[O+"calendar"],G=P["_"+O+"length"];if(d.isArrayOrTypedArray(B))for(var K,te=0;te<(G||B.length);te++)if(d.isArrayOrTypedArray(B[te]))for(var Y=0;Y<B[te].length;++Y)K=N.d2l(B[te][Y],0,W),!isNaN(K)&&isFinite(K)&&(L[0][F]=Math.min(L[0][F],K),L[1][F]=Math.max(L[1][F],K));else K=N.d2l(B[te],0,W),!isNaN(K)&&isFinite(K)&&(L[0][F]=Math.min(L[0][F],K),L[1][F]=Math.max(L[1][F],K));else L[0][F]=Math.min(L[0][F],0),L[1][F]=Math.max(L[1][F],G-1)}}w.plot=function(S,P,L){if(this.plotArgs=[S,P,L],!this.glplot.contextLost){var R,F,D,O,N,B,W=P[this.id],G=L[this.id];this.fullLayout=P,this.fullSceneLayout=W,this.axesOptions.merge(P,W),this.spikeOptions.merge(W),this.setViewport(W),this.updateFx(W.dragmode,W.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(y(W.bgcolor)),this.setConvert(N),S?Array.isArray(S)||(S=[S]):S=[];var K=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<S.length;++D)(R=S[D]).visible===!0&&R._length!==0&&E(this,R,K);(function(Me,we){for(var Ce=Me.fullSceneLayout,Fe=Ce.annotations||[],ze=0;ze<3;ze++)for(var $e=T[ze],Ke=$e.charAt(0),Re=Ce[$e],Ve=0;Ve<Fe.length;Ve++){var We=Fe[Ve];if(We.visible){var Ye=Re.r2l(We[Ke]);!isNaN(Ye)&&isFinite(Ye)&&(we[0][ze]=Math.min(we[0][ze],Ye),we[1][ze]=Math.max(we[1][ze],Ye))}}})(this,K);var te=[1,1,1];for(O=0;O<3;++O)K[1][O]===K[0][O]?te[O]=1:te[O]=1/(K[1][O]-K[0][O]);for(this.dataScale=te,this.convertAnnotations(this),D=0;D<S.length;++D)(R=S[D]).visible===!0&&R._length!==0&&((F=this.traces[R.uid])?F.data.type===R.type?F.update(R):(F.dispose(),F=R._module.plot(this,R),this.traces[R.uid]=F):(F=R._module.plot(this,R),this.traces[R.uid]=F),F.name=R.name);var Y=Object.keys(this.traces);e:for(D=0;D<Y.length;++D){for(O=0;O<S.length;++O)if(S[O].uid===Y[D]&&S[O].visible===!0&&S[O]._length!==0)continue e;(F=this.traces[Y[D]]).dispose(),delete this.traces[Y[D]]}this.glplot.objects.sort(function(Me,we){return Me._trace.data.index-we._trace.data.index});var J,re=[[0,0,0],[0,0,0]],U=[],V={};for(D=0;D<3;++D){if((B=(N=W[T[D]]).type)in V?(V[B].acc*=te[D],V[B].count+=1):V[B]={acc:te[D],count:1},N.autorange){re[0][D]=1/0,re[1][D]=-1/0;var H=this.glplot.objects,ne=this.fullSceneLayout.annotations||[],q=N._name.charAt(0);for(O=0;O<H.length;O++){var Q=H[O],ee=Q.bounds,ie=Q._trace.data._pad||0;Q.constructor.name==="ErrorBars"&&N._lowerLogErrorBound?re[0][D]=Math.min(re[0][D],N._lowerLogErrorBound):re[0][D]=Math.min(re[0][D],ee[0][D]/te[D]-ie),re[1][D]=Math.max(re[1][D],ee[1][D]/te[D]+ie)}for(O=0;O<ne.length;O++){var ae=ne[O];if(ae.visible){var ue=N.r2l(ae[q]);re[0][D]=Math.min(re[0][D],ue),re[1][D]=Math.max(re[1][D],ue)}}if("rangemode"in N&&N.rangemode==="tozero"&&(re[0][D]=Math.min(re[0][D],0),re[1][D]=Math.max(re[1][D],0)),re[0][D]>re[1][D])re[0][D]=-1,re[1][D]=1;else{var le=re[1][D]-re[0][D];re[0][D]-=le/32,re[1][D]+=le/32}if(N.autorange==="reversed"){var ge=re[0][D];re[0][D]=re[1][D],re[1][D]=ge}}else{var fe=N.range;re[0][D]=N.r2l(fe[0]),re[1][D]=N.r2l(fe[1])}re[0][D]===re[1][D]&&(re[0][D]-=1,re[1][D]+=1),U[D]=re[1][D]-re[0][D],this.glplot.setBounds(D,{min:re[0][D]*te[D],max:re[1][D]*te[D]})}var me=W.aspectmode;if(me==="cube")J=[1,1,1];else if(me==="manual"){var _e=W.aspectratio;J=[_e.x,_e.y,_e.z]}else{if(me!=="auto"&&me!=="data")throw new Error("scene.js aspectRatio was not one of the enumerated types");var Ae=[1,1,1];for(D=0;D<3;++D){var ke=V[B=(N=W[T[D]]).type];Ae[D]=Math.pow(ke.acc,1/ke.count)/te[D]}J=me==="data"||Math.max.apply(null,Ae)/Math.min.apply(null,Ae)<=4?Ae:[1,1,1]}W.aspectratio.x=G.aspectratio.x=J[0],W.aspectratio.y=G.aspectratio.y=J[1],W.aspectratio.z=G.aspectratio.z=J[2],this.glplot.setAspectratio(W.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:W.aspectratio.x,y:W.aspectratio.y,z:W.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=W.aspectmode);var Le=W.domain||null,de=P._size||null;if(Le&&de){var ve=this.container.style;ve.position="absolute",ve.left=de.l+Le.x[0]*de.w+"px",ve.top=de.t+(1-Le.y[1])*de.h+"px",ve.width=de.w*(Le.x[1]-Le.x[0])+"px",ve.height=de.h*(Le.y[1]-Le.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var S;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(S=this.camera).up[0],y:S.up[1],z:S.up[2]},center:{x:S.center[0],y:S.center[1],z:S.center[2]},eye:{x:S.eye[0],y:S.eye[1],z:S.eye[2]},projection:{type:S._ortho===!0?"orthographic":"perspective"}}},w.setViewport=function(S){var P,L=S.camera;this.camera.lookAt.apply(this,[[(P=L).eye.x,P.eye.y,P.eye.z],[P.center.x,P.center.y,P.center.z],[P.up.x,P.up.y,P.up.z]]),this.glplot.setAspectratio(S.aspectratio),L.projection.type==="orthographic"!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(S){var P=this.getCamera(),L=d.nestedProperty(S,this.id+".camera").get();function R(N,B,W,G){var K=["up","center","eye"],te=["x","y","z"];return B[K[W]]&&N[K[W]][te[G]]===B[K[W]][te[G]]}var F=!1;if(L===void 0)F=!0;else{for(var D=0;D<3;D++)for(var O=0;O<3;O++)if(!R(P,L,D,O)){F=!0;break}(!L.projection||P.projection&&P.projection.type!==L.projection.type)&&(F=!0)}return F},w.isAspectChanged=function(S){var P=this.glplot.getAspectratio(),L=d.nestedProperty(S,this.id+".aspectratio").get();return L===void 0||L.x!==P.x||L.y!==P.y||L.z!==P.z},w.saveLayout=function(S){var P,L,R,F,D,O,N=this.fullLayout,B=this.isCameraChanged(S),W=this.isAspectChanged(S),G=B||W;if(G){var K={};B&&(P=this.getCamera(),R=(L=d.nestedProperty(S,this.id+".camera")).get(),K[this.id+".camera"]=R),W&&(F=this.glplot.getAspectratio(),O=(D=d.nestedProperty(S,this.id+".aspectratio")).get(),K[this.id+".aspectratio"]=O),h.call("_storeDirectGUIEdit",S,N._preGUI,K),B&&(L.set(P),d.nestedProperty(N,this.id+".camera").set(P)),W&&(D.set(F),d.nestedProperty(N,this.id+".aspectratio").set(F),this.glplot.redraw())}return G},w.updateFx=function(S,P){var L=this.camera;if(L)if(S==="orbit")L.mode="orbit",L.keyBindingMode="rotate";else if(S==="turntable"){L.up=[0,0,1],L.mode="turntable",L.keyBindingMode="rotate";var R=this.graphDiv,F=R._fullLayout,D=this.fullSceneLayout.camera,O=D.up.x,N=D.up.y,B=D.up.z;if(B/Math.sqrt(O*O+N*N+B*B)<.999){var W=this.id+".camera.up",G={x:0,y:0,z:1},K={};K[W]=G;var te=R.layout;h.call("_storeDirectGUIEdit",te,F._preGUI,K),D.up=G,d.nestedProperty(te,W).set(G)}}else L.keyBindingMode=S;this.fullSceneLayout.hovermode=P},w.toImage=function(S){S||(S="png"),this.staticMode&&this.container.appendChild(r),this.glplot.redraw();var P=this.glplot.gl,L=P.drawingBufferWidth,R=P.drawingBufferHeight;P.bindFramebuffer(P.FRAMEBUFFER,null);var F=new Uint8Array(L*R*4);P.readPixels(0,0,L,R,P.RGBA,P.UNSIGNED_BYTE,F),function(W,G,K){for(var te=0,Y=K-1;te<Y;++te,--Y)for(var J=0;J<G;++J)for(var re=0;re<4;++re){var U=4*(G*te+J)+re,V=4*(G*Y+J)+re,H=W[U];W[U]=W[V],W[V]=H}}(F,L,R),function(W,G,K){for(var te=0;te<K;++te)for(var Y=0;Y<G;++Y){var J=4*(G*te+Y),re=W[J+3];if(re>0)for(var U=255/re,V=0;V<3;++V)W[J+V]=Math.min(U*W[J+V],255)}}(F,L,R);var D=document.createElement("canvas");D.width=L,D.height=R;var O,N=D.getContext("2d"),B=N.createImageData(L,R);switch(B.data.set(F),N.putImageData(B,0,0),S){case"jpeg":O=D.toDataURL("image/jpeg");break;case"webp":O=D.toDataURL("image/webp");break;default:O=D.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(r),O},w.setConvert=function(){for(var S=0;S<3;S++){var P=this.fullSceneLayout[T[S]];p.setConvert(P,this.fullLayout),P.setScale=d.noop}},w.make4thDimension=function(){var S=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,S)},o.exports=k},{"../../../stackgl_modules":1124,"../../components/fx":406,"../../lib":503,"../../lib/show_no_webgl_msg":525,"../../lib/str2rgbarray":528,"../../plots/cartesian/axes":554,"../../registry":638,"./layout/convert":602,"./layout/spikes":605,"./layout/tick_marks":606,"./project":607,"has-passive-events":229,"webgl-context":331}],609:[function(t,o,f){o.exports=function(r,a,l,c){c=c||r.length;for(var i=new Array(c),s=0;s<c;s++)i[s]=[r[s],a[s],l[s]];return i}},{}],610:[function(t,o,f){var r=t("./font_attributes"),a=t("./animation_attributes"),l=t("../components/color/attributes"),c=t("../components/shapes/draw_newshape/attributes"),i=t("./pad_attributes"),s=t("../lib/extend").extendFlat,u=r({editType:"calc"});u.family.dflt='"Open Sans", verdana, arial, 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types","strict"],dflt:"convert types",editType:"calc"},separators:{valType:"string",editType:"plot"},hidesources:{valType:"boolean",dflt:!1,editType:"plot"},showlegend:{valType:"boolean",editType:"legend"},colorway:{valType:"colorlist",dflt:l.defaults,editType:"calc"},datarevision:{valType:"any",editType:"calc"},uirevision:{valType:"any",editType:"none"},editrevision:{valType:"any",editType:"none"},selectionrevision:{valType:"any",editType:"none"},template:{valType:"any",editType:"calc"},newshape:c.newshape,activeshape:c.activeshape,meta:{valType:"any",arrayOk:!0,editType:"plot"},transition:s({},a.transition,{editType:"none"}),_deprecated:{title:{valType:"string",editType:"layoutstyle"},titlefont:r({editType:"layoutstyle"})}}},{"../components/color/attributes":365,"../components/shapes/draw_newshape/attributes":451,"../lib/extend":493,"./animation_attributes":548,"./font_attributes":585,"./pad_attributes":618}],611:[function(t,o,f){var r=t("../../lib/sort_object_keys"),a='© <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',l=['© <a target="_blank" href="https://carto.com/">Carto</a>',a].join(" "),c=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),i={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:a,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:l,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:l,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:c,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:c,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},s=r(i);o.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:i,styleValuesNonMapbox:s,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl version","Please install mapbox-gl@1.10.1."].join(`
-`),noAccessTokenErrorMsg:["Missing Mapbox access token.","Mapbox trace type require a Mapbox access token to be registered.","For example:","  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });","More info here: https://www.mapbox.com/help/define-access-token/"].join(`
-`),missingStyleErrorMsg:["No valid mapbox style found, please set `mapbox.style` to one of:",s.join(", "),"or register a Mapbox access token to use a Mapbox-served style."].join(`
-`),multipleTokensErrorMsg:["Set multiple mapbox access token across different mapbox subplot,","using first token found as mapbox-gl does not allow multipleaccess tokens on the same page."].join(`
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12.47,10.34"},styleRules:{map:"overflow:hidden;position:relative;","missing-css":"display:none;",canary:"background-color:salmon;","ctrl-bottom-left":"position: absolute; pointer-events: none; z-index: 2; bottom: 0; left: 0;","ctrl-bottom-right":"position: absolute; pointer-events: none; z-index: 2; right: 0; bottom: 0;",ctrl:"clear: both; pointer-events: auto; transform: translate(0, 0);","ctrl-attrib.mapboxgl-compact .mapboxgl-ctrl-attrib-inner":"display: none;","ctrl-attrib.mapboxgl-compact:hover .mapboxgl-ctrl-attrib-inner":"display: block; margin-top:2px","ctrl-attrib.mapboxgl-compact:hover":"padding: 2px 24px 2px 4px; visibility: visible; margin-top: 6px;","ctrl-attrib.mapboxgl-compact::after":`content: ""; cursor: pointer; position: absolute; background-image: url('data:image/svg+xml;charset=utf-8,%3Csvg viewBox="0 0 20 20" xmlns="http://www.w3.org/2000/svg"%3E %3Cpath fill="%23333333" fill-rule="evenodd" d="M4,10a6,6 0 1,0 12,0a6,6 0 1,0 -12,0 M9,7a1,1 0 1,0 2,0a1,1 0 1,0 -2,0 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"),i=c[0],s=c[1],u=r.isArrayOrTypedArray(l)?r.mean(l):l,h=.5+u/100,d=1.5+u/100,m=["",""],p=[0,0];switch(i){case"top":m[0]="top",p[1]=-d;break;case"bottom":m[0]="bottom",p[1]=d}switch(s){case"left":m[1]="right",p[0]=-h;break;case"right":m[1]="left",p[0]=h}return{anchor:m[0]&&m[1]?m.join("-"):m[0]?m[0]:m[1]?m[1]:"center",offset:p}}},{"../../lib":503}],613:[function(t,o,f){var r=t("mapbox-gl/dist/mapbox-gl-unminified"),a=t("../../lib"),l=a.strTranslate,c=a.strScale,i=t("../../plots/get_data").getSubplotCalcData,s=t("../../constants/xmlns_namespaces"),u=t("@plotly/d3"),h=t("../../components/drawing"),d=t("../../lib/svg_text_utils"),m=t("./mapbox"),p=f.constants=t("./constants");function g(y){return typeof y=="string"&&(p.styleValuesMapbox.indexOf(y)!==-1||y.indexOf("mapbox://")===0)}f.name="mapbox",f.attr="subplot",f.idRoot="mapbox",f.idRegex=f.attrRegex=a.counterRegex("mapbox"),f.attributes={subplot:{valType:"subplotid",dflt:"mapbox",editType:"calc"}},f.layoutAttributes=t("./layout_attributes"),f.supplyLayoutDefaults=t("./layout_defaults"),f.plot=function(y){var v=y._fullLayout,x=y.calcdata,_=v._subplots.mapbox;if(r.version!==p.requiredVersion)throw new Error(p.wrongVersionErrorMsg);var A=function(E,S){var P=E._fullLayout;if(E._context.mapboxAccessToken==="")return"";for(var L=[],R=[],F=!1,D=!1,O=0;O<S.length;O++){var N=P[S[O]],B=N.accesstoken;g(N.style)&&(B?a.pushUnique(L,B):(g(N._input.style)&&(a.error("Uses Mapbox map style, but did not set an access token."),F=!0),D=!0)),B&&a.pushUnique(R,B)}if(D){var W=F?p.noAccessTokenErrorMsg:p.missingStyleErrorMsg;throw a.error(W),new Error(W)}return L.length?(L.length>1&&a.warn(p.multipleTokensErrorMsg),L[0]):(R.length&&a.log(["Listed mapbox 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T=v._glimages.append("g");T.attr("transform",l(_.l+_.w*k.x[0]+10,_.t+_.h*(1-k.y[0])-31)),T.append("path").attr("d",p.mapboxLogo.path0).style({opacity:.9,fill:"#ffffff","enable-background":"new"}),T.append("path").attr("d",p.mapboxLogo.path1).style("opacity",.35).style("enable-background","new"),T.append("path").attr("d",p.mapboxLogo.path2).style("opacity",.35).style("enable-background","new"),T.append("polygon").attr("points",p.mapboxLogo.polygon).style({opacity:.9,fill:"#ffffff","enable-background":"new"})}var E=M.select(".mapboxgl-ctrl-attrib").text().replace("Improve this map",""),S=v._glimages.append("g"),P=S.append("text");P.text(E).classed("static-attribution",!0).attr({"font-size":12,"font-family":"Arial",color:"rgba(0, 0, 0, 0.75)","text-anchor":"end","data-unformatted":E});var L=h.bBox(P.node()),R=_.w*(k.x[1]-k.x[0]);if(L.width>R/2){var 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i(d,m){this.subplot=d,this.uid=d.uid+"-"+m,this.index=m,this.idSource="source-"+this.uid,this.idLayer=c.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var s=i.prototype;function u(d){if(!d.visible)return!1;var m=d.source;if(Array.isArray(m)&&m.length>0){for(var p=0;p<m.length;p++)if(typeof m[p]!="string"||m[p].length===0)return!1;return!0}return r.isPlainObject(m)||typeof m=="string"&&m.length>0}function h(d){var m={},p={};switch(d.type){case"circle":r.extendFlat(p,{"circle-radius":d.circle.radius,"circle-color":d.color,"circle-opacity":d.opacity});break;case"line":r.extendFlat(p,{"line-width":d.line.width,"line-color":d.color,"line-opacity":d.opacity,"line-dasharray":d.line.dash});break;case"fill":r.extendFlat(p,{"fill-color":d.color,"fill-outline-color":d.fill.outlinecolor,"fill-opacity":d.opacity});break;case"symbol":var g=d.symbol,y=l(g.textposition,g.iconsize);r.extendFlat(m,{"icon-image":g.icon+"-15","icon-size":g.iconsize/10,"text-field":g.text,"text-size":g.textfont.size,"text-anchor":y.anchor,"text-offset":y.offset,"symbol-placement":g.placement}),r.extendFlat(p,{"icon-color":d.color,"text-color":g.textfont.color,"text-opacity":d.opacity});break;case"raster":r.extendFlat(p,{"raster-fade-duration":0,"raster-opacity":d.opacity})}return{layout:m,paint:p}}s.update=function(d){this.visible?this.needsNewImage(d)?this.updateImage(d):this.needsNewSource(d)?(this.removeLayer(),this.updateSource(d),this.updateLayer(d)):this.needsNewLayer(d)?this.updateLayer(d):this.updateStyle(d):(this.updateSource(d),this.updateLayer(d)),this.visible=u(d)},s.needsNewImage=function(d){return this.subplot.map.getSource(this.idSource)&&this.sourceType==="image"&&d.sourcetype==="image"&&(this.source!==d.source||JSON.stringify(this.coordinates)!==JSON.stringify(d.coordinates))},s.needsNewSource=function(d){return this.sourceType!==d.sourcetype||JSON.stringify(this.source)!==JSON.stringify(d.source)||this.layerType!==d.type},s.needsNewLayer=function(d){return this.layerType!==d.type||this.below!==this.subplot.belowLookup["layout-"+this.index]},s.lookupBelow=function(){return this.subplot.belowLookup["layout-"+this.index]},s.updateImage=function(d){this.subplot.map.getSource(this.idSource).updateImage({url:d.source,coordinates:d.coordinates});var m=this.findFollowingMapboxLayerId(this.lookupBelow());m!==null&&this.subplot.map.moveLayer(this.idLayer,m)},s.updateSource=function(d){var m=this.subplot.map;if(m.getSource(this.idSource)&&m.removeSource(this.idSource),this.sourceType=d.sourcetype,this.source=d.source,u(d)){var p=function(g){var y,v=g.sourcetype,x=g.source,_={type:v};return v==="geojson"?y="data":v==="vector"?y=typeof x=="string"?"url":"tiles":v==="raster"?(y="tiles",_.tileSize=256):v==="image"&&(y="url",_.coordinates=g.coordinates),_[y]=x,g.sourceattribution&&(_.attribution=a(g.sourceattribution)),_}(d);m.addSource(this.idSource,p)}},s.findFollowingMapboxLayerId=function(d){if(d==="traces")for(var m=this.subplot.getMapLayers(),p=0;p<m.length;p++){var g=m[p].id;if(typeof g=="string"&&g.indexOf(c.traceLayerPrefix)===0){d=g;break}}return d},s.updateLayer=function(d){var m=this.subplot,p=h(d),g=this.lookupBelow(),y=this.findFollowingMapboxLayerId(g);this.removeLayer(),u(d)&&m.addLayer({id:this.idLayer,source:this.idSource,"source-layer":d.sourcelayer||"",type:d.type,minzoom:d.minzoom,maxzoom:d.maxzoom,layout:p.layout,paint:p.paint},y),this.layerType=d.type,this.below=g},s.updateStyle=function(d){if(u(d)){var m=h(d);this.subplot.setOptions(this.idLayer,"setLayoutProperty",m.layout),this.subplot.setOptions(this.idLayer,"setPaintProperty",m.paint)}},s.removeLayer=function(){var d=this.subplot.map;d.getLayer(this.idLayer)&&d.removeLayer(this.idLayer)},s.dispose=function(){var d=this.subplot.map;d.getLayer(this.idLayer)&&d.removeLayer(this.idLayer),d.getSource(this.idSource)&&d.removeSource(this.idSource)},o.exports=function(d,m,p){var g=new i(d,m);return g.update(p),g}},{"../../lib":503,"../../lib/svg_text_utils":529,"./constants":611,"./convert_text_opts":612}],615:[function(t,o,f){var r=t("../../lib"),a=t("../../components/color").defaultLine,l=t("../domain").attributes,c=t("../font_attributes"),i=t("../../traces/scatter/attributes").textposition,s=t("../../plot_api/edit_types").overrideAll,u=t("../../plot_api/plot_template").templatedArray,h=t("./constants"),d=c({});d.family.dflt="Open Sans Regular, Arial Unicode MS 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r=t("../../lib"),a=t("../subplot_defaults"),l=t("../array_container_defaults"),c=t("./layout_attributes");function i(u,h,d,m){d("accesstoken",m.accessToken),d("style"),d("center.lon"),d("center.lat"),d("zoom"),d("bearing"),d("pitch"),l(u,h,{name:"layers",handleItemDefaults:s}),h._input=u}function s(u,h){function d(v,x){return r.coerce(u,h,c.layers,v,x)}if(d("visible")){var m,p=d("sourcetype"),g=p==="raster"||p==="image";d("source"),d("sourceattribution"),p==="vector"&&d("sourcelayer"),p==="image"&&d("coordinates"),g&&(m="raster");var y=d("type",m);g&&y!=="raster"&&(y=h.type="raster",r.log("Source types *raster* and *image* must drawn *raster* layer 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r=t("mapbox-gl/dist/mapbox-gl-unminified"),a=t("../../lib"),l=t("../../lib/geo_location_utils"),c=t("../../registry"),i=t("../cartesian/axes"),s=t("../../components/dragelement"),u=t("../../components/fx"),h=t("../../components/dragelement/helpers"),d=h.rectMode,m=h.drawMode,p=h.selectMode,g=t("../cartesian/select").prepSelect,y=t("../cartesian/select").clearSelect,v=t("../cartesian/select").clearSelectionsCache,x=t("../cartesian/select").selectOnClick,_=t("./constants"),A=t("./layers");function b(S,P){this.id=P,this.gd=S;var L=S._fullLayout,R=S._context;this.container=L._glcontainer.node(),this.isStatic=R.staticPlot,this.uid=L._uid+"-"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(L),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var k=b.prototype;k.plot=function(S,P,L){var R,F=this,D=P[F.id];F.map&&D.accesstoken!==F.accessToken&&(F.map.remove(),F.map=null,F.styleObj=null,F.traceHash={},F.layerList=[]),R=F.map?new Promise(function(O,N){F.updateMap(S,P,O,N)}):new Promise(function(O,N){F.createMap(S,P,O,N)}),L.push(R)},k.createMap=function(S,P,L,R){var F=this,D=P[F.id],O=F.styleObj=M(D.style);F.accessToken=D.accesstoken;var N=F.map=new r.Map({container:F.div,style:O.style,center:E(D.center),zoom:D.zoom,bearing:D.bearing,pitch:D.pitch,interactive:!F.isStatic,preserveDrawingBuffer:F.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new r.AttributionControl({compact:!0}));N._canvas.style.left="0px",N._canvas.style.top="0px",F.rejectOnError(R),F.isStatic||F.initFx(S,P);var B=[];B.push(new Promise(function(W){N.once("load",W)})),B=B.concat(l.fetchTraceGeoData(S)),Promise.all(B).then(function(){F.fillBelowLookup(S,P),F.updateData(S),F.updateLayout(P),F.resolveOnRender(L)}).catch(R)},k.updateMap=function(S,P,L,R){var F=this,D=F.map,O=P[this.id];F.rejectOnError(R);var N=[],B=M(O.style);JSON.stringify(F.styleObj)!==JSON.stringify(B)&&(F.styleObj=B,D.setStyle(B.style),F.traceHash={},N.push(new Promise(function(W){D.once("styledata",W)}))),N=N.concat(l.fetchTraceGeoData(S)),Promise.all(N).then(function(){F.fillBelowLookup(S,P),F.updateData(S),F.updateLayout(P),F.resolveOnRender(L)}).catch(R)},k.fillBelowLookup=function(S,P){var L,R,F=P[this.id].layers,D=this.belowLookup={},O=!1;for(L=0;L<S.length;L++){var N=S[L][0].trace,B=N._module;typeof N.below=="string"?R=N.below:B.getBelow&&(R=B.getBelow(N,this)),R===""&&(O=!0),D["trace-"+N.uid]=R||""}for(L=0;L<F.length;L++){var W=F[L];R=typeof W.below=="string"?W.below:O?"traces":"",D["layout-"+L]=R}var G,K,te={};for(G in D)te[R=D[G]]?te[R].push(G):te[R]=[G];for(R in te){var Y=te[R];if(Y.length>1)for(L=0;L<Y.length;L++)(G=Y[L]).indexOf("trace-")===0?(K=G.split("trace-")[1],this.traceHash[K]&&(this.traceHash[K].below=null)):G.indexOf("layout-")===0&&(K=G.split("layout-")[1],this.layerList[K]&&(this.layerList[K].below=null))}};var w={choroplethmapbox:0,densitymapbox:1,scattermapbox:2};function M(S){var P={};return a.isPlainObject(S)?(P.id=S.id,P.style=S):typeof S=="string"?(P.id=S,_.styleValuesMapbox.indexOf(S)!==-1?P.style=T(S):_.stylesNonMapbox[S]?P.style=_.stylesNonMapbox[S]:P.style=S):(P.id=_.styleValueDflt,P.style=T(_.styleValueDflt)),P.transition={duration:0,delay:0},P}function T(S){return _.styleUrlPrefix+S+"-"+_.styleUrlSuffix}function E(S){return[S.lon,S.lat]}k.updateData=function(S){var P,L,R,F,D=this.traceHash,O=S.slice().sort(function(K,te){return w[K[0].trace.type]-w[te[0].trace.type]});for(R=0;R<O.length;R++){var N=O[R],B=!1;(P=D[(L=N[0].trace).uid])&&(P.type===L.type?(P.update(N),B=!0):P.dispose()),!B&&L._module&&(D[L.uid]=L._module.plot(this,N))}var W=Object.keys(D);e:for(R=0;R<W.length;R++){var G=W[R];for(F=0;F<S.length;F++)if(G===(L=S[F][0].trace).uid)continue e;(P=D[G]).dispose(),delete D[G]}},k.updateLayout=function(S){var P=this.map,L=S[this.id];this.dragging||this.wheeling||(P.setCenter(E(L.center)),P.setZoom(L.zoom),P.setBearing(L.bearing),P.setPitch(L.pitch)),this.updateLayers(S),this.updateFramework(S),this.updateFx(S),this.map.resize(),this.gd._context._scrollZoom.mapbox?P.scrollZoom.enable():P.scrollZoom.disable()},k.resolveOnRender=function(S){var P=this.map;P.on("render",function L(){P.loaded()&&(P.off("render",L),setTimeout(S,10))})},k.rejectOnError=function(S){var P=this.map;function L(){S(new Error(_.mapOnErrorMsg))}P.once("error",L),P.once("style.error",L),P.once("source.error",L),P.once("tile.error",L),P.once("layer.error",L)},k.createFramework=function(S){var P=this,L=P.div=document.createElement("div");L.id=P.uid,L.style.position="absolute",P.container.appendChild(L),P.xaxis={_id:"x",c2p:function(R){return P.project(R).x}},P.yaxis={_id:"y",c2p:function(R){return P.project(R).y}},P.updateFramework(S),P.mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},i.setConvert(P.mockAxis,S)},k.initFx=function(S,P){var L=this,R=L.gd,F=L.map;function D(){u.loneUnhover(P._hoverlayer)}function O(){var N=L.getView();R.emit("plotly_relayouting",L.getViewEditsWithDerived(N))}F.on("moveend",function(N){if(L.map){var B=R._fullLayout;if(N.originalEvent||L.wheeling){var W=B[L.id];c.call("_storeDirectGUIEdit",R.layout,B._preGUI,L.getViewEdits(W));var G=L.getView();W._input.center=W.center=G.center,W._input.zoom=W.zoom=G.zoom,W._input.bearing=W.bearing=G.bearing,W._input.pitch=W.pitch=G.pitch,R.emit("plotly_relayout",L.getViewEditsWithDerived(G))}N.originalEvent&&N.originalEvent.type==="mouseup"?L.dragging=!1:L.wheeling&&(L.wheeling=!1),B._rehover&&B._rehover()}}),F.on("wheel",function(){L.wheeling=!0}),F.on("mousemove",function(N){var B=L.div.getBoundingClientRect(),W=[N.originalEvent.offsetX,N.originalEvent.offsetY];N.target.getBoundingClientRect=function(){return B},L.xaxis.p2c=function(){return F.unproject(W).lng},L.yaxis.p2c=function(){return F.unproject(W).lat},R._fullLayout._rehover=function(){R._fullLayout._hoversubplot===L.id&&R._fullLayout[L.id]&&u.hover(R,N,L.id)},u.hover(R,N,L.id),R._fullLayout._hoversubplot=L.id}),F.on("dragstart",function(){L.dragging=!0,D()}),F.on("zoomstart",D),F.on("mouseout",function(){R._fullLayout._hoversubplot=null}),F.on("drag",O),F.on("zoom",O),F.on("dblclick",function(){var N=R._fullLayout[L.id];c.call("_storeDirectGUIEdit",R.layout,R._fullLayout._preGUI,L.getViewEdits(N));var B=L.viewInitial;F.setCenter(E(B.center)),F.setZoom(B.zoom),F.setBearing(B.bearing),F.setPitch(B.pitch);var W=L.getView();N._input.center=N.center=W.center,N._input.zoom=N.zoom=W.zoom,N._input.bearing=N.bearing=W.bearing,N._input.pitch=N.pitch=W.pitch,R.emit("plotly_doubleclick",null),R.emit("plotly_relayout",L.getViewEditsWithDerived(W))}),L.clearSelect=function(){v(L.dragOptions),y(L.dragOptions.gd)},L.onClickInPanFn=function(N){return function(B){var W=R._fullLayout.clickmode;W.indexOf("select")>-1&&x(B.originalEvent,R,[L.xaxis],[L.yaxis],L.id,N),W.indexOf("event")>-1&&u.click(R,B.originalEvent)}}},k.updateFx=function(S){var P=this,L=P.map,R=P.gd;if(!P.isStatic){var F,D=S.dragmode;F=d(D)?function(B,W){(B.range={})[P.id]=[N([W.xmin,W.ymin]),N([W.xmax,W.ymax])]}:function(B,W,G){(B.lassoPoints={})[P.id]=G.filtered.map(N)};var 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r=t("@plotly/d3"),a=t("tinycolor2"),l=t("../../registry"),c=t("../../lib"),i=c.strRotate,s=c.strTranslate,u=t("../../components/color"),h=t("../../components/drawing"),d=t("../plots"),m=t("../../plots/cartesian/axes"),p=t("../cartesian/set_convert"),g=t("./set_convert"),y=t("../cartesian/autorange").doAutoRange,v=t("../cartesian/dragbox"),x=t("../../components/dragelement"),_=t("../../components/fx"),A=t("../../components/titles"),b=t("../cartesian/select").prepSelect,k=t("../cartesian/select").selectOnClick,w=t("../cartesian/select").clearSelect,M=t("../../lib/setcursor"),T=t("../../lib/clear_gl_canvases"),E=t("../../plot_api/subroutines").redrawReglTraces,S=t("../../constants/alignment").MID_SHIFT,P=t("./constants"),L=t("./helpers"),R=t("../smith/helpers"),F=R.smith,D=R.reactanceArc,O=R.resistanceArc,N=R.smithTransform,B=c._,W=c.mod,G=c.deg2rad,K=c.rad2deg;function te(V,H,ne){this.isSmith=ne||!1,this.id=H,this.gd=V,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var q=V._fullLayout,Q="clip"+q._uid+H;this.clipIds.forTraces=Q+"-for-traces",this.clipPaths.forTraces=q._clips.append("clipPath").attr("id",this.clipIds.forTraces),this.clipPaths.forTraces.append("path"),this.framework=q["_"+(ne?"smith":"polar")+"layer"].append("g").attr("class",H),this.getHole=function(ee){return this.isSmith?0:ee.hole},this.getSector=function(ee){return this.isSmith?[0,360]:ee.sector},this.getRadial=function(ee){return this.isSmith?ee.realaxis:ee.radialaxis},this.getAngular=function(ee){return this.isSmith?ee.imaginaryaxis:ee.angularaxis},ne||(this.radialTickLayout=null,this.angularTickLayout=null)}var Y=te.prototype;function J(V){var H=V.ticks+String(V.ticklen)+String(V.showticklabels);return"side"in V&&(H+=V.side),H}function re(V,H){return H[c.findIndexOfMin(H,function(ne){return c.angleDist(V,ne)})]}function U(V,H,ne){return H?(V.attr("display",null),V.attr(ne)):V&&V.attr("display","none"),V}o.exports=function(V,H,ne){return new te(V,H,ne)},Y.plot=function(V,H){for(var ne=H[this.id],q=!1,Q=0;Q<V.length;Q++)if(V[Q][0].trace.cliponaxis===!1){q=!0;break}this._hasClipOnAxisFalse=q,this.updateLayers(H,ne),this.updateLayout(H,ne),d.generalUpdatePerTraceModule(this.gd,this,V,ne),this.updateFx(H,ne),this.isSmith&&(delete ne.realaxis.range,delete ne.imaginaryaxis.range)},Y.updateLayers=function(V,H){var ne=this.isSmith,q=this.layers,Q=this.getRadial(H),ee=this.getAngular(H),ie=P.layerNames,ae=ie.indexOf("frontplot"),ue=ie.slice(0,ae),le=ee.layer==="below traces",ge=Q.layer==="below 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ne=this.layers,q=V._size,Q=this.getRadial(H),ee=this.getAngular(H),ie=H.domain.x,ae=H.domain.y;this.xOffset=q.l+q.w*ie[0],this.yOffset=q.t+q.h*(1-ae[1]);var ue=this.xLength=q.w*(ie[1]-ie[0]),le=this.yLength=q.h*(ae[1]-ae[0]),ge=this.getSector(H);this.sectorInRad=ge.map(G);var fe,me,_e,Ae,ke,Le=this.sectorBBox=function(yt){var Ot,Tt,at,et,Lt=yt[0],Wt=yt[1]-Lt,Jt=W(Lt,360),Be=Jt+Wt,Ge=Math.cos(G(Jt)),kt=Math.sin(G(Jt)),dt=Math.cos(G(Be)),Oe=Math.sin(G(Be));return et=Jt<=90&&Be>=90||Jt>90&&Be>=450?1:kt<=0&&Oe<=0?0:Math.max(kt,Oe),Ot=Jt<=180&&Be>=180||Jt>180&&Be>=540?-1:Ge>=0&&dt>=0?0:Math.min(Ge,dt),Tt=Jt<=270&&Be>=270||Jt>270&&Be>=630?-1:kt>=0&&Oe>=0?0:Math.min(kt,Oe),at=Be>=360?1:Ge<=0&&dt<=0?0:Math.max(Ge,dt),[Ot,Tt,at,et]}(ge),de=Le[2]-Le[0],ve=Le[3]-Le[1],Me=le/ue,we=Math.abs(ve/de);Me>we?(fe=ue,ke=(le-(me=ue*we))/q.h/2,_e=[ie[0],ie[1]],Ae=[ae[0]+ke,ae[1]-ke]):(me=le,ke=(ue-(fe=le/we))/q.w/2,_e=[ie[0]+ke,ie[1]-ke],Ae=[ae[0],ae[1]]),this.xLength2=fe,this.yLength2=me,this.xDomain2=_e,this.yDomain2=Ae;var Ce,Fe=this.xOffset2=q.l+q.w*_e[0],ze=this.yOffset2=q.t+q.h*(1-Ae[1]),$e=this.radius=fe/de,Ke=this.innerRadius=this.getHole(H)*$e,Re=this.cx=Fe-$e*Le[0],Ve=this.cy=ze+$e*Le[3],We=this.cxx=Re-Fe,Ye=this.cyy=Ve-ze,nt=Q.side;nt==="counterclockwise"?(Ce=nt,nt="top"):nt==="clockwise"&&(Ce=nt,nt="bottom"),this.radialAxis=this.mockAxis(V,H,Q,{_id:"x",side:nt,_trueSide:Ce,domain:[Ke/q.w,$e/q.w]}),this.angularAxis=this.mockAxis(V,H,ee,{side:"right",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(V,H),this.updateAngularAxis(V,H),this.updateRadialAxis(V,H),this.updateRadialAxisTitle(V,H),this.xaxis=this.mockCartesianAxis(V,H,{_id:"x",domain:_e}),this.yaxis=this.mockCartesianAxis(V,H,{_id:"y",domain:Ae});var ft=this.pathSubplot();this.clipPaths.forTraces.select("path").attr("d",ft).attr("transform",s(We,Ye)),ne.frontplot.attr("transform",s(Fe,ze)).call(h.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces,this.gd),ne.bg.attr("d",ft).attr("transform",s(Re,Ve)).call(u.fill,H.bgcolor)},Y.mockAxis=function(V,H,ne,q){var Q=c.extendFlat({},ne,q);return g(Q,H,V),Q},Y.mockCartesianAxis=function(V,H,ne){var q=this,Q=q.isSmith,ee=ne._id,ie=c.extendFlat({type:"linear"},ne);p(ie,V);var ae={x:[0,2],y:[1,3]};return ie.setRange=function(){var ue=q.sectorBBox,le=ae[ee],ge=q.radialAxis._rl,fe=(ge[1]-ge[0])/(1-q.getHole(H));ie.range=[ue[le[0]]*fe,ue[le[1]]*fe]},ie.isPtWithinRange=ee!=="x"||Q?function(){return!0}:function(ue){return q.isPtInside(ue)},ie.setRange(),ie.setScale(),ie},Y.doAutoRange=function(V,H){var ne=this.gd,q=this.radialAxis,Q=this.getRadial(H);y(ne,q);var ee=q.range;Q.range=ee.slice(),Q._input.range=ee.slice(),q._rl=[q.r2l(ee[0],null,"gregorian"),q.r2l(ee[1],null,"gregorian")]},Y.updateRadialAxis=function(V,H){var ne=this,q=ne.gd,Q=ne.layers,ee=ne.radius,ie=ne.innerRadius,ae=ne.cx,ue=ne.cy,le=ne.getRadial(H),ge=W(ne.getSector(H)[0],360),fe=ne.radialAxis,me=ie<ee,_e=ne.isSmith;_e||(ne.fillViewInitialKey("radialaxis.angle",le.angle),ne.fillViewInitialKey("radialaxis.range",fe.range.slice()),fe.setGeometry()),fe.tickangle==="auto"&&ge>90&&ge<=270&&(fe.tickangle=180);var Ae=_e?function($e){var Ke=N(ne,F([$e.x,0]));return s(Ke[0]-ae,Ke[1]-ue)}:function($e){return s(fe.l2p($e.x)+ie,0)},ke=_e?function($e){return O(ne,$e.x,-1/0,1/0)}:function($e){return ne.pathArc(fe.r2p($e.x)+ie)},Le=J(le);if(ne.radialTickLayout!==Le&&(Q["radial-axis"].selectAll(".xtick").remove(),ne.radialTickLayout=Le),me){fe.setScale();var de=0,ve=_e?(fe.tickvals||[]).filter(function($e){return $e>=0}).map(function($e){return m.tickText(fe,$e,!0,!1)}):m.calcTicks(fe),Me=_e?ve:m.clipEnds(fe,ve),we=m.getTickSigns(fe)[2];_e&&((fe.ticks==="top"&&fe.side==="bottom"||fe.ticks==="bottom"&&fe.side==="top")&&(we=-we),fe.ticks==="top"&&fe.side==="top"&&(de=-fe.ticklen),fe.ticks==="bottom"&&fe.side==="bottom"&&(de=fe.ticklen)),m.drawTicks(q,fe,{vals:ve,layer:Q["radial-axis"],path:m.makeTickPath(fe,0,we),transFn:Ae,crisp:!1}),m.drawGrid(q,fe,{vals:Me,layer:Q["radial-grid"],path:ke,transFn:c.noop,crisp:!1}),m.drawLabels(q,fe,{vals:ve,layer:Q["radial-axis"],transFn:Ae,labelFns:m.makeLabelFns(fe,de)})}var Ce=ne.radialAxisAngle=ne.vangles?K(re(G(le.angle),ne.vangles)):le.angle,Fe=s(ae,ue),ze=Fe+i(-Ce);U(Q["radial-axis"],me&&(le.showticklabels||le.ticks),{transform:ze}),U(Q["radial-grid"],me&&le.showgrid,{transform:_e?"":Fe}),U(Q["radial-line"].select("line"),me&&le.showline,{x1:_e?-ee:ie,y1:0,x2:ee,y2:0,transform:ze}).attr("stroke-width",le.linewidth).call(u.stroke,le.linecolor)},Y.updateRadialAxisTitle=function(V,H,ne){if(!this.isSmith){var q=this.gd,Q=this.radius,ee=this.cx,ie=this.cy,ae=this.getRadial(H),ue=this.id+"title",le=0;if(ae.title){var ge=h.bBox(this.layers["radial-axis"].node()).height,fe=ae.title.font.size,me=ae.side;le=me==="top"?fe:me==="counterclockwise"?-(ge+.4*fe):ge+.8*fe}var _e=ne!==void 0?ne:this.radialAxisAngle,Ae=G(_e),ke=Math.cos(Ae),Le=Math.sin(Ae),de=ee+Q/2*ke+le*Le,ve=ie-Q/2*Le+le*ke;this.layers["radial-axis-title"]=A.draw(q,ue,{propContainer:ae,propName:this.id+".radialaxis.title",placeholder:B(q,"Click to enter radial axis 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Ke=me($e),Re=Math.cos(Ke),Ve=Math.sin(Ke);return"M"+[ae+ie*Re,ue-ie*Ve]+"L"+[ae+ee*Re,ue-ee*Ve]},de=m.makeLabelFns(ge,0).labelStandoff,ve={xFn:function($e){var Ke=me($e);return Math.cos(Ke)*de},yFn:function($e){var Ke=me($e),Re=Math.sin(Ke)>0?.2:1;return-Math.sin(Ke)*(de+$e.fontSize*Re)+Math.abs(Math.cos(Ke))*($e.fontSize*S)},anchorFn:function($e){var Ke=me($e),Re=Math.cos(Ke);return Math.abs(Re)<.1?"middle":Re>0?"start":"end"},heightFn:function($e,Ke,Re){var Ve=me($e);return-.5*(1+Math.sin(Ve))*Re}},Me=J(le);ne.angularTickLayout!==Me&&(Q["angular-axis"].selectAll("."+ge._id+"tick").remove(),ne.angularTickLayout=Me);var we,Ce=fe?[1/0].concat(ge.tickvals||[]).map(function($e){return m.tickText(ge,$e,!0,!1)}):m.calcTicks(ge);if(fe&&(Ce[0].text="∞",Ce[0].fontSize*=1.75),H.gridshape==="linear"?(we=Ce.map(me),c.angleDelta(we[0],we[1])<0&&(we=we.slice().reverse())):we=null,ne.vangles=we,ge.type==="category"&&(Ce=Ce.filter(function($e){return c.isAngleInsideSector(me($e),ne.sectorInRad)})),ge.visible){var Fe=ge.ticks==="inside"?-1:1,ze=(ge.linewidth||1)/2;m.drawTicks(q,ge,{vals:Ce,layer:Q["angular-axis"],path:"M"+Fe*ze+",0h"+Fe*ge.ticklen,transFn:ke,crisp:!1}),m.drawGrid(q,ge,{vals:Ce,layer:Q["angular-grid"],path:Le,transFn:c.noop,crisp:!1}),m.drawLabels(q,ge,{vals:Ce,layer:Q["angular-axis"],repositionOnUpdate:!0,transFn:Ae,labelFns:ve})}U(Q["angular-line"].select("path"),le.showline,{d:ne.pathSubplot(),transform:s(ae,ue)}).attr("stroke-width",le.linewidth).call(u.stroke,le.linecolor)},Y.updateFx=function(V,H){this.gd._context.staticPlot||(!this.isSmith&&(this.updateAngularDrag(V),this.updateRadialDrag(V,H,0),this.updateRadialDrag(V,H,1)),this.updateHoverAndMainDrag(V))},Y.updateHoverAndMainDrag=function(V){var H,ne,q=this,Q=q.isSmith,ee=q.gd,ie=q.layers,ae=V._zoomlayer,ue=P.MINZOOM,le=P.OFFEDGE,ge=q.radius,fe=q.innerRadius,me=q.cx,_e=q.cy,Ae=q.cxx,ke=q.cyy,Le=q.sectorInRad,de=q.vangles,ve=q.radialAxis,Me=L.clampTiny,we=L.findXYatLength,Ce=L.findEnclosingVertexAngles,Fe=P.cornerHalfWidth,ze=P.cornerLen/2,$e=v.makeDragger(ie,"path","maindrag","crosshair");r.select($e).attr("d",q.pathSubplot()).attr("transform",s(me,_e)),$e.onmousemove=function(rt){_.hover(ee,rt,q.id),ee._fullLayout._lasthover=$e,ee._fullLayout._hoversubplot=q.id},$e.onmouseout=function(rt){ee._dragging||x.unhover(ee,rt)};var Ke,Re,Ve,We,Ye,nt,ft,yt,Ot,Tt={element:$e,gd:ee,subplot:q.id,plotinfo:{id:q.id,xaxis:q.xaxis,yaxis:q.yaxis},xaxes:[q.xaxis],yaxes:[q.yaxis]};function at(rt,lt){return Math.sqrt(rt*rt+lt*lt)}function et(rt,lt){return at(rt-Ae,lt-ke)}function Lt(rt,lt){return Math.atan2(ke-lt,rt-Ae)}function Wt(rt,lt){return[rt*Math.cos(lt),rt*Math.sin(-lt)]}function Jt(rt,lt){if(rt===0)return q.pathSector(2*Fe);var ot=ze/rt,At=lt-ot,wt=lt+ot,$t=Math.max(0,Math.min(rt,ge)),Ut=$t-Fe,tt=$t+Fe;return"M"+Wt(Ut,At)+"A"+[Ut,Ut]+" 0,0,0 "+Wt(Ut,wt)+"L"+Wt(tt,wt)+"A"+[tt,tt]+" 0,0,1 "+Wt(tt,At)+"Z"}function Be(rt,lt,ot){if(rt===0)return q.pathSector(2*Fe);var At,wt,$t=Wt(rt,lt),Ut=Wt(rt,ot),tt=Me(($t[0]+Ut[0])/2),bt=Me(($t[1]+Ut[1])/2);if(tt&&bt){var Ft=bt/tt,Et=-1/Ft,Pt=we(Fe,Ft,tt,bt);At=we(ze,Et,Pt[0][0],Pt[0][1]),wt=we(ze,Et,Pt[1][0],Pt[1][1])}else{var De,Je;bt?(De=ze,Je=Fe):(De=Fe,Je=ze),At=[[tt-De,bt-Je],[tt+De,bt-Je]],wt=[[tt-De,bt+Je],[tt+De,bt+Je]]}return"M"+At.join("L")+"L"+wt.reverse().join("L")+"Z"}function Ge(rt,lt){return lt=Math.max(Math.min(lt,ge),fe),rt<le?rt=0:ge-rt<le?rt=ge:lt<le?lt=0:ge-lt<le&&(lt=ge),Math.abs(lt-rt)>ue?(rt<lt?(Ve=rt,We=lt):(Ve=lt,We=rt),!0):(Ve=null,We=null,!1)}function kt(rt,lt){rt=rt||Ye,lt=lt||"M0,0Z",yt.attr("d",rt),Ot.attr("d",lt),v.transitionZoombox(yt,Ot,nt,ft),nt=!0;var ot={};Pe(ot),ee.emit("plotly_relayouting",ot)}function dt(rt,lt){var ot,At,wt=Ke+(rt*=H),$t=Re+(lt*=ne),Ut=et(Ke,Re),tt=Math.min(et(wt,$t),ge),bt=Lt(Ke,Re);Ge(Ut,tt)&&(ot=Ye+q.pathSector(We),Ve&&(ot+=q.pathSector(Ve)),At=Jt(Ve,bt)+Jt(We,bt)),kt(ot,At)}function Oe(rt,lt,ot,At){var wt=L.findIntersectionXY(ot,At,ot,[rt-Ae,ke-lt]);return at(wt[0],wt[1])}function Ie(rt,lt){var ot,At,wt=Ke+rt,$t=Re+lt,Ut=Lt(Ke,Re),tt=Lt(wt,$t),bt=Ce(Ut,de),Ft=Ce(tt,de);Ge(Oe(Ke,Re,bt[0],bt[1]),Math.min(Oe(wt,$t,Ft[0],Ft[1]),ge))&&(ot=Ye+q.pathSector(We),Ve&&(ot+=q.pathSector(Ve)),At=[Be(Ve,bt[0],bt[1]),Be(We,bt[0],bt[1])].join(" ")),kt(ot,At)}function Te(){if(v.removeZoombox(ee),Ve!==null&&We!==null){var rt={};Pe(rt),v.showDoubleClickNotifier(ee),l.call("_guiRelayout",ee,rt)}}function Pe(rt){var lt=ve._rl,ot=(lt[1]-lt[0])/(1-fe/ge)/ge,At=[lt[0]+(Ve-fe)*ot,lt[0]+(We-fe)*ot];rt[q.id+".radialaxis.range"]=At}function qe(rt,lt){var ot=ee._fullLayout.clickmode;if(v.removeZoombox(ee),rt===2){var At={};for(var wt in 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r=t("../../components/color/attributes"),a=t("../domain").attributes,l=t("../cartesian/layout_attributes"),c=t("../../plot_api/edit_types").overrideAll,i=t("../../lib/extend").extendFlat,s={title:{text:l.title.text,font:l.title.font},color:l.color,tickmode:l.tickmode,nticks:i({},l.nticks,{dflt:6,min:1}),tick0:l.tick0,dtick:l.dtick,tickvals:l.tickvals,ticktext:l.ticktext,ticks:l.ticks,ticklen:l.ticklen,tickwidth:l.tickwidth,tickcolor:l.tickcolor,ticklabelstep:l.ticklabelstep,showticklabels:l.showticklabels,showtickprefix:l.showtickprefix,tickprefix:l.tickprefix,showticksuffix:l.showticksuffix,ticksuffix:l.ticksuffix,showexponent:l.showexponent,exponentformat:l.exponentformat,minexponent:l.minexponent,separatethousands:l.separatethousands,tickfont:l.tickfont,tickangle:l.tickangle,tickformat:l.tickformat,tickformatstops:l.tickformatstops,hoverformat:l.hoverformat,showline:i({},l.showline,{dflt:!0}),linecolor:l.linecolor,linewidth:l.linewidth,showgrid:i({},l.showgrid,{dflt:!0}),gridcolor:l.gridcolor,gridwidth:l.gridwidth,layer:l.layer,min:{valType:"number",dflt:0,min:0},_deprecated:{title:l._deprecated.title,titlefont:l._deprecated.titlefont}},u=o.exports=c({domain:a({name:"ternary"}),bgcolor:{valType:"color",dflt:r.background},sum:{valType:"number",dflt:1,min:0},aaxis:s,baxis:s,caxis:s},"plot","from-root");u.uirevision={valType:"any",editType:"none"},u.aaxis.uirevision=u.baxis.uirevision=u.caxis.uirevision={valType:"any",editType:"none"}},{"../../components/color/attributes":365,"../../lib/extend":493,"../../plot_api/edit_types":536,"../cartesian/layout_attributes":569,"../domain":584}],636:[function(t,o,f){var r=t("../../components/color"),a=t("../../plot_api/plot_template"),l=t("../../lib"),c=t("../subplot_defaults"),i=t("../cartesian/tick_label_defaults"),s=t("../cartesian/prefix_suffix_defaults"),u=t("../cartesian/tick_mark_defaults"),h=t("../cartesian/tick_value_defaults"),d=t("../cartesian/line_grid_defaults"),m=t("./layout_attributes"),p=["aaxis","baxis","caxis"];function g(v,x,_,A){var b,k,w,M=_("bgcolor"),T=_("sum");A.bgColor=r.combine(M,A.paper_bgcolor);for(var E=0;E<p.length;E++)k=v[b=p[E]]||{},(w=a.newContainer(x,b))._name=b,y(k,w,A,x);var S=x.aaxis,P=x.baxis,L=x.caxis;S.min+P.min+L.min>=T&&(S.min=0,P.min=0,L.min=0,v.aaxis&&delete v.aaxis.min,v.baxis&&delete v.baxis.min,v.caxis&&delete v.caxis.min)}function y(v,x,_,A){var b=m[x._name];function k(P,L){return l.coerce(v,x,b,P,L)}k("uirevision",A.uirevision),x.type="linear";var w=k("color"),M=w!==b.color.dflt?w:_.font.color,T=x._name.charAt(0).toUpperCase(),E="Component 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r=t("@plotly/d3"),a=t("tinycolor2"),l=t("../../registry"),c=t("../../lib"),i=c.strTranslate,s=c._,u=t("../../components/color"),h=t("../../components/drawing"),d=t("../cartesian/set_convert"),m=t("../../lib/extend").extendFlat,p=t("../plots"),g=t("../cartesian/axes"),y=t("../../components/dragelement"),v=t("../../components/fx"),x=t("../../components/dragelement/helpers"),_=x.freeMode,A=x.rectMode,b=t("../../components/titles"),k=t("../cartesian/select").prepSelect,w=t("../cartesian/select").selectOnClick,M=t("../cartesian/select").clearSelect,T=t("../cartesian/select").clearSelectionsCache,E=t("../cartesian/constants");function S(W,G){this.id=W.id,this.graphDiv=W.graphDiv,this.init(G),this.makeFramework(G),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}o.exports=S;var P=S.prototype;P.init=function(W){this.container=W._ternarylayer,this.defs=W._defs,this.layoutId=W._uid,this.traceHash={},this.layers={}},P.plot=function(W,G){var K=G[this.id],te=G._size;this._hasClipOnAxisFalse=!1;for(var Y=0;Y<W.length;Y++)if(W[Y][0].trace.cliponaxis===!1){this._hasClipOnAxisFalse=!0;break}this.updateLayers(K),this.adjustLayout(K,te),p.generalUpdatePerTraceModule(this.graphDiv,this,W,K),this.layers.plotbg.select("path").call(u.fill,K.bgcolor)},P.makeFramework=function(W){var G=this.graphDiv,K=W[this.id],te=this.clipId="clip"+this.layoutId+this.id,Y=this.clipIdRelative="clip-relative"+this.layoutId+this.id;this.clipDef=c.ensureSingleById(W._clips,"clipPath",te,function(J){J.append("path").attr("d","M0,0Z")}),this.clipDefRelative=c.ensureSingleById(W._clips,"clipPath",Y,function(J){J.append("path").attr("d","M0,0Z")}),this.plotContainer=c.ensureSingle(this.container,"g",this.id),this.updateLayers(K),h.setClipUrl(this.layers.backplot,te,G),h.setClipUrl(this.layers.grids,te,G)},P.updateLayers=function(W){var G=this.layers,K=["draglayer","plotbg","backplot","grids"];W.aaxis.layer==="below traces"&&K.push("aaxis","aline"),W.baxis.layer==="below traces"&&K.push("baxis","bline"),W.caxis.layer==="below traces"&&K.push("caxis","cline"),K.push("frontplot"),W.aaxis.layer==="above traces"&&K.push("aaxis","aline"),W.baxis.layer==="above traces"&&K.push("baxis","bline"),W.caxis.layer==="above traces"&&K.push("caxis","cline");var te=this.plotContainer.selectAll("g.toplevel").data(K,String),Y=["agrid","bgrid","cgrid"];te.enter().append("g").attr("class",function(J){return"toplevel "+J}).each(function(J){var re=r.select(this);G[J]=re,J==="frontplot"?re.append("g").classed("scatterlayer",!0):J==="backplot"?re.append("g").classed("maplayer",!0):J==="plotbg"?re.append("path").attr("d","M0,0Z"):J==="aline"||J==="bline"||J==="cline"?re.append("path"):J==="grids"&&Y.forEach(function(U){G[U]=re.append("g").classed("grid "+U,!0)})}),te.order()};var L=Math.sqrt(4/3);P.adjustLayout=function(W,G){var K,te,Y,J,re,U,V=this,H=W.domain,ne=(H.x[0]+H.x[1])/2,q=(H.y[0]+H.y[1])/2,Q=H.x[1]-H.x[0],ee=H.y[1]-H.y[0],ie=Q*G.w,ae=ee*G.h,ue=W.sum,le=W.aaxis.min,ge=W.baxis.min,fe=W.caxis.min;ie>L*ae?Y=(J=ae)*L:J=(Y=ie)/L,re=Q*Y/ie,U=ee*J/ae,K=G.l+G.w*ne-Y/2,te=G.t+G.h*(1-q)-J/2,V.x0=K,V.y0=te,V.w=Y,V.h=J,V.sum=ue,V.xaxis={type:"linear",range:[le+2*fe-ue,ue-le-2*ge],domain:[ne-re/2,ne+re/2],_id:"x"},d(V.xaxis,V.graphDiv._fullLayout),V.xaxis.setScale(),V.xaxis.isPtWithinRange=function(Fe){return Fe.a>=V.aaxis.range[0]&&Fe.a<=V.aaxis.range[1]&&Fe.b>=V.baxis.range[1]&&Fe.b<=V.baxis.range[0]&&Fe.c>=V.caxis.range[1]&&Fe.c<=V.caxis.range[0]},V.yaxis={type:"linear",range:[le,ue-ge-fe],domain:[q-U/2,q+U/2],_id:"y"},d(V.yaxis,V.graphDiv._fullLayout),V.yaxis.setScale(),V.yaxis.isPtWithinRange=function(){return!0};var me=V.yaxis.domain[0],_e=V.aaxis=m({},W.aaxis,{range:[le,ue-ge-fe],side:"left",tickangle:(+W.aaxis.tickangle||0)-30,domain:[me,me+U*L],anchor:"free",position:0,_id:"y",_length:Y});d(_e,V.graphDiv._fullLayout),_e.setScale();var Ae=V.baxis=m({},W.baxis,{range:[ue-le-fe,ge],side:"bottom",domain:V.xaxis.domain,anchor:"free",position:0,_id:"x",_length:Y});d(Ae,V.graphDiv._fullLayout),Ae.setScale();var ke=V.caxis=m({},W.caxis,{range:[ue-le-ge,fe],side:"right",tickangle:(+W.caxis.tickangle||0)+30,domain:[me,me+U*L],anchor:"free",position:0,_id:"y",_length:Y});d(ke,V.graphDiv._fullLayout),ke.setScale();var Le="M"+K+","+(te+J)+"h"+Y+"l-"+Y/2+",-"+J+"Z";V.clipDef.select("path").attr("d",Le),V.layers.plotbg.select("path").attr("d",Le);var de="M0,"+J+"h"+Y+"l-"+Y/2+",-"+J+"Z";V.clipDefRelative.select("path").attr("d",de);var ve=i(K,te);V.plotContainer.selectAll(".scatterlayer,.maplayer").attr("transform",ve),V.clipDefRelative.select("path").attr("transform",null);var Me=i(K-Ae._offset,te+J);V.layers.baxis.attr("transform",Me),V.layers.bgrid.attr("transform",Me);var we=i(K+Y/2,te)+"rotate(30)"+i(0,-_e._offset);V.layers.aaxis.attr("transform",we),V.layers.agrid.attr("transform",we);var Ce=i(K+Y/2,te)+"rotate(-30)"+i(0,-ke._offset);V.layers.caxis.attr("transform",Ce),V.layers.cgrid.attr("transform",Ce),V.drawAxes(!0),V.layers.aline.select("path").attr("d",_e.showline?"M"+K+","+(te+J)+"l"+Y/2+",-"+J:"M0,0").call(u.stroke,_e.linecolor||"#000").style("stroke-width",(_e.linewidth||0)+"px"),V.layers.bline.select("path").attr("d",Ae.showline?"M"+K+","+(te+J)+"h"+Y:"M0,0").call(u.stroke,Ae.linecolor||"#000").style("stroke-width",(Ae.linewidth||0)+"px"),V.layers.cline.select("path").attr("d",ke.showline?"M"+(K+Y/2)+","+te+"l"+Y/2+","+J:"M0,0").call(u.stroke,ke.linecolor||"#000").style("stroke-width",(ke.linewidth||0)+"px"),V.graphDiv._context.staticPlot||V.initInteractions(),h.setClipUrl(V.layers.frontplot,V._hasClipOnAxisFalse?null:V.clipId,V.graphDiv)},P.drawAxes=function(W){var G=this.graphDiv,K=this.id.substr(7)+"title",te=this.layers,Y=this.aaxis,J=this.baxis,re=this.caxis;if(this.drawAx(Y),this.drawAx(J),this.drawAx(re),W){var U=Math.max(Y.showticklabels?Y.tickfont.size/2:0,(re.showticklabels?.75*re.tickfont.size:0)+(re.ticks==="outside"?.87*re.ticklen:0)),V=(J.showticklabels?J.tickfont.size:0)+(J.ticks==="outside"?J.ticklen:0)+3;te["a-title"]=b.draw(G,"a"+K,{propContainer:Y,propName:this.id+".aaxis.title",placeholder:s(G,"Click to enter Component A title"),attributes:{x:this.x0+this.w/2,y:this.y0-Y.title.font.size/3-U,"text-anchor":"middle"}}),te["b-title"]=b.draw(G,"b"+K,{propContainer:J,propName:this.id+".baxis.title",placeholder:s(G,"Click to enter Component B title"),attributes:{x:this.x0-V,y:this.y0+this.h+.83*J.title.font.size+V,"text-anchor":"middle"}}),te["c-title"]=b.draw(G,"c"+K,{propContainer:re,propName:this.id+".caxis.title",placeholder:s(G,"Click to enter Component C title"),attributes:{x:this.x0+this.w+V,y:this.y0+this.h+.83*re.title.font.size+V,"text-anchor":"middle"}})}},P.drawAx=function(W){var G,K=this.graphDiv,te=W._name,Y=te.charAt(0),J=W._id,re=this.layers[te],U=Y+"tickLayout",V=(G=W).ticks+String(G.ticklen)+String(G.showticklabels);this[U]!==V&&(re.selectAll("."+J+"tick").remove(),this[U]=V),W.setScale();var H=g.calcTicks(W),ne=g.clipEnds(W,H),q=g.makeTransTickFn(W),Q=g.getTickSigns(W)[2],ee=c.deg2rad(30),ie=Q*(W.linewidth||1)/2,ae=Q*W.ticklen,ue=this.w,le=this.h,ge=Y==="b"?"M0,"+ie+"l"+Math.sin(ee)*ae+","+Math.cos(ee)*ae:"M"+ie+",0l"+Math.cos(ee)*ae+","+-Math.sin(ee)*ae,fe={a:"M0,0l"+le+",-"+ue/2,b:"M0,0l-"+ue/2+",-"+le,c:"M0,0l-"+le+","+ue/2}[Y];g.drawTicks(K,W,{vals:W.ticks==="inside"?ne:H,layer:re,path:ge,transFn:q,crisp:!1}),g.drawGrid(K,W,{vals:ne,layer:this.layers[Y+"grid"],path:fe,transFn:q,crisp:!1}),g.drawLabels(K,W,{vals:H,layer:re,transFn:q,labelFns:g.makeLabelFns(W,0,30)})};var R=E.MINZOOM/2+.87,F="m-0.87,.5h"+R+"v3h-"+(R+5.2)+"l"+(R/2+2.6)+",-"+(.87*R+4.5)+"l2.6,1.5l-"+R/2+","+.87*R+"Z",D="m0.87,.5h-"+R+"v3h"+(R+5.2)+"l-"+(R/2+2.6)+",-"+(.87*R+4.5)+"l-2.6,1.5l"+R/2+","+.87*R+"Z",O="m0,1l"+R/2+","+.87*R+"l2.6,-1.5l-"+(R/2+2.6)+",-"+(.87*R+4.5)+"l-"+(R/2+2.6)+","+(.87*R+4.5)+"l2.6,1.5l"+R/2+",-"+.87*R+"Z",N=!0;function B(W){r.select(W).selectAll(".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners").remove()}P.clearSelect=function(){T(this.dragOptions),M(this.dragOptions.gd)},P.initInteractions=function(){var W,G,K,te,Y,J,re,U,V,H,ne,q,Q=this,ee=Q.layers.plotbg.select("path").node(),ie=Q.graphDiv,ae=ie._fullLayout._zoomlayer;function ue(de){var ve={};return ve[Q.id+".aaxis.min"]=de.a,ve[Q.id+".baxis.min"]=de.b,ve[Q.id+".caxis.min"]=de.c,ve}function le(de,ve){var Me=ie._fullLayout.clickmode;B(ie),de===2&&(ie.emit("plotly_doubleclick",null),l.call("_guiRelayout",ie,ue({a:0,b:0,c:0}))),Me.indexOf("select")>-1&&de===1&&w(ve,ie,[Q.xaxis],[Q.yaxis],Q.id,Q.dragOptions),Me.indexOf("event")>-1&&v.click(ie,ve,Q.id)}function ge(de,ve){return 1-ve/Q.h}function fe(de,ve){return 1-(de+(Q.h-ve)/Math.sqrt(3))/Q.w}function me(de,ve){return(de-(Q.h-ve)/Math.sqrt(3))/Q.w}function _e(de,ve){var Me=K+de*W,we=te+ve*G,Ce=Math.max(0,Math.min(1,ge(0,te),ge(0,we))),Fe=Math.max(0,Math.min(1,fe(K,te),fe(Me,we))),ze=Math.max(0,Math.min(1,me(K,te),me(Me,we))),$e=(Ce/2+ze)*Q.w,Ke=(1-Ce/2-Fe)*Q.w,Re=($e+Ke)/2,Ve=Ke-$e,We=(1-Ce)*Q.h,Ye=We-Ve/L;Ve<E.MINZOOM?(re=Y,ne.attr("d",V),q.attr("d","M0,0Z")):(re={a:Y.a+Ce*J,b:Y.b+Fe*J,c:Y.c+ze*J},ne.attr("d",V+"M"+$e+","+We+"H"+Ke+"L"+Re+","+Ye+"L"+$e+","+We+"Z"),q.attr("d","M"+K+","+te+"m0.5,0.5h5v-2h-5v-5h-2v5h-5v2h5v5h2ZM"+$e+","+We+F+"M"+Ke+","+We+D+"M"+Re+","+Ye+O)),H||(ne.transition().style("fill",U>.2?"rgba(0,0,0,0.4)":"rgba(255,255,255,0.3)").duration(200),q.transition().style("opacity",1).duration(200),H=!0),ie.emit("plotly_relayouting",ue(re))}function Ae(){B(ie),re!==Y&&(l.call("_guiRelayout",ie,ue(re)),N&&ie.data&&ie._context.showTips&&(c.notifier(s(ie,"Double-click to zoom back out"),"long"),N=!1))}function ke(de,ve){var Me=de/Q.xaxis._m,we=ve/Q.yaxis._m,Ce=[(re={a:Y.a-we,b:Y.b+(Me+we)/2,c:Y.c-(Me-we)/2}).a,re.b,re.c].sort(c.sorterAsc),Fe=Ce.indexOf(re.a),ze=Ce.indexOf(re.b),$e=Ce.indexOf(re.c);Ce[0]<0&&(Ce[1]+Ce[0]/2<0?(Ce[2]+=Ce[0]+Ce[1],Ce[0]=Ce[1]=0):(Ce[2]+=Ce[0]/2,Ce[1]+=Ce[0]/2,Ce[0]=0),re={a:Ce[Fe],b:Ce[ze],c:Ce[$e]},ve=(Y.a-re.a)*Q.yaxis._m,de=(Y.c-re.c-Y.b+re.b)*Q.xaxis._m);var Ke=i(Q.x0+de,Q.y0+ve);Q.plotContainer.selectAll(".scatterlayer,.maplayer").attr("transform",Ke);var Re=i(-de,-ve);Q.clipDefRelative.select("path").attr("transform",Re),Q.aaxis.range=[re.a,Q.sum-re.b-re.c],Q.baxis.range=[Q.sum-re.a-re.c,re.b],Q.caxis.range=[Q.sum-re.a-re.b,re.c],Q.drawAxes(!1),Q._hasClipOnAxisFalse&&Q.plotContainer.select(".scatterlayer").selectAll(".trace").call(h.hideOutsideRangePoints,Q),ie.emit("plotly_relayouting",ue(re))}function Le(){l.call("_guiRelayout",ie,ue(re))}this.dragOptions={element:ee,gd:ie,plotinfo:{id:Q.id,domain:ie._fullLayout[Q.id].domain,xaxis:Q.xaxis,yaxis:Q.yaxis},subplot:Q.id,prepFn:function(de,ve,Me){Q.dragOptions.xaxes=[Q.xaxis],Q.dragOptions.yaxes=[Q.yaxis],W=ie._fullLayout._invScaleX,G=ie._fullLayout._invScaleY;var we=Q.dragOptions.dragmode=ie._fullLayout.dragmode;_(we)?Q.dragOptions.minDrag=1:Q.dragOptions.minDrag=void 0,we==="zoom"?(Q.dragOptions.moveFn=_e,Q.dragOptions.clickFn=le,Q.dragOptions.doneFn=Ae,function(Ce,Fe,ze){var $e=ee.getBoundingClientRect();K=Fe-$e.left,te=ze-$e.top,ie._fullLayout._calcInverseTransform(ie);var Ke=ie._fullLayout._invTransform,Re=c.apply3DTransform(Ke)(K,te);K=Re[0],te=Re[1],Y={a:Q.aaxis.range[0],b:Q.baxis.range[1],c:Q.caxis.range[1]},re=Y,J=Q.aaxis.range[1]-Y.a,U=a(Q.graphDiv._fullLayout[Q.id].bgcolor).getLuminance(),V="M0,"+Q.h+"L"+Q.w/2+", 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r=t("../../lib"),a=t("../../components/color"),l=t("../../registry"),c=t("../scatter/xy_defaults"),i=t("../scatter/period_defaults"),s=t("./style_defaults"),u=t("../../plots/cartesian/constraints").getAxisGroup,h=t("./attributes"),d=r.coerceFont;function m(g,y,v,x){var _=y.orientation,A=y[{v:"x",h:"y"}[_]+"axis"],b=u(v,A)+_,k=v._alignmentOpts||{},w=x("alignmentgroup"),M=k[b];M||(M=k[b]={});var T=M[w];T?T.traces.push(y):T=M[w]={traces:[y],alignmentIndex:Object.keys(M).length,offsetGroups:{}};var E=x("offsetgroup"),S=T.offsetGroups,P=S[E];E&&(P||(P=S[E]={offsetIndex:Object.keys(S).length}),y._offsetIndex=P.offsetIndex)}function p(g,y,v,x,_,A){var b=(A=A||{}).moduleHasSelected!==!1,k=A.moduleHasUnselected!==!1,w=A.moduleHasConstrain!==!1,M=A.moduleHasCliponaxis!==!1,T=A.moduleHasTextangle!==!1,E=A.moduleHasInsideanchor!==!1,S=!!A.hasPathbar,P=Array.isArray(_)||_==="auto",L=P||_==="inside",R=P||_==="outside";if(L||R){var 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r=t("fast-isnumeric"),a=t("tinycolor2"),l=t("../../lib").isArrayOrTypedArray;f.coerceString=function(c,i,s){if(typeof i=="string"){if(i||!c.noBlank)return i}else if((typeof i=="number"||i===!0)&&!c.strict)return String(i);return s!==void 0?s:c.dflt},f.coerceNumber=function(c,i,s){if(r(i)){i=+i;var u=c.min,h=c.max;if(!(u!==void 0&&i<u||h!==void 0&&i>h))return i}return s!==void 0?s:c.dflt},f.coerceColor=function(c,i,s){return a(i).isValid()?i:s!==void 0?s:c.dflt},f.coerceEnumerated=function(c,i,s){return c.coerceNumber&&(i=+i),c.values.indexOf(i)!==-1?i:s!==void 0?s:c.dflt},f.getValue=function(c,i){var s;return Array.isArray(c)?i<c.length&&(s=c[i]):s=c,s},f.getLineWidth=function(c,i){return 0<i.mlw?i.mlw:l(c.marker.line.width)?0:c.marker.line.width}},{"../../lib":503,"fast-isnumeric":190,tinycolor2:312}],655:[function(t,o,f){var r=t("../../components/fx"),a=t("../../registry"),l=t("../../components/color"),c=t("../../lib").fillText,i=t("./helpers").getLineWidth,s=t("../../plots/cartesian/axes").hoverLabelText,u=t("../../constants/numerical").BADNUM;function h(m,p,g,y,v){var x,_,A,b,k,w,M,T=m.cd,E=T[0].trace,S=T[0].t,P=y==="closest",L=E.type==="waterfall",R=m.maxHoverDistance,F=m.maxSpikeDistance;E.orientation==="h"?(x=g,_=p,A="y",b="x",k=U,w=J):(x=p,_=g,A="x",b="y",w=U,k=J);var D=E[A+"period"],O=P||D;function N(ae){return W(ae,-1)}function B(ae){return W(ae,1)}function W(ae,ue){var le=ae.w;return ae[A]+ue*le/2}function G(ae){return ae[A+"End"]-ae[A+"Start"]}var K=P?N:D?function(ae){return ae.p-G(ae)/2}:function(ae){return Math.min(N(ae),ae.p-S.bardelta/2)},te=P?B:D?function(ae){return ae.p+G(ae)/2}:function(ae){return Math.max(B(ae),ae.p+S.bardelta/2)};function Y(ae,ue,le){return v.finiteRange&&(le=0),r.inbox(ae-x,ue-x,le+Math.min(1,Math.abs(ue-ae)/M)-1)}function J(ae){return Y(K(ae),te(ae),R)}function 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r.inbox(le-ue,ge-ue,F+(ge-ue)/(ge-le)-1)}(q)+function(ae){return Y(N(ae),B(ae),F)}(q))/2,m[A+"Spike"]=V.c2p(q.p,!0),c(q,E,m),m.hovertemplate=E.hovertemplate,m}}function d(m,p){var g=p.mcc||m.marker.color,y=p.mlcc||m.marker.line.color,v=i(m,p);return l.opacity(g)?g:l.opacity(y)&&v?y:void 0}o.exports={hoverPoints:function(m,p,g,y,v){var x=h(m,p,g,y,v);if(x){var _=x.cd,A=_[0].trace,b=_[x.index];return 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r=t("../../registry"),a=t("../../plots/cartesian/axes"),l=t("../../lib"),c=t("./layout_attributes");o.exports=function(i,s,u){function h(A,b){return l.coerce(i,s,c,A,b)}for(var d=!1,m=!1,p=!1,g={},y=h("barmode"),v=0;v<u.length;v++){var x=u[v];if(r.traceIs(x,"bar")&&x.visible){if(d=!0,y==="group"){var _=x.xaxis+x.yaxis;g[_]&&(p=!0),g[_]=!0}x.visible&&x.type==="histogram"&&a.getFromId({_fullLayout:s},x[x.orientation==="v"?"xaxis":"yaxis"]).type!=="category"&&(m=!0)}}d?(y!=="overlay"&&h("barnorm"),h("bargap",m&&!p?0:.2),h("bargroupgap")):delete s.barmode}},{"../../lib":503,"../../plots/cartesian/axes":554,"../../registry":638,"./layout_attributes":657}],659:[function(t,o,f){var r=t("@plotly/d3"),a=t("fast-isnumeric"),l=t("../../lib"),c=t("../../lib/svg_text_utils"),i=t("../../components/color"),s=t("../../components/drawing"),u=t("../../registry"),h=t("../../plots/cartesian/axes").tickText,d=t("./uniform_text"),m=d.recordMinTextSize,p=d.clearMinTextSize,g=t("./style"),y=t("./helpers"),v=t("./constants"),x=t("./attributes"),_=x.text,A=x.textposition,b=t("../../components/fx/helpers").appendArrayPointValue,k=v.TEXTPAD;function w(F){return F.id}function M(F){if(F.ids)return w}function T(F,D){return F<D?1:-1}function E(F,D,O,N){var B;return!D.uniformtext.mode&&S(O)?(N&&(B=N()),F.transition().duration(O.duration).ease(O.easing).each("end",function(){B&&B()}).each("interrupt",function(){B&&B()})):F}function S(F){return F&&F.duration>0}function P(F){return F==="auto"?0:F}function L(F,D){var O=Math.PI/180*D,N=Math.abs(Math.sin(O)),B=Math.abs(Math.cos(O));return{x:F.width*B+F.height*N,y:F.width*N+F.height*B}}function R(F,D,O,N,B,W){var G=!!W.isHorizontal,K=!!W.constrained,te=W.angle||0,Y=W.anchor||"end",J=Y==="end",re=Y==="start",U=((W.leftToRight||0)+1)/2,V=1-U,H=B.width,ne=B.height,q=Math.abs(D-F),Q=Math.abs(N-O),ee=q>2*k&&Q>2*k?k:0;q-=2*ee,Q-=2*ee;var ie=P(te);te!=="auto"||H<=q&&ne<=Q||!(H>q||ne>Q)||(H>Q||ne>q)&&H<ne==q<Q||(ie+=90);var ae=L(B,ie),ue=1;K&&(ue=Math.min(1,q/ae.x,Q/ae.y));var le=B.left*V+B.right*U,ge=(B.top+B.bottom)/2,fe=(F+k)*V+(D-k)*U,me=(O+N)/2,_e=0,Ae=0;if(re||J){var ke=(G?ae.x:ae.y)/2,Le=G?T(F,D):T(O,N);G?re?(fe=F+Le*ee,_e=-Le*ke):(fe=D-Le*ee,_e=Le*ke):re?(me=O+Le*ee,Ae=-Le*ke):(me=N-Le*ee,Ae=Le*ke)}return{textX:le,textY:ge,targetX:fe,targetY:me,anchorX:_e,anchorY:Ae,scale:ue,rotate:ie}}o.exports={plot:function(F,D,O,N,B,W){var G=D.xaxis,K=D.yaxis,te=F._fullLayout;B||(B={mode:te.barmode,norm:te.barmode,gap:te.bargap,groupgap:te.bargroupgap},p("bar",te));var Y=l.makeTraceGroups(N,O,"trace bars").each(function(J){var re=r.select(this),U=J[0].trace,V=U.type==="waterfall",H=U.type==="funnel",ne=U.type==="bar"||H,q=0;V&&U.connector.visible&&U.connector.mode==="between"&&(q=U.connector.line.width/2);var Q=U.orientation==="h",ee=S(B),ie=l.ensureSingle(re,"g","points"),ae=M(U),ue=ie.selectAll("g.point").data(l.identity,ae);ue.enter().append("g").classed("point",!0),ue.exit().remove(),ue.each(function(ge,fe){var me,_e,Ae=r.select(this),ke=function(Re,Ve,We,Ye){var nt=[],ft=[],yt=Ye?Ve:We,Ot=Ye?We:Ve;return nt[0]=yt.c2p(Re.s0,!0),ft[0]=Ot.c2p(Re.p0,!0),nt[1]=yt.c2p(Re.s1,!0),ft[1]=Ot.c2p(Re.p1,!0),Ye?[nt,ft]:[ft,nt]}(ge,G,K,Q),Le=ke[0][0],de=ke[0][1],ve=ke[1][0],Me=ke[1][1],we=(Q?de-Le:Me-ve)==0;if(we&&ne&&y.getLineWidth(U,ge)&&(we=!1),we||(we=!(a(Le)&&a(de)&&a(ve)&&a(Me))),ge.isBlank=we,we&&(Q?de=Le:Me=ve),q&&!we&&(Q?(Le-=T(Le,de)*q,de+=T(Le,de)*q):(ve-=T(ve,Me)*q,Me+=T(ve,Me)*q)),U.type==="waterfall"){if(!we){var Ce=U[ge.dir].marker;me=Ce.line.width,_e=Ce.color}}else 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St,It,Zt=kt.textangle;Lt==="outside"?(It=kt.constraintext==="both"||kt.constraintext==="outside",St=function(Kt,qt,mn,Fn,pn,tn){var nn,sn=!!tn.isHorizontal,gn=!!tn.constrained,bn=tn.angle||0,In=pn.width,qn=pn.height,Wn=Math.abs(qt-Kt),ar=Math.abs(Fn-mn);nn=sn?ar>2*k?k:0:Wn>2*k?k:0;var Dr=1;gn&&(Dr=sn?Math.min(1,ar/qn):Math.min(1,Wn/In));var yr=P(bn),Sr=L(pn,yr),Kn=(sn?Sr.x:Sr.y)/2,Dn=(pn.left+pn.right)/2,lr=(pn.top+pn.bottom)/2,Yr=(Kt+qt)/2,Mn=(mn+Fn)/2,rr=0,nr=0,Bn=sn?T(qt,Kt):T(mn,Fn);return sn?(Yr=qt-Bn*nn,rr=Bn*Kn):(Mn=Fn+Bn*nn,nr=-Bn*Kn),{textX:Dn,textY:lr,targetX:Yr,targetY:Mn,anchorX:rr,anchorY:nr,scale:Dr,rotate:yr}}(ft,yt,Ot,Tt,$t,{isHorizontal:dt,constrained:It,angle:Zt})):(It=kt.constraintext==="both"||kt.constraintext==="inside",St=R(ft,yt,Ot,Tt,$t,{isHorizontal:dt,constrained:It,angle:Zt,anchor:kt.insidetextanchor})),St.fontSize=bt.size,m(kt.type==="histogram"?"bar":kt.type,St,Be),Te.transform=St,E(wt,Be,at,et).attr("transform",l.getTextTransform(St))})(F,D,Ae,J,fe,Le,de,ve,Me,B,W),D.layerClipId&&s.hideOutsideRangePoint(ge,Ae.select("text"),G,K,U.xcalendar,U.ycalendar)});var 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r=t("@plotly/d3"),a=t("../../components/color"),l=t("../../components/drawing"),c=t("../../lib"),i=t("../../registry"),s=t("./uniform_text").resizeText,u=t("./attributes"),h=u.textfont,d=u.insidetextfont,m=u.outsidetextfont,p=t("./helpers");function g(w,M,T){l.pointStyle(w.selectAll("path"),M,T),y(w,M,T)}function y(w,M,T){w.selectAll("text").each(function(E){var S=r.select(this),P=c.ensureUniformFontSize(T,v(S,E,M,T));l.font(S,P)})}function v(w,M,T,E){var S=E._fullLayout.font,P=T.textfont;if(w.classed("bartext-inside")){var L=k(M,T);P=_(T,M.i,S,L)}else w.classed("bartext-outside")&&(P=A(T,M.i,S));return P}function x(w,M,T){return b(h,w.textfont,M,T)}function _(w,M,T,E){var S=x(w,M,T);return(w._input.textfont===void 0||w._input.textfont.color===void 0||Array.isArray(w.textfont.color)&&w.textfont.color[M]===void 0)&&(S={color:a.contrast(E),family:S.family,size:S.size}),b(d,w.insidetextfont,M,S)}function A(w,M,T){var E=x(w,M,T);return b(m,w.outsidetextfont,M,E)}function 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r=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../lib/extend").extendFlat,l=t("../scatterpolar/attributes"),c=t("../bar/attributes");o.exports={r:l.r,theta:l.theta,r0:l.r0,dr:l.dr,theta0:l.theta0,dtheta:l.dtheta,thetaunit:l.thetaunit,base:a({},c.base,{}),offset:a({},c.offset,{}),width:a({},c.width,{}),text:a({},c.text,{}),hovertext:a({},c.hovertext,{}),marker:c.marker,hoverinfo:l.hoverinfo,hovertemplate:r(),selected:c.selected,unselected:c.unselected}},{"../../lib/extend":493,"../../plots/template_attributes":633,"../bar/attributes":648,"../scatterpolar/attributes":1002}],666:[function(t,o,f){var r=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/calc"),l=t("../bar/arrays_to_calcdata"),c=t("../bar/cross_trace_calc").setGroupPositions,i=t("../scatter/calc_selection"),s=t("../../registry").traceIs,u=t("../../lib").extendFlat;o.exports={calc:function(h,d){for(var m=h._fullLayout,p=d.subplot,g=m[p].radialaxis,y=m[p].angularaxis,v=g.makeCalcdata(d,"r"),x=y.makeCalcdata(d,"theta"),_=d._length,A=new Array(_),b=v,k=x,w=0;w<_;w++)A[w]={p:k[w],s:b[w]};function M(T){var E=d[T];E!==void 0&&(d["_"+T]=Array.isArray(E)?y.makeCalcdata(d,T):y.d2c(E,d.thetaunit))}return y.type==="linear"&&(M("width"),M("offset")),r(d,"marker")&&a(h,d,{vals:d.marker.color,containerStr:"marker",cLetter:"c"}),r(d,"marker.line")&&a(h,d,{vals:d.marker.line.color,containerStr:"marker.line",cLetter:"c"}),l(A,d),i(A,d),A},crossTraceCalc:function(h,d,m){for(var p=h.calcdata,g=[],y=0;y<p.length;y++){var v=p[y],x=v[0].trace;x.visible===!0&&s(x,"bar")&&x.subplot===m&&g.push(v)}var 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r=t("../../components/fx"),a=t("../../lib"),l=t("../bar/hover").getTraceColor,c=a.fillText,i=t("../scatterpolar/hover").makeHoverPointText,s=t("../../plots/polar/helpers").isPtInsidePolygon;o.exports=function(u,h,d){var m=u.cd,p=m[0].trace,g=u.subplot,y=g.radialAxis,v=g.angularAxis,x=g.vangles,_=x?s:a.isPtInsideSector,A=u.maxHoverDistance,b=v._period||2*Math.PI,k=Math.abs(y.g2p(Math.sqrt(h*h+d*d))),w=Math.atan2(d,h);if(y.range[0]>y.range[1]&&(w+=Math.PI),r.getClosest(m,function(E){return _(k,w,[E.rp0,E.rp1],[E.thetag0,E.thetag1],x)?A+Math.min(1,Math.abs(E.thetag1-E.thetag0)/b)-1+(E.rp1-k)/(E.rp1-E.rp0)-1:1/0},u),u.index!==!1){var M=m[u.index];u.x0=u.x1=M.ct[0],u.y0=u.y1=M.ct[1];var T=a.extendFlat({},M,{r:M.s,theta:M.p});return c(M,p,u),i(T,p,g,u),u.hovertemplate=p.hovertemplate,u.color=l(p,M),u.xLabelVal=u.yLabelVal=void 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r=t("../../lib"),a=t("./layout_attributes");o.exports=function(l,c,i){var s,u={};function h(p,g){return r.coerce(l[s]||{},c[s],a,p,g)}for(var d=0;d<i.length;d++){var m=i[d];m.type==="barpolar"&&m.visible===!0&&(u[s=m.subplot]||(h("barmode"),h("bargap"),u[s]=1))}}},{"../../lib":503,"./layout_attributes":670}],672:[function(t,o,f){var r=t("@plotly/d3"),a=t("fast-isnumeric"),l=t("../../lib"),c=t("../../components/drawing"),i=t("../../plots/polar/helpers");o.exports=function(s,u,h){var d=u.xaxis,m=u.yaxis,p=u.radialAxis,g=u.angularAxis,y=function(x){var _=x.cxx,A=x.cyy;return x.vangles?function(b,k,w,M){var T,E;l.angleDelta(w,M)>0?(T=w,E=M):(T=M,E=w);var S=[i.findEnclosingVertexAngles(T,x.vangles)[0],(T+E)/2,i.findEnclosingVertexAngles(E,x.vangles)[1]];return i.pathPolygonAnnulus(b,k,T,E,S,_,A)}:function(b,k,w,M){return l.pathAnnulus(b,k,w,M,_,A)}}(u),v=u.layers.frontplot.select("g.barlayer");l.makeTraceGroups(v,h,"trace bars").each(function(){var x=r.select(this),_=l.ensureSingle(x,"g","points").selectAll("g.point").data(l.identity);_.enter().append("g").style("vector-effect","non-scaling-stroke").style("stroke-miterlimit",2).classed("point",!0),_.exit().remove(),_.each(function(A){var b,k=r.select(this),w=A.rp0=p.c2p(A.s0),M=A.rp1=p.c2p(A.s1),T=A.thetag0=g.c2g(A.p0),E=A.thetag1=g.c2g(A.p1);if(a(w)&&a(M)&&a(T)&&a(E)&&w!==M&&T!==E){var S=p.c2g(A.s1),P=(T+E)/2;A.ct=[d.c2p(S*Math.cos(P)),m.c2p(S*Math.sin(P))],b=y(w,M,T,E)}else b="M0,0Z";l.ensureSingle(k,"path").attr("d",b)}),c.setClipUrl(x,u._hasClipOnAxisFalse?u.clipIds.forTraces:null,s)})}},{"../../components/drawing":388,"../../lib":503,"../../plots/polar/helpers":621,"@plotly/d3":58,"fast-isnumeric":190}],673:[function(t,o,f){var r=t("../scatter/attributes"),a=t("../bar/attributes"),l=t("../../components/color/attributes"),c=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,i=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../lib/extend").extendFlat,u=r.marker,h=u.line;o.exports={y:{valType:"data_array",editType:"calc+clearAxisTypes"},x:{valType:"data_array",editType:"calc+clearAxisTypes"},x0:{valType:"any",editType:"calc+clearAxisTypes"},y0:{valType:"any",editType:"calc+clearAxisTypes"},dx:{valType:"number",editType:"calc"},dy:{valType:"number",editType:"calc"},xperiod:r.xperiod,yperiod:r.yperiod,xperiod0:r.xperiod0,yperiod0:r.yperiod0,xperiodalignment:r.xperiodalignment,yperiodalignment:r.yperiodalignment,xhoverformat:c("x"),yhoverformat:c("y"),name:{valType:"string",editType:"calc+clearAxisTypes"},q1:{valType:"data_array",editType:"calc+clearAxisTypes"},median:{valType:"data_array",editType:"calc+clearAxisTypes"},q3:{valType:"data_array",editType:"calc+clearAxisTypes"},lowerfence:{valType:"data_array",editType:"calc"},upperfence:{valType:"data_array",editType:"calc"},notched:{valType:"boolean",editType:"calc"},notchwidth:{valType:"number",min:0,max:.5,dflt:.25,editType:"calc"},notchspan:{valType:"data_array",editType:"calc"},boxpoints:{valType:"enumerated",values:["all","outliers","suspectedoutliers",!1],editType:"calc"},jitter:{valType:"number",min:0,max:1,editType:"calc"},pointpos:{valType:"number",min:-2,max:2,editType:"calc"},boxmean:{valType:"enumerated",values:[!0,"sd",!1],editType:"calc"},mean:{valType:"data_array",editType:"calc"},sd:{valType:"data_array",editType:"calc"},orientation:{valType:"enumerated",values:["v","h"],editType:"calc+clearAxisTypes"},quartilemethod:{valType:"enumerated",values:["linear","exclusive","inclusive"],dflt:"linear",editType:"calc"},width:{valType:"number",min:0,dflt:0,editType:"calc"},marker:{outliercolor:{valType:"color",dflt:"rgba(0, 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0)",editType:"style"},symbol:s({},u.symbol,{arrayOk:!1,editType:"plot"}),opacity:s({},u.opacity,{arrayOk:!1,dflt:1,editType:"style"}),size:s({},u.size,{arrayOk:!1,editType:"calc"}),color:s({},u.color,{arrayOk:!1,editType:"style"}),line:{color:s({},h.color,{arrayOk:!1,dflt:l.defaultLine,editType:"style"}),width:s({},h.width,{arrayOk:!1,dflt:0,editType:"style"}),outliercolor:{valType:"color",editType:"style"},outlierwidth:{valType:"number",min:0,dflt:1,editType:"style"},editType:"style"},editType:"plot"},line:{color:{valType:"color",editType:"style"},width:{valType:"number",min:0,dflt:2,editType:"style"},editType:"plot"},fillcolor:r.fillcolor,whiskerwidth:{valType:"number",min:0,max:1,dflt:.5,editType:"calc"},offsetgroup:a.offsetgroup,alignmentgroup:a.alignmentgroup,selected:{marker:r.selected.marker,editType:"style"},unselected:{marker:r.unselected.marker,editType:"style"},text:s({},r.text,{}),hovertext:s({},r.hovertext,{}),hovertemplate:i({}),hoveron:{valType:"flaglist",flags:["boxes","points"],dflt:"boxes+points",editType:"style"}}},{"../../components/color/attributes":365,"../../lib/extend":493,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633,"../bar/attributes":648,"../scatter/attributes":927}],674:[function(t,o,f){var r=t("fast-isnumeric"),a=t("../../plots/cartesian/axes"),l=t("../../plots/cartesian/align_period"),c=t("../../lib"),i=t("../../constants/numerical").BADNUM,s=c._;o.exports=function(_,A){var b,k,w,M,T,E,S,P=_._fullLayout,L=a.getFromId(_,A.xaxis||"x"),R=a.getFromId(_,A.yaxis||"y"),F=[],D=A.type==="violin"?"_numViolins":"_numBoxes";A.orientation==="h"?(w=L,M="x",T=R,E="y",S=!!A.yperiodalignment):(w=R,M="y",T=L,E="x",S=!!A.xperiodalignment);var O,N,B,W,G,K,te=function(We,Ye,nt,ft){var yt,Ot=Ye+"0"in We,Tt="d"+Ye in We;if(Ye in We||Ot&&Tt){var at=nt.makeCalcdata(We,Ye);return[l(We,nt,Ye,at).vals,at]}yt=Ot?We[Ye+"0"]:"name"in We&&(nt.type==="category"||r(We.name)&&["linear","log"].indexOf(nt.type)!==-1||c.isDateTime(We.name)&&nt.type==="date")?We.name:ft;for(var et=nt.type==="multicategory"?nt.r2c_just_indices(yt):nt.d2c(yt,0,We[Ye+"calendar"]),Lt=We._length,Wt=new 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fe=q("notchspan");fe=fe!==i&&fe>0?fe:x(O,W),O.ln=O.med-fe,O.un=O.med+fe;var me=O.lf,_e=O.uf;A.boxpoints&&B.length&&(me=Math.min(me,B[0]),_e=Math.max(_e,B[W-1])),A.notched&&(me=Math.min(me,O.ln),_e=Math.max(_e,O.un)),O.min=me,O.max=_e}else{var Ae;c.warn(["Invalid input - make sure that q1 <= median <= q3","q1 = "+O.q1,"median = "+O.med,"q3 = "+O.q3].join(`
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Ke,Re;(O={}).pos=O[E]=U[b],N=O.pts=ve[b].sort(d),W=(B=O[M]=N.map(m)).length,O.min=B[0],O.max=B[W-1],O.mean=c.mean(B,W),O.sd=c.stdev(B,W,O.mean),O.med=c.interp(B,.5),W%2&&(ze||$e)?(ze?(Ke=B.slice(0,W/2),Re=B.slice(W/2+1)):$e&&(Ke=B.slice(0,W/2+1),Re=B.slice(W/2)),O.q1=c.interp(Ke,.5),O.q3=c.interp(Re,.5)):(O.q1=c.interp(B,.25),O.q3=c.interp(B,.75)),O.lf=p(O,B,W),O.uf=g(O,B,W),O.lo=y(O),O.uo=v(O);var Ve=x(O,W);O.ln=O.med-Ve,O.un=O.med+Ve,we=Math.min(we,O.ln),Ce=Math.max(Ce,O.un),O.pts2=N.filter(H),F.push(O)}A._extremes[w._id]=a.findExtremes(w,A.notched?ke.concat([we,Ce]):ke,{padded:!0})}return function(We,Ye){if(c.isArrayOrTypedArray(Ye.selectedpoints))for(var nt=0;nt<We.length;nt++){for(var ft=We[nt].pts||[],yt={},Ot=0;Ot<ft.length;Ot++)yt[ft[Ot].i]=Ot;c.tagSelected(ft,Ye,yt)}}(F,A),F.length>0?(F[0].t={num:P[D],dPos:V,posLetter:E,valLetter:M,labels:{med:s(_,"median:"),min:s(_,"min:"),q1:s(_,"q1:"),q3:s(_,"q3:"),max:s(_,"max:"),mean:A.boxmean==="sd"?s(_,"mean ± 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m,p,g,y=u.calcdata,v=u._fullLayout,x=d._id,_=x.charAt(0),A=[],b=0;for(m=0;m<h.length;m++)for(g=y[h[m]],p=0;p<g.length;p++)A.push(d.c2l(g[p].pos,!0)),b+=(g[p].pts2||[]).length;if(A.length){var k=a.distinctVals(A);d.type!=="category"&&d.type!=="multicategory"||(k.minDiff=1);var w=k.minDiff/2;r.minDtick(d,k.minDiff,k.vals[0],!0);var M=v[s==="violin"?"_numViolins":"_numBoxes"],T=v[s+"mode"]==="group"&&M>1,E=1-v[s+"gap"],S=1-v[s+"groupgap"];for(m=0;m<h.length;m++){var P,L,R,F,D,O,N=(g=y[h[m]])[0].trace,B=g[0].t,W=N.width,G=N.side;if(W)P=L=F=W/2,R=0;else if(P=w,T){var K=l(v,d._id)+N.orientation,te=(v._alignmentOpts[K]||{})[N.alignmentgroup]||{},Y=Object.keys(te.offsetGroups||{}).length,J=Y||M;L=P*E*S/J,R=2*P*(((Y?N._offsetIndex:B.num)+.5)/J-.5)*E,F=P*E/J}else L=P*E*S,R=0,F=P;B.dPos=P,B.bPos=R,B.bdPos=L,B.wHover=F;var re,U,V,H,ne,q,Q=R+L,ee=!!W;if(G==="positive"?(D=P*(W?1:.5),re=Q,O=re=R):G==="negative"?(D=re=R,O=P*(W?1:.5),U=Q):(D=O=P,re=U=Q),(N.boxpoints||N.points)&&b>0){var ie=N.pointpos,ae=N.jitter,ue=N.marker.size/2,le=0;ie+ae>=0&&((le=Q*(ie+ae))>D?(ee=!0,ne=ue,V=le):le>re&&(ne=ue,V=D)),le<=D&&(V=D);var ge=0;ie-ae<=0&&((ge=-Q*(ie-ae))>O?(ee=!0,q=ue,H=ge):ge>U&&(q=ue,H=O)),ge<=O&&(H=O)}else V=D,H=O;var fe=new Array(g.length);for(p=0;p<g.length;p++)fe[p]=g[p].pos;N._extremes[x]=r.findExtremes(d,fe,{padded:ee,vpadminus:H,vpadplus:V,vpadLinearized:!0,ppadminus:{x:q,y:ne}[_],ppadplus:{x:ne,y:q}[_]})}}}o.exports={crossTraceCalc:function(s,u){for(var h=s.calcdata,d=u.xaxis,m=u.yaxis,p=0;p<c.length;p++){for(var g=c[p],y=g==="h"?m:d,v=[],x=0;x<h.length;x++){var _=h[x],A=_[0].t,b=_[0].trace;b.visible!==!0||b.type!=="box"&&b.type!=="candlestick"||A.empty||(b.orientation||"v")!==g||b.xaxis!==d._id||b.yaxis!==m._id||v.push(x)}i("box",s,v,y)}},setPositionOffset:i}},{"../../lib":503,"../../plots/cartesian/axes":554,"../../plots/cartesian/constraints":562}],676:[function(t,o,f){var r=t("../../lib"),a=t("../../registry"),l=t("../../components/color"),c=t("../scatter/period_defaults"),i=t("../bar/defaults").handleGroupingDefaults,s=t("../../plots/cartesian/axis_autotype"),u=t("./attributes");function h(m,p,g,y){function v(G){var K=0;return G&&G.length&&(K+=1,r.isArrayOrTypedArray(G[0])&&G[0].length&&(K+=1)),K}function x(G){return r.validate(m[G],u[G])}var _,A=g("y"),b=g("x");if(p.type==="box"){var k=g("q1"),w=g("median"),M=g("q3");p._hasPreCompStats=k&&k.length&&w&&w.length&&M&&M.length,_=Math.min(r.minRowLength(k),r.minRowLength(w),r.minRowLength(M))}var T,E,S=v(A),P=v(b),L=S&&r.minRowLength(A),R=P&&r.minRowLength(b),F=y.calendar,D={autotypenumbers:y.autotypenumbers};if(p._hasPreCompStats)switch(String(P)+String(S)){case"00":var O=x("x0")||x("dx");T=(x("y0")||x("dy"))&&!O?"h":"v",E=_;break;case"10":T="v",E=Math.min(_,R);break;case"20":T="h",E=Math.min(_,b.length);break;case"01":T="h",E=Math.min(_,L);break;case"02":T="v",E=Math.min(_,A.length);break;case"12":T="v",E=Math.min(_,R,A.length);break;case"21":T="h",E=Math.min(_,b.length,L);break;case"11":E=0;break;case"22":var N,B=!1;for(N=0;N<b.length;N++)if(s(b[N],F,D)==="category"){B=!0;break}if(B)T="v",E=Math.min(_,R,A.length);else{for(N=0;N<A.length;N++)if(s(A[N],F,D)==="category"){B=!0;break}B?(T="h",E=Math.min(_,b.length,L)):(T="v",E=Math.min(_,R,A.length))}}else S>0?(T="v",E=P>0?Math.min(R,L):Math.min(L)):P>0?(T="h",E=Math.min(R)):E=0;if(E){p._length=E;var W=g("orientation",T);p._hasPreCompStats?W==="v"&&P===0?(g("x0",0),g("dx",1)):W==="h"&&S===0&&(g("y0",0),g("dy",1)):W==="v"&&P===0?g("x0"):W==="h"&&S===0&&g("y0"),a.getComponentMethod("calendars","handleTraceDefaults")(m,p,["x","y"],y)}else p.visible=!1}function d(m,p,g,y){var v=y.prefix,x=r.coerce2(m,p,u,"marker.outliercolor"),_=g("marker.line.outliercolor"),A="outliers";p._hasPreCompStats?A="all":(x||_)&&(A="suspectedoutliers");var b=g(v+"points",A);b?(g("jitter",b==="all"?.3:0),g("pointpos",b==="all"?-1.5:0),g("marker.symbol"),g("marker.opacity"),g("marker.size"),g("marker.color",p.line.color),g("marker.line.color"),g("marker.line.width"),b==="suspectedoutliers"&&(g("marker.line.outliercolor",p.marker.color),g("marker.line.outlierwidth")),g("selected.marker.color"),g("unselected.marker.color"),g("selected.marker.size"),g("unselected.marker.size"),g("text"),g("hovertext")):delete p.marker;var k=g("hoveron");k!=="all"&&k.indexOf("points")===-1||g("hovertemplate"),r.coerceSelectionMarkerOpacity(p,g)}o.exports={supplyDefaults:function(m,p,g,y){function v(M,T){return r.coerce(m,p,u,M,T)}if(h(m,p,v,y),p.visible!==!1){c(m,p,y,v),v("xhoverformat"),v("yhoverformat");var x=p._hasPreCompStats;x&&(v("lowerfence"),v("upperfence")),v("line.color",(m.marker||{}).color||g),v("line.width"),v("fillcolor",l.addOpacity(p.line.color,.5));var _=!1;if(x){var A=v("mean"),b=v("sd");A&&A.length&&(_=!0,b&&b.length&&(_="sd"))}v("boxmean",_),v("whiskerwidth"),v("width"),v("quartilemethod");var k=!1;if(x){var w=v("notchspan");w&&w.length&&(k=!0)}else r.validate(m.notchwidth,u.notchwidth)&&(k=!0);v("notched",k)&&v("notchwidth"),d(m,p,v,{prefix:"box"})}},crossTraceDefaults:function(m,p){var g,y;function v(A){return r.coerce(y._input,y,u,A)}for(var x=0;x<m.length;x++){var _=(y=m[x]).type;_!=="box"&&_!=="violin"||(g=y._input,p[_+"mode"]==="group"&&i(g,y,p,v))}},handleSampleDefaults:h,handlePointsDefaults:d}},{"../../components/color":366,"../../lib":503,"../../plots/cartesian/axis_autotype":555,"../../registry":638,"../bar/defaults":652,"../scatter/period_defaults":947,"./attributes":673}],677:[function(t,o,f){o.exports=function(r,a){return a.hoverOnBox&&(r.hoverOnBox=a.hoverOnBox),"xVal"in a&&(r.x=a.xVal),"yVal"in a&&(r.y=a.yVal),a.xa&&(r.xaxis=a.xa),a.ya&&(r.yaxis=a.ya),r}},{}],678:[function(t,o,f){var r=t("../../plots/cartesian/axes"),a=t("../../lib"),l=t("../../components/fx"),c=t("../../components/color"),i=a.fillText;function s(h,d,m,p){var g,y,v,x,_,A,b,k,w,M,T,E,S,P,L=h.cd,R=h.xa,F=h.ya,D=L[0].trace,O=L[0].t,N=D.type==="violin",B=[],W=O.bdPos,G=O.wHover,K=function(le){return v.c2l(le.pos)+O.bPos-v.c2l(A)};N&&D.side!=="both"?(D.side==="positive"&&(w=function(le){var ge=K(le);return l.inbox(ge,ge+G,M)},E=W,S=0),D.side==="negative"&&(w=function(le){var ge=K(le);return l.inbox(ge-G,ge,M)},E=0,S=W)):(w=function(le){var ge=K(le);return l.inbox(ge-G,ge+G,M)},E=S=W),P=N?function(le){return l.inbox(le.span[0]-_,le.span[1]-_,M)}:function(le){return l.inbox(le.min-_,le.max-_,M)},D.orientation==="h"?(_=d,A=m,b=P,k=w,g="y",v=F,y="x",x=R):(_=m,A=d,b=w,k=P,g="x",v=R,y="y",x=F);var te=Math.min(1,W/Math.abs(v.r2c(v.range[1])-v.r2c(v.range[0])));function Y(le){return(b(le)+k(le))/2}M=h.maxHoverDistance-te,T=h.maxSpikeDistance-te;var J=l.getDistanceFunction(p,b,k,Y);if(l.getClosest(L,J,h),h.index===!1)return[];var re=L[h.index],U=D.line.color,V=(D.marker||{}).color;c.opacity(U)&&D.line.width?h.color=U:c.opacity(V)&&D.boxpoints?h.color=V:h.color=D.fillcolor,h[g+"0"]=v.c2p(re.pos+O.bPos-S,!0),h[g+"1"]=v.c2p(re.pos+O.bPos+E,!0),h[g+"LabelVal"]=re.orig_p!==void 0?re.orig_p:re.pos;var H=g+"Spike";h.spikeDistance=Y(re)*T/M,h[H]=v.c2p(re.pos,!0);var ne={},q=["med","q1","q3","min","max"];(D.boxmean||(D.meanline||{}).visible)&&q.push("mean"),(D.boxpoints||D.points)&&q.push("lf","uf");for(var Q=0;Q<q.length;Q++){var ee=q[Q];if(ee in re&&!(re[ee]in ne)){ne[re[ee]]=!0;var ie=re[ee],ae=x.c2p(ie,!0),ue=a.extendFlat({},h);ue.attr=ee,ue[y+"0"]=ue[y+"1"]=ae,ue[y+"LabelVal"]=ie,ue[y+"Label"]=(O.labels?O.labels[ee]+" 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A.orientation==="h"?(F=_,p.xLabelVal=y.x,p.yLabelVal=O):(F=x,p.xLabelVal=O,p.yLabelVal=y.y),p[F._id.charAt(0)+"Spike"]=F.c2p(g.pos,!0),i(y,A,p),p}o.exports={hoverPoints:function(h,d,m,p){var g,y=h.cd[0].trace.hoveron,v=[];return y.indexOf("boxes")!==-1&&(v=v.concat(s(h,d,m,p))),y.indexOf("points")!==-1&&(g=u(h,d,m)),p==="closest"?g?[g]:v:(g&&v.push(g),v)},hoverOnBoxes:s,hoverOnPoints:u}},{"../../components/color":366,"../../components/fx":406,"../../lib":503,"../../plots/cartesian/axes":554}],679:[function(t,o,f){o.exports={attributes:t("./attributes"),layoutAttributes:t("./layout_attributes"),supplyDefaults:t("./defaults").supplyDefaults,crossTraceDefaults:t("./defaults").crossTraceDefaults,supplyLayoutDefaults:t("./layout_defaults").supplyLayoutDefaults,calc:t("./calc"),crossTraceCalc:t("./cross_trace_calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style").style,styleOnSelect:t("./style").styleOnSelect,hoverPoints:t("./hover").hoverPoints,eventData:t("./event_data"),selectPoints:t("./select"),moduleType:"trace",name:"box",basePlotModule:t("../../plots/cartesian"),categories:["cartesian","svg","symbols","oriented","box-violin","showLegend","boxLayout","zoomScale"],meta:{}}},{"../../plots/cartesian":568,"./attributes":673,"./calc":674,"./cross_trace_calc":675,"./defaults":676,"./event_data":677,"./hover":678,"./layout_attributes":680,"./layout_defaults":681,"./plot":682,"./select":683,"./style":684}],680:[function(t,o,f){o.exports={boxmode:{valType:"enumerated",values:["group","overlay"],dflt:"overlay",editType:"calc"},boxgap:{valType:"number",min:0,max:1,dflt:.3,editType:"calc"},boxgroupgap:{valType:"number",min:0,max:1,dflt:.3,editType:"calc"}}},{}],681:[function(t,o,f){var r=t("../../registry"),a=t("../../lib"),l=t("./layout_attributes");function c(i,s,u,h,d){for(var m=d+"Layout",p=!1,g=0;g<u.length;g++){var y=u[g];if(r.traceIs(y,m)){p=!0;break}}p&&(h(d+"mode"),h(d+"gap"),h(d+"groupgap"))}o.exports={supplyLayoutDefaults:function(i,s,u){c(0,0,u,function(h,d){return a.coerce(i,s,l,h,d)},"box")},_supply:c}},{"../../lib":503,"../../registry":638,"./layout_attributes":680}],682:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../lib"),l=t("../../components/drawing");function c(u,h,d,m){var p,g,y=d.orientation==="h",v=h.val,x=h.pos,_=!!x.rangebreaks,A=m.bPos,b=m.wdPos||0,k=m.bPosPxOffset||0,w=d.whiskerwidth||0,M=d.notched||!1,T=M?1-2*d.notchwidth:1;Array.isArray(m.bdPos)?(p=m.bdPos[0],g=m.bdPos[1]):(p=m.bdPos,g=m.bdPos);var E=u.selectAll("path.box").data(d.type!=="violin"||d.box.visible?a.identity:[]);E.enter().append("path").style("vector-effect","non-scaling-stroke").attr("class","box"),E.exit().remove(),E.each(function(S){if(S.empty)return"M0,0Z";var P=x.c2l(S.pos+A,!0),L=x.l2p(P-p)+k,R=x.l2p(P+g)+k,F=_?(L+R)/2:x.l2p(P)+k,D=d.whiskerwidth,O=_?L*D+(1-D)*F:x.l2p(P-b)+k,N=_?R*D+(1-D)*F:x.l2p(P+b)+k,B=x.l2p(P-p*T)+k,W=x.l2p(P+g*T)+k,G=v.c2p(S.q1,!0),K=v.c2p(S.q3,!0),te=a.constrain(v.c2p(S.med,!0),Math.min(G,K)+1,Math.max(G,K)-1),Y=S.lf===void 0||d.boxpoints===!1,J=v.c2p(Y?S.min:S.lf,!0),re=v.c2p(Y?S.max:S.uf,!0),U=v.c2p(S.ln,!0),V=v.c2p(S.un,!0);y?r.select(this).attr("d","M"+te+","+B+"V"+W+"M"+G+","+L+"V"+R+(M?"H"+U+"L"+te+","+W+"L"+V+","+R:"")+"H"+K+"V"+L+(M?"H"+V+"L"+te+","+B+"L"+U+","+L:"")+"ZM"+G+","+F+"H"+J+"M"+K+","+F+"H"+re+(w===0?"":"M"+J+","+O+"V"+N+"M"+re+","+O+"V"+N)):r.select(this).attr("d","M"+B+","+te+"H"+W+"M"+L+","+G+"H"+R+(M?"V"+U+"L"+W+","+te+"L"+R+","+V:"")+"V"+K+"H"+L+(M?"V"+V+"L"+B+","+te+"L"+L+","+U:"")+"ZM"+F+","+G+"V"+J+"M"+F+","+K+"V"+re+(w===0?"":"M"+O+","+J+"H"+N+"M"+O+","+re+"H"+N))})}function i(u,h,d,m){var p=h.x,g=h.y,y=m.bdPos,v=m.bPos,x=d.boxpoints||d.points;a.seedPseudoRandom();var _=u.selectAll("g.points").data(x?function(b){return b.forEach(function(k){k.t=m,k.trace=d}),b}:[]);_.enter().append("g").attr("class","points"),_.exit().remove();var A=_.selectAll("path").data(function(b){var k,w,M=b.pts2,T=Math.max((b.max-b.min)/10,b.q3-b.q1),E=1e-9*T,S=.01*T,P=[],L=0;if(d.jitter){if(T===0)for(L=1,P=new Array(M.length),k=0;k<M.length;k++)P[k]=1;else for(k=0;k<M.length;k++){var R=Math.max(0,k-5),F=M[R].v,D=Math.min(M.length-1,k+5),O=M[D].v;x!=="all"&&(M[k].v<b.lf?O=Math.min(O,b.lf):F=Math.max(F,b.uf));var N=Math.sqrt(S*(D-R)/(O-F+E))||0;N=a.constrain(Math.abs(N),0,1),P.push(N),L=Math.max(N,L)}w=2*d.jitter/(L||1)}for(k=0;k<M.length;k++){var B=M[k],W=B.v,G=d.jitter?w*P[k]*(a.pseudoRandom()-.5):0,K=b.pos+v+y*(d.pointpos+G);d.orientation==="h"?(B.y=K,B.x=W):(B.x=K,B.y=W),x==="suspectedoutliers"&&W<b.uo&&W>b.lo&&(B.so=!0)}return M});A.enter().append("path").classed("point",!0),A.exit().remove(),A.call(l.translatePoints,p,g)}function s(u,h,d,m){var p,g,y=h.val,v=h.pos,x=!!v.rangebreaks,_=m.bPos,A=m.bPosPxOffset||0,b=d.boxmean||(d.meanline||{}).visible;Array.isArray(m.bdPos)?(p=m.bdPos[0],g=m.bdPos[1]):(p=m.bdPos,g=m.bdPos);var k=u.selectAll("path.mean").data(d.type==="box"&&d.boxmean||d.type==="violin"&&d.box.visible&&d.meanline.visible?a.identity:[]);k.enter().append("path").attr("class","mean").style({fill:"none","vector-effect":"non-scaling-stroke"}),k.exit().remove(),k.each(function(w){var M=v.c2l(w.pos+_,!0),T=v.l2p(M-p)+A,E=v.l2p(M+g)+A,S=x?(T+E)/2:v.l2p(M)+A,P=y.c2p(w.mean,!0),L=y.c2p(w.mean-w.sd,!0),R=y.c2p(w.mean+w.sd,!0);d.orientation==="h"?r.select(this).attr("d","M"+P+","+T+"V"+E+(b==="sd"?"m0,0L"+L+","+S+"L"+P+","+T+"L"+R+","+S+"Z":"")):r.select(this).attr("d","M"+T+","+P+"H"+E+(b==="sd"?"m0,0L"+S+","+L+"L"+T+","+P+"L"+S+","+R+"Z":""))})}o.exports={plot:function(u,h,d,m){var p=h.xaxis,g=h.yaxis;a.makeTraceGroups(m,d,"trace boxes").each(function(y){var v,x,_=r.select(this),A=y[0],b=A.t,k=A.trace;b.wdPos=b.bdPos*k.whiskerwidth,k.visible!==!0||b.empty?_.remove():(k.orientation==="h"?(v=g,x=p):(v=p,x=g),c(_,{pos:v,val:x},k,b),i(_,{x:p,y:g},k,b),s(_,{pos:v,val:x},k,b))})},plotBoxAndWhiskers:c,plotPoints:i,plotBoxMean:s}},{"../../components/drawing":388,"../../lib":503,"@plotly/d3":58}],683:[function(t,o,f){o.exports=function(r,a){var l,c,i=r.cd,s=r.xaxis,u=r.yaxis,h=[];if(a===!1)for(l=0;l<i.length;l++)for(c=0;c<(i[l].pts||[]).length;c++)i[l].pts[c].selected=0;else for(l=0;l<i.length;l++)for(c=0;c<(i[l].pts||[]).length;c++){var d=i[l].pts[c],m=s.c2p(d.x),p=u.c2p(d.y);a.contains([m,p],null,d.i,r)?(h.push({pointNumber:d.i,x:s.c2d(d.x),y:u.c2d(d.y)}),d.selected=1):d.selected=0}return h}},{}],684:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/color"),l=t("../../components/drawing");o.exports={style:function(c,i,s){var u=s||r.select(c).selectAll("g.trace.boxes");u.style("opacity",function(h){return h[0].trace.opacity}),u.each(function(h){var d=r.select(this),m=h[0].trace,p=m.line.width;function g(x,_,A,b){x.style("stroke-width",_+"px").call(a.stroke,A).call(a.fill,b)}var y=d.selectAll("path.box");if(m.type==="candlestick")y.each(function(x){if(!x.empty){var _=r.select(this),A=m[x.dir];g(_,A.line.width,A.line.color,A.fillcolor),_.style("opacity",m.selectedpoints&&!x.selected?.3:1)}});else{g(y,p,m.line.color,m.fillcolor),d.selectAll("path.mean").style({"stroke-width":p,"stroke-dasharray":2*p+"px,"+p+"px"}).call(a.stroke,m.line.color);var v=d.selectAll("path.point");l.pointStyle(v,m,c)}})},styleOnSelect:function(c,i,s){var u=i[0].trace,h=s.selectAll("path.point");u.selectedpoints?l.selectedPointStyle(h,u):l.pointStyle(h,u,c)}}},{"../../components/color":366,"../../components/drawing":388,"@plotly/d3":58}],685:[function(t,o,f){var r=t("../../lib").extendFlat,a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,l=t("../ohlc/attributes"),c=t("../box/attributes");function 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r=t("./axis_defaults"),a=t("../../plot_api/plot_template");o.exports=function(l,c,i,s,u){s("a")||(s("da"),s("a0")),s("b")||(s("db"),s("b0")),function(h,d,m,p){["aaxis","baxis"].forEach(function(g){var y=g.charAt(0),v=h[g]||{},x=a.newContainer(d,g),_={noTicklabelstep:!0,tickfont:"x",id:y+"axis",letter:y,font:d.font,name:g,data:h[y],calendar:d.calendar,dfltColor:p,bgColor:m.paper_bgcolor,autotypenumbersDflt:m.autotypenumbers,fullLayout:m};r(v,x,_),x._categories=x._categories||[],h[g]||v.type==="-"||(h[g]={type:v.type})})}(l,c,i,u)}},{"../../plot_api/plot_template":543,"./axis_defaults":694}],690:[function(t,o,f){var r=t("../../lib").isArrayOrTypedArray;o.exports=function(a){return function l(c,i){if(!r(c)||i>=10)return null;for(var s=1/0,u=-1/0,h=c.length,d=0;d<h;d++){var m=c[d];if(r(m)){var p=l(m,i+1);p&&(s=Math.min(p[0],s),u=Math.max(p[1],u))}else s=Math.min(m,s),u=Math.max(m,u)}return[s,u]}(a,0)}},{"../../lib":503}],691:[function(t,o,f){var r=t("../../plots/font_attributes"),a=t("./axis_attributes"),l=t("../../components/color/attributes"),c=r({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=l.defaultLine,o.exports={carpet:{valType:"string",editType:"calc"},x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},a:{valType:"data_array",editType:"calc"},a0:{valType:"number",dflt:0,editType:"calc"},da:{valType:"number",dflt:1,editType:"calc"},b:{valType:"data_array",editType:"calc"},b0:{valType:"number",dflt:0,editType:"calc"},db:{valType:"number",dflt:1,editType:"calc"},cheaterslope:{valType:"number",dflt:1,editType:"calc"},aaxis:a,baxis:a,font:c,color:{valType:"color",dflt:l.defaultLine,editType:"plot"},transforms:void 0}},{"../../components/color/attributes":365,"../../plots/font_attributes":585,"./axis_attributes":693}],692:[function(t,o,f){var r=t("../../lib").isArrayOrTypedArray;o.exports=function(a,l,c,i){var 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r=t("./attributes"),a=t("../../components/color").addOpacity,l=t("../../registry"),c=t("../../lib"),i=t("../../plots/cartesian/tick_value_defaults"),s=t("../../plots/cartesian/tick_label_defaults"),u=t("../../plots/cartesian/prefix_suffix_defaults"),h=t("../../plots/cartesian/category_order_defaults"),d=t("../../plots/cartesian/set_convert"),m=t("../../plots/cartesian/axis_autotype");o.exports=function(p,g,y){var v=y.letter,x=y.font||{},_=r[v+"axis"];function A(D,O){return c.coerce(p,g,_,D,O)}function b(D,O){return c.coerce2(p,g,_,D,O)}y.name&&(g._name=y.name,g._id=y.name),A("autotypenumbers",y.autotypenumbersDflt);var k=A("type");k==="-"&&(y.data&&function(D,O){if(D.type==="-"){var N=D._id.charAt(0),B=D[N+"calendar"];D.type=m(O,B,{autotypenumbers:D.autotypenumbers})}}(g,y.data),g.type==="-"?g.type="linear":k=p.type=g.type),A("smoothing"),A("cheatertype"),A("showticklabels"),A("labelprefix",v+" = 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r=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../scattergeo/attributes"),l=t("../../components/colorscale/attributes"),c=t("../../plots/attributes"),i=t("../../components/color/attributes").defaultLine,s=t("../../lib/extend").extendFlat,u=a.marker.line;o.exports=s({locations:{valType:"data_array",editType:"calc"},locationmode:a.locationmode,z:{valType:"data_array",editType:"calc"},geojson:s({},a.geojson,{}),featureidkey:a.featureidkey,text:s({},a.text,{}),hovertext:s({},a.hovertext,{}),marker:{line:{color:s({},u.color,{dflt:i}),width:s({},u.width,{dflt:1}),editType:"calc"},opacity:{valType:"number",arrayOk:!0,min:0,max:1,dflt:1,editType:"style"},editType:"calc"},selected:{marker:{opacity:a.selected.marker.opacity,editType:"plot"},editType:"plot"},unselected:{marker:{opacity:a.unselected.marker.opacity,editType:"plot"},editType:"plot"},hoverinfo:s({},c.hoverinfo,{editType:"calc",flags:["location","z","text","name"]}),hovertemplate:r(),showlegend:s({},c.showlegend,{dflt:!1})},l("",{cLetter:"z",editTypeOverride:"calc"}))},{"../../components/color/attributes":365,"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/template_attributes":633,"../scattergeo/attributes":969}],717:[function(t,o,f){var 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r=t("@plotly/d3"),a=t("../../lib"),l=t("../../lib/geo_location_utils"),c=t("../../lib/topojson_utils").getTopojsonFeatures,i=t("../../plots/cartesian/autorange").findExtremes,s=t("./style").style;o.exports={calcGeoJSON:function(u,h){for(var d=u[0].trace,m=h[d.geo],p=m._subplot,g=d.locationmode,y=d._length,v=g==="geojson-id"?l.extractTraceFeature(u):c(d,p.topojson),x=[],_=[],A=0;A<y;A++){var b=u[A],k=g==="geojson-id"?b.fOut:l.locationToFeature(g,b.loc,v);if(k){b.geojson=k,b.ct=k.properties.ct,b._polygons=l.feature2polygons(k);var w=l.computeBbox(k);x.push(w[0],w[2]),_.push(w[1],w[3])}else b.geojson=null}if(m.fitbounds==="geojson"&&g==="geojson-id"){var M=l.computeBbox(l.getTraceGeojson(d));x=[M[0],M[2]],_=[M[1],M[3]]}var T={padded:!0};d._extremes.lon=i(m.lonaxis._ax,x,T),d._extremes.lat=i(m.lataxis._ax,_,T)},plot:function(u,h,d){var m=h.layers.backplot.select(".choroplethlayer");a.makeTraceGroups(m,d,"trace choropleth").each(function(p){var g=r.select(this).selectAll("path.choroplethlocation").data(a.identity);g.enter().append("path").classed("choroplethlocation",!0),g.exit().remove(),s(u,p)})}}},{"../../lib":503,"../../lib/geo_location_utils":496,"../../lib/topojson_utils":532,"../../plots/cartesian/autorange":553,"./style":724,"@plotly/d3":58}],723:[function(t,o,f){o.exports=function(r,a){var l,c,i,s,u,h=r.cd,d=r.xaxis,m=r.yaxis,p=[];if(a===!1)for(l=0;l<h.length;l++)h[l].selected=0;else for(l=0;l<h.length;l++)(i=(c=h[l]).ct)&&(s=d.c2p(i),u=m.c2p(i),a.contains([s,u],null,l,r)?(p.push({pointNumber:l,lon:i[0],lat:i[1]}),c.selected=1):c.selected=0);return p}},{}],724:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/color"),l=t("../../components/drawing"),c=t("../../components/colorscale");function i(s,u){var 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r=t("../choropleth/attributes"),a=t("../../components/colorscale/attributes"),l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../plots/attributes"),i=t("../../lib/extend").extendFlat;o.exports=i({locations:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},geojson:{valType:"any",editType:"calc"},featureidkey:i({},r.featureidkey,{}),below:{valType:"string",editType:"plot"},text:r.text,hovertext:r.hovertext,marker:{line:{color:i({},r.marker.line.color,{editType:"plot"}),width:i({},r.marker.line.width,{editType:"plot"}),editType:"calc"},opacity:i({},r.marker.opacity,{editType:"plot"}),editType:"calc"},selected:{marker:{opacity:i({},r.selected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},unselected:{marker:{opacity:i({},r.unselected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},hoverinfo:r.hoverinfo,hovertemplate:l({},{keys:["properties"]}),showlegend:i({},c.showlegend,{dflt:!1})},a("",{cLetter:"z",editTypeOverride:"calc"}))},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/template_attributes":633,"../choropleth/attributes":716}],726:[function(t,o,f){var r=t("fast-isnumeric"),a=t("../../lib"),l=t("../../components/colorscale"),c=t("../../components/drawing"),i=t("../../lib/geojson_utils").makeBlank,s=t("../../lib/geo_location_utils");function u(h){var d,m=h[0].trace,p=m._opts;if(m.selectedpoints){for(var g=c.makeSelectedPointStyleFns(m),y=0;y<h.length;y++){var v=h[y];v.fOut&&(v.fOut.properties.mo2=g.selectedOpacityFn(v))}d={type:"identity",property:"mo2"}}else d=a.isArrayOrTypedArray(m.marker.opacity)?{type:"identity",property:"mo"}:m.marker.opacity;return a.extendFlat(p.fill.paint,{"fill-opacity":d}),a.extendFlat(p.line.paint,{"line-opacity":d}),p}o.exports={convert:function(h){var d=h[0].trace,m=d.visible===!0&&d._length!==0,p={layout:{visibility:"none"},paint:{}},g={layout:{visibility:"none"},paint:{}},y=d._opts={fill:p,line:g,geojson:i()};if(!m)return y;var v=s.extractTraceFeature(h);if(!v)return y;var x,_,A,b=l.makeColorScaleFuncFromTrace(d),k=d.marker,w=k.line||{};a.isArrayOrTypedArray(k.opacity)&&(x=function(L){var R=L.mo;return 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r=t("../../lib"),a=t("../../components/colorscale/defaults"),l=t("./attributes");o.exports=function(c,i,s,u){function h(g,y){return r.coerce(c,i,l,g,y)}var d=h("locations"),m=h("z"),p=h("geojson");r.isArrayOrTypedArray(d)&&d.length&&r.isArrayOrTypedArray(m)&&m.length&&(typeof 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r=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../mesh3d/attributes"),i=t("../../plots/attributes"),s=t("../../lib/extend").extendFlat,u={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},sizemode:{valType:"enumerated",values:["scaled","absolute"],editType:"calc",dflt:"scaled"},sizeref:{valType:"number",editType:"calc",min:0},anchor:{valType:"enumerated",editType:"calc",values:["tip","tail","cm","center"],dflt:"cm"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertemplate:l({editType:"calc"},{keys:["norm"]}),uhoverformat:a("u",1),vhoverformat:a("v",1),whoverformat:a("w",1),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),showlegend:s({},i.showlegend,{dflt:!1})};s(u,r("",{colorAttr:"u/v/w 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r=t("../../../stackgl_modules").gl_cone3d,a=t("../../../stackgl_modules").gl_cone3d.createConeMesh,l=t("../../lib").simpleMap,c=t("../../lib/gl_format_color").parseColorScale,i=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3");function u(y,v){this.scene=y,this.uid=v,this.mesh=null,this.data=null}var h=u.prototype;h.handlePick=function(y){if(y.object===this.mesh){var v=y.index=y.data.index,x=this.data.x[v],_=this.data.y[v],A=this.data.z[v],b=this.data.u[v],k=this.data.v[v],w=this.data.w[v];y.traceCoordinate=[x,_,A,b,k,w,Math.sqrt(b*b+k*k+w*w)];var M=this.data.hovertext||this.data.text;return Array.isArray(M)&&M[v]!==void 0?y.textLabel=M[v]:M&&(y.textLabel=M),!0}};var d={xaxis:0,yaxis:1,zaxis:2},m={tip:1,tail:0,cm:.25,center:.5},p={tip:1,tail:1,cm:.75,center:.5};function g(y,v){var x=y.fullSceneLayout,_=y.dataScale,A={};function b(T,E){var S=x[E],P=_[d[E]];return l(T,function(L){return 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b.mesh=A,b.data=v,A._trace=b,y.glplot.add(A),b}},{"../../../stackgl_modules":1124,"../../components/colorscale":378,"../../lib":503,"../../lib/gl_format_color":499,"../../plots/gl3d/zip3":609}],733:[function(t,o,f){var r=t("../../lib"),a=t("../../components/colorscale/defaults"),l=t("./attributes");o.exports=function(c,i,s,u){function h(x,_){return r.coerce(c,i,l,x,_)}var d=h("u"),m=h("v"),p=h("w"),g=h("x"),y=h("y"),v=h("z");d&&d.length&&m&&m.length&&p&&p.length&&g&&g.length&&y&&y.length&&v&&v.length?(h("sizeref"),h("sizemode"),h("anchor"),h("lighting.ambient"),h("lighting.diffuse"),h("lighting.specular"),h("lighting.roughness"),h("lighting.fresnel"),h("lightposition.x"),h("lightposition.y"),h("lightposition.z"),a(c,i,u,h,{prefix:"",cLetter:"c"}),h("text"),h("hovertext"),h("hovertemplate"),h("uhoverformat"),h("vhoverformat"),h("whoverformat"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),i._length=null):i.visible=!1}},{"../../components/colorscale/defaults":376,"../../lib":503,"./attributes":730}],734:[function(t,o,f){o.exports={moduleType:"trace",name:"cone",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),eventData:function(r,a){return r.norm=a.traceCoordinate[6],r},meta:{}}},{"../../plots/gl3d":598,"./attributes":730,"./calc":731,"./convert":732,"./defaults":733}],735:[function(t,o,f){var r=t("../heatmap/attributes"),a=t("../scatter/attributes"),l=t("../../plots/cartesian/axis_format_attributes"),c=l.axisHoverFormat,i=l.descriptionOnlyNumbers,s=t("../../components/colorscale/attributes"),u=t("../../components/drawing/attributes").dash,h=t("../../plots/font_attributes"),d=t("../../lib/extend").extendFlat,m=t("../../constants/filter_ops"),p=m.COMPARISON_OPS2,g=m.INTERVAL_OPS,y=a.line;o.exports=d({z:r.z,x:r.x,x0:r.x0,dx:r.dx,y:r.y,y0:r.y0,dy:r.dy,xperiod:r.xperiod,yperiod:r.yperiod,xperiod0:a.xperiod0,yperiod0:a.yperiod0,xperiodalignment:r.xperiodalignment,yperiodalignment:r.yperiodalignment,text:r.text,hovertext:r.hovertext,transpose:r.transpose,xtype:r.xtype,ytype:r.ytype,xhoverformat:c("x"),yhoverformat:c("y"),zhoverformat:c("z",1),hovertemplate:r.hovertemplate,texttemplate:d({},r.texttemplate,{}),textfont:d({},r.textfont,{}),hoverongaps:r.hoverongaps,connectgaps:d({},r.connectgaps,{}),fillcolor:{valType:"color",editType:"calc"},autocontour:{valType:"boolean",dflt:!0,editType:"calc",impliedEdits:{"contours.start":void 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g=m.start,y=c(m),v=m.size||1,x=Math.floor((y-g)/v)+1;isFinite(v)||(v=1,x=1);var _=g-v/2;d=[_,_+x*v]}else d=h;return r.calc(i,s,{vals:d,cLetter:"z"}),u}},{"../../components/colorscale":378,"../heatmap/calc":793,"./end_plus":746,"./set_contours":754}],737:[function(t,o,f){o.exports=function(r,a){var l,c=r[0],i=c.z;switch(a.type){case"levels":var s=Math.min(i[0][0],i[0][1]);for(l=0;l<r.length;l++){var u=r[l];u.prefixBoundary=!u.edgepaths.length&&(s>u.level||u.starts.length&&s===u.level)}break;case"constraint":if(c.prefixBoundary=!1,c.edgepaths.length)return;var h=c.x.length,d=c.y.length,m=-1/0,p=1/0;for(l=0;l<d;l++)p=Math.min(p,i[l][0]),p=Math.min(p,i[l][h-1]),m=Math.max(m,i[l][0]),m=Math.max(m,i[l][h-1]);for(l=1;l<h-1;l++)p=Math.min(p,i[0][l]),p=Math.min(p,i[d-1][l]),m=Math.max(m,i[0][l]),m=Math.max(m,i[d-1][l]);var g,y,v=a.value;switch(a._operation){case">":v>m&&(c.prefixBoundary=!0);break;case"<":(v<p||c.starts.length&&v===p)&&(c.prefixBoundary=!0);break;case"[]":g=Math.min(v[0],v[1]),((y=Math.max(v[0],v[1]))<p||g>m||c.starts.length&&y===p)&&(c.prefixBoundary=!0);break;case"][":g=Math.min(v[0],v[1]),y=Math.max(v[0],v[1]),g<p&&y>m&&(c.prefixBoundary=!0)}}}},{}],738:[function(t,o,f){var r=t("../../components/colorscale"),a=t("./make_color_map"),l=t("./end_plus");o.exports={min:"zmin",max:"zmax",calc:function(c,i,s){var u=i.contours,h=i.line,d=u.size||1,m=u.coloring,p=a(i,{isColorbar:!0});if(m==="heatmap"){var g=r.extractOpts(i);s._fillgradient=g.reversescale?r.flipScale(g.colorscale):g.colorscale,s._zrange=[g.min,g.max]}else 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r=t("fast-isnumeric"),a=t("./label_defaults"),l=t("../../components/color"),c=l.addOpacity,i=l.opacity,s=t("../../constants/filter_ops"),u=s.CONSTRAINT_REDUCTION,h=s.COMPARISON_OPS2;o.exports=function(d,m,p,g,y,v){var x,_,A,b=m.contours,k=p("contours.operation");b._operation=u[k],function(w,M){var T;h.indexOf(M.operation)===-1?(w("contours.value",[0,1]),Array.isArray(M.value)?M.value.length>2?M.value=M.value.slice(2):M.length===0?M.value=[0,1]:M.length<2?(T=parseFloat(M.value[0]),M.value=[T,T+1]):M.value=[parseFloat(M.value[0]),parseFloat(M.value[1])]:r(M.value)&&(T=parseFloat(M.value),M.value=[T,T+1])):(w("contours.value",0),r(M.value)||(Array.isArray(M.value)?M.value=parseFloat(M.value[0]):M.value=0))}(p,b),k==="="?x=b.showlines=!0:(x=p("contours.showlines"),A=p("fillcolor",c((d.line||{}).color||y,.5))),x&&(_=p("line.color",A&&i(A)?c(m.fillcolor,1):y),p("line.width",2),p("line.dash")),p("line.smoothing"),a(p,g,_,v)}},{"../../components/color":366,"../../constants/filter_ops":475,"./label_defaults":750,"fast-isnumeric":190}],741:[function(t,o,f){var r=t("../../constants/filter_ops"),a=t("fast-isnumeric");function l(s,u){var h,d=Array.isArray(u);function m(p){return a(p)?+p:null}return r.COMPARISON_OPS2.indexOf(s)!==-1?h=m(d?u[0]:u):r.INTERVAL_OPS.indexOf(s)!==-1?h=d?[m(u[0]),m(u[1])]:[m(u),m(u)]:r.SET_OPS.indexOf(s)!==-1&&(h=d?u.map(m):[m(u)]),h}function c(s){return function(u){u=l(s,u);var h=Math.min(u[0],u[1]),d=Math.max(u[0],u[1]);return{start:h,end:d,size:d-h}}}function i(s){return function(u){return{start:u=l(s,u),end:1/0,size:1/0}}}o.exports={"[]":c("[]"),"][":c("]["),">":i(">"),"<":i("<"),"=":i("=")}},{"../../constants/filter_ops":475,"fast-isnumeric":190}],742:[function(t,o,f){o.exports=function(r,a,l,c){var i=c("contours.start"),s=c("contours.end"),u=i===!1||s===!1,h=l("contours.size");!(u?a.autocontour=!0:l("autocontour",!1))&&h||l("ncontours")}},{}],743:[function(t,o,f){var r=t("../../lib");function a(l){return r.extendFlat({},l,{edgepaths:r.extendDeep([],l.edgepaths),paths:r.extendDeep([],l.paths),starts:r.extendDeep([],l.starts)})}o.exports=function(l,c){var i,s,u,h=function(p){return p.reverse()},d=function(p){return p};switch(c){case"=":case"<":return 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r=t("../../lib"),a=t("../heatmap/xyz_defaults"),l=t("../scatter/period_defaults"),c=t("./constraint_defaults"),i=t("./contours_defaults"),s=t("./style_defaults"),u=t("../heatmap/label_defaults"),h=t("./attributes");o.exports=function(d,m,p,g){function y(x,_){return r.coerce(d,m,h,x,_)}if(a(d,m,y,g)){l(d,m,g,y),y("xhoverformat"),y("yhoverformat"),y("text"),y("hovertext"),y("hoverongaps"),y("hovertemplate");var v=y("contours.type")==="constraint";y("connectgaps",r.isArray1D(m.z)),v?c(d,m,y,g,p):(i(d,m,y,function(x){return r.coerce2(d,m,h,x)}),s(d,m,y,g)),m.contours&&m.contours.coloring==="heatmap"&&u(y,g)}else m.visible=!1}},{"../../lib":503,"../heatmap/label_defaults":802,"../heatmap/xyz_defaults":807,"../scatter/period_defaults":947,"./attributes":735,"./constraint_defaults":740,"./contours_defaults":742,"./style_defaults":756}],745:[function(t,o,f){var r=t("../../lib"),a=t("./constraint_mapping"),l=t("./end_plus");o.exports=function(c,i,s){for(var u=c.type==="constraint"?a[c._operation](c.value):c,h=u.size,d=[],m=l(u),p=s.trace._carpetTrace,g=p?{xaxis:p.aaxis,yaxis:p.baxis,x:s.a,y:s.b}:{xaxis:i.xaxis,yaxis:i.yaxis,x:s.x,y:s.y},y=u.start;y<m;y+=h)if(d.push(r.extendFlat({level:y,crossings:{},starts:[],edgepaths:[],paths:[],z:s.z,smoothing:s.trace.line.smoothing},g)),d.length>1e3){r.warn("Too many contours, clipping at 1000",c);break}return d}},{"../../lib":503,"./constraint_mapping":741,"./end_plus":746}],746:[function(t,o,f){o.exports=function(r){return r.end+r.size/1e6}},{}],747:[function(t,o,f){var r=t("../../lib"),a=t("./constants");function l(s,u,h,d){return Math.abs(s[0]-u[0])<h&&Math.abs(s[1]-u[1])<d}function c(s,u,h,d,m){var p,g=u.join(","),y=s.crossings[g],v=function(Q,ee,ie){var ae=0,ue=0;return Q>20&&ee?Q===208||Q===1114?ae=ie[0]===0?1:-1:ue=ie[1]===0?1:-1:a.BOTTOMSTART.indexOf(Q)!==-1?ue=1:a.LEFTSTART.indexOf(Q)!==-1?ae=1:a.TOPSTART.indexOf(Q)!==-1?ue=-1:ae=-1,[ae,ue]}(y,h,u),x=[i(s,u,[-v[0],-v[1]])],_=s.z.length,A=s.z[0].length,b=u.slice(),k=v.slice();for(p=0;p<1e4;p++){if(y>20?(y=a.CHOOSESADDLE[y][(v[0]||v[1])<0?0:1],s.crossings[g]=a.SADDLEREMAINDER[y]):delete s.crossings[g],!(v=a.NEWDELTA[y])){r.log("Found bad marching index:",y,u,s.level);break}x.push(i(s,u,v)),u[0]+=v[0],u[1]+=v[1],g=u.join(","),l(x[x.length-1],x[x.length-2],d,m)&&x.pop();var w=v[0]&&(u[0]<0||u[0]>A-2)||v[1]&&(u[1]<0||u[1]>_-2);if(u[0]===b[0]&&u[1]===b[1]&&v[0]===k[0]&&v[1]===k[1]||h&&w)break;y=s.crossings[g]}p===1e4&&r.log("Infinite loop in contour?");var M,T,E,S,P,L,R,F,D,O,N,B,W,G,K,te=l(x[0],x[x.length-1],d,m),Y=0,J=.2*s.smoothing,re=[],U=0;for(p=1;p<x.length;p++)B=x[p],W=x[p-1],G=void 0,K=void 0,G=B[2]-W[2],K=B[3]-W[3],Y+=R=Math.sqrt(G*G+K*K),re.push(R);var V=Y/re.length*J;function H(Q){return x[Q%x.length]}for(p=x.length-2;p>=U;p--)if((M=re[p])<V){for(E=0,T=p-1;T>=U&&M+re[T]<V;T--)M+=re[T];if(te&&p===x.length-2)for(E=0;E<T&&M+re[E]<V;E++)M+=re[E];P=p-T+E+1,L=Math.floor((p+T+E+2)/2),S=te||p!==x.length-2?te||T!==-1?P%2?H(L):[(H(L)[0]+H(L+1)[0])/2,(H(L)[1]+H(L+1)[1])/2]:x[0]:x[x.length-1],x.splice(T+1,p-T+1,S),p=T+1,E&&(U=E),te&&(p===x.length-2?x[E]=x[x.length-1]:p===0&&(x[x.length-1]=x[0]))}for(x.splice(0,U),p=0;p<x.length;p++)x[p].length=2;if(!(x.length<2))if(te)x.pop(),s.paths.push(x);else{h||r.log("Unclosed interior contour?",s.level,b.join(","),x.join("L"));var ne=!1;for(F=0;F<s.edgepaths.length;F++)if(O=s.edgepaths[F],!ne&&l(O[0],x[x.length-1],d,m)){x.pop(),ne=!0;var q=!1;for(D=0;D<s.edgepaths.length;D++)if(l((N=s.edgepaths[D])[N.length-1],x[0],d,m)){q=!0,x.shift(),s.edgepaths.splice(F,1),D===F?s.paths.push(x.concat(N)):(D>F&&D--,s.edgepaths[D]=N.concat(x,O));break}q||(s.edgepaths[F]=x.concat(O))}for(F=0;F<s.edgepaths.length&&!ne;F++)l((O=s.edgepaths[F])[O.length-1],x[0],d,m)&&(x.shift(),s.edgepaths[F]=O.concat(x),ne=!0);ne||s.edgepaths.push(x)}}function i(s,u,h){var d=u[0]+Math.max(h[0],0),m=u[1]+Math.max(h[1],0),p=s.z[m][d],g=s.xaxis,y=s.yaxis;if(h[1]){var v=(s.level-p)/(s.z[m][d+1]-p),x=(v!==1?(1-v)*g.c2l(s.x[d]):0)+(v!==0?v*g.c2l(s.x[d+1]):0);return[g.c2p(g.l2c(x),!0),y.c2p(s.y[m],!0),d+v,m]}var _=(s.level-p)/(s.z[m+1][d]-p),A=(_!==1?(1-_)*y.c2l(s.y[m]):0)+(_!==0?_*y.c2l(s.y[m+1]):0);return[g.c2p(s.x[d],!0),y.c2p(y.l2c(A),!0),d,m+_]}o.exports=function(s,u,h){var d,m,p,g;for(u=u||.01,h=h||.01,m=0;m<s.length;m++){for(p=s[m],g=0;g<p.starts.length;g++)c(p,p.starts[g],"edge",u,h);for(d=0;Object.keys(p.crossings).length&&d<1e4;)d++,c(p,Object.keys(p.crossings)[0].split(",").map(Number),void 0,u,h);d===1e4&&r.log("Infinite loop in contour?")}}},{"../../lib":503,"./constants":739}],748:[function(t,o,f){var r=t("../../components/color"),a=t("../heatmap/hover");o.exports=function(l,c,i,s,u){u||(u={}),u.isContour=!0;var h=a(l,c,i,s,u);return h&&h.forEach(function(d){var m=d.trace;m.contours.type==="constraint"&&(m.fillcolor&&r.opacity(m.fillcolor)?d.color=r.addOpacity(m.fillcolor,1):m.contours.showlines&&r.opacity(m.line.color)&&(d.color=r.addOpacity(m.line.color,1)))}),h}},{"../../components/color":366,"../heatmap/hover":799}],749:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot").plot,style:t("./style"),colorbar:t("./colorbar"),hoverPoints:t("./hover"),moduleType:"trace",name:"contour",basePlotModule:t("../../plots/cartesian"),categories:["cartesian","svg","2dMap","contour","showLegend"],meta:{}}},{"../../plots/cartesian":568,"./attributes":735,"./calc":736,"./colorbar":738,"./defaults":744,"./hover":748,"./plot":753,"./style":755}],750:[function(t,o,f){var r=t("../../lib");o.exports=function(a,l,c,i){if(i||(i={}),a("contours.showlabels")){var 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a.makeColorScaleFunc({domain:_,range:A},{noNumericCheck:!0})}},{"../../components/colorscale":378,"./end_plus":746,"@plotly/d3":58}],752:[function(t,o,f){var r=t("./constants");function a(l,c){var i=(c[0][0]>l?0:1)+(c[0][1]>l?0:2)+(c[1][1]>l?0:4)+(c[1][0]>l?0:8);return i===5||i===10?l>(c[0][0]+c[0][1]+c[1][0]+c[1][1])/4?i===5?713:1114:i===5?104:208:i===15?0:i}o.exports=function(l){var c,i,s,u,h,d,m,p,g,y=l[0].z,v=y.length,x=y[0].length,_=v===2||x===2;for(i=0;i<v-1;i++)for(u=[],i===0&&(u=u.concat(r.BOTTOMSTART)),i===v-2&&(u=u.concat(r.TOPSTART)),c=0;c<x-1;c++)for(s=u.slice(),c===0&&(s=s.concat(r.LEFTSTART)),c===x-2&&(s=s.concat(r.RIGHTSTART)),h=c+","+i,d=[[y[i][c],y[i][c+1]],[y[i+1][c],y[i+1][c+1]]],g=0;g<l.length;g++)(m=a((p=l[g]).level,d))&&(p.crossings[h]=m,s.indexOf(m)!==-1&&(p.starts.push([c,i]),_&&s.indexOf(m,s.indexOf(m)+1)!==-1&&p.starts.push([c,i])))}},{"./constants":739}],753:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../lib"),l=t("../../components/drawing"),c=t("../../components/colorscale"),i=t("../../lib/svg_text_utils"),s=t("../../plots/cartesian/axes"),u=t("../../plots/cartesian/set_convert"),h=t("../heatmap/plot"),d=t("./make_crossings"),m=t("./find_all_paths"),p=t("./empty_pathinfo"),g=t("./convert_to_constraints"),y=t("./close_boundaries"),v=t("./constants"),x=v.LABELOPTIMIZER;function _(k,w){var M,T,E,S,P,L,R,F="",D=0,O=k.edgepaths.map(function(te,Y){return Y}),N=!0;function B(te){return Math.abs(te[1]-w[2][1])<.01}function W(te){return Math.abs(te[0]-w[0][0])<.01}function G(te){return Math.abs(te[0]-w[2][0])<.01}for(;O.length;){for(L=l.smoothopen(k.edgepaths[D],k.smoothing),F+=N?L:L.replace(/^M/,"L"),O.splice(O.indexOf(D),1),M=k.edgepaths[D][k.edgepaths[D].length-1],S=-1,E=0;E<4;E++){if(!M){a.log("Missing end?",D,k);break}for(R=M,Math.abs(R[1]-w[0][1])<.01&&!G(M)?T=w[1]:W(M)?T=w[0]:B(M)?T=w[3]:G(M)&&(T=w[2]),P=0;P<k.edgepaths.length;P++){var K=k.edgepaths[P][0];Math.abs(M[0]-T[0])<.01?Math.abs(M[0]-K[0])<.01&&(K[1]-M[1])*(T[1]-K[1])>=0&&(T=K,S=P):Math.abs(M[1]-T[1])<.01?Math.abs(M[1]-K[1])<.01&&(K[0]-M[0])*(T[0]-K[0])>=0&&(T=K,S=P):a.log("endpt to newendpt is not vert. or horz.",M,T,K)}if(M=T,S>=0)break;F+="L"+T}if(S===k.edgepaths.length){a.log("unclosed perimeter path");break}D=S,(N=O.indexOf(D)===-1)&&(D=O[0],F+="Z")}for(D=0;D<k.paths.length;D++)F+=l.smoothclosed(k.paths[D],k.smoothing);return F}function A(k,w,M,T){var E=w.width/2,S=w.height/2,P=k.x,L=k.y,R=k.theta,F=Math.cos(R)*E,D=Math.sin(R)*E,O=(P>T.center?T.right-P:P-T.left)/(F+Math.abs(Math.sin(R)*S)),N=(L>T.middle?T.bottom-L:L-T.top)/(Math.abs(D)+Math.cos(R)*S);if(O<1||N<1)return 1/0;var B=x.EDGECOST*(1/(O-1)+1/(N-1));B+=x.ANGLECOST*R*R;for(var W=P-F,G=L-D,K=P+F,te=L+D,Y=0;Y<M.length;Y++){var 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r=t("../heatmap/attributes"),a=t("../contour/attributes"),l=t("../../components/colorscale/attributes"),c=t("../../lib/extend").extendFlat,i=a.contours;o.exports=c({carpet:{valType:"string",editType:"calc"},z:r.z,a:r.x,a0:r.x0,da:r.dx,b:r.y,b0:r.y0,db:r.dy,text:r.text,hovertext:r.hovertext,transpose:r.transpose,atype:r.xtype,btype:r.ytype,fillcolor:a.fillcolor,autocontour:a.autocontour,ncontours:a.ncontours,contours:{type:i.type,start:i.start,end:i.end,size:i.size,coloring:{valType:"enumerated",values:["fill","lines","none"],dflt:"fill",editType:"calc"},showlines:i.showlines,showlabels:i.showlabels,labelfont:i.labelfont,labelformat:i.labelformat,operation:i.operation,value:i.value,editType:"calc",impliedEdits:{autocontour:!1}},line:{color:a.line.color,width:a.line.width,dash:a.line.dash,smoothing:a.line.smoothing,editType:"plot"},transforms:void 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r=t("../../lib"),a=t("../heatmap/xyz_defaults"),l=t("./attributes"),c=t("../contour/constraint_defaults"),i=t("../contour/contours_defaults"),s=t("../contour/style_defaults");o.exports=function(u,h,d,m){function p(g,y){return r.coerce(u,h,l,g,y)}if(p("carpet"),u.a&&u.b){if(!a(u,h,p,m,"a","b"))return void(h.visible=!1);p("text"),p("contours.type")==="constraint"?c(u,h,p,m,d,{hasHover:!1}):(i(u,h,p,function(g){return r.coerce2(u,h,l,g)}),s(u,h,p,m,{hasHover:!1}))}else 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r=t("../../lib"),a=t("../bar/defaults").handleGroupingDefaults,l=t("../bar/defaults").handleText,c=t("../scatter/xy_defaults"),i=t("../scatter/period_defaults"),s=t("./attributes"),u=t("../../components/color");o.exports={supplyDefaults:function(h,d,m,p){function g(_,A){return r.coerce(h,d,s,_,A)}if(c(h,d,p,g)){i(h,d,p,g),g("xhoverformat"),g("yhoverformat"),g("orientation",d.y&&!d.x?"v":"h"),g("offset"),g("width");var y=g("text");g("hovertext"),g("hovertemplate");var v=g("textposition");l(h,d,p,g,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),d.textposition==="none"||d.texttemplate||g("textinfo",Array.isArray(y)?"text+value":"value");var x=g("marker.color",m);g("marker.line.color",u.defaultLine),g("marker.line.width"),g("connector.visible")&&(g("connector.fillcolor",function(_){var A=r.isArrayOrTypedArray(_)?"#000":_;return 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m=d.cd,p=m[0].trace,g=p.orientation==="h",y=m[d.index];d[(g?"x":"y")+"LabelVal"]=y.s,d.percentInitial=y.begR,d.percentInitialLabel=l(y.begR,1),d.percentPrevious=y.difR,d.percentPreviousLabel=l(y.difR,1),d.percentTotal=y.sumR,d.percentTotalLabel=l(y.sumR,1);var v=y.hi||p.hoverinfo,x=[];if(v&&v!=="none"&&v!=="skip"){var _=v==="all",A=v.split("+"),b=function(k){return _||A.indexOf(k)!==-1};b("percent initial")&&x.push(d.percentInitialLabel+" of initial"),b("percent previous")&&x.push(d.percentPreviousLabel+" of previous"),b("percent total")&&x.push(d.percentTotalLabel+" of total")}return d.extraText=x.join("<br>"),d.color=function(k,w){var M=k.marker,T=w.mc||M.color,E=w.mlc||M.line.color,S=w.mlw||M.line.width;if(r(T))return T;if(r(E)&&S)return E}(p,y),[d]}}},{"../../components/color":366,"../../lib":503,"../bar/hover":655}],778:[function(t,o,f){o.exports={attributes:t("./attributes"),layoutAttributes:t("./layout_attributes"),supplyDefaults:t("./defaults").supplyDefaults,crossTraceDefaults:t("./defaults").crossTraceDefaults,supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),crossTraceCalc:t("./cross_trace_calc"),plot:t("./plot"),style:t("./style").style,hoverPoints:t("./hover"),eventData:t("./event_data"),selectPoints:t("../bar/select"),moduleType:"trace",name:"funnel",basePlotModule:t("../../plots/cartesian"),categories:["bar-like","cartesian","svg","oriented","showLegend","zoomScale"],meta:{}}},{"../../plots/cartesian":568,"../bar/select":660,"./attributes":771,"./calc":772,"./cross_trace_calc":774,"./defaults":775,"./event_data":776,"./hover":777,"./layout_attributes":779,"./layout_defaults":780,"./plot":781,"./style":782}],779:[function(t,o,f){o.exports={funnelmode:{valType:"enumerated",values:["stack","group","overlay"],dflt:"stack",editType:"calc"},funnelgap:{valType:"number",min:0,max:1,editType:"calc"},funnelgroupgap:{valType:"number",min:0,max:1,dflt:0,editType:"calc"}}},{}],780:[function(t,o,f){var r=t("../../lib"),a=t("./layout_attributes");o.exports=function(l,c,i){var s=!1;function u(m,p){return r.coerce(l,c,a,m,p)}for(var h=0;h<i.length;h++){var d=i[h];if(d.visible&&d.type==="funnel"){s=!0;break}}s&&(u("funnelmode"),u("funnelgap",.2),u("funnelgroupgap"))}},{"../../lib":503,"./layout_attributes":779}],781:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../lib"),l=t("../../components/drawing"),c=t("../../constants/numerical").BADNUM,i=t("../bar/plot"),s=t("../bar/uniform_text").clearMinTextSize;function u(h,d,m,p){var g=[],y=[],v=p?d:m,x=p?m:d;return g[0]=v.c2p(h.s0,!0),y[0]=x.c2p(h.p0,!0),g[1]=v.c2p(h.s1,!0),y[1]=x.c2p(h.p1,!0),g[2]=v.c2p(h.nextS0,!0),y[2]=x.c2p(h.nextP0,!0),g[3]=v.c2p(h.nextS1,!0),y[3]=x.c2p(h.nextP1,!0),p?[g,y]:[y,g]}o.exports=function(h,d,m,p){var g=h._fullLayout;s("funnel",g),function(y,v,x,_){var A=v.xaxis,b=v.yaxis;a.makeTraceGroups(_,x,"trace bars").each(function(k){var w=r.select(this),M=k[0].trace,T=a.ensureSingle(w,"g","regions");if(M.connector&&M.connector.visible){var E=M.orientation==="h",S=T.selectAll("g.region").data(a.identity);S.enter().append("g").classed("region",!0),S.exit().remove();var P=S.size();S.each(function(L,R){if(R===P-1||L.cNext){var F=u(L,A,b,E),D=F[0],O=F[1],N="";D[0]!==c&&O[0]!==c&&D[1]!==c&&O[1]!==c&&D[2]!==c&&O[2]!==c&&D[3]!==c&&O[3]!==c&&(N+=E?"M"+D[0]+","+O[1]+"L"+D[2]+","+O[2]+"H"+D[3]+"L"+D[1]+","+O[1]+"Z":"M"+D[1]+","+O[1]+"L"+D[2]+","+O[3]+"V"+O[2]+"L"+D[1]+","+O[0]+"Z"),N===""&&(N="M0,0Z"),a.ensureSingle(r.select(this),"path").attr("d",N).call(l.setClipUrl,v.layerClipId,y)}})}else T.remove()})}(h,d,m,p),function(y,v,x,_){var A=v.xaxis,b=v.yaxis;a.makeTraceGroups(_,x,"trace bars").each(function(k){var w=r.select(this),M=k[0].trace,T=a.ensureSingle(w,"g","lines");if(M.connector&&M.connector.visible&&M.connector.line.width){var E=M.orientation==="h",S=T.selectAll("g.line").data(a.identity);S.enter().append("g").classed("line",!0),S.exit().remove();var P=S.size();S.each(function(L,R){if(R===P-1||L.cNext){var F=u(L,A,b,E),D=F[0],O=F[1],N="";D[3]!==void 0&&O[3]!==void 0&&(E?(N+="M"+D[0]+","+O[1]+"L"+D[2]+","+O[2],N+="M"+D[1]+","+O[1]+"L"+D[3]+","+O[2]):(N+="M"+D[1]+","+O[1]+"L"+D[2]+","+O[3],N+="M"+D[1]+","+O[0]+"L"+D[2]+","+O[2])),N===""&&(N="M0,0Z"),a.ensureSingle(r.select(this),"path").attr("d",N).call(l.setClipUrl,v.layerClipId,y)}})}else T.remove()})}(h,d,m,p),i.plot(h,d,m,p,{mode:g.funnelmode,norm:g.funnelmode,gap:g.funnelgap,groupgap:g.funnelgroupgap})}},{"../../components/drawing":388,"../../constants/numerical":479,"../../lib":503,"../bar/plot":659,"../bar/uniform_text":664,"@plotly/d3":58}],782:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/drawing"),l=t("../../components/color"),c=t("../../constants/interactions").DESELECTDIM,i=t("../bar/style"),s=t("../bar/uniform_text").resizeText,u=i.styleTextPoints;o.exports={style:function(h,d,m){var p=m||r.select(h).selectAll("g.funnellayer").selectAll("g.trace");s(h,p,"funnel"),p.style("opacity",function(g){return g[0].trace.opacity}),p.each(function(g){var y=r.select(this),v=g[0].trace;y.selectAll(".point > path").each(function(x){if(!x.isBlank){var _=v.marker;r.select(this).call(l.fill,x.mc||_.color).call(l.stroke,x.mlc||_.line.color).call(a.dashLine,_.line.dash,x.mlw||_.line.width).style("opacity",v.selectedpoints&&!x.selected?c:1)}}),u(y,v,h),y.selectAll(".regions").each(function(){r.select(this).selectAll("path").style("stroke-width",0).call(l.fill,v.connector.fillcolor)}),y.selectAll(".lines").each(function(){var x=v.connector.line;a.lineGroupStyle(r.select(this).selectAll("path"),x.width,x.color,x.dash)})})}}},{"../../components/color":366,"../../components/drawing":388,"../../constants/interactions":478,"../bar/style":662,"../bar/uniform_text":664,"@plotly/d3":58}],783:[function(t,o,f){var r=t("../pie/attributes"),a=t("../../plots/attributes"),l=t("../../plots/domain").attributes,c=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,s=t("../../lib/extend").extendFlat;o.exports={labels:r.labels,label0:r.label0,dlabel:r.dlabel,values:r.values,marker:{colors:r.marker.colors,line:{color:s({},r.marker.line.color,{dflt:null}),width:s({},r.marker.line.width,{dflt:1}),editType:"calc"},editType:"calc"},text:r.text,hovertext:r.hovertext,scalegroup:s({},r.scalegroup,{}),textinfo:s({},r.textinfo,{flags:["label","text","value","percent"]}),texttemplate:i({editType:"plot"},{keys:["label","color","value","text","percent"]}),hoverinfo:s({},a.hoverinfo,{flags:["label","text","value","percent","name"]}),hovertemplate:c({},{keys:["label","color","value","text","percent"]}),textposition:s({},r.textposition,{values:["inside","none"],dflt:"inside"}),textfont:r.textfont,insidetextfont:r.insidetextfont,title:{text:r.title.text,font:r.title.font,position:s({},r.title.position,{values:["top 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r=t("../../lib"),a=t("./attributes"),l=t("../../plots/domain").defaults,c=t("../bar/defaults").handleText,i=t("../pie/defaults").handleLabelsAndValues;o.exports=function(s,u,h,d){function m(k,w){return r.coerce(s,u,a,k,w)}var p=m("labels"),g=m("values"),y=i(p,g),v=y.len;if(u._hasLabels=y.hasLabels,u._hasValues=y.hasValues,!u._hasLabels&&u._hasValues&&(m("label0"),m("dlabel")),v){u._length=v,m("marker.line.width")&&m("marker.line.color",d.paper_bgcolor),m("marker.colors"),m("scalegroup");var x,_=m("text"),A=m("texttemplate");if(A||(x=m("textinfo",Array.isArray(_)?"text+percent":"percent")),m("hovertext"),m("hovertemplate"),A||x&&x!=="none"){var b=m("textposition");c(s,u,d,m,b,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}l(u,d,m),m("title.text")&&(m("title.position"),r.coerceFont(m,"title.font",d.font)),m("aspectratio"),m("baseratio")}else u.visible=!1}},{"../../lib":503,"../../plots/domain":584,"../bar/defaults":652,"../pie/defaults":904,"./attributes":783}],787:[function(t,o,f){o.exports={moduleType:"trace",name:"funnelarea",basePlotModule:t("./base_plot"),categories:["pie-like","funnelarea","showLegend"],attributes:t("./attributes"),layoutAttributes:t("./layout_attributes"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot"),style:t("./style"),styleOne:t("../pie/style_one"),meta:{}}},{"../pie/style_one":912,"./attributes":783,"./base_plot":784,"./calc":785,"./defaults":786,"./layout_attributes":788,"./layout_defaults":789,"./plot":790,"./style":791}],788:[function(t,o,f){var r=t("../pie/layout_attributes").hiddenlabels;o.exports={hiddenlabels:r,funnelareacolorway:{valType:"colorlist",editType:"calc"},extendfunnelareacolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{"../pie/layout_attributes":908}],789:[function(t,o,f){var r=t("../../lib"),a=t("./layout_attributes");o.exports=function(l,c){function i(s,u){return r.coerce(l,c,a,s,u)}i("hiddenlabels"),i("funnelareacolorway",c.colorway),i("extendfunnelareacolors")}},{"../../lib":503,"./layout_attributes":788}],790:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,i=l.strTranslate,s=t("../../lib/svg_text_utils"),u=t("../bar/plot").toMoveInsideBar,h=t("../bar/uniform_text"),d=h.recordMinTextSize,m=h.clearMinTextSize,p=t("../pie/helpers"),g=t("../pie/plot"),y=g.attachFxHandlers,v=g.determineInsideTextFont,x=g.layoutAreas,_=g.prerenderTitles,A=g.positionTitleOutside,b=g.formatSliceLabel;function k(w,M){return"l"+(M[0]-w[0])+","+(M[1]-w[1])}o.exports=function(w,M){var T=w._fullLayout;m("funnelarea",T),_(M,w),x(M,T._size),l.makeTraceGroups(T._funnelarealayer,M,"trace").each(function(E){var S=r.select(this),P=E[0],L=P.trace;(function(R){if(!R.length)return;var F=R[0],D=F.trace,O=D.aspectratio,N=D.baseratio;N>.999&&(N=.999);var B,W=Math.pow(N,2),G=F.vTotal,K=G,te=G*W/(1-W)/G;function Y(){var ke,Le={x:ke=Math.sqrt(te),y:-ke};return[Le.x,Le.y]}var J,re,U=[];for(U.push(Y()),J=R.length-1;J>-1;J--)if(!(re=R[J]).hidden){var V=re.v/K;te+=V,U.push(Y())}var H=1/0,ne=-1/0;for(J=0;J<U.length;J++)B=U[J],H=Math.min(H,B[1]),ne=Math.max(ne,B[1]);for(J=0;J<U.length;J++)U[J][1]-=(ne+H)/2;var q=U[U.length-1][0],Q=F.r,ee=(ne-H)/2,ie=Q/q,ae=Q/ee*O;for(F.r=ae*ee,J=0;J<U.length;J++)U[J][0]*=ie,U[J][1]*=ae;var ue=[-(B=U[0])[0],B[1]],le=[B[0],B[1]],ge=0;for(J=R.length-1;J>-1;J--)if(!(re=R[J]).hidden){var fe=U[ge+=1][0],me=U[ge][1];re.TL=[-fe,me],re.TR=[fe,me],re.BL=ue,re.BR=le,re.pxmid=(_e=re.TR,Ae=re.BR,[.5*(_e[0]+Ae[0]),.5*(_e[1]+Ae[1])]),ue=re.TL,le=re.TR}var _e,Ae})(E),S.each(function(){var R=r.select(this).selectAll("g.slice").data(E);R.enter().append("g").classed("slice",!0),R.exit().remove(),R.each(function(D,O){if(D.hidden)r.select(this).selectAll("path,g").remove();else{D.pointNumber=D.i,D.curveNumber=L.index;var N=P.cx,B=P.cy,W=r.select(this),G=W.selectAll("path.surface").data([D]);G.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),W.call(y,w,E);var K="M"+(N+D.TR[0])+","+(B+D.TR[1])+k(D.TR,D.BR)+k(D.BR,D.BL)+k(D.BL,D.TL)+"Z";G.attr("d",K),b(w,D,P);var te=p.castOption(L.textposition,D.pts),Y=W.selectAll("g.slicetext").data(D.text&&te!=="none"?[0]:[]);Y.enter().append("g").classed("slicetext",!0),Y.exit().remove(),Y.each(function(){var 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N=A(P,T._size);D.attr("transform",i(N.x,N.y)+c(Math.min(1,N.scale))+i(N.tx,N.ty))})})})}},{"../../components/drawing":388,"../../lib":503,"../../lib/svg_text_utils":529,"../bar/plot":659,"../bar/uniform_text":664,"../pie/helpers":906,"../pie/plot":910,"@plotly/d3":58}],791:[function(t,o,f){var r=t("@plotly/d3"),a=t("../pie/style_one"),l=t("../bar/uniform_text").resizeText;o.exports=function(c){var i=c._fullLayout._funnelarealayer.selectAll(".trace");l(c,i,"funnelarea"),i.each(function(s){var u=s[0].trace,h=r.select(this);h.style({opacity:u.opacity}),h.selectAll("path.surface").each(function(d){r.select(this).call(a,d,u)})})}},{"../bar/uniform_text":664,"../pie/style_one":912,"@plotly/d3":58}],792:[function(t,o,f){var 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v=p("textposition");c(u,h,m,p,v,{moduleHasSelected:!0,moduleHasUnselected:!0,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),p("hovertext"),p("hovertemplate"),p("xhoverformat"),p("yhoverformat");var x=p("orientation",y&&!g?"h":"v"),_=x==="v"?"x":"y",A=x==="v"?"y":"x",b=g&&y?Math.min(a.minRowLength(g)&&a.minRowLength(y)):a.minRowLength(h[_]||[]);if(b){h._length=b,r.getComponentMethod("calendars","handleTraceDefaults")(u,h,["x","y"],m),h[A]&&p("histfunc"),p("histnorm"),p("autobin"+_),i(u,h,p,d,m),a.coerceSelectionMarkerOpacity(h,p);var k=(h.marker.line||{}).color,w=r.getComponentMethod("errorbars","supplyDefaults");w(u,h,k||l.defaultLine,{axis:"y"}),w(u,h,k||l.defaultLine,{axis:"x",inherit:"y"})}else h.visible=!1}},{"../../components/color":366,"../../lib":503,"../../registry":638,"../bar/defaults":652,"../bar/style_defaults":663,"./attributes":812}],821:[function(t,o,f){o.exports=function(r,a,l,c,i){if(r.x="xVal"in a?a.xVal:a.x,r.y="yVal"in 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h}}},{"../../plots/cartesian/axes":554,"../bar/hover":655}],823:[function(t,o,f){o.exports={attributes:t("./attributes"),layoutAttributes:t("../bar/layout_attributes"),supplyDefaults:t("./defaults"),crossTraceDefaults:t("./cross_trace_defaults"),supplyLayoutDefaults:t("../bar/layout_defaults"),calc:t("./calc").calc,crossTraceCalc:t("../bar/cross_trace_calc").crossTraceCalc,plot:t("../bar/plot").plot,layerName:"barlayer",style:t("../bar/style").style,styleOnSelect:t("../bar/style").styleOnSelect,colorbar:t("../scatter/marker_colorbar"),hoverPoints:t("./hover"),selectPoints:t("../bar/select"),eventData:t("./event_data"),moduleType:"trace",name:"histogram",basePlotModule:t("../../plots/cartesian"),categories:["bar-like","cartesian","svg","bar","histogram","oriented","errorBarsOK","showLegend"],meta:{}}},{"../../plots/cartesian":568,"../bar/cross_trace_calc":651,"../bar/layout_attributes":657,"../bar/layout_defaults":658,"../bar/plot":659,"../bar/select":660,"../bar/style":662,"../scatter/marker_colorbar":945,"./attributes":812,"./calc":817,"./cross_trace_defaults":819,"./defaults":820,"./event_data":821,"./hover":822}],824:[function(t,o,f){o.exports={percent:function(r,a){for(var 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G=x.selectAll("image").data([v]);G.enter().append("svg:image").attr({xmlns:c.svg,preserveAspectRatio:"none"}),G.exit().remove();var K=A.zsmooth===!1?i.pixelatedStyle:"";if(b){var te=a.simpleMap(p.range,p.r2l),Y=a.simpleMap(g.range,g.r2l),J=te[1]<te[0],re=Y[1]>Y[0];if(J||re){var U=k+B/2,V=T+W/2;K+="transform:"+l(U+"px",V+"px")+"scale("+(J?-1:1)+","+(re?-1:1)+")"+l(-U+"px",-V+"px")+";"}}G.attr("style",K);var H=new Promise(function(q){if(A._hasZ)q();else if(A._hasSource)if(A._canvas&&A._canvas.el.width===F&&A._canvas.el.height===D&&A._canvas.source===A.source)q();else{var Q=document.createElement("canvas");Q.width=F,Q.height=D;var ee=Q.getContext("2d");A._image=A._image||new Image;var ie=A._image;ie.onload=function(){ee.drawImage(ie,0,0),A._canvas={el:Q,source:A.source},q()},ie.setAttribute("src",A.source)}}).then(function(){var q;if(A._hasZ)q=ne(function(ee,ie){return P[ie][ee]}).toDataURL("image/png");else if(A._hasSource)if(b)q=A.source;else{var 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r=t("@plotly/d3");o.exports=function(a){r.select(a).selectAll(".im image").style("opacity",function(l){return l[0].trace.opacity})}},{"@plotly/d3":58}],855:[function(t,o,f){var r=t("../../lib/extend").extendFlat,a=t("../../lib/extend").extendDeep,l=t("../../plot_api/edit_types").overrideAll,c=t("../../plots/font_attributes"),i=t("../../components/color/attributes"),s=t("../../plots/domain").attributes,u=t("../../plots/cartesian/layout_attributes"),h=t("../../plot_api/plot_template").templatedArray,d=t("../../constants/delta.js"),m=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,p=c({editType:"plot",colorEditType:"plot"}),g={color:{valType:"color",editType:"plot"},line:{color:{valType:"color",dflt:i.defaultLine,editType:"plot"},width:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"calc"},thickness:{valType:"number",min:0,max:1,dflt:1,editType:"plot"},editType:"calc"},y={valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},v=h("step",a({},g,{range:y}));o.exports={mode:{valType:"flaglist",editType:"calc",flags:["number","delta","gauge"],dflt:"number"},value:{valType:"number",editType:"calc",anim:!0},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},domain:s({name:"indicator",trace:!0,editType:"calc"}),title:{text:{valType:"string",editType:"plot"},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},font:r({},p,{}),editType:"plot"},number:{valueformat:{valType:"string",dflt:"",editType:"plot",description:m("value")},font:r({},p,{}),prefix:{valType:"string",dflt:"",editType:"plot"},suffix:{valType:"string",dflt:"",editType:"plot"},editType:"plot"},delta:{reference:{valType:"number",editType:"calc"},position:{valType:"enumerated",values:["top","bottom","left","right"],dflt:"bottom",editType:"plot"},relative:{valType:"boolean",editType:"plot",dflt:!1},valueformat:{valType:"string",editType:"plot",description:m("value")},increasing:{symbol:{valType:"string",dflt:d.INCREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:d.INCREASING.COLOR,editType:"plot"},editType:"plot"},decreasing:{symbol:{valType:"string",dflt:d.DECREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:d.DECREASING.COLOR,editType:"plot"},editType:"plot"},font:r({},p,{}),editType:"calc"},gauge:{shape:{valType:"enumerated",editType:"plot",dflt:"angular",values:["angular","bullet"]},bar:a({},g,{color:{dflt:"green"}}),bgcolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:1,editType:"plot"},axis:l({range:y,visible:r({},u.visible,{dflt:!0}),tickmode:u.tickmode,nticks:u.nticks,tick0:u.tick0,dtick:u.dtick,tickvals:u.tickvals,ticktext:u.ticktext,ticks:r({},u.ticks,{dflt:"outside"}),ticklen:u.ticklen,tickwidth:u.tickwidth,tickcolor:u.tickcolor,ticklabelstep:u.ticklabelstep,showticklabels:u.showticklabels,tickfont:c({}),tickangle:u.tickangle,tickformat:u.tickformat,tickformatstops:u.tickformatstops,tickprefix:u.tickprefix,showtickprefix:u.showtickprefix,ticksuffix:u.ticksuffix,showticksuffix:u.showticksuffix,separatethousands:u.separatethousands,exponentformat:u.exponentformat,minexponent:u.minexponent,showexponent:u.showexponent,editType:"plot"},"plot"),steps:v,threshold:{line:{color:r({},g.line.color,{}),width:r({},g.line.width,{dflt:1}),editType:"plot"},thickness:r({},g.thickness,{dflt:.85}),value:{valType:"number",editType:"calc",dflt:!1},editType:"plot"},editType:"plot"}}},{"../../components/color/attributes":365,"../../constants/delta.js":473,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../../plots/cartesian/axis_format_attributes":557,"../../plots/cartesian/layout_attributes":569,"../../plots/domain":584,"../../plots/font_attributes":585}],856:[function(t,o,f){var r=t("../../plots/plots");f.name="indicator",f.plot=function(a,l,c,i){r.plotBasePlot(f.name,a,l,c,i)},f.clean=function(a,l,c,i){r.cleanBasePlot(f.name,a,l,c,i)}},{"../../plots/plots":619}],857:[function(t,o,f){o.exports={calc:function(r,a){var l=[],c=a.value;typeof a._lastValue!="number"&&(a._lastValue=a.value);var i=a._lastValue,s=i;return a._hasDelta&&typeof a.delta.reference=="number"&&(s=a.delta.reference),l[0]={y:c,lastY:i,delta:c-s,relativeDelta:(c-s)/s},l}}},{}],858:[function(t,o,f){o.exports={defaultNumberFontSize:80,bulletNumberDomainSize:.25,bulletPadding:.025,innerRadius:.75,valueThickness:.5,titlePadding:5,horizontalPadding:10}},{}],859:[function(t,o,f){var r=t("../../lib"),a=t("./attributes"),l=t("../../plots/domain").defaults,c=t("../../plot_api/plot_template"),i=t("../../plots/array_container_defaults"),s=t("./constants.js"),u=t("../../plots/cartesian/tick_value_defaults"),h=t("../../plots/cartesian/tick_mark_defaults"),d=t("../../plots/cartesian/tick_label_defaults"),m=t("../../plots/cartesian/prefix_suffix_defaults");function p(g,y){function v(x,_){return r.coerce(g,y,a.gauge.steps,x,_)}v("color"),v("line.color"),v("line.width"),v("range"),v("thickness")}o.exports={supplyDefaults:function(g,y,v,x){function _(F,D){return r.coerce(g,y,a,F,D)}l(y,x,_),_("mode"),y._hasNumber=y.mode.indexOf("number")!==-1,y._hasDelta=y.mode.indexOf("delta")!==-1,y._hasGauge=y.mode.indexOf("gauge")!==-1;var A=_("value");y._range=[0,typeof A=="number"?1.5*A:1];var b,k,w,M,T,E,S=new Array(2);function P(F,D){return r.coerce(w,M,a.gauge,F,D)}function L(F,D){return r.coerce(T,E,a.gauge.axis,F,D)}if(y._hasNumber&&(_("number.valueformat"),_("number.font.color",x.font.color),_("number.font.family",x.font.family),_("number.font.size"),y.number.font.size===void 0&&(y.number.font.size=s.defaultNumberFontSize,S[0]=!0),_("number.prefix"),_("number.suffix"),b=y.number.font.size),y._hasDelta&&(_("delta.font.color",x.font.color),_("delta.font.family",x.font.family),_("delta.font.size"),y.delta.font.size===void 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R={outerTicks:!0};u(T,E,L,"linear"),m(T,E,L,"linear",R),d(T,E,L,"linear",R),h(T,E,L,R)}else _("title.align","center"),_("align","center"),y._isAngular=y._isBullet=!1;y._length=null}}},{"../../lib":503,"../../plot_api/plot_template":543,"../../plots/array_container_defaults":549,"../../plots/cartesian/prefix_suffix_defaults":573,"../../plots/cartesian/tick_label_defaults":578,"../../plots/cartesian/tick_mark_defaults":579,"../../plots/cartesian/tick_value_defaults":580,"../../plots/domain":584,"./attributes":855,"./constants.js":858}],860:[function(t,o,f){o.exports={moduleType:"trace",name:"indicator",basePlotModule:t("./base_plot"),categories:["svg","noOpacity","noHover"],animatable:!0,attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,calc:t("./calc").calc,plot:t("./plot"),meta:{}}},{"./attributes":855,"./base_plot":856,"./calc":857,"./defaults":859,"./plot":861}],861:[function(t,o,f){var r=t("@plotly/d3"),a=t("d3-interpolate").interpolate,l=t("d3-interpolate").interpolateNumber,c=t("../../lib"),i=c.strScale,s=c.strTranslate,u=c.rad2deg,h=t("../../constants/alignment").MID_SHIFT,d=t("../../components/drawing"),m=t("./constants"),p=t("../../lib/svg_text_utils"),g=t("../../plots/cartesian/axes"),y=t("../../plots/cartesian/axis_defaults"),v=t("../../plots/cartesian/position_defaults"),x=t("../../plots/cartesian/layout_attributes"),_=t("../../components/color"),A={left:"start",center:"middle",right:"end"},b={left:0,center:.5,right:1},k=/[yzafpn\xb5mkMGTPEZY]/;function w(L){return L&&L.duration>0}function M(L){L.each(function(R){_.stroke(r.select(this),R.line.color)}).each(function(R){_.fill(r.select(this),R.color)}).style("stroke-width",function(R){return R.line.width})}function T(L,R,F){var D=L._fullLayout,O=c.extendFlat({type:"linear",ticks:"outside",range:F,showline:!0},R),N={type:"linear",_id:"x"+R._id},B={letter:"x",font:D.font,noHover:!0,noTickson:!0};function W(G,K){return c.coerce(O,N,x,G,K)}return y(O,N,W,B,D),v(O,N,W,B),N}function E(L,R,F){return[Math.min(R/L.width,F/L.height),L,R+"x"+F]}function S(L,R,F,D){var O=document.createElementNS("http://www.w3.org/2000/svg","text"),N=r.select(O);return N.text(L).attr("x",0).attr("y",0).attr("text-anchor",F).attr("data-unformatted",L).call(p.convertToTspans,D).call(d.font,R),d.bBox(N.node())}function P(L,R,F,D,O,N){var B="_cache"+R;L[B]&&L[B].key===O||(L[B]={key:O,value:F});var W=c.aggNums(N,null,[L[B].value,D],2);return L[B].value=W,W}o.exports=function(L,R,F,D){var O,N=L._fullLayout;w(F)&&D&&(O=D()),c.makeTraceGroups(N._indicatorlayer,R,"trace").each(function(B){var W,G,K,te,Y,J=B[0].trace,re=r.select(this),U=J._hasGauge,V=J._isAngular,H=J._isBullet,ne=J.domain,q={w:N._size.w*(ne.x[1]-ne.x[0]),h:N._size.h*(ne.y[1]-ne.y[0]),l:N._size.l+N._size.w*ne.x[0],r:N._size.r+N._size.w*(1-ne.x[1]),t:N._size.t+N._size.h*(1-ne.y[1]),b:N._size.b+N._size.h*ne.y[0]},Q=q.l+q.w/2,ee=q.t+q.h/2,ie=Math.min(q.w/2,q.h),ae=m.innerRadius*ie,ue=J.align||"center";if(G=ee,U){if(V&&(W=Q,G=ee+ie/2,K=function(Le){return function(de,ve){var Me=Math.sqrt(de.width/2*(de.width/2)+de.height*de.height);return[ve/Me,de,ve]}(Le,.9*ae)}),H){var le=m.bulletPadding,ge=1-m.bulletNumberDomainSize+le;W=q.l+(ge+(1-ge)*b[ue])*q.w,K=function(Le){return E(Le,(m.bulletNumberDomainSize-le)*q.w,q.h)}}}else W=q.l+b[ue]*q.w,K=function(Le){return E(Le,q.w,q.h)};(function(Le,de,ve,Me){var we,Ce,Fe,ze=ve[0].trace,$e=Me.numbersX,Ke=Me.numbersY,Re=ze.align||"center",Ve=A[Re],We=Me.transitionOpts,Ye=Me.onComplete,nt=c.ensureSingle(de,"g","numbers"),ft=[];ze._hasNumber&&ft.push("number"),ze._hasDelta&&(ft.push("delta"),ze.delta.position==="left"&&ft.reverse());var yt=nt.selectAll("text").data(ft);function Ot(Ge,kt,dt,Oe){if(!Ge.match("s")||dt>=0==Oe>=0||kt(dt).slice(-1).match(k)||kt(Oe).slice(-1).match(k))return kt;var Ie=Ge.slice().replace("s","f").replace(/\d+/,function(Pe){return parseInt(Pe)-1}),Te=T(Le,{tickformat:Ie});return function(Pe){return Math.abs(Pe)<1?g.tickText(Te,Pe).text:kt(Pe)}}yt.enter().append("text"),yt.attr("text-anchor",function(){return Ve}).attr("class",function(Ge){return Ge}).attr("x",null).attr("y",null).attr("dx",null).attr("dy",null),yt.exit().remove();var Tt,at=ze.mode+ze.align;if(ze._hasDelta&&(Tt=function(){var Ge=T(Le,{tickformat:ze.delta.valueformat},ze._range);Ge.setScale(),g.prepTicks(Ge);var kt=function(qe){return g.tickText(Ge,qe).text},dt=function(qe){return ze.delta.relative?qe.relativeDelta:qe.delta},Oe=function(qe,rt){return qe===0||typeof qe!="number"||isNaN(qe)?"-":(qe>0?ze.delta.increasing.symbol:ze.delta.decreasing.symbol)+rt(qe)},Ie=function(qe){return qe.delta>=0?ze.delta.increasing.color:ze.delta.decreasing.color};ze._deltaLastValue===void 0&&(ze._deltaLastValue=dt(ve[0]));var Te=nt.select("text.delta");function Pe(){Te.text(Oe(dt(ve[0]),kt)).call(_.fill,Ie(ve[0])).call(p.convertToTspans,Le)}return Te.call(d.font,ze.delta.font).call(_.fill,Ie({delta:ze._deltaLastValue})),w(We)?Te.transition().duration(We.duration).ease(We.easing).tween("text",function(){var qe=r.select(this),rt=dt(ve[0]),lt=ze._deltaLastValue,ot=Ot(ze.delta.valueformat,kt,lt,rt),At=l(lt,rt);return ze._deltaLastValue=rt,function(wt){qe.text(Oe(At(wt),ot)),qe.call(_.fill,Ie({delta:At(wt)}))}}).each("end",function(){Pe(),Ye&&Ye()}).each("interrupt",function(){Pe(),Ye&&Ye()}):Pe(),Ce=S(Oe(dt(ve[0]),kt),ze.delta.font,Ve,Le),Te}(),at+=ze.delta.position+ze.delta.font.size+ze.delta.font.family+ze.delta.valueformat,at+=ze.delta.increasing.symbol+ze.delta.decreasing.symbol,Fe=Ce),ze._hasNumber&&(function(){var Ge=T(Le,{tickformat:ze.number.valueformat},ze._range);Ge.setScale(),g.prepTicks(Ge);var kt=function(Pe){return g.tickText(Ge,Pe).text},dt=ze.number.suffix,Oe=ze.number.prefix,Ie=nt.select("text.number");function Te(){var Pe=typeof ve[0].y=="number"?Oe+kt(ve[0].y)+dt:"-";Ie.text(Pe).call(d.font,ze.number.font).call(p.convertToTspans,Le)}w(We)?Ie.transition().duration(We.duration).ease(We.easing).each("end",function(){Te(),Ye&&Ye()}).each("interrupt",function(){Te(),Ye&&Ye()}).attrTween("text",function(){var Pe=r.select(this),qe=l(ve[0].lastY,ve[0].y);ze._lastValue=ve[0].y;var rt=Ot(ze.number.valueformat,kt,ve[0].lastY,ve[0].y);return function(lt){Pe.text(Oe+rt(qe(lt))+dt)}}):Te(),we=S(Oe+kt(ve[0].y)+dt,ze.number.font,Ve,Le)}(),at+=ze.number.font.size+ze.number.font.family+ze.number.valueformat+ze.number.suffix+ze.number.prefix,Fe=we),ze._hasDelta&&ze._hasNumber){var et,Lt,Wt=[(we.left+we.right)/2,(we.top+we.bottom)/2],Jt=[(Ce.left+Ce.right)/2,(Ce.top+Ce.bottom)/2],Be=.75*ze.delta.font.size;ze.delta.position==="left"&&(et=P(ze,"deltaPos",0,-1*(we.width*b[ze.align]+Ce.width*(1-b[ze.align])+Be),at,Math.min),Lt=Wt[1]-Jt[1],Fe={width:we.width+Ce.width+Be,height:Math.max(we.height,Ce.height),left:Ce.left+et,right:we.right,top:Math.min(we.top,Ce.top+Lt),bottom:Math.max(we.bottom,Ce.bottom+Lt)}),ze.delta.position==="right"&&(et=P(ze,"deltaPos",0,we.width*(1-b[ze.align])+Ce.width*b[ze.align]+Be,at,Math.max),Lt=Wt[1]-Jt[1],Fe={width:we.width+Ce.width+Be,height:Math.max(we.height,Ce.height),left:we.left,right:Ce.right+et,top:Math.min(we.top,Ce.top+Lt),bottom:Math.max(we.bottom,Ce.bottom+Lt)}),ze.delta.position==="bottom"&&(et=null,Lt=Ce.height,Fe={width:Math.max(we.width,Ce.width),height:we.height+Ce.height,left:Math.min(we.left,Ce.left),right:Math.max(we.right,Ce.right),top:we.bottom-we.height,bottom:we.bottom+Ce.height}),ze.delta.position==="top"&&(et=null,Lt=we.top,Fe={width:Math.max(we.width,Ce.width),height:we.height+Ce.height,left:Math.min(we.left,Ce.left),right:Math.max(we.right,Ce.right),top:we.bottom-we.height-Ce.height,bottom:we.bottom}),Tt.attr({dx:et,dy:Lt})}(ze._hasNumber||ze._hasDelta)&&nt.attr("transform",function(){var Ge=Me.numbersScaler(Fe);at+=Ge[2];var kt,dt=P(ze,"numbersScale",1,Ge[0],at,Math.min);ze._scaleNumbers||(dt=1),kt=ze._isAngular?Ke-dt*Fe.bottom:Ke-dt*(Fe.top+Fe.bottom)/2,ze._numbersTop=dt*Fe.top+kt;var Oe=Fe[Re];Re==="center"&&(Oe=(Fe.left+Fe.right)/2);var Ie=$e-dt*Oe;return Ie=P(ze,"numbersTranslate",0,Ie,at,Math.max),s(Ie,kt)+i(dt)})})(L,re,B,{numbersX:W,numbersY:G,numbersScaler:K,transitionOpts:F,onComplete:O}),U&&(te={range:J.gauge.axis.range,color:J.gauge.bgcolor,line:{color:J.gauge.bordercolor,width:0},thickness:1},Y={range:J.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:J.gauge.bordercolor,width:J.gauge.borderwidth},thickness:1});var fe=re.selectAll("g.angular").data(V?B:[]);fe.exit().remove();var me=re.selectAll("g.angularaxis").data(V?B:[]);me.exit().remove(),V&&function(Le,de,ve,Me){var we,Ce,Fe,ze,$e=ve[0].trace,Ke=Me.size,Re=Me.radius,Ve=Me.innerRadius,We=Me.gaugeBg,Ye=Me.gaugeOutline,nt=[Ke.l+Ke.w/2,Ke.t+Ke.h/2+Re/2],ft=Me.gauge,yt=Me.layer,Ot=Me.transitionOpts,Tt=Me.onComplete,at=Math.PI/2;function et(Ut){var tt=$e.gauge.axis.range[0],bt=(Ut-tt)/($e.gauge.axis.range[1]-tt)*Math.PI-at;return bt<-at?-at:bt>at?at:bt}function Lt(Ut){return r.svg.arc().innerRadius((Ve+Re)/2-Ut/2*(Re-Ve)).outerRadius((Ve+Re)/2+Ut/2*(Re-Ve)).startAngle(-at)}function Wt(Ut){Ut.attr("d",function(tt){return Lt(tt.thickness).startAngle(et(tt.range[0])).endAngle(et(tt.range[1]))()})}ft.enter().append("g").classed("angular",!0),ft.attr("transform",s(nt[0],nt[1])),yt.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),yt.selectAll("g.xangularaxistick,path,text").remove(),(we=T(Le,$e.gauge.axis)).type="linear",we.range=$e.gauge.axis.range,we._id="xangularaxis",we.ticklabeloverflow="allow",we.setScale();var Jt=function(Ut){return(we.range[0]-Ut.x)/(we.range[1]-we.range[0])*Math.PI+Math.PI},Be={},Ge=g.makeLabelFns(we,0).labelStandoff;Be.xFn=function(Ut){var tt=Jt(Ut);return Math.cos(tt)*Ge},Be.yFn=function(Ut){var tt=Jt(Ut),bt=Math.sin(tt)>0?.2:1;return-Math.sin(tt)*(Ge+Ut.fontSize*bt)+Math.abs(Math.cos(tt))*(Ut.fontSize*h)},Be.anchorFn=function(Ut){var tt=Jt(Ut),bt=Math.cos(tt);return Math.abs(bt)<.1?"middle":bt>0?"start":"end"},Be.heightFn=function(Ut,tt,bt){var Ft=Jt(Ut);return-.5*(1+Math.sin(Ft))*bt};var kt=function(Ut){return s(nt[0]+Re*Math.cos(Ut),nt[1]-Re*Math.sin(Ut))};if(Fe=function(Ut){return kt(Jt(Ut))},Ce=g.calcTicks(we),ze=g.getTickSigns(we)[2],we.visible){ze=we.ticks==="inside"?-1:1;var dt=(we.linewidth||1)/2;g.drawTicks(Le,we,{vals:Ce,layer:yt,path:"M"+ze*dt+",0h"+ze*we.ticklen,transFn:function(Ut){var tt=Jt(Ut);return kt(tt)+"rotate("+-u(tt)+")"}}),g.drawLabels(Le,we,{vals:Ce,layer:yt,transFn:Fe,labelFns:Be})}var Oe=[We].concat($e.gauge.steps),Ie=ft.selectAll("g.bg-arc").data(Oe);Ie.enter().append("g").classed("bg-arc",!0).append("path"),Ie.select("path").call(Wt).call(M),Ie.exit().remove();var Te=Lt($e.gauge.bar.thickness),Pe=ft.selectAll("g.value-arc").data([$e.gauge.bar]);Pe.enter().append("g").classed("value-arc",!0).append("path");var qe=Pe.select("path");w(Ot)?(qe.transition().duration(Ot.duration).ease(Ot.easing).each("end",function(){Tt&&Tt()}).each("interrupt",function(){Tt&&Tt()}).attrTween("d",(rt=Te,lt=et(ve[0].lastY),ot=et(ve[0].y),function(){var Ut=a(lt,ot);return function(tt){return rt.endAngle(Ut(tt))()}})),$e._lastValue=ve[0].y):qe.attr("d",typeof ve[0].y=="number"?Te.endAngle(et(ve[0].y)):"M0,0Z");var rt,lt,ot;qe.call(M),Pe.exit().remove(),Oe=[];var At=$e.gauge.threshold.value;(At||At===0)&&Oe.push({range:[At,At],color:$e.gauge.threshold.color,line:{color:$e.gauge.threshold.line.color,width:$e.gauge.threshold.line.width},thickness:$e.gauge.threshold.thickness});var wt=ft.selectAll("g.threshold-arc").data(Oe);wt.enter().append("g").classed("threshold-arc",!0).append("path"),wt.select("path").call(Wt).call(M),wt.exit().remove();var $t=ft.selectAll("g.gauge-outline").data([Ye]);$t.enter().append("g").classed("gauge-outline",!0).append("path"),$t.select("path").call(Wt).call(M),$t.exit().remove()}(L,0,B,{radius:ie,innerRadius:ae,gauge:fe,layer:me,size:q,gaugeBg:te,gaugeOutline:Y,transitionOpts:F,onComplete:O});var _e=re.selectAll("g.bullet").data(H?B:[]);_e.exit().remove();var Ae=re.selectAll("g.bulletaxis").data(H?B:[]);Ae.exit().remove(),H&&function(Le,de,ve,Me){var we,Ce,Fe,ze,$e,Ke=ve[0].trace,Re=Me.gauge,Ve=Me.layer,We=Me.gaugeBg,Ye=Me.gaugeOutline,nt=Me.size,ft=Ke.domain,yt=Me.transitionOpts,Ot=Me.onComplete;Re.enter().append("g").classed("bullet",!0),Re.attr("transform",s(nt.l,nt.t)),Ve.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),Ve.selectAll("g.xbulletaxistick,path,text").remove();var Tt=nt.h,at=Ke.gauge.bar.thickness*Tt,et=ft.x[0],Lt=ft.x[0]+(ft.x[1]-ft.x[0])*(Ke._hasNumber||Ke._hasDelta?1-m.bulletNumberDomainSize:1);(we=T(Le,Ke.gauge.axis))._id="xbulletaxis",we.domain=[et,Lt],we.setScale(),Ce=g.calcTicks(we),Fe=g.makeTransTickFn(we),ze=g.getTickSigns(we)[2],$e=nt.t+nt.h,we.visible&&(g.drawTicks(Le,we,{vals:we.ticks==="inside"?g.clipEnds(we,Ce):Ce,layer:Ve,path:g.makeTickPath(we,$e,ze),transFn:Fe}),g.drawLabels(Le,we,{vals:Ce,layer:Ve,transFn:Fe,labelFns:g.makeLabelFns(we,$e)}));function Wt(Ie){Ie.attr("width",function(Te){return Math.max(0,we.c2p(Te.range[1])-we.c2p(Te.range[0]))}).attr("x",function(Te){return we.c2p(Te.range[0])}).attr("y",function(Te){return .5*(1-Te.thickness)*Tt}).attr("height",function(Te){return Te.thickness*Tt})}var Jt=[We].concat(Ke.gauge.steps),Be=Re.selectAll("g.bg-bullet").data(Jt);Be.enter().append("g").classed("bg-bullet",!0).append("rect"),Be.select("rect").call(Wt).call(M),Be.exit().remove();var Ge=Re.selectAll("g.value-bullet").data([Ke.gauge.bar]);Ge.enter().append("g").classed("value-bullet",!0).append("rect"),Ge.select("rect").attr("height",at).attr("y",(Tt-at)/2).call(M),w(yt)?Ge.select("rect").transition().duration(yt.duration).ease(yt.easing).each("end",function(){Ot&&Ot()}).each("interrupt",function(){Ot&&Ot()}).attr("width",Math.max(0,we.c2p(Math.min(Ke.gauge.axis.range[1],ve[0].y)))):Ge.select("rect").attr("width",typeof ve[0].y=="number"?Math.max(0,we.c2p(Math.min(Ke.gauge.axis.range[1],ve[0].y))):0),Ge.exit().remove();var kt=ve.filter(function(){return Ke.gauge.threshold.value||Ke.gauge.threshold.value===0}),dt=Re.selectAll("g.threshold-bullet").data(kt);dt.enter().append("g").classed("threshold-bullet",!0).append("line"),dt.select("line").attr("x1",we.c2p(Ke.gauge.threshold.value)).attr("x2",we.c2p(Ke.gauge.threshold.value)).attr("y1",(1-Ke.gauge.threshold.thickness)/2*Tt).attr("y2",(1-(1-Ke.gauge.threshold.thickness)/2)*Tt).call(_.stroke,Ke.gauge.threshold.line.color).style("stroke-width",Ke.gauge.threshold.line.width),dt.exit().remove();var Oe=Re.selectAll("g.gauge-outline").data([Ye]);Oe.enter().append("g").classed("gauge-outline",!0).append("rect"),Oe.select("rect").call(Wt).call(M),Oe.exit().remove()}(L,0,B,{gauge:_e,layer:Ae,size:q,gaugeBg:te,gaugeOutline:Y,transitionOpts:F,onComplete:O});var ke=re.selectAll("text.title").data(B);ke.exit().remove(),ke.enter().append("text").classed("title",!0),ke.attr("text-anchor",function(){return 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r=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../mesh3d/attributes"),i=t("../../plots/attributes"),s=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll,h=o.exports=u(s({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:l(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),valuehoverformat:a("value",1),showlegend:s({},i.showlegend,{dflt:!1})},r("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:c.opacity,lightposition:c.lightposition,lighting:c.lighting,flatshading:c.flatshading,contour:c.contour,hoverinfo:s({},i.hoverinfo)}),"calc","nested");h.flatshading.dflt=!0,h.lighting.facenormalsepsilon.dflt=0,h.x.editType=h.y.editType=h.z.editType=h.value.editType="calc+clearAxisTypes",h.transforms=void 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Ot,Tt,at,et,Lt=!1,Wt=function(){Lt=ge(Re,[Ot,Tt,at],[-1,-1,-1],nt,ft)||Lt,Lt=ge(Re,[at,et,Ot],[-1,-1,-1],nt,ft)||Lt},Jt=yt[0],Be=yt[1],Ge=yt[2];return Jt&&(Ot=H(le([W(Ve,We-0,Ye-0)])[0],le([W(Ve-1,We-0,Ye-0)])[0],Jt),Tt=H(le([W(Ve,We-0,Ye-1)])[0],le([W(Ve-1,We-0,Ye-1)])[0],Jt),at=H(le([W(Ve,We-1,Ye-1)])[0],le([W(Ve-1,We-1,Ye-1)])[0],Jt),et=H(le([W(Ve,We-1,Ye-0)])[0],le([W(Ve-1,We-1,Ye-0)])[0],Jt),Wt()),Be&&(Ot=H(le([W(Ve-0,We,Ye-0)])[0],le([W(Ve-0,We-1,Ye-0)])[0],Be),Tt=H(le([W(Ve-0,We,Ye-1)])[0],le([W(Ve-0,We-1,Ye-1)])[0],Be),at=H(le([W(Ve-1,We,Ye-1)])[0],le([W(Ve-1,We-1,Ye-1)])[0],Be),et=H(le([W(Ve-1,We,Ye-0)])[0],le([W(Ve-1,We-1,Ye-0)])[0],Be),Wt()),Ge&&(Ot=H(le([W(Ve-0,We-0,Ye)])[0],le([W(Ve-0,We-0,Ye-1)])[0],Ge),Tt=H(le([W(Ve-0,We-1,Ye)])[0],le([W(Ve-0,We-1,Ye-1)])[0],Ge),at=H(le([W(Ve-1,We-1,Ye)])[0],le([W(Ve-1,We-1,Ye-1)])[0],Ge),et=H(le([W(Ve-1,We-0,Ye)])[0],le([W(Ve-1,We-0,Ye-1)])[0],Ge),Wt()),Lt}function Le(Re,Ve,We,Ye,nt,ft,yt,Ot,Tt,at,et,Lt){var Wt=Re;return 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ft=1;ft<D;ft++)Le(Re,W(ft-1,nt-1,Ye-1),W(ft-1,nt-1,Ye),W(ft-1,nt,Ye-1),W(ft-1,nt,Ye),W(ft,nt-1,Ye-1),W(ft,nt-1,Ye),W(ft,nt,Ye-1),W(ft,nt,Ye),Ve,We,(ft+nt+Ye)%2)}function Ce(Re,Ve,We){E=!0,we(Re,Ve,We),E=!1}function Fe(Re,Ve,We,Ye,nt,ft){for(var yt=[],Ot=0,Tt=0;Tt<Ve.length;Tt++)for(var at=Ve[Tt],et=1;et<N;et++)for(var Lt=1;Lt<O;Lt++)yt.push(ke(Re,at,Lt,et,We,Ye,nt[Tt],ft&&ft[Ot]&&ft[Ot])),Ot++;return yt}function ze(Re,Ve,We,Ye,nt,ft){for(var yt=[],Ot=0,Tt=0;Tt<Ve.length;Tt++)for(var at=Ve[Tt],et=1;et<D;et++)for(var Lt=1;Lt<N;Lt++)yt.push(ke(Re,et,at,Lt,We,Ye,nt[Tt],ft&&ft[Ot]&&ft[Ot])),Ot++;return yt}function $e(Re,Ve,We,Ye,nt,ft){for(var yt=[],Ot=0,Tt=0;Tt<Ve.length;Tt++)for(var at=Ve[Tt],et=1;et<O;et++)for(var Lt=1;Lt<D;Lt++)yt.push(ke(Re,Lt,et,at,We,Ye,nt[Tt],ft&&ft[Ot]&&ft[Ot])),Ot++;return yt}function Ke(Re,Ve){for(var We=[],Ye=Re;Ye<Ve;Ye++)We.push(Ye);return We}return function(){if(U(),function(){for(var Ot=0;Ot<D;Ot++)for(var Tt=0;Tt<O;Tt++)for(var at=0;at<N;at++){var et=W(Ot,Tt,at);V(p._x[et],p._y[et],p._z[et],p._value[et])}}(),w&&T&&(ne(T),S=!0,we(null,te,Y),S=!1),k&&M){ne(M);for(var Re=p.surface.pattern,Ve=p.surface.count,We=0;We<Ve;We++){var Ye=Ve===1?.5:We/(Ve-1),nt=(1-Ye)*te+Ye*Y,ft=Math.abs(nt-G)>Math.abs(nt-K)?[G,nt]:[nt,K];Ce(Re,ft[0],ft[1])}}var yt=[[Math.min(te,K),Math.max(te,K)],[Math.min(G,Y),Math.max(G,Y)]];["x","y","z"].forEach(function(Ot){for(var Tt=[],at=0;at<yt.length;at++){var et=0,Lt=yt[at][0],Wt=yt[at][1],Jt=p.slices[Ot];if(Jt.show&&Jt.fill){ne(Jt.fill);var Be=[],Ge=[],kt=[];if(Jt.locations.length)for(var dt=0;dt<Jt.locations.length;dt++){var Oe=s(Jt.locations[dt],Ot==="x"?L:Ot==="y"?R:F);Oe.distRatio===0?Be.push(Oe.id):Oe.id>0&&(Ge.push(Oe.id),Ot==="x"?kt.push([Oe.distRatio,0,0]):Ot==="y"?kt.push([0,Oe.distRatio,0]):kt.push([0,0,Oe.distRatio]))}else Be=Ke(1,Ot==="x"?D-1:Ot==="y"?O-1:N-1);Ge.length>0&&(Tt[et]=Ot==="x"?Fe(null,Ge,Lt,Wt,kt,Tt[et]):Ot==="y"?ze(null,Ge,Lt,Wt,kt,Tt[et]):$e(null,Ge,Lt,Wt,kt,Tt[et]),et++),Be.length>0&&(Tt[et]=Ot==="x"?de(null,Be,Lt,Wt,Tt[et]):Ot==="y"?ve(null,Be,Lt,Wt,Tt[et]):Me(null,Be,Lt,Wt,Tt[et]),et++)}var Ie=p.caps[Ot];Ie.show&&Ie.fill&&(ne(Ie.fill),Tt[et]=Ot==="x"?de(null,[0,D-1],Lt,Wt,Tt[et]):Ot==="y"?ve(null,[0,O-1],Lt,Wt,Tt[et]):Me(null,[0,N-1],Lt,Wt,Tt[et]),et++)}}),P===0&&U(),p._meshX=v,p._meshY=x,p._meshZ=_,p._meshIntensity=A,p._Xs=L,p._Ys=R,p._Zs=F}(),p}o.exports={findNearestOnAxis:s,generateIsoMeshes:m,createIsosurfaceTrace:function(p,g){var y=p.glplot.gl,v=r({gl:y}),x=new u(p,v,g.uid);return v._trace=x,x.update(g),p.glplot.add(v),x}}},{"../../../stackgl_modules":1124,"../../components/colorscale":378,"../../lib/gl_format_color":499,"../../lib/str2rgbarray":528,"../../plots/gl3d/zip3":609}],865:[function(t,o,f){var r=t("../../lib"),a=t("../../registry"),l=t("./attributes"),c=t("../../components/colorscale/defaults");function i(s,u,h,d,m){var p=m("isomin"),g=m("isomax");g!=null&&p!=null&&p>g&&(u.isomin=null,u.isomax=null);var y=m("x"),v=m("y"),x=m("z"),_=m("value");y&&y.length&&v&&v.length&&x&&x.length&&_&&_.length?(a.getComponentMethod("calendars","handleTraceDefaults")(s,u,["x","y","z"],d),m("valuehoverformat"),["x","y","z"].forEach(function(A){m(A+"hoverformat");var b="caps."+A;m(b+".show")&&m(b+".fill");var k="slices."+A;m(k+".show")&&(m(k+".fill"),m(k+".locations"))}),m("spaceframe.show")&&m("spaceframe.fill"),m("surface.show")&&(m("surface.count"),m("surface.fill"),m("surface.pattern")),m("contour.show")&&(m("contour.color"),m("contour.width")),["text","hovertext","hovertemplate","lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lighting.vertexnormalsepsilon","lighting.facenormalsepsilon","lightposition.x","lightposition.y","lightposition.z","flatshading","opacity"].forEach(function(A){m(A)}),c(s,u,d,m,{prefix:"",cLetter:"c"}),u._length=null):u.visible=!1}o.exports={supplyDefaults:function(s,u,h,d){i(s,u,h,d,function(m,p){return r.coerce(s,u,l,m,p)})},supplyIsoDefaults:i}},{"../../components/colorscale/defaults":376,"../../lib":503,"../../registry":638,"./attributes":862}],866:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,calc:t("./calc"),colorbar:{min:"cmin",max:"cmax"},plot:t("./convert").createIsosurfaceTrace,moduleType:"trace",name:"isosurface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],meta:{}}},{"../../plots/gl3d":598,"./attributes":862,"./calc":863,"./convert":864,"./defaults":865}],867:[function(t,o,f){var r=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../surface/attributes"),i=t("../../plots/attributes"),s=t("../../lib/extend").extendFlat;o.exports=s({x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},i:{valType:"data_array",editType:"calc"},j:{valType:"data_array",editType:"calc"},k:{valType:"data_array",editType:"calc"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertemplate:l({editType:"calc"}),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),delaunayaxis:{valType:"enumerated",values:["x","y","z"],dflt:"z",editType:"calc"},alphahull:{valType:"number",dflt:-1,editType:"calc"},intensity:{valType:"data_array",editType:"calc"},intensitymode:{valType:"enumerated",values:["vertex","cell"],dflt:"vertex",editType:"calc"},color:{valType:"color",editType:"calc"},vertexcolor:{valType:"data_array",editType:"calc"},facecolor:{valType:"data_array",editType:"calc"},transforms:void 0},r("",{colorAttr:"`intensity`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:c.opacity,flatshading:{valType:"boolean",dflt:!1,editType:"calc"},contour:{show:s({},c.contours.x.show,{}),color:c.contours.x.color,width:c.contours.x.width,editType:"calc"},lightposition:{x:s({},c.lightposition.x,{dflt:1e5}),y:s({},c.lightposition.y,{dflt:1e5}),z:s({},c.lightposition.z,{dflt:0}),editType:"calc"},lighting:s({vertexnormalsepsilon:{valType:"number",min:0,max:1,dflt:1e-12,editType:"calc"},facenormalsepsilon:{valType:"number",min:0,max:1,dflt:1e-6,editType:"calc"},editType:"calc"},c.lighting),hoverinfo:s({},i.hoverinfo,{editType:"calc"}),showlegend:s({},i.showlegend,{dflt:!1})})},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633,"../surface/attributes":1061}],868:[function(t,o,f){var r=t("../../components/colorscale/calc");o.exports=function(a,l){l.intensity&&r(a,l,{vals:l.intensity,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":374}],869:[function(t,o,f){var r=t("../../../stackgl_modules").gl_mesh3d,a=t("../../../stackgl_modules").delaunay_triangulate,l=t("../../../stackgl_modules").alpha_shape,c=t("../../../stackgl_modules").convex_hull,i=t("../../lib/gl_format_color").parseColorScale,s=t("../../lib/str2rgbarray"),u=t("../../components/colorscale").extractOpts,h=t("../../plots/gl3d/zip3");function d(x,_,A){this.scene=x,this.uid=A,this.mesh=_,this.name="",this.color="#fff",this.data=null,this.showContour=!1}var m=d.prototype;function p(x){for(var _=[],A=x.length,b=0;b<A;b++)_[b]=s(x[b]);return _}function g(x,_,A,b){for(var k=[],w=_.length,M=0;M<w;M++)k[M]=x.d2l(_[M],0,b)*A;return k}function y(x){for(var _=[],A=x.length,b=0;b<A;b++)_[b]=Math.round(x[b]);return _}function v(x,_){for(var A=x.length,b=0;b<A;b++)if(x[b]<=-.5||x[b]>=_-.5)return!1;return!0}m.handlePick=function(x){if(x.object===this.mesh){var _=x.index=x.data.index;x.data._cellCenter?x.traceCoordinate=x.data.dataCoordinate:x.traceCoordinate=[this.data.x[_],this.data.y[_],this.data.z[_]];var A=this.data.hovertext||this.data.text;return Array.isArray(A)&&A[_]!==void 0?x.textLabel=A[_]:A&&(x.textLabel=A),!0}},m.update=function(x){var _=this.scene,A=_.fullSceneLayout;this.data=x;var b,k=x.x.length,w=h(g(A.xaxis,x.x,_.dataScale[0],x.xcalendar),g(A.yaxis,x.y,_.dataScale[1],x.ycalendar),g(A.zaxis,x.z,_.dataScale[2],x.zcalendar));if(x.i&&x.j&&x.k){if(x.i.length!==x.j.length||x.j.length!==x.k.length||!v(x.i,k)||!v(x.j,k)||!v(x.k,k))return;b=h(y(x.i),y(x.j),y(x.k))}else b=x.alphahull===0?c(w):x.alphahull>0?l(x.alphahull,w):function(S,P){for(var L=["x","y","z"].indexOf(S),R=[],F=P.length,D=0;D<F;D++)R[D]=[P[D][(L+1)%3],P[D][(L+2)%3]];return a(R)}(x.delaunayaxis,w);var M={positions:w,cells:b,lightPosition:[x.lightposition.x,x.lightposition.y,x.lightposition.z],ambient:x.lighting.ambient,diffuse:x.lighting.diffuse,specular:x.lighting.specular,roughness:x.lighting.roughness,fresnel:x.lighting.fresnel,vertexNormalsEpsilon:x.lighting.vertexnormalsepsilon,faceNormalsEpsilon:x.lighting.facenormalsepsilon,opacity:x.opacity,contourEnable:x.contour.show,contourColor:s(x.contour.color).slice(0,3),contourWidth:x.contour.width,useFacetNormals:x.flatshading};if(x.intensity){var T=u(x);this.color="#fff";var E=x.intensitymode;M[E+"Intensity"]=x.intensity,M[E+"IntensityBounds"]=[T.min,T.max],M.colormap=i(x)}else x.vertexcolor?(this.color=x.vertexcolor[0],M.vertexColors=p(x.vertexcolor)):x.facecolor?(this.color=x.facecolor[0],M.cellColors=p(x.facecolor)):(this.color=x.color,M.meshColor=s(x.color));this.mesh.update(M)},m.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},o.exports=function(x,_){var A=x.glplot.gl,b=r({gl:A}),k=new d(x,b,_.uid);return b._trace=k,k.update(_),x.glplot.add(b),k}},{"../../../stackgl_modules":1124,"../../components/colorscale":378,"../../lib/gl_format_color":499,"../../lib/str2rgbarray":528,"../../plots/gl3d/zip3":609}],870:[function(t,o,f){var r=t("../../registry"),a=t("../../lib"),l=t("../../components/colorscale/defaults"),c=t("./attributes");o.exports=function(i,s,u,h){function d(p,g){return a.coerce(i,s,c,p,g)}function m(p){var g=p.map(function(y){var v=d(y);return v&&a.isArrayOrTypedArray(v)?v:null});return g.every(function(y){return y&&y.length===g[0].length})&&g}m(["x","y","z"])?(m(["i","j","k"]),(!s.i||s.j&&s.k)&&(!s.j||s.k&&s.i)&&(!s.k||s.i&&s.j)?(r.getComponentMethod("calendars","handleTraceDefaults")(i,s,["x","y","z"],h),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lighting.vertexnormalsepsilon","lighting.facenormalsepsilon","lightposition.x","lightposition.y","lightposition.z","flatshading","alphahull","delaunayaxis","opacity"].forEach(function(p){d(p)}),d("contour.show")&&(d("contour.color"),d("contour.width")),"intensity"in i?(d("intensity"),d("intensitymode"),l(i,s,h,d,{prefix:"",cLetter:"c"})):(s.showscale=!1,"facecolor"in i?d("facecolor"):"vertexcolor"in i?d("vertexcolor"):d("color",u)),d("text"),d("hovertext"),d("hovertemplate"),d("xhoverformat"),d("yhoverformat"),d("zhoverformat"),s._length=null):s.visible=!1):s.visible=!1}},{"../../components/colorscale/defaults":376,"../../lib":503,"../../registry":638,"./attributes":867}],871:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),colorbar:{min:"cmin",max:"cmax"},plot:t("./convert"),moduleType:"trace",name:"mesh3d",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],meta:{}}},{"../../plots/gl3d":598,"./attributes":867,"./calc":868,"./convert":869,"./defaults":870}],872:[function(t,o,f){var r=t("../../lib").extendFlat,a=t("../scatter/attributes"),l=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,c=t("../../components/drawing/attributes").dash,i=t("../../components/fx/attributes"),s=t("../../constants/delta.js"),u=s.INCREASING.COLOR,h=s.DECREASING.COLOR,d=a.line;function m(p){return{line:{color:r({},d.color,{dflt:p}),width:d.width,dash:c,editType:"style"},editType:"style"}}o.exports={xperiod:a.xperiod,xperiod0:a.xperiod0,xperiodalignment:a.xperiodalignment,xhoverformat:l("x"),yhoverformat:l("y"),x:{valType:"data_array",editType:"calc+clearAxisTypes"},open:{valType:"data_array",editType:"calc"},high:{valType:"data_array",editType:"calc"},low:{valType:"data_array",editType:"calc"},close:{valType:"data_array",editType:"calc"},line:{width:r({},d.width,{}),dash:r({},c,{}),editType:"style"},increasing:m(u),decreasing:m(h),text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},tickwidth:{valType:"number",min:0,max:.5,dflt:.3,editType:"calc"},hoverlabel:r({},i.hoverlabel,{split:{valType:"boolean",dflt:!1,editType:"style"}})}},{"../../components/drawing/attributes":387,"../../components/fx/attributes":397,"../../constants/delta.js":473,"../../lib":503,"../../plots/cartesian/axis_format_attributes":557,"../scatter/attributes":927}],873:[function(t,o,f){var r=t("../../lib"),a=r._,l=t("../../plots/cartesian/axes"),c=t("../../plots/cartesian/align_period"),i=t("../../constants/numerical").BADNUM;function s(h,d,m,p){return{o:h,h:d,l:m,c:p}}function u(h,d,m,p,g,y){for(var v=g.makeCalcdata(d,"open"),x=g.makeCalcdata(d,"high"),_=g.makeCalcdata(d,"low"),A=g.makeCalcdata(d,"close"),b=Array.isArray(d.text),k=Array.isArray(d.hovertext),w=!0,M=null,T=!!d.xperiodalignment,E=[],S=0;S<p.length;S++){var P=p[S],L=v[S],R=x[S],F=_[S],D=A[S];if(P!==i&&L!==i&&R!==i&&F!==i&&D!==i){D===L?M!==null&&D!==M&&(w=D>M):w=D>L,M=D;var O=y(L,R,F,D);O.pos=P,O.yc=(L+D)/2,O.i=S,O.dir=w?"increasing":"decreasing",O.x=O.pos,O.y=[F,R],T&&(O.orig_p=m[S]),b&&(O.tx=d.text[S]),k&&(O.htx=d.hovertext[S]),E.push(O)}else E.push({pos:P,empty:!0})}return d._extremes[g._id]=l.findExtremes(g,r.concat(_,x),{padded:!0}),E.length&&(E[0].t={labels:{open:a(h,"open:")+" ",high:a(h,"high:")+" ",low:a(h,"low:")+" ",close:a(h,"close:")+" "}}),E}o.exports={calc:function(h,d){var m=l.getFromId(h,d.xaxis),p=l.getFromId(h,d.yaxis),g=function(A,b,k){var w=k._minDiff;if(!w){var M,T=A._fullData,E=[];for(w=1/0,M=0;M<T.length;M++){var S=T[M];if(S.type==="ohlc"&&S.visible===!0&&S.xaxis===b._id){E.push(S);var P=b.makeCalcdata(S,"x");S._origX=P;var L=c(k,b,"x",P).vals;S._xcalc=L;var R=r.distinctVals(L).minDiff;R&&isFinite(R)&&(w=Math.min(w,R))}}for(w===1/0&&(w=1),M=0;M<E.length;M++)E[M]._minDiff=w}return w*k.tickwidth}(h,m,d),y=d._minDiff;d._minDiff=null;var v=d._origX;d._origX=null;var x=d._xcalc;d._xcalc=null;var _=u(h,d,v,x,p,s);return d._extremes[m._id]=l.findExtremes(m,x,{vpad:y/2}),_.length?(r.extendFlat(_[0].t,{wHover:y/2,tickLen:g}),_):[{t:{empty:!0}}]},calcCommon:u}},{"../../constants/numerical":479,"../../lib":503,"../../plots/cartesian/align_period":551,"../../plots/cartesian/axes":554}],874:[function(t,o,f){var r=t("../../lib"),a=t("./ohlc_defaults"),l=t("../scatter/period_defaults"),c=t("./attributes");function i(s,u,h,d){h(d+".line.color"),h(d+".line.width",u.line.width),h(d+".line.dash",u.line.dash)}o.exports=function(s,u,h,d){function m(p,g){return r.coerce(s,u,c,p,g)}a(s,u,m,d)?(l(s,u,d,m,{x:!0}),m("xhoverformat"),m("yhoverformat"),m("line.width"),m("line.dash"),i(s,u,m,"increasing"),i(s,u,m,"decreasing"),m("text"),m("hovertext"),m("tickwidth"),d._requestRangeslider[u.xaxis]=!0):u.visible=!1}},{"../../lib":503,"../scatter/period_defaults":947,"./attributes":872,"./ohlc_defaults":877}],875:[function(t,o,f){var r=t("../../plots/cartesian/axes"),a=t("../../lib"),l=t("../../components/fx"),c=t("../../components/color"),i=t("../../lib").fillText,s=t("../../constants/delta.js"),u={increasing:s.INCREASING.SYMBOL,decreasing:s.DECREASING.SYMBOL};function h(p,g,y,v){var x,_,A=p.cd,b=p.xa,k=A[0].trace,w=A[0].t,M=k.type,T=M==="ohlc"?"l":"min",E=M==="ohlc"?"h":"max",S=w.bPos||0,P=w.bdPos||w.tickLen,L=w.wHover,R=Math.min(1,P/Math.abs(b.r2c(b.range[1])-b.r2c(b.range[0])));function F(K){var te=function(Y){return Y.pos+S-g}(K);return l.inbox(te-L,te+L,x)}function D(K){var te=K[T],Y=K[E];return te===Y||l.inbox(te-y,Y-y,x)}function O(K){return(F(K)+D(K))/2}x=p.maxHoverDistance-R,_=p.maxSpikeDistance-R;var N=l.getDistanceFunction(v,F,D,O);if(l.getClosest(A,N,p),p.index===!1)return null;var B=A[p.index];if(B.empty)return null;var W=k[B.dir],G=W.line.color;return c.opacity(G)&&W.line.width?p.color=G:p.color=W.fillcolor,p.x0=b.c2p(B.pos+S-P,!0),p.x1=b.c2p(B.pos+S+P,!0),p.xLabelVal=B.orig_p!==void 0?B.orig_p:B.pos,p.spikeDistance=O(B)*_/x,p.xSpike=b.c2p(B.pos,!0),p}function d(p,g,y,v){var x=p.cd,_=p.ya,A=x[0].trace,b=x[0].t,k=[],w=h(p,g,y,v);if(!w)return[];var M=x[w.index].hi||A.hoverinfo,T=M.split("+");if(!(M==="all"||T.indexOf("y")!==-1))return[];for(var E=["high","open","close","low"],S={},P=0;P<E.length;P++){var L,R=E[P],F=A[R][w.index],D=_.c2p(F,!0);F in S?(L=S[F]).yLabel+="<br>"+b.labels[R]+r.hoverLabelText(_,F,A.yhoverformat):((L=a.extendFlat({},w)).y0=L.y1=D,L.yLabelVal=F,L.yLabel=b.labels[R]+r.hoverLabelText(_,F,A.yhoverformat),L.name="",k.push(L),S[F]=L)}return k}function m(p,g,y,v){var x=p.cd,_=p.ya,A=x[0].trace,b=x[0].t,k=h(p,g,y,v);if(!k)return[];var w=x[k.index],M=k.index=w.i,T=w.dir;function E(O){return b.labels[O]+r.hoverLabelText(_,A[O][M],A.yhoverformat)}var S=w.hi||A.hoverinfo,P=S.split("+"),L=S==="all",R=L||P.indexOf("y")!==-1,F=L||P.indexOf("text")!==-1,D=R?[E("open"),E("high"),E("low"),E("close")+"  "+u[T]]:[];return F&&i(w,A,D),k.extraText=D.join("<br>"),k.y0=k.y1=_.c2p(w.yc,!0),[k]}o.exports={hoverPoints:function(p,g,y,v){return p.cd[0].trace.hoverlabel.split?d(p,g,y,v):m(p,g,y,v)},hoverSplit:d,hoverOnPoints:m}},{"../../components/color":366,"../../components/fx":406,"../../constants/delta.js":473,"../../lib":503,"../../plots/cartesian/axes":554}],876:[function(t,o,f){o.exports={moduleType:"trace",name:"ohlc",basePlotModule:t("../../plots/cartesian"),categories:["cartesian","svg","showLegend"],meta:{},attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc").calc,plot:t("./plot"),style:t("./style"),hoverPoints:t("./hover").hoverPoints,selectPoints:t("./select")}},{"../../plots/cartesian":568,"./attributes":872,"./calc":873,"./defaults":874,"./hover":875,"./plot":878,"./select":879,"./style":880}],877:[function(t,o,f){var r=t("../../registry"),a=t("../../lib");o.exports=function(l,c,i,s){var u=i("x"),h=i("open"),d=i("high"),m=i("low"),p=i("close");if(i("hoverlabel.split"),r.getComponentMethod("calendars","handleTraceDefaults")(l,c,["x"],s),h&&d&&m&&p){var g=Math.min(h.length,d.length,m.length,p.length);return u&&(g=Math.min(g,a.minRowLength(u))),c._length=g,g}}},{"../../lib":503,"../../registry":638}],878:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../lib");o.exports=function(l,c,i,s){var u=c.yaxis,h=c.xaxis,d=!!h.rangebreaks;a.makeTraceGroups(s,i,"trace ohlc").each(function(m){var p=r.select(this),g=m[0],y=g.t;if(g.trace.visible!==!0||y.empty)p.remove();else{var v=y.tickLen,x=p.selectAll("path").data(a.identity);x.enter().append("path"),x.exit().remove(),x.attr("d",function(_){if(_.empty)return"M0,0Z";var A=h.c2p(_.pos-v,!0),b=h.c2p(_.pos+v,!0),k=d?(A+b)/2:h.c2p(_.pos,!0);return"M"+A+","+u.c2p(_.o,!0)+"H"+k+"M"+k+","+u.c2p(_.h,!0)+"V"+u.c2p(_.l,!0)+"M"+b+","+u.c2p(_.c,!0)+"H"+k})}})}},{"../../lib":503,"@plotly/d3":58}],879:[function(t,o,f){o.exports=function(r,a){var l,c=r.cd,i=r.xaxis,s=r.yaxis,u=[],h=c[0].t.bPos||0;if(a===!1)for(l=0;l<c.length;l++)c[l].selected=0;else for(l=0;l<c.length;l++){var d=c[l];a.contains([i.c2p(d.pos+h),s.c2p(d.yc)],null,d.i,r)?(u.push({pointNumber:d.i,x:i.c2d(d.pos),y:s.c2d(d.yc)}),d.selected=1):d.selected=0}return u}},{}],880:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/drawing"),l=t("../../components/color");o.exports=function(c,i,s){var u=s||r.select(c).selectAll("g.ohlclayer").selectAll("g.trace");u.style("opacity",function(h){return h[0].trace.opacity}),u.each(function(h){var d=h[0].trace;r.select(this).selectAll("path").each(function(m){if(!m.empty){var p=d[m.dir].line;r.select(this).style("fill","none").call(l.stroke,p.color).call(a.dashLine,p.dash,p.width).style("opacity",d.selectedpoints&&!m.selected?.3:1)}})})}},{"../../components/color":366,"../../components/drawing":388,"@plotly/d3":58}],881:[function(t,o,f){var r=t("../../lib/extend").extendFlat,a=t("../../plots/attributes"),l=t("../../plots/font_attributes"),c=t("../../components/colorscale/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/domain").attributes,u=r({editType:"calc"},c("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:i({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});o.exports={domain:s({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:r({},a.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:i({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:l({editType:"calc"}),tickfont:l({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category ascending","category descending","array"],dflt:"trace",editType:"calc"},categoryarray:{valType:"data_array",editType:"calc"},ticktext:{valType:"data_array",editType:"calc"},values:{valType:"data_array",dflt:[],editType:"calc"},displayindex:{valType:"integer",editType:"calc"},editType:"calc",visible:{valType:"boolean",dflt:!0,editType:"calc"}},line:u,counts:{valType:"number",min:0,dflt:1,arrayOk:!0,editType:"calc"},customdata:void 0,hoverlabel:void 0,ids:void 0,legendgroup:void 0,legendrank:void 0,opacity:void 0,selectedpoints:void 0,showlegend:void 0}},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/domain":584,"../../plots/font_attributes":585,"../../plots/template_attributes":633}],882:[function(t,o,f){var r=t("../../plots/get_data").getModuleCalcData,a=t("./plot");f.name="parcats",f.plot=function(l,c,i,s){var u=r(l.calcdata,"parcats");if(u.length){var h=u[0];a(l,h,i,s)}},f.clean=function(l,c,i,s){var u=s._has&&s._has("parcats"),h=c._has&&c._has("parcats");u&&!h&&s._paperdiv.selectAll(".parcats").remove()}},{"../../plots/get_data":593,"./plot":887}],883:[function(t,o,f){var r=t("../../lib/gup").wrap,a=t("../../components/colorscale/helpers").hasColorscale,l=t("../../components/colorscale/calc"),c=t("../../lib/filter_unique.js"),i=t("../../components/drawing"),s=t("../../lib"),u=t("fast-isnumeric");function h(p,g,y){p.valueInds.push(g),p.count+=y}function d(p,g,y){return{categoryInds:p,color:g,rawColor:y,valueInds:[],count:0}}function m(p,g,y){p.valueInds.push(g),p.count+=y}o.exports=function(p,g){var y=s.filterVisible(g.dimensions);if(y.length===0)return[];var v,x,_,A=y.map(function(J){var re;if(J.categoryorder==="trace")re=null;else if(J.categoryorder==="array")re=J.categoryarray;else{re=c(J.values);for(var U=!0,V=0;V<re.length;V++)if(!u(re[V])){U=!1;break}re.sort(U?s.sorterAsc:void 0),J.categoryorder==="category descending"&&(re=re.reverse())}return function(H,ne){ne=ne==null?[]:ne.map(function(ge){return ge});var q={},Q={},ee=[];ne.forEach(function(ge,fe){q[ge]=0,Q[ge]=fe});for(var ie=0;ie<H.length;ie++){var ae,ue=H[ie];q[ue]===void 0?(q[ue]=1,ae=ne.push(ue)-1,Q[ue]=ae):(q[ue]++,ae=Q[ue]),ee.push(ae)}var le=ne.map(function(ge){return q[ge]});return{uniqueValues:ne,uniqueCounts:le,inds:ee}}(J.values,re)});v=s.isArrayOrTypedArray(g.counts)?g.counts:[g.counts],function(J){var re;if(function(U){for(var V=new Array(U.length),H=0;H<U.length;H++){if(U[H]<0||U[H]>=U.length||V[U[H]]!==void 0)return!1;V[U[H]]=!0}return!0}(J.map(function(U){return U.displayindex})))for(re=0;re<J.length;re++)J[re]._displayindex=J[re].displayindex;else for(re=0;re<J.length;re++)J[re]._displayindex=re}(y),y.forEach(function(J,re){(function(U,V){U._categoryarray=V.uniqueValues,U.ticktext===null||U.ticktext===void 0?U._ticktext=[]:U._ticktext=U.ticktext.slice();for(var H=U._ticktext.length;H<V.uniqueValues.length;H++)U._ticktext.push(V.uniqueValues[H])})(J,A[re])});var b,k=g.line;k?(a(g,"line")&&l(p,g,{vals:g.line.color,containerStr:"line",cLetter:"c"}),b=i.tryColorscale(k)):b=s.identity;var w,M,T,E,S,P=y[0].values.length,L={},R=A.map(function(J){return J.inds});for(_=0,w=0;w<P;w++){var F=[];for(M=0;M<R.length;M++)F.push(R[M][w]);x=v[w%v.length],_+=x;var D=(T=w,E=void 0,S=void 0,s.isArrayOrTypedArray(k.color)?S=E=k.color[T%k.color.length]:E=k.color,{color:b(E),rawColor:S}),O=F+"-"+D.rawColor;L[O]===void 0&&(L[O]=d(F,D.color,D.rawColor)),m(L[O],w,x)}var N,B=y.map(function(J,re){return function(U,V,H,ne,q){return{dimensionInd:U,containerInd:V,displayInd:H,dimensionLabel:ne,count:q,categories:[],dragX:null}}(re,J._index,J._displayindex,J.label,_)});for(w=0;w<P;w++)for(x=v[w%v.length],M=0;M<B.length;M++){var W=B[M].containerInd,G=A[M].inds[w],K=B[M].categories;if(K[G]===void 0){var te=g.dimensions[W]._categoryarray[G],Y=g.dimensions[W]._ticktext[G];K[G]={dimensionInd:M,categoryInd:N=G,categoryValue:te,displayInd:N,categoryLabel:Y,valueInds:[],count:0,dragY:null}}h(K[G],w,x)}return r(function(J,re,U){var V=J.map(function(H){return H.categories.length}).reduce(function(H,ne){return Math.max(H,ne)});return{dimensions:J,paths:re,trace:void 0,maxCats:V,count:U}}(B,L,_))}},{"../../components/colorscale/calc":374,"../../components/colorscale/helpers":377,"../../components/drawing":388,"../../lib":503,"../../lib/filter_unique.js":494,"../../lib/gup":500,"fast-isnumeric":190}],884:[function(t,o,f){var r=t("../../lib"),a=t("../../components/colorscale/helpers").hasColorscale,l=t("../../components/colorscale/defaults"),c=t("../../plots/domain").defaults,i=t("../../plots/array_container_defaults"),s=t("./attributes"),u=t("../parcoords/merge_length");function h(d,m){function p(b,k){return r.coerce(d,m,s.dimensions,b,k)}var g=p("values"),y=p("visible");if(g&&g.length||(y=m.visible=!1),y){p("label"),p("displayindex",m._index);var v,x=d.categoryarray,_=Array.isArray(x)&&x.length>0;_&&(v="array");var A=p("categoryorder",v);A==="array"?(p("categoryarray"),p("ticktext")):(delete d.categoryarray,delete d.ticktext),_||A!=="array"||(m.categoryorder="trace")}}o.exports=function(d,m,p,g){function y(b,k){return r.coerce(d,m,s,b,k)}var v=i(d,m,{name:"dimensions",handleItemDefaults:h}),x=function(b,k,w,M,T){T("line.shape"),T("line.hovertemplate");var E=T("line.color",M.colorway[0]);if(a(b,"line")&&r.isArrayOrTypedArray(E)){if(E.length)return T("line.colorscale"),l(b,k,M,T,{prefix:"line.",cLetter:"c"}),E.length;k.line.color=w}return 1/0}(d,m,p,g,y);c(m,g,y),Array.isArray(v)&&v.length||(m.visible=!1),u(m,v,"values",x),y("hoveron"),y("hovertemplate"),y("arrangement"),y("bundlecolors"),y("sortpaths"),y("counts");var _={family:g.font.family,size:Math.round(g.font.size),color:g.font.color};r.coerceFont(y,"labelfont",_);var A={family:g.font.family,size:Math.round(g.font.size/1.2),color:g.font.color};r.coerceFont(y,"tickfont",A)}},{"../../components/colorscale/defaults":376,"../../components/colorscale/helpers":377,"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/domain":584,"../parcoords/merge_length":898,"./attributes":881}],885:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcats",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":881,"./base_plot":882,"./calc":883,"./defaults":884,"./plot":887}],886:[function(t,o,f){var r=t("@plotly/d3"),a=t("d3-interpolate").interpolateNumber,l=t("../../plot_api/plot_api"),c=t("../../components/fx"),i=t("../../lib"),s=i.strTranslate,u=t("../../components/drawing"),h=t("tinycolor2"),d=t("../../lib/svg_text_utils");function m(U,V,H,ne){var q=U.map(K.bind(0,V,H)),Q=ne.selectAll("g.parcatslayer").data([null]);Q.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var ee=Q.selectAll("g.trace.parcats").data(q,p),ie=ee.enter().append("g").attr("class","trace parcats");ee.attr("transform",function(ke){return s(ke.x,ke.y)}),ie.append("g").attr("class","paths");var ae=ee.select("g.paths").selectAll("path.path").data(function(ke){return ke.paths},p);ae.attr("fill",function(ke){return ke.model.color});var ue=ae.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",function(ke){return ke.model.color}).attr("fill-opacity",0);k(ue),ae.attr("d",function(ke){return ke.svgD}),ue.empty()||ae.sort(y),ae.exit().remove(),ae.on("mouseover",v).on("mouseout",x).on("click",b),ie.append("g").attr("class","dimensions");var le=ee.select("g.dimensions").selectAll("g.dimension").data(function(ke){return ke.dimensions},p);le.enter().append("g").attr("class","dimension"),le.attr("transform",function(ke){return s(ke.x,0)}),le.exit().remove();var ge=le.selectAll("g.category").data(function(ke){return ke.categories},p),fe=ge.enter().append("g").attr("class","category");ge.attr("transform",function(ke){return s(0,ke.y)}),fe.append("rect").attr("class","catrect").attr("pointer-events","none"),ge.select("rect.catrect").attr("fill","none").attr("width",function(ke){return ke.width}).attr("height",function(ke){return ke.height}),M(fe);var me=ge.selectAll("rect.bandrect").data(function(ke){return ke.bands},p);me.each(function(){i.raiseToTop(this)}),me.attr("fill",function(ke){return ke.color});var _e=me.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",function(ke){return ke.color}).attr("fill-opacity",0);me.attr("fill",function(ke){return ke.color}).attr("width",function(ke){return ke.width}).attr("height",function(ke){return ke.height}).attr("y",function(ke){return ke.y}).attr("cursor",function(ke){return ke.parcatsViewModel.arrangement==="fixed"?"default":ke.parcatsViewModel.arrangement==="perpendicular"?"ns-resize":"move"}),T(_e),me.exit().remove(),fe.append("text").attr("class","catlabel").attr("pointer-events","none");var Ae=V._fullLayout.paper_bgcolor;ge.select("text.catlabel").attr("text-anchor",function(ke){return g(ke)?"start":"end"}).attr("alignment-baseline","middle").style("text-shadow",d.makeTextShadow(Ae)).style("fill","rgb(0, 0, 0)").attr("x",function(ke){return g(ke)?ke.width+5:-5}).attr("y",function(ke){return ke.height/2}).text(function(ke){return ke.model.categoryLabel}).each(function(ke){u.font(r.select(this),ke.parcatsViewModel.categorylabelfont),d.convertToTspans(r.select(this),V)}),fe.append("text").attr("class","dimlabel"),ge.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",function(ke){return ke.parcatsViewModel.arrangement==="fixed"?"default":"ew-resize"}).attr("x",function(ke){return ke.width/2}).attr("y",-5).text(function(ke,Le){return Le===0?ke.parcatsViewModel.model.dimensions[ke.model.dimensionInd].dimensionLabel:null}).each(function(ke){u.font(r.select(this),ke.parcatsViewModel.labelfont)}),ge.selectAll("rect.bandrect").on("mouseover",R).on("mouseout",F),ge.exit().remove(),le.call(r.behavior.drag().origin(function(ke){return{x:ke.x,y:0}}).on("dragstart",D).on("drag",O).on("dragend",N)),ee.each(function(ke){ke.traceSelection=r.select(this),ke.pathSelection=r.select(this).selectAll("g.paths").selectAll("path.path"),ke.dimensionSelection=r.select(this).selectAll("g.dimensions").selectAll("g.dimension")}),ee.exit().remove()}function p(U){return U.key}function g(U){var V=U.parcatsViewModel.dimensions.length,H=U.parcatsViewModel.dimensions[V-1].model.dimensionInd;return U.model.dimensionInd===H}function y(U,V){return U.model.rawColor>V.model.rawColor?1:U.model.rawColor<V.model.rawColor?-1:0}function v(U){if(!U.parcatsViewModel.dragDimension&&U.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1){i.raiseToTop(this),w(r.select(this));var V=_(U),H=A(U);if(U.parcatsViewModel.graphDiv.emit("plotly_hover",{points:V,event:r.event,constraints:H}),U.parcatsViewModel.hoverinfoItems.indexOf("none")===-1){var ne,q,Q,ee=r.mouse(this)[0],ie=U.parcatsViewModel.graphDiv,ae=U.parcatsViewModel.trace,ue=ie._fullLayout,le=ue._paperdiv.node().getBoundingClientRect(),ge=U.parcatsViewModel.graphDiv.getBoundingClientRect();for(Q=0;Q<U.leftXs.length-1;Q++)if(U.leftXs[Q]+U.dimWidths[Q]-2<=ee&&ee<=U.leftXs[Q+1]+2){var fe=U.parcatsViewModel.dimensions[Q],me=U.parcatsViewModel.dimensions[Q+1];ne=(fe.x+fe.width+me.x)/2,q=(U.topYs[Q]+U.topYs[Q+1]+U.height)/2;break}var _e=U.parcatsViewModel.x+ne,Ae=U.parcatsViewModel.y+q,ke=h.mostReadable(U.model.color,["black","white"]),Le=U.model.count,de=Le/U.parcatsViewModel.model.count,ve={countLabel:Le,probabilityLabel:de.toFixed(3)},Me=[];U.parcatsViewModel.hoverinfoItems.indexOf("count")!==-1&&Me.push(["Count:",ve.countLabel].join(" ")),U.parcatsViewModel.hoverinfoItems.indexOf("probability")!==-1&&Me.push(["P:",ve.probabilityLabel].join(" "));var we=Me.join("<br>"),Ce=r.mouse(ie)[0];c.loneHover({trace:ae,x:_e-le.left+ge.left,y:Ae-le.top+ge.top,text:we,color:U.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:ke,idealAlign:Ce<_e?"right":"left",hovertemplate:(ae.line||{}).hovertemplate,hovertemplateLabels:ve,eventData:[{data:ae._input,fullData:ae,count:Le,probability:de}]},{container:ue._hoverlayer.node(),outerContainer:ue._paper.node(),gd:ie})}}}function x(U){if(!U.parcatsViewModel.dragDimension&&(k(r.select(this)),c.loneUnhover(U.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),U.parcatsViewModel.pathSelection.sort(y),U.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1)){var V=_(U),H=A(U);U.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:V,event:r.event,constraints:H})}}function _(U){for(var V=[],H=B(U.parcatsViewModel),ne=0;ne<U.model.valueInds.length;ne++){var q=U.model.valueInds[ne];V.push({curveNumber:H,pointNumber:q})}return V}function A(U){for(var V={},H=U.parcatsViewModel.model.dimensions,ne=0;ne<H.length;ne++){var q=H[ne],Q=q.categories[U.model.categoryInds[ne]];V[q.containerInd]=Q.categoryValue}return U.model.rawColor!==void 0&&(V.color=U.model.rawColor),V}function b(U){if(U.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1){var V=_(U),H=A(U);U.parcatsViewModel.graphDiv.emit("plotly_click",{points:V,event:r.event,constraints:H})}}function k(U){U.attr("fill",function(V){return V.model.color}).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(U){U.attr("fill-opacity",.8).attr("stroke",function(V){return h.mostReadable(V.model.color,["black","white"])}).attr("stroke-width",.3)}function M(U){U.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function T(U){U.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function E(U){var V=U.parcatsViewModel.pathSelection,H=U.categoryViewModel.model.dimensionInd,ne=U.categoryViewModel.model.categoryInd;return V.filter(function(q){return q.model.categoryInds[H]===ne&&q.model.color===U.color})}function S(U,V,H){var ne=r.select(U).datum(),q=ne.categoryViewModel.model,Q=ne.parcatsViewModel.graphDiv,ee=r.select(U.parentNode).selectAll("rect.bandrect"),ie=[];ee.each(function(ue){E(ue).each(function(le){Array.prototype.push.apply(ie,_(le))})});var ae={};ae[q.dimensionInd]=q.categoryValue,Q.emit(V,{points:ie,event:H,constraints:ae})}function P(U,V,H){var ne=r.select(U).datum(),q=ne.categoryViewModel.model,Q=ne.parcatsViewModel.graphDiv,ee=E(ne),ie=[];ee.each(function(ue){Array.prototype.push.apply(ie,_(ue))});var ae={};ae[q.dimensionInd]=q.categoryValue,ne.rawColor!==void 0&&(ae.color=ne.rawColor),Q.emit(V,{points:ie,event:H,constraints:ae})}function L(U,V,H){U._fullLayout._calcInverseTransform(U);var ne,q,Q=U._fullLayout._invScaleX,ee=U._fullLayout._invScaleY,ie=r.select(H.parentNode).select("rect.catrect"),ae=ie.node().getBoundingClientRect(),ue=ie.datum(),le=ue.parcatsViewModel,ge=le.model.dimensions[ue.model.dimensionInd],fe=le.trace,me=ae.top+ae.height/2;le.dimensions.length>1&&ge.displayInd===le.dimensions.length-1?(ne=ae.left,q="left"):(ne=ae.left+ae.width,q="right");var _e=ue.model.count,Ae=ue.model.categoryLabel,ke=_e/ue.parcatsViewModel.model.count,Le={countLabel:_e,categoryLabel:Ae,probabilityLabel:ke.toFixed(3)},de=[];ue.parcatsViewModel.hoverinfoItems.indexOf("count")!==-1&&de.push(["Count:",Le.countLabel].join(" ")),ue.parcatsViewModel.hoverinfoItems.indexOf("probability")!==-1&&de.push(["P("+Le.categoryLabel+"):",Le.probabilityLabel].join(" "));var ve=de.join("<br>");return{trace:fe,x:Q*(ne-V.left),y:ee*(me-V.top),text:ve,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:q,hovertemplate:fe.hovertemplate,hovertemplateLabels:Le,eventData:[{data:fe._input,fullData:fe,count:_e,category:Ae,probability:ke}]}}function R(U){if(!U.parcatsViewModel.dragDimension&&U.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1){if(r.mouse(this)[1]<-1)return;var V,H=U.parcatsViewModel.graphDiv,ne=H._fullLayout,q=ne._paperdiv.node().getBoundingClientRect(),Q=U.parcatsViewModel.hoveron;Q==="color"?(function(ee){var ie=r.select(ee).datum(),ae=E(ie);w(ae),ae.each(function(){i.raiseToTop(this)}),r.select(ee.parentNode).selectAll("rect.bandrect").filter(function(ue){return ue.color===ie.color}).each(function(){i.raiseToTop(this),r.select(this).attr("stroke","black").attr("stroke-width",1.5)})}(this),P(this,"plotly_hover",r.event)):(function(ee){r.select(ee.parentNode).selectAll("rect.bandrect").each(function(ie){var ae=E(ie);w(ae),ae.each(function(){i.raiseToTop(this)})}),r.select(ee.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),S(this,"plotly_hover",r.event)),U.parcatsViewModel.hoverinfoItems.indexOf("none")===-1&&(Q==="category"?V=L(H,q,this):Q==="color"?V=function(ee,ie,ae){ee._fullLayout._calcInverseTransform(ee);var ue,le,ge=ee._fullLayout._invScaleX,fe=ee._fullLayout._invScaleY,me=ae.getBoundingClientRect(),_e=r.select(ae).datum(),Ae=_e.categoryViewModel,ke=Ae.parcatsViewModel,Le=ke.model.dimensions[Ae.model.dimensionInd],de=ke.trace,ve=me.y+me.height/2;ke.dimensions.length>1&&Le.displayInd===ke.dimensions.length-1?(ue=me.left,le="left"):(ue=me.left+me.width,le="right");var Me=Ae.model.categoryLabel,we=_e.parcatsViewModel.model.count,Ce=0;_e.categoryViewModel.bands.forEach(function(ft){ft.color===_e.color&&(Ce+=ft.count)});var Fe=Ae.model.count,ze=0;ke.pathSelection.each(function(ft){ft.model.color===_e.color&&(ze+=ft.model.count)});var $e=Ce/we,Ke=Ce/ze,Re=Ce/Fe,Ve={countLabel:we,categoryLabel:Me,probabilityLabel:$e.toFixed(3)},We=[];Ae.parcatsViewModel.hoverinfoItems.indexOf("count")!==-1&&We.push(["Count:",Ve.countLabel].join(" ")),Ae.parcatsViewModel.hoverinfoItems.indexOf("probability")!==-1&&(We.push("P(color ∩ "+Me+"): "+Ve.probabilityLabel),We.push("P("+Me+" | color): "+Ke.toFixed(3)),We.push("P(color | "+Me+"): "+Re.toFixed(3)));var Ye=We.join("<br>"),nt=h.mostReadable(_e.color,["black","white"]);return{trace:de,x:ge*(ue-ie.left),y:fe*(ve-ie.top),text:Ye,color:_e.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:nt,fontSize:10,idealAlign:le,hovertemplate:de.hovertemplate,hovertemplateLabels:Ve,eventData:[{data:de._input,fullData:de,category:Me,count:we,probability:$e,categorycount:Fe,colorcount:ze,bandcolorcount:Ce}]}}(H,q,this):Q==="dimension"&&(V=function(ee,ie,ae){var ue=[];return r.select(ae.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each(function(){ue.push(L(ee,ie,this))}),ue}(H,q,this)),V&&c.loneHover(V,{container:ne._hoverlayer.node(),outerContainer:ne._paper.node(),gd:H}))}}function F(U){var V=U.parcatsViewModel;!V.dragDimension&&(k(V.pathSelection),M(V.dimensionSelection.selectAll("g.category")),T(V.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),c.loneUnhover(V.graphDiv._fullLayout._hoverlayer.node()),V.pathSelection.sort(y),V.hoverinfoItems.indexOf("skip")===-1)&&(U.parcatsViewModel.hoveron==="color"?P(this,"plotly_unhover",r.event):S(this,"plotly_unhover",r.event))}function D(U){U.parcatsViewModel.arrangement!=="fixed"&&(U.dragDimensionDisplayInd=U.model.displayInd,U.initialDragDimensionDisplayInds=U.parcatsViewModel.model.dimensions.map(function(V){return V.displayInd}),U.dragHasMoved=!1,U.dragCategoryDisplayInd=null,r.select(this).selectAll("g.category").select("rect.catrect").each(function(V){var H=r.mouse(this)[0],ne=r.mouse(this)[1];-2<=H&&H<=V.width+2&&-2<=ne&&ne<=V.height+2&&(U.dragCategoryDisplayInd=V.model.displayInd,U.initialDragCategoryDisplayInds=U.model.categories.map(function(q){return q.displayInd}),V.model.dragY=V.y,i.raiseToTop(this.parentNode),r.select(this.parentNode).selectAll("rect.bandrect").each(function(q){q.y<ne&&ne<=q.y+q.height&&(U.potentialClickBand=this)}))}),U.parcatsViewModel.dragDimension=U,c.loneUnhover(U.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function O(U){if(U.parcatsViewModel.arrangement!=="fixed"&&(U.dragHasMoved=!0,U.dragDimensionDisplayInd!==null)){var V=U.dragDimensionDisplayInd,H=V-1,ne=V+1,q=U.parcatsViewModel.dimensions[V];if(U.dragCategoryDisplayInd!==null){var Q=q.categories[U.dragCategoryDisplayInd];Q.model.dragY+=r.event.dy;var ee=Q.model.dragY,ie=Q.model.displayInd,ae=q.categories,ue=ae[ie-1],le=ae[ie+1];ue!==void 0&&ee<ue.y+ue.height/2&&(Q.model.displayInd=ue.model.displayInd,ue.model.displayInd=ie),le!==void 0&&ee+Q.height>le.y+le.height/2&&(Q.model.displayInd=le.model.displayInd,le.model.displayInd=ie),U.dragCategoryDisplayInd=Q.model.displayInd}if(U.dragCategoryDisplayInd===null||U.parcatsViewModel.arrangement==="freeform"){q.model.dragX=r.event.x;var ge=U.parcatsViewModel.dimensions[H],fe=U.parcatsViewModel.dimensions[ne];ge!==void 0&&q.model.dragX<ge.x+ge.width&&(q.model.displayInd=ge.model.displayInd,ge.model.displayInd=V),fe!==void 0&&q.model.dragX+q.width>fe.x&&(q.model.displayInd=fe.model.displayInd,fe.model.displayInd=U.dragDimensionDisplayInd),U.dragDimensionDisplayInd=q.model.displayInd}J(U.parcatsViewModel),Y(U.parcatsViewModel),G(U.parcatsViewModel),W(U.parcatsViewModel)}}function N(U){if(U.parcatsViewModel.arrangement!=="fixed"&&U.dragDimensionDisplayInd!==null){r.select(this).selectAll("text").attr("font-weight","normal");var V={},H=B(U.parcatsViewModel),ne=U.parcatsViewModel.model.dimensions.map(function(le){return le.displayInd}),q=U.initialDragDimensionDisplayInds.some(function(le,ge){return le!==ne[ge]});q&&ne.forEach(function(le,ge){var fe=U.parcatsViewModel.model.dimensions[ge].containerInd;V["dimensions["+fe+"].displayindex"]=le});var Q=!1;if(U.dragCategoryDisplayInd!==null){var ee=U.model.categories.map(function(le){return le.displayInd});if(Q=U.initialDragCategoryDisplayInds.some(function(le,ge){return le!==ee[ge]})){var ie=U.model.categories.slice().sort(function(le,ge){return le.displayInd-ge.displayInd}),ae=ie.map(function(le){return le.categoryValue}),ue=ie.map(function(le){return le.categoryLabel});V["dimensions["+U.model.containerInd+"].categoryarray"]=[ae],V["dimensions["+U.model.containerInd+"].ticktext"]=[ue],V["dimensions["+U.model.containerInd+"].categoryorder"]="array"}}U.parcatsViewModel.hoverinfoItems.indexOf("skip")===-1&&!U.dragHasMoved&&U.potentialClickBand&&(U.parcatsViewModel.hoveron==="color"?P(U.potentialClickBand,"plotly_click",r.event.sourceEvent):S(U.potentialClickBand,"plotly_click",r.event.sourceEvent)),U.model.dragX=null,U.dragCategoryDisplayInd!==null&&(U.parcatsViewModel.dimensions[U.dragDimensionDisplayInd].categories[U.dragCategoryDisplayInd].model.dragY=null,U.dragCategoryDisplayInd=null),U.dragDimensionDisplayInd=null,U.parcatsViewModel.dragDimension=null,U.dragHasMoved=null,U.potentialClickBand=null,J(U.parcatsViewModel),Y(U.parcatsViewModel),r.transition().duration(300).ease("cubic-in-out").each(function(){G(U.parcatsViewModel,!0),W(U.parcatsViewModel,!0)}).each("end",function(){(q||Q)&&l.restyle(U.parcatsViewModel.graphDiv,V,[H])})}}function B(U){for(var V,H=U.graphDiv._fullData,ne=0;ne<H.length;ne++)if(U.key===H[ne].uid){V=ne;break}return V}function W(U,V){var H;V===void 0&&(V=!1),U.pathSelection.data(function(ne){return ne.paths},p),(H=U.pathSelection,V?H.transition():H).attr("d",function(ne){return ne.svgD})}function G(U,V){function H(ee){return V?ee.transition():ee}V===void 0&&(V=!1),U.dimensionSelection.data(function(ee){return ee.dimensions},p);var ne=U.dimensionSelection.selectAll("g.category").data(function(ee){return ee.categories},p);H(U.dimensionSelection).attr("transform",function(ee){return s(ee.x,0)}),H(ne).attr("transform",function(ee){return s(0,ee.y)}),ne.select(".dimlabel").text(function(ee,ie){return ie===0?ee.parcatsViewModel.model.dimensions[ee.model.dimensionInd].dimensionLabel:null}),ne.select(".catlabel").attr("text-anchor",function(ee){return g(ee)?"start":"end"}).attr("x",function(ee){return g(ee)?ee.width+5:-5}).each(function(ee){var ie,ae;g(ee)?(ie=ee.width+5,ae="start"):(ie=-5,ae="end"),r.select(this).selectAll("tspan").attr("x",ie).attr("text-anchor",ae)});var q=ne.selectAll("rect.bandrect").data(function(ee){return ee.bands},p),Q=q.enter().append("rect").attr("class","bandrect").attr("cursor","move").attr("stroke-opacity",0).attr("fill",function(ee){return ee.color}).attr("fill-opacity",0);q.attr("fill",function(ee){return ee.color}).attr("width",function(ee){return ee.width}).attr("height",function(ee){return ee.height}).attr("y",function(ee){return ee.y}),T(Q),q.each(function(){i.raiseToTop(this)}),q.exit().remove()}function K(U,V,H){var ne,q=H[0],Q=V.margin||{l:80,r:80,t:100,b:80},ee=q.trace,ie=ee.domain,ae=V.width,ue=V.height,le=Math.floor(ae*(ie.x[1]-ie.x[0])),ge=Math.floor(ue*(ie.y[1]-ie.y[0])),fe=ie.x[0]*ae+Q.l,me=V.height-ie.y[1]*V.height+Q.t,_e=ee.line.shape;ne=ee.hoverinfo==="all"?["count","probability"]:(ee.hoverinfo||"").split("+");var Ae={trace:ee,key:ee.uid,model:q,x:fe,y:me,width:le,height:ge,hoveron:ee.hoveron,hoverinfoItems:ne,arrangement:ee.arrangement,bundlecolors:ee.bundlecolors,sortpaths:ee.sortpaths,labelfont:ee.labelfont,categorylabelfont:ee.tickfont,pathShape:_e,dragDimension:null,margin:Q,paths:[],dimensions:[],graphDiv:U,traceSelection:null,pathSelection:null,dimensionSelection:null};return q.dimensions&&(J(Ae),Y(Ae)),Ae}function te(U,V,H,ne,q){var Q,ee,ie=[],ae=[];for(ee=0;ee<H.length-1;ee++)Q=a(H[ee]+U[ee],U[ee+1]),ie.push(Q(q)),ae.push(Q(1-q));var ue="M "+U[0]+","+V[0];for(ue+="l"+H[0]+",0 ",ee=1;ee<H.length;ee++)ue+="C"+ie[ee-1]+","+V[ee-1]+" "+ae[ee-1]+","+V[ee]+" "+U[ee]+","+V[ee],ue+="l"+H[ee]+",0 ";for(ue+="l0,"+ne+" ",ue+="l -"+H[H.length-1]+",0 ",ee=H.length-2;ee>=0;ee--)ue+="C"+ae[ee]+","+(V[ee+1]+ne)+" "+ie[ee]+","+(V[ee]+ne)+" "+(U[ee]+H[ee])+","+(V[ee]+ne),ue+="l-"+H[ee]+",0 ";return ue+="Z"}function Y(U){var V=U.dimensions,H=U.model,ne=V.map(function(We){return We.categories.map(function(Ye){return Ye.y})}),q=U.model.dimensions.map(function(We){return We.categories.map(function(Ye){return Ye.displayInd})}),Q=U.model.dimensions.map(function(We){return We.displayInd}),ee=U.dimensions.map(function(We){return We.model.dimensionInd}),ie=V.map(function(We){return We.x}),ae=V.map(function(We){return We.width}),ue=[];for(var le in H.paths)H.paths.hasOwnProperty(le)&&ue.push(H.paths[le]);function ge(We){var Ye=We.categoryInds.map(function(nt,ft){return q[ft][nt]});return ee.map(function(nt){return Ye[nt]})}ue.sort(function(We,Ye){var nt=ge(We),ft=ge(Ye);return U.sortpaths==="backward"&&(nt.reverse(),ft.reverse()),nt.push(We.valueInds[0]),ft.push(Ye.valueInds[0]),U.bundlecolors&&(nt.unshift(We.rawColor),ft.unshift(Ye.rawColor)),nt<ft?-1:nt>ft?1:0});for(var fe=new Array(ue.length),me=V[0].model.count,_e=V[0].categories.map(function(We){return We.height}).reduce(function(We,Ye){return We+Ye}),Ae=0;Ae<ue.length;Ae++){var ke,Le=ue[Ae];ke=me>0?_e*(Le.count/me):0;for(var de,ve=new Array(ne.length),Me=0;Me<Le.categoryInds.length;Me++){var we=Le.categoryInds[Me],Ce=q[Me][we],Fe=Q[Me];ve[Fe]=ne[Fe][Ce],ne[Fe][Ce]+=ke;var ze=U.dimensions[Fe].categories[Ce],$e=ze.bands.length,Ke=ze.bands[$e-1];if(Ke===void 0||Le.rawColor!==Ke.rawColor){var Re=Ke===void 0?0:Ke.y+Ke.height;ze.bands.push({key:Re,color:Le.color,rawColor:Le.rawColor,height:ke,width:ze.width,count:Le.count,y:Re,categoryViewModel:ze,parcatsViewModel:U})}else{var Ve=ze.bands[$e-1];Ve.height+=ke,Ve.count+=Le.count}}de=U.pathShape==="hspline"?te(ie,ve,ae,ke,.5):te(ie,ve,ae,ke,0),fe[Ae]={key:Le.valueInds[0],model:Le,height:ke,leftXs:ie,topYs:ve,dimWidths:ae,svgD:de,parcatsViewModel:U}}U.paths=fe}function J(U){var V=U.model.dimensions.map(function(ee){return{displayInd:ee.displayInd,dimensionInd:ee.dimensionInd}});V.sort(function(ee,ie){return ee.displayInd-ie.displayInd});var H=[];for(var ne in V){var q=V[ne].dimensionInd,Q=U.model.dimensions[q];H.push(re(U,Q))}U.dimensions=H}function re(U,V){var H,ne=U.model.dimensions.length,q=V.displayInd;H=40+(ne>1?(U.width-80-16)/(ne-1):0)*q;var Q,ee,ie,ae,ue,le=[],ge=U.model.maxCats,fe=V.categories.length,me=V.count,_e=U.height-8*(ge-1),Ae=8*(ge-fe)/2,ke=V.categories.map(function(Le){return{displayInd:Le.displayInd,categoryInd:Le.categoryInd}});for(ke.sort(function(Le,de){return Le.displayInd-de.displayInd}),ue=0;ue<fe;ue++)ae=ke[ue].categoryInd,ee=V.categories[ae],Q=me>0?ee.count/me*_e:0,ie={key:ee.valueInds[0],model:ee,width:16,height:Q,y:ee.dragY!==null?ee.dragY:Ae,bands:[],parcatsViewModel:U},Ae=Ae+Q+8,le.push(ie);return{key:V.dimensionInd,x:V.dragX!==null?V.dragX:H,y:0,width:16,model:V,categories:le,parcatsViewModel:U,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}o.exports=function(U,V,H,ne){m(H,U,ne,V)}},{"../../components/drawing":388,"../../components/fx":406,"../../lib":503,"../../lib/svg_text_utils":529,"../../plot_api/plot_api":540,"@plotly/d3":58,"d3-interpolate":116,tinycolor2:312}],887:[function(t,o,f){var r=t("./parcats");o.exports=function(a,l,c,i){var s=a._fullLayout,u=s._paper,h=s._size;r(a,u,l,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},c,i)}},{"./parcats":886}],888:[function(t,o,f){var r=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/layout_attributes"),l=t("../../plots/font_attributes"),c=t("../../plots/domain").attributes,i=t("../../lib/extend").extendFlat,s=t("../../plot_api/plot_template").templatedArray;o.exports={domain:c({name:"parcoords",trace:!0,editType:"plot"}),labelangle:{valType:"angle",dflt:0,editType:"plot"},labelside:{valType:"enumerated",values:["top","bottom"],dflt:"top",editType:"plot"},labelfont:l({editType:"plot"}),tickfont:l({editType:"plot"}),rangefont:l({editType:"plot"}),dimensions:s("dimension",{label:{valType:"string",editType:"plot"},tickvals:i({},a.tickvals,{editType:"plot"}),ticktext:i({},a.ticktext,{editType:"plot"}),tickformat:i({},a.tickformat,{editType:"plot"}),visible:{valType:"boolean",dflt:!0,editType:"plot"},range:{valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},constraintrange:{valType:"info_array",freeLength:!0,dimensions:"1-2",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},multiselect:{valType:"boolean",dflt:!0,editType:"plot"},values:{valType:"data_array",editType:"calc"},editType:"calc"}),line:i({editType:"calc"},r("line",{colorscaleDflt:"Viridis",autoColorDflt:!1,editTypeOverride:"calc"}))}},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/plot_template":543,"../../plots/cartesian/layout_attributes":569,"../../plots/domain":584,"../../plots/font_attributes":585}],889:[function(t,o,f){var r=t("./constants"),a=t("@plotly/d3"),l=t("../../lib/gup").keyFun,c=t("../../lib/gup").repeat,i=t("../../lib").sorterAsc,s=t("../../lib").strTranslate,u=r.bar.snapRatio;function h(L,R){return L*(1-u)+R*u}var d=r.bar.snapClose;function m(L,R){return L*(1-d)+R*d}function p(L,R,F,D){if(function(re,U){for(var V=0;V<U.length;V++)if(re>=U[V][0]&&re<=U[V][1])return!0;return!1}(F,D))return F;var O=L?-1:1,N=0,B=R.length-1;if(O<0){var W=N;N=B,B=W}for(var G=R[N],K=G,te=N;O*te<O*B;te+=O){var Y=te+O,J=R[Y];if(O*F<O*m(G,J))return h(G,K);if(O*F<O*J||Y===B)return h(J,G);K=G,G=J}}function g(L){L.attr("x",-r.bar.captureWidth/2).attr("width",r.bar.captureWidth)}function y(L){L.attr("visibility","visible").style("visibility","visible").attr("fill","yellow").attr("opacity",0)}function v(L){if(!L.brush.filterSpecified)return"0,"+L.height;for(var R,F,D,O=x(L.brush.filter.getConsolidated(),L.height),N=[0],B=O.length?O[0][0]:null,W=0;W<O.length;W++)F=(R=O[W])[1]-R[0],N.push(B),N.push(F),(D=W+1)<O.length&&(B=O[D][0]-R[1]);return N.push(L.height),N}function x(L,R){return L.map(function(F){return F.map(function(D){return Math.max(0,D*R)}).sort(i)})}function _(){a.select(document.body).style("cursor",null)}function A(L){L.attr("stroke-dasharray",v)}function b(L,R){var F=a.select(L).selectAll(".highlight, .highlight-shadow");A(R?F.transition().duration(r.bar.snapDuration).each("end",R):F)}function k(L,R){var F,D=L.brush,O=NaN,N={};if(D.filterSpecified){var B=L.height,W=D.filter.getConsolidated(),G=x(W,B),K=NaN,te=NaN,Y=NaN;for(F=0;F<=G.length;F++){var J=G[F];if(J&&J[0]<=R&&R<=J[1]){K=F;break}if(te=F?F-1:NaN,J&&J[0]>R){Y=F;break}}if(O=K,isNaN(O)&&(O=isNaN(te)||isNaN(Y)?isNaN(te)?Y:te:R-G[te][1]<G[Y][0]-R?te:Y),!isNaN(O)){var re=G[O],U=function(q,Q){var ee=r.bar.handleHeight;if(!(Q>q[1]+ee||Q<q[0]-ee))return Q>=.9*q[1]+.1*q[0]?"n":Q<=.9*q[0]+.1*q[1]?"s":"ns"}(re,R);U&&(N.interval=W[O],N.intervalPix=re,N.region=U)}}if(L.ordinal&&!N.region){var V=L.unitTickvals,H=L.unitToPaddedPx.invert(R);for(F=0;F<V.length;F++){var ne=[.25*V[Math.max(F-1,0)]+.75*V[F],.25*V[Math.min(F+1,V.length-1)]+.75*V[F]];if(H>=ne[0]&&H<=ne[1]){N.clickableOrdinalRange=ne;break}}}return N}function w(L,R){a.event.sourceEvent.stopPropagation();var F=R.height-a.mouse(L)[1]-2*r.verticalPadding,D=R.brush.svgBrush;D.wasDragged=!0,D._dragging=!0,D.grabbingBar?D.newExtent=[F-D.grabPoint,F+D.barLength-D.grabPoint].map(R.unitToPaddedPx.invert):D.newExtent=[D.startExtent,R.unitToPaddedPx.invert(F)].sort(i),R.brush.filterSpecified=!0,D.extent=D.stayingIntervals.concat([D.newExtent]),D.brushCallback(R),b(L.parentNode)}function M(L,R){var F=k(R,R.height-a.mouse(L)[1]-2*r.verticalPadding),D="crosshair";F.clickableOrdinalRange?D="pointer":F.region&&(D=F.region+"-resize"),a.select(document.body).style("cursor",D)}function T(L){L.on("mousemove",function(R){a.event.preventDefault(),R.parent.inBrushDrag||M(this,R)}).on("mouseleave",function(R){R.parent.inBrushDrag||_()}).call(a.behavior.drag().on("dragstart",function(R){(function(F,D){a.event.sourceEvent.stopPropagation();var O=D.height-a.mouse(F)[1]-2*r.verticalPadding,N=D.unitToPaddedPx.invert(O),B=D.brush,W=k(D,O),G=W.interval,K=B.svgBrush;if(K.wasDragged=!1,K.grabbingBar=W.region==="ns",K.grabbingBar){var te=G.map(D.unitToPaddedPx);K.grabPoint=O-te[0]-r.verticalPadding,K.barLength=te[1]-te[0]}K.clickableOrdinalRange=W.clickableOrdinalRange,K.stayingIntervals=D.multiselect&&B.filterSpecified?B.filter.getConsolidated():[],G&&(K.stayingIntervals=K.stayingIntervals.filter(function(Y){return Y[0]!==G[0]&&Y[1]!==G[1]})),K.startExtent=W.region?G[W.region==="s"?1:0]:N,D.parent.inBrushDrag=!0,K.brushStartCallback()})(this,R)}).on("drag",function(R){w(this,R)}).on("dragend",function(R){(function(F,D){var O=D.brush,N=O.filter,B=O.svgBrush;B._dragging||(M(F,D),w(F,D),D.brush.svgBrush.wasDragged=!1),B._dragging=!1,a.event.sourceEvent.stopPropagation();var W=B.grabbingBar;if(B.grabbingBar=!1,B.grabLocation=void 0,D.parent.inBrushDrag=!1,_(),!B.wasDragged)return B.wasDragged=void 0,B.clickableOrdinalRange?O.filterSpecified&&D.multiselect?B.extent.push(B.clickableOrdinalRange):(B.extent=[B.clickableOrdinalRange],O.filterSpecified=!0):W?(B.extent=B.stayingIntervals,B.extent.length===0&&S(O)):S(O),B.brushCallback(D),b(F.parentNode),void B.brushEndCallback(O.filterSpecified?N.getConsolidated():[]);var G=function(){N.set(N.getConsolidated())};if(D.ordinal){var K=D.unitTickvals;K[K.length-1]<K[0]&&K.reverse(),B.newExtent=[p(0,K,B.newExtent[0],B.stayingIntervals),p(1,K,B.newExtent[1],B.stayingIntervals)];var te=B.newExtent[1]>B.newExtent[0];B.extent=B.stayingIntervals.concat(te?[B.newExtent]:[]),B.extent.length||S(O),B.brushCallback(D),te?b(F.parentNode,G):(G(),b(F.parentNode))}else G();B.brushEndCallback(O.filterSpecified?N.getConsolidated():[])})(this,R)}))}function E(L,R){return L[0]-R[0]}function S(L){L.filterSpecified=!1,L.svgBrush.extent=[[-1/0,1/0]]}function P(L){for(var R,F=L.slice(),D=[],O=F.shift();O;){for(R=O.slice();(O=F.shift())&&O[0]<=R[1];)R[1]=Math.max(R[1],O[1]);D.push(R)}return D.length===1&&D[0][0]>D[0][1]&&(D=[]),D}o.exports={makeBrush:function(L,R,F,D,O,N){var B,W=function(){var G,K,te=[];return{set:function(Y){(te=Y.map(function(J){return J.slice().sort(i)}).sort(E)).length===1&&te[0][0]===-1/0&&te[0][1]===1/0&&(te=[[0,-1]]),G=P(te),K=te.reduce(function(J,re){return[Math.min(J[0],re[0]),Math.max(J[1],re[1])]},[1/0,-1/0])},get:function(){return te.slice()},getConsolidated:function(){return G},getBounds:function(){return K}}}();return W.set(F),{filter:W,filterSpecified:R,svgBrush:{extent:[],brushStartCallback:D,brushCallback:(B=O,function(G){var K=G.brush,te=function(Y){return Y.svgBrush.extent.map(function(J){return J.slice()})}(K).slice();K.filter.set(te),B()}),brushEndCallback:N}}},ensureAxisBrush:function(L,R){var F=L.selectAll("."+r.cn.axisBrush).data(c,l);F.enter().append("g").classed(r.cn.axisBrush,!0),function(D,O){var N=D.selectAll(".background").data(c);N.enter().append("rect").classed("background",!0).call(g).call(y).style("pointer-events","auto").attr("transform",s(0,r.verticalPadding)),N.call(T).attr("height",function(G){return G.height-r.verticalPadding});var B=D.selectAll(".highlight-shadow").data(c);B.enter().append("line").classed("highlight-shadow",!0).attr("x",-r.bar.width/2).attr("stroke-width",r.bar.width+r.bar.strokeWidth).attr("stroke",O).attr("opacity",r.bar.strokeOpacity).attr("stroke-linecap","butt"),B.attr("y1",function(G){return G.height}).call(A);var W=D.selectAll(".highlight").data(c);W.enter().append("line").classed("highlight",!0).attr("x",-r.bar.width/2).attr("stroke-width",r.bar.width-r.bar.strokeWidth).attr("stroke",r.bar.fillColor).attr("opacity",r.bar.fillOpacity).attr("stroke-linecap","butt"),W.attr("y1",function(G){return G.height}).call(A)}(F,R)},cleanRanges:function(L,R){if(Array.isArray(L[0])?(L=L.map(function(D){return D.sort(i)}),L=R.multiselect?P(L.sort(E)):[L[0]]):L=[L.sort(i)],R.tickvals){var F=R.tickvals.slice().sort(i);if(!(L=L.map(function(D){var O=[p(0,F,D[0],[]),p(1,F,D[1],[])];if(O[1]>O[0])return O}).filter(function(D){return D})).length)return}return L.length>1?L:L[0]}}},{"../../lib":503,"../../lib/gup":500,"./constants":893,"@plotly/d3":58}],890:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":888,"./base_plot":891,"./calc":892,"./defaults":894}],891:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../plots/get_data").getModuleCalcData,l=t("./plot"),c=t("../../constants/xmlns_namespaces");f.name="parcoords",f.plot=function(i){var s=a(i.calcdata,"parcoords")[0];s.length&&l(i,s)},f.clean=function(i,s,u,h){var d=h._has&&h._has("parcoords"),m=s._has&&s._has("parcoords");d&&!m&&(h._paperdiv.selectAll(".parcoords").remove(),h._glimages.selectAll("*").remove())},f.toSVG=function(i){var s=i._fullLayout._glimages,u=r.select(i).selectAll(".svg-container");u.filter(function(h,d){return d===u.size()-1}).selectAll(".gl-canvas-context, .gl-canvas-focus").each(function(){var h=this.toDataURL("image/png");s.append("svg:image").attr({xmlns:c.svg,"xlink:href":h,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})}),window.setTimeout(function(){r.selectAll("#filterBarPattern").attr("id","filterBarPattern")},60)}},{"../../constants/xmlns_namespaces":480,"../../plots/get_data":593,"./plot":900,"@plotly/d3":58}],892:[function(t,o,f){var r=t("../../lib").isArrayOrTypedArray,a=t("../../components/colorscale"),l=t("../../lib/gup").wrap;o.exports=function(c,i){var s,u;return a.hasColorscale(i,"line")&&r(i.line.color)?(s=i.line.color,u=a.extractOpts(i.line).colorscale,a.calc(c,i,{vals:s,containerStr:"line",cLetter:"c"})):(s=function(h){for(var d=new Array(h),m=0;m<h;m++)d[m]=.5;return d}(i._length),u=[[0,i.line.color],[1,i.line.color]]),l({lineColor:s,cscale:u})}},{"../../components/colorscale":378,"../../lib":503,"../../lib/gup":500}],893:[function(t,o,f){o.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:"#777",bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}},{}],894:[function(t,o,f){var r=t("../../lib"),a=t("../../components/colorscale/helpers").hasColorscale,l=t("../../components/colorscale/defaults"),c=t("../../plots/domain").defaults,i=t("../../plots/array_container_defaults"),s=t("../../plots/cartesian/axes"),u=t("./attributes"),h=t("./axisbrush"),d=t("./constants").maxDimensionCount,m=t("./merge_length");function p(g,y,v,x){function _(M,T){return r.coerce(g,y,u.dimensions,M,T)}var A=_("values"),b=_("visible");if(A&&A.length||(b=y.visible=!1),b){_("label"),_("tickvals"),_("ticktext"),_("tickformat");var k=_("range");y._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:k},s.setConvert(y._ax,x.layout),_("multiselect");var w=_("constraintrange");w&&(y.constraintrange=h.cleanRanges(w,y))}}o.exports=function(g,y,v,x){function _(M,T){return r.coerce(g,y,u,M,T)}var A=g.dimensions;Array.isArray(A)&&A.length>d&&(r.log("parcoords traces support up to "+d+" dimensions at the moment"),A.splice(d));var b=i(g,y,{name:"dimensions",layout:x,handleItemDefaults:p}),k=function(M,T,E,S,P){var L=P("line.color",E);if(a(M,"line")&&r.isArrayOrTypedArray(L)){if(L.length)return P("line.colorscale"),l(M,T,S,P,{prefix:"line.",cLetter:"c"}),L.length;T.line.color=E}return 1/0}(g,y,v,x,_);c(y,x,_),Array.isArray(b)&&b.length||(y.visible=!1),m(y,b,"values",k);var w={family:x.font.family,size:Math.round(x.font.size/1.2),color:x.font.color};r.coerceFont(_,"labelfont",w),r.coerceFont(_,"tickfont",w),r.coerceFont(_,"rangefont",w),_("labelangle"),_("labelside")}},{"../../components/colorscale/defaults":376,"../../components/colorscale/helpers":377,"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/cartesian/axes":554,"../../plots/domain":584,"./attributes":888,"./axisbrush":889,"./constants":893,"./merge_length":898}],895:[function(t,o,f){var r=t("../../lib").isTypedArray;f.convertTypedArray=function(a){return r(a)?Array.prototype.slice.call(a):a},f.isOrdinal=function(a){return!!a.tickvals},f.isVisible=function(a){return a.visible||!("visible"in a)}},{"../../lib":503}],896:[function(t,o,f){var r=t("./base_index");r.plot=t("./plot"),o.exports=r},{"./base_index":890,"./plot":900}],897:[function(t,o,f){var r=t("glslify"),a=r([`precision highp float;
-#define GLSLIFY 1
-
-varying vec4 fragColor;
-
-attribute vec4 p01_04, p05_08, p09_12, p13_16,
-               p17_20, p21_24, p25_28, p29_32,
-               p33_36, p37_40, p41_44, p45_48,
-               p49_52, p53_56, p57_60, colors;
-
-uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,
-             loA, hiA, loB, hiB, loC, hiC, loD, hiD;
-
-uniform vec2 resolution, viewBoxPos, viewBoxSize;
-uniform float maskHeight;
-uniform float drwLayer; // 0: context, 1: focus, 2: pick
-uniform vec4 contextColor;
-uniform sampler2D maskTexture, palette;
-
-bool isPick    = (drwLayer > 1.5);
-bool isContext = (drwLayer < 0.5);
-
-const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);
-const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);
-
-float val(mat4 p, mat4 v) {
-    return dot(matrixCompMult(p, v) * UNITS, UNITS);
-}
-
-float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {
-    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);
-    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);
-    return y1 * (1.0 - ratio) + y2 * ratio;
-}
-
-int iMod(int a, int b) {
-    return a - b * (a / b);
-}
-
-bool fOutside(float p, float lo, float hi) {
-    return (lo < hi) && (lo > p || p > hi);
-}
-
-bool vOutside(vec4 p, vec4 lo, vec4 hi) {
-    return (
-        fOutside(p[0], lo[0], hi[0]) ||
-        fOutside(p[1], lo[1], hi[1]) ||
-        fOutside(p[2], lo[2], hi[2]) ||
-        fOutside(p[3], lo[3], hi[3])
-    );
-}
-
-bool mOutside(mat4 p, mat4 lo, mat4 hi) {
-    return (
-        vOutside(p[0], lo[0], hi[0]) ||
-        vOutside(p[1], lo[1], hi[1]) ||
-        vOutside(p[2], lo[2], hi[2]) ||
-        vOutside(p[3], lo[3], hi[3])
-    );
-}
-
-bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {
-    return mOutside(A, loA, hiA) ||
-           mOutside(B, loB, hiB) ||
-           mOutside(C, loC, hiC) ||
-           mOutside(D, loD, hiD);
-}
-
-bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {
-    mat4 pnts[4];
-    pnts[0] = A;
-    pnts[1] = B;
-    pnts[2] = C;
-    pnts[3] = D;
-
-    for(int i = 0; i < 4; ++i) {
-        for(int j = 0; j < 4; ++j) {
-            for(int k = 0; k < 4; ++k) {
-                if(0 == iMod(
-                    int(255.0 * texture2D(maskTexture,
-                        vec2(
-                            (float(i * 2 + j / 2) + 0.5) / 8.0,
-                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight
-                        ))[3]
-                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),
-                    2
-                )) return true;
-            }
-        }
-    }
-    return false;
-}
-
-vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {
-    float x = 0.5 * sign(v) + 0.5;
-    float y = axisY(x, A, B, C, D);
-    float z = 1.0 - abs(v);
-
-    z += isContext ? 0.0 : 2.0 * float(
-        outsideBoundingBox(A, B, C, D) ||
-        outsideRasterMask(A, B, C, D)
-    );
-
-    return vec4(
-        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,
-        z,
-        1.0
-    );
-}
-
-void main() {
-    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);
-    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);
-    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);
-    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);
-
-    float v = colors[3];
-
-    gl_Position = position(isContext, v, A, B, C, D);
-
-    fragColor =
-        isContext ? vec4(contextColor) :
-        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));
-}
-`]),l=r([`precision highp float;
-#define GLSLIFY 1
-
-varying vec4 fragColor;
-
-void main() {
-    gl_FragColor = fragColor;
-}
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L=4*b+P,R=w[64*E+L];L===63&&S===0&&(R*=-1),M[T++]=R}return M}function v(b){var k="0"+b;return k.substr(k.length-2)}function x(b){return b<c?"p"+v(b+1)+"_"+v(b+4):"colors"}function _(b,k,w,M,T,E,S,P,L,R,F,D,O,N){for(var B=[[],[]],W=0;W<64;W++)B[0][W]=W===T?1:0,B[1][W]=W===E?1:0;S*=N,P*=N,L*=N,R*=N;var G=b.lines.canvasOverdrag*N,K=b.domain,te=b.canvasWidth*N,Y=b.canvasHeight*N,J=b.pad.l*N,re=b.pad.b*N,U=b.layoutHeight*N,V=b.layoutWidth*N,H=b.deselectedLines.color;return 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ke=fe.selectAll("."+_.cn.axisExtent).data(y,g);ke.enter().append("g").classed(_.cn.axisExtent,!0);var Le=ke.selectAll("."+_.cn.axisExtentTop).data(y,g);Le.enter().append("g").classed(_.cn.axisExtentTop,!0),Le.attr("transform",u(0,-_.axisExtentOffset));var de=Le.selectAll("."+_.cn.axisExtentTopText).data(y,g);de.enter().append("text").classed(_.cn.axisExtentTopText,!0).call(D),de.text(function(we){return W(we,!0)}).each(function(we){d.font(r.select(this),we.model.rangeFont)});var ve=ke.selectAll("."+_.cn.axisExtentBottom).data(y,g);ve.enter().append("g").classed(_.cn.axisExtentBottom,!0),ve.attr("transform",function(we){return u(0,we.model.height+_.axisExtentOffset)});var Me=ve.selectAll("."+_.cn.axisExtentBottomText).data(y,g);Me.enter().append("text").classed(_.cn.axisExtentBottomText,!0).attr("dy","0.75em").call(D),Me.text(function(we){return W(we,!1)}).each(function(we){d.font(r.select(this),we.model.rangeFont)}),A.ensureAxisBrush(fe,H)}},{"../../components/colorscale":378,"../../components/drawing":388,"../../lib":503,"../../lib/gup":500,"../../lib/svg_text_utils":529,"../../plots/cartesian/axes":554,"./axisbrush":889,"./constants":893,"./helpers":895,"./lines":897,"@plotly/d3":58,"color-rgba":91}],900:[function(t,o,f){var r=t("./parcoords"),a=t("../../lib/prepare_regl"),l=t("./helpers").isVisible,c={};function i(s,u,h){var d=u.indexOf(h),m=s.indexOf(d);return m===-1&&(m+=u.length),m}(o.exports=function(s,u){var h=s._fullLayout;if(a(s,[],c)){var d={},m={},p={},g={},y=h._size;u.forEach(function(v,x){var _=v[0].trace;p[x]=_.index;var A=g[x]=_._fullInput.index;d[x]=s.data[A].dimensions,m[x]=s.data[A].dimensions.slice()}),r(s,u,{width:y.w,height:y.h,margin:{t:y.t,r:y.r,b:y.b,l:y.l}},{filterChanged:function(v,x,_){var A=m[v][x],b=_.map(function(S){return S.slice()}),k="dimensions["+x+"].constraintrange",w=h._tracePreGUI[s._fullData[p[v]]._fullInput.uid];if(w[k]===void 0){var M=A.constraintrange;w[k]=M||null}var T=s._fullData[p[v]].dimensions[x];b.length?(b.length===1&&(b=b[0]),A.constraintrange=b,T.constraintrange=b.slice(),b=[b]):(delete A.constraintrange,delete T.constraintrange,b=null);var E={};E[k]=b,s.emit("plotly_restyle",[E,[g[v]]])},hover:function(v){s.emit("plotly_hover",v)},unhover:function(v){s.emit("plotly_unhover",v)},axesMoved:function(v,x){var _=function(A,b){return function(k,w){return i(A,b,k)-i(A,b,w)}}(x,m[v].filter(l));d[v].sort(_),m[v].filter(function(A){return!l(A)}).sort(function(A){return m[v].indexOf(A)}).forEach(function(A){d[v].splice(d[v].indexOf(A),1),d[v].splice(m[v].indexOf(A),0,A)}),s.emit("plotly_restyle",[{dimensions:[d[v]]},[g[v]]])}})}}).reglPrecompiled=c},{"../../lib/prepare_regl":516,"./helpers":895,"./parcoords":899}],901:[function(t,o,f){var r=t("../../plots/attributes"),a=t("../../plots/domain").attributes,l=t("../../plots/font_attributes"),c=t("../../components/color/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/template_attributes").texttemplateAttrs,u=t("../../lib/extend").extendFlat,h=l({editType:"plot",arrayOk:!0,colorEditType:"plot"});o.exports={labels:{valType:"data_array",editType:"calc"},label0:{valType:"number",dflt:0,editType:"calc"},dlabel:{valType:"number",dflt:1,editType:"calc"},values:{valType:"data_array",editType:"calc"},marker:{colors:{valType:"data_array",editType:"calc"},line:{color:{valType:"color",dflt:c.defaultLine,arrayOk:!0,editType:"style"},width:{valType:"number",min:0,dflt:0,arrayOk:!0,editType:"style"},editType:"calc"},editType:"calc"},text:{valType:"data_array",editType:"plot"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"style"},scalegroup:{valType:"string",dflt:"",editType:"calc"},textinfo:{valType:"flaglist",flags:["label","text","value","percent"],extras:["none"],editType:"calc"},hoverinfo:u({},r.hoverinfo,{flags:["label","text","value","percent","name"]}),hovertemplate:i({},{keys:["label","color","value","percent","text"]}),texttemplate:s({editType:"plot"},{keys:["label","color","value","percent","text"]}),textposition:{valType:"enumerated",values:["inside","outside","auto","none"],dflt:"auto",arrayOk:!0,editType:"plot"},textfont:u({},h,{}),insidetextorientation:{valType:"enumerated",values:["horizontal","radial","tangential","auto"],dflt:"auto",editType:"plot"},insidetextfont:u({},h,{}),outsidetextfont:u({},h,{}),automargin:{valType:"boolean",dflt:!1,editType:"plot"},title:{text:{valType:"string",dflt:"",editType:"plot"},font:u({},h,{}),position:{valType:"enumerated",values:["top left","top center","top right","middle center","bottom left","bottom center","bottom right"],editType:"plot"},editType:"plot"},domain:a({name:"pie",trace:!0,editType:"calc"}),hole:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},sort:{valType:"boolean",dflt:!0,editType:"calc"},direction:{valType:"enumerated",values:["clockwise","counterclockwise"],dflt:"counterclockwise",editType:"calc"},rotation:{valType:"number",min:-360,max:360,dflt:0,editType:"calc"},pull:{valType:"number",min:0,max:1,dflt:0,arrayOk:!0,editType:"calc"},_deprecated:{title:{valType:"string",dflt:"",editType:"calc"},titlefont:u({},h,{}),titleposition:{valType:"enumerated",values:["top left","top center","top right","middle center","bottom left","bottom center","bottom right"],editType:"calc"}}}},{"../../components/color/attributes":365,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/domain":584,"../../plots/font_attributes":585,"../../plots/template_attributes":633}],902:[function(t,o,f){var r=t("../../plots/plots");f.name="pie",f.plot=function(a,l,c,i){r.plotBasePlot(f.name,a,l,c,i)},f.clean=function(a,l,c,i){r.cleanBasePlot(f.name,a,l,c,i)}},{"../../plots/plots":619}],903:[function(t,o,f){var r=t("fast-isnumeric"),a=t("tinycolor2"),l=t("../../components/color"),c={};function i(u){return function(h,d){return!!h&&!!(h=a(h)).isValid()&&(h=l.addOpacity(h,h.getAlpha()),u[d]||(u[d]=h),h)}}function s(u,h){var d,m=JSON.stringify(u),p=h[m];if(!p){for(p=u.slice(),d=0;d<u.length;d++)p.push(a(u[d]).lighten(20).toHexString());for(d=0;d<u.length;d++)p.push(a(u[d]).darken(20).toHexString());h[m]=p}return p}o.exports={calc:function(u,h){var d,m,p=[],g=u._fullLayout,y=g.hiddenlabels||[],v=h.labels,x=h.marker.colors||[],_=h.values,A=h._length,b=h._hasValues&&A;if(h.dlabel)for(v=new Array(A),d=0;d<A;d++)v[d]=String(h.label0+d*h.dlabel);var k={},w=i(g["_"+h.type+"colormap"]),M=0,T=!1;for(d=0;d<A;d++){var E,S,P;if(b){if(E=_[d],!r(E))continue;E=+E}else E=1;(S=v[d])!==void 0&&S!==""||(S=d);var L=k[S=String(S)];L===void 0?(k[S]=p.length,(P=y.indexOf(S)!==-1)||(M+=E),p.push({v:E,label:S,color:w(x[d],S),i:d,pts:[d],hidden:P})):(T=!0,(m=p[L]).v+=E,m.pts.push(d),m.hidden||(M+=E),m.color===!1&&x[d]&&(m.color=w(x[d],S)))}return p=p.filter(function(R){return R.v>=0}),(h.type==="funnelarea"?T:h.sort)&&p.sort(function(R,F){return F.v-R.v}),p[0]&&(p[0].vTotal=M),p},crossTraceCalc:function(u,h){var d=(h||{}).type;d||(d="pie");var m=u._fullLayout,p=u.calcdata,g=m[d+"colorway"],y=m["_"+d+"colormap"];m["extend"+d+"colors"]&&(g=s(g,c));for(var v=0,x=0;x<p.length;x++){var _=p[x];if(_[0].trace.type===d)for(var A=0;A<_.length;A++){var b=_[A];b.color===!1&&(y[b.label]?b.color=y[b.label]:(y[b.label]=b.color=g[v%g.length],v++))}}},makePullColorFn:i,generateExtendedColors:s}},{"../../components/color":366,"fast-isnumeric":190,tinycolor2:312}],904:[function(t,o,f){var r=t("fast-isnumeric"),a=t("../../lib"),l=t("./attributes"),c=t("../../plots/domain").defaults,i=t("../bar/defaults").handleText;function s(u,h){var d=Array.isArray(u),m=a.isArrayOrTypedArray(h),p=Math.min(d?u.length:1/0,m?h.length:1/0);if(isFinite(p)||(p=0),p&&m){for(var g,y=0;y<p;y++){var v=h[y];if(r(v)&&v>0){g=!0;break}}g||(p=0)}return{hasLabels:d,hasValues:m,len:p}}o.exports={handleLabelsAndValues:s,supplyDefaults:function(u,h,d,m){function p(w,M){return a.coerce(u,h,l,w,M)}var g=s(p("labels"),p("values")),y=g.len;if(h._hasLabels=g.hasLabels,h._hasValues=g.hasValues,!h._hasLabels&&h._hasValues&&(p("label0"),p("dlabel")),y){h._length=y,p("marker.line.width")&&p("marker.line.color"),p("marker.colors"),p("scalegroup");var v,x=p("text"),_=p("texttemplate");if(_||(v=p("textinfo",Array.isArray(x)?"text+percent":"percent")),p("hovertext"),p("hovertemplate"),_||v&&v!=="none"){var A=p("textposition");i(u,h,m,p,A,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(A)||A==="auto"||A==="outside")&&p("automargin"),(A==="inside"||A==="auto"||Array.isArray(A))&&p("insidetextorientation")}c(h,m,p);var b=p("hole");if(p("title.text")){var k=p("title.position",b?"middle center":"top center");b||k!=="middle center"||(h.title.position="top center"),a.coerceFont(p,"title.font",m.font)}p("sort"),p("direction"),p("rotation"),p("pull")}else h.visible=!1}}},{"../../lib":503,"../../plots/domain":584,"../bar/defaults":652,"./attributes":901,"fast-isnumeric":190}],905:[function(t,o,f){var r=t("../../components/fx/helpers").appendArrayMultiPointValues;o.exports=function(a,l){var c={curveNumber:l.index,pointNumbers:a.pts,data:l._input,fullData:l,label:a.label,color:a.color,value:a.v,percent:a.percent,text:a.text,bbox:a.bbox,v:a.v};return a.pts.length===1&&(c.pointNumber=c.i=a.pts[0]),r(c,l,a.pts),l.type==="funnelarea"&&(delete c.v,delete c.i),c}},{"../../components/fx/helpers":402}],906:[function(t,o,f){var r=t("../../lib");function a(l){return l.indexOf("e")!==-1?l.replace(/[.]?0+e/,"e"):l.indexOf(".")!==-1?l.replace(/[.]?0+$/,""):l}f.formatPiePercent=function(l,c){var i=a((100*l).toPrecision(3));return r.numSeparate(i,c)+"%"},f.formatPieValue=function(l,c){var i=a(l.toPrecision(10));return r.numSeparate(i,c)},f.getFirstFilled=function(l,c){if(Array.isArray(l))for(var i=0;i<c.length;i++){var s=l[c[i]];if(s||s===0||s==="")return s}},f.castOption=function(l,c){return Array.isArray(l)?f.getFirstFilled(l,c):l||void 0},f.getRotationAngle=function(l){return(l==="auto"?0:l)*Math.PI/180}},{"../../lib":503}],907:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":901,"./base_plot":902,"./calc":903,"./defaults":904,"./layout_attributes":908,"./layout_defaults":909,"./plot":910,"./style":911,"./style_one":912}],908:[function(t,o,f){o.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],909:[function(t,o,f){var r=t("../../lib"),a=t("./layout_attributes");o.exports=function(l,c){function i(s,u){return r.coerce(l,c,a,s,u)}i("hiddenlabels"),i("piecolorway",c.colorway),i("extendpiecolors")}},{"../../lib":503,"./layout_attributes":908}],910:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../plots/plots"),l=t("../../components/fx"),c=t("../../components/color"),i=t("../../components/drawing"),s=t("../../lib"),u=s.strScale,h=s.strTranslate,d=t("../../lib/svg_text_utils"),m=t("../bar/uniform_text"),p=m.recordMinTextSize,g=m.clearMinTextSize,y=t("../bar/constants").TEXTPAD,v=t("./helpers"),x=t("./event_data"),_=t("../../lib").isValidTextValue;function A(K,te,Y){var J=Y[0],re=J.cx,U=J.cy,V=J.trace,H=V.type==="funnelarea";"_hasHoverLabel"in V||(V._hasHoverLabel=!1),"_hasHoverEvent"in V||(V._hasHoverEvent=!1),K.on("mouseover",function(ne){var q=te._fullLayout,Q=te._fullData[V.index];if(!te._dragging&&q.hovermode!==!1){var ee=Q.hoverinfo;if(Array.isArray(ee)&&(ee=l.castHoverinfo({hoverinfo:[v.castOption(ee,ne.pts)],_module:V._module},q,0)),ee==="all"&&(ee="label+text+value+percent+name"),Q.hovertemplate||ee!=="none"&&ee!=="skip"&&ee){var ie=ne.rInscribed||0,ae=re+ne.pxmid[0]*(1-ie),ue=U+ne.pxmid[1]*(1-ie),le=q.separators,ge=[];if(ee&&ee.indexOf("label")!==-1&&ge.push(ne.label),ne.text=v.castOption(Q.hovertext||Q.text,ne.pts),ee&&ee.indexOf("text")!==-1){var fe=ne.text;s.isValidTextValue(fe)&&ge.push(fe)}ne.value=ne.v,ne.valueLabel=v.formatPieValue(ne.v,le),ee&&ee.indexOf("value")!==-1&&ge.push(ne.valueLabel),ne.percent=ne.v/J.vTotal,ne.percentLabel=v.formatPiePercent(ne.percent,le),ee&&ee.indexOf("percent")!==-1&&ge.push(ne.percentLabel);var me=Q.hoverlabel,_e=me.font,Ae=[];l.loneHover({trace:V,x0:ae-ie*J.r,x1:ae+ie*J.r,y:ue,_x0:H?re+ne.TL[0]:ae-ie*J.r,_x1:H?re+ne.TR[0]:ae+ie*J.r,_y0:H?U+ne.TL[1]:ue-ie*J.r,_y1:H?U+ne.BL[1]:ue+ie*J.r,text:ge.join("<br>"),name:Q.hovertemplate||ee.indexOf("name")!==-1?Q.name:void 0,idealAlign:ne.pxmid[0]<0?"left":"right",color:v.castOption(me.bgcolor,ne.pts)||ne.color,borderColor:v.castOption(me.bordercolor,ne.pts),fontFamily:v.castOption(_e.family,ne.pts),fontSize:v.castOption(_e.size,ne.pts),fontColor:v.castOption(_e.color,ne.pts),nameLength:v.castOption(me.namelength,ne.pts),textAlign:v.castOption(me.align,ne.pts),hovertemplate:v.castOption(Q.hovertemplate,ne.pts),hovertemplateLabels:ne,eventData:[x(ne,Q)]},{container:q._hoverlayer.node(),outerContainer:q._paper.node(),gd:te,inOut_bbox:Ae}),ne.bbox=Ae[0],V._hasHoverLabel=!0}V._hasHoverEvent=!0,te.emit("plotly_hover",{points:[x(ne,Q)],event:r.event})}}),K.on("mouseout",function(ne){var q=te._fullLayout,Q=te._fullData[V.index],ee=r.select(this).datum();V._hasHoverEvent&&(ne.originalEvent=r.event,te.emit("plotly_unhover",{points:[x(ee,Q)],event:r.event}),V._hasHoverEvent=!1),V._hasHoverLabel&&(l.loneUnhover(q._hoverlayer.node()),V._hasHoverLabel=!1)}),K.on("click",function(ne){var q=te._fullLayout,Q=te._fullData[V.index];te._dragging||q.hovermode===!1||(te._hoverdata=[x(ne,Q)],l.click(te,r.event))})}function b(K,te,Y){var J=v.castOption(K.insidetextfont.color,te.pts);!J&&K._input.textfont&&(J=v.castOption(K._input.textfont.color,te.pts));var re=v.castOption(K.insidetextfont.family,te.pts)||v.castOption(K.textfont.family,te.pts)||Y.family,U=v.castOption(K.insidetextfont.size,te.pts)||v.castOption(K.textfont.size,te.pts)||Y.size;return{color:J||c.contrast(te.color),family:re,size:U}}function k(K,te){for(var Y,J,re=0;re<K.length;re++)if((J=(Y=K[re][0]).trace).title.text){var U=J.title.text;J._meta&&(U=s.templateString(U,J._meta));var V=i.tester.append("text").attr("data-notex",1).text(U).call(i.font,J.title.font).call(d.convertToTspans,te),H=i.bBox(V.node(),!0);Y.titleBox={width:H.width,height:H.height},V.remove()}}function w(K,te,Y){var J=Y.r||te.rpx1,re=te.rInscribed;if(te.startangle===te.stopangle)return{rCenter:1-re,scale:0,rotate:0,textPosAngle:0};var U,V=te.ring,H=V===1&&Math.abs(te.startangle-te.stopangle)===2*Math.PI,ne=te.halfangle,q=te.midangle,Q=Y.trace.insidetextorientation,ee=Q==="horizontal",ie=Q==="tangential",ae=Q==="radial",ue=Q==="auto",le=[];if(!ue){var ge,fe=function(de,ve){if(function(Fe,ze){var $e=Fe.startangle,Ke=Fe.stopangle;return $e>ze&&ze>Ke||$e<ze&&ze<Ke}(te,de)){var Me=Math.abs(de-te.startangle),we=Math.abs(de-te.stopangle),Ce=Me<we?Me:we;(U=ve==="tan"?T(K,J,V,Ce,0):M(K,J,V,Ce,Math.PI/2)).textPosAngle=de,le.push(U)}};if(ee||ie){for(ge=4;ge>=-4;ge-=2)fe(Math.PI*ge,"tan");for(ge=4;ge>=-4;ge-=2)fe(Math.PI*(ge+1),"tan")}if(ee||ae){for(ge=4;ge>=-4;ge-=2)fe(Math.PI*(ge+1.5),"rad");for(ge=4;ge>=-4;ge-=2)fe(Math.PI*(ge+.5),"rad")}}if(H||ue||ee){var me=Math.sqrt(K.width*K.width+K.height*K.height);if((U={scale:re*J*2/me,rCenter:1-re,rotate:0}).textPosAngle=(te.startangle+te.stopangle)/2,U.scale>=1)return U;le.push(U)}(ue||ae)&&((U=M(K,J,V,ne,q)).textPosAngle=(te.startangle+te.stopangle)/2,le.push(U)),(ue||ie)&&((U=T(K,J,V,ne,q)).textPosAngle=(te.startangle+te.stopangle)/2,le.push(U));for(var _e=0,Ae=0,ke=0;ke<le.length;ke++){var Le=le[ke].scale;if(Ae<Le&&(Ae=Le,_e=ke),!ue&&Ae>=1)break}return le[_e]}function M(K,te,Y,J,re){te=Math.max(0,te-2*y);var U=K.width/K.height,V=P(U,J,te,Y);return{scale:2*V/K.height,rCenter:E(U,V/te),rotate:S(re)}}function T(K,te,Y,J,re){te=Math.max(0,te-2*y);var U=K.height/K.width,V=P(U,J,te,Y);return{scale:2*V/K.width,rCenter:E(U,V/te),rotate:S(re+Math.PI/2)}}function E(K,te){return Math.cos(te)-K*te}function S(K){return(180/Math.PI*K+720)%180-90}function P(K,te,Y,J){var re=K+1/(2*Math.tan(te));return Y*Math.min(1/(Math.sqrt(re*re+.5)+re),J/(Math.sqrt(K*K+J/2)+K))}function L(K,te){return K.v!==te.vTotal||te.trace.hole?Math.min(1/(1+1/Math.sin(K.halfangle)),K.ring/2):1}function R(K,te){var Y=te.pxmid[0],J=te.pxmid[1],re=K.width/2,U=K.height/2;return Y<0&&(re*=-1),J<0&&(U*=-1),{scale:1,rCenter:1,rotate:0,x:re+Math.abs(U)*(re>0?1:-1)/2,y:U/(1+Y*Y/(J*J)),outside:!0}}function F(K,te){var Y,J,re,U=K.trace,V={x:K.cx,y:K.cy},H={tx:0,ty:0};H.ty+=U.title.font.size,re=O(U),U.title.position.indexOf("top")!==-1?(V.y-=(1+re)*K.r,H.ty-=K.titleBox.height):U.title.position.indexOf("bottom")!==-1&&(V.y+=(1+re)*K.r);var ne,q,Q=(ne=K.r,q=K.trace.aspectratio,ne/(q===void 0?1:q)),ee=te.w*(U.domain.x[1]-U.domain.x[0])/2;return U.title.position.indexOf("left")!==-1?(ee+=Q,V.x-=(1+re)*Q,H.tx+=K.titleBox.width/2):U.title.position.indexOf("center")!==-1?ee*=2:U.title.position.indexOf("right")!==-1&&(ee+=Q,V.x+=(1+re)*Q,H.tx-=K.titleBox.width/2),Y=ee/K.titleBox.width,J=D(K,te)/K.titleBox.height,{x:V.x,y:V.y,scale:Math.min(Y,J),tx:H.tx,ty:H.ty}}function D(K,te){var Y=K.trace,J=te.h*(Y.domain.y[1]-Y.domain.y[0]);return Math.min(K.titleBox.height,J/2)}function O(K){var te,Y=K.pull;if(!Y)return 0;if(Array.isArray(Y))for(Y=0,te=0;te<K.pull.length;te++)K.pull[te]>Y&&(Y=K.pull[te]);return Y}function N(K,te){for(var Y=[],J=0;J<K.length;J++){var re=K[J][0],U=re.trace,V=U.domain,H=te.w*(V.x[1]-V.x[0]),ne=te.h*(V.y[1]-V.y[0]);U.title.text&&U.title.position!=="middle center"&&(ne-=D(re,te));var q=H/2,Q=ne/2;U.type!=="funnelarea"||U.scalegroup||(Q/=U.aspectratio),re.r=Math.min(q,Q)/(1+O(U)),re.cx=te.l+te.w*(U.domain.x[1]+U.domain.x[0])/2,re.cy=te.t+te.h*(1-U.domain.y[0])-ne/2,U.title.text&&U.title.position.indexOf("bottom")!==-1&&(re.cy-=D(re,te)),U.scalegroup&&Y.indexOf(U.scalegroup)===-1&&Y.push(U.scalegroup)}(function(ee,ie){for(var ae,ue,le,ge=0;ge<ie.length;ge++){var fe=1/0,me=ie[ge];for(ue=0;ue<ee.length;ue++)if(ae=ee[ue][0],(le=ae.trace).scalegroup===me){var _e;if(le.type==="pie")_e=ae.r*ae.r;else if(le.type==="funnelarea"){var Ae,ke;le.aspectratio>1?(Ae=ae.r,ke=Ae/le.aspectratio):(ke=ae.r,Ae=ke*le.aspectratio),Ae*=(1+le.baseratio)/2,_e=Ae*ke}fe=Math.min(fe,_e/ae.vTotal)}for(ue=0;ue<ee.length;ue++)if(ae=ee[ue][0],(le=ae.trace).scalegroup===me){var Le=fe*ae.vTotal;le.type==="funnelarea"&&(Le/=(1+le.baseratio)/2,Le/=le.aspectratio),ae.r=Math.sqrt(Le)}}})(K,Y)}function B(K,te){return[K*Math.sin(te),-K*Math.cos(te)]}function W(K,te,Y){var J=K._fullLayout,re=Y.trace,U=re.texttemplate,V=re.textinfo;if(!U&&V&&V!=="none"){var H,ne=V.split("+"),q=function(_e){return ne.indexOf(_e)!==-1},Q=q("label"),ee=q("text"),ie=q("value"),ae=q("percent"),ue=J.separators;if(H=Q?[te.label]:[],ee){var le=v.getFirstFilled(re.text,te.pts);_(le)&&H.push(le)}ie&&H.push(v.formatPieValue(te.v,ue)),ae&&H.push(v.formatPiePercent(te.v/Y.vTotal,ue)),te.text=H.join("<br>")}if(U){var ge=s.castOption(re,te.i,"texttemplate");if(ge){var fe=function(_e){return{label:_e.label,value:_e.v,valueLabel:v.formatPieValue(_e.v,J.separators),percent:_e.v/Y.vTotal,percentLabel:v.formatPiePercent(_e.v/Y.vTotal,J.separators),color:_e.color,text:_e.text,customdata:s.castOption(re,_e.i,"customdata")}}(te),me=v.getFirstFilled(re.text,te.pts);(_(me)||me==="")&&(fe.text=me),te.text=s.texttemplateString(ge,fe,K._fullLayout._d3locale,fe,re._meta||{})}else te.text=""}}function G(K,te){var Y=K.rotate*Math.PI/180,J=Math.cos(Y),re=Math.sin(Y),U=(te.left+te.right)/2,V=(te.top+te.bottom)/2;K.textX=U*J-V*re,K.textY=U*re+V*J,K.noCenter=!0}o.exports={plot:function(K,te){var Y=K._fullLayout,J=Y._size;g("pie",Y),k(te,K),N(te,J);var re=s.makeTraceGroups(Y._pielayer,te,"trace").each(function(U){var V=r.select(this),H=U[0],ne=H.trace;(function(q){var Q,ee,ie,ae=q[0],ue=ae.r,le=ae.trace,ge=v.getRotationAngle(le.rotation),fe=2*Math.PI/ae.vTotal,me="px0",_e="px1";if(le.direction==="counterclockwise"){for(Q=0;Q<q.length&&q[Q].hidden;Q++);if(Q===q.length)return;ge+=fe*q[Q].v,fe*=-1,me="px1",_e="px0"}for(ie=B(ue,ge),Q=0;Q<q.length;Q++)(ee=q[Q]).hidden||(ee[me]=ie,ee.startangle=ge,ge+=fe*ee.v/2,ee.pxmid=B(ue,ge),ee.midangle=ge,ge+=fe*ee.v/2,ie=B(ue,ge),ee.stopangle=ge,ee[_e]=ie,ee.largeArc=ee.v>ae.vTotal/2?1:0,ee.halfangle=Math.PI*Math.min(ee.v/ae.vTotal,.5),ee.ring=1-le.hole,ee.rInscribed=L(ee,ae))})(U),V.attr("stroke-linejoin","round"),V.each(function(){var q=r.select(this).selectAll("g.slice").data(U);q.enter().append("g").classed("slice",!0),q.exit().remove();var Q=[[[],[]],[[],[]]],ee=!1;q.each(function(_e,Ae){if(_e.hidden)r.select(this).selectAll("path,g").remove();else{_e.pointNumber=_e.i,_e.curveNumber=ne.index,Q[_e.pxmid[1]<0?0:1][_e.pxmid[0]<0?0:1].push(_e);var ke=H.cx,Le=H.cy,de=r.select(this),ve=de.selectAll("path.surface").data([_e]);if(ve.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),de.call(A,K,U),ne.pull){var Me=+v.castOption(ne.pull,_e.pts)||0;Me>0&&(ke+=Me*_e.pxmid[0],Le+=Me*_e.pxmid[1])}_e.cxFinal=ke,_e.cyFinal=Le;var we=ne.hole;if(_e.v===H.vTotal){var Ce="M"+(ke+_e.px0[0])+","+(Le+_e.px0[1])+Re(_e.px0,_e.pxmid,!0,1)+Re(_e.pxmid,_e.px0,!0,1)+"Z";we?ve.attr("d","M"+(ke+we*_e.px0[0])+","+(Le+we*_e.px0[1])+Re(_e.px0,_e.pxmid,!1,we)+Re(_e.pxmid,_e.px0,!1,we)+"Z"+Ce):ve.attr("d",Ce)}else{var Fe=Re(_e.px0,_e.px1,!0,1);if(we){var ze=1-we;ve.attr("d","M"+(ke+we*_e.px1[0])+","+(Le+we*_e.px1[1])+Re(_e.px1,_e.px0,!1,we)+"l"+ze*_e.px0[0]+","+ze*_e.px0[1]+Fe+"Z")}else ve.attr("d","M"+ke+","+Le+"l"+_e.px0[0]+","+_e.px0[1]+Fe+"Z")}W(K,_e,H);var $e=v.castOption(ne.textposition,_e.pts),Ke=de.selectAll("g.slicetext").data(_e.text&&$e!=="none"?[0]:[]);Ke.enter().append("g").classed("slicetext",!0),Ke.exit().remove(),Ke.each(function(){var Ve=s.ensureSingle(r.select(this),"text","",function(at){at.attr("data-notex",1)}),We=s.ensureUniformFontSize(K,$e==="outside"?function(at,et,Lt){var Wt=v.castOption(at.outsidetextfont.color,et.pts)||v.castOption(at.textfont.color,et.pts)||Lt.color,Jt=v.castOption(at.outsidetextfont.family,et.pts)||v.castOption(at.textfont.family,et.pts)||Lt.family,Be=v.castOption(at.outsidetextfont.size,et.pts)||v.castOption(at.textfont.size,et.pts)||Lt.size;return{color:Wt,family:Jt,size:Be}}(ne,_e,Y.font):b(ne,_e,Y.font));Ve.text(_e.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(i.font,We).call(d.convertToTspans,K);var Ye,nt=i.bBox(Ve.node());if($e==="outside")Ye=R(nt,_e);else if(Ye=w(nt,_e,H),$e==="auto"&&Ye.scale<1){var ft=s.ensureUniformFontSize(K,ne.outsidetextfont);Ve.call(i.font,ft),Ye=R(nt=i.bBox(Ve.node()),_e)}var yt=Ye.textPosAngle,Ot=yt===void 0?_e.pxmid:B(H.r,yt);if(Ye.targetX=ke+Ot[0]*Ye.rCenter+(Ye.x||0),Ye.targetY=Le+Ot[1]*Ye.rCenter+(Ye.y||0),G(Ye,nt),Ye.outside){var Tt=Ye.targetY;_e.yLabelMin=Tt-nt.height/2,_e.yLabelMid=Tt,_e.yLabelMax=Tt+nt.height/2,_e.labelExtraX=0,_e.labelExtraY=0,ee=!0}Ye.fontSize=We.size,p(ne.type,Ye,Y),U[Ae].transform=Ye,Ve.attr("transform",s.getTextTransform(Ye))})}function Re(Ve,We,Ye,nt){var ft=nt*(We[0]-Ve[0]),yt=nt*(We[1]-Ve[1]);return"a"+nt*H.r+","+nt*H.r+" 0 "+_e.largeArc+(Ye?" 1 ":" 0 ")+ft+","+yt}});var ie=r.select(this).selectAll("g.titletext").data(ne.title.text?[0]:[]);if(ie.enter().append("g").classed("titletext",!0),ie.exit().remove(),ie.each(function(){var _e,Ae=s.ensureSingle(r.select(this),"text","",function(Le){Le.attr("data-notex",1)}),ke=ne.title.text;ne._meta&&(ke=s.templateString(ke,ne._meta)),Ae.text(ke).attr({class:"titletext",transform:"","text-anchor":"middle"}).call(i.font,ne.title.font).call(d.convertToTspans,K),_e=ne.title.position==="middle center"?function(Le){var de=Math.sqrt(Le.titleBox.width*Le.titleBox.width+Le.titleBox.height*Le.titleBox.height);return{x:Le.cx,y:Le.cy,scale:Le.trace.hole*Le.r*2/de,tx:0,ty:-Le.titleBox.height/2+Le.trace.title.font.size}}(H):F(H,J),Ae.attr("transform",h(_e.x,_e.y)+u(Math.min(1,_e.scale))+h(_e.tx,_e.ty))}),ee&&function(_e,Ae){var ke,Le,de,ve,Me,we,Ce,Fe,ze,$e,Ke,Re,Ve;function We(yt,Ot){return yt.pxmid[1]-Ot.pxmid[1]}function Ye(yt,Ot){return Ot.pxmid[1]-yt.pxmid[1]}function nt(yt,Ot){Ot||(Ot={});var Tt,at,et,Lt,Wt=Ot.labelExtraY+(Le?Ot.yLabelMax:Ot.yLabelMin),Jt=Le?yt.yLabelMin:yt.yLabelMax,Be=Le?yt.yLabelMax:yt.yLabelMin,Ge=yt.cyFinal+Me(yt.px0[1],yt.px1[1]),kt=Wt-Jt;if(kt*Ce>0&&(yt.labelExtraY=kt),Array.isArray(Ae.pull))for(at=0;at<$e.length;at++)(et=$e[at])===yt||(v.castOption(Ae.pull,yt.pts)||0)>=(v.castOption(Ae.pull,et.pts)||0)||((yt.pxmid[1]-et.pxmid[1])*Ce>0?(kt=et.cyFinal+Me(et.px0[1],et.px1[1])-Jt-yt.labelExtraY)*Ce>0&&(yt.labelExtraY+=kt):(Be+yt.labelExtraY-Ge)*Ce>0&&(Tt=3*we*Math.abs(at-$e.indexOf(yt)),(Lt=et.cxFinal+ve(et.px0[0],et.px1[0])+Tt-(yt.cxFinal+yt.pxmid[0])-yt.labelExtraX)*we>0&&(yt.labelExtraX+=Lt)))}for(Le=0;Le<2;Le++)for(de=Le?We:Ye,Me=Le?Math.max:Math.min,Ce=Le?1:-1,ke=0;ke<2;ke++){for(ve=ke?Math.max:Math.min,we=ke?1:-1,(Fe=_e[Le][ke]).sort(de),ze=_e[1-Le][ke],$e=ze.concat(Fe),Re=[],Ke=0;Ke<Fe.length;Ke++)Fe[Ke].yLabelMid!==void 0&&Re.push(Fe[Ke]);for(Ve=!1,Ke=0;Le&&Ke<ze.length;Ke++)if(ze[Ke].yLabelMid!==void 0){Ve=ze[Ke];break}for(Ke=0;Ke<Re.length;Ke++){var ft=Ke&&Re[Ke-1];Ve&&!Ke&&(ft=Ve),nt(Re[Ke],ft)}}}(Q,ne),function(_e,Ae){_e.each(function(ke){var Le=r.select(this);if(ke.labelExtraX||ke.labelExtraY){var de=Le.select("g.slicetext text");ke.transform.targetX+=ke.labelExtraX,ke.transform.targetY+=ke.labelExtraY,de.attr("transform",s.getTextTransform(ke.transform));var ve=ke.cxFinal+ke.pxmid[0],Me="M"+ve+","+(ke.cyFinal+ke.pxmid[1]),we=(ke.yLabelMax-ke.yLabelMin)*(ke.pxmid[0]<0?-1:1)/4;if(ke.labelExtraX){var Ce=ke.labelExtraX*ke.pxmid[1]/ke.pxmid[0],Fe=ke.yLabelMid+ke.labelExtraY-(ke.cyFinal+ke.pxmid[1]);Math.abs(Ce)>Math.abs(Fe)?Me+="l"+Fe*ke.pxmid[0]/ke.pxmid[1]+","+Fe+"H"+(ve+ke.labelExtraX+we):Me+="l"+ke.labelExtraX+","+Ce+"v"+(Fe-Ce)+"h"+we}else Me+="V"+(ke.yLabelMid+ke.labelExtraY)+"h"+we;s.ensureSingle(Le,"path","textline").call(c.stroke,Ae.outsidetextfont.color).attr({"stroke-width":Math.min(2,Ae.outsidetextfont.size/8),d:Me,fill:"none"})}else Le.select("path.textline").remove()})}(q,ne),ee&&ne.automargin){var ae=i.bBox(V.node()),ue=ne.domain,le=J.w*(ue.x[1]-ue.x[0]),ge=J.h*(ue.y[1]-ue.y[0]),fe=(.5*le-H.r)/J.w,me=(.5*ge-H.r)/J.h;a.autoMargin(K,"pie."+ne.uid+".automargin",{xl:ue.x[0]-fe,xr:ue.x[1]+fe,yb:ue.y[0]-me,yt:ue.y[1]+me,l:Math.max(H.cx-H.r-ae.left,0),r:Math.max(ae.right-(H.cx+H.r),0),b:Math.max(ae.bottom-(H.cy+H.r),0),t:Math.max(H.cy-H.r-ae.top,0),pad:5})}})});setTimeout(function(){re.selectAll("tspan").each(function(){var U=r.select(this);U.attr("dy")&&U.attr("dy",U.attr("dy"))})},0)},formatSliceLabel:W,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:F,prerenderTitles:k,layoutAreas:N,attachFxHandlers:A,computeTransform:G}},{"../../components/color":366,"../../components/drawing":388,"../../components/fx":406,"../../lib":503,"../../lib/svg_text_utils":529,"../../plots/plots":619,"../bar/constants":650,"../bar/uniform_text":664,"./event_data":905,"./helpers":906,"@plotly/d3":58}],911:[function(t,o,f){var r=t("@plotly/d3"),a=t("./style_one"),l=t("../bar/uniform_text").resizeText;o.exports=function(c){var i=c._fullLayout._pielayer.selectAll(".trace");l(c,i,"pie"),i.each(function(s){var u=s[0].trace,h=r.select(this);h.style({opacity:u.opacity}),h.selectAll("path.surface").each(function(d){r.select(this).call(a,d,u)})})}},{"../bar/uniform_text":664,"./style_one":912,"@plotly/d3":58}],912:[function(t,o,f){var r=t("../../components/color"),a=t("./helpers").castOption;o.exports=function(l,c,i){var s=i.marker.line,u=a(s.color,c.pts)||r.defaultLine,h=a(s.width,c.pts)||0;l.style("stroke-width",h).call(r.fill,c.color).call(r.stroke,u)}},{"../../components/color":366,"./helpers":906}],913:[function(t,o,f){var r=t("../scatter/attributes");o.exports={x:r.x,y:r.y,xy:{valType:"data_array",editType:"calc"},indices:{valType:"data_array",editType:"calc"},xbounds:{valType:"data_array",editType:"calc"},ybounds:{valType:"data_array",editType:"calc"},text:r.text,marker:{color:{valType:"color",arrayOk:!1,editType:"calc"},opacity:{valType:"number",min:0,max:1,dflt:1,arrayOk:!1,editType:"calc"},blend:{valType:"boolean",dflt:null,editType:"calc"},sizemin:{valType:"number",min:.1,max:2,dflt:.5,editType:"calc"},sizemax:{valType:"number",min:.1,dflt:20,editType:"calc"},border:{color:{valType:"color",arrayOk:!1,editType:"calc"},arearatio:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},editType:"calc"},editType:"calc"},transforms:void 0}},{"../scatter/attributes":927}],914:[function(t,o,f){var r=t("../../../stackgl_modules").gl_pointcloud2d,a=t("../../lib/str2rgbarray"),l=t("../../plots/cartesian/autorange").findExtremes,c=t("../scatter/get_trace_color");function i(u,h){this.scene=u,this.uid=h,this.type="pointcloud",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color="rgb(0, 0, 0)",this.name="",this.hoverinfo="all",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=r(u.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var s=i.prototype;s.handlePick=function(u){var h=this.idToIndex[u.pointId];return{trace:this,dataCoord:u.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*h],this.pickXYData[2*h+1]]:[this.pickXData[h],this.pickYData[h]],textLabel:Array.isArray(this.textLabels)?this.textLabels[h]:this.textLabels,color:this.color,name:this.name,pointIndex:h,hoverinfo:this.hoverinfo}},s.update=function(u){this.index=u.index,this.textLabels=u.text,this.name=u.name,this.hoverinfo=u.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(u),this.color=c(u,{})},s.updateFast=function(u){var h,d,m,p,g,y,v=this.xData=this.pickXData=u.x,x=this.yData=this.pickYData=u.y,_=this.pickXYData=u.xy,A=u.xbounds&&u.ybounds,b=u.indices,k=this.bounds;if(_){if(m=_,h=_.length>>>1,A)k[0]=u.xbounds[0],k[2]=u.xbounds[1],k[1]=u.ybounds[0],k[3]=u.ybounds[1];else for(y=0;y<h;y++)p=m[2*y],g=m[2*y+1],p<k[0]&&(k[0]=p),p>k[2]&&(k[2]=p),g<k[1]&&(k[1]=g),g>k[3]&&(k[3]=g);if(b)d=b;else for(d=new Int32Array(h),y=0;y<h;y++)d[y]=y}else for(h=v.length,m=new Float32Array(2*h),d=new Int32Array(h),y=0;y<h;y++)p=v[y],g=x[y],d[y]=y,m[2*y]=p,m[2*y+1]=g,p<k[0]&&(k[0]=p),p>k[2]&&(k[2]=p),g<k[1]&&(k[1]=g),g>k[3]&&(k[3]=g);this.idToIndex=d,this.pointcloudOptions.idToIndex=d,this.pointcloudOptions.positions=m;var w=a(u.marker.color),M=a(u.marker.border.color),T=u.opacity*u.marker.opacity;w[3]*=T,this.pointcloudOptions.color=w;var E=u.marker.blend;E===null&&(E=v.length<100||x.length<100),this.pointcloudOptions.blend=E,M[3]*=T,this.pointcloudOptions.borderColor=M;var S=u.marker.sizemin,P=Math.max(u.marker.sizemax,u.marker.sizemin);this.pointcloudOptions.sizeMin=S,this.pointcloudOptions.sizeMax=P,this.pointcloudOptions.areaRatio=u.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var L=this.scene.xaxis,R=this.scene.yaxis,F=P/2||.5;u._extremes[L._id]=l(L,[k[0],k[2]],{ppad:F}),u._extremes[R._id]=l(R,[k[1],k[3]],{ppad:F})},s.dispose=function(){this.pointcloud.dispose()},o.exports=function(u,h){var d=new i(u,h.uid);return d.update(h),d}},{"../../../stackgl_modules":1124,"../../lib/str2rgbarray":528,"../../plots/cartesian/autorange":553,"../scatter/get_trace_color":937}],915:[function(t,o,f){var r=t("../../lib"),a=t("./attributes");o.exports=function(l,c,i){function s(u,h){return r.coerce(l,c,a,u,h)}s("x"),s("y"),s("xbounds"),s("ybounds"),l.xy&&l.xy instanceof Float32Array&&(c.xy=l.xy),l.indices&&l.indices instanceof 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S=h.node.groups,P={};for(d=0;d<S.length;d++){var L=S[d];for(T=0;T<L.length;T++){var R=L[T],F=E+d;P.hasOwnProperty(R)?a.warn("Node "+R+" is already part of a group."):P[R]=F}}var D={source:[],target:[]};for(d=0;d<p.value.length;d++){var O=p.value[d],N=p.source[d],B=p.target[d];if(O>0&&i(N,E)&&i(B,E)&&(!P.hasOwnProperty(N)||!P.hasOwnProperty(B)||P[N]!==P[B])){P.hasOwnProperty(B)&&(B=P[B]),P.hasOwnProperty(N)&&(N=P[N]),B=+B,x[N=+N]=x[B]=!0;var W="";p.label&&p.label[d]&&(W=p.label[d]);var G=null;W&&_.hasOwnProperty(W)&&(G=_[W]),g.push({pointNumber:d,label:W,color:y?p.color[d]:p.color,customdata:v?p.customdata[d]:p.customdata,concentrationscale:G,source:N,target:B,value:+O}),D.source.push(N),D.target.push(B)}}var K=E+S.length,te=c(m.color),Y=c(m.customdata),J=[];for(d=0;d<K;d++)if(x[d]){var re=m.label[d];J.push({group:d>E-1,childrenNodes:[],pointNumber:d,label:re,color:te?m.color[d]:m.color,customdata:Y?m.customdata[d]:m.customdata})}var U=!1;return function(V,H,ne){for(var 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r=t("../../lib"),a=t("./attributes"),l=t("../../components/color"),c=t("tinycolor2"),i=t("../../plots/domain").defaults,s=t("../../components/fx/hoverlabel_defaults"),u=t("../../plot_api/plot_template"),h=t("../../plots/array_container_defaults");function d(m,p){function g(y,v){return r.coerce(m,p,a.link.colorscales,y,v)}g("label"),g("cmin"),g("cmax"),g("colorscale")}o.exports=function(m,p,g,y){function v(P,L){return r.coerce(m,p,a,P,L)}var x=r.extendDeep(y.hoverlabel,m.hoverlabel),_=m.node,A=u.newContainer(p,"node");function b(P,L){return r.coerce(_,A,a.node,P,L)}b("label"),b("groups"),b("x"),b("y"),b("pad"),b("thickness"),b("line.color"),b("line.width"),b("hoverinfo",m.hoverinfo),s(_,A,b,x),b("hovertemplate");var k=y.colorway;b("color",A.label.map(function(P,L){return l.addOpacity(function(R){return k[R%k.length]}(L),.8)})),b("customdata");var w=m.link||{},M=u.newContainer(p,"link");function T(P,L){return 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U,V=_(J),H=V.trace,ne=H.domain,q=H.orientation==="h",Q=H.node.pad,ee=H.node.thickness,ie=Y.width*(ne.x[1]-ne.x[0]),ae=Y.height*(ne.y[1]-ne.y[0]),ue=V._nodes,le=V._links,ge=V.circular;(U=ge?i.sankeyCircular().circularLinkGap(0):c.sankey()).iterations(s.sankeyIterations).size(q?[ie,ae]:[ae,ie]).nodeWidth(ee).nodePadding(Q).nodeId(function(Ce){return Ce.pointNumber}).nodes(ue).links(le);var fe,me,_e,Ae=U();for(var ke in U.nodePadding()<Q&&m.warn("node.pad was reduced to ",U.nodePadding()," to fit within the figure."),V._groupLookup){var Le,de=parseInt(V._groupLookup[ke]);for(fe=0;fe<Ae.nodes.length;fe++)if(Ae.nodes[fe].pointNumber===de){Le=Ae.nodes[fe];break}if(Le){var ve={pointNumber:parseInt(ke),x0:Le.x0,x1:Le.x1,y0:Le.y0,y1:Le.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};Ae.nodes.unshift(ve),Le.childrenNodes.unshift(ve)}}if(function(){for(fe=0;fe<Ae.nodes.length;fe++){var Ce,Fe,ze=Ae.nodes[fe],$e={};for(me=0;me<ze.targetLinks.length;me++)Ce=(Fe=ze.targetLinks[me]).source.pointNumber+":"+Fe.target.pointNumber,$e.hasOwnProperty(Ce)||($e[Ce]=[]),$e[Ce].push(Fe);var Ke=Object.keys($e);for(me=0;me<Ke.length;me++){var Re=$e[Ce=Ke[me]],Ve=0,We={};for(_e=0;_e<Re.length;_e++)We[(Fe=Re[_e]).label]||(We[Fe.label]=0),We[Fe.label]+=Fe.value,Ve+=Fe.value;for(_e=0;_e<Re.length;_e++)(Fe=Re[_e]).flow={value:Ve,labelConcentration:We[Fe.label]/Ve,concentration:Fe.value/Ve,links:Re},Fe.concentrationscale&&(Fe.color=u(Fe.concentrationscale(Fe.flow.labelConcentration)))}var Ye=0;for(me=0;me<ze.sourceLinks.length;me++)Ye+=ze.sourceLinks[me].value;for(me=0;me<ze.sourceLinks.length;me++)(Fe=ze.sourceLinks[me]).concentrationOut=Fe.value/Ye;var nt=0;for(me=0;me<ze.targetLinks.length;me++)nt+=ze.targetLinks[me].value;for(me=0;me<ze.targetLinks.length;me++)(Fe=ze.targetLinks[me]).concenrationIn=Fe.value/nt}}(),H.node.x.length&&H.node.y.length){for(fe=0;fe<Math.min(H.node.x.length,H.node.y.length,Ae.nodes.length);fe++)if(H.node.x[fe]&&H.node.y[fe]){var Me=[H.node.x[fe]*ie,H.node.y[fe]*ae];Ae.nodes[fe].x0=Me[0]-ee/2,Ae.nodes[fe].x1=Me[0]+ee/2;var we=Ae.nodes[fe].y1-Ae.nodes[fe].y0;Ae.nodes[fe].y0=Me[1]-we/2,Ae.nodes[fe].y1=Me[1]+we/2}H.arrangement==="snap"&&function(Ce){Ce.forEach(function(Fe){var ze,$e,Ke,Re=0,Ve=Fe.length;for(Fe.sort(function(We,Ye){return We.y0-Ye.y0}),Ke=0;Ke<Ve;++Ke)(ze=Fe[Ke]).y0>=Re||($e=Re-ze.y0)>1e-6&&(ze.y0+=$e,ze.y1+=$e),Re=ze.y1+Q})}(function(Ce){var Fe,ze,$e=Ce.map(function(Ye,nt){return{x0:Ye.x0,index:nt}}).sort(function(Ye,nt){return Ye.x0-nt.x0}),Ke=[],Re=-1,Ve=-1/0;for(fe=0;fe<$e.length;fe++){var We=Ce[$e[fe].index];We.x0>Ve+ee&&(Re+=1,Fe=We.x0),Ve=We.x0,Ke[Re]||(Ke[Re]=[]),Ke[Re].push(We),ze=Fe-We.x0,We.x0+=ze,We.x1+=ze}return Ke}(ue=Ae.nodes)),U.update(Ae)}return{circular:ge,key:re,trace:H,guid:m.randstr(),horizontal:q,width:ie,height:ae,nodePad:H.node.pad,nodeLineColor:H.node.line.color,nodeLineWidth:H.node.line.width,linkLineColor:H.link.line.color,linkLineWidth:H.link.line.width,valueFormat:H.valueformat,valueSuffix:H.valuesuffix,textFont:H.textfont,translateX:ne.x[0]*Y.width+Y.margin.l,translateY:Y.height-ne.y[1]*Y.height+Y.margin.t,dragParallel:q?ae:ie,dragPerpendicular:q?ie:ae,arrangement:H.arrangement,sankey:U,graph:Ae,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function E(Y,J,re){var U=u(J.color),V=J.source.label+"|"+J.target.label+"__"+re;return J.trace=Y.trace,J.curveNumber=Y.trace.index,{circular:Y.circular,key:V,traceId:Y.key,pointNumber:J.pointNumber,link:J,tinyColorHue:h.tinyRGB(U),tinyColorAlpha:U.getAlpha(),linkPath:S,linkLineColor:Y.linkLineColor,linkLineWidth:Y.linkLineWidth,valueFormat:Y.valueFormat,valueSuffix:Y.valueSuffix,sankey:Y.sankey,parent:Y,interactionState:Y.interactionState,flow:J.flow}}function S(){return function(Y){if(Y.link.circular)return J=Y.link,re=J.width/2,U=J.circularPathData,J.circularLinkType==="top"?"M "+U.targetX+" "+(U.targetY+re)+" L"+U.rightInnerExtent+" "+(U.targetY+re)+"A"+(U.rightLargeArcRadius+re)+" "+(U.rightSmallArcRadius+re)+" 0 0 1 "+(U.rightFullExtent-re)+" "+(U.targetY-U.rightSmallArcRadius)+"L"+(U.rightFullExtent-re)+" "+U.verticalRightInnerExtent+"A"+(U.rightLargeArcRadius+re)+" "+(U.rightLargeArcRadius+re)+" 0 0 1 "+U.rightInnerExtent+" "+(U.verticalFullExtent-re)+"L"+U.leftInnerExtent+" "+(U.verticalFullExtent-re)+"A"+(U.leftLargeArcRadius+re)+" "+(U.leftLargeArcRadius+re)+" 0 0 1 "+(U.leftFullExtent+re)+" "+U.verticalLeftInnerExtent+"L"+(U.leftFullExtent+re)+" "+(U.sourceY-U.leftSmallArcRadius)+"A"+(U.leftLargeArcRadius+re)+" "+(U.leftSmallArcRadius+re)+" 0 0 1 "+U.leftInnerExtent+" "+(U.sourceY+re)+"L"+U.sourceX+" "+(U.sourceY+re)+"L"+U.sourceX+" "+(U.sourceY-re)+"L"+U.leftInnerExtent+" "+(U.sourceY-re)+"A"+(U.leftLargeArcRadius-re)+" "+(U.leftSmallArcRadius-re)+" 0 0 0 "+(U.leftFullExtent-re)+" "+(U.sourceY-U.leftSmallArcRadius)+"L"+(U.leftFullExtent-re)+" "+U.verticalLeftInnerExtent+"A"+(U.leftLargeArcRadius-re)+" "+(U.leftLargeArcRadius-re)+" 0 0 0 "+U.leftInnerExtent+" "+(U.verticalFullExtent+re)+"L"+U.rightInnerExtent+" "+(U.verticalFullExtent+re)+"A"+(U.rightLargeArcRadius-re)+" "+(U.rightLargeArcRadius-re)+" 0 0 0 "+(U.rightFullExtent+re)+" "+U.verticalRightInnerExtent+"L"+(U.rightFullExtent+re)+" "+(U.targetY-U.rightSmallArcRadius)+"A"+(U.rightLargeArcRadius-re)+" "+(U.rightSmallArcRadius-re)+" 0 0 0 "+U.rightInnerExtent+" 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"+(U.sourceY+U.leftSmallArcRadius)+"L"+(U.leftFullExtent-re)+" "+U.verticalLeftInnerExtent+"A"+(U.leftLargeArcRadius-re)+" "+(U.leftLargeArcRadius-re)+" 0 0 1 "+U.leftInnerExtent+" "+(U.verticalFullExtent-re)+"L"+U.rightInnerExtent+" "+(U.verticalFullExtent-re)+"A"+(U.rightLargeArcRadius-re)+" "+(U.rightLargeArcRadius-re)+" 0 0 1 "+(U.rightFullExtent+re)+" "+U.verticalRightInnerExtent+"L"+(U.rightFullExtent+re)+" "+(U.targetY+U.rightSmallArcRadius)+"A"+(U.rightLargeArcRadius-re)+" "+(U.rightSmallArcRadius-re)+" 0 0 1 "+U.rightInnerExtent+" "+(U.targetY+re)+"L"+U.targetX+" "+(U.targetY+re)+"Z";var J,re,U,V=Y.link.source.x1,H=Y.link.target.x0,ne=a(V,H),q=ne(.5),Q=ne(.5),ee=Y.link.y0-Y.link.width/2,ie=Y.link.y0+Y.link.width/2,ae=Y.link.y1-Y.link.width/2,ue=Y.link.y1+Y.link.width/2;return"M"+V+","+ee+"C"+q+","+ee+" "+Q+","+ae+" "+H+","+ae+"L"+H+","+ue+"C"+Q+","+ue+" "+q+","+ie+" "+V+","+ie+"Z"}}function P(Y,J){var re=u(J.color),U=s.nodePadAcross,V=Y.nodePad/2;J.dx=J.x1-J.x0,J.dy=J.y1-J.y0;var H=J.dx,ne=Math.max(.5,J.dy),q="node_"+J.pointNumber;return J.group&&(q=m.randstr()),J.trace=Y.trace,J.curveNumber=Y.trace.index,{index:J.pointNumber,key:q,partOfGroup:J.partOfGroup||!1,group:J.group,traceId:Y.key,trace:Y.trace,node:J,nodePad:Y.nodePad,nodeLineColor:Y.nodeLineColor,nodeLineWidth:Y.nodeLineWidth,textFont:Y.textFont,size:Y.horizontal?Y.height:Y.width,visibleWidth:Math.ceil(H),visibleHeight:ne,zoneX:-U,zoneY:-V,zoneWidth:H+2*U,zoneHeight:ne+2*V,labelY:Y.horizontal?J.dy/2+1:J.dx/2+1,left:J.originalLayer===1,sizeAcross:Y.width,forceLayouts:Y.forceLayouts,horizontal:Y.horizontal,darkBackground:re.getBrightness()<=128,tinyColorHue:h.tinyRGB(re),tinyColorAlpha:re.getAlpha(),valueFormat:Y.valueFormat,valueSuffix:Y.valueSuffix,sankey:Y.sankey,graph:Y.graph,arrangement:Y.arrangement,uniqueNodeLabelPathId:[Y.guid,Y.key,q].join("_"),interactionState:Y.interactionState,figure:Y}}function L(Y){Y.attr("transform",function(J){return p(J.node.x0.toFixed(3),J.node.y0.toFixed(3))})}function R(Y){Y.call(L)}function F(Y,J){Y.call(R),J.attr("d",S())}function D(Y){Y.attr("width",function(J){return J.node.x1-J.node.x0}).attr("height",function(J){return J.visibleHeight})}function O(Y){return Y.link.width>1||Y.linkLineWidth>0}function N(Y){return p(Y.translateX,Y.translateY)+(Y.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function B(Y,J,re){Y.on(".basic",null).on("mouseover.basic",function(U){U.interactionState.dragInProgress||U.partOfGroup||(re.hover(this,U,J),U.interactionState.hovered=[this,U])}).on("mousemove.basic",function(U){U.interactionState.dragInProgress||U.partOfGroup||(re.follow(this,U),U.interactionState.hovered=[this,U])}).on("mouseout.basic",function(U){U.interactionState.dragInProgress||U.partOfGroup||(re.unhover(this,U,J),U.interactionState.hovered=!1)}).on("click.basic",function(U){U.interactionState.hovered&&(re.unhover(this,U,J),U.interactionState.hovered=!1),U.interactionState.dragInProgress||U.partOfGroup||re.select(this,U,J)})}function W(Y,J,re,U){var V=l.behavior.drag().origin(function(H){return{x:H.node.x0+H.visibleWidth/2,y:H.node.y0+H.visibleHeight/2}}).on("dragstart",function(H){if(H.arrangement!=="fixed"&&(m.ensureSingle(U._fullLayout._infolayer,"g","dragcover",function(q){U._fullLayout._dragCover=q}),m.raiseToTop(this),H.interactionState.dragInProgress=H.node,K(H.node),H.interactionState.hovered&&(re.nodeEvents.unhover.apply(0,H.interactionState.hovered),H.interactionState.hovered=!1),H.arrangement==="snap")){var ne=H.traceId+"|"+H.key;H.forceLayouts[ne]?H.forceLayouts[ne].alpha(1):function(q,Q,ee,ie){(function(ue){for(var le=0;le<ue.length;le++)ue[le].y=(ue[le].y0+ue[le].y1)/2,ue[le].x=(ue[le].x0+ue[le].x1)/2})(ee.graph.nodes);var ae=ee.graph.nodes.filter(function(ue){return ue.originalX===ee.node.originalX}).filter(function(ue){return!ue.partOfGroup});ee.forceLayouts[Q]=r.forceSimulation(ae).alphaDecay(0).force("collide",r.forceCollide().radius(function(ue){return ue.dy/2+ee.nodePad/2}).strength(1).iterations(s.forceIterations)).force("constrain",function(ue,le,ge,fe){return function(){for(var me=0,_e=0;_e<ge.length;_e++){var Ae=ge[_e];Ae===fe.interactionState.dragInProgress?(Ae.x=Ae.lastDraggedX,Ae.y=Ae.lastDraggedY):(Ae.vx=(Ae.originalX-Ae.x)/s.forceTicksPerFrame,Ae.y=Math.min(fe.size-Ae.dy/2,Math.max(Ae.dy/2,Ae.y))),me=Math.max(me,Math.abs(Ae.vx),Math.abs(Ae.vy))}!fe.interactionState.dragInProgress&&me<.1&&fe.forceLayouts[le].alpha()>0&&fe.forceLayouts[le].alpha(0)}}(0,Q,ae,ee)).stop()}(0,ne,H),function(q,Q,ee,ie,ae){window.requestAnimationFrame(function ue(){var le;for(le=0;le<s.forceTicksPerFrame;le++)ee.forceLayouts[ie].tick();if(function(fe){for(var me=0;me<fe.length;me++)fe[me].y0=fe[me].y-fe[me].dy/2,fe[me].y1=fe[me].y0+fe[me].dy,fe[me].x0=fe[me].x-fe[me].dx/2,fe[me].x1=fe[me].x0+fe[me].dx}(ee.graph.nodes),ee.sankey.update(ee.graph),F(q.filter(te(ee)),Q),ee.forceLayouts[ie].alpha()>0)window.requestAnimationFrame(ue);else{var ge=ee.node.originalX;ee.node.x0=ge-ee.visibleWidth/2,ee.node.x1=ge+ee.visibleWidth/2,G(ee,ae)}})}(Y,J,H,ne,U)}}).on("drag",function(H){if(H.arrangement!=="fixed"){var ne=l.event.x,q=l.event.y;H.arrangement==="snap"?(H.node.x0=ne-H.visibleWidth/2,H.node.x1=ne+H.visibleWidth/2,H.node.y0=q-H.visibleHeight/2,H.node.y1=q+H.visibleHeight/2):(H.arrangement==="freeform"&&(H.node.x0=ne-H.visibleWidth/2,H.node.x1=ne+H.visibleWidth/2),q=Math.max(0,Math.min(H.size-H.visibleHeight/2,q)),H.node.y0=q-H.visibleHeight/2,H.node.y1=q+H.visibleHeight/2),K(H.node),H.arrangement!=="snap"&&(H.sankey.update(H.graph),F(Y.filter(te(H)),J))}}).on("dragend",function(H){if(H.arrangement!=="fixed"){H.interactionState.dragInProgress=!1;for(var ne=0;ne<H.node.childrenNodes.length;ne++)H.node.childrenNodes[ne].x=H.node.x,H.node.childrenNodes[ne].y=H.node.y;H.arrangement!=="snap"&&G(H,U)}});Y.on(".drag",null).call(V)}function G(Y,J){for(var re=[],U=[],V=0;V<Y.graph.nodes.length;V++){var H=(Y.graph.nodes[V].x0+Y.graph.nodes[V].x1)/2,ne=(Y.graph.nodes[V].y0+Y.graph.nodes[V].y1)/2;re.push(H/Y.figure.width),U.push(ne/Y.figure.height)}b.call("_guiRestyle",J,{"node.x":[re],"node.y":[U]},Y.trace.index).then(function(){J._fullLayout._dragCover&&J._fullLayout._dragCover.remove()})}function K(Y){Y.lastDraggedX=Y.x0+Y.dx/2,Y.lastDraggedY=Y.y0+Y.dy/2}function te(Y){return function(J){return J.node.originalX===Y.node.originalX}}o.exports=function(Y,J,re,U,V){var H=!1;m.ensureSingle(Y._fullLayout._infolayer,"g","first-render",function(){H=!0});var ne=Y._fullLayout._dragCover,q=re.filter(function(fe){return _(fe).trace.visible}).map(T.bind(null,U)),Q=J.selectAll("."+s.cn.sankey).data(q,v);Q.exit().remove(),Q.enter().append("g").classed(s.cn.sankey,!0).style("box-sizing","content-box").style("position","absolute").style("left",0).style("shape-rendering","geometricPrecision").style("pointer-events","auto").attr("transform",N),Q.each(function(fe,me){Y._fullData[me]._sankey=fe;var _e="bgsankey-"+fe.trace.uid+"-"+me;m.ensureSingle(Y._fullLayout._draggers,"rect",_e),Y._fullData[me]._bgRect=l.select("."+_e),Y._fullData[me]._bgRect.style("pointer-events","all").attr("width",fe.width).attr("height",fe.height).attr("x",fe.translateX).attr("y",fe.translateY).classed("bgsankey",!0).style({fill:"transparent","stroke-width":0})}),Q.transition().ease(s.ease).duration(s.duration).attr("transform",N);var ee=Q.selectAll("."+s.cn.sankeyLinks).data(x,v);ee.enter().append("g").classed(s.cn.sankeyLinks,!0).style("fill","none");var ie=ee.selectAll("."+s.cn.sankeyLink).data(function(fe){return fe.graph.links.filter(function(me){return me.value}).map(E.bind(null,fe))},v);ie.enter().append("path").classed(s.cn.sankeyLink,!0).call(B,Q,V.linkEvents),ie.style("stroke",function(fe){return O(fe)?h.tinyRGB(u(fe.linkLineColor)):fe.tinyColorHue}).style("stroke-opacity",function(fe){return O(fe)?h.opacity(fe.linkLineColor):fe.tinyColorAlpha}).style("fill",function(fe){return fe.tinyColorHue}).style("fill-opacity",function(fe){return fe.tinyColorAlpha}).style("stroke-width",function(fe){return O(fe)?fe.linkLineWidth:1}).attr("d",S()),ie.style("opacity",function(){return Y._context.staticPlot||H||ne?1:0}).transition().ease(s.ease).duration(s.duration).style("opacity",1),ie.exit().transition().ease(s.ease).duration(s.duration).style("opacity",0).remove();var ae=Q.selectAll("."+s.cn.sankeyNodeSet).data(x,v);ae.enter().append("g").classed(s.cn.sankeyNodeSet,!0),ae.style("cursor",function(fe){switch(fe.arrangement){case"fixed":return"default";case"perpendicular":return"ns-resize";default:return"move"}});var ue=ae.selectAll("."+s.cn.sankeyNode).data(function(fe){var me=fe.graph.nodes;return function(_e){var Ae,ke=[];for(Ae=0;Ae<_e.length;Ae++)_e[Ae].originalX=(_e[Ae].x0+_e[Ae].x1)/2,_e[Ae].originalY=(_e[Ae].y0+_e[Ae].y1)/2,ke.indexOf(_e[Ae].originalX)===-1&&ke.push(_e[Ae].originalX);for(ke.sort(function(Le,de){return Le-de}),Ae=0;Ae<_e.length;Ae++)_e[Ae].originalLayerIndex=ke.indexOf(_e[Ae].originalX),_e[Ae].originalLayer=_e[Ae].originalLayerIndex/(ke.length-1)}(me),me.map(P.bind(null,fe))},v);ue.enter().append("g").classed(s.cn.sankeyNode,!0).call(L).style("opacity",function(fe){return!Y._context.staticPlot&&!H||fe.partOfGroup?0:1}),ue.call(B,Q,V.nodeEvents).call(W,ie,V,Y),ue.transition().ease(s.ease).duration(s.duration).call(L).style("opacity",function(fe){return fe.partOfGroup?0:1}),ue.exit().transition().ease(s.ease).duration(s.duration).style("opacity",0).remove();var le=ue.selectAll("."+s.cn.nodeRect).data(x);le.enter().append("rect").classed(s.cn.nodeRect,!0).call(D),le.style("stroke-width",function(fe){return fe.nodeLineWidth}).style("stroke",function(fe){return h.tinyRGB(u(fe.nodeLineColor))}).style("stroke-opacity",function(fe){return h.opacity(fe.nodeLineColor)}).style("fill",function(fe){return fe.tinyColorHue}).style("fill-opacity",function(fe){return fe.tinyColorAlpha}),le.transition().ease(s.ease).duration(s.duration).call(D);var ge=ue.selectAll("."+s.cn.nodeLabel).data(x);ge.enter().append("text").classed(s.cn.nodeLabel,!0).style("cursor","default"),ge.attr("data-notex",1).text(function(fe){return fe.node.label}).each(function(fe){var me=l.select(this);d.font(me,fe.textFont),A.convertToTspans(me,Y)}).style("text-shadow",A.makeTextShadow(Y._fullLayout.paper_bgcolor)).attr("text-anchor",function(fe){return fe.horizontal&&fe.left?"end":"start"}).attr("transform",function(fe){var me=l.select(this),_e=A.lineCount(me),Ae=fe.textFont.size*((_e-1)*M-w),ke=fe.nodeLineWidth/2+3,Le=((fe.horizontal?fe.visibleHeight:fe.visibleWidth)-Ae)/2;fe.horizontal&&(fe.left?ke=-ke:ke+=fe.visibleWidth);var de=fe.horizontal?"":"scale(-1,1)"+g(90);return 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r=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").texttemplateAttrs,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../components/colorscale/attributes"),i=t("../../plots/font_attributes"),s=t("../../components/drawing/attributes").dash,u=t("../../components/drawing/attributes").pattern,h=t("../../components/drawing"),d=t("./constants"),m=t("../../lib/extend").extendFlat;o.exports={x:{valType:"data_array",editType:"calc+clearAxisTypes",anim:!0},x0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes",anim:!0},dx:{valType:"number",dflt:1,editType:"calc",anim:!0},y:{valType:"data_array",editType:"calc+clearAxisTypes",anim:!0},y0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes",anim:!0},dy:{valType:"number",dflt:1,editType:"calc",anim:!0},xperiod:{valType:"any",dflt:0,editType:"calc"},yperiod:{valType:"any",dflt:0,editType:"calc"},xperiod0:{valType:"any",editType:"calc"},yperiod0:{valType:"any",editType:"calc"},xperiodalignment:{valType:"enumerated",values:["start","middle","end"],dflt:"middle",editType:"calc"},yperiodalignment:{valType:"enumerated",values:["start","middle","end"],dflt:"middle",editType:"calc"},xhoverformat:r("x"),yhoverformat:r("y"),stackgroup:{valType:"string",dflt:"",editType:"calc"},orientation:{valType:"enumerated",values:["v","h"],editType:"calc"},groupnorm:{valType:"enumerated",values:["","fraction","percent"],dflt:"",editType:"calc"},stackgaps:{valType:"enumerated",values:["infer 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k=x.stackgroup;if(k){var w=_._scatterStackOpts[A._id+b._id][k],M=w.orientation==="v"?b:A;return M.type==="linear"||M.type==="log"?w:void 0}}o.exports={calc:function(x,_){var A,b,k,w,M,T,E=x._fullLayout,S=l.getFromId(x,_.xaxis||"x"),P=l.getFromId(x,_.yaxis||"y"),L=S.makeCalcdata(_,"x"),R=P.makeCalcdata(_,"y"),F=c(_,S,"x",L),D=c(_,P,"y",R),O=F.vals,N=D.vals,B=_._length,W=new Array(B),G=_.ids,K=v(_,E,S,P),te=!1;g(E,_);var Y,J="x",re="y";K?(a.pushUnique(K.traceIndices,_._expandedIndex),(A=K.orientation==="v")?(re="s",Y="x"):(J="s",Y="y"),M=K.stackgaps==="interpolate"):m(x,_,S,P,O,N,p(_,B));var U=!!_.xperiodalignment,V=!!_.yperiodalignment;for(b=0;b<B;b++){var 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r=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/calc"),l=t("./subtypes");o.exports=function(c,i){l.hasLines(i)&&r(i,"line")&&a(c,i,{vals:i.line.color,containerStr:"line",cLetter:"c"}),l.hasMarkers(i)&&(r(i,"marker")&&a(c,i,{vals:i.marker.color,containerStr:"marker",cLetter:"c"}),r(i,"marker.line")&&a(c,i,{vals:i.marker.line.color,containerStr:"marker.line",cLetter:"c"}))}},{"../../components/colorscale/calc":374,"../../components/colorscale/helpers":377,"./subtypes":952}],931:[function(t,o,f){o.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},{}],932:[function(t,o,f){var r=t("./calc");function a(l,c,i,s,u,h,d){u[s]=!0;var m={i:null,gap:!0,s:0};if(m[d]=i,l.splice(c,0,m),c&&i===l[c-1][d]){var p=l[c-1];m.s=p.s,m.i=p.i,m.gap=p.gap}else h&&(m.s=function(g,y,v,x){var _=g[y-1],A=g[y+1];return A?_?_.s+(A.s-_.s)*(v-_[x])/(A[x]-_[x]):A.s:_.s}(l,c,i,d));c||(l[0].t=l[1].t,l[0].trace=l[1].trace,delete 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O=(y=S[L[d]])[0].trace,N=r.calcMarkerSize(O,O._rawLength),B=Array.isArray(N);if(N&&E[d]||B){var W=N;for(N=new Array(F),m=0;m<F;m++)N[m]=y[m].gap?0:B?W[y[m].i]:W}var G=new Array(F),K=new Array(F);for(m=0;m<F;m++)G[m]=y[m].x,K[m]=y[m].y;r.calcAxisExpansion(l,O,i,s,G,K,N),y[0].t.orientation=b.orientation}}}}}},{"./calc":928}],933:[function(t,o,f){o.exports=function(r){for(var a=0;a<r.length;a++){var l=r[a];if(l.type==="scatter"){var c=l.fill;if(c!=="none"&&c!=="toself"&&(l.opacity=void 0,c==="tonexty"||c==="tonextx"))for(var i=a-1;i>=0;i--){var s=r[i];if(s.type==="scatter"&&s.xaxis===l.xaxis&&s.yaxis===l.yaxis){s.opacity=void 0;break}}}}}},{}],934:[function(t,o,f){var r=t("../../lib"),a=t("../../registry"),l=t("./attributes"),c=t("./constants"),i=t("./subtypes"),s=t("./xy_defaults"),u=t("./period_defaults"),h=t("./stack_defaults"),d=t("./marker_defaults"),m=t("./line_defaults"),p=t("./line_shape_defaults"),g=t("./text_defaults"),y=t("./fillcolor_defaults"),v=t("../../lib").coercePattern;o.exports=function(x,_,A,b){function k(R,F){return r.coerce(x,_,l,R,F)}var w=s(x,_,b,k);if(w||(_.visible=!1),_.visible){u(x,_,b,k),k("xhoverformat"),k("yhoverformat");var M=h(x,_,b,k),T=!M&&w<c.PTS_LINESONLY?"lines+markers":"lines";k("text"),k("hovertext"),k("mode",T),i.hasLines(_)&&(m(x,_,A,b,k),p(x,_,k),k("connectgaps"),k("line.simplify")),i.hasMarkers(_)&&d(x,_,A,b,k,{gradient:!0}),i.hasText(_)&&(k("texttemplate"),g(x,_,b,k));var E=[];(i.hasMarkers(_)||i.hasText(_))&&(k("cliponaxis"),k("marker.maxdisplayed"),E.push("points")),k("fill",M?M.fillDflt:"none"),_.fill!=="none"&&(y(x,_,A,k),i.hasLines(_)||p(x,_,k),v(k,"fillpattern",_.fillcolor,!1));var S=(_.line||{}).color,P=(_.marker||{}).color;_.fill!=="tonext"&&_.fill!=="toself"||E.push("fills"),k("hoveron",E.join("+")||"points"),_.hoveron!=="fills"&&k("hovertemplate");var L=a.getComponentMethod("errorbars","supplyDefaults");L(x,_,S||P||A,{axis:"y"}),L(x,_,S||P||A,{axis:"x",inherit:"y"}),r.coerceSelectionMarkerOpacity(_,k)}}},{"../../lib":503,"../../registry":638,"./attributes":927,"./constants":931,"./fillcolor_defaults":935,"./line_defaults":940,"./line_shape_defaults":942,"./marker_defaults":946,"./period_defaults":947,"./stack_defaults":950,"./subtypes":952,"./text_defaults":953,"./xy_defaults":954}],935:[function(t,o,f){var r=t("../../components/color"),a=t("../../lib").isArrayOrTypedArray;o.exports=function(l,c,i,s){var u=!1;if(c.marker){var h=c.marker.color,d=(c.marker.line||{}).color;h&&!a(h)?u=h:d&&!a(d)&&(u=d)}s("fillcolor",r.addOpacity((c.line||{}).color||u||i,.5))}},{"../../components/color":366,"../../lib":503}],936:[function(t,o,f){var r=t("../../plots/cartesian/axes");o.exports=function(a,l,c){var i={},s={_fullLayout:c},u=r.getFromTrace(s,l,"x"),h=r.getFromTrace(s,l,"y");return i.xLabel=r.tickText(u,u.c2l(a.x),!0).text,i.yLabel=r.tickText(h,h.c2l(a.y),!0).text,i}},{"../../plots/cartesian/axes":554}],937:[function(t,o,f){var r=t("../../components/color"),a=t("./subtypes");o.exports=function(l,c){var i,s;if(l.mode==="lines")return(i=l.line.color)&&r.opacity(i)?i:l.fillcolor;if(l.mode==="none")return l.fill?l.fillcolor:"";var u=c.mcc||(l.marker||{}).color,h=c.mlcc||((l.marker||{}).line||{}).color;return(s=u&&r.opacity(u)?u:h&&r.opacity(h)&&(c.mlw||((l.marker||{}).line||{}).width)?h:"")?r.opacity(s)<.3?r.addOpacity(s,.3):s:(i=(l.line||{}).color)&&r.opacity(i)&&a.hasLines(l)&&l.line.width?i:l.fillcolor}},{"../../components/color":366,"./subtypes":952}],938:[function(t,o,f){var r=t("../../lib"),a=t("../../components/fx"),l=t("../../registry"),c=t("./get_trace_color"),i=t("../../components/color"),s=r.fillText;o.exports=function(u,h,d,m){var p=u.cd,g=p[0].trace,y=u.xa,v=u.ya,x=y.c2p(h),_=v.c2p(d),A=[x,_],b=g.hoveron||"",k=g.mode.indexOf("markers")!==-1?3:.5,w=!!g.xperiodalignment,M=!!g.yperiodalignment;if(b.indexOf("points")!==-1){var T=function(le){var ge=Math.max(k,le.mrc||0),fe=y.c2p(le.x)-x,me=v.c2p(le.y)-_;return Math.max(Math.sqrt(fe*fe+me*me)-ge,1-k/ge)},E=a.getDistanceFunction(m,function(le){if(w){var ge=y.c2p(le.xStart),fe=y.c2p(le.xEnd);return x>=Math.min(ge,fe)&&x<=Math.max(ge,fe)?0:1/0}var me=Math.max(3,le.mrc||0),_e=1-1/me,Ae=Math.abs(y.c2p(le.x)-x);return Ae<me?_e*Ae/me:Ae-me+_e},function(le){if(M){var ge=v.c2p(le.yStart),fe=v.c2p(le.yEnd);return _>=Math.min(ge,fe)&&_<=Math.max(ge,fe)?0:1/0}var me=Math.max(3,le.mrc||0),_e=1-1/me,Ae=Math.abs(v.c2p(le.y)-_);return Ae<me?_e*Ae/me:Ae-me+_e},T);if(a.getClosest(p,E,u),u.index!==!1){var 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i.opacity(g.fillcolor)?ue=g.fillcolor:i.opacity((g.line||{}).color)&&(ue=g.line.color),r.extendFlat(u,{distance:u.maxHoverDistance,x0:q,x1:Q,y0:ae,y1:ae,color:ue,hovertemplate:!1}),delete u.index,g.text&&!Array.isArray(g.text)?u.text=String(g.text):u.text=g.name,[u]}}}},{"../../components/color":366,"../../components/fx":406,"../../lib":503,"../../registry":638,"./get_trace_color":937}],939:[function(t,o,f){var 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r=t("../scatter/attributes"),a=t("../../components/colorscale/attributes"),l=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,c=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,s=t("../../plots/attributes"),u=t("../../constants/gl3d_dashes"),h=t("../../constants/gl3d_markers"),d=t("../../lib/extend").extendFlat,m=t("../../plot_api/edit_types").overrideAll,p=t("../../lib/sort_object_keys"),g=r.line,y=r.marker,v=y.line,x=d({width:g.width,dash:{valType:"enumerated",values:p(u),dflt:"solid"}},a("line")),_=o.exports=m({x:r.x,y:r.y,z:{valType:"data_array"},text:d({},r.text,{}),texttemplate:i({},{}),hovertext:d({},r.hovertext,{}),hovertemplate:c(),xhoverformat:l("x"),yhoverformat:l("y"),zhoverformat:l("z"),mode:d({},r.mode,{dflt:"lines+markers"}),surfaceaxis:{valType:"enumerated",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:"color"},projection:{x:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},y:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},z:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}}},connectgaps:r.connectgaps,line:x,marker:d({symbol:{valType:"enumerated",values:p(h),dflt:"circle",arrayOk:!0},size:d({},y.size,{dflt:8}),sizeref:y.sizeref,sizemin:y.sizemin,sizemode:y.sizemode,opacity:d({},y.opacity,{arrayOk:!1}),colorbar:y.colorbar,line:d({width:d({},v.width,{arrayOk:!1})},a("marker.line"))},a("marker")),textposition:d({},r.textposition,{dflt:"top 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r=t("../../../stackgl_modules").gl_line3d,a=t("../../../stackgl_modules").gl_scatter3d,l=t("../../../stackgl_modules").gl_error3d,c=t("../../../stackgl_modules").gl_mesh3d,i=t("../../../stackgl_modules").delaunay_triangulate,s=t("../../lib"),u=t("../../lib/str2rgbarray"),h=t("../../lib/gl_format_color").formatColor,d=t("../scatter/make_bubble_size_func"),m=t("../../constants/gl3d_dashes"),p=t("../../constants/gl3d_markers"),g=t("../../plots/cartesian/axes"),y=t("../../components/fx/helpers").appendArrayPointValue,v=t("./calc_errors");function x(P,L){this.scene=P,this.uid=L,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode="",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var _=x.prototype;function A(P){return P==null?0:P.indexOf("left")>-1?-1:P.indexOf("right")>-1?1:0}function b(P){return 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Me=L.projection[ve[N]];(O.project[N]=Me.show)&&(O.projectOpacity[N]=Me.opacity,O.projectScale[N]=Me.scale)}O.errorBounds=v(L,K,G);var we=function(Ce){for(var Fe=[0,0,0],ze=[[0,0,0],[0,0,0],[0,0,0]],$e=[1,1,1],Ke=0;Ke<3;Ke++){var Re=Ce[Ke];Re&&Re.copy_zstyle!==!1&&Ce[2].visible!==!1&&(Re=Ce[2]),Re&&Re.visible&&(Fe[Ke]=Re.width/2,ze[Ke]=u(Re.color),$e[Ke]=Re.thickness)}return{capSize:Fe,color:ze,lineWidth:$e}}([L.error_x,L.error_y,L.error_z]);return O.errorColor=we.color,O.errorLineWidth=we.lineWidth,O.errorCapSize=we.capSize,O.delaunayAxis=L.surfaceaxis,O.delaunayColor=u(L.surfacecolor),O}function E(P){if(s.isArrayOrTypedArray(P)){var L=P[0];return s.isArrayOrTypedArray(L)&&(P=L),"rgb("+P.slice(0,3).map(function(R){return Math.round(255*R)})+")"}return null}function S(P){return s.isArrayOrTypedArray(P)?P.length===4&&typeof 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W=P.opacity;if(P.marker&&P.marker.opacity&&(W*=P.marker.opacity),R={gl:this.scene.glplot.gl,position:B.position,color:B.scatterColor,size:B.scatterSize,glyph:B.scatterMarker,opacity:W,orthographic:!0,lineWidth:B.scatterLineWidth,lineColor:B.scatterLineColor,project:B.project,projectScale:B.projectScale,projectOpacity:B.projectOpacity},this.mode.indexOf("markers")!==-1?this.scatterPlot?this.scatterPlot.update(R):(this.scatterPlot=a(R),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),D={gl:this.scene.glplot.gl,position:B.position,glyph:B.text,color:B.textColor,size:B.textSize,angle:B.textAngle,alignment:B.textOffset,font:B.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:P.opacity},this.textLabels=P.hovertext||P.text,this.mode.indexOf("text")!==-1?this.textMarkers?this.textMarkers.update(D):(this.textMarkers=a(D),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),F={gl:this.scene.glplot.gl,position:B.position,color:B.errorColor,error:B.errorBounds,lineWidth:B.errorLineWidth,capSize:B.errorCapSize,opacity:P.opacity},this.errorBars?B.errorBounds?this.errorBars.update(F):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):B.errorBounds&&(this.errorBars=l(F),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),B.delaunayAxis>=0){var G=function(K,te,Y){var J,re=(Y+1)%3,U=(Y+2)%3,V=[],H=[];for(J=0;J<K.length;++J){var ne=K[J];!isNaN(ne[re])&&isFinite(ne[re])&&!isNaN(ne[U])&&isFinite(ne[U])&&(V.push([ne[re],ne[U]]),H.push(J))}var q=i(V);for(J=0;J<q.length;++J)for(var Q=q[J],ee=0;ee<Q.length;++ee)Q[ee]=H[Q[ee]];return{positions:K,cells:q,meshColor:te}}(B.position,B.delaunayColor,B.delaunayAxis);G.opacity=P.opacity,this.delaunayMesh?this.delaunayMesh.update(G):(G.gl=O,this.delaunayMesh=c(G),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},_.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},o.exports=function(P,L){var R=new x(P,L.uid);return R.update(L),R}},{"../../../stackgl_modules":1124,"../../components/fx/helpers":402,"../../constants/gl3d_dashes":476,"../../constants/gl3d_markers":477,"../../lib":503,"../../lib/gl_format_color":499,"../../lib/str2rgbarray":528,"../../plots/cartesian/axes":554,"../scatter/make_bubble_size_func":944,"./calc_errors":957}],959:[function(t,o,f){var r=t("../../registry"),a=t("../../lib"),l=t("../scatter/subtypes"),c=t("../scatter/marker_defaults"),i=t("../scatter/line_defaults"),s=t("../scatter/text_defaults"),u=t("./attributes");o.exports=function(h,d,m,p){function g(k,w){return a.coerce(h,d,u,k,w)}if(function(k,w,M,T){var E=0,S=M("x"),P=M("y"),L=M("z");return r.getComponentMethod("calendars","handleTraceDefaults")(k,w,["x","y","z"],T),S&&P&&L&&(E=Math.min(S.length,P.length,L.length),w._length=w._xlength=w._ylength=w._zlength=E),E}(h,d,g,p)){g("text"),g("hovertext"),g("hovertemplate"),g("xhoverformat"),g("yhoverformat"),g("zhoverformat"),g("mode"),l.hasLines(d)&&(g("connectgaps"),i(h,d,m,p,g)),l.hasMarkers(d)&&c(h,d,m,p,g,{noSelect:!0}),l.hasText(d)&&(g("texttemplate"),s(h,d,p,g,{noSelect:!0}));var y=(d.line||{}).color,v=(d.marker||{}).color;g("surfaceaxis")>=0&&g("surfacecolor",y||v);for(var x=["x","y","z"],_=0;_<3;++_){var A="projection."+x[_];g(A+".show")&&(g(A+".opacity"),g(A+".scale"))}var b=r.getComponentMethod("errorbars","supplyDefaults");b(h,d,y||v||m,{axis:"z"}),b(h,d,y||v||m,{axis:"y",inherit:"z"}),b(h,d,y||v||m,{axis:"x",inherit:"z"})}else d.visible=!1}},{"../../lib":503,"../../registry":638,"../scatter/line_defaults":940,"../scatter/marker_defaults":946,"../scatter/subtypes":952,"../scatter/text_defaults":953,"./attributes":955}],960:[function(t,o,f){o.exports={plot:t("./convert"),attributes:t("./attributes"),markerSymbols:t("../../constants/gl3d_markers"),supplyDefaults:t("./defaults"),colorbar:[{container:"marker",min:"cmin",max:"cmax"},{container:"line",min:"cmin",max:"cmax"}],calc:t("./calc"),moduleType:"trace",name:"scatter3d",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","symbols","showLegend","scatter-like"],meta:{}}},{"../../constants/gl3d_markers":477,"../../plots/gl3d":598,"./attributes":955,"./calc":956,"./convert":958,"./defaults":959}],961:[function(t,o,f){var r=t("../scatter/attributes"),a=t("../../plots/attributes"),l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../plots/template_attributes").texttemplateAttrs,i=t("../../components/colorscale/attributes"),s=t("../../lib/extend").extendFlat,u=r.marker,h=r.line,d=u.line;o.exports={carpet:{valType:"string",editType:"calc"},a:{valType:"data_array",editType:"calc"},b:{valType:"data_array",editType:"calc"},mode:s({},r.mode,{dflt:"markers"}),text:s({},r.text,{}),texttemplate:c({editType:"plot"},{keys:["a","b","text"]}),hovertext:s({},r.hovertext,{}),line:{color:h.color,width:h.width,dash:h.dash,shape:s({},h.shape,{values:["linear","spline"]}),smoothing:h.smoothing,editType:"calc"},connectgaps:r.connectgaps,fill:s({},r.fill,{values:["none","toself","tonext"],dflt:"none"}),fillcolor:r.fillcolor,marker:s({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:s({width:d.width,editType:"calc"},i("marker.line")),gradient:u.gradient,editType:"calc"},i("marker")),textfont:r.textfont,textposition:r.textposition,selected:r.selected,unselected:r.unselected,hoverinfo:s({},a.hoverinfo,{flags:["a","b","text","name"]}),hoveron:r.hoveron,hovertemplate:l()}},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/template_attributes":633,"../scatter/attributes":927}],962:[function(t,o,f){var r=t("fast-isnumeric"),a=t("../scatter/colorscale_calc"),l=t("../scatter/arrays_to_calcdata"),c=t("../scatter/calc_selection"),i=t("../scatter/calc").calcMarkerSize,s=t("../carpet/lookup_carpetid");o.exports=function(u,h){var d=h._carpetTrace=s(u,h);if(d&&d.visible&&d.visible!=="legendonly"){var m;h.xaxis=d.xaxis,h.yaxis=d.yaxis;var p,g,y=h._length,v=new Array(y),x=!1;for(m=0;m<y;m++)if(p=h.a[m],g=h.b[m],r(p)&&r(g)){var _=d.ab2xy(+p,+g,!0),A=d.isVisible(+p,+g);A||(x=!0),v[m]={x:_[0],y:_[1],a:p,b:g,vis:A}}else v[m]={x:!1,y:!1};return h._needsCull=x,v[0].carpet=d,v[0].trace=h,i(h,y),a(u,h),l(v,h),c(v,h),v}}},{"../carpet/lookup_carpetid":708,"../scatter/arrays_to_calcdata":926,"../scatter/calc":928,"../scatter/calc_selection":929,"../scatter/colorscale_calc":930,"fast-isnumeric":190}],963:[function(t,o,f){var r=t("../../lib"),a=t("../scatter/constants"),l=t("../scatter/subtypes"),c=t("../scatter/marker_defaults"),i=t("../scatter/line_defaults"),s=t("../scatter/line_shape_defaults"),u=t("../scatter/text_defaults"),h=t("../scatter/fillcolor_defaults"),d=t("./attributes");o.exports=function(m,p,g,y){function v(k,w){return r.coerce(m,p,d,k,w)}v("carpet"),p.xaxis="x",p.yaxis="y";var x=v("a"),_=v("b"),A=Math.min(x.length,_.length);if(A){p._length=A,v("text"),v("texttemplate"),v("hovertext"),v("mode",A<a.PTS_LINESONLY?"lines+markers":"lines"),l.hasLines(p)&&(i(m,p,g,y,v),s(m,p,v),v("connectgaps")),l.hasMarkers(p)&&c(m,p,g,y,v,{gradient:!0}),l.hasText(p)&&u(m,p,y,v);var b=[];(l.hasMarkers(p)||l.hasText(p))&&(v("marker.maxdisplayed"),b.push("points")),v("fill"),p.fill!=="none"&&(h(m,p,g,v),l.hasLines(p)||s(m,p,v)),p.fill!=="tonext"&&p.fill!=="toself"||b.push("fills"),v("hoveron",b.join("+")||"points")!=="fills"&&v("hovertemplate"),r.coerceSelectionMarkerOpacity(p,v)}else p.visible=!1}},{"../../lib":503,"../scatter/constants":931,"../scatter/fillcolor_defaults":935,"../scatter/line_defaults":940,"../scatter/line_shape_defaults":942,"../scatter/marker_defaults":946,"../scatter/subtypes":952,"../scatter/text_defaults":953,"./attributes":961}],964:[function(t,o,f){o.exports=function(r,a,l,c,i){var s=c[i];return r.a=s.a,r.b=s.b,r.y=s.y,r}},{}],965:[function(t,o,f){o.exports=function(r,a){var l={},c=a._carpet,i=c.ab2ij([r.a,r.b]),s=Math.floor(i[0]),u=i[0]-s,h=Math.floor(i[1]),d=i[1]-h,m=c.evalxy([],s,h,u,d);return l.yLabel=m[1].toFixed(3),l}},{}],966:[function(t,o,f){var r=t("../scatter/hover"),a=t("../../lib").fillText;o.exports=function(l,c,i,s){var u=r(l,c,i,s);if(u&&u[0].index!==!1){var h=u[0];if(h.index===void 0){var d=1-h.y0/l.ya._length,m=l.xa._length,p=m*d/2,g=m-p;return h.x0=Math.max(Math.min(h.x0,g),p),h.x1=Math.max(Math.min(h.x1,g),p),u}var y=h.cd[h.index];h.a=y.a,h.b=y.b,h.xLabelVal=void 0,h.yLabelVal=void 0;var v=h.trace,x=v._carpet,_=v._module.formatLabels(y,v);h.yLabel=_.yLabel,delete h.text;var A=[];if(!v.hovertemplate){var b=(y.hi||v.hoverinfo).split("+");b.indexOf("all")!==-1&&(b=["a","b","text"]),b.indexOf("a")!==-1&&k(x.aaxis,y.a),b.indexOf("b")!==-1&&k(x.baxis,y.b),A.push("y: "+h.yLabel),b.indexOf("text")!==-1&&a(y,v,A),h.extraText=A.join("<br>")}return u}function k(w,M){var T;T=w.labelprefix&&w.labelprefix.length>0?w.labelprefix.replace(/ = $/,""):w._hovertitle,A.push(T+": 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r=t("fast-isnumeric"),a=t("../../constants/numerical").BADNUM,l=t("../scatter/colorscale_calc"),c=t("../scatter/arrays_to_calcdata"),i=t("../scatter/calc_selection"),s=t("../../lib")._;function u(h){return h&&typeof h=="string"}o.exports=function(h,d){var m,p=Array.isArray(d.locations),g=p?d.locations.length:d._length,y=new Array(g);m=d.geojson?function(k){return u(k)||r(k)}:u;for(var v=0;v<g;v++){var x=y[v]={};if(p){var _=d.locations[v];x.loc=m(_)?_:null}else{var A=d.lon[v],b=d.lat[v];r(A)&&r(b)?x.lonlat=[+A,+b]:x.lonlat=[a,a]}}return c(y,d),l(h,d),i(y,d),g&&(y[0].t={labels:{lat:s(h,"lat:")+" ",lon:s(h,"lon:")+" "}}),y}},{"../../constants/numerical":479,"../../lib":503,"../scatter/arrays_to_calcdata":926,"../scatter/calc_selection":929,"../scatter/colorscale_calc":930,"fast-isnumeric":190}],971:[function(t,o,f){var 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A=g("lon")||[],b=g("lat")||[];y=Math.min(A.length,b.length)}y?(d._length=y,g("text"),g("hovertext"),g("hovertemplate"),g("mode"),a.hasLines(d)&&(c(h,d,m,p,g),g("connectgaps")),a.hasMarkers(d)&&l(h,d,m,p,g,{gradient:!0}),a.hasText(d)&&(g("texttemplate"),i(h,d,p,g)),g("fill"),d.fill!=="none"&&s(h,d,m,g),r.coerceSelectionMarkerOpacity(d,g)):d.visible=!1}},{"../../lib":503,"../scatter/fillcolor_defaults":935,"../scatter/line_defaults":940,"../scatter/marker_defaults":946,"../scatter/subtypes":952,"../scatter/text_defaults":953,"./attributes":969}],972:[function(t,o,f){o.exports=function(r,a,l,c,i){r.lon=a.lon,r.lat=a.lat,r.location=a.loc?a.loc:null;var s=c[i];return s.fIn&&s.fIn.properties&&(r.properties=s.fIn.properties),r}},{}],973:[function(t,o,f){var r=t("../../plots/cartesian/axes");o.exports=function(a,l,c){var i={},s=c[l.geo]._subplot.mockAxis,u=a.lonlat;return i.lonLabel=r.tickText(s,s.c2l(u[0]),!0).text,i.latLabel=r.tickText(s,s.c2l(u[1]),!0).text,i}},{"../../plots/cartesian/axes":554}],974:[function(t,o,f){var r=t("../../components/fx"),a=t("../../constants/numerical").BADNUM,l=t("../scatter/get_trace_color"),c=t("../../lib").fillText,i=t("./attributes");o.exports=function(s,u,h){var d=s.cd,m=d[0].trace,p=s.xa,g=s.ya,y=s.subplot,v=y.projection.isLonLatOverEdges,x=y.project;if(r.getClosest(d,function(T){var E=T.lonlat;if(E[0]===a||v(E))return 1/0;var S=x(E),P=x([u,h]),L=Math.abs(S[0]-P[0]),R=Math.abs(S[1]-P[1]),F=Math.max(3,T.mrc||0);return Math.max(Math.sqrt(L*L+R*R)-F,1-3/F)},s),s.index!==!1){var _=d[s.index],A=_.lonlat,b=[p.c2p(A),g.c2p(A)],k=_.mrc||1;s.x0=b[0]-k,s.x1=b[0]+k,s.y0=b[1]-k,s.y1=b[1]+k,s.loc=_.loc,s.lon=A[0],s.lat=A[1];var w={};w[m.geo]={_subplot:y};var M=m._module.formatLabels(_,m,w);return s.lonLabel=M.lonLabel,s.latLabel=M.latLabel,s.color=l(m,_),s.extraText=function(T,E,S,P){if(T.hovertemplate)return;var L=E.hi||T.hoverinfo,R=L==="all"?i.hoverinfo.flags:L.split("+"),F=R.indexOf("location")!==-1&&Array.isArray(T.locations),D=R.indexOf("lon")!==-1,O=R.indexOf("lat")!==-1,N=R.indexOf("text")!==-1,B=[];function W(G){return G+"°"}return F?B.push(E.loc):D&&O?B.push("("+W(S.latLabel)+", 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r=t("@plotly/d3"),a=t("../../lib"),l=t("../../lib/topojson_utils").getTopojsonFeatures,c=t("../../lib/geojson_utils"),i=t("../../lib/geo_location_utils"),s=t("../../plots/cartesian/autorange").findExtremes,u=t("../../constants/numerical").BADNUM,h=t("../scatter/calc").calcMarkerSize,d=t("../scatter/subtypes"),m=t("./style");o.exports={calcGeoJSON:function(p,g){var y,v,x=p[0].trace,_=g[x.geo],A=_._subplot,b=x._length;if(Array.isArray(x.locations)){var k=x.locationmode,w=k==="geojson-id"?i.extractTraceFeature(p):l(x,A.topojson);for(y=0;y<b;y++){v=p[y];var M=k==="geojson-id"?v.fOut:i.locationToFeature(k,v.loc,w);v.lonlat=M?M.properties.ct:[u,u]}}var T,E,S={padded:!0};if(_.fitbounds==="geojson"&&x.locationmode==="geojson-id"){var P=i.computeBbox(i.getTraceGeojson(x));T=[P[0],P[2]],E=[P[1],P[3]]}else{for(T=new Array(b),E=new 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w=c.calcTraceToLineCoords(A),M=k.fill!=="none"?c.makePolygon(w):c.makeLine(w);b.selectAll("path.js-line").data([{geojson:M,trace:k}]).enter().append("path").classed("js-line",!0).style("stroke-miterlimit",2)}d.hasMarkers(k)&&b.selectAll("path.point").data(a.identity).enter().append("path").classed("point",!0).each(function(T){_(T,this)}),d.hasText(k)&&b.selectAll("g").data(a.identity).enter().append("g").append("text").each(function(T){_(T,this)}),m(p,A)})}}},{"../../constants/numerical":479,"../../lib":503,"../../lib/geo_location_utils":496,"../../lib/geojson_utils":497,"../../lib/topojson_utils":532,"../../plots/cartesian/autorange":553,"../scatter/calc":928,"../scatter/subtypes":952,"./style":978,"@plotly/d3":58}],977:[function(t,o,f){var r=t("../scatter/subtypes"),a=t("../../constants/numerical").BADNUM;o.exports=function(l,c){var i,s,u,h,d,m=l.cd,p=l.xaxis,g=l.yaxis,y=[],v=m[0].trace;if(!r.hasMarkers(v)&&!r.hasText(v))return[];if(c===!1)for(d=0;d<m.length;d++)m[d].selected=0;else 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r=t("../../plots/attributes"),a=t("../scatter/attributes"),l=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,c=t("../../components/colorscale/attributes"),i=t("../../lib/sort_object_keys"),s=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll,h=t("./constants").DASHES,d=a.line,m=a.marker,p=m.line,g=o.exports=u({x:a.x,x0:a.x0,dx:a.dx,y:a.y,y0:a.y0,dy:a.dy,xperiod:a.xperiod,yperiod:a.yperiod,xperiod0:a.xperiod0,yperiod0:a.yperiod0,xperiodalignment:a.xperiodalignment,yperiodalignment:a.yperiodalignment,xhoverformat:l("x"),yhoverformat:l("y"),text:a.text,hovertext:a.hovertext,textposition:a.textposition,textfont:a.textfont,mode:{valType:"flaglist",flags:["lines","markers","text"],extras:["none"]},line:{color:d.color,width:d.width,shape:{valType:"enumerated",values:["linear","hv","vh","hvh","vhv"],dflt:"linear",editType:"plot"},dash:{valType:"enumerated",values:i(h),dflt:"solid"}},marker:s({},c("marker"),{symbol:m.symbol,size:m.size,sizeref:m.sizeref,sizemin:m.sizemin,sizemode:m.sizemode,opacity:m.opacity,colorbar:m.colorbar,line:s({},c("marker.line"),{width:p.width})}),connectgaps:a.connectgaps,fill:s({},a.fill,{dflt:"none"}),fillcolor:a.fillcolor,selected:{marker:a.selected.marker,textfont:a.selected.textfont},unselected:{marker:a.unselected.marker,textfont:a.unselected.textfont},opacity:r.opacity},"calc","nested");g.x.editType=g.y.editType=g.x0.editType=g.y0.editType="calc+clearAxisTypes",g.hovertemplate=a.hovertemplate,g.texttemplate=a.texttemplate},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../lib/sort_object_keys":526,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../scatter/attributes":927,"./constants":982}],980:[function(t,o,f){var 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r=t("../../lib"),a=t("../../registry"),l=t("./helpers"),c=t("./attributes"),i=t("../scatter/constants"),s=t("../scatter/subtypes"),u=t("../scatter/xy_defaults"),h=t("../scatter/period_defaults"),d=t("../scatter/marker_defaults"),m=t("../scatter/line_defaults"),p=t("../scatter/fillcolor_defaults"),g=t("../scatter/text_defaults");o.exports=function(y,v,x,_){function A(P,L){return r.coerce(y,v,c,P,L)}var b=!!y.marker&&l.isOpenSymbol(y.marker.symbol),k=s.isBubble(y),w=u(y,v,_,A);if(w){h(y,v,_,A),A("xhoverformat"),A("yhoverformat");var M=w<i.PTS_LINESONLY?"lines+markers":"lines";A("text"),A("hovertext"),A("hovertemplate"),A("mode",M),s.hasLines(v)&&(A("connectgaps"),m(y,v,x,_,A),A("line.shape")),s.hasMarkers(v)&&(d(y,v,x,_,A),A("marker.line.width",b||k?1:0)),s.hasText(v)&&(A("texttemplate"),g(y,v,_,A));var T=(v.line||{}).color,E=(v.marker||{}).color;A("fill"),v.fill!=="none"&&p(y,v,x,A);var S=a.getComponentMethod("errorbars","supplyDefaults");S(y,v,T||E||x,{axis:"y"}),S(y,v,T||E||x,{axis:"x",inherit:"y"}),r.coerceSelectionMarkerOpacity(v,A)}else v.visible=!1}},{"../../lib":503,"../../registry":638,"../scatter/constants":931,"../scatter/fillcolor_defaults":935,"../scatter/line_defaults":940,"../scatter/marker_defaults":946,"../scatter/period_defaults":947,"../scatter/subtypes":952,"../scatter/text_defaults":953,"../scatter/xy_defaults":954,"./attributes":979,"./helpers":987}],985:[function(t,o,f){var r=t("../../lib"),a=t("../../components/color"),l=t("../../constants/interactions").DESELECTDIM;o.exports={styleTextSelection:function(c){var i,s,u=c[0],h=u.trace,d=u.t,m=d._scene,p=d.index,g=m.selectBatch[p],y=m.unselectBatch[p],v=m.textOptions[p],x=m.textSelectedOptions[p]||{},_=m.textUnselectedOptions[p]||{},A=r.extendFlat({},v);if(g.length||y.length){var b=x.color,k=_.color,w=v.color,M=Array.isArray(w);for(A.color=new Array(h._length),i=0;i<g.length;i++)s=g[i],A.color[s]=b||(M?w[s]:w);for(i=0;i<y.length;i++){s=y[i];var T=M?w[s]:w;A.color[s]=k||(b?T:a.addOpacity(T,l))}}m.glText[p].update(A)}}},{"../../components/color":366,"../../constants/interactions":478,"../../lib":503}],986:[function(t,o,f){var r=t("../scatter/format_labels");o.exports=function(a,l,c){var i=a.i;return"x"in a||(a.x=l._x[i]),"y"in a||(a.y=l._y[i]),r(a,l,c)}},{"../scatter/format_labels":936}],987:[function(t,o,f){var r=t("./constants");f.isOpenSymbol=function(a){return typeof a=="string"?r.OPEN_RE.test(a):a%200>100},f.isDotSymbol=function(a){return typeof a=="string"?r.DOT_RE.test(a):a>200}},{"./constants":982}],988:[function(t,o,f){var r=t("../../registry"),a=t("../../lib"),l=t("../scatter/get_trace_color");function c(i,s,u,h){var d=i.xa,m=i.ya,p=i.distance,g=i.dxy,y=i.index,v={pointNumber:y,x:s[y],y:u[y]};v.tx=Array.isArray(h.text)?h.text[y]:h.text,v.htx=Array.isArray(h.hovertext)?h.hovertext[y]:h.hovertext,v.data=Array.isArray(h.customdata)?h.customdata[y]:h.customdata,v.tp=Array.isArray(h.textposition)?h.textposition[y]:h.textposition;var x=h.textfont;x&&(v.ts=a.isArrayOrTypedArray(x.size)?x.size[y]:x.size,v.tc=Array.isArray(x.color)?x.color[y]:x.color,v.tf=Array.isArray(x.family)?x.family[y]:x.family);var _=h.marker;_&&(v.ms=a.isArrayOrTypedArray(_.size)?_.size[y]:_.size,v.mo=a.isArrayOrTypedArray(_.opacity)?_.opacity[y]:_.opacity,v.mx=a.isArrayOrTypedArray(_.symbol)?_.symbol[y]:_.symbol,v.mc=a.isArrayOrTypedArray(_.color)?_.color[y]:_.color);var A=_&&_.line;A&&(v.mlc=Array.isArray(A.color)?A.color[y]:A.color,v.mlw=a.isArrayOrTypedArray(A.width)?A.width[y]:A.width);var b=_&&_.gradient;b&&b.type!=="none"&&(v.mgt=Array.isArray(b.type)?b.type[y]:b.type,v.mgc=Array.isArray(b.color)?b.color[y]:b.color);var k=d.c2p(v.x,!0),w=m.c2p(v.y,!0),M=v.mrc||1,T=h.hoverlabel;T&&(v.hbg=Array.isArray(T.bgcolor)?T.bgcolor[y]:T.bgcolor,v.hbc=Array.isArray(T.bordercolor)?T.bordercolor[y]:T.bordercolor,v.hts=a.isArrayOrTypedArray(T.font.size)?T.font.size[y]:T.font.size,v.htc=Array.isArray(T.font.color)?T.font.color[y]:T.font.color,v.htf=Array.isArray(T.font.family)?T.font.family[y]:T.font.family,v.hnl=a.isArrayOrTypedArray(T.namelength)?T.namelength[y]:T.namelength);var E=h.hoverinfo;E&&(v.hi=Array.isArray(E)?E[y]:E);var S=h.hovertemplate;S&&(v.ht=Array.isArray(S)?S[y]:S);var P={};P[i.index]=v;var L=h._origX,R=h._origY,F=a.extendFlat({},i,{color:l(h,v),x0:k-M,x1:k+M,xLabelVal:L?L[y]:v.x,y0:w-M,y1:w+M,yLabelVal:R?R[y]:v.y,cd:P,distance:p,spikeDistance:g,hovertemplate:v.ht});return v.htx?F.text=v.htx:v.tx?F.text=v.tx:h.text&&(F.text=h.text),a.fillText(v,h,F),r.getComponentMethod("errorbars","hoverInfo")(v,h,F),F}o.exports={hoverPoints:function(i,s,u,h){var d,m,p,g,y,v,x,_,A,b,k=i.cd,w=k[0].t,M=k[0].trace,T=i.xa,E=i.ya,S=w.x,P=w.y,L=T.c2p(s),R=E.c2p(u),F=i.distance;if(w.tree){var D=T.p2c(L-F),O=T.p2c(L+F),N=E.p2c(R-F),B=E.p2c(R+F);d=h==="x"?w.tree.range(Math.min(D,O),Math.min(E._rl[0],E._rl[1]),Math.max(D,O),Math.max(E._rl[0],E._rl[1])):w.tree.range(Math.min(D,O),Math.min(N,B),Math.max(D,O),Math.max(N,B))}else d=w.ids;var W=F;if(h==="x"){var G=!!M.xperiodalignment,K=!!M.yperiodalignment;for(v=0;v<d.length;v++){if(g=S[m=d[v]],x=Math.abs(T.c2p(g)-L),G){var te=T.c2p(M._xStarts[m]),Y=T.c2p(M._xEnds[m]);x=L>=Math.min(te,Y)&&L<=Math.max(te,Y)?0:1/0}if(x<W){if(W=x,y=P[m],_=E.c2p(y)-R,K){var J=E.c2p(M._yStarts[m]),re=E.c2p(M._yEnds[m]);_=R>=Math.min(J,re)&&R<=Math.max(J,re)?0:1/0}b=Math.sqrt(x*x+_*_),p=d[v]}}}else for(v=d.length-1;v>-1;v--)g=S[m=d[v]],y=P[m],x=T.c2p(g)-L,_=E.c2p(y)-R,(A=Math.sqrt(x*x+_*_))<W&&(W=b=A,p=m);return i.index=p,i.distance=W,i.dxy=b,p===void 0?[i]:[c(i,S,P,M)]},calcHover:c}},{"../../lib":503,"../../registry":638,"../scatter/get_trace_color":937}],989:[function(t,o,f){arguments[4][896][0].apply(f,arguments)},{"./base_index":980,"./plot":990,dup:896}],990:[function(t,o,f){var r=t("regl-scatter2d"),a=t("regl-line2d"),l=t("regl-error2d"),c=t("gl-text"),i=t("../../lib"),s=t("../../components/dragelement/helpers").selectMode,u=t("../../lib/prepare_regl"),h=t("../scatter/subtypes"),d=t("../scatter/link_traces"),m=t("./edit_style").styleTextSelection,p={};function g(y,v,x,_){var A=y._size,b=y.width*_,k=y.height*_,w=A.l*_,M=A.b*_,T=A.r*_,E=A.t*_,S=A.w*_,P=A.h*_;return[w+v.domain[0]*S,M+x.domain[0]*P,b-T-(1-v.domain[1])*S,k-E-(1-x.domain[1])*P]}(o.exports=function(y,v,x){if(x.length){var _,A,b=y._fullLayout,k=v._scene,w=v.xaxis,M=v.yaxis;if(k)if(u(y,["ANGLE_instanced_arrays","OES_element_index_uint"],p)){var T=k.count,E=b._glcanvas.data()[0].regl;if(d(y,v,x),k.dirty){if(k.error2d===!0&&(k.error2d=l(E)),k.line2d===!0&&(k.line2d=a(E)),k.scatter2d===!0&&(k.scatter2d=r(E)),k.fill2d===!0&&(k.fill2d=a(E)),k.glText===!0)for(k.glText=new Array(T),_=0;_<T;_++)k.glText[_]=new c(E);if(k.glText){if(T>k.glText.length){var S=T-k.glText.length;for(_=0;_<S;_++)k.glText.push(new c(E))}else if(T<k.glText.length){var P=k.glText.length-T;k.glText.splice(T,P).forEach(function(Q){Q.destroy()})}for(_=0;_<T;_++)k.glText[_].update(k.textOptions[_])}if(k.line2d&&(k.line2d.update(k.lineOptions),k.lineOptions=k.lineOptions.map(function(Q){if(Q&&Q.positions){for(var ee=Q.positions,ie=0;ie<ee.length&&(isNaN(ee[ie])||isNaN(ee[ie+1]));)ie+=2;for(var ae=ee.length-2;ae>ie&&(isNaN(ee[ae])||isNaN(ee[ae+1]));)ae-=2;Q.positions=ee.slice(ie,ae+2)}return Q}),k.line2d.update(k.lineOptions)),k.error2d){var L=(k.errorXOptions||[]).concat(k.errorYOptions||[]);k.error2d.update(L)}k.scatter2d&&k.scatter2d.update(k.markerOptions),k.fillOrder=i.repeat(null,T),k.fill2d&&(k.fillOptions=k.fillOptions.map(function(Q,ee){var ie=x[ee];if(Q&&ie&&ie[0]&&ie[0].trace){var ae,ue,le=ie[0],ge=le.trace,fe=le.t,me=k.lineOptions[ee],_e=[];ge._ownfill&&_e.push(ee),ge._nexttrace&&_e.push(ee+1),_e.length&&(k.fillOrder[ee]=_e);var Ae,ke,Le=[],de=me&&me.positions||fe.positions;if(ge.fill==="tozeroy"){for(Ae=0;Ae<de.length&&isNaN(de[Ae+1]);)Ae+=2;for(ke=de.length-2;ke>Ae&&isNaN(de[ke+1]);)ke-=2;de[Ae+1]!==0&&(Le=[de[Ae],0]),Le=Le.concat(de.slice(Ae,ke+2)),de[ke+1]!==0&&(Le=Le.concat([de[ke],0]))}else if(ge.fill==="tozerox"){for(Ae=0;Ae<de.length&&isNaN(de[Ae]);)Ae+=2;for(ke=de.length-2;ke>Ae&&isNaN(de[ke]);)ke-=2;de[Ae]!==0&&(Le=[0,de[Ae+1]]),Le=Le.concat(de.slice(Ae,ke+2)),de[ke]!==0&&(Le=Le.concat([0,de[ke+1]]))}else if(ge.fill==="toself"||ge.fill==="tonext"){for(Le=[],ae=0,Q.splitNull=!0,ue=0;ue<de.length;ue+=2)(isNaN(de[ue])||isNaN(de[ue+1]))&&((Le=Le.concat(de.slice(ae,ue))).push(de[ae],de[ae+1]),Le.push(null,null),ae=ue+2);Le=Le.concat(de.slice(ae)),ae&&Le.push(de[ae],de[ae+1])}else{var ve=ge._nexttrace;if(ve){var Me=k.lineOptions[ee+1];if(Me){var we=Me.positions;if(ge.fill==="tonexty"){for(Le=de.slice(),ee=Math.floor(we.length/2);ee--;){var Ce=we[2*ee],Fe=we[2*ee+1];isNaN(Ce)||isNaN(Fe)||Le.push(Ce,Fe)}Q.fill=ve.fillcolor}}}}if(ge._prevtrace&&ge._prevtrace.fill==="tonext"){var ze=k.lineOptions[ee-1].positions,$e=Le.length/2,Ke=[ae=$e];for(ue=0;ue<ze.length;ue+=2)(isNaN(ze[ue])||isNaN(ze[ue+1]))&&(Ke.push(ue/2+$e+1),ae=ue+2);Le=Le.concat(ze),Q.hole=Ke}return Q.fillmode=ge.fill,Q.opacity=ge.opacity,Q.positions=Le,Q}}),k.fill2d.update(k.fillOptions))}var R=b.dragmode,F=s(R),D=b.clickmode.indexOf("select")>-1;for(_=0;_<T;_++){var O=x[_][0],N=O.trace,B=O.t,W=B.index,G=N._length,K=B.x,te=B.y;if(N.selectedpoints||F||D){if(F||(F=!0),N.selectedpoints){var Y=k.selectBatch[W]=i.selIndices2selPoints(N),J={};for(A=0;A<Y.length;A++)J[Y[A]]=1;var re=[];for(A=0;A<G;A++)J[A]||re.push(A);k.unselectBatch[W]=re}var U=B.xpx=new Array(G),V=B.ypx=new Array(G);for(A=0;A<G;A++)U[A]=w.c2p(K[A]),V[A]=M.c2p(te[A])}else B.xpx=B.ypx=null}if(F){if(k.select2d||(k.select2d=r(b._glcanvas.data()[1].regl)),k.scatter2d){var H=new Array(T);for(_=0;_<T;_++)H[_]=k.selectBatch[_].length||k.unselectBatch[_].length?k.markerUnselectedOptions[_]:{};k.scatter2d.update(H)}k.select2d&&(k.select2d.update(k.markerOptions),k.select2d.update(k.markerSelectedOptions)),k.glText&&x.forEach(function(Q){var ee=((Q||[])[0]||{}).trace||{};h.hasText(ee)&&m(Q)})}else k.scatter2d&&k.scatter2d.update(k.markerOptions);var ne={viewport:g(b,w,M,y._context.plotGlPixelRatio),range:[(w._rl||w.range)[0],(M._rl||M.range)[0],(w._rl||w.range)[1],(M._rl||M.range)[1]]},q=i.repeat(ne,k.count);k.fill2d&&k.fill2d.update(q),k.line2d&&k.line2d.update(q),k.error2d&&k.error2d.update(q.concat(q)),k.scatter2d&&k.scatter2d.update(q),k.select2d&&k.select2d.update(q),k.glText&&k.glText.forEach(function(Q){Q.update(ne)})}else k.init()}}).reglPrecompiled=p},{"../../components/dragelement/helpers":384,"../../lib":503,"../../lib/prepare_regl":516,"../scatter/link_traces":943,"../scatter/subtypes":952,"./edit_style":985,"gl-text":225,"regl-error2d":279,"regl-line2d":280,"regl-scatter2d":281}],991:[function(t,o,f){var r=t("../../lib");o.exports=function(a,l){var c=l._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},s={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return l._scene||((c=l._scene={}).init=function(){r.extendFlat(c,s,i)},c.init(),c.update=function(u){var h=r.repeat(u,c.count);if(c.fill2d&&c.fill2d.update(h),c.scatter2d&&c.scatter2d.update(h),c.line2d&&c.line2d.update(h),c.error2d&&c.error2d.update(h.concat(h)),c.select2d&&c.select2d.update(h),c.glText)for(var d=0;d<c.count;d++)c.glText[d].update(u)},c.draw=function(){for(var u=c.count,h=c.fill2d,d=c.error2d,m=c.line2d,p=c.scatter2d,g=c.glText,y=c.select2d,v=c.selectBatch,x=c.unselectBatch,_=0;_<u;_++){if(h&&c.fillOrder[_]&&h.draw(c.fillOrder[_]),m&&c.lineOptions[_]&&m.draw(_),d&&(c.errorXOptions[_]&&d.draw(_),c.errorYOptions[_]&&d.draw(_+u)),p&&c.markerOptions[_])if(x[_].length){var A=r.repeat([],c.count);A[_]=x[_],p.draw(A)}else v[_].length||p.draw(_);g[_]&&c.textOptions[_]&&g[_].render()}y&&y.draw(v),c.dirty=!1},c.destroy=function(){c.fill2d&&c.fill2d.destroy&&c.fill2d.destroy(),c.scatter2d&&c.scatter2d.destroy&&c.scatter2d.destroy(),c.error2d&&c.error2d.destroy&&c.error2d.destroy(),c.line2d&&c.line2d.destroy&&c.line2d.destroy(),c.select2d&&c.select2d.destroy&&c.select2d.destroy(),c.glText&&c.glText.forEach(function(u){u.destroy&&u.destroy()}),c.lineOptions=null,c.fillOptions=null,c.markerOptions=null,c.markerSelectedOptions=null,c.markerUnselectedOptions=null,c.errorXOptions=null,c.errorYOptions=null,c.textOptions=null,c.textSelectedOptions=null,c.textUnselectedOptions=null,c.selectBatch=null,c.unselectBatch=null,l._scene=null}),c.dirty||r.extendFlat(c,i),c}},{"../../lib":503}],992:[function(t,o,f){var r=t("../scatter/subtypes"),a=t("./edit_style").styleTextSelection;o.exports=function(l,c){var i=l.cd,s=l.xaxis,u=l.yaxis,h=[],d=i[0].trace,m=i[0].t,p=d._length,g=m.x,y=m.y,v=m._scene,x=m.index;if(!v)return h;var _=r.hasText(d),A=r.hasMarkers(d),b=!A&&!_;if(d.visible!==!0||b)return h;var k=[],w=[];if(c!==!1&&!c.degenerate)for(var M=0;M<p;M++)c.contains([m.xpx[M],m.ypx[M]],!1,M,l)?(k.push(M),h.push({pointNumber:M,x:s.c2d(g[M]),y:u.c2d(y[M])})):w.push(M);if(A){var T=v.scatter2d;if(k.length||w.length){if(!v.selectBatch[x].length&&!v.unselectBatch[x].length){var E=new Array(v.count);E[x]=v.markerUnselectedOptions[x],T.update.apply(T,E)}}else{var S=new Array(v.count);S[x]=v.markerOptions[x],T.update.apply(T,S)}}return v.selectBatch[x]=k,v.unselectBatch[x]=w,_&&a(i),h}},{"../scatter/subtypes":952,"./edit_style":985}],993:[function(t,o,f){var r=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../plots/template_attributes").texttemplateAttrs,l=t("../scattergeo/attributes"),c=t("../scatter/attributes"),i=t("../../plots/mapbox/layout_attributes"),s=t("../../plots/attributes"),u=t("../../components/colorscale/attributes"),h=t("../../lib/extend").extendFlat,d=t("../../plot_api/edit_types").overrideAll,m=l.line,p=l.marker;o.exports=d({lon:l.lon,lat:l.lat,mode:h({},c.mode,{dflt:"markers"}),text:h({},c.text,{}),texttemplate:a({editType:"plot"},{keys:["lat","lon","text"]}),hovertext:h({},c.hovertext,{}),line:{color:m.color,width:m.width},connectgaps:c.connectgaps,marker:h({symbol:{valType:"string",dflt:"circle",arrayOk:!0},angle:{valType:"number",dflt:"auto",arrayOk:!0},allowoverlap:{valType:"boolean",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},u("marker")),fill:l.fill,fillcolor:c.fillcolor,textfont:i.layers.symbol.textfont,textposition:i.layers.symbol.textposition,below:{valType:"string"},selected:{marker:c.selected.marker},unselected:{marker:c.unselected.marker},hoverinfo:h({},s.hoverinfo,{flags:["lon","lat","text","name"]}),hovertemplate:r()},"calc","nested")},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/mapbox/layout_attributes":615,"../../plots/template_attributes":633,"../scatter/attributes":927,"../scattergeo/attributes":969}],994:[function(t,o,f){var r=t("fast-isnumeric"),a=t("../../lib"),l=t("../../constants/numerical").BADNUM,c=t("../../lib/geojson_utils"),i=t("../../components/colorscale"),s=t("../../components/drawing"),u=t("../scatter/make_bubble_size_func"),h=t("../scatter/subtypes"),d=t("../../plots/mapbox/convert_text_opts"),m=t("../../components/fx/helpers").appendArrayPointValue,p=t("../../lib/svg_text_utils").NEWLINES,g=t("../../lib/svg_text_utils").BR_TAG_ALL;function y(){return{geojson:c.makeBlank(),layout:{visibility:"none"},paint:{}}}function v(A,b){return a.isArrayOrTypedArray(A)?b?function(k){return r(A[k])?+A[k]:0}:function(k){return A[k]}:A?function(){return A}:x}function x(){return""}function _(A){return A[0]===l}o.exports=function(A,b){var k,w=b[0].trace,M=w.visible===!0&&w._length!==0,T=w.fill!=="none",E=h.hasLines(w),S=h.hasMarkers(w),P=h.hasText(w),L=S&&w.marker.symbol==="circle",R=S&&w.marker.symbol!=="circle",F=y(),D=y(),O=y(),N=y(),B={fill:F,line:D,circle:O,symbol:N};if(!M)return B;if((T||E)&&(k=c.calcTraceToLineCoords(b)),T&&(F.geojson=c.makePolygon(k),F.layout.visibility="visible",a.extendFlat(F.paint,{"fill-color":w.fillcolor})),E&&(D.geojson=c.makeLine(k),D.layout.visibility="visible",a.extendFlat(D.paint,{"line-width":w.line.width,"line-color":w.line.color,"line-opacity":w.opacity})),L){var W=function(te){var Y,J,re,U,V=te[0].trace,H=V.marker,ne=V.selectedpoints,q=a.isArrayOrTypedArray(H.color),Q=a.isArrayOrTypedArray(H.size),ee=a.isArrayOrTypedArray(H.opacity);function ie(Ae){return V.opacity*Ae}q&&(J=i.hasColorscale(V,"marker")?i.makeColorScaleFuncFromTrace(H):a.identity),Q&&(re=u(V)),ee&&(U=function(Ae){return ie(r(Ae)?+a.constrain(Ae,0,1):0)});var ae,ue=[];for(Y=0;Y<te.length;Y++){var le=te[Y],ge=le.lonlat;if(!_(ge)){var 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r=t("../../registry"),a=t("../../components/grid");o.exports={moduleType:"trace",name:"splom",categories:["gl","regl","cartesian","symbols","showLegend","scatter-like"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:t("../scatter/marker_colorbar"),calc:t("./calc"),plot:t("./plot"),hoverPoints:t("./hover").hoverPoints,selectPoints:t("./select"),editStyle:t("./edit_style"),meta:{}},r.register(a)},{"../../components/grid":410,"../../registry":638,"../scatter/marker_colorbar":945,"./attributes":1032,"./calc":1035,"./defaults":1036,"./edit_style":1037,"./hover":1039,"./plot":1041,"./select":1043}],1034:[function(t,o,f){var r=t("regl-line2d"),a=t("../../registry"),l=t("../../lib/prepare_regl"),c=t("../../plots/get_data").getModuleCalcData,i=t("../../plots/cartesian"),s=t("../../plots/cartesian/axis_ids").getFromId,u=t("../../plots/cartesian/axes").shouldShowZeroLine,h={};function d(p,g,y){for(var v=y.matrixOptions.data.length,x=g._visibleDims,_=y.viewOpts.ranges=new 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x=g[v][0].trace,_=y._splomScenes[x.uid];x.type==="splom"&&_&&_.matrix&&d(p,x,_)}},updateGrid:m,clean:function(p,g,y,v){var x,_={};if(v._splomScenes){for(x=0;x<p.length;x++){var A=p[x];A.type==="splom"&&(_[A.uid]=1)}for(x=0;x<y.length;x++){var b=y[x];if(!_[b.uid]){var k=v._splomScenes[b.uid];k&&k.destroy&&k.destroy(),v._splomScenes[b.uid]=null,delete v._splomScenes[b.uid]}}}Object.keys(v._splomScenes||{}).length===0&&delete v._splomScenes,v._splomGrid&&!g._hasOnlyLargeSploms&&v._hasOnlyLargeSploms&&(v._splomGrid.destroy(),v._splomGrid=null,delete v._splomGrid),i.clean(p,g,y,v)},updateFx:i.updateFx,toSVG:i.toSVG,reglPrecompiled:h}},{"../../lib/prepare_regl":516,"../../plots/cartesian":568,"../../plots/cartesian/axes":554,"../../plots/cartesian/axis_ids":558,"../../plots/get_data":593,"../../registry":638,"regl-line2d":280}],1035:[function(t,o,f){var 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R=s._context.plotGlPixelRatio,F=v.l*R,D=v.b*R,O=v.w*R,N=v.h*R;S.viewport=[F,D,O+F,N+D],A.matrix===!0&&(A.matrix=r(w));var B=y.clickmode.indexOf("select")>-1,W=!0;if(c(M)||x.selectedpoints||B){var G=x._length;if(x.selectedpoints){A.selectBatch=x.selectedpoints;var K=x.selectedpoints,te={};for(m=0;m<K.length;m++)te[K[m]]=!0;var Y=[];for(m=0;m<G;m++)te[m]||Y.push(m);A.unselectBatch=Y}var J=_.xpx=new Array(E),re=_.ypx=new Array(E);for(g=0;g<T.length;g++){if(m=T[g],h=l.getFromId(s,x._diag[m][0]))for(J[g]=new Array(G),p=0;p<G;p++)J[g][p]=h.c2p(k[g][p]);if(d=l.getFromId(s,x._diag[m][1]))for(re[g]=new Array(G),p=0;p<G;p++)re[g][p]=d.c2p(k[g][p])}if(A.selectBatch.length||A.unselectBatch.length){var U=a.extendFlat({},b,A.unselectedOptions,S),V=a.extendFlat({},b,A.selectedOptions,S);A.matrix.update(U,V),W=!1}}else _.xpx=_.ypx=null;if(W){var H=a.extendFlat({},b,S);A.matrix.update(H,null)}}}o.exports=function(s,u,h){if(h.length)for(var 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ae=q(u,h,d),ue=q(u,h,d+1),le=q(u,h+1,d),ge=q(u+1,h,d);if(Y[ae]*Q<Y[ue]*Q&&J[ae]*ee<J[le]*ee&&re[ae]*ie<re[ge]*ie||(ne=!0),ne)break}if(ne)break}if(ne)break}return ne&&(r.warn("Encountered arbitrary coordinates! 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w=d[y];h.xMax=Math.max(h.xMax,w),h.xMin=Math.min(h.xMin,w);var M=m[y];h.yMax=Math.max(h.yMax,M),h.yMin=Math.min(h.yMin,M);var T=p[y];h.zMax=Math.max(h.zMax,T),h.zMin=Math.min(h.zMin,T)}u._slen=g,u._normMax=v,u._xbnds=[h.xMin,h.xMax],u._ybnds=[h.yMin,h.yMax],u._zbnds=[h.zMin,h.zMax]},filter:i,processGrid:l}},{"../../components/colorscale/calc":374,"../../lib":503}],1046:[function(t,o,f){var r=t("../../../stackgl_modules").gl_streamtube3d,a=r.createTubeMesh,l=t("../../lib"),c=t("../../lib/gl_format_color").parseColorScale,i=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3"),u={xaxis:0,yaxis:1,zaxis:2};function h(y,v){this.scene=y,this.uid=v,this.mesh=null,this.data=null}var d=h.prototype;function m(y){var v=y.length;return v>2?y.slice(1,v-1):v===2?[(y[0]+y[1])/2]:y}function p(y){var v=y.length;return v===1?[.5,.5]:[y[1]-y[0],y[v-1]-y[v-2]]}function g(y,v){var x=y.fullSceneLayout,_=y.dataScale,A=v._len,b={};function k(U,V){var H=x[V],ne=_[u[V]];return l.simpleMap(U,function(q){return H.d2l(q)*ne})}if(b.vectors=s(k(v._u,"xaxis"),k(v._v,"yaxis"),k(v._w,"zaxis"),A),!A)return{positions:[],cells:[]};var w=k(v._Xs,"xaxis"),M=k(v._Ys,"yaxis"),T=k(v._Zs,"zaxis");if(b.meshgrid=[w,M,T],b.gridFill=v._gridFill,v._slen)b.startingPositions=s(k(v._startsX,"xaxis"),k(v._startsY,"yaxis"),k(v._startsZ,"zaxis"));else{for(var E=M[0],S=m(w),P=m(T),L=new Array(S.length*P.length),R=0,F=0;F<S.length;F++)for(var D=0;D<P.length;D++)L[R++]=[S[F],E,P[D]];b.startingPositions=L}b.colormap=c(v),b.tubeSize=v.sizeref,b.maxLength=v.maxdisplayed;var O=k(v._xbnds,"xaxis"),N=k(v._ybnds,"yaxis"),B=k(v._zbnds,"zaxis"),W=p(w),G=p(M),K=p(T),te=[[O[0]-W[0],N[0]-G[0],B[0]-K[0]],[O[1]+W[1],N[1]+G[1],B[1]+K[1]]],Y=r(b,te),J=i(v);Y.vertexIntensityBounds=[J.min/v._normMax,J.max/v._normMax];var re=v.lightposition;return Y.lightPosition=[re.x,re.y,re.z],Y.ambient=v.lighting.ambient,Y.diffuse=v.lighting.diffuse,Y.specular=v.lighting.specular,Y.roughness=v.lighting.roughness,Y.fresnel=v.lighting.fresnel,Y.opacity=v.opacity,v._pad=Y.tubeScale*v.sizeref*2,Y}d.handlePick=function(y){var v=this.scene.fullSceneLayout,x=this.scene.dataScale;function _(k,w){var M=v[w],T=x[u[w]];return M.l2c(k)/T}if(y.object===this.mesh){var A=y.data.position,b=y.data.velocity;return y.traceCoordinate=[_(A[0],"xaxis"),_(A[1],"yaxis"),_(A[2],"zaxis"),_(b[0],"xaxis"),_(b[1],"yaxis"),_(b[2],"zaxis"),y.data.intensity*this.data._normMax,y.data.divergence],y.textLabel=this.data.hovertext||this.data.text,!0}},d.update=function(y){this.data=y;var v=g(this.scene,y);this.mesh.update(v)},d.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},o.exports=function(y,v){var x=y.glplot.gl,_=g(y,v),A=a(x,_),b=new h(y,v.uid);return b.mesh=A,b.data=v,A._trace=b,y.glplot.add(A),b}},{"../../../stackgl_modules":1124,"../../components/colorscale":378,"../../lib":503,"../../lib/gl_format_color":499,"../../plots/gl3d/zip3":609}],1047:[function(t,o,f){var r=t("../../lib"),a=t("../../components/colorscale/defaults"),l=t("./attributes");o.exports=function(c,i,s,u){function h(x,_){return r.coerce(c,i,l,x,_)}var d=h("u"),m=h("v"),p=h("w"),g=h("x"),y=h("y"),v=h("z");d&&d.length&&m&&m.length&&p&&p.length&&g&&g.length&&y&&y.length&&v&&v.length?(h("starts.x"),h("starts.y"),h("starts.z"),h("maxdisplayed"),h("sizeref"),h("lighting.ambient"),h("lighting.diffuse"),h("lighting.specular"),h("lighting.roughness"),h("lighting.fresnel"),h("lightposition.x"),h("lightposition.y"),h("lightposition.z"),a(c,i,u,h,{prefix:"",cLetter:"c"}),h("text"),h("hovertext"),h("hovertemplate"),h("uhoverformat"),h("vhoverformat"),h("whoverformat"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),i._length=null):i.visible=!1}},{"../../components/colorscale/defaults":376,"../../lib":503,"./attributes":1044}],1048:[function(t,o,f){o.exports={moduleType:"trace",name:"streamtube",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc").calc,plot:t("./convert"),eventData:function(r,a){return r.tubex=r.x,r.tubey=r.y,r.tubez=r.z,r.tubeu=a.traceCoordinate[3],r.tubev=a.traceCoordinate[4],r.tubew=a.traceCoordinate[5],r.norm=a.traceCoordinate[6],r.divergence=a.traceCoordinate[7],delete r.x,delete r.y,delete r.z,r},meta:{}}},{"../../plots/gl3d":598,"./attributes":1044,"./calc":1045,"./convert":1046,"./defaults":1047}],1049:[function(t,o,f){var r=t("../../plots/attributes"),a=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/template_attributes").texttemplateAttrs,c=t("../../components/colorscale/attributes"),i=t("../../plots/domain").attributes,s=t("../pie/attributes"),u=t("./constants"),h=t("../../lib/extend").extendFlat;o.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:h({colors:{valType:"data_array",editType:"calc"},line:{color:h({},s.marker.line.color,{dflt:null}),width:h({},s.marker.line.width,{dflt:1}),editType:"calc"},editType:"calc"},c("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:s.text,textinfo:{valType:"flaglist",flags:["label","text","value","current path","percent root","percent entry","percent parent"],extras:["none"],editType:"plot"},texttemplate:l({editType:"plot"},{keys:u.eventDataKeys.concat(["label","value"])}),hovertext:s.hovertext,hoverinfo:h({},r.hoverinfo,{flags:["label","text","value","name","current path","percent root","percent entry","percent parent"],dflt:"label+text+value+name"}),hovertemplate:a({},{keys:u.eventDataKeys}),textfont:s.textfont,insidetextorientation:s.insidetextorientation,insidetextfont:s.insidetextfont,outsidetextfont:h({},s.outsidetextfont,{}),rotation:{valType:"angle",dflt:0,editType:"plot"},sort:s.sort,root:{color:{valType:"color",editType:"calc",dflt:"rgba(0,0,0,0)"},editType:"calc"},domain:i({name:"sunburst",trace:!0,editType:"calc"})}},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/domain":584,"../../plots/template_attributes":633,"../pie/attributes":901,"./constants":1052}],1050:[function(t,o,f){var r=t("../../plots/plots");f.name="sunburst",f.plot=function(a,l,c,i){r.plotBasePlot(f.name,a,l,c,i)},f.clean=function(a,l,c,i){r.cleanBasePlot(f.name,a,l,c,i)}},{"../../plots/plots":619}],1051:[function(t,o,f){var r=t("d3-hierarchy"),a=t("fast-isnumeric"),l=t("../../lib"),c=t("../../components/colorscale").makeColorScaleFuncFromTrace,i=t("../pie/calc").makePullColorFn,s=t("../pie/calc").generateExtendedColors,u=t("../../components/colorscale").calc,h=t("../../constants/numerical").ALMOST_EQUAL,d={},m={},p={};f.calc=function(g,y){var v,x,_,A,b,k,w=g._fullLayout,M=y.ids,T=l.isArrayOrTypedArray(M),E=y.labels,S=y.parents,P=y.values,L=l.isArrayOrTypedArray(P),R=[],F={},D={},O=function(ee){return ee||typeof ee=="number"},N=function(ee){return!L||a(P[ee])&&P[ee]>=0};T?(v=Math.min(M.length,S.length),x=function(ee){return O(M[ee])&&N(ee)},_=function(ee){return String(M[ee])}):(v=Math.min(E.length,S.length),x=function(ee){return O(E[ee])&&N(ee)},_=function(ee){return String(E[ee])}),L&&(v=Math.min(v,P.length));for(var B=0;B<v;B++)if(x(B)){var W=_(B),G=O(S[B])?String(S[B]):"",K={i:B,id:W,pid:G,label:O(E[B])?String(E[B]):""};L&&(K.v=+P[B]),R.push(K),b=W,F[A=G]?F[A].push(b):F[A]=[b],D[b]=1}if(F[""]){if(F[""].length>1){for(var te=l.randstr(),Y=0;Y<R.length;Y++)R[Y].pid===""&&(R[Y].pid=te);R.unshift({hasMultipleRoots:!0,id:te,pid:"",label:""})}}else{var J,re=[];for(J in F)D[J]||re.push(J);if(re.length!==1)return l.warn(["Multiple implied roots, cannot build",y.type,"hierarchy of",y.name+".","These roots include:",re.join(", ")].join(" "));J=re[0],R.unshift({hasImpliedRoot:!0,id:J,pid:"",label:J})}try{k=r.stratify().id(function(ee){return ee.id}).parentId(function(ee){return ee.pid})(R)}catch(ee){return l.warn(["Failed to build",y.type,"hierarchy of",y.name+".","Error:",ee.message].join(" "))}var U=r.hierarchy(k),V=!1;if(L)switch(y.branchvalues){case"remainder":U.sum(function(ee){return ee.data.v});break;case"total":U.each(function(ee){var ie=ee.data.data,ae=ie.v;if(ee.children){var ue=ee.children.reduce(function(le,ge){return le+ge.data.data.v},0);if((ie.hasImpliedRoot||ie.hasMultipleRoots)&&(ae=ue),ae<ue*h)return V=!0,l.warn(["Total value for node",ee.data.data.id,"of",y.name,"is smaller than the sum of its children.",`
-parent value =`,ae,`
-children sum =`,ue].join(" "))}ee.value=ae})}else(function ee(ie,ae,ue){var le=0,ge=ie.children;if(ge){for(var fe=ge.length,me=0;me<fe;me++)le+=ee(ge[me],ae,ue);ue.branches&&le++}else ue.leaves&&le++;return ie.value=ie.data.data.value=le,ae._values||(ae._values=[]),ae._values[ie.data.data.i]=le,le})(U,y,{branches:y.count.indexOf("branches")!==-1,leaves:y.count.indexOf("leaves")!==-1});if(!V){var H,ne;y.sort&&U.sort(function(ee,ie){return ie.value-ee.value});var q=y.marker.colors||[],Q=!!q.length;return y._hasColorscale?(Q||(q=L?y.values:y._values),u(g,y,{vals:q,containerStr:"marker",cLetter:"c"}),ne=c(y.marker)):H=i(w["_"+y.type+"colormap"]),U.each(function(ee){var ie=ee.data.data;ie.color=y._hasColorscale?ne(q[ie.i]):H(q[ie.i],ie.id)}),R[0].hierarchy=U,R}},f._runCrossTraceCalc=function(g,y){var v=y._fullLayout,x=y.calcdata,_=v[g+"colorway"],A=v["_"+g+"colormap"];v["extend"+g+"colors"]&&(_=s(_,g==="icicle"?p:g==="treemap"?m:d));var b,k=0;function w(E){var S=E.data.data,P=S.id;S.color===!1&&(A[P]?S.color=A[P]:E.parent?E.parent.parent?S.color=E.parent.data.data.color:(A[P]=S.color=_[k%_.length],k++):S.color=b)}for(var M=0;M<x.length;M++){var T=x[M][0];T.trace.type===g&&T.hierarchy&&(b=T.trace.root.color,T.hierarchy.each(w))}},f.crossTraceCalc=function(g){return f._runCrossTraceCalc("sunburst",g)}},{"../../components/colorscale":378,"../../constants/numerical":479,"../../lib":503,"../pie/calc":903,"d3-hierarchy":115,"fast-isnumeric":190}],1052:[function(t,o,f){o.exports={CLICK_TRANSITION_TIME:750,CLICK_TRANSITION_EASING:"linear",eventDataKeys:["currentPath","root","entry","percentRoot","percentEntry","percentParent"]}},{}],1053:[function(t,o,f){var r=t("../../lib"),a=t("./attributes"),l=t("../../plots/domain").defaults,c=t("../bar/defaults").handleText,i=t("../../components/colorscale"),s=i.hasColorscale,u=i.handleDefaults;o.exports=function(h,d,m,p){function g(b,k){return r.coerce(h,d,a,b,k)}var y=g("labels"),v=g("parents");if(y&&y.length&&v&&v.length){var x=g("values");x&&x.length?g("branchvalues"):g("count"),g("level"),g("maxdepth"),g("marker.line.width")&&g("marker.line.color",p.paper_bgcolor),g("marker.colors");var _=d._hasColorscale=s(h,"marker","colors")||(h.marker||{}).coloraxis;_&&u(h,d,p,g,{prefix:"marker.",cLetter:"c"}),g("leaf.opacity",_?1:.7);var A=g("text");g("texttemplate"),d.texttemplate||g("textinfo",Array.isArray(A)?"text+label":"label"),g("hovertext"),g("hovertemplate"),c(h,d,p,g,"auto",{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),g("insidetextorientation"),g("sort"),g("rotation"),g("root.color"),l(d,p,g),d._length=null}else d.visible=!1}},{"../../components/colorscale":378,"../../lib":503,"../../plots/domain":584,"../bar/defaults":652,"./attributes":1049}],1054:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../registry"),l=t("../../components/fx/helpers").appendArrayPointValue,c=t("../../components/fx"),i=t("../../lib"),s=t("../../lib/events"),u=t("./helpers"),h=t("../pie/helpers").formatPieValue;function d(m,p,g){for(var y=m.data.data,v={curveNumber:p.index,pointNumber:y.i,data:p._input,fullData:p},x=0;x<g.length;x++){var _=g[x];_ in m&&(v[_]=m[_])}return"parentString"in m&&!u.isHierarchyRoot(m)&&(v.parent=m.parentString),l(v,p,y.i),v}o.exports=function(m,p,g,y,v){var x=y[0],_=x.trace,A=x.hierarchy,b=_.type==="sunburst",k=_.type==="treemap"||_.type==="icicle";"_hasHoverLabel"in _||(_._hasHoverLabel=!1),"_hasHoverEvent"in _||(_._hasHoverEvent=!1),m.on("mouseover",function(w){var M=g._fullLayout;if(!g._dragging&&M.hovermode!==!1){var T,E=g._fullData[_.index],S=w.data.data,P=S.i,L=u.isHierarchyRoot(w),R=u.getParent(A,w),F=u.getValue(w),D=function(Q){return i.castOption(E,P,Q)},O=D("hovertemplate"),N=c.castHoverinfo(E,M,P),B=M.separators;if(O||N&&N!=="none"&&N!=="skip"){var W,G;b&&(W=x.cx+w.pxmid[0]*(1-w.rInscribed),G=x.cy+w.pxmid[1]*(1-w.rInscribed)),k&&(W=w._hoverX,G=w._hoverY);var K,te={},Y=[],J=[],re=function(Q){return Y.indexOf(Q)!==-1};N&&(Y=N==="all"?E._module.attributes.hoverinfo.flags:N.split("+")),te.label=S.label,re("label")&&te.label&&J.push(te.label),S.hasOwnProperty("v")&&(te.value=S.v,te.valueLabel=h(te.value,B),re("value")&&J.push(te.valueLabel)),te.currentPath=w.currentPath=u.getPath(w.data),re("current path")&&!L&&J.push(te.currentPath);var U=[],V=function(){U.indexOf(K)===-1&&(J.push(K),U.push(K))};te.percentParent=w.percentParent=F/u.getValue(R),te.parent=w.parentString=u.getPtLabel(R),re("percent parent")&&(K=u.formatPercent(te.percentParent,B)+" of "+te.parent,V()),te.percentEntry=w.percentEntry=F/u.getValue(p),te.entry=w.entry=u.getPtLabel(p),!re("percent entry")||L||w.onPathbar||(K=u.formatPercent(te.percentEntry,B)+" of "+te.entry,V()),te.percentRoot=w.percentRoot=F/u.getValue(A),te.root=w.root=u.getPtLabel(A),re("percent root")&&!L&&(K=u.formatPercent(te.percentRoot,B)+" of "+te.root,V()),te.text=D("hovertext")||D("text"),re("text")&&(K=te.text,i.isValidTextValue(K)&&J.push(K)),T=[d(w,E,v.eventDataKeys)];var H={trace:E,y:G,_x0:w._x0,_x1:w._x1,_y0:w._y0,_y1:w._y1,text:J.join("<br>"),name:O||re("name")?E.name:void 0,color:D("hoverlabel.bgcolor")||S.color,borderColor:D("hoverlabel.bordercolor"),fontFamily:D("hoverlabel.font.family"),fontSize:D("hoverlabel.font.size"),fontColor:D("hoverlabel.font.color"),nameLength:D("hoverlabel.namelength"),textAlign:D("hoverlabel.align"),hovertemplate:O,hovertemplateLabels:te,eventData:T};b&&(H.x0=W-w.rInscribed*w.rpx1,H.x1=W+w.rInscribed*w.rpx1,H.idealAlign=w.pxmid[0]<0?"left":"right"),k&&(H.x=W,H.idealAlign=W<0?"left":"right");var ne=[];c.loneHover(H,{container:M._hoverlayer.node(),outerContainer:M._paper.node(),gd:g,inOut_bbox:ne}),T[0].bbox=ne[0],_._hasHoverLabel=!0}if(k){var q=m.select("path.surface");v.styleOne(q,w,E,{hovered:!0})}_._hasHoverEvent=!0,g.emit("plotly_hover",{points:T||[d(w,E,v.eventDataKeys)],event:r.event})}}),m.on("mouseout",function(w){var M=g._fullLayout,T=g._fullData[_.index],E=r.select(this).datum();if(_._hasHoverEvent&&(w.originalEvent=r.event,g.emit("plotly_unhover",{points:[d(E,T,v.eventDataKeys)],event:r.event}),_._hasHoverEvent=!1),_._hasHoverLabel&&(c.loneUnhover(M._hoverlayer.node()),_._hasHoverLabel=!1),k){var S=m.select("path.surface");v.styleOne(S,E,T,{hovered:!1})}}),m.on("click",function(w){var M=g._fullLayout,T=g._fullData[_.index],E=b&&(u.isHierarchyRoot(w)||u.isLeaf(w)),S=u.getPtId(w),P=u.isEntry(w)?u.findEntryWithChild(A,S):u.findEntryWithLevel(A,S),L=u.getPtId(P),R={points:[d(w,T,v.eventDataKeys)],event:r.event};E||(R.nextLevel=L);var F=s.triggerHandler(g,"plotly_"+_.type+"click",R);if(F!==!1&&M.hovermode&&(g._hoverdata=[d(w,T,v.eventDataKeys)],c.click(g,r.event)),!E&&F!==!1&&!g._dragging&&!g._transitioning){a.call("_storeDirectGUIEdit",T,M._tracePreGUI[T.uid],{level:T.level});var D={data:[{level:L}],traces:[_.index]},O={frame:{redraw:!1,duration:v.transitionTime},transition:{duration:v.transitionTime,easing:v.transitionEasing},mode:"immediate",fromcurrent:!0};c.loneUnhover(M._hoverlayer.node()),a.call("animate",g,D,O)}})}},{"../../components/fx":406,"../../components/fx/helpers":402,"../../lib":503,"../../lib/events":492,"../../registry":638,"../pie/helpers":906,"./helpers":1055,"@plotly/d3":58}],1055:[function(t,o,f){var r=t("../../lib"),a=t("../../components/color"),l=t("../../lib/setcursor"),c=t("../pie/helpers");function i(s){return s.data.data.pid}f.findEntryWithLevel=function(s,u){var h;return u&&s.eachAfter(function(d){if(f.getPtId(d)===u)return h=d.copy()}),h||s},f.findEntryWithChild=function(s,u){var h;return s.eachAfter(function(d){for(var m=d.children||[],p=0;p<m.length;p++){var g=m[p];if(f.getPtId(g)===u)return h=d.copy()}}),h||s},f.isEntry=function(s){return!s.parent},f.isLeaf=function(s){return!s.children},f.getPtId=function(s){return s.data.data.id},f.getPtLabel=function(s){return s.data.data.label},f.getValue=function(s){return s.value},f.isHierarchyRoot=function(s){return i(s)===""},f.setSliceCursor=function(s,u,h){var d=h.isTransitioning;if(!d){var m=s.datum();d=h.hideOnRoot&&f.isHierarchyRoot(m)||h.hideOnLeaves&&f.isLeaf(m)}l(s,d?null:"pointer")},f.getInsideTextFontKey=function(s,u,h,d,m){var p=(m||{}).onPathbar?"pathbar.textfont":"insidetextfont",g=h.data.data.i;return r.castOption(u,g,p+"."+s)||r.castOption(u,g,"textfont."+s)||d.size},f.getOutsideTextFontKey=function(s,u,h,d){var m=h.data.data.i;return r.castOption(u,m,"outsidetextfont."+s)||r.castOption(u,m,"textfont."+s)||d.size},f.isOutsideText=function(s,u){return!s._hasColorscale&&f.isHierarchyRoot(u)},f.determineTextFont=function(s,u,h,d){return f.isOutsideText(s,u)?function(m,p,g){return{color:f.getOutsideTextFontKey("color",m,p,g),family:f.getOutsideTextFontKey("family",m,p,g),size:f.getOutsideTextFontKey("size",m,p,g)}}(s,u,h):function(m,p,g,y){var v=(y||{}).onPathbar,x=p.data.data,_=x.i,A=r.castOption(m,_,(v?"pathbar.textfont":"insidetextfont")+".color");return!A&&m._input.textfont&&(A=r.castOption(m._input,_,"textfont.color")),{color:A||a.contrast(x.color),family:f.getInsideTextFontKey("family",m,p,g,y),size:f.getInsideTextFontKey("size",m,p,g,y)}}(s,u,h,d)},f.hasTransition=function(s){return!!(s&&s.duration>0)},f.getMaxDepth=function(s){return s.maxdepth>=0?s.maxdepth:1/0},f.isHeader=function(s,u){return!(f.isLeaf(s)||s.depth===u._maxDepth-1)},f.getParent=function(s,u){return f.findEntryWithLevel(s,i(u))},f.listPath=function(s,u){var h=s.parent;if(!h)return[];var d=u?[h.data[u]]:[h];return f.listPath(h,u).concat(d)},f.getPath=function(s){return f.listPath(s,"label").join("/")+"/"},f.formatValue=c.formatPieValue,f.formatPercent=function(s,u){var h=r.formatPercent(s,0);return h==="0%"&&(h=c.formatPiePercent(s,u)),h}},{"../../components/color":366,"../../lib":503,"../../lib/setcursor":524,"../pie/helpers":906}],1056:[function(t,o,f){o.exports={moduleType:"trace",name:"sunburst",basePlotModule:t("./base_plot"),categories:[],animatable:!0,attributes:t("./attributes"),layoutAttributes:t("./layout_attributes"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style").style,colorbar:t("../scatter/marker_colorbar"),meta:{}}},{"../scatter/marker_colorbar":945,"./attributes":1049,"./base_plot":1050,"./calc":1051,"./defaults":1053,"./layout_attributes":1057,"./layout_defaults":1058,"./plot":1059,"./style":1060}],1057:[function(t,o,f){o.exports={sunburstcolorway:{valType:"colorlist",editType:"calc"},extendsunburstcolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],1058:[function(t,o,f){var r=t("../../lib"),a=t("./layout_attributes");o.exports=function(l,c){function i(s,u){return r.coerce(l,c,a,s,u)}i("sunburstcolorway",c.colorway),i("extendsunburstcolors")}},{"../../lib":503,"./layout_attributes":1057}],1059:[function(t,o,f){var r=t("@plotly/d3"),a=t("d3-hierarchy"),l=t("d3-interpolate").interpolate,c=t("../../components/drawing"),i=t("../../lib"),s=t("../../lib/svg_text_utils"),u=t("../bar/uniform_text"),h=u.recordMinTextSize,d=u.clearMinTextSize,m=t("../pie/plot"),p=t("../pie/helpers").getRotationAngle,g=m.computeTransform,y=m.transformInsideText,v=t("./style").styleOne,x=t("../bar/style").resizeText,_=t("./fx"),A=t("./constants"),b=t("./helpers");function k(M,T,E,S){var P=M._fullLayout,L=!P.uniformtext.mode&&b.hasTransition(S),R=r.select(E).selectAll("g.slice"),F=T[0],D=F.trace,O=F.hierarchy,N=b.findEntryWithLevel(O,D.level),B=b.getMaxDepth(D),W=P._size,G=D.domain,K=W.w*(G.x[1]-G.x[0]),te=W.h*(G.y[1]-G.y[0]),Y=.5*Math.min(K,te),J=F.cx=W.l+W.w*(G.x[1]+G.x[0])/2,re=F.cy=W.t+W.h*(1-G.y[0])-te/2;if(!N)return R.remove();var U=null,V={};L&&R.each(function(Le){V[b.getPtId(Le)]={rpx0:Le.rpx0,rpx1:Le.rpx1,x0:Le.x0,x1:Le.x1,transform:Le.transform},!U&&b.isEntry(Le)&&(U=Le)});var H=function(Le){return a.partition().size([2*Math.PI,Le.height+1])(Le)}(N).descendants(),ne=N.height+1,q=0,Q=B;F.hasMultipleRoots&&b.isHierarchyRoot(N)&&(H=H.slice(1),ne-=1,q=1,Q+=1),H=H.filter(function(Le){return Le.y1<=Q});var ee=p(D.rotation);ee&&H.forEach(function(Le){Le.x0+=ee,Le.x1+=ee});var ie=Math.min(ne,B),ae=function(Le){return(Le-q)/ie*Y},ue=function(Le,de){return[Le*Math.cos(de),-Le*Math.sin(de)]},le=function(Le){return i.pathAnnulus(Le.rpx0,Le.rpx1,Le.x0,Le.x1,J,re)},ge=function(Le){return J+w(Le)[0]*(Le.transform.rCenter||0)+(Le.transform.x||0)},fe=function(Le){return re+w(Le)[1]*(Le.transform.rCenter||0)+(Le.transform.y||0)};(R=R.data(H,b.getPtId)).enter().append("g").classed("slice",!0),L?R.exit().transition().each(function(){var Le=r.select(this);Le.select("path.surface").transition().attrTween("d",function(de){var ve=function(Me){var we,Ce=b.getPtId(Me),Fe=V[Ce],ze=V[b.getPtId(N)];if(ze){var $e=(Me.x1>ze.x1?2*Math.PI:0)+ee;we=Me.rpx1<ze.rpx1?{x0:Me.x0,x1:Me.x1,rpx0:0,rpx1:0}:{x0:$e,x1:$e,rpx0:Me.rpx0,rpx1:Me.rpx1}}else{var Ke,Re=b.getPtId(Me.parent);R.each(function(ft){if(b.getPtId(ft)===Re)return Ke=ft});var Ve,We=Ke.children;We.forEach(function(ft,yt){if(b.getPtId(ft)===Ce)return Ve=yt});var Ye=We.length,nt=l(Ke.x0,Ke.x1);we={rpx0:Y,rpx1:Y,x0:nt(Ve/Ye),x1:nt((Ve+1)/Ye)}}return l(Fe,we)}(de);return function(Me){return le(ve(Me))}}),Le.select("g.slicetext").attr("opacity",0)}).remove():R.exit().remove(),R.order();var me=null;if(L&&U){var _e=b.getPtId(U);R.each(function(Le){me===null&&b.getPtId(Le)===_e&&(me=Le.x1)})}var Ae=R;function ke(Le){var de=Le.parent,ve=V[b.getPtId(de)],Me={};if(ve){var we=de.children,Ce=we.indexOf(Le),Fe=we.length,ze=l(ve.x0,ve.x1);Me.x0=ze(Ce/Fe),Me.x1=ze(Ce/Fe)}else Me.x0=Me.x1=0;return Me}L&&(Ae=Ae.transition().each("end",function(){var Le=r.select(this);b.setSliceCursor(Le,M,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})})),Ae.each(function(Le){var de=r.select(this),ve=i.ensureSingle(de,"path","surface",function($e){$e.style("pointer-events","all")});Le.rpx0=ae(Le.y0),Le.rpx1=ae(Le.y1),Le.xmid=(Le.x0+Le.x1)/2,Le.pxmid=ue(Le.rpx1,Le.xmid),Le.midangle=-(Le.xmid-Math.PI/2),Le.startangle=-(Le.x0-Math.PI/2),Le.stopangle=-(Le.x1-Math.PI/2),Le.halfangle=.5*Math.min(i.angleDelta(Le.x0,Le.x1)||Math.PI,Math.PI),Le.ring=1-Le.rpx0/Le.rpx1,Le.rInscribed=function($e){return $e.rpx0===0&&i.isFullCircle([$e.x0,$e.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin($e.halfangle)),$e.ring/2))}(Le),L?ve.transition().attrTween("d",function($e){var Ke=function(Re){var Ve,We=V[b.getPtId(Re)],Ye={x0:Re.x0,x1:Re.x1,rpx0:Re.rpx0,rpx1:Re.rpx1};if(We)Ve=We;else if(U)if(Re.parent)if(me){var nt=(Re.x1>me?2*Math.PI:0)+ee;Ve={x0:nt,x1:nt}}else Ve={rpx0:Y,rpx1:Y},i.extendFlat(Ve,ke(Re));else Ve={rpx0:0,rpx1:0};else Ve={x0:ee,x1:ee};return l(Ve,Ye)}($e);return function(Re){return le(Ke(Re))}}):ve.attr("d",le),de.call(_,N,M,T,{eventDataKeys:A.eventDataKeys,transitionTime:A.CLICK_TRANSITION_TIME,transitionEasing:A.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,M,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:M._transitioning}),ve.call(v,Le,D);var Me=i.ensureSingle(de,"g","slicetext"),we=i.ensureSingle(Me,"text","",function($e){$e.attr("data-notex",1)}),Ce=i.ensureUniformFontSize(M,b.determineTextFont(D,Le,P.font));we.text(f.formatSliceLabel(Le,N,D,T,P)).classed("slicetext",!0).attr("text-anchor","middle").call(c.font,Ce).call(s.convertToTspans,M);var Fe=c.bBox(we.node());Le.transform=y(Fe,Le,F),Le.transform.targetX=ge(Le),Le.transform.targetY=fe(Le);var ze=function($e,Ke){var Re=$e.transform;return g(Re,Ke),Re.fontSize=Ce.size,h(D.type,Re,P),i.getTextTransform(Re)};L?we.transition().attrTween("transform",function($e){var Ke=function(Re){var Ve,We=V[b.getPtId(Re)],Ye=Re.transform;if(We)Ve=We;else if(Ve={rpx1:Re.rpx1,transform:{textPosAngle:Ye.textPosAngle,scale:0,rotate:Ye.rotate,rCenter:Ye.rCenter,x:Ye.x,y:Ye.y}},U)if(Re.parent)if(me){var nt=Re.x1>me?2*Math.PI:0;Ve.x0=Ve.x1=nt}else i.extendFlat(Ve,ke(Re));else Ve.x0=Ve.x1=ee;else Ve.x0=Ve.x1=ee;var ft=l(Ve.transform.textPosAngle,Re.transform.textPosAngle),yt=l(Ve.rpx1,Re.rpx1),Ot=l(Ve.x0,Re.x0),Tt=l(Ve.x1,Re.x1),at=l(Ve.transform.scale,Ye.scale),et=l(Ve.transform.rotate,Ye.rotate),Lt=Ye.rCenter===0?3:Ve.transform.rCenter===0?1/3:1,Wt=l(Ve.transform.rCenter,Ye.rCenter);return function(Jt){var Be=yt(Jt),Ge=Ot(Jt),kt=Tt(Jt),dt=function(Ie){return Wt(Math.pow(Ie,Lt))}(Jt),Oe={pxmid:ue(Be,(Ge+kt)/2),rpx1:Be,transform:{textPosAngle:ft(Jt),rCenter:dt,x:Ye.x,y:Ye.y}};return h(D.type,Ye,P),{transform:{targetX:ge(Oe),targetY:fe(Oe),scale:at(Jt),rotate:et(Jt),rCenter:dt}}}}($e);return function(Re){return ze(Ke(Re),Fe)}}):we.attr("transform",ze(Le,Fe))})}function w(M){return T=M.rpx1,E=M.transform.textPosAngle,[T*Math.sin(E),-T*Math.cos(E)];var T,E}f.plot=function(M,T,E,S){var P,L,R=M._fullLayout,F=R._sunburstlayer,D=!E,O=!R.uniformtext.mode&&b.hasTransition(E);d("sunburst",R),(P=F.selectAll("g.trace.sunburst").data(T,function(N){return N[0].trace.uid})).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),P.order(),O?(S&&(L=S()),r.transition().duration(E.duration).ease(E.easing).each("end",function(){L&&L()}).each("interrupt",function(){L&&L()}).each(function(){F.selectAll("g.trace").each(function(N){k(M,N,this,E)})})):(P.each(function(N){k(M,N,this,E)}),R.uniformtext.mode&&x(M,R._sunburstlayer.selectAll(".trace"),"sunburst")),D&&P.exit().remove()},f.formatSliceLabel=function(M,T,E,S,P){var L=E.texttemplate,R=E.textinfo;if(!(L||R&&R!=="none"))return"";var F=P.separators,D=S[0],O=M.data.data,N=D.hierarchy,B=b.isHierarchyRoot(M),W=b.getParent(N,M),G=b.getValue(M);if(!L){var K,te=R.split("+"),Y=function(ee){return te.indexOf(ee)!==-1},J=[];if(Y("label")&&O.label&&J.push(O.label),O.hasOwnProperty("v")&&Y("value")&&J.push(b.formatValue(O.v,F)),!B){Y("current path")&&J.push(b.getPath(M.data));var re=0;Y("percent parent")&&re++,Y("percent entry")&&re++,Y("percent root")&&re++;var U=re>1;if(re){var V,H=function(ee){K=b.formatPercent(V,F),U&&(K+=" of "+ee),J.push(K)};Y("percent parent")&&!B&&(V=G/b.getValue(W),H("parent")),Y("percent entry")&&(V=G/b.getValue(T),H("entry")),Y("percent root")&&(V=G/b.getValue(N),H("root"))}}return Y("text")&&(K=i.castOption(E,O.i,"text"),i.isValidTextValue(K)&&J.push(K)),J.join("<br>")}var ne=i.castOption(E,O.i,"texttemplate");if(!ne)return"";var q={};O.label&&(q.label=O.label),O.hasOwnProperty("v")&&(q.value=O.v,q.valueLabel=b.formatValue(O.v,F)),q.currentPath=b.getPath(M.data),B||(q.percentParent=G/b.getValue(W),q.percentParentLabel=b.formatPercent(q.percentParent,F),q.parent=b.getPtLabel(W)),q.percentEntry=G/b.getValue(T),q.percentEntryLabel=b.formatPercent(q.percentEntry,F),q.entry=b.getPtLabel(T),q.percentRoot=G/b.getValue(N),q.percentRootLabel=b.formatPercent(q.percentRoot,F),q.root=b.getPtLabel(N),O.hasOwnProperty("color")&&(q.color=O.color);var Q=i.castOption(E,O.i,"text");return(i.isValidTextValue(Q)||Q==="")&&(q.text=Q),q.customdata=i.castOption(E,O.i,"customdata"),i.texttemplateString(ne,q,P._d3locale,q,E._meta||{})}},{"../../components/drawing":388,"../../lib":503,"../../lib/svg_text_utils":529,"../bar/style":662,"../bar/uniform_text":664,"../pie/helpers":906,"../pie/plot":910,"./constants":1052,"./fx":1054,"./helpers":1055,"./style":1060,"@plotly/d3":58,"d3-hierarchy":115,"d3-interpolate":116}],1060:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/color"),l=t("../../lib"),c=t("../bar/uniform_text").resizeText;function i(s,u,h){var d=u.data.data,m=!u.children,p=d.i,g=l.castOption(h,p,"marker.line.color")||a.defaultLine,y=l.castOption(h,p,"marker.line.width")||0;s.style("stroke-width",y).call(a.fill,d.color).call(a.stroke,g).style("opacity",m?h.leaf.opacity:null)}o.exports={style:function(s){var u=s._fullLayout._sunburstlayer.selectAll(".trace");c(s,u,"sunburst"),u.each(function(h){var 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h(m){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:r.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:r.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var d=o.exports=u(s({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:c(),xhoverformat:l("x"),yhoverformat:l("y"),zhoverformat:l("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},a("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{contours:{x:h(),y:h(),z:h()},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05},roughness:{valType:"number",min:0,max:1,dflt:.5},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},opacityscale:{valType:"any",editType:"calc"},_deprecated:{zauto:s({},a.zauto,{}),zmin:s({},a.zmin,{}),zmax:s({},a.zmax,{})},hoverinfo:s({},i.hoverinfo),showlegend:s({},i.showlegend,{dflt:!1})}),"calc","nested");d.x.editType=d.y.editType=d.z.editType="calc+clearAxisTypes",d.transforms=void 0},{"../../components/color":366,"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633}],1062:[function(t,o,f){var r=t("../../components/colorscale/calc");o.exports=function(a,l){l.surfacecolor?r(a,l,{vals:l.surfacecolor,containerStr:"",cLetter:"c"}):r(a,l,{vals:l.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":374}],1063:[function(t,o,f){var r=t("../../../stackgl_modules").gl_surface3d,a=t("../../../stackgl_modules").ndarray,l=t("../../../stackgl_modules").ndarray_linear_interpolate.d2,c=t("../heatmap/interp2d"),i=t("../heatmap/find_empties"),s=t("../../lib").isArrayOrTypedArray,u=t("../../lib/gl_format_color").parseColorScale,h=t("../../lib/str2rgbarray"),d=t("../../components/colorscale").extractOpts;function m(E,S,P){this.scene=E,this.uid=P,this.surface=S,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=m.prototype;p.getXat=function(E,S,P,L){var R=s(this.data.x)?s(this.data.x[0])?this.data.x[S][E]:this.data.x[E]:E;return P===void 0?R:L.d2l(R,0,P)},p.getYat=function(E,S,P,L){var R=s(this.data.y)?s(this.data.y[0])?this.data.y[S][E]:this.data.y[S]:S;return P===void 0?R:L.d2l(R,0,P)},p.getZat=function(E,S,P,L){var R=this.data.z[S][E];return R===null&&this.data.connectgaps&&this.data._interpolatedZ&&(R=this.data._interpolatedZ[S][E]),P===void 0?R:L.d2l(R,0,P)},p.handlePick=function(E){if(E.object===this.surface){var S=(E.data.index[0]-1)/this.dataScaleX-1,P=(E.data.index[1]-1)/this.dataScaleY-1,L=Math.max(Math.min(Math.round(S),this.data.z[0].length-1),0),R=Math.max(Math.min(Math.round(P),this.data._ylength-1),0);E.index=[L,R],E.traceCoordinate=[this.getXat(L,R),this.getYat(L,R),this.getZat(L,R)],E.dataCoordinate=[this.getXat(L,R,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(L,R,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(L,R,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var F=0;F<3;F++){var D=E.dataCoordinate[F];D!=null&&(E.dataCoordinate[F]*=this.scene.dataScale[F])}var O=this.data.hovertext||this.data.text;return Array.isArray(O)&&O[R]&&O[R][L]!==void 0?E.textLabel=O[R][L]:E.textLabel=O||"",E.data.dataCoordinate=E.dataCoordinate.slice(),this.surface.highlight(E.data),this.scene.glplot.spikes.position=E.dataCoordinate,!0}};var g=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function y(E,S){if(E<S)return 0;for(var P=0;Math.floor(E%S)===0;)E/=S,P++;return P}function v(E){for(var S=[],P=0;P<g.length;P++){var L=g[P];S.push(y(E,L))}return S}function x(E){for(var S=v(E),P=E,L=0;L<g.length;L++)if(S[L]>0){P=g[L];break}return P}function _(E,S){if(!(E<1||S<1)){for(var P=v(E),L=v(S),R=1,F=0;F<g.length;F++)R*=Math.pow(g[F],Math.max(P[F],L[F]));return R}}p.calcXnums=function(E){var S,P=[];for(S=1;S<E;S++){var L=this.getXat(S-1,0),R=this.getXat(S,0);P[S-1]=R!==L&&L!=null&&R!=null?Math.abs(R-L):0}var F=0;for(S=1;S<E;S++)F+=P[S-1];for(S=1;S<E;S++)P[S-1]===0?P[S-1]=1:P[S-1]=Math.round(F/P[S-1]);return P},p.calcYnums=function(E){var S,P=[];for(S=1;S<E;S++){var L=this.getYat(0,S-1),R=this.getYat(0,S);P[S-1]=R!==L&&L!=null&&R!=null?Math.abs(R-L):0}var F=0;for(S=1;S<E;S++)F+=P[S-1];for(S=1;S<E;S++)P[S-1]===0?P[S-1]=1:P[S-1]=Math.round(F/P[S-1]);return P};var A=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=A[9],k=A[13];function w(E,S,P){var L=P[8]+P[2]*S[0]+P[5]*S[1];return E[0]=(P[6]+P[0]*S[0]+P[3]*S[1])/L,E[1]=(P[7]+P[1]*S[0]+P[4]*S[1])/L,E}function M(E,S,P){return function(L,R,F,D){for(var O=[0,0],N=L.shape[0],B=L.shape[1],W=0;W<N;W++)for(var G=0;G<B;G++)F(O,[W,G],D),L.set(W,G,l(R,O[0],O[1]))}(E,S,w,P),E}function T(E,S){for(var P=!1,L=0;L<E.length;L++)if(S===E[L]){P=!0;break}P===!1&&E.push(S)}p.estimateScale=function(E,S){for(var P=1+function(R){if(R.length!==0){for(var F=1,D=0;D<R.length;D++)F=_(F,R[D]);return F}}(S===0?this.calcXnums(E):this.calcYnums(E));P<b;)P*=2;for(;P>k;)P--,P/=x(P),++P<b&&(P=k);var L=Math.round(P/E);return L>1?L:1},p.refineCoords=function(E){for(var S=this.dataScaleX,P=this.dataScaleY,L=E[0].shape[0],R=E[0].shape[1],F=0|Math.floor(E[0].shape[0]*S+1),D=0|Math.floor(E[0].shape[1]*P+1),O=1+L+1,N=1+R+1,B=a(new Float32Array(O*N),[O,N]),W=[1/S,0,0,0,1/P,0,0,0,1],G=0;G<E.length;++G){this.surface.padField(B,E[G]);var K=a(new Float32Array(F*D),[F,D]);M(K,B,W),E[G]=K}},p.setContourLevels=function(){var E,S,P,L=[[],[],[]],R=[!1,!1,!1],F=!1;for(E=0;E<3;++E)if(this.showContour[E]&&(F=!0,this.contourSize[E]>0&&this.contourStart[E]!==null&&this.contourEnd[E]!==null&&this.contourEnd[E]>this.contourStart[E]))for(R[E]=!0,S=this.contourStart[E];S<this.contourEnd[E];S+=this.contourSize[E])P=S*this.scene.dataScale[E],T(L[E],P);if(F){var D=[[],[],[]];for(E=0;E<3;++E)this.showContour[E]&&(D[E]=R[E]?L[E]:this.scene.contourLevels[E]);this.surface.update({levels:D})}},p.update=function(E){var S,P,L,R,F=this.scene,D=F.fullSceneLayout,O=this.surface,N=u(E),B=F.dataScale,W=E.z[0].length,G=E._ylength,K=F.contourLevels;this.data=E;var te=[];for(S=0;S<3;S++)for(te[S]=[],P=0;P<W;P++)te[S][P]=[];for(P=0;P<W;P++)for(L=0;L<G;L++)te[0][P][L]=this.getXat(P,L,E.xcalendar,D.xaxis),te[1][P][L]=this.getYat(P,L,E.ycalendar,D.yaxis),te[2][P][L]=this.getZat(P,L,E.zcalendar,D.zaxis);if(E.connectgaps)for(E._emptypoints=i(te[2]),c(te[2],E._emptypoints),E._interpolatedZ=[],P=0;P<W;P++)for(E._interpolatedZ[P]=[],L=0;L<G;L++)E._interpolatedZ[P][L]=te[2][P][L];for(S=0;S<3;S++)for(P=0;P<W;P++)for(L=0;L<G;L++)(R=te[S][P][L])==null?te[S][P][L]=NaN:R=te[S][P][L]*=B[S];for(S=0;S<3;S++)for(P=0;P<W;P++)for(L=0;L<G;L++)(R=te[S][P][L])!=null&&(this.minValues[S]>R&&(this.minValues[S]=R),this.maxValues[S]<R&&(this.maxValues[S]=R));for(S=0;S<3;S++)this.objectOffset[S]=.5*(this.minValues[S]+this.maxValues[S]);for(S=0;S<3;S++)for(P=0;P<W;P++)for(L=0;L<G;L++)(R=te[S][P][L])!=null&&(te[S][P][L]-=this.objectOffset[S]);var Y=[a(new Float32Array(W*G),[W,G]),a(new Float32Array(W*G),[W,G]),a(new Float32Array(W*G),[W,G])];for(S=0;S<3;S++)for(P=0;P<W;P++)for(L=0;L<G;L++)Y[S].set(P,L,te[S][P][L]);te=[];var J={colormap:N,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!E.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:E.opacityscale,opacity:E.opacity},re=d(E);if(J.intensityBounds=[re.min,re.max],E.surfacecolor){var U=a(new Float32Array(W*G),[W,G]);for(P=0;P<W;P++)for(L=0;L<G;L++)U.set(P,L,E.surfacecolor[L][P]);Y.push(U)}else J.intensityBounds[0]*=B[2],J.intensityBounds[1]*=B[2];(k<Y[0].shape[0]||k<Y[0].shape[1])&&(this.refineData=!1),this.refineData===!0&&(this.dataScaleX=this.estimateScale(Y[0].shape[0],0),this.dataScaleY=this.estimateScale(Y[0].shape[1],1),this.dataScaleX===1&&this.dataScaleY===1||this.refineCoords(Y)),E.surfacecolor&&(J.intensity=Y.pop());var V=[!0,!0,!0],H=["x","y","z"];for(S=0;S<3;++S){var ne=E.contours[H[S]];V[S]=ne.highlight,J.showContour[S]=ne.show||ne.highlight,J.showContour[S]&&(J.contourProject[S]=[ne.project.x,ne.project.y,ne.project.z],ne.show?(this.showContour[S]=!0,J.levels[S]=K[S],O.highlightColor[S]=J.contourColor[S]=h(ne.color),ne.usecolormap?O.highlightTint[S]=J.contourTint[S]=0:O.highlightTint[S]=J.contourTint[S]=1,J.contourWidth[S]=ne.width,this.contourStart[S]=ne.start,this.contourEnd[S]=ne.end,this.contourSize[S]=ne.size):(this.showContour[S]=!1,this.contourStart[S]=null,this.contourEnd[S]=null,this.contourSize[S]=0),ne.highlight&&(J.dynamicColor[S]=h(ne.highlightcolor),J.dynamicWidth[S]=ne.highlightwidth))}(function(q){var Q=q[0].rgb,ee=q[q.length-1].rgb;return Q[0]===ee[0]&&Q[1]===ee[1]&&Q[2]===ee[2]&&Q[3]===ee[3]})(N)&&(J.vertexColor=!0),J.objectOffset=this.objectOffset,J.coords=Y,O.update(J),O.visible=E.visible,O.enableDynamic=V,O.enableHighlight=V,O.snapToData=!0,"lighting"in E&&(O.ambientLight=E.lighting.ambient,O.diffuseLight=E.lighting.diffuse,O.specularLight=E.lighting.specular,O.roughness=E.lighting.roughness,O.fresnel=E.lighting.fresnel),"lightposition"in E&&(O.lightPosition=[E.lightposition.x,E.lightposition.y,E.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},o.exports=function(E,S){var P=E.glplot.gl,L=r({gl:P}),R=new m(E,L,S.uid);return L._trace=R,R.update(S),E.glplot.add(L),R}},{"../../../stackgl_modules":1124,"../../components/colorscale":378,"../../lib":503,"../../lib/gl_format_color":499,"../../lib/str2rgbarray":528,"../heatmap/find_empties":798,"../heatmap/interp2d":801}],1064:[function(t,o,f){var r=t("../../registry"),a=t("../../lib"),l=t("../../components/colorscale/defaults"),c=t("./attributes");function i(u,h,d,m){var p=m("opacityscale");p==="max"?h.opacityscale=[[0,.1],[1,1]]:p==="min"?h.opacityscale=[[0,1],[1,.1]]:p==="extremes"?h.opacityscale=function(g,y){for(var v=[],x=0;x<32;x++){var _=x/31,A=y+(1-y)*(1-Math.pow(Math.sin(g*_*Math.PI),2));v.push([_,Math.max(0,Math.min(1,A))])}return v}(1,.1):function(g){var y=0;if(!Array.isArray(g)||g.length<2||!g[0]||!g[g.length-1]||+g[0][0]!=0||+g[g.length-1][0]!=1)return!1;for(var v=0;v<g.length;v++){var x=g[v];if(x.length!==2||+x[0]<y)return!1;y=+x[0]}return!0}(p)||(h.opacityscale=void 0)}function s(u,h,d){h in u&&!(d in u)&&(u[d]=u[h])}o.exports={supplyDefaults:function(u,h,d,m){var p,g;function y(T,E){return a.coerce(u,h,c,T,E)}var v=y("x"),x=y("y"),_=y("z");if(!_||!_.length||v&&v.length<1||x&&x.length<1)h.visible=!1;else{h._xlength=Array.isArray(v)&&a.isArrayOrTypedArray(v[0])?_.length:_[0].length,h._ylength=_.length,r.getComponentMethod("calendars","handleTraceDefaults")(u,h,["x","y","z"],m),y("text"),y("hovertext"),y("hovertemplate"),y("xhoverformat"),y("yhoverformat"),y("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach(function(T){y(T)});var A=y("surfacecolor"),b=["x","y","z"];for(p=0;p<3;++p){var k="contours."+b[p],w=y(k+".show"),M=y(k+".highlight");if(w||M)for(g=0;g<3;++g)y(k+".project."+b[g]);w&&(y(k+".color"),y(k+".width"),y(k+".usecolormap")),M&&(y(k+".highlightcolor"),y(k+".highlightwidth")),y(k+".start"),y(k+".end"),y(k+".size")}A||(s(u,"zmin","cmin"),s(u,"zmax","cmax"),s(u,"zauto","cauto")),l(u,h,m,y,{prefix:"",cLetter:"c"}),i(u,h,m,y),h._length=null}},opacityscaleDefaults:i}},{"../../components/colorscale/defaults":376,"../../lib":503,"../../registry":638,"./attributes":1061}],1065:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":598,"./attributes":1061,"./calc":1062,"./convert":1063,"./defaults":1064}],1066:[function(t,o,f){var r=t("../../components/annotations/attributes"),a=t("../../lib/extend").extendFlat,l=t("../../plot_api/edit_types").overrideAll,c=t("../../plots/font_attributes"),i=t("../../plots/domain").attributes,s=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(o.exports=l({domain:i({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:s("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:a({},r.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:a({},c({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:s("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:20},align:a({},r.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:a({},c({arrayOk:!0}))}},"calc","from-root")).transforms=void 0},{"../../components/annotations/attributes":349,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/cartesian/axis_format_attributes":557,"../../plots/domain":584,"../../plots/font_attributes":585}],1067:[function(t,o,f){var r=t("../../plots/get_data").getModuleCalcData,a=t("./plot");f.name="table",f.plot=function(l){var c=r(l.calcdata,"table")[0];c.length&&a(l,c)},f.clean=function(l,c,i,s){var u=s._has&&s._has("table"),h=c._has&&c._has("table");u&&!h&&s._paperdiv.selectAll(".table").remove()}},{"../../plots/get_data":593,"./plot":1074}],1068:[function(t,o,f){var r=t("../../lib/gup").wrap;o.exports=function(){return r({})}},{"../../lib/gup":500}],1069:[function(t,o,f){o.exports={cellPad:8,columnExtentOffset:10,columnTitleOffset:28,emptyHeaderHeight:16,latexCheck:/^\$.*\$$/,goldenRatio:1.618,lineBreaker:"<br>",maxDimensionCount:60,overdrag:45,releaseTransitionDuration:120,releaseTransitionEase:"cubic-out",scrollbarCaptureWidth:18,scrollbarHideDelay:1e3,scrollbarHideDuration:1e3,scrollbarOffset:5,scrollbarWidth:8,transitionDuration:100,transitionEase:"cubic-out",uplift:5,wrapSpacer:" ",wrapSplitCharacter:" 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r=t("./constants"),a=t("@plotly/d3"),l=t("../../lib").numberFormat,c=t("../../lib/gup"),i=t("../../components/drawing"),s=t("../../lib/svg_text_utils"),u=t("../../lib").raiseToTop,h=t("../../lib").strTranslate,d=t("../../lib").cancelTransition,m=t("./data_preparation_helper"),p=t("./data_split_helpers"),g=t("../../components/color");function y(Y){return Math.ceil(Y.calcdata.maxLineWidth/2)}function v(Y,J){return"clip"+Y._fullLayout._uid+"_scrollAreaBottomClip_"+J.key}function x(Y,J){return"clip"+Y._fullLayout._uid+"_columnBoundaryClippath_"+J.calcdata.key+"_"+J.specIndex}function _(Y){return[].concat.apply([],Y.map(function(J){return J})).map(function(J){return J.__data__})}function A(Y,J,re){var U=Y.selectAll("."+r.cn.scrollbarKit).data(c.repeat,c.keyFun);U.enter().append("g").classed(r.cn.scrollbarKit,!0).style("shape-rendering","geometricPrecision"),U.each(function(Q){var ee=Q.scrollbarState;ee.totalHeight=function(ie){var ae=ie.rowBlocks;return 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x=v.barDifY,_=v.width,A=v.height,b=v.viewX,k=v.viewY,w=v.pathSlice,M=v.toMoveInsideSlice,T=v.strTransform,E=v.hasTransition,S=v.handleSlicesExit,P=v.makeUpdateSliceInterpolator,L=v.makeUpdateTextInterpolator,R={},F=m._fullLayout,D=p[0],O=D.trace,N=D.hierarchy,B=_/O._entryDepth,W=h.listPath(g.data,"id"),G=i(N.copy(),[_,A],{packing:"dice",pad:{inner:0,top:0,left:0,right:0,bottom:0}}).descendants();(G=G.filter(function(te){var Y=W.indexOf(te.data.id);return Y!==-1&&(te.x0=B*Y,te.x1=B*(Y+1),te.y0=x,te.y1=x+A,te.onPathbar=!0,!0)})).reverse(),(y=y.data(G,h.getPtId)).enter().append("g").classed("pathbar",!0),S(y,!0,R,[_,A],w),y.order();var K=y;E&&(K=K.transition().each("end",function(){var te=r.select(this);h.setSliceCursor(te,m,{hideOnRoot:!1,hideOnLeaves:!1,isTransitioning:!1})})),K.each(function(te){te._x0=b(te.x0),te._x1=b(te.x1),te._y0=k(te.y0),te._y1=k(te.y1),te._hoverX=b(te.x1-Math.min(_,A)/2),te._hoverY=k(te.y1-A/2);var 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ne=L(H,!0,R,[_,A]);return function(q){return T(ne(q))}}):U.attr("transform",T(te))})}},{"../../components/drawing":388,"../../lib":503,"../../lib/svg_text_utils":529,"../sunburst/fx":1054,"../sunburst/helpers":1055,"./constants":1078,"./partition":1087,"./style":1090,"@plotly/d3":58}],1082:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../lib"),l=t("../../components/drawing"),c=t("../../lib/svg_text_utils"),i=t("./partition"),s=t("./style").styleOne,u=t("./constants"),h=t("../sunburst/helpers"),d=t("../sunburst/fx"),m=t("../sunburst/plot").formatSliceLabel;o.exports=function(p,g,y,v,x){var 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T(ue(le))}}):ee.attr("transform",T(V))}),Y}},{"../../components/drawing":388,"../../lib":503,"../../lib/svg_text_utils":529,"../sunburst/fx":1054,"../sunburst/helpers":1055,"../sunburst/plot":1059,"./constants":1078,"./partition":1087,"./style":1090,"@plotly/d3":58}],1083:[function(t,o,f){o.exports=function r(a,l,c){var i;c.swapXY&&(i=a.x0,a.x0=a.y0,a.y0=i,i=a.x1,a.x1=a.y1,a.y1=i),c.flipX&&(i=a.x0,a.x0=l[0]-a.x1,a.x1=l[0]-i),c.flipY&&(i=a.y0,a.y0=l[1]-a.y1,a.y1=l[1]-i);var s=a.children;if(s)for(var u=0;u<s.length;u++)r(s[u],l,c)}},{}],1084:[function(t,o,f){o.exports={moduleType:"trace",name:"treemap",basePlotModule:t("./base_plot"),categories:[],animatable:!0,attributes:t("./attributes"),layoutAttributes:t("./layout_attributes"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot"),style:t("./style").style,colorbar:t("../scatter/marker_colorbar"),meta:{}}},{"../scatter/marker_colorbar":945,"./attributes":1075,"./base_plot":1076,"./calc":1077,"./defaults":1079,"./layout_attributes":1085,"./layout_defaults":1086,"./plot":1088,"./style":1090}],1085:[function(t,o,f){o.exports={treemapcolorway:{valType:"colorlist",editType:"calc"},extendtreemapcolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],1086:[function(t,o,f){var r=t("../../lib"),a=t("./layout_attributes");o.exports=function(l,c){function i(s,u){return 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r=t("../box/attributes"),a=t("../../lib/extend").extendFlat,l=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat;o.exports={y:r.y,x:r.x,x0:r.x0,y0:r.y0,xhoverformat:l("x"),yhoverformat:l("y"),name:a({},r.name,{}),orientation:a({},r.orientation,{}),bandwidth:{valType:"number",min:0,editType:"calc"},scalegroup:{valType:"string",dflt:"",editType:"calc"},scalemode:{valType:"enumerated",values:["width","count"],dflt:"width",editType:"calc"},spanmode:{valType:"enumerated",values:["soft","hard","manual"],dflt:"soft",editType:"calc"},span:{valType:"info_array",items:[{valType:"any",editType:"calc"},{valType:"any",editType:"calc"}],editType:"calc"},line:{color:{valType:"color",editType:"style"},width:{valType:"number",min:0,dflt:2,editType:"style"},editType:"plot"},fillcolor:r.fillcolor,points:a({},r.boxpoints,{}),jitter:a({},r.jitter,{}),pointpos:a({},r.pointpos,{}),width:a({},r.width,{}),marker:r.marker,text:r.text,hovertext:r.hovertext,hovertemplate:r.hovertemplate,box:{visible:{valType:"boolean",dflt:!1,editType:"plot"},width:{valType:"number",min:0,max:1,dflt:.25,editType:"plot"},fillcolor:{valType:"color",editType:"style"},line:{color:{valType:"color",editType:"style"},width:{valType:"number",min:0,editType:"style"},editType:"style"},editType:"plot"},meanline:{visible:{valType:"boolean",dflt:!1,editType:"plot"},color:{valType:"color",editType:"style"},width:{valType:"number",min:0,editType:"style"},editType:"plot"},side:{valType:"enumerated",values:["both","positive","negative"],dflt:"both",editType:"calc"},offsetgroup:r.offsetgroup,alignmentgroup:r.alignmentgroup,selected:r.selected,unselected:r.unselected,hoveron:{valType:"flaglist",flags:["violins","points","kde"],dflt:"violins+points+kde",extras:["all"],editType:"style"}}},{"../../lib/extend":493,"../../plots/cartesian/axis_format_attributes":557,"../box/attributes":673}],1092:[function(t,o,f){var r=t("../../lib"),a=t("../../plots/cartesian/axes"),l=t("../box/calc"),c=t("./helpers"),i=t("../../constants/numerical").BADNUM;function s(h,d,m){var p=d.max-d.min;if(!p)return h.bandwidth?h.bandwidth:0;if(h.bandwidth)return Math.max(h.bandwidth,p/1e4);var g=m.length,y=r.stdev(m,g-1,d.mean);return Math.max(function(v,x,_){return 1.059*Math.min(x,_/1.349)*Math.pow(v,-.2)}(g,y,d.q3-d.q1),p/100)}function u(h,d,m,p){var g,y=h.spanmode,v=h.span||[],x=[d.min,d.max],_=[d.min-2*p,d.max+2*p];function A(k){var w=v[k],M=m.type==="multicategory"?m.r2c(w):m.d2c(w,0,h[d.valLetter+"calendar"]);return M===i?_[k]:M}var b={type:"linear",range:g=y==="soft"?_:y==="hard"?x:[A(0),A(1)]};return a.setConvert(b),b.cleanRange(),g}o.exports=function(h,d){var m=l(h,d);if(m[0].t.empty)return m;for(var p=h._fullLayout,g=a.getFromId(h,d[d.orientation==="h"?"xaxis":"yaxis"]),y=1/0,v=-1/0,x=0,_=0,A=0;A<m.length;A++){var b=m[A],k=b.pts.map(c.extractVal),w=b.bandwidth=s(d,b,k),M=b.span=u(d,b,g,w);if(b.min===b.max&&w===0)M=b.span=[b.min,b.max],b.density=[{v:1,t:M[0]}],b.bandwidth=w,x=Math.max(x,1);else{var T=M[1]-M[0],E=Math.ceil(T/(w/3)),S=T/E;if(!isFinite(S)||!isFinite(E))return r.error("Something went wrong with computing the violin span"),m[0].t.empty=!0,m;var P=c.makeKDE(b,d,k);b.density=new Array(E);for(var L=0,R=M[0];R<M[1]+S/2;L++,R+=S){var F=P(R);b.density[L]={v:F,t:R},x=Math.max(x,F)}}_=Math.max(_,k.length),y=Math.min(y,M[0]),v=Math.max(v,M[1])}var D=a.findExtremes(g,[y,v],{padded:!0});if(d._extremes[g._id]=D,d.width)m[0].t.maxKDE=x;else{var O=p._violinScaleGroupStats,N=d.scalegroup,B=O[N];B?(B.maxKDE=Math.max(B.maxKDE,x),B.maxCount=Math.max(B.maxCount,_)):O[N]={maxKDE:x,maxCount:_}}return m[0].t.labels.kde=r._(h,"kde:"),m}},{"../../constants/numerical":479,"../../lib":503,"../../plots/cartesian/axes":554,"../box/calc":674,"./helpers":1095}],1093:[function(t,o,f){var r=t("../box/cross_trace_calc").setPositionOffset,a=["v","h"];o.exports=function(l,c){for(var i=l.calcdata,s=c.xaxis,u=c.yaxis,h=0;h<a.length;h++){for(var d=a[h],m=d==="h"?u:s,p=[],g=0;g<i.length;g++){var y=i[g],v=y[0].t,x=y[0].trace;x.visible!==!0||x.type!=="violin"||v.empty||x.orientation!==d||x.xaxis!==s._id||x.yaxis!==u._id||p.push(g)}r("violin",l,p,m)}}},{"../box/cross_trace_calc":675}],1094:[function(t,o,f){var r=t("../../lib"),a=t("../../components/color"),l=t("../box/defaults"),c=t("./attributes");o.exports=function(i,s,u,h){function d(T,E){return r.coerce(i,s,c,T,E)}function m(T,E){return r.coerce2(i,s,c,T,E)}if(l.handleSampleDefaults(i,s,d,h),s.visible!==!1){d("bandwidth"),d("side"),d("width")||(d("scalegroup",s.name),d("scalemode"));var p,g=d("span");Array.isArray(g)&&(p="manual"),d("spanmode",p);var y=d("line.color",(i.marker||{}).color||u),v=d("line.width"),x=d("fillcolor",a.addOpacity(s.line.color,.5));l.handlePointsDefaults(i,s,d,{prefix:""});var 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r=t("../../lib"),a=t("../../plots/cartesian/axes"),l=t("../box/hover"),c=t("./helpers");o.exports=function(i,s,u,h,d){d||(d={});var m,p,g=d.hoverLayer,y=i.cd,v=y[0].trace,x=v.hoveron,_=x.indexOf("violins")!==-1,A=x.indexOf("kde")!==-1,b=[];if(_||A){var k=l.hoverOnBoxes(i,s,u,h);if(A&&k.length>0){var w,M,T,E,S,P=i.xa,L=i.ya;v.orientation==="h"?(S=s,w="y",T=L,M="x",E=P):(S=u,w="x",T=P,M="y",E=L);var R=y[i.index];if(S>=R.span[0]&&S<=R.span[1]){var F=r.extendFlat({},i),D=E.c2p(S,!0),O=c.getKdeValue(R,v,S),N=c.getPositionOnKdePath(R,v,D),B=T._offset,W=T._length;F[w+"0"]=N[0],F[w+"1"]=N[1],F[M+"0"]=F[M+"1"]=D,F[M+"Label"]=M+": "+a.hoverLabelText(E,S,v[M+"hoverformat"])+", "+y[0].t.labels.kde+" "+O.toFixed(3),F.spikeDistance=k[0].spikeDistance;var G=w+"Spike";F[G]=k[0][G],k[0].spikeDistance=void 0,k[0][G]=void 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r=t("../box/layout_attributes"),a=t("../../lib").extendFlat;o.exports={violinmode:a({},r.boxmode,{}),violingap:a({},r.boxgap,{}),violingroupgap:a({},r.boxgroupgap,{})}},{"../../lib":503,"../box/layout_attributes":680}],1099:[function(t,o,f){var r=t("../../lib"),a=t("./layout_attributes"),l=t("../box/layout_defaults");o.exports=function(c,i,s){l._supply(c,i,s,function(u,h){return r.coerce(c,i,a,u,h)},"violin")}},{"../../lib":503,"../box/layout_defaults":681,"./layout_attributes":1098}],1100:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../lib"),l=t("../../components/drawing"),c=t("../box/plot"),i=t("../scatter/line_points"),s=t("./helpers");o.exports=function(u,h,d,m){var p=u._fullLayout,g=h.xaxis,y=h.yaxis;function v(x){var _=i(x,{xaxis:g,yaxis:y,connectGaps:!0,baseTolerance:.75,shape:"spline",simplify:!0,linearized:!0});return l.smoothopen(_[0],1)}a.makeTraceGroups(m,d,"trace violins").each(function(x){var 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te=T.c2p(K.mean,!0),Y=s.getPositionOnKdePath(K,k,te);r.select(this).attr("d",k.orientation==="h"?"M"+te+","+Y[0]+"V"+Y[1]:"M"+Y[0]+","+te+"H"+Y[1])}),c.plotPoints(_,{x:g,y},k,b)}})}},{"../../components/drawing":388,"../../lib":503,"../box/plot":682,"../scatter/line_points":941,"./helpers":1095,"@plotly/d3":58}],1101:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../components/color"),l=t("../scatter/style").stylePoints;o.exports=function(c){var i=r.select(c).selectAll("g.trace.violins");i.style("opacity",function(s){return s[0].trace.opacity}),i.each(function(s){var u=s[0].trace,h=r.select(this),d=u.box||{},m=d.line||{},p=u.meanline||{},g=p.width;h.selectAll("path.violin").style("stroke-width",u.line.width+"px").call(a.stroke,u.line.color).call(a.fill,u.fillcolor),h.selectAll("path.box").style("stroke-width",m.width+"px").call(a.stroke,m.color).call(a.fill,d.fillcolor);var y={"stroke-width":g+"px","stroke-dasharray":2*g+"px,"+g+"px"};h.selectAll("path.mean").style(y).call(a.stroke,p.color),h.selectAll("path.meanline").style(y).call(a.stroke,p.color),l(h,u,c)})}},{"../../components/color":366,"../scatter/style":951,"@plotly/d3":58}],1102:[function(t,o,f){var r=t("../../components/colorscale/attributes"),a=t("../isosurface/attributes"),l=t("../surface/attributes"),c=t("../../plots/attributes"),i=t("../../lib/extend").extendFlat,s=t("../../plot_api/edit_types").overrideAll,u=o.exports=s(i({x:a.x,y:a.y,z:a.z,value:a.value,isomin:a.isomin,isomax:a.isomax,surface:a.surface,spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:1}},slices:a.slices,caps:a.caps,text:a.text,hovertext:a.hovertext,xhoverformat:a.xhoverformat,yhoverformat:a.yhoverformat,zhoverformat:a.zhoverformat,valuehoverformat:a.valuehoverformat,hovertemplate:a.hovertemplate},r("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{colorbar:a.colorbar,opacity:a.opacity,opacityscale:l.opacityscale,lightposition:a.lightposition,lighting:a.lighting,flatshading:a.flatshading,contour:a.contour,hoverinfo:i({},c.hoverinfo),showlegend:i({},c.showlegend,{dflt:!1})}),"calc","nested");u.x.editType=u.y.editType=u.z.editType=u.value.editType="calc+clearAxisTypes",u.transforms=void 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p=m.data.index,g=this.data._meshX[p],y=this.data._meshY[p],v=this.data._meshZ[p],x=this.data._Ys.length,_=this.data._Zs.length,A=s(g,this.data._Xs).id,b=s(y,this.data._Ys).id,k=s(v,this.data._Zs).id,w=m.index=k+_*b+_*x*A;m.traceCoordinate=[this.data._meshX[w],this.data._meshY[w],this.data._meshZ[w],this.data._value[w]];var M=this.data.hovertext||this.data.text;return Array.isArray(M)&&M[w]!==void 0?m.textLabel=M[w]:M&&(m.textLabel=M),!0}},d.update=function(m){var p=this.scene,g=p.fullSceneLayout;function y(_,A,b,k){return A.map(function(w){return _.d2l(w,0,k)*b})}this.data=u(m);var v={positions:i(y(g.xaxis,m._meshX,p.dataScale[0],m.xcalendar),y(g.yaxis,m._meshY,p.dataScale[1],m.ycalendar),y(g.zaxis,m._meshZ,p.dataScale[2],m.zcalendar)),cells:i(m._meshI,m._meshJ,m._meshK),lightPosition:[m.lightposition.x,m.lightposition.y,m.lightposition.z],ambient:m.lighting.ambient,diffuse:m.lighting.diffuse,specular:m.lighting.specular,roughness:m.lighting.roughness,fresnel:m.lighting.fresnel,vertexNormalsEpsilon:m.lighting.vertexnormalsepsilon,faceNormalsEpsilon:m.lighting.facenormalsepsilon,opacity:m.opacity,opacityscale:m.opacityscale,contourEnable:m.contour.show,contourColor:l(m.contour.color).slice(0,3),contourWidth:m.contour.width,useFacetNormals:m.flatshading},x=c(m);v.vertexIntensity=m._meshIntensity,v.vertexIntensityBounds=[x.min,x.max],v.colormap=a(m),this.mesh.update(v)},d.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},o.exports=function(m,p){var g=m.glplot.gl,y=r({gl:g}),v=new h(m,y,p.uid);return y._trace=v,v.update(p),m.glplot.add(y),v}},{"../../../stackgl_modules":1124,"../../components/colorscale":378,"../../lib/gl_format_color":499,"../../lib/str2rgbarray":528,"../../plots/gl3d/zip3":609,"../isosurface/convert":864}],1104:[function(t,o,f){var r=t("../../lib"),a=t("./attributes"),l=t("../isosurface/defaults").supplyIsoDefaults,c=t("../surface/defaults").opacityscaleDefaults;o.exports=function(i,s,u,h){function d(m,p){return r.coerce(i,s,a,m,p)}l(i,s,u,h,d),c(i,s,h,d)}},{"../../lib":503,"../isosurface/defaults":865,"../surface/defaults":1064,"./attributes":1102}],1105:[function(t,o,f){o.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("../isosurface/calc"),colorbar:{min:"cmin",max:"cmax"},plot:t("./convert"),moduleType:"trace",name:"volume",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],meta:{}}},{"../../plots/gl3d":598,"../isosurface/calc":863,"./attributes":1102,"./convert":1103,"./defaults":1104}],1106:[function(t,o,f){var r=t("../bar/attributes"),a=t("../scatter/attributes").line,l=t("../../plots/attributes"),c=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,i=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/template_attributes").texttemplateAttrs,u=t("./constants"),h=t("../../lib/extend").extendFlat,d=t("../../components/color");function m(p){return{marker:{color:h({},r.marker.color,{arrayOk:!1,editType:"style"}),line:{color:h({},r.marker.line.color,{arrayOk:!1,editType:"style"}),width:h({},r.marker.line.width,{arrayOk:!1,editType:"style"}),editType:"style"},editType:"style"},editType:"style"}}o.exports={measure:{valType:"data_array",dflt:[],editType:"calc"},base:{valType:"number",dflt:null,arrayOk:!1,editType:"calc"},x:r.x,x0:r.x0,dx:r.dx,y:r.y,y0:r.y0,dy:r.dy,xperiod:r.xperiod,yperiod:r.yperiod,xperiod0:r.xperiod0,yperiod0:r.yperiod0,xperiodalignment:r.xperiodalignment,yperiodalignment:r.yperiodalignment,xhoverformat:c("x"),yhoverformat:c("y"),hovertext:r.hovertext,hovertemplate:i({},{keys:u.eventDataKeys}),hoverinfo:h({},l.hoverinfo,{flags:["name","x","y","text","initial","delta","final"]}),textinfo:{valType:"flaglist",flags:["label","text","initial","delta","final"],extras:["none"],editType:"plot",arrayOk:!1},texttemplate:s({editType:"plot"},{keys:u.eventDataKeys.concat(["label"])}),text:r.text,textposition:r.textposition,insidetextanchor:r.insidetextanchor,textangle:r.textangle,textfont:r.textfont,insidetextfont:r.insidetextfont,outsidetextfont:r.outsidetextfont,constraintext:r.constraintext,cliponaxis:r.cliponaxis,orientation:r.orientation,offset:r.offset,width:r.width,increasing:m(),decreasing:m(),totals:m(),connector:{line:{color:h({},a.color,{dflt:d.defaultLine}),width:h({},a.width,{editType:"plot"}),dash:a.dash,editType:"plot"},mode:{valType:"enumerated",values:["spanning","between"],dflt:"between",editType:"plot"},visible:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},offsetgroup:r.offsetgroup,alignmentgroup:r.alignmentgroup}},{"../../components/color":366,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633,"../bar/attributes":648,"../scatter/attributes":927,"./constants":1108}],1107:[function(t,o,f){var r=t("../../plots/cartesian/axes"),a=t("../../plots/cartesian/align_period"),l=t("../../lib").mergeArray,c=t("../scatter/calc_selection"),i=t("../../constants/numerical").BADNUM;function s(h){return h==="a"||h==="absolute"}function u(h){return h==="t"||h==="total"}o.exports=function(h,d){var m,p,g,y,v,x,_=r.getFromId(h,d.xaxis||"x"),A=r.getFromId(h,d.yaxis||"y");d.orientation==="h"?(m=_.makeCalcdata(d,"x"),g=A.makeCalcdata(d,"y"),y=a(d,A,"y",g),v=!!d.yperiodalignment,x="y"):(m=A.makeCalcdata(d,"y"),g=_.makeCalcdata(d,"x"),y=a(d,_,"x",g),v=!!d.xperiodalignment,x="x"),p=y.vals;for(var b,k=Math.min(p.length,m.length),w=new Array(k),M=0,T=!1,E=0;E<k;E++){var S=m[E]||0,P=!1;(m[E]!==i||u(d.measure[E])||s(d.measure[E]))&&E+1<k&&(m[E+1]!==i||u(d.measure[E+1])||s(d.measure[E+1]))&&(P=!0);var L=w[E]={i:E,p:p[E],s:S,rawS:S,cNext:P};s(d.measure[E])?(M=L.s,L.isSum=!0,L.dir="totals",L.s=M):u(d.measure[E])?(L.isSum=!0,L.dir="totals",L.s=M):(L.isSum=!1,L.dir=L.rawS<0?"decreasing":"increasing",b=L.s,L.s=M+b,M+=b),L.dir==="totals"&&(T=!0),v&&(w[E].orig_p=g[E],w[E][x+"End"]=y.ends[E],w[E][x+"Start"]=y.starts[E]),d.ids&&(L.id=String(d.ids[E])),L.v=(d.base||0)+M}return w.length&&(w[0].hasTotals=T),l(d.text,w,"tx"),l(d.hovertext,w,"htx"),c(w,d),w}},{"../../constants/numerical":479,"../../lib":503,"../../plots/cartesian/align_period":551,"../../plots/cartesian/axes":554,"../scatter/calc_selection":929}],1108:[function(t,o,f){o.exports={eventDataKeys:["initial","delta","final"]}},{}],1109:[function(t,o,f){var r=t("../bar/cross_trace_calc").setGroupPositions;o.exports=function(a,l){var c,i,s=a._fullLayout,u=a._fullData,h=a.calcdata,d=l.xaxis,m=l.yaxis,p=[],g=[],y=[];for(i=0;i<u.length;i++){var v=u[i];v.visible===!0&&v.xaxis===d._id&&v.yaxis===m._id&&v.type==="waterfall"&&(c=h[i],v.orientation==="h"?y.push(c):g.push(c),p.push(c))}var x={mode:s.waterfallmode,norm:s.waterfallnorm,gap:s.waterfallgap,groupgap:s.waterfallgroupgap};for(r(a,d,m,g,x),r(a,m,d,y,x),i=0;i<p.length;i++){c=p[i];for(var _=0;_<c.length;_++){var A=c[_];A.isSum===!1&&(A.s0+=_===0?0:c[_-1].s),_+1<c.length&&(c[_].nextP0=c[_+1].p0,c[_].nextS0=c[_+1].s0)}}}},{"../bar/cross_trace_calc":651}],1110:[function(t,o,f){var r=t("../../lib"),a=t("../bar/defaults").handleGroupingDefaults,l=t("../bar/defaults").handleText,c=t("../scatter/xy_defaults"),i=t("../scatter/period_defaults"),s=t("./attributes"),u=t("../../components/color"),h=t("../../constants/delta.js"),d=h.INCREASING.COLOR,m=h.DECREASING.COLOR;function p(g,y,v){g(y+".marker.color",v),g(y+".marker.line.color",u.defaultLine),g(y+".marker.line.width")}o.exports={supplyDefaults:function(g,y,v,x){function _(b,k){return r.coerce(g,y,s,b,k)}if(c(g,y,x,_)){i(g,y,x,_),_("xhoverformat"),_("yhoverformat"),_("measure"),_("orientation",y.x&&!y.y?"h":"v"),_("base"),_("offset"),_("width"),_("text"),_("hovertext"),_("hovertemplate");var A=_("textposition");l(g,y,x,_,A,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),y.textposition!=="none"&&(_("texttemplate"),y.texttemplate||_("textinfo")),p(_,"increasing",d),p(_,"decreasing",m),p(_,"totals","#4499FF"),_("connector.visible")&&(_("connector.mode"),_("connector.line.width")&&(_("connector.line.color"),_("connector.line.dash")))}else y.visible=!1},crossTraceDefaults:function(g,y){var v,x;function _(b){return r.coerce(x._input,x,s,b)}if(y.waterfallmode==="group")for(var A=0;A<g.length;A++)v=(x=g[A])._input,a(v,x,y,_)}}},{"../../components/color":366,"../../constants/delta.js":473,"../../lib":503,"../bar/defaults":652,"../scatter/period_defaults":947,"../scatter/xy_defaults":954,"./attributes":1106}],1111:[function(t,o,f){o.exports=function(r,a){return r.x="xVal"in a?a.xVal:a.x,r.y="yVal"in a?a.yVal:a.y,"initial"in a&&(r.initial=a.initial),"delta"in a&&(r.delta=a.delta),"final"in a&&(r.final=a.final),a.xa&&(r.xaxis=a.xa),a.ya&&(r.yaxis=a.ya),r}},{}],1112:[function(t,o,f){var r=t("../../plots/cartesian/axes").hoverLabelText,a=t("../../components/color").opacity,l=t("../bar/hover").hoverOnBars,c=t("../../constants/delta.js"),i=c.INCREASING.SYMBOL,s=c.DECREASING.SYMBOL;o.exports=function(u,h,d,m,p){var g=l(u,h,d,m,p);if(g){var y=g.cd,v=y[0].trace,x=v.orientation==="h",_=x?"x":"y",A=x?u.xa:u.ya,b=y[g.index],k=b.isSum?b.b+b.s:b.rawS;if(!b.isSum){g.initial=b.b+b.s-k,g.delta=k,g.final=g.initial+g.delta;var w=L(Math.abs(g.delta));g.deltaLabel=k<0?"("+w+")":w,g.finalLabel=L(g.final),g.initialLabel=L(g.initial)}var M=b.hi||v.hoverinfo,T=[];if(M&&M!=="none"&&M!=="skip"){var E=M==="all",S=M.split("+"),P=function(R){return E||S.indexOf(R)!==-1};b.isSum||(!P("final")||P(x?"x":"y")||T.push(g.finalLabel),P("delta")&&(k<0?T.push(g.deltaLabel+" "+s):T.push(g.deltaLabel+" "+i)),P("initial")&&T.push("Initial: "+g.initialLabel))}return T.length&&(g.extraText=T.join("<br>")),g.color=function(R,F){var D=R[F.dir].marker,O=D.color,N=D.line.color,B=D.line.width;if(a(O))return O;if(a(N)&&B)return N}(v,b),[g]}function L(R){return r(A,R,v[_+"hoverformat"])}}},{"../../components/color":366,"../../constants/delta.js":473,"../../plots/cartesian/axes":554,"../bar/hover":655}],1113:[function(t,o,f){o.exports={attributes:t("./attributes"),layoutAttributes:t("./layout_attributes"),supplyDefaults:t("./defaults").supplyDefaults,crossTraceDefaults:t("./defaults").crossTraceDefaults,supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),crossTraceCalc:t("./cross_trace_calc"),plot:t("./plot"),style:t("./style").style,hoverPoints:t("./hover"),eventData:t("./event_data"),selectPoints:t("../bar/select"),moduleType:"trace",name:"waterfall",basePlotModule:t("../../plots/cartesian"),categories:["bar-like","cartesian","svg","oriented","showLegend","zoomScale"],meta:{}}},{"../../plots/cartesian":568,"../bar/select":660,"./attributes":1106,"./calc":1107,"./cross_trace_calc":1109,"./defaults":1110,"./event_data":1111,"./hover":1112,"./layout_attributes":1114,"./layout_defaults":1115,"./plot":1116,"./style":1117}],1114:[function(t,o,f){o.exports={waterfallmode:{valType:"enumerated",values:["group","overlay"],dflt:"group",editType:"calc"},waterfallgap:{valType:"number",min:0,max:1,editType:"calc"},waterfallgroupgap:{valType:"number",min:0,max:1,dflt:0,editType:"calc"}}},{}],1115:[function(t,o,f){var r=t("../../lib"),a=t("./layout_attributes");o.exports=function(l,c,i){var s=!1;function u(m,p){return r.coerce(l,c,a,m,p)}for(var h=0;h<i.length;h++){var d=i[h];if(d.visible&&d.type==="waterfall"){s=!0;break}}s&&(u("waterfallmode"),u("waterfallgap",.2),u("waterfallgroupgap"))}},{"../../lib":503,"./layout_attributes":1114}],1116:[function(t,o,f){var r=t("@plotly/d3"),a=t("../../lib"),l=t("../../components/drawing"),c=t("../../constants/numerical").BADNUM,i=t("../bar/plot"),s=t("../bar/uniform_text").clearMinTextSize;o.exports=function(u,h,d,m){var p=u._fullLayout;s("waterfall",p),i.plot(u,h,d,m,{mode:p.waterfallmode,norm:p.waterfallmode,gap:p.waterfallgap,groupgap:p.waterfallgroupgap}),function(g,y,v,x){var _=y.xaxis,A=y.yaxis;a.makeTraceGroups(x,v,"trace bars").each(function(b){var k=r.select(this),w=b[0].trace,M=a.ensureSingle(k,"g","lines");if(w.connector&&w.connector.visible){var 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O>=4096&&(N+=13,O>>>=13),O>=64&&(N+=7,O>>>=7),O>=8&&(N+=4,O>>>=4),O>=2&&(N+=2,O>>>=2),N+O},d.prototype._zeroBits=function(D){if(D===0)return 26;var O=D,N=0;return!(8191&O)&&(N+=13,O>>>=13),!(127&O)&&(N+=7,O>>>=7),!(15&O)&&(N+=4,O>>>=4),!(3&O)&&(N+=2,O>>>=2),!(1&O)&&N++,N},d.prototype.bitLength=function(){var D=this.words[this.length-1],O=this._countBits(D);return 26*(this.length-1)+O},d.prototype.zeroBits=function(){if(this.isZero())return 0;for(var D=0,O=0;O<this.length;O++){var N=this._zeroBits(this.words[O]);if(D+=N,N!==26)break}return D},d.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},d.prototype.toTwos=function(D){return this.negative!==0?this.abs().inotn(D).iaddn(1):this.clone()},d.prototype.fromTwos=function(D){return this.testn(D-1)?this.notn(D).iaddn(1).ineg():this.clone()},d.prototype.isNeg=function(){return this.negative!==0},d.prototype.neg=function(){return this.clone().ineg()},d.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},d.prototype.iuor=function(D){for(;this.length<D.length;)this.words[this.length++]=0;for(var O=0;O<D.length;O++)this.words[O]=this.words[O]|D.words[O];return this.strip()},d.prototype.ior=function(D){return u((this.negative|D.negative)==0),this.iuor(D)},d.prototype.or=function(D){return this.length>D.length?this.clone().ior(D):D.clone().ior(this)},d.prototype.uor=function(D){return this.length>D.length?this.clone().iuor(D):D.clone().iuor(this)},d.prototype.iuand=function(D){var O;O=this.length>D.length?D:this;for(var N=0;N<O.length;N++)this.words[N]=this.words[N]&D.words[N];return this.length=O.length,this.strip()},d.prototype.iand=function(D){return u((this.negative|D.negative)==0),this.iuand(D)},d.prototype.and=function(D){return this.length>D.length?this.clone().iand(D):D.clone().iand(this)},d.prototype.uand=function(D){return this.length>D.length?this.clone().iuand(D):D.clone().iuand(this)},d.prototype.iuxor=function(D){var O,N;this.length>D.length?(O=this,N=D):(O=D,N=this);for(var B=0;B<N.length;B++)this.words[B]=O.words[B]^N.words[B];if(this!==O)for(;B<O.length;B++)this.words[B]=O.words[B];return this.length=O.length,this.strip()},d.prototype.ixor=function(D){return u((this.negative|D.negative)==0),this.iuxor(D)},d.prototype.xor=function(D){return this.length>D.length?this.clone().ixor(D):D.clone().ixor(this)},d.prototype.uxor=function(D){return this.length>D.length?this.clone().iuxor(D):D.clone().iuxor(this)},d.prototype.inotn=function(D){u(typeof D=="number"&&D>=0);var O=0|Math.ceil(D/26),N=D%26;this._expand(O),N>0&&O--;for(var B=0;B<O;B++)this.words[B]=67108863&~this.words[B];return N>0&&(this.words[B]=~this.words[B]&67108863>>26-N),this.strip()},d.prototype.notn=function(D){return this.clone().inotn(D)},d.prototype.setn=function(D,O){u(typeof D=="number"&&D>=0);var N=D/26|0,B=D%26;return this._expand(N+1),this.words[N]=O?this.words[N]|1<<B:this.words[N]&~(1<<B),this.strip()},d.prototype.iadd=function(D){var O,N,B;if(this.negative!==0&&D.negative===0)return this.negative=0,O=this.isub(D),this.negative^=1,this._normSign();if(this.negative===0&&D.negative!==0)return D.negative=0,O=this.isub(D),D.negative=1,O._normSign();this.length>D.length?(N=this,B=D):(N=D,B=this);for(var W=0,G=0;G<B.length;G++)O=(0|N.words[G])+(0|B.words[G])+W,this.words[G]=67108863&O,W=O>>>26;for(;W!==0&&G<N.length;G++)O=(0|N.words[G])+W,this.words[G]=67108863&O,W=O>>>26;if(this.length=N.length,W!==0)this.words[this.length]=W,this.length++;else if(N!==this)for(;G<N.length;G++)this.words[G]=N.words[G];return this},d.prototype.add=function(D){var O;return D.negative!==0&&this.negative===0?(D.negative=0,O=this.sub(D),D.negative^=1,O):D.negative===0&&this.negative!==0?(this.negative=0,O=D.sub(this),this.negative=1,O):this.length>D.length?this.clone().iadd(D):D.clone().iadd(this)},d.prototype.isub=function(D){if(D.negative!==0){D.negative=0;var O=this.iadd(D);return D.negative=1,O._normSign()}if(this.negative!==0)return this.negative=0,this.iadd(D),this.negative=1,this._normSign();var N,B,W=this.cmp(D);if(W===0)return this.negative=0,this.length=1,this.words[0]=0,this;W>0?(N=this,B=D):(N=D,B=this);for(var G=0,K=0;K<B.length;K++)G=(O=(0|N.words[K])-(0|B.words[K])+G)>>26,this.words[K]=67108863&O;for(;G!==0&&K<N.length;K++)G=(O=(0|N.words[K])+G)>>26,this.words[K]=67108863&O;if(G===0&&K<N.length&&N!==this)for(;K<N.length;K++)this.words[K]=N.words[K];return this.length=Math.max(this.length,K),N!==this&&(this.negative=1),this.strip()},d.prototype.sub=function(D){return this.clone().isub(D)};var b=function(D,O,N){var B,W,G,K=D.words,te=O.words,Y=N.words,J=0,re=0|K[0],U=8191&re,V=re>>>13,H=0|K[1],ne=8191&H,q=H>>>13,Q=0|K[2],ee=8191&Q,ie=Q>>>13,ae=0|K[3],ue=8191&ae,le=ae>>>13,ge=0|K[4],fe=8191&ge,me=ge>>>13,_e=0|K[5],Ae=8191&_e,ke=_e>>>13,Le=0|K[6],de=8191&Le,ve=Le>>>13,Me=0|K[7],we=8191&Me,Ce=Me>>>13,Fe=0|K[8],ze=8191&Fe,$e=Fe>>>13,Ke=0|K[9],Re=8191&Ke,Ve=Ke>>>13,We=0|te[0],Ye=8191&We,nt=We>>>13,ft=0|te[1],yt=8191&ft,Ot=ft>>>13,Tt=0|te[2],at=8191&Tt,et=Tt>>>13,Lt=0|te[3],Wt=8191&Lt,Jt=Lt>>>13,Be=0|te[4],Ge=8191&Be,kt=Be>>>13,dt=0|te[5],Oe=8191&dt,Ie=dt>>>13,Te=0|te[6],Pe=8191&Te,qe=Te>>>13,rt=0|te[7],lt=8191&rt,ot=rt>>>13,At=0|te[8],wt=8191&At,$t=At>>>13,Ut=0|te[9],tt=8191&Ut,bt=Ut>>>13;N.negative=D.negative^O.negative,N.length=19;var Ft=(J+(B=Math.imul(U,Ye))|0)+((8191&(W=(W=Math.imul(U,nt))+Math.imul(V,Ye)|0))<<13)|0;J=((G=Math.imul(V,nt))+(W>>>13)|0)+(Ft>>>26)|0,Ft&=67108863,B=Math.imul(ne,Ye),W=(W=Math.imul(ne,nt))+Math.imul(q,Ye)|0,G=Math.imul(q,nt);var Et=(J+(B=B+Math.imul(U,yt)|0)|0)+((8191&(W=(W=W+Math.imul(U,Ot)|0)+Math.imul(V,yt)|0))<<13)|0;J=((G=G+Math.imul(V,Ot)|0)+(W>>>13)|0)+(Et>>>26)|0,Et&=67108863,B=Math.imul(ee,Ye),W=(W=Math.imul(ee,nt))+Math.imul(ie,Ye)|0,G=Math.imul(ie,nt),B=B+Math.imul(ne,yt)|0,W=(W=W+Math.imul(ne,Ot)|0)+Math.imul(q,yt)|0,G=G+Math.imul(q,Ot)|0;var Pt=(J+(B=B+Math.imul(U,at)|0)|0)+((8191&(W=(W=W+Math.imul(U,et)|0)+Math.imul(V,at)|0))<<13)|0;J=((G=G+Math.imul(V,et)|0)+(W>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,B=Math.imul(ue,Ye),W=(W=Math.imul(ue,nt))+Math.imul(le,Ye)|0,G=Math.imul(le,nt),B=B+Math.imul(ee,yt)|0,W=(W=W+Math.imul(ee,Ot)|0)+Math.imul(ie,yt)|0,G=G+Math.imul(ie,Ot)|0,B=B+Math.imul(ne,at)|0,W=(W=W+Math.imul(ne,et)|0)+Math.imul(q,at)|0,G=G+Math.imul(q,et)|0;var De=(J+(B=B+Math.imul(U,Wt)|0)|0)+((8191&(W=(W=W+Math.imul(U,Jt)|0)+Math.imul(V,Wt)|0))<<13)|0;J=((G=G+Math.imul(V,Jt)|0)+(W>>>13)|0)+(De>>>26)|0,De&=67108863,B=Math.imul(fe,Ye),W=(W=Math.imul(fe,nt))+Math.imul(me,Ye)|0,G=Math.imul(me,nt),B=B+Math.imul(ue,yt)|0,W=(W=W+Math.imul(ue,Ot)|0)+Math.imul(le,yt)|0,G=G+Math.imul(le,Ot)|0,B=B+Math.imul(ee,at)|0,W=(W=W+Math.imul(ee,et)|0)+Math.imul(ie,at)|0,G=G+Math.imul(ie,et)|0,B=B+Math.imul(ne,Wt)|0,W=(W=W+Math.imul(ne,Jt)|0)+Math.imul(q,Wt)|0,G=G+Math.imul(q,Jt)|0;var Je=(J+(B=B+Math.imul(U,Ge)|0)|0)+((8191&(W=(W=W+Math.imul(U,kt)|0)+Math.imul(V,Ge)|0))<<13)|0;J=((G=G+Math.imul(V,kt)|0)+(W>>>13)|0)+(Je>>>26)|0,Je&=67108863,B=Math.imul(Ae,Ye),W=(W=Math.imul(Ae,nt))+Math.imul(ke,Ye)|0,G=Math.imul(ke,nt),B=B+Math.imul(fe,yt)|0,W=(W=W+Math.imul(fe,Ot)|0)+Math.imul(me,yt)|0,G=G+Math.imul(me,Ot)|0,B=B+Math.imul(ue,at)|0,W=(W=W+Math.imul(ue,et)|0)+Math.imul(le,at)|0,G=G+Math.imul(le,et)|0,B=B+Math.imul(ee,Wt)|0,W=(W=W+Math.imul(ee,Jt)|0)+Math.imul(ie,Wt)|0,G=G+Math.imul(ie,Jt)|0,B=B+Math.imul(ne,Ge)|0,W=(W=W+Math.imul(ne,kt)|0)+Math.imul(q,Ge)|0,G=G+Math.imul(q,kt)|0;var st=(J+(B=B+Math.imul(U,Oe)|0)|0)+((8191&(W=(W=W+Math.imul(U,Ie)|0)+Math.imul(V,Oe)|0))<<13)|0;J=((G=G+Math.imul(V,Ie)|0)+(W>>>13)|0)+(st>>>26)|0,st&=67108863,B=Math.imul(de,Ye),W=(W=Math.imul(de,nt))+Math.imul(ve,Ye)|0,G=Math.imul(ve,nt),B=B+Math.imul(Ae,yt)|0,W=(W=W+Math.imul(Ae,Ot)|0)+Math.imul(ke,yt)|0,G=G+Math.imul(ke,Ot)|0,B=B+Math.imul(fe,at)|0,W=(W=W+Math.imul(fe,et)|0)+Math.imul(me,at)|0,G=G+Math.imul(me,et)|0,B=B+Math.imul(ue,Wt)|0,W=(W=W+Math.imul(ue,Jt)|0)+Math.imul(le,Wt)|0,G=G+Math.imul(le,Jt)|0,B=B+Math.imul(ee,Ge)|0,W=(W=W+Math.imul(ee,kt)|0)+Math.imul(ie,Ge)|0,G=G+Math.imul(ie,kt)|0,B=B+Math.imul(ne,Oe)|0,W=(W=W+Math.imul(ne,Ie)|0)+Math.imul(q,Oe)|0,G=G+Math.imul(q,Ie)|0;var St=(J+(B=B+Math.imul(U,Pe)|0)|0)+((8191&(W=(W=W+Math.imul(U,qe)|0)+Math.imul(V,Pe)|0))<<13)|0;J=((G=G+Math.imul(V,qe)|0)+(W>>>13)|0)+(St>>>26)|0,St&=67108863,B=Math.imul(we,Ye),W=(W=Math.imul(we,nt))+Math.imul(Ce,Ye)|0,G=Math.imul(Ce,nt),B=B+Math.imul(de,yt)|0,W=(W=W+Math.imul(de,Ot)|0)+Math.imul(ve,yt)|0,G=G+Math.imul(ve,Ot)|0,B=B+Math.imul(Ae,at)|0,W=(W=W+Math.imul(Ae,et)|0)+Math.imul(ke,at)|0,G=G+Math.imul(ke,et)|0,B=B+Math.imul(fe,Wt)|0,W=(W=W+Math.imul(fe,Jt)|0)+Math.imul(me,Wt)|0,G=G+Math.imul(me,Jt)|0,B=B+Math.imul(ue,Ge)|0,W=(W=W+Math.imul(ue,kt)|0)+Math.imul(le,Ge)|0,G=G+Math.imul(le,kt)|0,B=B+Math.imul(ee,Oe)|0,W=(W=W+Math.imul(ee,Ie)|0)+Math.imul(ie,Oe)|0,G=G+Math.imul(ie,Ie)|0,B=B+Math.imul(ne,Pe)|0,W=(W=W+Math.imul(ne,qe)|0)+Math.imul(q,Pe)|0,G=G+Math.imul(q,qe)|0;var It=(J+(B=B+Math.imul(U,lt)|0)|0)+((8191&(W=(W=W+Math.imul(U,ot)|0)+Math.imul(V,lt)|0))<<13)|0;J=((G=G+Math.imul(V,ot)|0)+(W>>>13)|0)+(It>>>26)|0,It&=67108863,B=Math.imul(ze,Ye),W=(W=Math.imul(ze,nt))+Math.imul($e,Ye)|0,G=Math.imul($e,nt),B=B+Math.imul(we,yt)|0,W=(W=W+Math.imul(we,Ot)|0)+Math.imul(Ce,yt)|0,G=G+Math.imul(Ce,Ot)|0,B=B+Math.imul(de,at)|0,W=(W=W+Math.imul(de,et)|0)+Math.imul(ve,at)|0,G=G+Math.imul(ve,et)|0,B=B+Math.imul(Ae,Wt)|0,W=(W=W+Math.imul(Ae,Jt)|0)+Math.imul(ke,Wt)|0,G=G+Math.imul(ke,Jt)|0,B=B+Math.imul(fe,Ge)|0,W=(W=W+Math.imul(fe,kt)|0)+Math.imul(me,Ge)|0,G=G+Math.imul(me,kt)|0,B=B+Math.imul(ue,Oe)|0,W=(W=W+Math.imul(ue,Ie)|0)+Math.imul(le,Oe)|0,G=G+Math.imul(le,Ie)|0,B=B+Math.imul(ee,Pe)|0,W=(W=W+Math.imul(ee,qe)|0)+Math.imul(ie,Pe)|0,G=G+Math.imul(ie,qe)|0,B=B+Math.imul(ne,lt)|0,W=(W=W+Math.imul(ne,ot)|0)+Math.imul(q,lt)|0,G=G+Math.imul(q,ot)|0;var Zt=(J+(B=B+Math.imul(U,wt)|0)|0)+((8191&(W=(W=W+Math.imul(U,$t)|0)+Math.imul(V,wt)|0))<<13)|0;J=((G=G+Math.imul(V,$t)|0)+(W>>>13)|0)+(Zt>>>26)|0,Zt&=67108863,B=Math.imul(Re,Ye),W=(W=Math.imul(Re,nt))+Math.imul(Ve,Ye)|0,G=Math.imul(Ve,nt),B=B+Math.imul(ze,yt)|0,W=(W=W+Math.imul(ze,Ot)|0)+Math.imul($e,yt)|0,G=G+Math.imul($e,Ot)|0,B=B+Math.imul(we,at)|0,W=(W=W+Math.imul(we,et)|0)+Math.imul(Ce,at)|0,G=G+Math.imul(Ce,et)|0,B=B+Math.imul(de,Wt)|0,W=(W=W+Math.imul(de,Jt)|0)+Math.imul(ve,Wt)|0,G=G+Math.imul(ve,Jt)|0,B=B+Math.imul(Ae,Ge)|0,W=(W=W+Math.imul(Ae,kt)|0)+Math.imul(ke,Ge)|0,G=G+Math.imul(ke,kt)|0,B=B+Math.imul(fe,Oe)|0,W=(W=W+Math.imul(fe,Ie)|0)+Math.imul(me,Oe)|0,G=G+Math.imul(me,Ie)|0,B=B+Math.imul(ue,Pe)|0,W=(W=W+Math.imul(ue,qe)|0)+Math.imul(le,Pe)|0,G=G+Math.imul(le,qe)|0,B=B+Math.imul(ee,lt)|0,W=(W=W+Math.imul(ee,ot)|0)+Math.imul(ie,lt)|0,G=G+Math.imul(ie,ot)|0,B=B+Math.imul(ne,wt)|0,W=(W=W+Math.imul(ne,$t)|0)+Math.imul(q,wt)|0,G=G+Math.imul(q,$t)|0;var Kt=(J+(B=B+Math.imul(U,tt)|0)|0)+((8191&(W=(W=W+Math.imul(U,bt)|0)+Math.imul(V,tt)|0))<<13)|0;J=((G=G+Math.imul(V,bt)|0)+(W>>>13)|0)+(Kt>>>26)|0,Kt&=67108863,B=Math.imul(Re,yt),W=(W=Math.imul(Re,Ot))+Math.imul(Ve,yt)|0,G=Math.imul(Ve,Ot),B=B+Math.imul(ze,at)|0,W=(W=W+Math.imul(ze,et)|0)+Math.imul($e,at)|0,G=G+Math.imul($e,et)|0,B=B+Math.imul(we,Wt)|0,W=(W=W+Math.imul(we,Jt)|0)+Math.imul(Ce,Wt)|0,G=G+Math.imul(Ce,Jt)|0,B=B+Math.imul(de,Ge)|0,W=(W=W+Math.imul(de,kt)|0)+Math.imul(ve,Ge)|0,G=G+Math.imul(ve,kt)|0,B=B+Math.imul(Ae,Oe)|0,W=(W=W+Math.imul(Ae,Ie)|0)+Math.imul(ke,Oe)|0,G=G+Math.imul(ke,Ie)|0,B=B+Math.imul(fe,Pe)|0,W=(W=W+Math.imul(fe,qe)|0)+Math.imul(me,Pe)|0,G=G+Math.imul(me,qe)|0,B=B+Math.imul(ue,lt)|0,W=(W=W+Math.imul(ue,ot)|0)+Math.imul(le,lt)|0,G=G+Math.imul(le,ot)|0,B=B+Math.imul(ee,wt)|0,W=(W=W+Math.imul(ee,$t)|0)+Math.imul(ie,wt)|0,G=G+Math.imul(ie,$t)|0;var qt=(J+(B=B+Math.imul(ne,tt)|0)|0)+((8191&(W=(W=W+Math.imul(ne,bt)|0)+Math.imul(q,tt)|0))<<13)|0;J=((G=G+Math.imul(q,bt)|0)+(W>>>13)|0)+(qt>>>26)|0,qt&=67108863,B=Math.imul(Re,at),W=(W=Math.imul(Re,et))+Math.imul(Ve,at)|0,G=Math.imul(Ve,et),B=B+Math.imul(ze,Wt)|0,W=(W=W+Math.imul(ze,Jt)|0)+Math.imul($e,Wt)|0,G=G+Math.imul($e,Jt)|0,B=B+Math.imul(we,Ge)|0,W=(W=W+Math.imul(we,kt)|0)+Math.imul(Ce,Ge)|0,G=G+Math.imul(Ce,kt)|0,B=B+Math.imul(de,Oe)|0,W=(W=W+Math.imul(de,Ie)|0)+Math.imul(ve,Oe)|0,G=G+Math.imul(ve,Ie)|0,B=B+Math.imul(Ae,Pe)|0,W=(W=W+Math.imul(Ae,qe)|0)+Math.imul(ke,Pe)|0,G=G+Math.imul(ke,qe)|0,B=B+Math.imul(fe,lt)|0,W=(W=W+Math.imul(fe,ot)|0)+Math.imul(me,lt)|0,G=G+Math.imul(me,ot)|0,B=B+Math.imul(ue,wt)|0,W=(W=W+Math.imul(ue,$t)|0)+Math.imul(le,wt)|0,G=G+Math.imul(le,$t)|0;var mn=(J+(B=B+Math.imul(ee,tt)|0)|0)+((8191&(W=(W=W+Math.imul(ee,bt)|0)+Math.imul(ie,tt)|0))<<13)|0;J=((G=G+Math.imul(ie,bt)|0)+(W>>>13)|0)+(mn>>>26)|0,mn&=67108863,B=Math.imul(Re,Wt),W=(W=Math.imul(Re,Jt))+Math.imul(Ve,Wt)|0,G=Math.imul(Ve,Jt),B=B+Math.imul(ze,Ge)|0,W=(W=W+Math.imul(ze,kt)|0)+Math.imul($e,Ge)|0,G=G+Math.imul($e,kt)|0,B=B+Math.imul(we,Oe)|0,W=(W=W+Math.imul(we,Ie)|0)+Math.imul(Ce,Oe)|0,G=G+Math.imul(Ce,Ie)|0,B=B+Math.imul(de,Pe)|0,W=(W=W+Math.imul(de,qe)|0)+Math.imul(ve,Pe)|0,G=G+Math.imul(ve,qe)|0,B=B+Math.imul(Ae,lt)|0,W=(W=W+Math.imul(Ae,ot)|0)+Math.imul(ke,lt)|0,G=G+Math.imul(ke,ot)|0,B=B+Math.imul(fe,wt)|0,W=(W=W+Math.imul(fe,$t)|0)+Math.imul(me,wt)|0,G=G+Math.imul(me,$t)|0;var Fn=(J+(B=B+Math.imul(ue,tt)|0)|0)+((8191&(W=(W=W+Math.imul(ue,bt)|0)+Math.imul(le,tt)|0))<<13)|0;J=((G=G+Math.imul(le,bt)|0)+(W>>>13)|0)+(Fn>>>26)|0,Fn&=67108863,B=Math.imul(Re,Ge),W=(W=Math.imul(Re,kt))+Math.imul(Ve,Ge)|0,G=Math.imul(Ve,kt),B=B+Math.imul(ze,Oe)|0,W=(W=W+Math.imul(ze,Ie)|0)+Math.imul($e,Oe)|0,G=G+Math.imul($e,Ie)|0,B=B+Math.imul(we,Pe)|0,W=(W=W+Math.imul(we,qe)|0)+Math.imul(Ce,Pe)|0,G=G+Math.imul(Ce,qe)|0,B=B+Math.imul(de,lt)|0,W=(W=W+Math.imul(de,ot)|0)+Math.imul(ve,lt)|0,G=G+Math.imul(ve,ot)|0,B=B+Math.imul(Ae,wt)|0,W=(W=W+Math.imul(Ae,$t)|0)+Math.imul(ke,wt)|0,G=G+Math.imul(ke,$t)|0;var pn=(J+(B=B+Math.imul(fe,tt)|0)|0)+((8191&(W=(W=W+Math.imul(fe,bt)|0)+Math.imul(me,tt)|0))<<13)|0;J=((G=G+Math.imul(me,bt)|0)+(W>>>13)|0)+(pn>>>26)|0,pn&=67108863,B=Math.imul(Re,Oe),W=(W=Math.imul(Re,Ie))+Math.imul(Ve,Oe)|0,G=Math.imul(Ve,Ie),B=B+Math.imul(ze,Pe)|0,W=(W=W+Math.imul(ze,qe)|0)+Math.imul($e,Pe)|0,G=G+Math.imul($e,qe)|0,B=B+Math.imul(we,lt)|0,W=(W=W+Math.imul(we,ot)|0)+Math.imul(Ce,lt)|0,G=G+Math.imul(Ce,ot)|0,B=B+Math.imul(de,wt)|0,W=(W=W+Math.imul(de,$t)|0)+Math.imul(ve,wt)|0,G=G+Math.imul(ve,$t)|0;var tn=(J+(B=B+Math.imul(Ae,tt)|0)|0)+((8191&(W=(W=W+Math.imul(Ae,bt)|0)+Math.imul(ke,tt)|0))<<13)|0;J=((G=G+Math.imul(ke,bt)|0)+(W>>>13)|0)+(tn>>>26)|0,tn&=67108863,B=Math.imul(Re,Pe),W=(W=Math.imul(Re,qe))+Math.imul(Ve,Pe)|0,G=Math.imul(Ve,qe),B=B+Math.imul(ze,lt)|0,W=(W=W+Math.imul(ze,ot)|0)+Math.imul($e,lt)|0,G=G+Math.imul($e,ot)|0,B=B+Math.imul(we,wt)|0,W=(W=W+Math.imul(we,$t)|0)+Math.imul(Ce,wt)|0,G=G+Math.imul(Ce,$t)|0;var nn=(J+(B=B+Math.imul(de,tt)|0)|0)+((8191&(W=(W=W+Math.imul(de,bt)|0)+Math.imul(ve,tt)|0))<<13)|0;J=((G=G+Math.imul(ve,bt)|0)+(W>>>13)|0)+(nn>>>26)|0,nn&=67108863,B=Math.imul(Re,lt),W=(W=Math.imul(Re,ot))+Math.imul(Ve,lt)|0,G=Math.imul(Ve,ot),B=B+Math.imul(ze,wt)|0,W=(W=W+Math.imul(ze,$t)|0)+Math.imul($e,wt)|0,G=G+Math.imul($e,$t)|0;var sn=(J+(B=B+Math.imul(we,tt)|0)|0)+((8191&(W=(W=W+Math.imul(we,bt)|0)+Math.imul(Ce,tt)|0))<<13)|0;J=((G=G+Math.imul(Ce,bt)|0)+(W>>>13)|0)+(sn>>>26)|0,sn&=67108863,B=Math.imul(Re,wt),W=(W=Math.imul(Re,$t))+Math.imul(Ve,wt)|0,G=Math.imul(Ve,$t);var gn=(J+(B=B+Math.imul(ze,tt)|0)|0)+((8191&(W=(W=W+Math.imul(ze,bt)|0)+Math.imul($e,tt)|0))<<13)|0;J=((G=G+Math.imul($e,bt)|0)+(W>>>13)|0)+(gn>>>26)|0,gn&=67108863;var bn=(J+(B=Math.imul(Re,tt))|0)+((8191&(W=(W=Math.imul(Re,bt))+Math.imul(Ve,tt)|0))<<13)|0;return J=((G=Math.imul(Ve,bt))+(W>>>13)|0)+(bn>>>26)|0,bn&=67108863,Y[0]=Ft,Y[1]=Et,Y[2]=Pt,Y[3]=De,Y[4]=Je,Y[5]=st,Y[6]=St,Y[7]=It,Y[8]=Zt,Y[9]=Kt,Y[10]=qt,Y[11]=mn,Y[12]=Fn,Y[13]=pn,Y[14]=tn,Y[15]=nn,Y[16]=sn,Y[17]=gn,Y[18]=bn,J!==0&&(Y[19]=J,N.length++),N};function k(D,O,N){return new w().mulp(D,O,N)}function w(D,O){this.x=D,this.y=O}Math.imul||(b=A),d.prototype.mulTo=function(D,O){var N=this.length+D.length;return this.length===10&&D.length===10?b(this,D,O):N<63?A(this,D,O):N<1024?function(B,W,G){G.negative=W.negative^B.negative,G.length=B.length+W.length;for(var K=0,te=0,Y=0;Y<G.length-1;Y++){var J=te;te=0;for(var re=67108863&K,U=Math.min(Y,W.length-1),V=Math.max(0,Y-B.length+1);V<=U;V++){var H=Y-V,ne=(0|B.words[H])*(0|W.words[V]),q=67108863&ne;re=67108863&(q=q+re|0),te+=(J=(J=J+(ne/67108864|0)|0)+(q>>>26)|0)>>>26,J&=67108863}G.words[Y]=re,K=J,J=te}return K!==0?G.words[Y]=K:G.length--,G.strip()}(this,D,O):k(this,D,O)},w.prototype.makeRBT=function(D){for(var O=new 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W=8192*Math.round(D[2*B+1]/O)+Math.round(D[2*B]/O)+N;D[B]=67108863&W,N=W<67108864?0:W/67108864|0}return D},w.prototype.convert13b=function(D,O,N,B){for(var W=0,G=0;G<O;G++)W+=0|D[G],N[2*G]=8191&W,W>>>=13,N[2*G+1]=8191&W,W>>>=13;for(G=2*O;G<B;++G)N[G]=0;u(W===0),u((-8192&W)==0)},w.prototype.stub=function(D){for(var O=new Array(D),N=0;N<D;N++)O[N]=0;return O},w.prototype.mulp=function(D,O,N){var B=2*this.guessLen13b(D.length,O.length),W=this.makeRBT(B),G=this.stub(B),K=new Array(B),te=new Array(B),Y=new Array(B),J=new Array(B),re=new Array(B),U=new Array(B),V=N.words;V.length=B,this.convert13b(D.words,D.length,K,B),this.convert13b(O.words,O.length,J,B),this.transform(K,G,te,Y,B,W),this.transform(J,G,re,U,B,W);for(var H=0;H<B;H++){var ne=te[H]*re[H]-Y[H]*U[H];Y[H]=te[H]*U[H]+Y[H]*re[H],te[H]=ne}return this.conjugate(te,Y,B),this.transform(te,Y,V,G,B,W),this.conjugate(V,G,B),this.normalize13b(V,B),N.negative=D.negative^O.negative,N.length=D.length+O.length,N.strip()},d.prototype.mul=function(D){var O=new d(null);return O.words=new Array(this.length+D.length),this.mulTo(D,O)},d.prototype.mulf=function(D){var O=new d(null);return O.words=new Array(this.length+D.length),k(this,D,O)},d.prototype.imul=function(D){return this.clone().mulTo(D,this)},d.prototype.imuln=function(D){u(typeof D=="number"),u(D<67108864);for(var O=0,N=0;N<this.length;N++){var B=(0|this.words[N])*D,W=(67108863&B)+(67108863&O);O>>=26,O+=B/67108864|0,O+=W>>>26,this.words[N]=67108863&W}return O!==0&&(this.words[N]=O,this.length++),this},d.prototype.muln=function(D){return this.clone().imuln(D)},d.prototype.sqr=function(){return this.mul(this)},d.prototype.isqr=function(){return this.imul(this.clone())},d.prototype.pow=function(D){var O=function(G){for(var K=new Array(G.bitLength()),te=0;te<K.length;te++){var Y=te/26|0,J=te%26;K[te]=(G.words[Y]&1<<J)>>>J}return K}(D);if(O.length===0)return new d(1);for(var N=this,B=0;B<O.length&&O[B]===0;B++,N=N.sqr());if(++B<O.length)for(var W=N.sqr();B<O.length;B++,W=W.sqr())O[B]!==0&&(N=N.mul(W));return N},d.prototype.iushln=function(D){u(typeof D=="number"&&D>=0);var O,N=D%26,B=(D-N)/26,W=67108863>>>26-N<<26-N;if(N!==0){var G=0;for(O=0;O<this.length;O++){var K=this.words[O]&W,te=(0|this.words[O])-K<<N;this.words[O]=te|G,G=K>>>26-N}G&&(this.words[O]=G,this.length++)}if(B!==0){for(O=this.length-1;O>=0;O--)this.words[O+B]=this.words[O];for(O=0;O<B;O++)this.words[O]=0;this.length+=B}return this.strip()},d.prototype.ishln=function(D){return u(this.negative===0),this.iushln(D)},d.prototype.iushrn=function(D,O,N){var B;u(typeof D=="number"&&D>=0),B=O?(O-O%26)/26:0;var W=D%26,G=Math.min((D-W)/26,this.length),K=67108863^67108863>>>W<<W,te=N;if(B-=G,B=Math.max(0,B),te){for(var Y=0;Y<G;Y++)te.words[Y]=this.words[Y];te.length=G}if(G!==0)if(this.length>G)for(this.length-=G,Y=0;Y<this.length;Y++)this.words[Y]=this.words[Y+G];else this.words[0]=0,this.length=1;var J=0;for(Y=this.length-1;Y>=0&&(J!==0||Y>=B);Y--){var re=0|this.words[Y];this.words[Y]=J<<26-W|re>>>W,J=re&K}return te&&J!==0&&(te.words[te.length++]=J),this.length===0&&(this.words[0]=0,this.length=1),this.strip()},d.prototype.ishrn=function(D,O,N){return u(this.negative===0),this.iushrn(D,O,N)},d.prototype.shln=function(D){return this.clone().ishln(D)},d.prototype.ushln=function(D){return this.clone().iushln(D)},d.prototype.shrn=function(D){return this.clone().ishrn(D)},d.prototype.ushrn=function(D){return this.clone().iushrn(D)},d.prototype.testn=function(D){u(typeof D=="number"&&D>=0);var O=D%26,N=(D-O)/26,B=1<<O;return!(this.length<=N)&&!!(this.words[N]&B)},d.prototype.imaskn=function(D){u(typeof D=="number"&&D>=0);var O=D%26,N=(D-O)/26;if(u(this.negative===0,"imaskn works only with positive numbers"),this.length<=N)return this;if(O!==0&&N++,this.length=Math.min(N,this.length),O!==0){var B=67108863^67108863>>>O<<O;this.words[this.length-1]&=B}return this.strip()},d.prototype.maskn=function(D){return this.clone().imaskn(D)},d.prototype.iaddn=function(D){return u(typeof D=="number"),u(D<67108864),D<0?this.isubn(-D):this.negative!==0?this.length===1&&(0|this.words[0])<D?(this.words[0]=D-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(D),this.negative=1,this):this._iaddn(D)},d.prototype._iaddn=function(D){this.words[0]+=D;for(var O=0;O<this.length&&this.words[O]>=67108864;O++)this.words[O]-=67108864,O===this.length-1?this.words[O+1]=1:this.words[O+1]++;return this.length=Math.max(this.length,O+1),this},d.prototype.isubn=function(D){if(u(typeof D=="number"),u(D<67108864),D<0)return this.iaddn(-D);if(this.negative!==0)return this.negative=0,this.iaddn(D),this.negative=1,this;if(this.words[0]-=D,this.length===1&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var O=0;O<this.length&&this.words[O]<0;O++)this.words[O]+=67108864,this.words[O+1]-=1;return this.strip()},d.prototype.addn=function(D){return this.clone().iaddn(D)},d.prototype.subn=function(D){return this.clone().isubn(D)},d.prototype.iabs=function(){return this.negative=0,this},d.prototype.abs=function(){return this.clone().iabs()},d.prototype._ishlnsubmul=function(D,O,N){var B,W,G=D.length+N;this._expand(G);var K=0;for(B=0;B<D.length;B++){W=(0|this.words[B+N])+K;var te=(0|D.words[B])*O;K=((W-=67108863&te)>>26)-(te/67108864|0),this.words[B+N]=67108863&W}for(;B<this.length-N;B++)K=(W=(0|this.words[B+N])+K)>>26,this.words[B+N]=67108863&W;if(K===0)return this.strip();for(u(K===-1),K=0,B=0;B<this.length;B++)K=(W=-(0|this.words[B])+K)>>26,this.words[B]=67108863&W;return this.negative=1,this.strip()},d.prototype._wordDiv=function(D,O){var N=(this.length,D.length),B=this.clone(),W=D,G=0|W.words[W.length-1];(N=26-this._countBits(G))!==0&&(W=W.ushln(N),B.iushln(N),G=0|W.words[W.length-1]);var K,te=B.length-W.length;if(O!=="mod"){(K=new d(null)).length=te+1,K.words=new Array(K.length);for(var Y=0;Y<K.length;Y++)K.words[Y]=0}var J=B.clone()._ishlnsubmul(W,1,te);J.negative===0&&(B=J,K&&(K.words[te]=1));for(var re=te-1;re>=0;re--){var U=67108864*(0|B.words[W.length+re])+(0|B.words[W.length+re-1]);for(U=Math.min(U/G|0,67108863),B._ishlnsubmul(W,U,re);B.negative!==0;)U--,B.negative=0,B._ishlnsubmul(W,1,re),B.isZero()||(B.negative^=1);K&&(K.words[re]=U)}return K&&K.strip(),B.strip(),O!=="div"&&N!==0&&B.iushrn(N),{div:K||null,mod:B}},d.prototype.divmod=function(D,O,N){return u(!D.isZero()),this.isZero()?{div:new d(0),mod:new d(0)}:this.negative!==0&&D.negative===0?(G=this.neg().divmod(D,O),O!=="mod"&&(B=G.div.neg()),O!=="div"&&(W=G.mod.neg(),N&&W.negative!==0&&W.iadd(D)),{div:B,mod:W}):this.negative===0&&D.negative!==0?(G=this.divmod(D.neg(),O),O!=="mod"&&(B=G.div.neg()),{div:B,mod:G.mod}):this.negative&D.negative?(G=this.neg().divmod(D.neg(),O),O!=="div"&&(W=G.mod.neg(),N&&W.negative!==0&&W.isub(D)),{div:G.div,mod:W}):D.length>this.length||this.cmp(D)<0?{div:new d(0),mod:this}:D.length===1?O==="div"?{div:this.divn(D.words[0]),mod:null}:O==="mod"?{div:null,mod:new d(this.modn(D.words[0]))}:{div:this.divn(D.words[0]),mod:new d(this.modn(D.words[0]))}:this._wordDiv(D,O);var B,W,G},d.prototype.div=function(D){return this.divmod(D,"div",!1).div},d.prototype.mod=function(D){return this.divmod(D,"mod",!1).mod},d.prototype.umod=function(D){return this.divmod(D,"mod",!0).mod},d.prototype.divRound=function(D){var O=this.divmod(D);if(O.mod.isZero())return O.div;var N=O.div.negative!==0?O.mod.isub(D):O.mod,B=D.ushrn(1),W=D.andln(1),G=N.cmp(B);return G<0||W===1&&G===0?O.div:O.div.negative!==0?O.div.isubn(1):O.div.iaddn(1)},d.prototype.modn=function(D){u(D<=67108863);for(var O=(1<<26)%D,N=0,B=this.length-1;B>=0;B--)N=(O*N+(0|this.words[B]))%D;return N},d.prototype.idivn=function(D){u(D<=67108863);for(var O=0,N=this.length-1;N>=0;N--){var B=(0|this.words[N])+67108864*O;this.words[N]=B/D|0,O=B%D}return this.strip()},d.prototype.divn=function(D){return this.clone().idivn(D)},d.prototype.egcd=function(D){u(D.negative===0),u(!D.isZero());var O=this,N=D.clone();O=O.negative!==0?O.umod(D):O.clone();for(var B=new d(1),W=new d(0),G=new d(0),K=new d(1),te=0;O.isEven()&&N.isEven();)O.iushrn(1),N.iushrn(1),++te;for(var Y=N.clone(),J=O.clone();!O.isZero();){for(var re=0,U=1;!(O.words[0]&U)&&re<26;++re,U<<=1);if(re>0)for(O.iushrn(re);re-- >0;)(B.isOdd()||W.isOdd())&&(B.iadd(Y),W.isub(J)),B.iushrn(1),W.iushrn(1);for(var V=0,H=1;!(N.words[0]&H)&&V<26;++V,H<<=1);if(V>0)for(N.iushrn(V);V-- >0;)(G.isOdd()||K.isOdd())&&(G.iadd(Y),K.isub(J)),G.iushrn(1),K.iushrn(1);O.cmp(N)>=0?(O.isub(N),B.isub(G),W.isub(K)):(N.isub(O),G.isub(B),K.isub(W))}return{a:G,b:K,gcd:N.iushln(te)}},d.prototype._invmp=function(D){u(D.negative===0),u(!D.isZero());var O=this,N=D.clone();O=O.negative!==0?O.umod(D):O.clone();for(var B,W=new d(1),G=new d(0),K=N.clone();O.cmpn(1)>0&&N.cmpn(1)>0;){for(var te=0,Y=1;!(O.words[0]&Y)&&te<26;++te,Y<<=1);if(te>0)for(O.iushrn(te);te-- >0;)W.isOdd()&&W.iadd(K),W.iushrn(1);for(var J=0,re=1;!(N.words[0]&re)&&J<26;++J,re<<=1);if(J>0)for(N.iushrn(J);J-- >0;)G.isOdd()&&G.iadd(K),G.iushrn(1);O.cmp(N)>=0?(O.isub(N),W.isub(G)):(N.isub(O),G.isub(W))}return(B=O.cmpn(1)===0?W:G).cmpn(0)<0&&B.iadd(D),B},d.prototype.gcd=function(D){if(this.isZero())return D.abs();if(D.isZero())return this.abs();var O=this.clone(),N=D.clone();O.negative=0,N.negative=0;for(var B=0;O.isEven()&&N.isEven();B++)O.iushrn(1),N.iushrn(1);for(;;){for(;O.isEven();)O.iushrn(1);for(;N.isEven();)N.iushrn(1);var W=O.cmp(N);if(W<0){var G=O;O=N,N=G}else if(W===0||N.cmpn(1)===0)break;O.isub(N)}return N.iushln(B)},d.prototype.invm=function(D){return this.egcd(D).a.umod(D)},d.prototype.isEven=function(){return(1&this.words[0])==0},d.prototype.isOdd=function(){return(1&this.words[0])==1},d.prototype.andln=function(D){return this.words[0]&D},d.prototype.bincn=function(D){u(typeof D=="number");var O=D%26,N=(D-O)/26,B=1<<O;if(this.length<=N)return this._expand(N+1),this.words[N]|=B,this;for(var W=B,G=N;W!==0&&G<this.length;G++){var K=0|this.words[G];W=(K+=W)>>>26,K&=67108863,this.words[G]=K}return W!==0&&(this.words[G]=W,this.length++),this},d.prototype.isZero=function(){return this.length===1&&this.words[0]===0},d.prototype.cmpn=function(D){var O,N=D<0;if(this.negative!==0&&!N)return-1;if(this.negative===0&&N)return 1;if(this.strip(),this.length>1)O=1;else{N&&(D=-D),u(D<=67108863,"Number is too big");var B=0|this.words[0];O=B===D?0:B<D?-1:1}return this.negative!==0?0|-O:O},d.prototype.cmp=function(D){if(this.negative!==0&&D.negative===0)return-1;if(this.negative===0&&D.negative!==0)return 1;var O=this.ucmp(D);return this.negative!==0?0|-O:O},d.prototype.ucmp=function(D){if(this.length>D.length)return 1;if(this.length<D.length)return-1;for(var O=0,N=this.length-1;N>=0;N--){var B=0|this.words[N],W=0|D.words[N];if(B!==W){B<W?O=-1:B>W&&(O=1);break}}return O},d.prototype.gtn=function(D){return this.cmpn(D)===1},d.prototype.gt=function(D){return this.cmp(D)===1},d.prototype.gten=function(D){return this.cmpn(D)>=0},d.prototype.gte=function(D){return this.cmp(D)>=0},d.prototype.ltn=function(D){return this.cmpn(D)===-1},d.prototype.lt=function(D){return this.cmp(D)===-1},d.prototype.lten=function(D){return this.cmpn(D)<=0},d.prototype.lte=function(D){return this.cmp(D)<=0},d.prototype.eqn=function(D){return this.cmpn(D)===0},d.prototype.eq=function(D){return this.cmp(D)===0},d.red=function(D){return new R(D)},d.prototype.toRed=function(D){return u(!this.red,"Already a number in reduction context"),u(this.negative===0,"red works only with positives"),D.convertTo(this)._forceRed(D)},d.prototype.fromRed=function(){return u(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},d.prototype._forceRed=function(D){return this.red=D,this},d.prototype.forceRed=function(D){return u(!this.red,"Already a number in reduction context"),this._forceRed(D)},d.prototype.redAdd=function(D){return u(this.red,"redAdd works only with red numbers"),this.red.add(this,D)},d.prototype.redIAdd=function(D){return u(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,D)},d.prototype.redSub=function(D){return u(this.red,"redSub works only with red numbers"),this.red.sub(this,D)},d.prototype.redISub=function(D){return u(this.red,"redISub works only with red numbers"),this.red.isub(this,D)},d.prototype.redShl=function(D){return u(this.red,"redShl works only with red numbers"),this.red.shl(this,D)},d.prototype.redMul=function(D){return u(this.red,"redMul works only with red numbers"),this.red._verify2(this,D),this.red.mul(this,D)},d.prototype.redIMul=function(D){return u(this.red,"redMul works only with red numbers"),this.red._verify2(this,D),this.red.imul(this,D)},d.prototype.redSqr=function(){return u(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},d.prototype.redISqr=function(){return u(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},d.prototype.redSqrt=function(){return u(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},d.prototype.redInvm=function(){return u(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},d.prototype.redNeg=function(){return u(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},d.prototype.redPow=function(D){return u(this.red&&!D.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,D)};var M={k256:null,p224:null,p192:null,p25519:null};function T(D,O){this.name=D,this.p=new d(O,16),this.n=this.p.bitLength(),this.k=new d(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function E(){T.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function S(){T.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function P(){T.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function L(){T.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function R(D){if(typeof D=="string"){var O=d._prime(D);this.m=O.p,this.prime=O}else u(D.gtn(1),"modulus must be greater than 1"),this.m=D,this.prime=null}function F(D){R.call(this,D),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new d(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}T.prototype._tmp=function(){var D=new d(null);return D.words=new Array(Math.ceil(this.n/13)),D},T.prototype.ireduce=function(D){var O,N=D;do this.split(N,this.tmp),O=(N=(N=this.imulK(N)).iadd(this.tmp)).bitLength();while(O>this.n);var B=O<this.n?-1:N.ucmp(this.p);return B===0?(N.words[0]=0,N.length=1):B>0?N.isub(this.p):N.strip!==void 0?N.strip():N._strip(),N},T.prototype.split=function(D,O){D.iushrn(this.n,0,O)},T.prototype.imulK=function(D){return D.imul(this.k)},h(E,T),E.prototype.split=function(D,O){for(var N=Math.min(D.length,9),B=0;B<N;B++)O.words[B]=D.words[B];if(O.length=N,D.length<=9)return D.words[0]=0,void(D.length=1);var W=D.words[9];for(O.words[O.length++]=4194303&W,B=10;B<D.length;B++){var G=0|D.words[B];D.words[B-10]=(4194303&G)<<4|W>>>22,W=G}W>>>=22,D.words[B-10]=W,W===0&&D.length>10?D.length-=10:D.length-=9},E.prototype.imulK=function(D){D.words[D.length]=0,D.words[D.length+1]=0,D.length+=2;for(var O=0,N=0;N<D.length;N++){var B=0|D.words[N];O+=977*B,D.words[N]=67108863&O,O=64*B+(O/67108864|0)}return D.words[D.length-1]===0&&(D.length--,D.words[D.length-1]===0&&D.length--),D},h(S,T),h(P,T),h(L,T),L.prototype.imulK=function(D){for(var O=0,N=0;N<D.length;N++){var B=19*(0|D.words[N])+O,W=67108863&B;B>>>=26,D.words[N]=W,O=B}return O!==0&&(D.words[D.length++]=O),D},d._prime=function(D){if(M[D])return M[D];var O;if(D==="k256")O=new E;else if(D==="p224")O=new S;else if(D==="p192")O=new P;else{if(D!=="p25519")throw new Error("Unknown prime "+D);O=new L}return M[D]=O,O},R.prototype._verify1=function(D){u(D.negative===0,"red works only with positives"),u(D.red,"red works only with red numbers")},R.prototype._verify2=function(D,O){u((D.negative|O.negative)==0,"red works only with positives"),u(D.red&&D.red===O.red,"red works only with red numbers")},R.prototype.imod=function(D){return this.prime?this.prime.ireduce(D)._forceRed(this):D.umod(this.m)._forceRed(this)},R.prototype.neg=function(D){return D.isZero()?D.clone():this.m.sub(D)._forceRed(this)},R.prototype.add=function(D,O){this._verify2(D,O);var N=D.add(O);return N.cmp(this.m)>=0&&N.isub(this.m),N._forceRed(this)},R.prototype.iadd=function(D,O){this._verify2(D,O);var N=D.iadd(O);return N.cmp(this.m)>=0&&N.isub(this.m),N},R.prototype.sub=function(D,O){this._verify2(D,O);var N=D.sub(O);return N.cmpn(0)<0&&N.iadd(this.m),N._forceRed(this)},R.prototype.isub=function(D,O){this._verify2(D,O);var N=D.isub(O);return N.cmpn(0)<0&&N.iadd(this.m),N},R.prototype.shl=function(D,O){return this._verify1(D),this.imod(D.ushln(O))},R.prototype.imul=function(D,O){return this._verify2(D,O),this.imod(D.imul(O))},R.prototype.mul=function(D,O){return this._verify2(D,O),this.imod(D.mul(O))},R.prototype.isqr=function(D){return this.imul(D,D.clone())},R.prototype.sqr=function(D){return this.mul(D,D)},R.prototype.sqrt=function(D){if(D.isZero())return D.clone();var O=this.m.andln(3);if(u(O%2==1),O===3){var N=this.m.add(new d(1)).iushrn(2);return this.pow(D,N)}for(var B=this.m.subn(1),W=0;!B.isZero()&&B.andln(1)===0;)W++,B.iushrn(1);u(!B.isZero());var G=new d(1).toRed(this),K=G.redNeg(),te=this.m.subn(1).iushrn(1),Y=this.m.bitLength();for(Y=new d(2*Y*Y).toRed(this);this.pow(Y,te).cmp(K)!==0;)Y.redIAdd(K);for(var J=this.pow(Y,B),re=this.pow(D,B.addn(1).iushrn(1)),U=this.pow(D,B),V=W;U.cmp(G)!==0;){for(var H=U,ne=0;H.cmp(G)!==0;ne++)H=H.redSqr();u(ne<V);var q=this.pow(J,new d(1).iushln(V-ne-1));re=re.redMul(q),J=q.redSqr(),U=U.redMul(J),V=ne}return re},R.prototype.invm=function(D){var O=D._invmp(this.m);return O.negative!==0?(O.negative=0,this.imod(O).redNeg()):this.imod(O)},R.prototype.pow=function(D,O){if(O.isZero())return new d(1).toRed(this);if(O.cmpn(1)===0)return D.clone();var N=new Array(16);N[0]=new d(1).toRed(this),N[1]=D;for(var B=2;B<N.length;B++)N[B]=this.mul(N[B-1],D);var W=N[0],G=0,K=0,te=O.bitLength()%26;for(te===0&&(te=26),B=O.length-1;B>=0;B--){for(var Y=O.words[B],J=te-1;J>=0;J--){var re=Y>>J&1;W!==N[0]&&(W=this.sqr(W)),re!==0||G!==0?(G<<=1,G|=re,(++K===4||B===0&&J===0)&&(W=this.mul(W,N[G]),K=0,G=0)):K=0}te=26}return W},R.prototype.convertTo=function(D){var O=D.umod(this.m);return O===D?O.clone():O},R.prototype.convertFrom=function(D){var O=D.clone();return O.red=null,O},d.mont=function(D){return new F(D)},h(F,R),F.prototype.convertTo=function(D){return this.imod(D.ushln(this.shift))},F.prototype.convertFrom=function(D){var O=this.imod(D.mul(this.rinv));return O.red=null,O},F.prototype.imul=function(D,O){if(D.isZero()||O.isZero())return D.words[0]=0,D.length=1,D;var N=D.imul(O),B=N.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),W=N.isub(B).iushrn(this.shift),G=W;return W.cmp(this.m)>=0?G=W.isub(this.m):W.cmpn(0)<0&&(G=W.iadd(this.m)),G._forceRed(this)},F.prototype.mul=function(D,O){if(D.isZero()||O.isZero())return new d(0)._forceRed(this);var N=D.mul(O),B=N.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),W=N.isub(B).iushrn(this.shift),G=W;return W.cmp(this.m)>=0?G=W.isub(this.m):W.cmpn(0)<0&&(G=W.iadd(this.m)),G._forceRed(this)},F.prototype.invm=function(D){return this.imod(D._invmp(this.m).mul(this.r2))._forceRed(this)}})(l===void 0||l,this)},{buffer:2}],34:[function(a,l,c){l.exports=function(i){var s,u,h,d=i.length,m=0;for(s=0;s<d;++s)m+=i[s].length;var p=new Array(m),g=0;for(s=0;s<d;++s){var y=i[s],v=y.length;for(u=0;u<v;++u){var x=p[g++]=new Array(v-1),_=0;for(h=0;h<v;++h)h!==u&&(x[_++]=y[h]);if(1&u){var A=x[1];x[1]=x[0],x[0]=A}}}return 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K=2*S,te=B*K,Y=W[te+P];e:for(var J=F,re=F*K;J<D;++J,re+=K){var U=O[re+P],V=O[re+P+S];if(!(Y<U||V<Y)&&(!R||Y!==U)){for(var H,ne=N[J],q=P+1;q<S;++q){U=O[re+q],V=O[re+q+S];var Q=W[te+q],ee=W[te+q+S];if(V<Q||ee<U)continue e}if((H=R?L(G,ne):L(ne,G))!==void 0)return H}}}function E(S,P,L,R,F,D,O,N,B,W){var G=2*S,K=N*G,te=B[K+P];e:for(var Y=R,J=R*G;Y<F;++Y,J+=G){var re=O[Y];if(re!==W){var U=D[J+P],V=D[J+P+S];if(!(te<U||V<te)){for(var H=P+1;H<S;++H){U=D[J+H],V=D[J+H+S];var ne=B[K+H],q=B[K+H+S];if(V<ne||q<U)continue e}var Q=L(re,W);if(Q!==void 0)return Q}}}}},{"./brute":36,"./median":38,"./partition":39,"./sweep":41,"bit-twiddle":32,"typedarray-pool":308}],38:[function(a,l,c){l.exports=function(u,h,d,m,p,g){if(m<=d+1)return d;for(var y=d,v=m,x=m+d>>>1,_=2*u,A=x,b=p[_*x+h];y<v;){if(v-y<8){s(u,h,y,v,p,g),b=p[_*x+h];break}var k=v-y,w=Math.random()*k+y|0,M=p[_*w+h],T=Math.random()*k+y|0,E=p[_*T+h],S=Math.random()*k+y|0,P=p[_*S+h];M<=E?P>=E?(A=T,b=E):M>=P?(A=w,b=M):(A=S,b=P):E>=P?(A=T,b=E):P>=M?(A=w,b=M):(A=S,b=P);for(var L=_*(v-1),R=_*A,F=0;F<_;++F,++L,++R){var D=p[L];p[L]=p[R],p[R]=D}var O=g[v-1];for(g[v-1]=g[A],g[A]=O,A=i(u,h,y,v-1,p,g,b),L=_*(v-1),R=_*A,F=0;F<_;++F,++L,++R)D=p[L],p[L]=p[R],p[R]=D;if(O=g[v-1],g[v-1]=g[A],g[A]=O,x<A){for(v=A-1;y<v&&p[_*(v-1)+h]===b;)v-=1;v+=1}else{if(!(A<x))break;for(y=A+1;y<v&&p[_*y+h]===b;)y+=1}}return i(u,h,d,x,p,g,p[_*x+h])};var i=a("./partition")("lo<p0");function s(u,h,d,m,p,g){for(var y=2*u,v=y*(d+1)+h,x=d+1;x<m;++x,v+=y)for(var _=p[v],A=x,b=y*(x-1);A>d&&p[b+h]>_;--A,b-=y){for(var k=b,w=b+y,M=0;M<y;++M,++k,++w){var T=p[k];p[k]=p[w],p[w]=T}var E=g[A];g[A]=g[A-1],g[A-1]=E}}},{"./partition":39}],39:[function(a,l,c){l.exports=function(s){return i[s]};var i={"lo===p0":function(s,u,h,d,m,p,g){for(var 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B=A[2*P],W=A[2*P+1],G=A[2*R],K=A[2*R+1],te=2*S,Y=2*L,J=2*F,re=2*k,U=2*M,V=2*w,H=0;H<2;++H){var ne=A[te+H],q=A[Y+H],Q=A[J+H];A[re+H]=ne,A[U+H]=q,A[V+H]=Q}u(T,x,A),u(E,_,A);for(var ee=D;ee<=O;++ee)if(p(ee,B,W,A))ee!==D&&s(ee,D,A),++D;else if(!p(ee,G,K,A))for(;;){if(p(O,G,K,A)){p(O,B,W,A)?(h(ee,D,O,A),++D,--O):(s(ee,O,A),--O);break}if(--O<ee)break}d(x,D-1,B,W,A),d(_,O+1,G,K,A),D-2-x<=32?i(x,D-2,A):v(x,D-2,A),_-(O+2)<=32?i(O+2,_,A):v(O+2,_,A),O-D<=32?i(D,O,A):v(D,O,A)}(0,y-1,g)};function i(g,y,v){for(var x=2*(g+1),_=g+1;_<=y;++_){for(var A=v[x++],b=v[x++],k=_,w=x-2;k-- >g;){var M=v[w-2],T=v[w-1];if(M<A||M===A&&T<b)break;v[w]=M,v[w+1]=T,w-=2}v[w]=A,v[w+1]=b}}function s(g,y,v){y*=2;var x=v[g*=2],_=v[g+1];v[g]=v[y],v[g+1]=v[y+1],v[y]=x,v[y+1]=_}function u(g,y,v){y*=2,v[g*=2]=v[y],v[g+1]=v[y+1]}function h(g,y,v,x){y*=2,v*=2;var _=x[g*=2],A=x[g+1];x[g]=x[y],x[g+1]=x[y+1],x[y]=x[v],x[y+1]=x[v+1],x[v]=_,x[v+1]=A}function d(g,y,v,x,_){y*=2,_[g*=2]=_[y],_[y]=v,_[g+1]=_[y+1],_[y+1]=x}function 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te=K+W,Y=O*K;v[D++]=E[Y+N],v[D++]=-te,v[D++]=E[Y+B],v[D++]=te}for(K=P;K<L;++K){te=K+G;var J=O*K;v[D++]=R[J+N],v[D++]=-te}var re=D>>>1;u(v,re);var U=0;for(K=0;K<re;++K){var V=0|v[2*K+1];if(V<0){var H=!1;if((te=-V)>=1<<28?(H=!w,te-=1<<28):(H=!!w,te-=1),H)_(h,d,U++,te);else{var ne=F[te],q=O*te,Q=R[q+b+1],ee=R[q+b+1+A];e:for(var ie=0;ie<U;++ie){var ae=h[ie],ue=O*ae;if(!(ee<E[ue+b+1]||E[ue+b+1+A]<Q)){for(var le=b+2;le<A;++le)if(R[q+le+A]<E[ue+le]||E[ue+le+A]<R[q+le])continue e;var ge,fe=S[ae];if((ge=w?k(ne,fe):k(fe,ne))!==void 0)return ge}}}}else x(h,d,U--,V-W)}},scanComplete:function(A,b,k,w,M,T,E,S,P,L,R){for(var F=0,D=2*A,O=b,N=b+A,B=w;B<M;++B){var W=B+268435456,G=D*B;v[F++]=T[G+O],v[F++]=-W,v[F++]=T[G+N],v[F++]=W}for(B=S;B<P;++B){W=B+1;var K=D*B;v[F++]=L[K+O],v[F++]=-W}var te=F>>>1;u(v,te);var Y=0;for(B=0;B<te;++B){var J=0|v[2*B+1];if(J<0)if((W=-J)>=1<<28)h[Y++]=W-(1<<28);else{var re=R[W-=1],U=D*W,V=L[U+b+1],H=L[U+b+1+A];e:for(var ne=0;ne<Y;++ne){var q=h[ne],Q=E[q];if(Q===re)break;var ee=D*q;if(!(H<T[ee+b+1]||T[ee+b+1+A]<V)){for(var ie=b+2;ie<A;++ie)if(L[U+ie+A]<T[ee+ie]||T[ee+ie+A]<L[U+ie])continue e;var ae=k(Q,re);if(ae!==void 0)return ae}}}else{for(W=J-(1<<28),ne=Y-1;ne>=0;--ne)if(h[ne]===W){for(ie=ne+1;ie<Y;++ie)h[ie-1]=h[ie];break}--Y}}}};var i=a("typedarray-pool"),s=a("bit-twiddle"),u=a("./sort"),h=i.mallocInt32(1024),d=i.mallocInt32(1024),m=i.mallocInt32(1024),p=i.mallocInt32(1024),g=i.mallocInt32(1024),y=i.mallocInt32(1024),v=i.mallocDouble(8192);function x(A,b,k,w){var M=b[w],T=A[k-1];A[M]=T,b[T]=M}function _(A,b,k,w){A[k]=w,b[w]=k}},{"./sort":40,"bit-twiddle":32,"typedarray-pool":308}],42:[function(a,l,c){var i=a("./lib/monotone"),s=a("./lib/triangulation"),u=a("./lib/delaunay"),h=a("./lib/filter");function d(g){return[Math.min(g[0],g[1]),Math.max(g[0],g[1])]}function m(g,y){return g[0]-y[0]||g[1]-y[1]}function p(g,y,v){return y in g?g[y]:v}l.exports=function(g,y,v){Array.isArray(y)?(v=v||{},y=y||[]):(v=y||{},y=[]);var 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b=d.pop(),k=(g=d.pop(),x=-1,_=-1,y=p[g],1);k<y.length;k+=2){var w=y[k-1],M=y[k];w===b?_=M:M===b&&(x=w)}x<0||_<0||i(u[g],u[b],u[x],u[_])>=0||(h.flip(g,b),s(u,h,d,x,g,_),s(u,h,d,g,_,x),s(u,h,d,_,b,x),s(u,h,d,b,x,_))}}},{"binary-search-bounds":31,"robust-in-sphere":282}],44:[function(a,l,c){var i,s=a("binary-search-bounds");function u(d,m,p,g,y,v,x){this.cells=d,this.neighbor=m,this.flags=g,this.constraint=p,this.active=y,this.next=v,this.boundary=x}function h(d,m){return d[0]-m[0]||d[1]-m[1]||d[2]-m[2]}l.exports=function(d,m,p){var g=function(P,L){for(var R=P.cells(),F=R.length,D=0;D<F;++D){var O=(U=R[D])[0],N=U[1],B=U[2];N<B?N<O&&(U[0]=N,U[1]=B,U[2]=O):B<O&&(U[0]=B,U[1]=O,U[2]=N)}R.sort(h);var W=new Array(F);for(D=0;D<W.length;++D)W[D]=0;var G=[],K=[],te=new Array(3*F),Y=new Array(3*F),J=null;L&&(J=[]);var re=new u(R,te,Y,W,G,K,J);for(D=0;D<F;++D)for(var U=R[D],V=0;V<3;++V){O=U[V],N=U[(V+1)%3];var H=te[3*D+V]=re.locate(N,O,P.opposite(N,O)),ne=Y[3*D+V]=P.isConstraint(O,N);H<0&&(ne?K.push(D):(G.push(D),W[D]=1),L&&J.push([N,O,-1]))}return re}(d,p);if(m===0)return p?g.cells.concat(g.boundary):g.cells;for(var y=1,v=g.active,x=g.next,_=g.flags,A=g.cells,b=g.constraint,k=g.neighbor;v.length>0||x.length>0;){for(;v.length>0;){var w=v.pop();if(_[w]!==-y){_[w]=y,A[w];for(var M=0;M<3;++M){var T=k[3*w+M];T>=0&&_[T]===0&&(b[3*w+M]?x.push(T):(v.push(T),_[T]=y))}}}var E=x;x=v,v=E,x.length=0,y=-y}var S=function(P,L,R){for(var F=0,D=0;D<P.length;++D)L[D]===R&&(P[F++]=P[D]);return P.length=F,P}(A,_,m);return p?S.concat(g.boundary):S},u.prototype.locate=(i=[0,0,0],function(d,m,p){var g=d,y=m,v=p;return m<p?m<d&&(g=m,y=p,v=d):p<d&&(g=p,y=d,v=m),g<0?-1:(i[0]=g,i[1]=y,i[2]=v,s.eq(this.cells,i,h))})},{"binary-search-bounds":31}],45:[function(a,l,c){var i=a("binary-search-bounds"),s=a("robust-orientation")[3];function u(x,_,A,b,k){this.a=x,this.b=_,this.idx=A,this.lowerIds=b,this.upperIds=k}function h(x,_,A,b){this.a=x,this.b=_,this.type=A,this.idx=b}function d(x,_){var A=x.a[0]-_.a[0]||x.a[1]-_.a[1]||x.type-_.type;return A||(x.type!==0&&(A=s(x.a,x.b,_.b))?A:x.idx-_.idx)}function m(x,_){return s(x.a,x.b,_)}function p(x,_,A,b,k){for(var w=i.lt(_,b,m),M=i.gt(_,b,m),T=w;T<M;++T){for(var E=_[T],S=E.lowerIds,P=S.length;P>1&&s(A[S[P-2]],A[S[P-1]],b)>0;)x.push([S[P-1],S[P-2],k]),P-=1;S.length=P,S.push(k);var L=E.upperIds;for(P=L.length;P>1&&s(A[L[P-2]],A[L[P-1]],b)<0;)x.push([L[P-2],L[P-1],k]),P-=1;L.length=P,L.push(k)}}function g(x,_){var A;return(A=x.a[0]<_.a[0]?s(x.a,x.b,_.a):s(_.b,_.a,x.a))?A:(A=_.b[0]<x.b[0]?s(x.a,x.b,_.b):s(_.b,_.a,x.b))||x.idx-_.idx}function y(x,_,A){var b=i.le(x,A,g),k=x[b],w=k.upperIds,M=w[w.length-1];k.upperIds=[M],x.splice(b+1,0,new u(A.a,A.b,A.idx,[M],w))}function v(x,_,A){var b=A.a;A.a=A.b,A.b=b;var k=i.eq(x,A,g),w=x[k];x[k-1].upperIds=w.upperIds,x.splice(k,1)}l.exports=function(x,_){for(var A=x.length,b=_.length,k=[],w=0;w<A;++w)k.push(new h(x[w],null,0,w));for(w=0;w<b;++w){var M=_[w],T=x[M[0]],E=x[M[1]];T[0]<E[0]?k.push(new h(T,E,2,w),new h(E,T,1,w)):T[0]>E[0]&&k.push(new h(E,T,2,w),new h(T,E,1,w))}k.sort(d);for(var S=k[0].a[0]-(1+Math.abs(k[0].a[0]))*Math.pow(2,-52),P=[new u([S,1],[S,0],-1,[],[])],L=[],R=(w=0,k.length);w<R;++w){var F=k[w],D=F.type;D===0?p(L,P,x,F.a,F.idx):D===2?y(P,x,F):v(P,x,F)}return L}},{"binary-search-bounds":31,"robust-orientation":284}],46:[function(a,l,c){var i=a("binary-search-bounds");function s(d,m){this.stars=d,this.edges=m}l.exports=function(d,m){for(var p=new Array(d),g=0;g<d;++g)p[g]=[];return new s(p,m)};var u=s.prototype;function h(d,m,p){for(var g=1,y=d.length;g<y;g+=2)if(d[g-1]===m&&d[g]===p)return d[g-1]=d[y-2],d[g]=d[y-1],void(d.length=y-2)}u.isConstraint=function(){var d=[0,0];function m(p,g){return p[0]-g[0]||p[1]-g[1]}return function(p,g){return d[0]=Math.min(p,g),d[1]=Math.max(p,g),i.eq(this.edges,d,m)>=0}}(),u.removeTriangle=function(d,m,p){var g=this.stars;h(g[d],m,p),h(g[m],p,d),h(g[p],d,m)},u.addTriangle=function(d,m,p){var g=this.stars;g[d].push(m,p),g[m].push(p,d),g[p].push(d,m)},u.opposite=function(d,m){for(var p=this.stars[m],g=1,y=p.length;g<y;g+=2)if(p[g]===d)return p[g-1];return-1},u.flip=function(d,m){var p=this.opposite(d,m),g=this.opposite(m,d);this.removeTriangle(d,m,p),this.removeTriangle(m,d,g),this.addTriangle(d,g,p),this.addTriangle(m,p,g)},u.edges=function(){for(var d=this.stars,m=[],p=0,g=d.length;p<g;++p)for(var y=d[p],v=0,x=y.length;v<x;v+=2)m.push([y[v],y[v+1]]);return m},u.cells=function(){for(var d=this.stars,m=[],p=0,g=d.length;p<g;++p)for(var y=d[p],v=0,x=y.length;v<x;v+=2){var _=y[v],A=y[v+1];p<Math.min(_,A)&&m.push([p,_,A])}return m}},{"binary-search-bounds":31}],47:[function(a,l,c){l.exports=function(i){for(var s=1,u=1;u<i.length;++u)for(var h=0;h<u;++h)if(i[u]<i[h])s=-s;else if(i[h]===i[u])return 0;return s}},{}],48:[function(a,l,c){var i=a("dup"),s=a("robust-linear-solve");function 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R=T[P-2];if(R.left===L)if(L.left===M){if(!(F=R.right)||F._color!==0){R._color=0,R.left=L.right,L._color=1,L.right=R,T[P-2]=L,T[P-1]=M,h(R),h(L),P>=3&&((D=T[P-3]).left===R?D.left=L:D.right=L);break}L._color=1,R.right=u(1,F),R._color=0,P-=1}else{if(!(F=R.right)||F._color!==0){L.right=M.left,R._color=0,R.left=M.right,M._color=1,M.left=L,M.right=R,T[P-2]=M,T[P-1]=L,h(R),h(L),h(M),P>=3&&((D=T[P-3]).left===R?D.left=M:D.right=M);break}L._color=1,R.right=u(1,F),R._color=0,P-=1}else if(L.right===M){if(!(F=R.left)||F._color!==0){R._color=0,R.right=L.left,L._color=1,L.left=R,T[P-2]=L,T[P-1]=M,h(R),h(L),P>=3&&((D=T[P-3]).right===R?D.right=L:D.left=L);break}L._color=1,R.left=u(1,F),R._color=0,P-=1}else{var F;if(!(F=R.left)||F._color!==0){var D;L.left=M.right,R._color=0,R.right=M.left,M._color=1,M.right=L,M.left=R,T[P-2]=M,T[P-1]=L,h(R),h(L),h(M),P>=3&&((D=T[P-3]).right===R?D.right=M:D.left=M);break}L._color=1,R.left=u(1,F),R._color=0,P-=1}}return T[0]._color=1,new 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E=k(b,w.key);M.push(w),E<0&&(T=M.length),w=E<0?w.left:w.right}return M.length=T,new v(this,M)},m.lt=function(b){for(var k=this._compare,w=this.root,M=[],T=0;w;){var E=k(b,w.key);M.push(w),E>0&&(T=M.length),w=E<=0?w.left:w.right}return M.length=T,new v(this,M)},m.le=function(b){for(var k=this._compare,w=this.root,M=[],T=0;w;){var E=k(b,w.key);M.push(w),E>=0&&(T=M.length),w=E<0?w.left:w.right}return M.length=T,new v(this,M)},m.find=function(b){for(var k=this._compare,w=this.root,M=[];w;){var T=k(b,w.key);if(M.push(w),T===0)return new v(this,M);w=T<=0?w.left:w.right}return new v(this,[])},m.remove=function(b){var k=this.find(b);return k?k.remove():this},m.get=function(b){for(var k=this._compare,w=this.root;w;){var M=k(b,w.key);if(M===0)return w.value;w=M<=0?w.left:w.right}};var x=v.prototype;function _(b,k){b.key=k.key,b.value=k.value,b.left=k.left,b.right=k.right,b._color=k._color,b._count=k._count}function A(b,k){return b<k?-1:b>k?1:0}Object.defineProperty(x,"valid",{get:function(){return this._stack.length>0}}),Object.defineProperty(x,"node",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),x.clone=function(){return new v(this.tree,this._stack.slice())},x.remove=function(){var b=this._stack;if(b.length===0)return this.tree;var k=new Array(b.length),w=b[b.length-1];k[k.length-1]=new i(w._color,w.key,w.value,w.left,w.right,w._count);for(var M=b.length-2;M>=0;--M)(w=b[M]).left===b[M+1]?k[M]=new i(w._color,w.key,w.value,k[M+1],w.right,w._count):k[M]=new i(w._color,w.key,w.value,w.left,k[M+1],w._count);if((w=k[k.length-1]).left&&w.right){var T=k.length;for(w=w.left;w.right;)k.push(w),w=w.right;var E=k[T-1];for(k.push(new i(w._color,E.key,E.value,w.left,w.right,w._count)),k[T-1].key=w.key,k[T-1].value=w.value,M=k.length-2;M>=T;--M)w=k[M],k[M]=new i(w._color,w.key,w.value,w.left,k[M+1],w._count);k[T-1].left=k[T]}if((w=k[k.length-1])._color===0){var S=k[k.length-2];for(S.left===w?S.left=null:S.right===w&&(S.right=null),k.pop(),M=0;M<k.length;++M)k[M]._count--;return new d(this.tree._compare,k[0])}if(w.left||w.right){for(w.left?_(w,w.left):w.right&&_(w,w.right),w._color=1,M=0;M<k.length-1;++M)k[M]._count--;return new d(this.tree._compare,k[0])}if(k.length===1)return new d(this.tree._compare,null);for(M=0;M<k.length;++M)k[M]._count--;var P=k[k.length-2];return function(L){for(var R,F,D,O,N=L.length-1;N>=0;--N){if(R=L[N],N===0)return void(R._color=1);if((F=L[N-1]).left===R){if((D=F.right).right&&D.right._color===0)return O=(D=F.right=s(D)).right=s(D.right),F.right=D.left,D.left=F,D.right=O,D._color=F._color,R._color=1,F._color=1,O._color=1,h(F),h(D),N>1&&((B=L[N-2]).left===F?B.left=D:B.right=D),void(L[N-1]=D);if(D.left&&D.left._color===0)return O=(D=F.right=s(D)).left=s(D.left),F.right=O.left,D.left=O.right,O.left=F,O.right=D,O._color=F._color,F._color=1,D._color=1,R._color=1,h(F),h(D),h(O),N>1&&((B=L[N-2]).left===F?B.left=O:B.right=O),void(L[N-1]=O);if(D._color===1){if(F._color===0)return F._color=1,void(F.right=u(0,D));F.right=u(0,D);continue}D=s(D),F.right=D.left,D.left=F,D._color=F._color,F._color=0,h(F),h(D),N>1&&((B=L[N-2]).left===F?B.left=D:B.right=D),L[N-1]=D,L[N]=F,N+1<L.length?L[N+1]=R:L.push(R),N+=2}else{if((D=F.left).left&&D.left._color===0)return O=(D=F.left=s(D)).left=s(D.left),F.left=D.right,D.right=F,D.left=O,D._color=F._color,R._color=1,F._color=1,O._color=1,h(F),h(D),N>1&&((B=L[N-2]).right===F?B.right=D:B.left=D),void(L[N-1]=D);if(D.right&&D.right._color===0)return 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g(T){this.gl=T,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=u(T)}var y=g.prototype;function v(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}y.update=function(T){function E(K,te,Y){if(Y in T){var J,re=T[Y],U=this[Y];(K?Array.isArray(re)&&Array.isArray(re[0]):Array.isArray(re))?this[Y]=J=[te(re[0]),te(re[1]),te(re[2])]:this[Y]=J=[te(re),te(re),te(re)];for(var V=0;V<3;++V)if(J[V]!==U[V])return!0}return!1}T=T||{};var S,P=E.bind(this,!1,Number),L=E.bind(this,!1,Boolean),R=E.bind(this,!1,String),F=E.bind(this,!0,function(K){if(Array.isArray(K)){if(K.length===3)return[+K[0],+K[1],+K[2],1];if(K.length===4)return[+K[0],+K[1],+K[2],+K[3]]}return[0,0,0,1]}),D=!1,O=!1;if("bounds"in T)for(var N=T.bounds,B=0;B<2;++B)for(var W=0;W<3;++W)N[B][W]!==this.bounds[B][W]&&(O=!0),this.bounds[B][W]=N[B][W];if("ticks"in T)for(S=T.ticks,D=!0,this.autoTicks=!1,B=0;B<3;++B)this.tickSpacing[B]=0;else P("tickSpacing")&&(this.autoTicks=!0,O=!0);if(this._firstInit&&("ticks"in T||"tickSpacing"in T||(this.autoTicks=!0),O=!0,D=!0,this._firstInit=!1),O&&this.autoTicks&&(S=d.create(this.bounds,this.tickSpacing),D=!0),D){for(B=0;B<3;++B)S[B].sort(function(K,te){return K.x-te.x});d.equal(S,this.ticks)?D=!1:this.ticks=S}L("tickEnable"),R("tickFont")&&(D=!0),P("tickSize"),P("tickAngle"),P("tickPad"),F("tickColor");var G=R("labels");R("labelFont")&&(G=!0),L("labelEnable"),P("labelSize"),P("labelPad"),F("labelColor"),L("lineEnable"),L("lineMirror"),P("lineWidth"),F("lineColor"),L("lineTickEnable"),L("lineTickMirror"),P("lineTickLength"),P("lineTickWidth"),F("lineTickColor"),L("gridEnable"),P("gridWidth"),F("gridColor"),L("zeroEnable"),F("zeroLineColor"),P("zeroLineWidth"),L("backgroundEnable"),F("backgroundColor"),this._text?this._text&&(G||D)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=i(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&D&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=s(this.gl,this.bounds,this.ticks))};var x=[new v,new v,new v];function _(T,E,S,P,L){for(var R=T.primalOffset,F=T.primalMinor,D=T.mirrorOffset,O=T.mirrorMinor,N=P[E],B=0;B<3;++B)if(E!==B){var W=R,G=D,K=F,te=O;N&1<<B&&(W=D,G=R,K=O,te=F),W[B]=S[0][B],G[B]=S[1][B],L[B]>0?(K[B]=-1,te[B]=0):(K[B]=0,te[B]=1)}}var A=[0,0,0],b={model:m,view:m,projection:m,_ortho:!1};y.isOpaque=function(){return!0},y.isTransparent=function(){return!1},y.drawTransparent=function(T){};var k=[0,0,0],w=[0,0,0],M=[0,0,0];y.draw=function(T){T=T||b;for(var E=this.gl,S=T.model||m,P=T.view||m,L=T.projection||m,R=this.bounds,F=T._ortho||!1,D=h(S,P,L,R,F),O=D.cubeEdges,N=D.axis,B=P[12],W=P[13],G=P[14],K=P[15],te=(F?2:1)*this.pixelRatio*(L[3]*B+L[7]*W+L[11]*G+L[15]*K)/E.drawingBufferHeight,Y=0;Y<3;++Y)this.lastCubeProps.cubeEdges[Y]=O[Y],this.lastCubeProps.axis[Y]=N[Y];var J=x;for(Y=0;Y<3;++Y)_(x[Y],Y,this.bounds,O,N);E=this.gl;var re,U=A;for(Y=0;Y<3;++Y)this.backgroundEnable[Y]?U[Y]=N[Y]:U[Y]=0;for(this._background.draw(S,P,L,R,U,this.backgroundColor),this._lines.bind(S,P,L,this),Y=0;Y<3;++Y){var V=[0,0,0];N[Y]>0?V[Y]=R[1][Y]:V[Y]=R[0][Y];for(var H=0;H<2;++H){var ne=(Y+1+H)%3,q=(Y+1+(1^H))%3;this.gridEnable[ne]&&this._lines.drawGrid(ne,q,this.bounds,V,this.gridColor[ne],this.gridWidth[ne]*this.pixelRatio)}for(H=0;H<2;++H)ne=(Y+1+H)%3,q=(Y+1+(1^H))%3,this.zeroEnable[q]&&Math.min(R[0][q],R[1][q])<=0&&Math.max(R[0][q],R[1][q])>=0&&this._lines.drawZero(ne,q,this.bounds,V,this.zeroLineColor[q],this.zeroLineWidth[q]*this.pixelRatio)}for(Y=0;Y<3;++Y){this.lineEnable[Y]&&this._lines.drawAxisLine(Y,this.bounds,J[Y].primalOffset,this.lineColor[Y],this.lineWidth[Y]*this.pixelRatio),this.lineMirror[Y]&&this._lines.drawAxisLine(Y,this.bounds,J[Y].mirrorOffset,this.lineColor[Y],this.lineWidth[Y]*this.pixelRatio);var Q=p(k,J[Y].primalMinor),ee=p(w,J[Y].mirrorMinor),ie=this.lineTickLength;for(H=0;H<3;++H){var ae=te/S[5*H];Q[H]*=ie[H]*ae,ee[H]*=ie[H]*ae}this.lineTickEnable[Y]&&this._lines.drawAxisTicks(Y,J[Y].primalOffset,Q,this.lineTickColor[Y],this.lineTickWidth[Y]*this.pixelRatio),this.lineTickMirror[Y]&&this._lines.drawAxisTicks(Y,J[Y].mirrorOffset,ee,this.lineTickColor[Y],this.lineTickWidth[Y]*this.pixelRatio)}this._lines.unbind(),this._text.bind(S,P,L,this.pixelRatio);var ue,le;function ge(Le){(le=[0,0,0])[Le]=1}function fe(Le,de,ve){var Me=(Le+1)%3,we=(Le+2)%3,Ce=de[Me],Fe=de[we],ze=ve[Me],$e=ve[we];Ce>0&&$e>0||Ce>0&&$e<0||Ce<0&&$e>0||Ce<0&&$e<0?ge(Me):(Fe>0&&ze>0||Fe>0&&ze<0||Fe<0&&ze>0||Fe<0&&ze<0)&&ge(we)}for(Y=0;Y<3;++Y){var me=J[Y].primalMinor,_e=J[Y].mirrorMinor,Ae=p(M,J[Y].primalOffset);for(H=0;H<3;++H)this.lineTickEnable[Y]&&(Ae[H]+=te*me[H]*Math.max(this.lineTickLength[H],0)/S[5*H]);var ke=[0,0,0];if(ke[Y]=1,this.tickEnable[Y]){for(this.tickAngle[Y]===-3600?(this.tickAngle[Y]=0,this.tickAlign[Y]="auto"):this.tickAlign[Y]=-1,ue=1,(re=[this.tickAlign[Y],.5,ue])[0]==="auto"?re[0]=0:re[0]=parseInt(""+re[0]),le=[0,0,0],fe(Y,me,_e),H=0;H<3;++H)Ae[H]+=te*me[H]*this.tickPad[H]/S[5*H];this._text.drawTicks(Y,this.tickSize[Y],this.tickAngle[Y],Ae,this.tickColor[Y],ke,le,re)}if(this.labelEnable[Y]){for(ue=0,le=[0,0,0],this.labels[Y].length>4&&(ge(Y),ue=1),(re=[this.labelAlign[Y],.5,ue])[0]==="auto"?re[0]=0:re[0]=parseInt(""+re[0]),H=0;H<3;++H)Ae[H]+=te*me[H]*this.labelPad[H]/S[5*H];Ae[Y]+=.5*(R[0][Y]+R[1][Y]),this._text.drawLabel(Y,this.labelSize[Y],this.labelAngle[Y],Ae,this.labelColor[Y],[0,0,0],le,re)}}this._text.unbind()},y.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":71,"./lib/cube.js":72,"./lib/lines.js":73,"./lib/text.js":75,"./lib/ticks.js":76}],71:[function(a,l,c){l.exports=function(m){for(var p=[],g=[],y=0,v=0;v<3;++v)for(var x=(v+1)%3,_=(v+2)%3,A=[0,0,0],b=[0,0,0],k=-1;k<=1;k+=2){g.push(y,y+2,y+1,y+1,y+2,y+3),A[v]=k,b[v]=k;for(var w=-1;w<=1;w+=2){A[x]=w;for(var M=-1;M<=1;M+=2)A[_]=M,p.push(A[0],A[1],A[2],b[0],b[1],b[2]),y+=1}var T=x;x=_,_=T}var E=i(m,new Float32Array(p)),S=i(m,new Uint16Array(g),m.ELEMENT_ARRAY_BUFFER),P=s(m,[{buffer:E,type:m.FLOAT,size:3,offset:0,stride:24},{buffer:E,type:m.FLOAT,size:3,offset:12,stride:24}],S),L=u(m);return L.attributes.position.location=0,L.attributes.normal.location=1,new h(m,E,P,L)};var i=a("gl-buffer"),s=a("gl-vao"),u=a("./shaders").bg;function h(m,p,g,y){this.gl=m,this.buffer=p,this.vao=g,this.shader=y}var d=h.prototype;d.draw=function(m,p,g,y,v,x){for(var _=!1,A=0;A<3;++A)_=_||v[A];if(_){var b=this.gl;b.enable(b.POLYGON_OFFSET_FILL),b.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:m,view:p,projection:g,bounds:y,enable:v,colors:x},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),b.disable(b.POLYGON_OFFSET_FILL)}},d.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],72:[function(a,l,c){l.exports=function(w,M,T,E,S){s(d,M,w),s(d,T,d);for(var P=0,L=0;L<2;++L){g[2]=E[L][2];for(var R=0;R<2;++R){g[1]=E[R][1];for(var F=0;F<2;++F)g[0]=E[F][0],v(m[P],g,d),P+=1}}var D=-1;for(L=0;L<8;++L){for(var O=m[L][3],N=0;N<3;++N)p[L][N]=m[L][N]/O;S&&(p[L][2]*=-1),O<0&&(D<0||p[L][2]<p[D][2])&&(D=L)}if(D<0){D=0;for(var B=0;B<3;++B){for(var W=(B+2)%3,G=(B+1)%3,K=-1,te=-1,Y=0;Y<2;++Y){var J=(U=Y<<B)+(Y<<W)+(1-Y<<G),re=U+(1-Y<<W)+(Y<<G);h(p[U],p[J],p[re],y)<0||(Y?K=1:te=1)}if(K<0||te<0)te>K&&(D|=1<<B);else{for(Y=0;Y<2;++Y){J=(U=Y<<B)+(Y<<W)+(1-Y<<G),re=U+(1-Y<<W)+(Y<<G);var U,V=_([m[U],m[J],m[re],m[U+(1<<W)+(1<<G)]]);Y?K=V:te=V}te>K&&(D|=1<<B)}}}var H=7^D,ne=-1;for(L=0;L<8;++L)L!==D&&L!==H&&(ne<0||p[ne][1]>p[L][1])&&(ne=L);var q=-1;for(L=0;L<3;++L)(ee=ne^1<<L)!==D&&ee!==H&&(q<0&&(q=ee),(G=p[ee])[0]<p[q][0]&&(q=ee));var Q=-1;for(L=0;L<3;++L){var ee;(ee=ne^1<<L)!==D&&ee!==H&&ee!==q&&(Q<0&&(Q=ee),(G=p[ee])[0]>p[Q][0]&&(Q=ee))}var ie=A;ie[0]=ie[1]=ie[2]=0,ie[i.log2(q^ne)]=ne&q,ie[i.log2(ne^Q)]=ne&Q;var ae=7^Q;ae===D||ae===H?(ae=7^q,ie[i.log2(Q^ae)]=ae&Q):ie[i.log2(q^ae)]=ae&q;var ue=b,le=D;for(B=0;B<3;++B)ue[B]=le&1<<B?-1:1;return k};var i=a("bit-twiddle"),s=a("gl-mat4/multiply"),u=a("split-polygon"),h=a("robust-orientation"),d=new Array(16),m=new Array(8),p=new Array(8),g=new Array(3),y=[0,0,0];function v(w,M,T){for(var E=0;E<4;++E){w[E]=T[12+E];for(var S=0;S<3;++S)w[E]+=M[S]*T[4*S+E]}}(function(){for(var w=0;w<8;++w)m[w]=[1,1,1,1],p[w]=[1,1,1]})();var x=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function _(w){for(var M=0;M<x.length;++M)if((w=u.positive(w,x[M])).length<3)return 0;var T=w[0],E=T[0]/T[3],S=T[1]/T[3],P=0;for(M=1;M+1<w.length;++M){var L=w[M],R=w[M+1],F=L[0]/L[3]-E,D=L[1]/L[3]-S,O=R[0]/R[3]-E,N=R[1]/R[3]-S;P+=Math.abs(F*N-D*O)}return P}var A=[1,1,1],b=[0,0,0],k={cubeEdges:A,axis:b}},{"bit-twiddle":32,"gl-mat4/multiply":100,"robust-orientation":284,"split-polygon":300}],73:[function(a,l,c){l.exports=function(A,b,k){var w=[],M=[0,0,0],T=[0,0,0],E=[0,0,0],S=[0,0,0];w.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var P=0;P<3;++P){for(var L=w.length/3|0,R=0;R<k[P].length;++R){var F=+k[P][R].x;w.push(F,0,1,F,1,1,F,0,-1,F,0,-1,F,1,1,F,1,-1)}var D=w.length/3|0;M[P]=L,T[P]=D-L,L=w.length/3|0;for(var O=0;O<k[P].length;++O)F=+k[P][O].x,w.push(F,0,1,F,1,1,F,0,-1,F,0,-1,F,1,1,F,1,-1);D=w.length/3|0,E[P]=L,S[P]=D-L}var N=i(A,new Float32Array(w)),B=s(A,[{buffer:N,type:A.FLOAT,size:3,stride:0,offset:0}]),W=u(A);return W.attributes.position.location=0,new x(A,N,B,W,T,M,S,E)};var i=a("gl-buffer"),s=a("gl-vao"),u=a("./shaders").line,h=[0,0,0],d=[0,0,0],m=[0,0,0],p=[0,0,0],g=[1,1];function y(A){return A[0]=A[1]=A[2]=0,A}function v(A,b){return A[0]=b[0],A[1]=b[1],A[2]=b[2],A}function x(A,b,k,w,M,T,E,S){this.gl=A,this.vertBuffer=b,this.vao=k,this.shader=w,this.tickCount=M,this.tickOffset=T,this.gridCount=E,this.gridOffset=S}var _=x.prototype;_.bind=function(A,b,k){this.shader.bind(),this.shader.uniforms.model=A,this.shader.uniforms.view=b,this.shader.uniforms.projection=k,g[0]=this.gl.drawingBufferWidth,g[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=g,this.vao.bind()},_.unbind=function(){this.vao.unbind()},_.drawAxisLine=function(A,b,k,w,M){var T=y(d);this.shader.uniforms.majorAxis=d,T[A]=b[1][A]-b[0][A],this.shader.uniforms.minorAxis=T;var E,S=v(p,k);S[A]+=b[0][A],this.shader.uniforms.offset=S,this.shader.uniforms.lineWidth=M,this.shader.uniforms.color=w,(E=y(m))[(A+2)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6),(E=y(m))[(A+1)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6)},_.drawAxisTicks=function(A,b,k,w,M){if(this.tickCount[A]){var T=y(h);T[A]=1,this.shader.uniforms.majorAxis=T,this.shader.uniforms.offset=b,this.shader.uniforms.minorAxis=k,this.shader.uniforms.color=w,this.shader.uniforms.lineWidth=M;var E=y(m);E[A]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,this.tickCount[A],this.tickOffset[A])}},_.drawGrid=function(A,b,k,w,M,T){if(this.gridCount[A]){var E=y(d);E[b]=k[1][b]-k[0][b],this.shader.uniforms.minorAxis=E;var S=v(p,w);S[b]+=k[0][b],this.shader.uniforms.offset=S;var P=y(h);P[A]=1,this.shader.uniforms.majorAxis=P;var L=y(m);L[A]=1,this.shader.uniforms.screenAxis=L,this.shader.uniforms.lineWidth=T,this.shader.uniforms.color=M,this.vao.draw(this.gl.TRIANGLES,this.gridCount[A],this.gridOffset[A])}},_.drawZero=function(A,b,k,w,M,T){var E=y(d);this.shader.uniforms.majorAxis=E,E[A]=k[1][A]-k[0][A],this.shader.uniforms.minorAxis=E;var S=v(p,w);S[A]+=k[0][A],this.shader.uniforms.offset=S;var P=y(m);P[b]=1,this.shader.uniforms.screenAxis=P,this.shader.uniforms.lineWidth=T,this.shader.uniforms.color=M,this.vao.draw(this.gl.TRIANGLES,6)},_.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],74:[function(a,l,c){var i=a("glslify"),s=a("gl-shader"),u=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position;
-
-uniform mat4 model, view, projection;
-uniform vec3 offset, majorAxis, minorAxis, screenAxis;
-uniform float lineWidth;
-uniform vec2 screenShape;
-
-vec3 project(vec3 p) {
-  vec4 pp = projection * view * model * vec4(p, 1.0);
-  return pp.xyz / max(pp.w, 0.0001);
-}
-
-void main() {
-  vec3 major = position.x * majorAxis;
-  vec3 minor = position.y * minorAxis;
-
-  vec3 vPosition = major + minor + offset;
-  vec3 pPosition = project(vPosition);
-  vec3 offset = project(vPosition + screenAxis * position.z);
-
-  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;
-
-  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);
-}
-`]),h=i([`precision highp float;
-#define GLSLIFY 1
-
-uniform vec4 color;
-void main() {
-  gl_FragColor = color;
-}`]);c.line=function(y){return s(y,u,h,null,[{name:"position",type:"vec3"}])};var d=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position;
-
-uniform mat4 model, view, projection;
-uniform vec3 offset, axis, alignDir, alignOpt;
-uniform float scale, angle, pixelScale;
-uniform vec2 resolution;
-
-vec3 project(vec3 p) {
-  vec4 pp = projection * view * model * vec4(p, 1.0);
-  return pp.xyz / max(pp.w, 0.0001);
-}
-
-float computeViewAngle(vec3 a, vec3 b) {
-  vec3 A = project(a);
-  vec3 B = project(b);
-
-  return atan(
-    (B.y - A.y) * resolution.y,
-    (B.x - A.x) * resolution.x
-  );
-}
-
-const float PI = 3.141592;
-const float TWO_PI = 2.0 * PI;
-const float HALF_PI = 0.5 * PI;
-const float ONE_AND_HALF_PI = 1.5 * PI;
-
-int option = int(floor(alignOpt.x + 0.001));
-float hv_ratio =       alignOpt.y;
-bool enableAlign =    (alignOpt.z != 0.0);
-
-float mod_angle(float a) {
-  return mod(a, PI);
-}
-
-float positive_angle(float a) {
-  return mod_angle((a < 0.0) ?
-    a + TWO_PI :
-    a
-  );
-}
-
-float look_upwards(float a) {
-  float b = positive_angle(a);
-  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?
-    b - PI :
-    b;
-}
-
-float look_horizontal_or_vertical(float a, float ratio) {
-  // ratio controls the ratio between being horizontal to (vertical + horizontal)
-  // if ratio is set to 0.5 then it is 50%, 50%.
-  // when using a higher ratio e.g. 0.75 the result would
-  // likely be more horizontal than vertical.
-
-  float b = positive_angle(a);
-
-  return
-    (b < (      ratio) * HALF_PI) ? 0.0 :
-    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :
-    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :
-    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :
-                                    0.0;
-}
-
-float roundTo(float a, float b) {
-  return float(b * floor((a + 0.5 * b) / b));
-}
-
-float look_round_n_directions(float a, int n) {
-  float b = positive_angle(a);
-  float div = TWO_PI / float(n);
-  float c = roundTo(b, div);
-  return look_upwards(c);
-}
-
-float applyAlignOption(float rawAngle, float delta) {
-  return
-    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions
-    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical
-    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis
-    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards
-    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal
-                    rawAngle;                // otherwise return back raw input angle
-}
-
-bool isAxisTitle = (axis.x == 0.0) &&
-                   (axis.y == 0.0) &&
-                   (axis.z == 0.0);
-
-void main() {
-  //Compute world offset
-  float axisDistance = position.z;
-  vec3 dataPosition = axisDistance * axis + offset;
-
-  float beta = angle; // i.e. user defined attributes for each tick
-
-  float axisAngle;
-  float clipAngle;
-  float flip;
-
-  if (enableAlign) {
-    axisAngle = (isAxisTitle) ? HALF_PI :
-                      computeViewAngle(dataPosition, dataPosition + axis);
-    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);
-
-    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;
-    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;
-
-    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),
-                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;
-
-    beta += applyAlignOption(clipAngle, flip * PI);
-  }
-
-  //Compute plane offset
-  vec2 planeCoord = position.xy * pixelScale;
-
-  mat2 planeXform = scale * mat2(
-     cos(beta), sin(beta),
-    -sin(beta), cos(beta)
-  );
-
-  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;
-
-  //Compute clip position
-  vec3 clipPosition = project(dataPosition);
-
-  //Apply text offset in clip coordinates
-  clipPosition += vec3(viewOffset, 0.0);
-
-  //Done
-  gl_Position = vec4(clipPosition, 1.0);
-}`]),m=i([`precision highp float;
-#define GLSLIFY 1
-
-uniform vec4 color;
-void main() {
-  gl_FragColor = color;
-}`]);c.text=function(y){return s(y,d,m,null,[{name:"position",type:"vec3"}])};var p=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position;
-attribute vec3 normal;
-
-uniform mat4 model, view, projection;
-uniform vec3 enable;
-uniform vec3 bounds[2];
-
-varying vec3 colorChannel;
-
-void main() {
-
-  vec3 signAxis = sign(bounds[1] - bounds[0]);
-
-  vec3 realNormal = signAxis * normal;
-
-  if(dot(realNormal, enable) > 0.0) {
-    vec3 minRange = min(bounds[0], bounds[1]);
-    vec3 maxRange = max(bounds[0], bounds[1]);
-    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));
-    gl_Position = projection * view * model * vec4(nPosition, 1.0);
-  } else {
-    gl_Position = vec4(0,0,0,0);
-  }
-
-  colorChannel = abs(realNormal);
-}`]),g=i([`precision highp float;
-#define GLSLIFY 1
-
-uniform vec4 colors[3];
-
-varying vec3 colorChannel;
-
-void main() {
-  gl_FragColor = colorChannel.x * colors[0] +
-                 colorChannel.y * colors[1] +
-                 colorChannel.z * colors[2];
-}`]);c.bg=function(y){return s(y,p,g,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":132,glslify:231}],75:[function(a,l,c){(function(i){(function(){l.exports=function(x,_,A,b,k,w){var M=s(x),T=u(x,[{buffer:M,size:3}]),E=d(x);E.attributes.position.location=0;var S=new g(x,E,M,T);return S.update(_,A,b,k,w),S};var s=a("gl-buffer"),u=a("gl-vao"),h=a("vectorize-text"),d=a("./shaders").text,m=window||i.global||{},p=m.__TEXT_CACHE||{};m.__TEXT_CACHE={};function g(x,_,A,b){this.gl=x,this.shader=_,this.buffer=A,this.vao=b,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var y=g.prototype,v=[0,0];y.bind=function(x,_,A,b){this.vao.bind(),this.shader.bind();var k=this.shader.uniforms;k.model=x,k.view=_,k.projection=A,k.pixelScale=b,v[0]=this.gl.drawingBufferWidth,v[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=v},y.unbind=function(){this.vao.unbind()},y.update=function(x,_,A,b,k){var w=[];function M(D,O,N,B,W,G){var K=p[N];K||(K=p[N]={});var te=K[O];te||(te=K[O]=function(Q,ee){try{return h(Q,ee)}catch(ie){return console.warn('error vectorizing text:"'+Q+'" error:',ie),{cells:[],positions:[]}}}(O,{triangles:!0,font:N,textAlign:"center",textBaseline:"middle",lineSpacing:W,styletags:G}));for(var Y=(B||12)/12,J=te.positions,re=te.cells,U=0,V=re.length;U<V;++U)for(var H=re[U],ne=2;ne>=0;--ne){var q=J[H[ne]];w.push(Y*q[0],-Y*q[1],D)}}for(var T=[0,0,0],E=[0,0,0],S=[0,0,0],P=[0,0,0],L={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},R=0;R<3;++R){S[R]=w.length/3|0,M(.5*(x[0][R]+x[1][R]),_[R],A[R],12,1.25,L),P[R]=(w.length/3|0)-S[R],T[R]=w.length/3|0;for(var F=0;F<b[R].length;++F)b[R][F].text&&M(b[R][F].x,b[R][F].text,b[R][F].font||k,b[R][F].fontSize||12,1.25,L);E[R]=(w.length/3|0)-T[R]}this.buffer.update(w),this.tickOffset=T,this.tickCount=E,this.labelOffset=S,this.labelCount=P},y.drawTicks=function(x,_,A,b,k,w,M,T){this.tickCount[x]&&(this.shader.uniforms.axis=w,this.shader.uniforms.color=k,this.shader.uniforms.angle=A,this.shader.uniforms.scale=_,this.shader.uniforms.offset=b,this.shader.uniforms.alignDir=M,this.shader.uniforms.alignOpt=T,this.vao.draw(this.gl.TRIANGLES,this.tickCount[x],this.tickOffset[x]))},y.drawLabel=function(x,_,A,b,k,w,M,T){this.labelCount[x]&&(this.shader.uniforms.axis=w,this.shader.uniforms.color=k,this.shader.uniforms.angle=A,this.shader.uniforms.scale=_,this.shader.uniforms.offset=b,this.shader.uniforms.alignDir=M,this.shader.uniforms.alignOpt=T,this.vao.draw(this.gl.TRIANGLES,this.labelCount[x],this.labelOffset[x]))},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,a("_process"))},{"./shaders":74,_process:5,"gl-buffer":78,"gl-vao":150,"vectorize-text":311}],76:[function(a,l,c){function i(s,u){var h=s+"",d=h.indexOf("."),m=0;d>=0&&(m=h.length-d-1);var p=Math.pow(10,m),g=Math.round(s*u*p),y=g+"";if(y.indexOf("e")>=0)return y;var v=g/p,x=g%p;g<0?(v=0|-Math.ceil(v),x=0|-x):(v=0|Math.floor(v),x|=0);var _=""+v;if(g<0&&(_="-"+_),m){for(var A=""+x;A.length<m;)A="0"+A;return _+"."+A}return _}c.create=function(s,u){for(var h=[],d=0;d<3;++d){for(var m=[],p=(s[0][d],s[1][d],0);p*u[d]<=s[1][d];++p)m.push({x:p*u[d],text:i(u[d],p)});for(p=-1;p*u[d]>=s[0][d];--p)m.push({x:p*u[d],text:i(u[d],p)});h.push(m)}return h},c.equal=function(s,u){for(var h=0;h<3;++h){if(s[h].length!==u[h].length)return!1;for(var d=0;d<s[h].length;++d){var m=s[h][d],p=u[h][d];if(m.x!==p.x||m.text!==p.text||m.font!==p.font||m.fontColor!==p.fontColor||m.fontSize!==p.fontSize||m.dx!==p.dx||m.dy!==p.dy)return!1}}return!0}},{}],77:[function(a,l,c){l.exports=function(k,w,M,T,E){var S=w.model||p,P=w.view||p,L=w.projection||p,R=w._ortho||!1,F=k.bounds,D=(E=E||u(S,P,L,F,R)).axis;h(g,P,S),h(g,L,g);for(var O=A,N=0;N<3;++N)O[N].lo=1/0,O[N].hi=-1/0,O[N].pixelsPerDataUnit=1/0;var B=i(d(g,g));d(g,g);for(var W=0;W<3;++W){var G=(W+1)%3,K=(W+2)%3,te=b;e:for(N=0;N<2;++N){var Y=[];if(D[W]<0!=!!N){te[W]=F[N][W];for(var J=0;J<2;++J){te[G]=F[J^N][G];for(var re=0;re<2;++re)te[K]=F[re^J^N][K],Y.push(te.slice())}var U=R?5:4;for(J=U;J===U;++J){if(Y.length===0)continue e;Y=s.positive(Y,B[J])}for(J=0;J<Y.length;++J){K=Y[J];var V=_(b,g,K,M,T);for(re=0;re<3;++re)O[re].lo=Math.min(O[re].lo,K[re]),O[re].hi=Math.max(O[re].hi,K[re]),re!==W&&(O[re].pixelsPerDataUnit=Math.min(O[re].pixelsPerDataUnit,Math.abs(V[re])))}}}}return O};var i=a("extract-frustum-planes"),s=a("split-polygon"),u=a("./lib/cube.js"),h=a("gl-mat4/multiply"),d=a("gl-mat4/transpose"),m=a("gl-vec4/transformMat4"),p=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),g=new Float32Array(16);function y(k,w,M){this.lo=k,this.hi=w,this.pixelsPerDataUnit=M}var v=[0,0,0,1],x=[0,0,0,1];function _(k,w,M,T,E){for(var S=0;S<3;++S){for(var P=v,L=x,R=0;R<3;++R)L[R]=P[R]=M[R];L[3]=P[3]=1,L[S]+=1,m(L,L,w),L[3]<0&&(k[S]=1/0),P[S]-=1,m(P,P,w),P[3]<0&&(k[S]=1/0);var F=(P[0]/P[3]-L[0]/L[3])*T,D=(P[1]/P[3]-L[1]/L[3])*E;k[S]=.25*Math.sqrt(F*F+D*D)}return k}var A=[new y(1/0,-1/0,1/0),new y(1/0,-1/0,1/0),new y(1/0,-1/0,1/0)],b=[0,0,0]},{"./lib/cube.js":72,"extract-frustum-planes":67,"gl-mat4/multiply":100,"gl-mat4/transpose":109,"gl-vec4/transformMat4":221,"split-polygon":300}],78:[function(a,l,c){var i=a("typedarray-pool"),s=a("ndarray-ops"),u=a("ndarray"),h=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function d(y,v,x,_,A){this.gl=y,this.type=v,this.handle=x,this.length=_,this.usage=A}var m=d.prototype;function p(y,v,x,_,A,b){var k=A.length*A.BYTES_PER_ELEMENT;if(b<0)return y.bufferData(v,A,_),k;if(k+b>x)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return y.bufferSubData(v,b,A),x}function g(y,v){for(var x=i.malloc(y.length,v),_=y.length,A=0;A<_;++A)x[A]=y[A];return x}m.bind=function(){this.gl.bindBuffer(this.type,this.handle)},m.unbind=function(){this.gl.bindBuffer(this.type,null)},m.dispose=function(){this.gl.deleteBuffer(this.handle)},m.update=function(y,v){if(typeof v!="number"&&(v=-1),this.bind(),typeof y=="object"&&y.shape!==void 0){var x=y.dtype;if(h.indexOf(x)<0&&(x="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER&&(x=gl.getExtension("OES_element_index_uint")&&x!=="uint16"?"uint32":"uint16"),x===y.dtype&&function(k,w){for(var M=1,T=w.length-1;T>=0;--T){if(w[T]!==M)return!1;M*=k[T]}return!0}(y.shape,y.stride))y.offset===0&&y.data.length===y.shape[0]?this.length=p(this.gl,this.type,this.length,this.usage,y.data,v):this.length=p(this.gl,this.type,this.length,this.usage,y.data.subarray(y.offset,y.shape[0]),v);else{var _=i.malloc(y.size,x),A=u(_,y.shape);s.assign(A,y),this.length=p(this.gl,this.type,this.length,this.usage,v<0?_:_.subarray(0,y.size),v),i.free(_)}}else if(Array.isArray(y)){var b;b=this.type===this.gl.ELEMENT_ARRAY_BUFFER?g(y,"uint16"):g(y,"float32"),this.length=p(this.gl,this.type,this.length,this.usage,v<0?b:b.subarray(0,y.length),v),i.free(b)}else if(typeof y=="object"&&typeof y.length=="number")this.length=p(this.gl,this.type,this.length,this.usage,y,v);else{if(typeof y!="number"&&y!==void 0)throw new Error("gl-buffer: Invalid data type");if(v>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(y|=0)<=0&&(y=1),this.gl.bufferData(this.type,0|y,this.usage),this.length=y}},l.exports=function(y,v,x,_){if(x=x||y.ARRAY_BUFFER,_=_||y.DYNAMIC_DRAW,x!==y.ARRAY_BUFFER&&x!==y.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(_!==y.DYNAMIC_DRAW&&_!==y.STATIC_DRAW&&_!==y.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var A=y.createBuffer(),b=new d(y,x,A,0,_);return b.update(v),b}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],79:[function(a,l,c){var i=a("gl-vec3");l.exports=function(u,h){var d=u.positions,m=u.vectors,p={positions:[],vertexIntensity:[],vertexIntensityBounds:u.vertexIntensityBounds,vectors:[],cells:[],coneOffset:u.coneOffset,colormap:u.colormap};if(u.positions.length===0)return h&&(h[0]=[0,0,0],h[1]=[0,0,0]),p;for(var g=0,y=1/0,v=-1/0,x=1/0,_=-1/0,A=1/0,b=-1/0,k=null,w=null,M=[],T=1/0,E=!1,S=0;S<d.length;S++){var P=d[S];y=Math.min(P[0],y),v=Math.max(P[0],v),x=Math.min(P[1],x),_=Math.max(P[1],_),A=Math.min(P[2],A),b=Math.max(P[2],b);var L=m[S];if(i.length(L)>g&&(g=i.length(L)),S){var R=2*i.distance(k,P)/(i.length(w)+i.length(L));R?(T=Math.min(T,R),E=!1):E=!0}E||(k=P,w=L),M.push(L)}var F=[y,x,A],D=[v,_,b];h&&(h[0]=F,h[1]=D),g===0&&(g=1);var O=1/g;isFinite(T)||(T=1),p.vectorScale=T;var N=u.coneSize||.5;u.absoluteConeSize&&(N=u.absoluteConeSize*O),p.coneScale=N,S=0;for(var B=0;S<d.length;S++)for(var W=(P=d[S])[0],G=P[1],K=P[2],te=M[S],Y=i.length(te)*O,J=0;J<8;J++){p.positions.push([W,G,K,B++]),p.positions.push([W,G,K,B++]),p.positions.push([W,G,K,B++]),p.positions.push([W,G,K,B++]),p.positions.push([W,G,K,B++]),p.positions.push([W,G,K,B++]),p.vectors.push(te),p.vectors.push(te),p.vectors.push(te),p.vectors.push(te),p.vectors.push(te),p.vectors.push(te),p.vertexIntensity.push(Y,Y,Y),p.vertexIntensity.push(Y,Y,Y);var re=p.positions.length;p.cells.push([re-6,re-5,re-4],[re-3,re-2,re-1])}return p};var s=a("./lib/shaders");l.exports.createMesh=a("./create_mesh"),l.exports.createConeMesh=function(u,h){return l.exports.createMesh(u,h,{shaders:s,traceType:"cone"})}},{"./create_mesh":80,"./lib/shaders":81,"gl-vec3":169}],80:[function(a,l,c){var i=a("gl-shader"),s=a("gl-buffer"),u=a("gl-vao"),h=a("gl-texture2d"),d=a("gl-mat4/multiply"),m=a("gl-mat4/invert"),p=a("ndarray"),g=a("colormap"),y=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function v(b,k,w,M,T,E,S,P,L,R,F){this.gl=b,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=k,this.dirty=!0,this.triShader=w,this.pickShader=M,this.trianglePositions=T,this.triangleVectors=E,this.triangleColors=P,this.triangleUVs=L,this.triangleIds=S,this.triangleVAO=R,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=F,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=y,this._view=y,this._projection=y,this._resolution=[1,1]}var x=v.prototype;function _(b,k){var w=i(b,k.meshShader.vertex,k.meshShader.fragment,null,k.meshShader.attributes);return w.attributes.position.location=0,w.attributes.color.location=2,w.attributes.uv.location=3,w.attributes.vector.location=4,w}function A(b,k){var w=i(b,k.pickShader.vertex,k.pickShader.fragment,null,k.pickShader.attributes);return w.attributes.position.location=0,w.attributes.id.location=1,w.attributes.vector.location=4,w}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(b){this.pickId=b},x.update=function(b){b=b||{};var k=this.gl;this.dirty=!0,"lightPosition"in b&&(this.lightPosition=b.lightPosition),"opacity"in b&&(this.opacity=b.opacity),"ambient"in b&&(this.ambientLight=b.ambient),"diffuse"in b&&(this.diffuseLight=b.diffuse),"specular"in b&&(this.specularLight=b.specular),"roughness"in b&&(this.roughness=b.roughness),"fresnel"in b&&(this.fresnel=b.fresnel),b.tubeScale!==void 0&&(this.tubeScale=b.tubeScale),b.vectorScale!==void 0&&(this.vectorScale=b.vectorScale),b.coneScale!==void 0&&(this.coneScale=b.coneScale),b.coneOffset!==void 0&&(this.coneOffset=b.coneOffset),b.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=k.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=k.LINEAR,this.texture.setPixels(function(ne){for(var q=g({colormap:ne,nshades:256,format:"rgba"}),Q=new Uint8Array(1024),ee=0;ee<256;++ee){for(var ie=q[ee],ae=0;ae<3;++ae)Q[4*ee+ae]=ie[ae];Q[4*ee+3]=255*ie[3]}return p(Q,[256,256,4],[4,0,1])}(b.colormap)),this.texture.generateMipmap());var w=b.cells,M=b.positions,T=b.vectors;if(M&&w&&T){var E=[],S=[],P=[],L=[],R=[];this.cells=w,this.positions=M,this.vectors=T;var F=b.meshColor||[1,1,1,1],D=b.vertexIntensity,O=1/0,N=-1/0;if(D)if(b.vertexIntensityBounds)O=+b.vertexIntensityBounds[0],N=+b.vertexIntensityBounds[1];else for(var B=0;B<D.length;++B){var W=D[B];O=Math.min(O,W),N=Math.max(N,W)}else for(B=0;B<M.length;++B)W=M[B][2],O=Math.min(O,W),N=Math.max(N,W);for(this.intensity=D||function(ne){for(var q=ne.length,Q=new Array(q),ee=0;ee<q;++ee)Q[ee]=ne[ee][2];return Q}(M),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],B=0;B<M.length;++B)for(var G=M[B],K=0;K<3;++K)!isNaN(G[K])&&isFinite(G[K])&&(this.bounds[0][K]=Math.min(this.bounds[0][K],G[K]),this.bounds[1][K]=Math.max(this.bounds[1][K],G[K]));var te=0;e:for(B=0;B<w.length;++B){var Y=w[B];switch(Y.length){case 3:for(K=0;K<3;++K){G=M[re=Y[K]];for(var J=0;J<3;++J)if(isNaN(G[J])||!isFinite(G[J]))continue e}for(K=0;K<3;++K){var re;G=M[re=Y[2-K]],E.push(G[0],G[1],G[2],G[3]);var U=T[re];S.push(U[0],U[1],U[2],U[3]||0);var V,H=F;H.length===3?P.push(H[0],H[1],H[2],1):P.push(H[0],H[1],H[2],H[3]),V=D?[(D[re]-O)/(N-O),0]:[(G[2]-O)/(N-O),0],L.push(V[0],V[1]),R.push(B)}te+=1}}this.triangleCount=te,this.trianglePositions.update(E),this.triangleVectors.update(S),this.triangleColors.update(P),this.triangleUVs.update(L),this.triangleIds.update(new Uint32Array(R))}},x.drawTransparent=x.draw=function(b){b=b||{};for(var k=this.gl,w=b.model||y,M=b.view||y,T=b.projection||y,E=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],S=0;S<3;++S)E[0][S]=Math.max(E[0][S],this.clipBounds[0][S]),E[1][S]=Math.min(E[1][S],this.clipBounds[1][S]);var P={model:w,view:M,projection:T,inverseModel:y.slice(),clipBounds:E,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};P.inverseModel=m(P.inverseModel,P.model),k.disable(k.CULL_FACE),this.texture.bind(0);var L=new Array(16);for(d(L,P.view,P.model),d(L,P.projection,L),m(L,L),S=0;S<3;++S)P.eyePosition[S]=L[12+S]/L[15];var R=L[15];for(S=0;S<3;++S)R+=this.lightPosition[S]*L[4*S+3];for(S=0;S<3;++S){for(var F=L[12+S],D=0;D<3;++D)F+=L[4*D+S]*this.lightPosition[D];P.lightPosition[S]=F/R}if(this.triangleCount>0){var O=this.triShader;O.bind(),O.uniforms=P,this.triangleVAO.bind(),k.drawArrays(k.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},x.drawPick=function(b){b=b||{};for(var k=this.gl,w=b.model||y,M=b.view||y,T=b.projection||y,E=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],S=0;S<3;++S)E[0][S]=Math.max(E[0][S],this.clipBounds[0][S]),E[1][S]=Math.min(E[1][S],this.clipBounds[1][S]);this._model=[].slice.call(w),this._view=[].slice.call(M),this._projection=[].slice.call(T),this._resolution=[k.drawingBufferWidth,k.drawingBufferHeight];var P={model:w,view:M,projection:T,clipBounds:E,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},L=this.pickShader;L.bind(),L.uniforms=P,this.triangleCount>0&&(this.triangleVAO.bind(),k.drawArrays(k.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},x.pick=function(b){if(!b||b.id!==this.pickId)return null;var k=b.value[0]+256*b.value[1]+65536*b.value[2],w=this.cells[k],M=this.positions[w[1]].slice(0,3),T={position:M,dataCoordinate:M,index:Math.floor(w[1]/48)};return this.traceType==="cone"?T.index=Math.floor(w[1]/48):this.traceType==="streamtube"&&(T.intensity=this.intensity[w[1]],T.velocity=this.vectors[w[1]].slice(0,3),T.divergence=this.vectors[w[1]][3],T.index=k),T},x.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},l.exports=function(b,k,w){var M=w.shaders;arguments.length===1&&(b=(k=b).gl);var T=_(b,M),E=A(b,M),S=h(b,p(new Uint8Array([255,255,255,255]),[1,1,4]));S.generateMipmap(),S.minFilter=b.LINEAR_MIPMAP_LINEAR,S.magFilter=b.LINEAR;var P=s(b),L=s(b),R=s(b),F=s(b),D=s(b),O=u(b,[{buffer:P,type:b.FLOAT,size:4},{buffer:D,type:b.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:R,type:b.FLOAT,size:4},{buffer:F,type:b.FLOAT,size:2},{buffer:L,type:b.FLOAT,size:4}]),N=new v(b,S,T,E,P,L,D,R,F,O,w.traceType||"cone");return N.update(k),N}},{colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259}],81:[function(a,l,c){var i=a("glslify"),s=i([`precision highp float;
-
-precision highp float;
-#define GLSLIFY 1
-
-vec3 getOrthogonalVector(vec3 v) {
-  // Return up-vector for only-z vector.
-  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
-  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
-  // Assign z = 0, x = -b, y = a:
-  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
-  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
-    return normalize(vec3(-v.y, v.x, 0.0));
-  } else {
-    return normalize(vec3(0.0, v.z, -v.y));
-  }
-}
-
-// Calculate the cone vertex and normal at the given index.
-//
-// The returned vertex is for a cone with its top at origin and height of 1.0,
-// pointing in the direction of the vector attribute.
-//
-// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.
-// These vertices are used to make up the triangles of the cone by the following:
-//   segment + 0 top vertex
-//   segment + 1 perimeter vertex a+1
-//   segment + 2 perimeter vertex a
-//   segment + 3 center base vertex
-//   segment + 4 perimeter vertex a
-//   segment + 5 perimeter vertex a+1
-// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.
-// To go from index to segment, floor(index / 6)
-// To go from segment to angle, 2*pi * (segment/segmentCount)
-// To go from index to segment index, index - (segment*6)
-//
-vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {
-
-  const float segmentCount = 8.0;
-
-  float index = rawIndex - floor(rawIndex /
-    (segmentCount * 6.0)) *
-    (segmentCount * 6.0);
-
-  float segment = floor(0.001 + index/6.0);
-  float segmentIndex = index - (segment*6.0);
-
-  normal = -normalize(d);
-
-  if (segmentIndex > 2.99 && segmentIndex < 3.01) {
-    return mix(vec3(0.0), -d, coneOffset);
-  }
-
-  float nextAngle = (
-    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||
-    (segmentIndex > 4.99 &&  segmentIndex < 5.01)
-  ) ? 1.0 : 0.0;
-  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);
-
-  vec3 v1 = mix(d, vec3(0.0), coneOffset);
-  vec3 v2 = v1 - d;
-
-  vec3 u = getOrthogonalVector(d);
-  vec3 v = normalize(cross(u, d));
-
-  vec3 x = u * cos(angle) * length(d)*0.25;
-  vec3 y = v * sin(angle) * length(d)*0.25;
-  vec3 v3 = v2 + x + y;
-  if (segmentIndex < 3.0) {
-    vec3 tx = u * sin(angle);
-    vec3 ty = v * -cos(angle);
-    vec3 tangent = tx + ty;
-    normal = normalize(cross(v3 - v1, tangent));
-  }
-
-  if (segmentIndex == 0.0) {
-    return mix(d, vec3(0.0), coneOffset);
-  }
-  return v3;
-}
-
-attribute vec3 vector;
-attribute vec4 color, position;
-attribute vec2 uv;
-
-uniform float vectorScale, coneScale, coneOffset;
-uniform mat4 model, view, projection, inverseModel;
-uniform vec3 eyePosition, lightPosition;
-
-varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
-varying vec4 f_color;
-varying vec2 f_uv;
-
-void main() {
-  // Scale the vector magnitude to stay constant with
-  // model & view changes.
-  vec3 normal;
-  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);
-  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
-
-  //Lighting geometry parameters
-  vec4 cameraCoordinate = view * conePosition;
-  cameraCoordinate.xyz /= cameraCoordinate.w;
-  f_lightDirection = lightPosition - cameraCoordinate.xyz;
-  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;
-  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);
-
-  // vec4 m_position  = model * vec4(conePosition, 1.0);
-  vec4 t_position  = view * conePosition;
-  gl_Position      = projection * t_position;
-
-  f_color          = color;
-  f_data           = conePosition.xyz;
-  f_position       = position.xyz;
-  f_uv             = uv;
-}
-`]),u=i([`#extension GL_OES_standard_derivatives : enable
-
-precision highp float;
-#define GLSLIFY 1
-
-float beckmannDistribution(float x, float roughness) {
-  float NdotH = max(x, 0.0001);
-  float cos2Alpha = NdotH * NdotH;
-  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
-  float roughness2 = roughness * roughness;
-  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
-  return exp(tan2Alpha / roughness2) / denom;
-}
-
-float cookTorranceSpecular(
-  vec3 lightDirection,
-  vec3 viewDirection,
-  vec3 surfaceNormal,
-  float roughness,
-  float fresnel) {
-
-  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);
-  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);
-
-  //Half angle vector
-  vec3 H = normalize(lightDirection + viewDirection);
-
-  //Geometric term
-  float NdotH = max(dot(surfaceNormal, H), 0.0);
-  float VdotH = max(dot(viewDirection, H), 0.000001);
-  float LdotH = max(dot(lightDirection, H), 0.000001);
-  float G1 = (2.0 * NdotH * VdotN) / VdotH;
-  float G2 = (2.0 * NdotH * LdotN) / LdotH;
-  float G = min(1.0, min(G1, G2));
-  
-  //Distribution term
-  float D = beckmannDistribution(NdotH, roughness);
-
-  //Fresnel term
-  float F = pow(1.0 - VdotN, fresnel);
-
-  //Multiply terms and done
-  return  G * F * D / max(3.14159265 * VdotN, 0.000001);
-}
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 clipBounds[2];
-uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;
-uniform sampler2D texture;
-
-varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
-varying vec4 f_color;
-varying vec2 f_uv;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
-  vec3 N = normalize(f_normal);
-  vec3 L = normalize(f_lightDirection);
-  vec3 V = normalize(f_eyeDirection);
-
-  if(gl_FrontFacing) {
-    N = -N;
-  }
-
-  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));
-  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
-
-  vec4 surfaceColor = f_color * texture2D(texture, f_uv);
-  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
-
-  gl_FragColor = litColor * opacity;
-}
-`]),h=i([`precision highp float;
-
-precision highp float;
-#define GLSLIFY 1
-
-vec3 getOrthogonalVector(vec3 v) {
-  // Return up-vector for only-z vector.
-  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
-  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
-  // Assign z = 0, x = -b, y = a:
-  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
-  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
-    return normalize(vec3(-v.y, v.x, 0.0));
-  } else {
-    return normalize(vec3(0.0, v.z, -v.y));
-  }
-}
-
-// Calculate the cone vertex and normal at the given index.
-//
-// The returned vertex is for a cone with its top at origin and height of 1.0,
-// pointing in the direction of the vector attribute.
-//
-// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.
-// These vertices are used to make up the triangles of the cone by the following:
-//   segment + 0 top vertex
-//   segment + 1 perimeter vertex a+1
-//   segment + 2 perimeter vertex a
-//   segment + 3 center base vertex
-//   segment + 4 perimeter vertex a
-//   segment + 5 perimeter vertex a+1
-// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.
-// To go from index to segment, floor(index / 6)
-// To go from segment to angle, 2*pi * (segment/segmentCount)
-// To go from index to segment index, index - (segment*6)
-//
-vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {
-
-  const float segmentCount = 8.0;
-
-  float index = rawIndex - floor(rawIndex /
-    (segmentCount * 6.0)) *
-    (segmentCount * 6.0);
-
-  float segment = floor(0.001 + index/6.0);
-  float segmentIndex = index - (segment*6.0);
-
-  normal = -normalize(d);
-
-  if (segmentIndex > 2.99 && segmentIndex < 3.01) {
-    return mix(vec3(0.0), -d, coneOffset);
-  }
-
-  float nextAngle = (
-    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||
-    (segmentIndex > 4.99 &&  segmentIndex < 5.01)
-  ) ? 1.0 : 0.0;
-  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);
-
-  vec3 v1 = mix(d, vec3(0.0), coneOffset);
-  vec3 v2 = v1 - d;
-
-  vec3 u = getOrthogonalVector(d);
-  vec3 v = normalize(cross(u, d));
-
-  vec3 x = u * cos(angle) * length(d)*0.25;
-  vec3 y = v * sin(angle) * length(d)*0.25;
-  vec3 v3 = v2 + x + y;
-  if (segmentIndex < 3.0) {
-    vec3 tx = u * sin(angle);
-    vec3 ty = v * -cos(angle);
-    vec3 tangent = tx + ty;
-    normal = normalize(cross(v3 - v1, tangent));
-  }
-
-  if (segmentIndex == 0.0) {
-    return mix(d, vec3(0.0), coneOffset);
-  }
-  return v3;
-}
-
-attribute vec4 vector;
-attribute vec4 position;
-attribute vec4 id;
-
-uniform mat4 model, view, projection;
-uniform float vectorScale, coneScale, coneOffset;
-
-varying vec3 f_position;
-varying vec4 f_id;
-
-void main() {
-  vec3 normal;
-  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);
-  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
-  gl_Position = projection * view * conePosition;
-  f_id        = id;
-  f_position  = position.xyz;
-}
-`]),d=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3  clipBounds[2];
-uniform float pickId;
-
-varying vec3 f_position;
-varying vec4 f_id;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
-
-  gl_FragColor = vec4(pickId, f_id.xyz);
-}`]);c.meshShader={vertex:s,fragment:u,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},c.pickShader={vertex:h,fragment:d,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:231}],82:[function(a,l,c){l.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],83:[function(a,l,c){var i=a("./1.0/numbers");l.exports=function(s){return i[s]}},{"./1.0/numbers":82}],84:[function(a,l,c){l.exports=function(v){var x=v.gl,_=i(x),A=s(x,[{buffer:_,type:x.FLOAT,size:3,offset:0,stride:40},{buffer:_,type:x.FLOAT,size:4,offset:12,stride:40},{buffer:_,type:x.FLOAT,size:3,offset:28,stride:40}]),b=u(x);b.attributes.position.location=0,b.attributes.color.location=1,b.attributes.offset.location=2;var k=new d(x,_,A,b);return k.update(v),k};var i=a("gl-buffer"),s=a("gl-vao"),u=a("./shaders/index"),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(v,x,_,A){this.gl=v,this.shader=A,this.buffer=x,this.vao=_,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var m=d.prototype;function p(v,x){for(var _=0;_<3;++_)v[0][_]=Math.min(v[0][_],x[_]),v[1][_]=Math.max(v[1][_],x[_])}m.isOpaque=function(){return!this.hasAlpha},m.isTransparent=function(){return this.hasAlpha},m.drawTransparent=m.draw=function(v){var x=this.gl,_=this.shader.uniforms;this.shader.bind();var A=_.view=v.view||h,b=_.projection=v.projection||h;_.model=v.model||h,_.clipBounds=this.clipBounds,_.opacity=this.opacity;var k=A[12],w=A[13],M=A[14],T=A[15],E=(v._ortho?2:1)*this.pixelRatio*(b[3]*k+b[7]*w+b[11]*M+b[15]*T)/x.drawingBufferHeight;this.vao.bind();for(var S=0;S<3;++S)x.lineWidth(this.lineWidth[S]*this.pixelRatio),_.capSize=this.capSize[S]*E,this.lineCount[S]&&x.drawArrays(x.LINES,this.lineOffset[S],this.lineCount[S]);this.vao.unbind()};var g=function(){for(var v=new Array(3),x=0;x<3;++x){for(var _=[],A=1;A<=2;++A)for(var b=-1;b<=1;b+=2){var k=[0,0,0];k[(A+x)%3]=b,_.push(k)}v[x]=_}return v}();function y(v,x,_,A){for(var b=g[A],k=0;k<b.length;++k){var w=b[k];v.push(x[0],x[1],x[2],_[0],_[1],_[2],_[3],w[0],w[1],w[2])}return b.length}m.update=function(v){"lineWidth"in(v=v||{})&&(this.lineWidth=v.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in v&&(this.capSize=v.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in v&&(this.opacity=+v.opacity,this.opacity<1&&(this.hasAlpha=!0));var x=v.color||[[0,0,0],[0,0,0],[0,0,0]],_=v.position,A=v.error;if(Array.isArray(x[0])||(x=[x,x,x]),_&&A){var b=[],k=_.length,w=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var M=0;M<3;++M){this.lineOffset[M]=w;e:for(var T=0;T<k;++T){for(var E=_[T],S=0;S<3;++S)if(isNaN(E[S])||!isFinite(E[S]))continue e;var P=A[T],L=x[M];if(Array.isArray(L[0])&&(L=x[T]),L.length===3?L=[L[0],L[1],L[2],1]:L.length===4&&(L=[L[0],L[1],L[2],L[3]],!this.hasAlpha&&L[3]<1&&(this.hasAlpha=!0)),!isNaN(P[0][M])&&!isNaN(P[1][M])){var R;P[0][M]<0&&((R=E.slice())[M]+=P[0][M],b.push(E[0],E[1],E[2],L[0],L[1],L[2],L[3],0,0,0,R[0],R[1],R[2],L[0],L[1],L[2],L[3],0,0,0),p(this.bounds,R),w+=2+y(b,R,L,M)),P[1][M]>0&&((R=E.slice())[M]+=P[1][M],b.push(E[0],E[1],E[2],L[0],L[1],L[2],L[3],0,0,0,R[0],R[1],R[2],L[0],L[1],L[2],L[3],0,0,0),p(this.bounds,R),w+=2+y(b,R,L,M))}}this.lineCount[M]=w-this.lineOffset[M]}this.buffer.update(b)}},m.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":85,"gl-buffer":78,"gl-vao":150}],85:[function(a,l,c){var i=a("glslify"),s=a("gl-shader"),u=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position, offset;
-attribute vec4 color;
-uniform mat4 model, view, projection;
-uniform float capSize;
-varying vec4 fragColor;
-varying vec3 fragPosition;
-
-void main() {
-  vec4 worldPosition  = model * vec4(position, 1.0);
-  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);
-  gl_Position         = projection * view * worldPosition;
-  fragColor           = color;
-  fragPosition        = position;
-}`]),h=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 clipBounds[2];
-uniform float opacity;
-varying vec3 fragPosition;
-varying vec4 fragColor;
-
-void main() {
-  if (
-    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||
-    fragColor.a * opacity == 0.
-  ) discard;
-
-  gl_FragColor = opacity * fragColor;
-}`]);l.exports=function(d){return s(d,u,h,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":132,glslify:231}],86:[function(a,l,c){var i=a("gl-texture2d");l.exports=function(k,w,M,T){s||(s=k.FRAMEBUFFER_UNSUPPORTED,u=k.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,h=k.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,d=k.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var E=k.getExtension("WEBGL_draw_buffers");if(!m&&E&&function(O,N){var B=O.getParameter(N.MAX_COLOR_ATTACHMENTS_WEBGL);m=new Array(B+1);for(var W=0;W<=B;++W){for(var G=new Array(B),K=0;K<W;++K)G[K]=O.COLOR_ATTACHMENT0+K;for(K=W;K<B;++K)G[K]=O.NONE;m[W]=G}}(k,E),Array.isArray(w)&&(T=M,M=0|w[1],w=0|w[0]),typeof w!="number")throw new Error("gl-fbo: Missing shape parameter");var S=k.getParameter(k.MAX_RENDERBUFFER_SIZE);if(w<0||w>S||M<0||M>S)throw new Error("gl-fbo: Parameters are too large for FBO");var P=1;if("color"in(T=T||{})){if((P=Math.max(0|T.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(P>1){if(!E)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(P>k.getParameter(E.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+P+" draw buffers")}}var L=k.UNSIGNED_BYTE,R=k.getExtension("OES_texture_float");if(T.float&&P>0){if(!R)throw new Error("gl-fbo: Context does not support floating point textures");L=k.FLOAT}else T.preferFloat&&P>0&&R&&(L=k.FLOAT);var F=!0;"depth"in T&&(F=!!T.depth);var D=!1;return"stencil"in T&&(D=!!T.stencil),new _(k,w,M,L,P,F,D,E)};var s,u,h,d,m=null;function p(k){return[k.getParameter(k.FRAMEBUFFER_BINDING),k.getParameter(k.RENDERBUFFER_BINDING),k.getParameter(k.TEXTURE_BINDING_2D)]}function g(k,w){k.bindFramebuffer(k.FRAMEBUFFER,w[0]),k.bindRenderbuffer(k.RENDERBUFFER,w[1]),k.bindTexture(k.TEXTURE_2D,w[2])}function y(k){switch(k){case s:throw new Error("gl-fbo: Framebuffer unsupported");case u:throw new Error("gl-fbo: Framebuffer incomplete attachment");case h:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case d:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function v(k,w,M,T,E,S){if(!T)return null;var P=i(k,w,M,E,T);return P.magFilter=k.NEAREST,P.minFilter=k.NEAREST,P.mipSamples=1,P.bind(),k.framebufferTexture2D(k.FRAMEBUFFER,S,k.TEXTURE_2D,P.handle,0),P}function x(k,w,M,T,E){var S=k.createRenderbuffer();return k.bindRenderbuffer(k.RENDERBUFFER,S),k.renderbufferStorage(k.RENDERBUFFER,T,w,M),k.framebufferRenderbuffer(k.FRAMEBUFFER,E,k.RENDERBUFFER,S),S}function _(k,w,M,T,E,S,P,L){this.gl=k,this._shape=[0|w,0|M],this._destroyed=!1,this._ext=L,this.color=new Array(E);for(var R=0;R<E;++R)this.color[R]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=T,this._useDepth=S,this._useStencil=P;var F=this,D=[0|w,0|M];Object.defineProperties(D,{0:{get:function(){return F._shape[0]},set:function(O){return F.width=O}},1:{get:function(){return F._shape[1]},set:function(O){return F.height=O}}}),this._shapeVector=D,function(O){var N=p(O.gl),B=O.gl,W=O.handle=B.createFramebuffer(),G=O._shape[0],K=O._shape[1],te=O.color.length,Y=O._ext,J=O._useStencil,re=O._useDepth,U=O._colorType;B.bindFramebuffer(B.FRAMEBUFFER,W);for(var V=0;V<te;++V)O.color[V]=v(B,G,K,U,B.RGBA,B.COLOR_ATTACHMENT0+V);te===0?(O._color_rb=x(B,G,K,B.RGBA4,B.COLOR_ATTACHMENT0),Y&&Y.drawBuffersWEBGL(m[0])):te>1&&Y.drawBuffersWEBGL(m[te]);var H=B.getExtension("WEBGL_depth_texture");H?J?O.depth=v(B,G,K,H.UNSIGNED_INT_24_8_WEBGL,B.DEPTH_STENCIL,B.DEPTH_STENCIL_ATTACHMENT):re&&(O.depth=v(B,G,K,B.UNSIGNED_SHORT,B.DEPTH_COMPONENT,B.DEPTH_ATTACHMENT)):re&&J?O._depth_rb=x(B,G,K,B.DEPTH_STENCIL,B.DEPTH_STENCIL_ATTACHMENT):re?O._depth_rb=x(B,G,K,B.DEPTH_COMPONENT16,B.DEPTH_ATTACHMENT):J&&(O._depth_rb=x(B,G,K,B.STENCIL_INDEX,B.STENCIL_ATTACHMENT));var ne=B.checkFramebufferStatus(B.FRAMEBUFFER);if(ne!==B.FRAMEBUFFER_COMPLETE){for(O._destroyed=!0,B.bindFramebuffer(B.FRAMEBUFFER,null),B.deleteFramebuffer(O.handle),O.handle=null,O.depth&&(O.depth.dispose(),O.depth=null),O._depth_rb&&(B.deleteRenderbuffer(O._depth_rb),O._depth_rb=null),V=0;V<O.color.length;++V)O.color[V].dispose(),O.color[V]=null;O._color_rb&&(B.deleteRenderbuffer(O._color_rb),O._color_rb=null),g(B,N),y(ne)}g(B,N)}(this)}var A=_.prototype;function b(k,w,M){if(k._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(k._shape[0]!==w||k._shape[1]!==M){var T=k.gl,E=T.getParameter(T.MAX_RENDERBUFFER_SIZE);if(w<0||w>E||M<0||M>E)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");k._shape[0]=w,k._shape[1]=M;for(var S=p(T),P=0;P<k.color.length;++P)k.color[P].shape=k._shape;k._color_rb&&(T.bindRenderbuffer(T.RENDERBUFFER,k._color_rb),T.renderbufferStorage(T.RENDERBUFFER,T.RGBA4,k._shape[0],k._shape[1])),k.depth&&(k.depth.shape=k._shape),k._depth_rb&&(T.bindRenderbuffer(T.RENDERBUFFER,k._depth_rb),k._useDepth&&k._useStencil?T.renderbufferStorage(T.RENDERBUFFER,T.DEPTH_STENCIL,k._shape[0],k._shape[1]):k._useDepth?T.renderbufferStorage(T.RENDERBUFFER,T.DEPTH_COMPONENT16,k._shape[0],k._shape[1]):k._useStencil&&T.renderbufferStorage(T.RENDERBUFFER,T.STENCIL_INDEX,k._shape[0],k._shape[1])),T.bindFramebuffer(T.FRAMEBUFFER,k.handle);var L=T.checkFramebufferStatus(T.FRAMEBUFFER);L!==T.FRAMEBUFFER_COMPLETE&&(k.dispose(),g(T,S),y(L)),g(T,S)}}Object.defineProperties(A,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(k){if(Array.isArray(k)||(k=[0|k,0|k]),k.length!==2)throw new Error("gl-fbo: Shape vector must be length 2");var w=0|k[0],M=0|k[1];return b(this,w,M),[w,M]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(k){return b(this,k|=0,this._shape[1]),k},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(k){return k|=0,b(this,this._shape[0],k),k},enumerable:!1}}),A.bind=function(){if(!this._destroyed){var k=this.gl;k.bindFramebuffer(k.FRAMEBUFFER,this.handle),k.viewport(0,0,this._shape[0],this._shape[1])}},A.dispose=function(){if(!this._destroyed){this._destroyed=!0;var k=this.gl;k.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(k.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var w=0;w<this.color.length;++w)this.color[w].dispose(),this.color[w]=null;this._color_rb&&(k.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":146}],87:[function(a,l,c){var i=a("sprintf-js").sprintf,s=a("gl-constants/lookup"),u=a("glsl-shader-name"),h=a("add-line-numbers");l.exports=function(d,m,p){var g=u(m)||"of unknown name (see npm glsl-shader-name)",y="unknown type";p!==void 0&&(y=p===s.FRAGMENT_SHADER?"fragment":"vertex");for(var v=i(`Error compiling %s shader %s:
-`,y,g),x=i("%s%s",v,d),_=d.split(`
-`),A={},b=0;b<_.length;b++){var k=_[b];if(k!==""&&k!=="\0"){var w=parseInt(k.split(":")[2]);if(isNaN(w))throw new Error(i("Could not parse error: %s",k));A[w]=k}}var M=h(m).split(`
-`);for(b=0;b<M.length;b++)if(A[b+3]||A[b+2]||A[b+1]){var T=M[b];if(v+=T+`
-`,A[b+1]){var E=A[b+1];E=E.substr(E.split(":",3).join(":").length+1).trim(),v+=i(`^^^ %s
-
-`,E)}}return{long:v.trim(),short:x.trim()}}},{"add-line-numbers":9,"gl-constants/lookup":83,"glsl-shader-name":223,"sprintf-js":301}],88:[function(a,l,c){l.exports=function(x,_){var A=x.gl,b=h(A,m.vertex,m.fragment),k=h(A,m.pickVertex,m.pickFragment),w=d(A),M=d(A),T=d(A),E=d(A),S=new p(x,b,k,w,M,T,E);return S.update(_),x.addObject(S),S};var i=a("binary-search-bounds"),s=a("iota-array"),u=a("typedarray-pool"),h=a("gl-shader"),d=a("gl-buffer"),m=a("./lib/shaders");function p(x,_,A,b,k,w,M){this.plot=x,this.shader=_,this.pickShader=A,this.positionBuffer=b,this.weightBuffer=k,this.colorBuffer=w,this.idBuffer=M,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var g,y=p.prototype,v=[0,0,1,0,0,1,1,0,1,1,0,1];y.draw=(g=[1,0,0,0,1,0,0,0,1],function(){var x=this.plot,_=this.shader,A=this.bounds,b=this.numVertices;if(!(b<=0)){var k=x.gl,w=x.dataBox,M=A[2]-A[0],T=A[3]-A[1],E=w[2]-w[0],S=w[3]-w[1];g[0]=2*M/E,g[4]=2*T/S,g[6]=2*(A[0]-w[0])/E-1,g[7]=2*(A[1]-w[1])/S-1,_.bind();var P=_.uniforms;P.viewTransform=g,P.shape=this.shape;var L=_.attributes;this.positionBuffer.bind(),L.position.pointer(),this.weightBuffer.bind(),L.weight.pointer(k.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),L.color.pointer(k.UNSIGNED_BYTE,!0),k.drawArrays(k.TRIANGLES,0,b)}}),y.drawPick=function(){var x=[1,0,0,0,1,0,0,0,1],_=[0,0,0,0];return function(A){var b=this.plot,k=this.pickShader,w=this.bounds,M=this.numVertices;if(!(M<=0)){var T=b.gl,E=b.dataBox,S=w[2]-w[0],P=w[3]-w[1],L=E[2]-E[0],R=E[3]-E[1];x[0]=2*S/L,x[4]=2*P/R,x[6]=2*(w[0]-E[0])/L-1,x[7]=2*(w[1]-E[1])/R-1;for(var F=0;F<4;++F)_[F]=A>>8*F&255;this.pickOffset=A,k.bind();var D=k.uniforms;D.viewTransform=x,D.pickOffset=_,D.shape=this.shape;var O=k.attributes;return this.positionBuffer.bind(),O.position.pointer(),this.weightBuffer.bind(),O.weight.pointer(T.UNSIGNED_BYTE,!1),this.idBuffer.bind(),O.pickId.pointer(T.UNSIGNED_BYTE,!1),T.drawArrays(T.TRIANGLES,0,M),A+this.shape[0]*this.shape[1]}}}(),y.pick=function(x,_,A){var b=this.pickOffset,k=this.shape[0]*this.shape[1];if(A<b||A>=b+k)return null;var w=A-b,M=this.xData,T=this.yData;return{object:this,pointId:w,dataCoord:[M[w%this.shape[0]],T[w/this.shape[0]|0]]}},y.update=function(x){var _=(x=x||{}).shape||[0,0],A=x.x||s(_[0]),b=x.y||s(_[1]),k=x.z||new Float32Array(_[0]*_[1]),w=x.zsmooth!==!1;this.xData=A,this.yData=b;var M,T,E,S,P=x.colorLevels||[0],L=x.colorValues||[0,0,0,1],R=P.length,F=this.bounds;w?(M=F[0]=A[0],T=F[1]=b[0],E=F[2]=A[A.length-1],S=F[3]=b[b.length-1]):(M=F[0]=A[0]+(A[1]-A[0])/2,T=F[1]=b[0]+(b[1]-b[0])/2,E=F[2]=A[A.length-1]+(A[A.length-1]-A[A.length-2])/2,S=F[3]=b[b.length-1]+(b[b.length-1]-b[b.length-2])/2);var D=1/(E-M),O=1/(S-T),N=_[0],B=_[1];this.shape=[N,B];var W=(w?(N-1)*(B-1):N*B)*(v.length>>>1);this.numVertices=W;for(var G=u.mallocUint8(4*W),K=u.mallocFloat32(2*W),te=u.mallocUint8(2*W),Y=u.mallocUint32(W),J=0,re=w?N-1:N,U=w?B-1:B,V=0;V<U;++V){var H,ne;w?(H=O*(b[V]-T),ne=O*(b[V+1]-T)):(H=V<B-1?O*(b[V]-(b[V+1]-b[V])/2-T):O*(b[V]-(b[V]-b[V-1])/2-T),ne=V<B-1?O*(b[V]+(b[V+1]-b[V])/2-T):O*(b[V]+(b[V]-b[V-1])/2-T));for(var q=0;q<re;++q){var Q,ee;w?(Q=D*(A[q]-M),ee=D*(A[q+1]-M)):(Q=q<N-1?D*(A[q]-(A[q+1]-A[q])/2-M):D*(A[q]-(A[q]-A[q-1])/2-M),ee=q<N-1?D*(A[q]+(A[q+1]-A[q])/2-M):D*(A[q]+(A[q]-A[q-1])/2-M));for(var ie=0;ie<v.length;ie+=2){var ae,ue,le,ge,fe=v[ie],me=v[ie+1],_e=k[w?(V+me)*N+(q+fe):V*N+q],Ae=i.le(P,_e);if(Ae<0)ae=L[0],ue=L[1],le=L[2],ge=L[3];else if(Ae===R-1)ae=L[4*R-4],ue=L[4*R-3],le=L[4*R-2],ge=L[4*R-1];else{var ke=(_e-P[Ae])/(P[Ae+1]-P[Ae]),Le=1-ke,de=4*Ae,ve=4*(Ae+1);ae=Le*L[de]+ke*L[ve],ue=Le*L[de+1]+ke*L[ve+1],le=Le*L[de+2]+ke*L[ve+2],ge=Le*L[de+3]+ke*L[ve+3]}G[4*J]=255*ae,G[4*J+1]=255*ue,G[4*J+2]=255*le,G[4*J+3]=255*ge,K[2*J]=.5*Q+.5*ee,K[2*J+1]=.5*H+.5*ne,te[2*J]=fe,te[2*J+1]=me,Y[J]=V*N+q,J+=1}}}this.positionBuffer.update(K),this.weightBuffer.update(te),this.colorBuffer.update(G),this.idBuffer.update(Y),u.free(K),u.free(G),u.free(te),u.free(Y)},y.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":89,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"iota-array":235,"typedarray-pool":308}],89:[function(a,l,c){var i=a("glslify");l.exports={fragment:i([`precision lowp float;
-#define GLSLIFY 1
-varying vec4 fragColor;
-void main() {
-  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);
-}
-`]),vertex:i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec2 position;
-attribute vec4 color;
-attribute vec2 weight;
-
-uniform vec2 shape;
-uniform mat3 viewTransform;
-
-varying vec4 fragColor;
-
-void main() {
-  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);
-  fragColor = color;
-  gl_Position = vec4(vPosition.xy, 0, vPosition.z);
-}
-`]),pickFragment:i([`precision mediump float;
-#define GLSLIFY 1
-
-varying vec4 fragId;
-varying vec2 vWeight;
-
-uniform vec2 shape;
-uniform vec4 pickOffset;
-
-void main() {
-  vec2 d = step(.5, vWeight);
-  vec4 id = fragId + pickOffset;
-  id.x += d.x + d.y*shape.x;
-
-  id.y += floor(id.x / 256.0);
-  id.x -= floor(id.x / 256.0) * 256.0;
-
-  id.z += floor(id.y / 256.0);
-  id.y -= floor(id.y / 256.0) * 256.0;
-
-  id.w += floor(id.z / 256.0);
-  id.z -= floor(id.z / 256.0) * 256.0;
-
-  gl_FragColor = id/255.;
-}
-`]),pickVertex:i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec2 position;
-attribute vec4 pickId;
-attribute vec2 weight;
-
-uniform vec2 shape;
-uniform mat3 viewTransform;
-
-varying vec4 fragId;
-varying vec2 vWeight;
-
-void main() {
-  vWeight = weight;
-
-  fragId = pickId;
-
-  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);
-  gl_Position = vec4(vPosition.xy, 0, vPosition.z);
-}
-`])}},{glslify:231}],90:[function(a,l,c){var i=a("glslify"),s=a("gl-shader"),u=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position, nextPosition;
-attribute float arcLength, lineWidth;
-attribute vec4 color;
-
-uniform vec2 screenShape;
-uniform float pixelRatio;
-uniform mat4 model, view, projection;
-
-varying vec4 fragColor;
-varying vec3 worldPosition;
-varying float pixelArcLength;
-
-vec4 project(vec3 p) {
-  return projection * view * model * vec4(p, 1.0);
-}
-
-void main() {
-  vec4 startPoint = project(position);
-  vec4 endPoint   = project(nextPosition);
-
-  vec2 A = startPoint.xy / startPoint.w;
-  vec2 B =   endPoint.xy /   endPoint.w;
-
-  float clipAngle = atan(
-    (B.y - A.y) * screenShape.y,
-    (B.x - A.x) * screenShape.x
-  );
-
-  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(
-    sin(clipAngle),
-    -cos(clipAngle)
-  ) / screenShape;
-
-  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);
-
-  worldPosition = position;
-  pixelArcLength = arcLength;
-  fragColor = color;
-}
-`]),h=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3      clipBounds[2];
-uniform sampler2D dashTexture;
-uniform float     dashScale;
-uniform float     opacity;
-
-varying vec3    worldPosition;
-varying float   pixelArcLength;
-varying vec4    fragColor;
-
-void main() {
-  if (
-    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||
-    fragColor.a * opacity == 0.
-  ) discard;
-
-  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;
-  if(dashWeight < 0.5) {
-    discard;
-  }
-  gl_FragColor = fragColor * opacity;
-}
-`]),d=i([`precision highp float;
-#define GLSLIFY 1
-
-#define FLOAT_MAX  1.70141184e38
-#define FLOAT_MIN  1.17549435e-38
-
-// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl
-vec4 packFloat(float v) {
-  float av = abs(v);
-
-  //Handle special cases
-  if(av < FLOAT_MIN) {
-    return vec4(0.0, 0.0, 0.0, 0.0);
-  } else if(v > FLOAT_MAX) {
-    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;
-  } else if(v < -FLOAT_MAX) {
-    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;
-  }
-
-  vec4 c = vec4(0,0,0,0);
-
-  //Compute exponent and mantissa
-  float e = floor(log2(av));
-  float m = av * pow(2.0, -e) - 1.0;
-
-  //Unpack mantissa
-  c[1] = floor(128.0 * m);
-  m -= c[1] / 128.0;
-  c[2] = floor(32768.0 * m);
-  m -= c[2] / 32768.0;
-  c[3] = floor(8388608.0 * m);
-
-  //Unpack exponent
-  float ebias = e + 127.0;
-  c[0] = floor(ebias / 2.0);
-  ebias -= c[0] * 2.0;
-  c[1] += floor(ebias) * 128.0;
-
-  //Unpack sign bit
-  c[0] += 128.0 * step(0.0, -v);
-
-  //Scale back to range
-  return c / 255.0;
-}
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform float pickId;
-uniform vec3 clipBounds[2];
-
-varying vec3 worldPosition;
-varying float pixelArcLength;
-varying vec4 fragColor;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;
-
-  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);
-}`]),m=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];c.createShader=function(p){return s(p,u,h,null,m)},c.createPickShader=function(p){return s(p,u,d,null,m)}},{"gl-shader":132,glslify:231}],91:[function(a,l,c){l.exports=function(M){var T=M.gl||M.scene&&M.scene.gl,E=y(T);E.attributes.position.location=0,E.attributes.nextPosition.location=1,E.attributes.arcLength.location=2,E.attributes.lineWidth.location=3,E.attributes.color.location=4;var S=v(T);S.attributes.position.location=0,S.attributes.nextPosition.location=1,S.attributes.arcLength.location=2,S.attributes.lineWidth.location=3,S.attributes.color.location=4;for(var P=i(T),L=s(T,[{buffer:P,size:3,offset:0,stride:48},{buffer:P,size:3,offset:12,stride:48},{buffer:P,size:1,offset:24,stride:48},{buffer:P,size:1,offset:28,stride:48},{buffer:P,size:4,offset:32,stride:48}]),R=p(new Array(1024),[256,1,4]),F=0;F<1024;++F)R.data[F]=255;var D=u(T,R);D.wrap=T.REPEAT;var O=new k(T,E,S,P,L,D);return O.update(M),O};var i=a("gl-buffer"),s=a("gl-vao"),u=a("gl-texture2d"),h=new Uint8Array(4),d=new Float32Array(h.buffer),m=a("binary-search-bounds"),p=a("ndarray"),g=a("./lib/shaders"),y=g.createShader,v=g.createPickShader,x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function _(M,T){for(var E=0,S=0;S<3;++S){var P=M[S]-T[S];E+=P*P}return Math.sqrt(E)}function A(M){for(var T=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],E=0;E<3;++E)T[0][E]=Math.max(M[0][E],T[0][E]),T[1][E]=Math.min(M[1][E],T[1][E]);return T}function b(M,T,E,S){this.arcLength=M,this.position=T,this.index=E,this.dataCoordinate=S}function k(M,T,E,S,P,L){this.gl=M,this.shader=T,this.pickShader=E,this.buffer=S,this.vao=P,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=L,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var w=k.prototype;w.isTransparent=function(){return this.hasAlpha},w.isOpaque=function(){return!this.hasAlpha},w.pickSlots=1,w.setPickBase=function(M){this.pickId=M},w.drawTransparent=w.draw=function(M){if(this.vertexCount){var T=this.gl,E=this.shader,S=this.vao;E.bind(),E.uniforms={model:M.model||x,view:M.view||x,projection:M.projection||x,clipBounds:A(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[T.drawingBufferWidth,T.drawingBufferHeight],pixelRatio:this.pixelRatio},S.bind(),S.draw(T.TRIANGLE_STRIP,this.vertexCount),S.unbind()}},w.drawPick=function(M){if(this.vertexCount){var T=this.gl,E=this.pickShader,S=this.vao;E.bind(),E.uniforms={model:M.model||x,view:M.view||x,projection:M.projection||x,pickId:this.pickId,clipBounds:A(this.clipBounds),screenShape:[T.drawingBufferWidth,T.drawingBufferHeight],pixelRatio:this.pixelRatio},S.bind(),S.draw(T.TRIANGLE_STRIP,this.vertexCount),S.unbind()}},w.update=function(M){var T,E;this.dirty=!0;var S=!!M.connectGaps;"dashScale"in M&&(this.dashScale=M.dashScale),this.hasAlpha=!1,"opacity"in M&&(this.opacity=+M.opacity,this.opacity<1&&(this.hasAlpha=!0));var P=[],L=[],R=[],F=0,D=0,O=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],N=M.position||M.positions;if(N){var B=M.color||M.colors||[0,0,0,1],W=M.lineWidth||1,G=!1;e:for(T=1;T<N.length;++T){var K,te,Y,J=N[T-1],re=N[T];for(L.push(F),R.push(J.slice()),E=0;E<3;++E){if(isNaN(J[E])||isNaN(re[E])||!isFinite(J[E])||!isFinite(re[E])){if(!S&&P.length>0){for(var U=0;U<24;++U)P.push(P[P.length-12]);D+=2,G=!0}continue e}O[0][E]=Math.min(O[0][E],J[E],re[E]),O[1][E]=Math.max(O[1][E],J[E],re[E])}Array.isArray(B[0])?(K=B.length>T-1?B[T-1]:B.length>0?B[B.length-1]:[0,0,0,1],te=B.length>T?B[T]:B.length>0?B[B.length-1]:[0,0,0,1]):K=te=B,K.length===3&&(K=[K[0],K[1],K[2],1]),te.length===3&&(te=[te[0],te[1],te[2],1]),!this.hasAlpha&&K[3]<1&&(this.hasAlpha=!0),Y=Array.isArray(W)?W.length>T-1?W[T-1]:W.length>0?W[W.length-1]:[0,0,0,1]:W;var V=F;if(F+=_(J,re),G){for(E=0;E<2;++E)P.push(J[0],J[1],J[2],re[0],re[1],re[2],V,Y,K[0],K[1],K[2],K[3]);D+=2,G=!1}P.push(J[0],J[1],J[2],re[0],re[1],re[2],V,Y,K[0],K[1],K[2],K[3],J[0],J[1],J[2],re[0],re[1],re[2],V,-Y,K[0],K[1],K[2],K[3],re[0],re[1],re[2],J[0],J[1],J[2],F,-Y,te[0],te[1],te[2],te[3],re[0],re[1],re[2],J[0],J[1],J[2],F,Y,te[0],te[1],te[2],te[3]),D+=4}}if(this.buffer.update(P),L.push(F),R.push(N[N.length-1].slice()),this.bounds=O,this.vertexCount=D,this.points=R,this.arcLength=L,"dashes"in M){var H=M.dashes.slice();for(H.unshift(0),T=1;T<H.length;++T)H[T]=H[T-1]+H[T];var ne=p(new Array(1024),[256,1,4]);for(T=0;T<256;++T){for(E=0;E<4;++E)ne.set(T,0,E,0);1&m.le(H,H[H.length-1]*T/255)?ne.set(T,0,0,0):ne.set(T,0,0,255)}this.texture.setPixels(ne)}},w.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},w.pick=function(M){if(!M||M.id!==this.pickId)return null;var T=function(N,B,W,G){return h[0]=G,h[1]=W,h[2]=B,h[3]=N,d[0]}(M.value[0],M.value[1],M.value[2],0),E=m.le(this.arcLength,T);if(E<0)return null;if(E===this.arcLength.length-1)return new b(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),E);for(var S=this.points[E],P=this.points[Math.min(E+1,this.points.length-1)],L=(T-this.arcLength[E])/(this.arcLength[E+1]-this.arcLength[E]),R=1-L,F=[0,0,0],D=0;D<3;++D)F[D]=R*S[D]+L*P[D];var O=Math.min(L<.5?E:E+1,this.points.length-1);return new b(T,F,O,this.points[O])}},{"./lib/shaders":90,"binary-search-bounds":31,"gl-buffer":78,"gl-texture2d":146,"gl-vao":150,ndarray:259}],92:[function(a,l,c){l.exports=function(i){var s=new Float32Array(16);return s[0]=i[0],s[1]=i[1],s[2]=i[2],s[3]=i[3],s[4]=i[4],s[5]=i[5],s[6]=i[6],s[7]=i[7],s[8]=i[8],s[9]=i[9],s[10]=i[10],s[11]=i[11],s[12]=i[12],s[13]=i[13],s[14]=i[14],s[15]=i[15],s}},{}],93:[function(a,l,c){l.exports=function(){var i=new Float32Array(16);return i[0]=1,i[1]=0,i[2]=0,i[3]=0,i[4]=0,i[5]=1,i[6]=0,i[7]=0,i[8]=0,i[9]=0,i[10]=1,i[11]=0,i[12]=0,i[13]=0,i[14]=0,i[15]=1,i}},{}],94:[function(a,l,c){l.exports=function(i){var s=i[0],u=i[1],h=i[2],d=i[3],m=i[4],p=i[5],g=i[6],y=i[7],v=i[8],x=i[9],_=i[10],A=i[11],b=i[12],k=i[13],w=i[14],M=i[15];return(s*p-u*m)*(_*M-A*w)-(s*g-h*m)*(x*M-A*k)+(s*y-d*m)*(x*w-_*k)+(u*g-h*p)*(v*M-A*b)-(u*y-d*p)*(v*w-_*b)+(h*y-d*g)*(v*k-x*b)}},{}],95:[function(a,l,c){l.exports=function(i,s){var u=s[0],h=s[1],d=s[2],m=s[3],p=u+u,g=h+h,y=d+d,v=u*p,x=h*p,_=h*g,A=d*p,b=d*g,k=d*y,w=m*p,M=m*g,T=m*y;return i[0]=1-_-k,i[1]=x+T,i[2]=A-M,i[3]=0,i[4]=x-T,i[5]=1-v-k,i[6]=b+w,i[7]=0,i[8]=A+M,i[9]=b-w,i[10]=1-v-_,i[11]=0,i[12]=0,i[13]=0,i[14]=0,i[15]=1,i}},{}],96:[function(a,l,c){l.exports=function(i,s,u){var h=s[0],d=s[1],m=s[2],p=s[3],g=h+h,y=d+d,v=m+m,x=h*g,_=h*y,A=h*v,b=d*y,k=d*v,w=m*v,M=p*g,T=p*y,E=p*v;return i[0]=1-(b+w),i[1]=_+E,i[2]=A-T,i[3]=0,i[4]=_-E,i[5]=1-(x+w),i[6]=k+M,i[7]=0,i[8]=A+T,i[9]=k-M,i[10]=1-(x+b),i[11]=0,i[12]=u[0],i[13]=u[1],i[14]=u[2],i[15]=1,i}},{}],97:[function(a,l,c){l.exports=function(i){return i[0]=1,i[1]=0,i[2]=0,i[3]=0,i[4]=0,i[5]=1,i[6]=0,i[7]=0,i[8]=0,i[9]=0,i[10]=1,i[11]=0,i[12]=0,i[13]=0,i[14]=0,i[15]=1,i}},{}],98:[function(a,l,c){l.exports=function(i,s){var u=s[0],h=s[1],d=s[2],m=s[3],p=s[4],g=s[5],y=s[6],v=s[7],x=s[8],_=s[9],A=s[10],b=s[11],k=s[12],w=s[13],M=s[14],T=s[15],E=u*g-h*p,S=u*y-d*p,P=u*v-m*p,L=h*y-d*g,R=h*v-m*g,F=d*v-m*y,D=x*w-_*k,O=x*M-A*k,N=x*T-b*k,B=_*M-A*w,W=_*T-b*w,G=A*T-b*M,K=E*G-S*W+P*B+L*N-R*O+F*D;return K?(K=1/K,i[0]=(g*G-y*W+v*B)*K,i[1]=(d*W-h*G-m*B)*K,i[2]=(w*F-M*R+T*L)*K,i[3]=(A*R-_*F-b*L)*K,i[4]=(y*N-p*G-v*O)*K,i[5]=(u*G-d*N+m*O)*K,i[6]=(M*P-k*F-T*S)*K,i[7]=(x*F-A*P+b*S)*K,i[8]=(p*W-g*N+v*D)*K,i[9]=(h*N-u*W-m*D)*K,i[10]=(k*R-w*P+T*E)*K,i[11]=(_*P-x*R-b*E)*K,i[12]=(g*O-p*B-y*D)*K,i[13]=(u*B-h*O+d*D)*K,i[14]=(w*S-k*L-M*E)*K,i[15]=(x*L-_*S+A*E)*K,i):null}},{}],99:[function(a,l,c){var i=a("./identity");l.exports=function(s,u,h,d){var m,p,g,y,v,x,_,A,b,k,w=u[0],M=u[1],T=u[2],E=d[0],S=d[1],P=d[2],L=h[0],R=h[1],F=h[2];return Math.abs(w-L)<1e-6&&Math.abs(M-R)<1e-6&&Math.abs(T-F)<1e-6?i(s):(_=w-L,A=M-R,b=T-F,k=1/Math.sqrt(_*_+A*A+b*b),m=S*(b*=k)-P*(A*=k),p=P*(_*=k)-E*b,g=E*A-S*_,(k=Math.sqrt(m*m+p*p+g*g))?(m*=k=1/k,p*=k,g*=k):(m=0,p=0,g=0),y=A*g-b*p,v=b*m-_*g,x=_*p-A*m,(k=Math.sqrt(y*y+v*v+x*x))?(y*=k=1/k,v*=k,x*=k):(y=0,v=0,x=0),s[0]=m,s[1]=y,s[2]=_,s[3]=0,s[4]=p,s[5]=v,s[6]=A,s[7]=0,s[8]=g,s[9]=x,s[10]=b,s[11]=0,s[12]=-(m*w+p*M+g*T),s[13]=-(y*w+v*M+x*T),s[14]=-(_*w+A*M+b*T),s[15]=1,s)}},{"./identity":97}],100:[function(a,l,c){l.exports=function(i,s,u){var h=s[0],d=s[1],m=s[2],p=s[3],g=s[4],y=s[5],v=s[6],x=s[7],_=s[8],A=s[9],b=s[10],k=s[11],w=s[12],M=s[13],T=s[14],E=s[15],S=u[0],P=u[1],L=u[2],R=u[3];return i[0]=S*h+P*g+L*_+R*w,i[1]=S*d+P*y+L*A+R*M,i[2]=S*m+P*v+L*b+R*T,i[3]=S*p+P*x+L*k+R*E,S=u[4],P=u[5],L=u[6],R=u[7],i[4]=S*h+P*g+L*_+R*w,i[5]=S*d+P*y+L*A+R*M,i[6]=S*m+P*v+L*b+R*T,i[7]=S*p+P*x+L*k+R*E,S=u[8],P=u[9],L=u[10],R=u[11],i[8]=S*h+P*g+L*_+R*w,i[9]=S*d+P*y+L*A+R*M,i[10]=S*m+P*v+L*b+R*T,i[11]=S*p+P*x+L*k+R*E,S=u[12],P=u[13],L=u[14],R=u[15],i[12]=S*h+P*g+L*_+R*w,i[13]=S*d+P*y+L*A+R*M,i[14]=S*m+P*v+L*b+R*T,i[15]=S*p+P*x+L*k+R*E,i}},{}],101:[function(a,l,c){l.exports=function(i,s,u,h,d,m,p){var g=1/(s-u),y=1/(h-d),v=1/(m-p);return i[0]=-2*g,i[1]=0,i[2]=0,i[3]=0,i[4]=0,i[5]=-2*y,i[6]=0,i[7]=0,i[8]=0,i[9]=0,i[10]=2*v,i[11]=0,i[12]=(s+u)*g,i[13]=(d+h)*y,i[14]=(p+m)*v,i[15]=1,i}},{}],102:[function(a,l,c){l.exports=function(i,s,u,h,d){var m=1/Math.tan(s/2),p=1/(h-d);return i[0]=m/u,i[1]=0,i[2]=0,i[3]=0,i[4]=0,i[5]=m,i[6]=0,i[7]=0,i[8]=0,i[9]=0,i[10]=(d+h)*p,i[11]=-1,i[12]=0,i[13]=0,i[14]=2*d*h*p,i[15]=0,i}},{}],103:[function(a,l,c){l.exports=function(i,s,u,h){var d,m,p,g,y,v,x,_,A,b,k,w,M,T,E,S,P,L,R,F,D,O,N,B,W=h[0],G=h[1],K=h[2],te=Math.sqrt(W*W+G*G+K*K);return Math.abs(te)<1e-6?null:(W*=te=1/te,G*=te,K*=te,d=Math.sin(u),m=Math.cos(u),p=1-m,g=s[0],y=s[1],v=s[2],x=s[3],_=s[4],A=s[5],b=s[6],k=s[7],w=s[8],M=s[9],T=s[10],E=s[11],S=W*W*p+m,P=G*W*p+K*d,L=K*W*p-G*d,R=W*G*p-K*d,F=G*G*p+m,D=K*G*p+W*d,O=W*K*p+G*d,N=G*K*p-W*d,B=K*K*p+m,i[0]=g*S+_*P+w*L,i[1]=y*S+A*P+M*L,i[2]=v*S+b*P+T*L,i[3]=x*S+k*P+E*L,i[4]=g*R+_*F+w*D,i[5]=y*R+A*F+M*D,i[6]=v*R+b*F+T*D,i[7]=x*R+k*F+E*D,i[8]=g*O+_*N+w*B,i[9]=y*O+A*N+M*B,i[10]=v*O+b*N+T*B,i[11]=x*O+k*N+E*B,s!==i&&(i[12]=s[12],i[13]=s[13],i[14]=s[14],i[15]=s[15]),i)}},{}],104:[function(a,l,c){l.exports=function(i,s,u){var h=Math.sin(u),d=Math.cos(u),m=s[4],p=s[5],g=s[6],y=s[7],v=s[8],x=s[9],_=s[10],A=s[11];return s!==i&&(i[0]=s[0],i[1]=s[1],i[2]=s[2],i[3]=s[3],i[12]=s[12],i[13]=s[13],i[14]=s[14],i[15]=s[15]),i[4]=m*d+v*h,i[5]=p*d+x*h,i[6]=g*d+_*h,i[7]=y*d+A*h,i[8]=v*d-m*h,i[9]=x*d-p*h,i[10]=_*d-g*h,i[11]=A*d-y*h,i}},{}],105:[function(a,l,c){l.exports=function(i,s,u){var h=Math.sin(u),d=Math.cos(u),m=s[0],p=s[1],g=s[2],y=s[3],v=s[8],x=s[9],_=s[10],A=s[11];return s!==i&&(i[4]=s[4],i[5]=s[5],i[6]=s[6],i[7]=s[7],i[12]=s[12],i[13]=s[13],i[14]=s[14],i[15]=s[15]),i[0]=m*d-v*h,i[1]=p*d-x*h,i[2]=g*d-_*h,i[3]=y*d-A*h,i[8]=m*h+v*d,i[9]=p*h+x*d,i[10]=g*h+_*d,i[11]=y*h+A*d,i}},{}],106:[function(a,l,c){l.exports=function(i,s,u){var h=Math.sin(u),d=Math.cos(u),m=s[0],p=s[1],g=s[2],y=s[3],v=s[4],x=s[5],_=s[6],A=s[7];return s!==i&&(i[8]=s[8],i[9]=s[9],i[10]=s[10],i[11]=s[11],i[12]=s[12],i[13]=s[13],i[14]=s[14],i[15]=s[15]),i[0]=m*d+v*h,i[1]=p*d+x*h,i[2]=g*d+_*h,i[3]=y*d+A*h,i[4]=v*d-m*h,i[5]=x*d-p*h,i[6]=_*d-g*h,i[7]=A*d-y*h,i}},{}],107:[function(a,l,c){l.exports=function(i,s,u){var h=u[0],d=u[1],m=u[2];return i[0]=s[0]*h,i[1]=s[1]*h,i[2]=s[2]*h,i[3]=s[3]*h,i[4]=s[4]*d,i[5]=s[5]*d,i[6]=s[6]*d,i[7]=s[7]*d,i[8]=s[8]*m,i[9]=s[9]*m,i[10]=s[10]*m,i[11]=s[11]*m,i[12]=s[12],i[13]=s[13],i[14]=s[14],i[15]=s[15],i}},{}],108:[function(a,l,c){l.exports=function(i,s,u){var h,d,m,p,g,y,v,x,_,A,b,k,w=u[0],M=u[1],T=u[2];return s===i?(i[12]=s[0]*w+s[4]*M+s[8]*T+s[12],i[13]=s[1]*w+s[5]*M+s[9]*T+s[13],i[14]=s[2]*w+s[6]*M+s[10]*T+s[14],i[15]=s[3]*w+s[7]*M+s[11]*T+s[15]):(h=s[0],d=s[1],m=s[2],p=s[3],g=s[4],y=s[5],v=s[6],x=s[7],_=s[8],A=s[9],b=s[10],k=s[11],i[0]=h,i[1]=d,i[2]=m,i[3]=p,i[4]=g,i[5]=y,i[6]=v,i[7]=x,i[8]=_,i[9]=A,i[10]=b,i[11]=k,i[12]=h*w+g*M+_*T+s[12],i[13]=d*w+y*M+A*T+s[13],i[14]=m*w+v*M+b*T+s[14],i[15]=p*w+x*M+k*T+s[15]),i}},{}],109:[function(a,l,c){l.exports=function(i,s){if(i===s){var u=s[1],h=s[2],d=s[3],m=s[6],p=s[7],g=s[11];i[1]=s[4],i[2]=s[8],i[3]=s[12],i[4]=u,i[6]=s[9],i[7]=s[13],i[8]=h,i[9]=m,i[11]=s[14],i[12]=d,i[13]=p,i[14]=g}else i[0]=s[0],i[1]=s[4],i[2]=s[8],i[3]=s[12],i[4]=s[1],i[5]=s[5],i[6]=s[9],i[7]=s[13],i[8]=s[2],i[9]=s[6],i[10]=s[10],i[11]=s[14],i[12]=s[3],i[13]=s[7],i[14]=s[11],i[15]=s[15];return i}},{}],110:[function(a,l,c){var i=a("barycentric"),s=a("polytope-closest-point/lib/closest_point_2d.js");function u(m,p){for(var g=[0,0,0,0],y=0;y<4;++y)for(var v=0;v<4;++v)g[v]+=m[4*y+v]*p[y];return g}function h(m,p,g,y,v){for(var x=u(y,u(g,u(p,[m[0],m[1],m[2],1]))),_=0;_<3;++_)x[_]/=x[3];return[.5*v[0]*(1+x[0]),.5*v[1]*(1-x[1])]}function d(m,p){for(var g=[0,0,0],y=0;y<m.length;++y)for(var v=m[y],x=p[y],_=0;_<3;++_)g[_]+=x*v[_];return g}l.exports=function(m,p,g,y,v,x){if(m.length===1)return[0,m[0].slice()];for(var _=new Array(m.length),A=0;A<m.length;++A)_[A]=h(m[A],g,y,v,x);var b=0,k=1/0;for(A=0;A<_.length;++A){for(var w=0,M=0;M<2;++M)w+=Math.pow(_[A][M]-p[M],2);w<k&&(k=w,b=A)}var T=function(S,P){if(S.length===2){for(var L=0,R=0,F=0;F<2;++F)L+=Math.pow(P[F]-S[0][F],2),R+=Math.pow(P[F]-S[1][F],2);return L=Math.sqrt(L),R=Math.sqrt(R),L+R<1e-6?[1,0]:[R/(L+R),L/(R+L)]}if(S.length===3){var D=[0,0];return s(S[0],S[1],S[2],P,D),i(S,D)}return[]}(_,p),E=0;for(A=0;A<3;++A){if(T[A]<-.001||T[A]>1.0001)return null;E+=T[A]}return Math.abs(E-1)>.001?null:[b,d(m,T),T]}},{barycentric:14,"polytope-closest-point/lib/closest_point_2d.js":270}],111:[function(a,l,c){var i=a("glslify"),s=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position, normal;
-attribute vec4 color;
-attribute vec2 uv;
-
-uniform mat4 model
-           , view
-           , projection
-           , inverseModel;
-uniform vec3 eyePosition
-           , lightPosition;
-
-varying vec3 f_normal
-           , f_lightDirection
-           , f_eyeDirection
-           , f_data;
-varying vec4 f_color;
-varying vec2 f_uv;
-
-vec4 project(vec3 p) {
-  return projection * view * model * vec4(p, 1.0);
-}
-
-void main() {
-  gl_Position      = project(position);
-
-  //Lighting geometry parameters
-  vec4 cameraCoordinate = view * vec4(position , 1.0);
-  cameraCoordinate.xyz /= cameraCoordinate.w;
-  f_lightDirection = lightPosition - cameraCoordinate.xyz;
-  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;
-  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);
-
-  f_color          = color;
-  f_data           = position;
-  f_uv             = uv;
-}
-`]),u=i([`#extension GL_OES_standard_derivatives : enable
-
-precision highp float;
-#define GLSLIFY 1
-
-float beckmannDistribution(float x, float roughness) {
-  float NdotH = max(x, 0.0001);
-  float cos2Alpha = NdotH * NdotH;
-  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
-  float roughness2 = roughness * roughness;
-  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
-  return exp(tan2Alpha / roughness2) / denom;
-}
-
-float cookTorranceSpecular(
-  vec3 lightDirection,
-  vec3 viewDirection,
-  vec3 surfaceNormal,
-  float roughness,
-  float fresnel) {
-
-  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);
-  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);
-
-  //Half angle vector
-  vec3 H = normalize(lightDirection + viewDirection);
-
-  //Geometric term
-  float NdotH = max(dot(surfaceNormal, H), 0.0);
-  float VdotH = max(dot(viewDirection, H), 0.000001);
-  float LdotH = max(dot(lightDirection, H), 0.000001);
-  float G1 = (2.0 * NdotH * VdotN) / VdotH;
-  float G2 = (2.0 * NdotH * LdotN) / LdotH;
-  float G = min(1.0, min(G1, G2));
-  
-  //Distribution term
-  float D = beckmannDistribution(NdotH, roughness);
-
-  //Fresnel term
-  float F = pow(1.0 - VdotN, fresnel);
-
-  //Multiply terms and done
-  return  G * F * D / max(3.14159265 * VdotN, 0.000001);
-}
-
-//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 clipBounds[2];
-uniform float roughness
-            , fresnel
-            , kambient
-            , kdiffuse
-            , kspecular;
-uniform sampler2D texture;
-
-varying vec3 f_normal
-           , f_lightDirection
-           , f_eyeDirection
-           , f_data;
-varying vec4 f_color;
-varying vec2 f_uv;
-
-void main() {
-  if (f_color.a == 0.0 ||
-    outOfRange(clipBounds[0], clipBounds[1], f_data)
-  ) discard;
-
-  vec3 N = normalize(f_normal);
-  vec3 L = normalize(f_lightDirection);
-  vec3 V = normalize(f_eyeDirection);
-
-  if(gl_FrontFacing) {
-    N = -N;
-  }
-
-  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));
-  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d
-
-  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
-
-  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);
-  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
-
-  gl_FragColor = litColor * f_color.a;
-}
-`]),h=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position;
-attribute vec4 color;
-attribute vec2 uv;
-
-uniform mat4 model, view, projection;
-
-varying vec4 f_color;
-varying vec3 f_data;
-varying vec2 f_uv;
-
-void main() {
-  gl_Position = projection * view * model * vec4(position, 1.0);
-  f_color = color;
-  f_data  = position;
-  f_uv    = uv;
-}`]),d=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 clipBounds[2];
-uniform sampler2D texture;
-uniform float opacity;
-
-varying vec4 f_color;
-varying vec3 f_data;
-varying vec2 f_uv;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;
-
-  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;
-}`]),m=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-attribute vec3 position;
-attribute vec4 color;
-attribute vec2 uv;
-attribute float pointSize;
-
-uniform mat4 model, view, projection;
-uniform vec3 clipBounds[2];
-
-varying vec4 f_color;
-varying vec2 f_uv;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
-
-    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);
-  } else {
-    gl_Position = projection * view * model * vec4(position, 1.0);
-  }
-  gl_PointSize = pointSize;
-  f_color = color;
-  f_uv = uv;
-}`]),p=i([`precision highp float;
-#define GLSLIFY 1
-
-uniform sampler2D texture;
-uniform float opacity;
-
-varying vec4 f_color;
-varying vec2 f_uv;
-
-void main() {
-  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);
-  if(dot(pointR, pointR) > 0.25) {
-    discard;
-  }
-  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;
-}`]),g=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position;
-attribute vec4 id;
-
-uniform mat4 model, view, projection;
-
-varying vec3 f_position;
-varying vec4 f_id;
-
-void main() {
-  gl_Position = projection * view * model * vec4(position, 1.0);
-  f_id        = id;
-  f_position  = position;
-}`]),y=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3  clipBounds[2];
-uniform float pickId;
-
-varying vec3 f_position;
-varying vec4 f_id;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
-
-  gl_FragColor = vec4(pickId, f_id.xyz);
-}`]),v=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-attribute vec3  position;
-attribute float pointSize;
-attribute vec4  id;
-
-uniform mat4 model, view, projection;
-uniform vec3 clipBounds[2];
-
-varying vec3 f_position;
-varying vec4 f_id;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
-
-    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);
-  } else {
-    gl_Position  = projection * view * model * vec4(position, 1.0);
-    gl_PointSize = pointSize;
-  }
-  f_id         = id;
-  f_position   = position;
-}`]),x=i([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec3 position;
-
-uniform mat4 model, view, projection;
-
-void main() {
-  gl_Position = projection * view * model * vec4(position, 1.0);
-}`]),_=i([`precision highp float;
-#define GLSLIFY 1
-
-uniform vec3 contourColor;
-
-void main() {
-  gl_FragColor = vec4(contourColor, 1.0);
-}
-`]);c.meshShader={vertex:s,fragment:u,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},c.wireShader={vertex:h,fragment:d,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},c.pointShader={vertex:m,fragment:p,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},c.pickShader={vertex:g,fragment:y,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},c.pointPickShader={vertex:v,fragment:y,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},c.contourShader={vertex:x,fragment:_,attributes:[{name:"position",type:"vec3"}]}},{glslify:231}],112:[function(a,l,c){var i=a("gl-shader"),s=a("gl-buffer"),u=a("gl-vao"),h=a("gl-texture2d"),d=a("normals"),m=a("gl-mat4/multiply"),p=a("gl-mat4/invert"),g=a("ndarray"),y=a("colormap"),v=a("simplicial-complex-contour"),x=a("typedarray-pool"),_=a("./lib/shaders"),A=a("./lib/closest-point"),b=_.meshShader,k=_.wireShader,w=_.pointShader,M=_.pickShader,T=_.pointPickShader,E=_.contourShader,S=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function P(G,K,te,Y,J,re,U,V,H,ne,q,Q,ee,ie,ae,ue,le,ge,fe,me,_e,Ae,ke,Le,de,ve,Me){this.gl=G,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=K,this.dirty=!0,this.triShader=te,this.lineShader=Y,this.pointShader=J,this.pickShader=re,this.pointPickShader=U,this.contourShader=V,this.trianglePositions=H,this.triangleColors=q,this.triangleNormals=ee,this.triangleUVs=Q,this.triangleIds=ne,this.triangleVAO=ie,this.triangleCount=0,this.lineWidth=1,this.edgePositions=ae,this.edgeColors=le,this.edgeUVs=ge,this.edgeIds=ue,this.edgeVAO=fe,this.edgeCount=0,this.pointPositions=me,this.pointColors=Ae,this.pointUVs=ke,this.pointSizes=Le,this.pointIds=_e,this.pointVAO=de,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=ve,this.contourVAO=Me,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=S,this._view=S,this._projection=S,this._resolution=[1,1]}var L=P.prototype;function R(G,K){if(!K||!K.length)return 1;for(var te=0;te<K.length;++te){if(K.length<2)return 1;if(K[te][0]===G)return K[te][1];if(K[te][0]>G&&te>0){var Y=(K[te][0]-G)/(K[te][0]-K[te-1][0]);return K[te][1]*(1-Y)+Y*K[te-1][1]}}return 1}function F(G){var K=i(G,b.vertex,b.fragment);return K.attributes.position.location=0,K.attributes.color.location=2,K.attributes.uv.location=3,K.attributes.normal.location=4,K}function D(G){var K=i(G,k.vertex,k.fragment);return K.attributes.position.location=0,K.attributes.color.location=2,K.attributes.uv.location=3,K}function O(G){var K=i(G,w.vertex,w.fragment);return K.attributes.position.location=0,K.attributes.color.location=2,K.attributes.uv.location=3,K.attributes.pointSize.location=4,K}function N(G){var K=i(G,M.vertex,M.fragment);return K.attributes.position.location=0,K.attributes.id.location=1,K}function B(G){var K=i(G,T.vertex,T.fragment);return K.attributes.position.location=0,K.attributes.id.location=1,K.attributes.pointSize.location=4,K}function W(G){var K=i(G,E.vertex,E.fragment);return K.attributes.position.location=0,K}L.isOpaque=function(){return!this.hasAlpha},L.isTransparent=function(){return this.hasAlpha},L.pickSlots=1,L.setPickBase=function(G){this.pickId=G},L.highlight=function(G){if(G&&this.contourEnable){for(var K=v(this.cells,this.intensity,G.intensity),te=K.cells,Y=K.vertexIds,J=K.vertexWeights,re=te.length,U=x.mallocFloat32(6*re),V=0,H=0;H<re;++H)for(var ne=te[H],q=0;q<2;++q){var Q=ne[0];ne.length===2&&(Q=ne[q]);for(var ee=Y[Q][0],ie=Y[Q][1],ae=J[Q],ue=1-ae,le=this.positions[ee],ge=this.positions[ie],fe=0;fe<3;++fe)U[V++]=ae*le[fe]+ue*ge[fe]}this.contourCount=V/3|0,this.contourPositions.update(U.subarray(0,V)),x.free(U)}else this.contourCount=0},L.update=function(G){G=G||{};var K=this.gl;this.dirty=!0,"contourEnable"in G&&(this.contourEnable=G.contourEnable),"contourColor"in G&&(this.contourColor=G.contourColor),"lineWidth"in G&&(this.lineWidth=G.lineWidth),"lightPosition"in G&&(this.lightPosition=G.lightPosition),this.hasAlpha=!1,"opacity"in G&&(this.opacity=G.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in G&&(this.opacityscale=G.opacityscale,this.hasAlpha=!0),"ambient"in G&&(this.ambientLight=G.ambient),"diffuse"in G&&(this.diffuseLight=G.diffuse),"specular"in G&&(this.specularLight=G.specular),"roughness"in G&&(this.roughness=G.roughness),"fresnel"in G&&(this.fresnel=G.fresnel),G.texture?(this.texture.dispose(),this.texture=h(K,G.texture)):G.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=K.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=K.LINEAR,this.texture.setPixels(function(Jt,Be){for(var Ge=y({colormap:Jt,nshades:256,format:"rgba"}),kt=new Uint8Array(1024),dt=0;dt<256;++dt){for(var Oe=Ge[dt],Ie=0;Ie<3;++Ie)kt[4*dt+Ie]=Oe[Ie];kt[4*dt+3]=Be?255*R(dt/255,Be):255*Oe[3]}return g(kt,[256,256,4],[4,0,1])}(G.colormap,this.opacityscale)),this.texture.generateMipmap());var te=G.cells,Y=G.positions;if(Y&&te){var J=[],re=[],U=[],V=[],H=[],ne=[],q=[],Q=[],ee=[],ie=[],ae=[],ue=[],le=[],ge=[];this.cells=te,this.positions=Y;var fe=G.vertexNormals,me=G.cellNormals,_e=G.vertexNormalsEpsilon===void 0?1e-6:G.vertexNormalsEpsilon,Ae=G.faceNormalsEpsilon===void 0?1e-6:G.faceNormalsEpsilon;G.useFacetNormals&&!me&&(me=d.faceNormals(te,Y,Ae)),me||fe||(fe=d.vertexNormals(te,Y,_e));var ke=G.vertexColors,Le=G.cellColors,de=G.meshColor||[1,1,1,1],ve=G.vertexUVs,Me=G.vertexIntensity,we=G.cellUVs,Ce=G.cellIntensity,Fe=1/0,ze=-1/0;if(!ve&&!we)if(Me)if(G.vertexIntensityBounds)Fe=+G.vertexIntensityBounds[0],ze=+G.vertexIntensityBounds[1];else for(var $e=0;$e<Me.length;++$e){var Ke=Me[$e];Fe=Math.min(Fe,Ke),ze=Math.max(ze,Ke)}else if(Ce)if(G.cellIntensityBounds)Fe=+G.cellIntensityBounds[0],ze=+G.cellIntensityBounds[1];else for($e=0;$e<Ce.length;++$e)Ke=Ce[$e],Fe=Math.min(Fe,Ke),ze=Math.max(ze,Ke);else for($e=0;$e<Y.length;++$e)Ke=Y[$e][2],Fe=Math.min(Fe,Ke),ze=Math.max(ze,Ke);this.intensity=Me||Ce||function(Jt){for(var Be=Jt.length,Ge=new Array(Be),kt=0;kt<Be;++kt)Ge[kt]=Jt[kt][2];return Ge}(Y),this.pickVertex=!(Ce||Le);var Re=G.pointSizes,Ve=G.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],$e=0;$e<Y.length;++$e)for(var We=Y[$e],Ye=0;Ye<3;++Ye)!isNaN(We[Ye])&&isFinite(We[Ye])&&(this.bounds[0][Ye]=Math.min(this.bounds[0][Ye],We[Ye]),this.bounds[1][Ye]=Math.max(this.bounds[1][Ye],We[Ye]));var nt=0,ft=0,yt=0;e:for($e=0;$e<te.length;++$e){var Ot=te[$e];switch(Ot.length){case 1:for(We=Y[at=Ot[0]],Ye=0;Ye<3;++Ye)if(isNaN(We[Ye])||!isFinite(We[Ye]))continue e;ie.push(We[0],We[1],We[2]),et=ke?ke[at]:Le?Le[$e]:de,this.opacityscale&&Me?re.push(et[0],et[1],et[2],this.opacity*R((Me[at]-Fe)/(ze-Fe),this.opacityscale)):et.length===3?ae.push(et[0],et[1],et[2],this.opacity):(ae.push(et[0],et[1],et[2],et[3]*this.opacity),et[3]<1&&(this.hasAlpha=!0)),Lt=ve?ve[at]:Me?[(Me[at]-Fe)/(ze-Fe),0]:we?we[$e]:Ce?[(Ce[$e]-Fe)/(ze-Fe),0]:[(We[2]-Fe)/(ze-Fe),0],ue.push(Lt[0],Lt[1]),Re?le.push(Re[at]):le.push(Ve),ge.push($e),yt+=1;break;case 2:for(Ye=0;Ye<2;++Ye){We=Y[at=Ot[Ye]];for(var Tt=0;Tt<3;++Tt)if(isNaN(We[Tt])||!isFinite(We[Tt]))continue e}for(Ye=0;Ye<2;++Ye)We=Y[at=Ot[Ye]],ne.push(We[0],We[1],We[2]),et=ke?ke[at]:Le?Le[$e]:de,this.opacityscale&&Me?re.push(et[0],et[1],et[2],this.opacity*R((Me[at]-Fe)/(ze-Fe),this.opacityscale)):et.length===3?q.push(et[0],et[1],et[2],this.opacity):(q.push(et[0],et[1],et[2],et[3]*this.opacity),et[3]<1&&(this.hasAlpha=!0)),Lt=ve?ve[at]:Me?[(Me[at]-Fe)/(ze-Fe),0]:we?we[$e]:Ce?[(Ce[$e]-Fe)/(ze-Fe),0]:[(We[2]-Fe)/(ze-Fe),0],Q.push(Lt[0],Lt[1]),ee.push($e);ft+=1;break;case 3:for(Ye=0;Ye<3;++Ye)for(We=Y[at=Ot[Ye]],Tt=0;Tt<3;++Tt)if(isNaN(We[Tt])||!isFinite(We[Tt]))continue e;for(Ye=0;Ye<3;++Ye){var at,et,Lt,Wt;We=Y[at=Ot[2-Ye]],J.push(We[0],We[1],We[2]),(et=ke?ke[at]:Le?Le[$e]:de)?this.opacityscale&&Me?re.push(et[0],et[1],et[2],this.opacity*R((Me[at]-Fe)/(ze-Fe),this.opacityscale)):et.length===3?re.push(et[0],et[1],et[2],this.opacity):(re.push(et[0],et[1],et[2],et[3]*this.opacity),et[3]<1&&(this.hasAlpha=!0)):re.push(.5,.5,.5,1),Lt=ve?ve[at]:Me?[(Me[at]-Fe)/(ze-Fe),0]:we?we[$e]:Ce?[(Ce[$e]-Fe)/(ze-Fe),0]:[(We[2]-Fe)/(ze-Fe),0],V.push(Lt[0],Lt[1]),Wt=fe?fe[at]:me[$e],U.push(Wt[0],Wt[1],Wt[2]),H.push($e)}nt+=1}}this.pointCount=yt,this.edgeCount=ft,this.triangleCount=nt,this.pointPositions.update(ie),this.pointColors.update(ae),this.pointUVs.update(ue),this.pointSizes.update(le),this.pointIds.update(new Uint32Array(ge)),this.edgePositions.update(ne),this.edgeColors.update(q),this.edgeUVs.update(Q),this.edgeIds.update(new Uint32Array(ee)),this.trianglePositions.update(J),this.triangleColors.update(re),this.triangleUVs.update(V),this.triangleNormals.update(U),this.triangleIds.update(new Uint32Array(H))}},L.drawTransparent=L.draw=function(G){G=G||{};for(var K=this.gl,te=G.model||S,Y=G.view||S,J=G.projection||S,re=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],U=0;U<3;++U)re[0][U]=Math.max(re[0][U],this.clipBounds[0][U]),re[1][U]=Math.min(re[1][U],this.clipBounds[1][U]);var V={model:te,view:Y,projection:J,inverseModel:S.slice(),clipBounds:re,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};V.inverseModel=p(V.inverseModel,V.model),K.disable(K.CULL_FACE),this.texture.bind(0);var H=new Array(16);for(m(H,V.view,V.model),m(H,V.projection,H),p(H,H),U=0;U<3;++U)V.eyePosition[U]=H[12+U]/H[15];var ne,q=H[15];for(U=0;U<3;++U)q+=this.lightPosition[U]*H[4*U+3];for(U=0;U<3;++U){for(var Q=H[12+U],ee=0;ee<3;++ee)Q+=H[4*ee+U]*this.lightPosition[ee];V.lightPosition[U]=Q/q}this.triangleCount>0&&((ne=this.triShader).bind(),ne.uniforms=V,this.triangleVAO.bind(),K.drawArrays(K.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((ne=this.lineShader).bind(),ne.uniforms=V,this.edgeVAO.bind(),K.lineWidth(this.lineWidth*this.pixelRatio),K.drawArrays(K.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((ne=this.pointShader).bind(),ne.uniforms=V,this.pointVAO.bind(),K.drawArrays(K.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((ne=this.contourShader).bind(),ne.uniforms=V,this.contourVAO.bind(),K.drawArrays(K.LINES,0,this.contourCount),this.contourVAO.unbind())},L.drawPick=function(G){G=G||{};for(var K=this.gl,te=G.model||S,Y=G.view||S,J=G.projection||S,re=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],U=0;U<3;++U)re[0][U]=Math.max(re[0][U],this.clipBounds[0][U]),re[1][U]=Math.min(re[1][U],this.clipBounds[1][U]);this._model=[].slice.call(te),this._view=[].slice.call(Y),this._projection=[].slice.call(J),this._resolution=[K.drawingBufferWidth,K.drawingBufferHeight];var V,H={model:te,view:Y,projection:J,clipBounds:re,pickId:this.pickId/255};(V=this.pickShader).bind(),V.uniforms=H,this.triangleCount>0&&(this.triangleVAO.bind(),K.drawArrays(K.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),K.lineWidth(this.lineWidth*this.pixelRatio),K.drawArrays(K.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((V=this.pointPickShader).bind(),V.uniforms=H,this.pointVAO.bind(),K.drawArrays(K.POINTS,0,this.pointCount),this.pointVAO.unbind())},L.pick=function(G){if(!G||G.id!==this.pickId)return null;for(var K=G.value[0]+256*G.value[1]+65536*G.value[2],te=this.cells[K],Y=this.positions,J=new Array(te.length),re=0;re<te.length;++re)J[re]=Y[te[re]];var U=G.coord[0],V=G.coord[1];if(!this.pickVertex){var H=this.positions[te[0]],ne=this.positions[te[1]],q=this.positions[te[2]],Q=[(H[0]+ne[0]+q[0])/3,(H[1]+ne[1]+q[1])/3,(H[2]+ne[2]+q[2])/3];return{_cellCenter:!0,position:[U,V],index:K,cell:te,cellId:K,intensity:this.intensity[K],dataCoordinate:Q}}var ee=A(J,[U*this.pixelRatio,this._resolution[1]-V*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!ee)return null;var ie=ee[2],ae=0;for(re=0;re<te.length;++re)ae+=ie[re]*this.intensity[te[re]];return{position:ee[1],index:te[ee[0]],cell:te,cellId:K,intensity:ae,dataCoordinate:this.positions[te[ee[0]]]}},L.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},l.exports=function(G,K){arguments.length===1&&(G=(K=G).gl);var te=G.getExtension("OES_standard_derivatives")||G.getExtension("MOZ_OES_standard_derivatives")||G.getExtension("WEBKIT_OES_standard_derivatives");if(!te)throw new Error("derivatives not supported");var Y=F(G),J=D(G),re=O(G),U=N(G),V=B(G),H=W(G),ne=h(G,g(new Uint8Array([255,255,255,255]),[1,1,4]));ne.generateMipmap(),ne.minFilter=G.LINEAR_MIPMAP_LINEAR,ne.magFilter=G.LINEAR;var q=s(G),Q=s(G),ee=s(G),ie=s(G),ae=s(G),ue=u(G,[{buffer:q,type:G.FLOAT,size:3},{buffer:ae,type:G.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Q,type:G.FLOAT,size:4},{buffer:ee,type:G.FLOAT,size:2},{buffer:ie,type:G.FLOAT,size:3}]),le=s(G),ge=s(G),fe=s(G),me=s(G),_e=u(G,[{buffer:le,type:G.FLOAT,size:3},{buffer:me,type:G.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:ge,type:G.FLOAT,size:4},{buffer:fe,type:G.FLOAT,size:2}]),Ae=s(G),ke=s(G),Le=s(G),de=s(G),ve=s(G),Me=u(G,[{buffer:Ae,type:G.FLOAT,size:3},{buffer:ve,type:G.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:ke,type:G.FLOAT,size:4},{buffer:Le,type:G.FLOAT,size:2},{buffer:de,type:G.FLOAT,size:1}]),we=s(G),Ce=u(G,[{buffer:we,type:G.FLOAT,size:3}]),Fe=new P(G,ne,Y,J,re,U,V,H,q,ae,Q,ee,ie,ue,le,me,ge,fe,_e,Ae,ve,ke,Le,de,Me,we,Ce);return Fe.update(K),Fe}},{"./lib/closest-point":110,"./lib/shaders":111,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259,normals:261,"simplicial-complex-contour":291,"typedarray-pool":308}],113:[function(a,l,c){l.exports=function(g){var y=g.gl,v=i(y,[0,0,0,1,1,0,1,1]),x=s(y,u.boxVert,u.lineFrag);return new h(g,v,x)};var i=a("gl-buffer"),s=a("gl-shader"),u=a("./shaders");function h(g,y,v){this.plot=g,this.vbo=y,this.shader=v}var d,m,p=h.prototype;p.bind=function(){var g=this.shader;this.vbo.bind(),this.shader.bind(),g.attributes.coord.pointer(),g.uniforms.screenBox=this.plot.screenBox},p.drawBox=(d=[0,0],m=[0,0],function(g,y,v,x,_){var A=this.plot,b=this.shader,k=A.gl;d[0]=g,d[1]=y,m[0]=v,m[1]=x,b.uniforms.lo=d,b.uniforms.hi=m,b.uniforms.color=_,k.drawArrays(k.TRIANGLE_STRIP,0,4)}),p.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],114:[function(a,l,c){l.exports=function(A){var b=A.gl,k=i(b),w=s(b,h.gridVert,h.gridFrag),M=s(b,h.tickVert,h.gridFrag);return new d(A,k,w,M)};var i=a("gl-buffer"),s=a("gl-shader"),u=a("binary-search-bounds"),h=a("./shaders");function d(A,b,k,w){this.plot=A,this.vbo=b,this.shader=k,this.tickShader=w,this.ticks=[[],[]]}function m(A,b){return A-b}var p,g,y,v,x,_=d.prototype;_.draw=(p=[0,0],g=[0,0],y=[0,0],function(){for(var A=this.plot,b=this.vbo,k=this.shader,w=this.ticks,M=A.gl,T=A._tickBounds,E=A.dataBox,S=A.viewBox,P=A.gridLineWidth,L=A.gridLineColor,R=A.gridLineEnable,F=A.pixelRatio,D=0;D<2;++D){var O=T[D],N=T[D+2]-O,B=.5*(E[D+2]+E[D]),W=E[D+2]-E[D];g[D]=2*N/W,p[D]=2*(O-B)/W}k.bind(),b.bind(),k.attributes.dataCoord.pointer(),k.uniforms.dataShift=p,k.uniforms.dataScale=g;var G=0;for(D=0;D<2;++D){y[0]=y[1]=0,y[D]=1,k.uniforms.dataAxis=y,k.uniforms.lineWidth=P[D]/(S[D+2]-S[D])*F,k.uniforms.color=L[D];var K=6*w[D].length;R[D]&&K&&M.drawArrays(M.TRIANGLES,G,K),G+=K}}),_.drawTickMarks=function(){var A=[0,0],b=[0,0],k=[1,0],w=[0,1],M=[0,0],T=[0,0];return function(){for(var E=this.plot,S=this.vbo,P=this.tickShader,L=this.ticks,R=E.gl,F=E._tickBounds,D=E.dataBox,O=E.viewBox,N=E.pixelRatio,B=E.screenBox,W=B[2]-B[0],G=B[3]-B[1],K=O[2]-O[0],te=O[3]-O[1],Y=0;Y<2;++Y){var J=F[Y],re=F[Y+2]-J,U=.5*(D[Y+2]+D[Y]),V=D[Y+2]-D[Y];b[Y]=2*re/V,A[Y]=2*(J-U)/V}b[0]*=K/W,A[0]*=K/W,b[1]*=te/G,A[1]*=te/G,P.bind(),S.bind(),P.attributes.dataCoord.pointer();var H=P.uniforms;H.dataShift=A,H.dataScale=b;var ne=E.tickMarkLength,q=E.tickMarkWidth,Q=E.tickMarkColor,ee=6*L[0].length,ie=Math.min(u.ge(L[0],(D[0]-F[0])/(F[2]-F[0]),m),L[0].length),ae=Math.min(u.gt(L[0],(D[2]-F[0])/(F[2]-F[0]),m),L[0].length),ue=0+6*ie,le=6*Math.max(0,ae-ie),ge=Math.min(u.ge(L[1],(D[1]-F[1])/(F[3]-F[1]),m),L[1].length),fe=Math.min(u.gt(L[1],(D[3]-F[1])/(F[3]-F[1]),m),L[1].length),me=ee+6*ge,_e=6*Math.max(0,fe-ge);M[0]=2*(O[0]-ne[1])/W-1,M[1]=(O[3]+O[1])/G-1,T[0]=ne[1]*N/W,T[1]=q[1]*N/G,_e&&(H.color=Q[1],H.tickScale=T,H.dataAxis=w,H.screenOffset=M,R.drawArrays(R.TRIANGLES,me,_e)),M[0]=(O[2]+O[0])/W-1,M[1]=2*(O[1]-ne[0])/G-1,T[0]=q[0]*N/W,T[1]=ne[0]*N/G,le&&(H.color=Q[0],H.tickScale=T,H.dataAxis=k,H.screenOffset=M,R.drawArrays(R.TRIANGLES,ue,le)),M[0]=2*(O[2]+ne[3])/W-1,M[1]=(O[3]+O[1])/G-1,T[0]=ne[3]*N/W,T[1]=q[3]*N/G,_e&&(H.color=Q[3],H.tickScale=T,H.dataAxis=w,H.screenOffset=M,R.drawArrays(R.TRIANGLES,me,_e)),M[0]=(O[2]+O[0])/W-1,M[1]=2*(O[3]+ne[2])/G-1,T[0]=q[2]*N/W,T[1]=ne[2]*N/G,le&&(H.color=Q[2],H.tickScale=T,H.dataAxis=k,H.screenOffset=M,R.drawArrays(R.TRIANGLES,ue,le))}}(),_.update=(v=[1,1,-1,-1,1,-1],x=[1,-1,1,1,-1,-1],function(A){for(var b=A.ticks,k=A.bounds,w=new Float32Array(18*(b[0].length+b[1].length)),M=(this.plot.zeroLineEnable,0),T=[[],[]],E=0;E<2;++E)for(var S=T[E],P=b[E],L=k[E],R=k[E+2],F=0;F<P.length;++F){var D=(P[F].x-L)/(R-L);S.push(D);for(var O=0;O<6;++O)w[M++]=D,w[M++]=v[O],w[M++]=x[O]}this.ticks=T,this.vbo.update(w)}),_.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132}],115:[function(a,l,c){l.exports=function(g){var y=g.gl,v=i(y,[-1,-1,-1,1,1,-1,1,1]),x=s(y,u.lineVert,u.lineFrag);return new h(g,v,x)};var i=a("gl-buffer"),s=a("gl-shader"),u=a("./shaders");function h(g,y,v){this.plot=g,this.vbo=y,this.shader=v}var d,m,p=h.prototype;p.bind=function(){var g=this.shader;this.vbo.bind(),this.shader.bind(),g.attributes.coord.pointer(),g.uniforms.screenBox=this.plot.screenBox},p.drawLine=(d=[0,0],m=[0,0],function(g,y,v,x,_,A){var b=this.plot,k=this.shader,w=b.gl;d[0]=g,d[1]=y,m[0]=v,m[1]=x,k.uniforms.start=d,k.uniforms.end=m,k.uniforms.width=_*b.pixelRatio,k.uniforms.color=A,w.drawArrays(w.TRIANGLE_STRIP,0,4)}),p.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],116:[function(a,l,c){var i=a("glslify"),s=i([`precision lowp float;
-#define GLSLIFY 1
-uniform vec4 color;
-void main() {
-  gl_FragColor = vec4(color.xyz * color.w, color.w);
-}
-`]);l.exports={lineVert:i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec2 coord;
-
-uniform vec4 screenBox;
-uniform vec2 start, end;
-uniform float width;
-
-vec2 perp(vec2 v) {
-  return vec2(v.y, -v.x);
-}
-
-vec2 screen(vec2 v) {
-  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;
-}
-
-void main() {
-  vec2 delta = normalize(perp(start - end));
-  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));
-  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);
-}
-`]),lineFrag:s,textVert:i([`#define GLSLIFY 1
-attribute vec3 textCoordinate;
-
-uniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;
-uniform float angle;
-
-void main() {
-  float dataOffset  = textCoordinate.z;
-  vec2 glyphOffset  = textCoordinate.xy;
-  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));
-  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +
-    glyphMatrix * glyphOffset * textScale + screenOffset;
-  gl_Position = vec4(screenCoordinate, 0, 1);
-}
-`]),textFrag:s,gridVert:i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec3 dataCoord;
-
-uniform vec2 dataAxis, dataShift, dataScale;
-uniform float lineWidth;
-
-void main() {
-  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);
-  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;
-  gl_Position = vec4(pos, 0, 1);
-}
-`]),gridFrag:s,boxVert:i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec2 coord;
-
-uniform vec4 screenBox;
-uniform vec2 lo, hi;
-
-vec2 screen(vec2 v) {
-  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;
-}
-
-void main() {
-  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);
-}
-`]),tickVert:i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec3 dataCoord;
-
-uniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;
-
-void main() {
-  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);
-  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);
-}
-`])}},{glslify:231}],117:[function(a,l,c){l.exports=function(b){var k=b.gl,w=i(k),M=s(k,d.textVert,d.textFrag);return new m(b,w,M)};var i=a("gl-buffer"),s=a("gl-shader"),u=a("text-cache"),h=a("binary-search-bounds"),d=a("./shaders");function m(b,k,w){this.plot=b,this.vbo=k,this.shader=w,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var p,g,y,v,x,_,A=m.prototype;A.drawTicks=(p=[0,0],g=[0,0],y=[0,0],function(b){var k=this.plot,w=this.shader,M=this.tickX[b],T=this.tickOffset[b],E=k.gl,S=k.viewBox,P=k.dataBox,L=k.screenBox,R=k.pixelRatio,F=k.tickEnable,D=k.tickPad,O=k.tickColor,N=k.tickAngle,B=k.labelEnable,W=k.labelPad,G=k.labelColor,K=k.labelAngle,te=this.labelOffset[b],Y=this.labelCount[b],J=h.lt(M,P[b]),re=h.le(M,P[b+2]);p[0]=p[1]=0,p[b]=1,g[b]=(S[2+b]+S[b])/(L[2+b]-L[b])-1;var U=2/L[2+(1^b)]-L[1^b];g[1^b]=U*S[1^b]-1,F[b]&&(g[1^b]-=U*R*D[b],J<re&&T[re]>T[J]&&(w.uniforms.dataAxis=p,w.uniforms.screenOffset=g,w.uniforms.color=O[b],w.uniforms.angle=N[b],E.drawArrays(E.TRIANGLES,T[J],T[re]-T[J]))),B[b]&&Y&&(g[1^b]-=U*R*W[b],w.uniforms.dataAxis=y,w.uniforms.screenOffset=g,w.uniforms.color=G[b],w.uniforms.angle=K[b],E.drawArrays(E.TRIANGLES,te,Y)),g[1^b]=U*S[2+(1^b)]-1,F[b+2]&&(g[1^b]+=U*R*D[b+2],J<re&&T[re]>T[J]&&(w.uniforms.dataAxis=p,w.uniforms.screenOffset=g,w.uniforms.color=O[b+2],w.uniforms.angle=N[b+2],E.drawArrays(E.TRIANGLES,T[J],T[re]-T[J]))),B[b+2]&&Y&&(g[1^b]+=U*R*W[b+2],w.uniforms.dataAxis=y,w.uniforms.screenOffset=g,w.uniforms.color=G[b+2],w.uniforms.angle=K[b+2],E.drawArrays(E.TRIANGLES,te,Y))}),A.drawTitle=function(){var b=[0,0],k=[0,0];return function(){var w=this.plot,M=this.shader,T=w.gl,E=w.screenBox,S=w.titleCenter,P=w.titleAngle,L=w.titleColor,R=w.pixelRatio;if(this.titleCount){for(var F=0;F<2;++F)k[F]=2*(S[F]*R-E[F])/(E[2+F]-E[F])-1;M.bind(),M.uniforms.dataAxis=b,M.uniforms.screenOffset=k,M.uniforms.angle=P,M.uniforms.color=L,T.drawArrays(T.TRIANGLES,this.titleOffset,this.titleCount)}}}(),A.bind=(v=[0,0],x=[0,0],_=[0,0],function(){var b=this.plot,k=this.shader,w=b._tickBounds,M=b.dataBox,T=b.screenBox,E=b.viewBox;k.bind();for(var S=0;S<2;++S){var P=w[S],L=w[S+2]-P,R=.5*(M[S+2]+M[S]),F=M[S+2]-M[S],D=E[S],O=E[S+2]-D,N=T[S],B=T[S+2]-N;x[S]=2*L/F*O/B,v[S]=2*(P-R)/F*O/B}_[1]=2*b.pixelRatio/(T[3]-T[1]),_[0]=_[1]*(T[3]-T[1])/(T[2]-T[0]),k.uniforms.dataScale=x,k.uniforms.dataShift=v,k.uniforms.textScale=_,this.vbo.bind(),k.attributes.textCoordinate.pointer()}),A.update=function(b){var k,w,M,T,E,S=[],P=b.ticks,L=b.bounds;for(E=0;E<2;++E){var R=[Math.floor(S.length/3)],F=[-1/0],D=P[E];for(k=0;k<D.length;++k){var O=D[k],N=O.x,B=O.text,W=O.font||"sans-serif";T=O.fontSize||12;for(var G=1/(L[E+2]-L[E]),K=L[E],te=B.split(`
-`),Y=0;Y<te.length;Y++)for(M=u(W,te[Y]).data,w=0;w<M.length;w+=2)S.push(M[w]*T,-M[w+1]*T-Y*T*1.2,(N-K)*G);R.push(Math.floor(S.length/3)),F.push(N)}this.tickOffset[E]=R,this.tickX[E]=F}for(E=0;E<2;++E){for(this.labelOffset[E]=Math.floor(S.length/3),M=u(b.labelFont[E],b.labels[E],{textAlign:"center"}).data,T=b.labelSize[E],k=0;k<M.length;k+=2)S.push(M[k]*T,-M[k+1]*T,0);this.labelCount[E]=Math.floor(S.length/3)-this.labelOffset[E]}for(this.titleOffset=Math.floor(S.length/3),M=u(b.titleFont,b.title).data,T=b.titleSize,k=0;k<M.length;k+=2)S.push(M[k]*T,-M[k+1]*T,0);this.titleCount=Math.floor(S.length/3)-this.titleOffset,this.vbo.update(S)},A.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"text-cache":303}],118:[function(a,l,c){l.exports=function(v){var x=v.gl,_=i(x,[x.drawingBufferWidth,x.drawingBufferHeight]),A=new m(x,_);return A.grid=s(A),A.text=u(A),A.line=h(A),A.box=d(A),A.update(v),A};var i=a("gl-select-static"),s=a("./lib/grid"),u=a("./lib/text"),h=a("./lib/line"),d=a("./lib/box");function m(v,x){this.gl=v,this.pickBuffer=x,this.screenBox=[0,0,v.drawingBufferWidth,v.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var p=m.prototype;function g(v){for(var x=v.slice(),_=0;_<x.length;++_)x[_]=x[_].slice();return x}function y(v,x){return v.x-x.x}p.setDirty=function(){this.dirty=this.pickDirty=!0},p.setOverlayDirty=function(){this.dirty=!0},p.nextDepthValue=function(){return this._depthCounter++/65536},p.draw=function(){var v=this.gl,x=this.screenBox,_=this.viewBox,A=this.dataBox,b=this.pixelRatio,k=this.grid,w=this.line,M=this.text,T=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,v.bindFramebuffer(v.FRAMEBUFFER,null),v.enable(v.SCISSOR_TEST),v.disable(v.DEPTH_TEST),v.depthFunc(v.LESS),v.depthMask(!1),v.enable(v.BLEND),v.blendEquation(v.FUNC_ADD,v.FUNC_ADD),v.blendFunc(v.ONE,v.ONE_MINUS_SRC_ALPHA),this.borderColor){v.scissor(x[0],x[1],x[2]-x[0],x[3]-x[1]);var E=this.borderColor;v.clearColor(E[0]*E[3],E[1]*E[3],E[2]*E[3],E[3]),v.clear(v.COLOR_BUFFER_BIT|v.DEPTH_BUFFER_BIT)}v.scissor(_[0],_[1],_[2]-_[0],_[3]-_[1]),v.viewport(_[0],_[1],_[2]-_[0],_[3]-_[1]);var S=this.backgroundColor;v.clearColor(S[0]*S[3],S[1]*S[3],S[2]*S[3],S[3]),v.clear(v.COLOR_BUFFER_BIT),k.draw();var P=this.zeroLineEnable,L=this.zeroLineColor,R=this.zeroLineWidth;if(P[0]||P[1]){w.bind();for(var F=0;F<2;++F)if(P[F]&&A[F]<=0&&A[F+2]>=0){var D=x[F]-A[F]*(x[F+2]-x[F])/(A[F+2]-A[F]);F===0?w.drawLine(D,x[1],D,x[3],R[F],L[F]):w.drawLine(x[0],D,x[2],D,R[F],L[F])}}for(F=0;F<T.length;++F)T[F].draw();v.viewport(x[0],x[1],x[2]-x[0],x[3]-x[1]),v.scissor(x[0],x[1],x[2]-x[0],x[3]-x[1]),this.grid.drawTickMarks(),w.bind();var O=this.borderLineEnable,N=this.borderLineWidth,B=this.borderLineColor;for(O[1]&&w.drawLine(_[0],_[1]-.5*N[1]*b,_[0],_[3]+.5*N[3]*b,N[1],B[1]),O[0]&&w.drawLine(_[0]-.5*N[0]*b,_[1],_[2]+.5*N[2]*b,_[1],N[0],B[0]),O[3]&&w.drawLine(_[2],_[1]-.5*N[1]*b,_[2],_[3]+.5*N[3]*b,N[3],B[3]),O[2]&&w.drawLine(_[0]-.5*N[0]*b,_[3],_[2]+.5*N[2]*b,_[3],N[2],B[2]),M.bind(),F=0;F<2;++F)M.drawTicks(F);this.titleEnable&&M.drawTitle();var W=this.overlays;for(F=0;F<W.length;++F)W[F].draw();v.disable(v.SCISSOR_TEST),v.disable(v.BLEND),v.depthMask(!0)}},p.drawPick=function(){if(!this.static){var v=this.pickBuffer;this.gl,this._pickTimeout=null,v.begin();for(var x=1,_=this.objects,A=0;A<_.length;++A)x=_[A].drawPick(x);v.end()}},p.pick=function(v,x){if(!this.static){var _=this.pixelRatio,A=this.pickPixelRatio,b=this.viewBox,k=0|Math.round((v-b[0]/_)*A),w=0|Math.round((x-b[1]/_)*A),M=this.pickBuffer.query(k,w,this.pickRadius);if(!M)return null;for(var T=M.id+(M.value[0]<<8)+(M.value[1]<<16)+(M.value[2]<<24),E=this.objects,S=0;S<E.length;++S){var P=E[S].pick(k,w,T);if(P)return P}return null}},p.setScreenBox=function(v){var x=this.screenBox,_=this.pixelRatio;x[0]=0|Math.round(v[0]*_),x[1]=0|Math.round(v[1]*_),x[2]=0|Math.round(v[2]*_),x[3]=0|Math.round(v[3]*_),this.setDirty()},p.setDataBox=function(v){var x=this.dataBox;(x[0]!==v[0]||x[1]!==v[1]||x[2]!==v[2]||x[3]!==v[3])&&(x[0]=v[0],x[1]=v[1],x[2]=v[2],x[3]=v[3],this.setDirty())},p.setViewBox=function(v){var x=this.pixelRatio,_=this.viewBox;_[0]=0|Math.round(v[0]*x),_[1]=0|Math.round(v[1]*x),_[2]=0|Math.round(v[2]*x),_[3]=0|Math.round(v[3]*x);var A=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((v[2]-v[0])*A),0|Math.round((v[3]-v[1])*A)],this.setDirty()},p.update=function(v){v=v||{};var x=this.gl;this.pixelRatio=v.pixelRatio||1;var _=this.pixelRatio;this.pickPixelRatio=Math.max(_,1),this.setScreenBox(v.screenBox||[0,0,x.drawingBufferWidth/_,x.drawingBufferHeight/_]),this.screenBox,this.setViewBox(v.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/_,.125*(this.screenBox[3]-this.screenBox[1])/_,.875*(this.screenBox[2]-this.screenBox[0])/_,.875*(this.screenBox[3]-this.screenBox[1])/_]);var A=this.viewBox,b=(A[2]-A[0])/(A[3]-A[1]);this.setDataBox(v.dataBox||[-10,-10/b,10,10/b]),this.borderColor=v.borderColor!==!1&&(v.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(v.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(v.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(v.gridLineWidth||[1,1]).slice(),this.gridLineColor=g(v.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(v.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(v.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=g(v.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(v.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(v.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=g(v.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(v.titleCenter||[.5*(A[0]+A[2])/_,(A[3]+120)/_]).slice(),this.titleEnable=!("titleEnable"in v)||!!v.titleEnable,this.titleAngle=v.titleAngle||0,this.titleColor=(v.titleColor||[0,0,0,1]).slice(),this.labelPad=(v.labelPad||[15,15,15,15]).slice(),this.labelAngle=(v.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(v.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=g(v.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(v.tickPad||[15,15,15,15]).slice(),this.tickAngle=(v.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(v.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=g(v.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(v.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(v.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=g(v.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var k=v.ticks||[[],[]],w=this._tickBounds;w[0]=w[1]=1/0,w[2]=w[3]=-1/0;for(var M=0;M<2;++M){var T=k[M].slice(0);T.length!==0&&(T.sort(y),w[M]=Math.min(w[M],T[0].x),w[M+2]=Math.max(w[M+2],T[T.length-1].x))}this.grid.update({bounds:w,ticks:k}),this.text.update({bounds:w,ticks:k,labels:v.labels||["x","y"],labelSize:v.labelSize||[12,12],labelFont:v.labelFont||["sans-serif","sans-serif"],title:v.title||"",titleSize:v.titleSize||18,titleFont:v.titleFont||"sans-serif"}),this.static=!!v.static,this.setDirty()},p.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var v=this.objects.length-1;v>=0;--v)this.objects[v].dispose();for(this.objects.length=0,v=this.overlays.length-1;v>=0;--v)this.overlays[v].dispose();this.overlays.length=0,this.gl=null},p.addObject=function(v){this.objects.indexOf(v)<0&&(this.objects.push(v),this.setDirty())},p.removeObject=function(v){for(var x=this.objects,_=0;_<x.length;++_)if(x[_]===v){x.splice(_,1),this.setDirty();break}},p.addOverlay=function(v){this.overlays.indexOf(v)<0&&(this.overlays.push(v),this.setOverlayDirty())},p.removeOverlay=function(v){for(var x=this.overlays,_=0;_<x.length;++_)if(x[_]===v){x.splice(_,1),this.setOverlayDirty();break}}},{"./lib/box":113,"./lib/grid":114,"./lib/line":115,"./lib/text":117,"gl-select-static":131}],119:[function(a,l,c){l.exports=function(p,g){p=p||document.body,g=g||{};var y=[.01,1/0];"distanceLimits"in g&&(y[0]=g.distanceLimits[0],y[1]=g.distanceLimits[1]),"zoomMin"in g&&(y[0]=g.zoomMin),"zoomMax"in g&&(y[1]=g.zoomMax);var v=s({center:g.center||[0,0,0],up:g.up||[0,1,0],eye:g.eye||[0,0,10],mode:g.mode||"orbit",distanceLimits:y}),x=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],_=0,A=p.clientWidth,b=p.clientHeight,k={keyBindingMode:"rotate",enableWheel:!0,view:v,element:p,delay:g.delay||16,rotateSpeed:g.rotateSpeed||1,zoomSpeed:g.zoomSpeed||1,translateSpeed:g.translateSpeed||1,flipX:!!g.flipX,flipY:!!g.flipY,modes:v.modes,_ortho:g._ortho||g.projection&&g.projection.type==="orthographic"||!1,tick:function(){var w=i(),M=this.delay,T=w-2*M;v.idle(w-M),v.recalcMatrix(T),v.flush(w-(100+2*M));for(var E=!0,S=v.computedMatrix,P=0;P<16;++P)E=E&&x[P]===S[P],x[P]=S[P];var L=p.clientWidth===A&&p.clientHeight===b;return A=p.clientWidth,b=p.clientHeight,E?!L:(_=Math.exp(v.computedRadius[0]),!0)},lookAt:function(w,M,T){v.lookAt(v.lastT(),w,M,T)},rotate:function(w,M,T){v.rotate(v.lastT(),w,M,T)},pan:function(w,M,T){v.pan(v.lastT(),w,M,T)},translate:function(w,M,T){v.translate(v.lastT(),w,M,T)}};return Object.defineProperties(k,{matrix:{get:function(){return v.computedMatrix},set:function(w){return v.setMatrix(v.lastT(),w),v.computedMatrix},enumerable:!0},mode:{get:function(){return v.getMode()},set:function(w){var M=v.computedUp.slice(),T=v.computedEye.slice(),E=v.computedCenter.slice();if(v.setMode(w),w==="turntable"){var S=i();v._active.lookAt(S,T,E,M),v._active.lookAt(S+500,T,E,[0,0,1]),v._active.flush(S)}return v.getMode()},enumerable:!0},center:{get:function(){return v.computedCenter},set:function(w){return v.lookAt(v.lastT(),null,w),v.computedCenter},enumerable:!0},eye:{get:function(){return v.computedEye},set:function(w){return v.lookAt(v.lastT(),w),v.computedEye},enumerable:!0},up:{get:function(){return v.computedUp},set:function(w){return v.lookAt(v.lastT(),null,null,w),v.computedUp},enumerable:!0},distance:{get:function(){return _},set:function(w){return v.setDistance(v.lastT(),w),w},enumerable:!0},distanceLimits:{get:function(){return v.getDistanceLimits(y)},set:function(w){return v.setDistanceLimits(w),w},enumerable:!0}}),p.addEventListener("contextmenu",function(w){return w.preventDefault(),!1}),k._lastX=-1,k._lastY=-1,k._lastMods={shift:!1,control:!1,alt:!1,meta:!1},k.enableMouseListeners=function(){function w(M,T,E,S){var P=k.keyBindingMode;if(P!==!1){var L=P==="rotate",R=P==="pan",F=P==="zoom",D=!!S.control,O=!!S.alt,N=!!S.shift,B=!!(1&M),W=!!(2&M),G=!!(4&M),K=1/p.clientHeight,te=K*(T-k._lastX),Y=K*(E-k._lastY),J=k.flipX?1:-1,re=k.flipY?1:-1,U=Math.PI*k.rotateSpeed,V=i();if(k._lastX!==-1&&k._lastY!==-1&&((L&&B&&!D&&!O&&!N||B&&!D&&!O&&N)&&v.rotate(V,J*U*te,-re*U*Y,0),(R&&B&&!D&&!O&&!N||W||B&&D&&!O&&!N)&&v.pan(V,-k.translateSpeed*te*_,k.translateSpeed*Y*_,0),F&&B&&!D&&!O&&!N||G||B&&!D&&O&&!N)){var H=-k.zoomSpeed*Y/window.innerHeight*(V-v.lastT())*100;v.pan(V,0,0,_*(Math.exp(H)-1))}return k._lastX=T,k._lastY=E,k._lastMods=S,!0}}k.mouseListener=u(p,w),p.addEventListener("touchstart",function(M){var T=d(M.changedTouches[0],p);w(0,T[0],T[1],k._lastMods),w(1,T[0],T[1],k._lastMods)},!!m&&{passive:!0}),p.addEventListener("touchmove",function(M){var T=d(M.changedTouches[0],p);w(1,T[0],T[1],k._lastMods),M.preventDefault()},!!m&&{passive:!1}),p.addEventListener("touchend",function(M){w(0,k._lastX,k._lastY,k._lastMods)},!!m&&{passive:!0}),k.wheelListener=h(p,function(M,T){if(k.keyBindingMode!==!1&&k.enableWheel){var E=k.flipX?1:-1,S=k.flipY?1:-1,P=i();if(Math.abs(M)>Math.abs(T))v.rotate(P,0,0,-M*E*Math.PI*k.rotateSpeed/window.innerWidth);else if(!k._ortho){var L=-k.zoomSpeed*S*T/window.innerHeight*(P-v.lastT())/20;v.pan(P,0,0,_*(Math.exp(L)-1))}}},!0)},k.enableMouseListeners(),k};var i=a("right-now"),s=a("3d-view"),u=a("mouse-change"),h=a("mouse-wheel"),d=a("mouse-event-offset"),m=a("has-passive-events")},{"3d-view":7,"has-passive-events":232,"mouse-change":247,"mouse-event-offset":248,"mouse-wheel":250,"right-now":278}],120:[function(a,l,c){var i=a("glslify"),s=a("gl-shader"),u=i([`precision mediump float;
-#define GLSLIFY 1
-attribute vec2 position;
-varying vec2 uv;
-void main() {
-  uv = position;
-  gl_Position = vec4(position, 0, 1);
-}`]),h=i([`precision mediump float;
-#define GLSLIFY 1
-
-uniform sampler2D accumBuffer;
-varying vec2 uv;
-
-void main() {
-  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));
-  gl_FragColor = min(vec4(1,1,1,1), accum);
-}`]);l.exports=function(d){return s(d,u,h,null,[{name:"position",type:"vec2"}])}},{"gl-shader":132,glslify:231}],121:[function(a,l,c){var i=a("./camera.js"),s=a("gl-axes3d"),u=a("gl-axes3d/properties"),h=a("gl-spikes3d"),d=a("gl-select-static"),m=a("gl-fbo"),p=a("a-big-triangle"),g=a("mouse-change"),y=a("gl-mat4/perspective"),v=a("gl-mat4/ortho"),x=a("./lib/shader"),_=a("is-mobile")({tablet:!0,featureDetect:!0});function A(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function b(w){var M=Math.round(Math.log(Math.abs(w))/Math.log(10));if(M<0){var T=Math.round(Math.pow(10,-M));return Math.ceil(w*T)/T}return M>0?(T=Math.round(Math.pow(10,M)),Math.ceil(w/T)*T):Math.ceil(w)}function k(w){return typeof w!="boolean"||w}l.exports={createScene:function(w){(w=w||{}).camera=w.camera||{};var M=w.canvas;M||(M=document.createElement("canvas"),w.container?w.container.appendChild(M):document.body.appendChild(M));var T=w.gl;if(T||(w.glOptions&&(_=!!w.glOptions.preserveDrawingBuffer),T=function(fe,me){var _e=null;try{(_e=fe.getContext("webgl",me))||(_e=fe.getContext("experimental-webgl",me))}catch{return null}return _e}(M,w.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:_})),!T)throw new Error("webgl not supported");var E=w.bounds||[[-10,-10,-10],[10,10,10]],S=new A,P=m(T,T.drawingBufferWidth,T.drawingBufferHeight,{preferFloat:!_}),L=x(T),R=w.cameraObject&&w.cameraObject._ortho===!0||w.camera.projection&&w.camera.projection.type==="orthographic"||!1,F={eye:w.camera.eye||[2,0,0],center:w.camera.center||[0,0,0],up:w.camera.up||[0,1,0],zoomMin:w.camera.zoomMax||.1,zoomMax:w.camera.zoomMin||100,mode:w.camera.mode||"turntable",_ortho:R},D=w.axes||{},O=s(T,D);O.enable=!D.disable;var N=w.spikes||{},B=h(T,N),W=[],G=[],K=[],te=[],Y=!0,J=!0,re=new Array(16),U=new Array(16),V={view:null,projection:re,model:U,_ortho:!1},H=(J=!0,[T.drawingBufferWidth,T.drawingBufferHeight]),ne=w.cameraObject||i(M,F),q={gl:T,contextLost:!1,pixelRatio:w.pixelRatio||1,canvas:M,selection:S,camera:ne,axes:O,axesPixels:null,spikes:B,bounds:E,objects:W,shape:H,aspect:w.aspectRatio||[1,1,1],pickRadius:w.pickRadius||10,zNear:w.zNear||.01,zFar:w.zFar||1e3,fovy:w.fovy||Math.PI/4,clearColor:w.clearColor||[0,0,0,0],autoResize:k(w.autoResize),autoBounds:k(w.autoBounds),autoScale:!!w.autoScale,autoCenter:k(w.autoCenter),clipToBounds:k(w.clipToBounds),snapToData:!!w.snapToData,onselect:w.onselect||null,onrender:w.onrender||null,onclick:w.onclick||null,cameraParams:V,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(fe){this.aspect[0]=fe.x,this.aspect[1]=fe.y,this.aspect[2]=fe.z,J=!0},setBounds:function(fe,me){this.bounds[0][fe]=me.min,this.bounds[1][fe]=me.max},setClearColor:function(fe){this.clearColor=fe},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},Q=[T.drawingBufferWidth/q.pixelRatio|0,T.drawingBufferHeight/q.pixelRatio|0];function ee(){if(!q._stopped&&q.autoResize){var fe=M.parentNode,me=1,_e=1;fe&&fe!==document.body?(me=fe.clientWidth,_e=fe.clientHeight):(me=window.innerWidth,_e=window.innerHeight);var Ae=0|Math.ceil(me*q.pixelRatio),ke=0|Math.ceil(_e*q.pixelRatio);if(Ae!==M.width||ke!==M.height){M.width=Ae,M.height=ke;var Le=M.style;Le.position=Le.position||"absolute",Le.left="0px",Le.top="0px",Le.width=me+"px",Le.height=_e+"px",Y=!0}}}q.autoResize&&ee();function ie(){for(var fe=W.length,me=te.length,_e=0;_e<me;++_e)K[_e]=0;e:for(_e=0;_e<fe;++_e){var Ae=W[_e],ke=Ae.pickSlots;if(ke){for(var Le=0;Le<me;++Le)if(K[Le]+ke<255){G[_e]=Le,Ae.setPickBase(K[Le]+1),K[Le]+=ke;continue e}var de=d(T,H);G[_e]=me,te.push(de),K.push(ke),Ae.setPickBase(1),me+=1}else G[_e]=-1}for(;me>0&&K[me-1]===0;)K.pop(),te.pop().dispose()}function ae(){if(q.contextLost)return!0;T.isContextLost()&&(q.contextLost=!0,q.mouseListener.enabled=!1,q.selection.object=null,q.oncontextloss&&q.oncontextloss())}window.addEventListener("resize",ee),q.update=function(fe){q._stopped||(Y=!0,J=!0)},q.add=function(fe){q._stopped||(fe.axes=O,W.push(fe),G.push(-1),Y=!0,J=!0,ie())},q.remove=function(fe){if(!q._stopped){var me=W.indexOf(fe);me<0||(W.splice(me,1),G.pop(),Y=!0,J=!0,ie())}},q.dispose=function(){if(!q._stopped&&(q._stopped=!0,window.removeEventListener("resize",ee),M.removeEventListener("webglcontextlost",ae),q.mouseListener.enabled=!1,!q.contextLost)){O.dispose(),B.dispose();for(var fe=0;fe<W.length;++fe)W[fe].dispose();for(P.dispose(),fe=0;fe<te.length;++fe)te[fe].dispose();L.dispose(),T=null,O=null,B=null,W=[]}},q._mouseRotating=!1,q._prevButtons=0,q.enableMouseListeners=function(){q.mouseListener=g(M,function(fe,me,_e){if(!q._stopped){var Ae=te.length,ke=W.length,Le=S.object;S.distance=1/0,S.mouse[0]=me,S.mouse[1]=_e,S.object=null,S.screen=null,S.dataCoordinate=S.dataPosition=null;var de=!1;if(fe&&q._prevButtons)q._mouseRotating=!0;else{q._mouseRotating&&(J=!0),q._mouseRotating=!1;for(var ve=0;ve<Ae;++ve){var Me=te[ve].query(me,Q[1]-_e-1,q.pickRadius);if(Me){if(Me.distance>S.distance)continue;for(var we=0;we<ke;++we){var Ce=W[we];if(G[we]===ve){var Fe=Ce.pick(Me);Fe&&(S.buttons=fe,S.screen=Me.coord,S.distance=Me.distance,S.object=Ce,S.index=Fe.distance,S.dataPosition=Fe.position,S.dataCoordinate=Fe.dataCoordinate,S.data=Fe,de=!0)}}}}}Le&&Le!==S.object&&(Le.highlight&&Le.highlight(null),Y=!0),S.object&&(S.object.highlight&&S.object.highlight(S.data),Y=!0),(de=de||S.object!==Le)&&q.onselect&&q.onselect(S),1&fe&&!(1&q._prevButtons)&&q.onclick&&q.onclick(S),q._prevButtons=fe}})},M.addEventListener("webglcontextlost",ae);var ue=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],le=[ue[0].slice(),ue[1].slice()];function ge(){if(!ae()){ee();var fe=q.camera.tick();V.view=q.camera.matrix,Y=Y||fe,J=J||fe,O.pixelRatio=q.pixelRatio,B.pixelRatio=q.pixelRatio;var me=W.length,_e=ue[0],Ae=ue[1];_e[0]=_e[1]=_e[2]=1/0,Ae[0]=Ae[1]=Ae[2]=-1/0;for(var ke=0;ke<me;++ke){(Ve=W[ke]).pixelRatio=q.pixelRatio,Ve.axes=q.axes,Y=Y||!!Ve.dirty,J=J||!!Ve.dirty;var Le=Ve.bounds;if(Le)for(var de=Le[0],ve=Le[1],Me=0;Me<3;++Me)_e[Me]=Math.min(_e[Me],de[Me]),Ae[Me]=Math.max(Ae[Me],ve[Me])}var we=q.bounds;if(q.autoBounds)for(Me=0;Me<3;++Me){if(Ae[Me]<_e[Me])_e[Me]=-1,Ae[Me]=1;else{_e[Me]===Ae[Me]&&(_e[Me]-=1,Ae[Me]+=1);var Ce=.05*(Ae[Me]-_e[Me]);_e[Me]=_e[Me]-Ce,Ae[Me]=Ae[Me]+Ce}we[0][Me]=_e[Me],we[1][Me]=Ae[Me]}var Fe=!1;for(Me=0;Me<3;++Me)Fe=Fe||le[0][Me]!==we[0][Me]||le[1][Me]!==we[1][Me],le[0][Me]=we[0][Me],le[1][Me]=we[1][Me];if(J=J||Fe,Y=Y||Fe){if(Fe){var ze=[0,0,0];for(ke=0;ke<3;++ke)ze[ke]=b((we[1][ke]-we[0][ke])/10);O.autoTicks?O.update({bounds:we,tickSpacing:ze}):O.update({bounds:we})}var $e=T.drawingBufferWidth,Ke=T.drawingBufferHeight;for(H[0]=$e,H[1]=Ke,Q[0]=0|Math.max($e/q.pixelRatio,1),Q[1]=0|Math.max(Ke/q.pixelRatio,1),function(We,Ye){var nt=We.bounds,ft=We.cameraParams,yt=ft.projection,Ot=ft.model,Tt=We.gl.drawingBufferWidth,at=We.gl.drawingBufferHeight,et=We.zNear,Lt=We.zFar,Wt=We.fovy,Jt=Tt/at;Ye?(v(yt,-Jt,Jt,-1,1,et,Lt),ft._ortho=!0):(y(yt,Wt,Jt,et,Lt),ft._ortho=!1);for(var Be=0;Be<16;++Be)Ot[Be]=0;Ot[15]=1;var Ge=0;for(Be=0;Be<3;++Be)Ge=Math.max(Ge,nt[1][Be]-nt[0][Be]);for(Be=0;Be<3;++Be)We.autoScale?Ot[5*Be]=We.aspect[Be]/(nt[1][Be]-nt[0][Be]):Ot[5*Be]=1/Ge,We.autoCenter&&(Ot[12+Be]=.5*-Ot[5*Be]*(nt[0][Be]+nt[1][Be]))}(q,R),ke=0;ke<me;++ke)(Ve=W[ke]).axesBounds=we,q.clipToBounds&&(Ve.clipBounds=we);S.object&&(q.snapToData?B.position=S.dataCoordinate:B.position=S.dataPosition,B.bounds=we),J&&(J=!1,function(){if(!ae()){T.colorMask(!0,!0,!0,!0),T.depthMask(!0),T.disable(T.BLEND),T.enable(T.DEPTH_TEST),T.depthFunc(T.LEQUAL);for(var We=W.length,Ye=te.length,nt=0;nt<Ye;++nt){var ft=te[nt];ft.shape=Q,ft.begin();for(var yt=0;yt<We;++yt)if(G[yt]===nt){var Ot=W[yt];Ot.drawPick&&(Ot.pixelRatio=1,Ot.drawPick(V))}ft.end()}}}()),q.axesPixels=u(q.axes,V,$e,Ke),q.onrender&&q.onrender(),T.bindFramebuffer(T.FRAMEBUFFER,null),T.viewport(0,0,$e,Ke),q.clearRGBA(),T.depthMask(!0),T.colorMask(!0,!0,!0,!0),T.enable(T.DEPTH_TEST),T.depthFunc(T.LEQUAL),T.disable(T.BLEND),T.disable(T.CULL_FACE);var Re=!1;for(O.enable&&(Re=Re||O.isTransparent(),O.draw(V)),B.axes=O,S.object&&B.draw(V),T.disable(T.CULL_FACE),ke=0;ke<me;++ke)(Ve=W[ke]).axes=O,Ve.pixelRatio=q.pixelRatio,Ve.isOpaque&&Ve.isOpaque()&&Ve.draw(V),Ve.isTransparent&&Ve.isTransparent()&&(Re=!0);if(Re){for(P.shape=H,P.bind(),T.clear(T.DEPTH_BUFFER_BIT),T.colorMask(!1,!1,!1,!1),T.depthMask(!0),T.depthFunc(T.LESS),O.enable&&O.isTransparent()&&O.drawTransparent(V),ke=0;ke<me;++ke)(Ve=W[ke]).isOpaque&&Ve.isOpaque()&&Ve.draw(V);for(T.enable(T.BLEND),T.blendEquation(T.FUNC_ADD),T.blendFunc(T.ONE,T.ONE_MINUS_SRC_ALPHA),T.colorMask(!0,!0,!0,!0),T.depthMask(!1),T.clearColor(0,0,0,0),T.clear(T.COLOR_BUFFER_BIT),O.isTransparent()&&O.drawTransparent(V),ke=0;ke<me;++ke){var Ve;(Ve=W[ke]).isTransparent&&Ve.isTransparent()&&Ve.drawTransparent(V)}T.bindFramebuffer(T.FRAMEBUFFER,null),T.blendFunc(T.ONE,T.ONE_MINUS_SRC_ALPHA),T.disable(T.DEPTH_TEST),L.bind(),P.color[0].bind(0),L.uniforms.accumBuffer=0,p(T),T.disable(T.BLEND)}for(Y=!1,ke=0;ke<me;++ke)W[ke].dirty=!1}}}return q.enableMouseListeners(),function fe(){q._stopped||q.contextLost||(ge(),requestAnimationFrame(fe))}(),q.redraw=function(){q._stopped||(Y=!0,ge())},q},createCamera:i}},{"./camera.js":119,"./lib/shader":120,"a-big-triangle":8,"gl-axes3d":70,"gl-axes3d/properties":77,"gl-fbo":86,"gl-mat4/ortho":101,"gl-mat4/perspective":102,"gl-select-static":131,"gl-spikes3d":141,"is-mobile":238,"mouse-change":247}],122:[function(a,l,c){var i=a("glslify");c.pointVertex=i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec2 position;
-
-uniform mat3 matrix;
-uniform float pointSize;
-uniform float pointCloud;
-
-highp float rand(vec2 co) {
-  highp float a = 12.9898;
-  highp float b = 78.233;
-  highp float c = 43758.5453;
-  highp float d = dot(co.xy, vec2(a, b));
-  highp float e = mod(d, 3.14);
-  return fract(sin(e) * c);
-}
-
-void main() {
-  vec3 hgPosition = matrix * vec3(position, 1);
-  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);
-    // if we don't jitter the point size a bit, overall point cloud
-    // saturation 'jumps' on zooming, which is disturbing and confusing
-  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);
-  if(pointCloud != 0.0) { // pointCloud is truthy
-    // get the same square surface as circle would be
-    gl_PointSize *= 0.886;
-  }
-}`]),c.pointFragment=i([`precision mediump float;
-#define GLSLIFY 1
-
-uniform vec4 color, borderColor;
-uniform float centerFraction;
-uniform float pointCloud;
-
-void main() {
-  float radius;
-  vec4 baseColor;
-  if(pointCloud != 0.0) { // pointCloud is truthy
-    if(centerFraction == 1.0) {
-      gl_FragColor = color;
-    } else {
-      gl_FragColor = mix(borderColor, color, centerFraction);
-    }
-  } else {
-    radius = length(2.0 * gl_PointCoord.xy - 1.0);
-    if(radius > 1.0) {
-      discard;
-    }
-    baseColor = mix(borderColor, color, step(radius, centerFraction));
-    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);
-  }
-}
-`]),c.pickVertex=i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec2 position;
-attribute vec4 pickId;
-
-uniform mat3 matrix;
-uniform float pointSize;
-uniform vec4 pickOffset;
-
-varying vec4 fragId;
-
-void main() {
-  vec3 hgPosition = matrix * vec3(position, 1);
-  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);
-  gl_PointSize = pointSize;
-
-  vec4 id = pickId + pickOffset;
-  id.y += floor(id.x / 256.0);
-  id.x -= floor(id.x / 256.0) * 256.0;
-
-  id.z += floor(id.y / 256.0);
-  id.y -= floor(id.y / 256.0) * 256.0;
-
-  id.w += floor(id.z / 256.0);
-  id.z -= floor(id.z / 256.0) * 256.0;
-
-  fragId = id;
-}
-`]),c.pickFragment=i([`precision mediump float;
-#define GLSLIFY 1
-
-varying vec4 fragId;
-
-void main() {
-  float radius = length(2.0 * gl_PointCoord.xy - 1.0);
-  if(radius > 1.0) {
-    discard;
-  }
-  gl_FragColor = fragId / 255.0;
-}
-`])},{glslify:231}],123:[function(a,l,c){var i=a("gl-shader"),s=a("gl-buffer"),u=a("typedarray-pool"),h=a("./lib/shader");function d(y,v,x,_,A){this.plot=y,this.offsetBuffer=v,this.pickBuffer=x,this.shader=_,this.pickShader=A,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}l.exports=function(y,v){var x=y.gl,_=s(x),A=s(x),b=i(x,h.pointVertex,h.pointFragment),k=i(x,h.pickVertex,h.pickFragment),w=new d(y,_,A,b,k);return w.update(v),y.addObject(w),w};var m,p,g=d.prototype;g.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},g.update=function(y){var v;function x(T,E){return T in y?y[T]:E}y=y||{},this.sizeMin=x("sizeMin",.5),this.sizeMax=x("sizeMax",20),this.color=x("color",[1,0,0,1]).slice(),this.areaRatio=x("areaRatio",1),this.borderColor=x("borderColor",[0,0,0,1]).slice(),this.blend=x("blend",!1);var _=y.positions.length>>>1,A=y.positions instanceof Float32Array,b=y.idToIndex instanceof Int32Array&&y.idToIndex.length>=_,k=y.positions,w=A?k:u.mallocFloat32(k.length),M=b?y.idToIndex:u.mallocInt32(_);if(A||w.set(k),!b)for(w.set(k),v=0;v<_;v++)M[v]=v;this.points=k,this.offsetBuffer.update(w),this.pickBuffer.update(M),A||u.free(w),b||u.free(M),this.pointCount=_,this.pickOffset=0},g.unifiedDraw=(m=[1,0,0,0,1,0,0,0,1],p=[0,0,0,0],function(y){var v=y!==void 0,x=v?this.pickShader:this.shader,_=this.plot.gl,A=this.plot.dataBox;if(this.pointCount===0)return y;var b=A[2]-A[0],k=A[3]-A[1],w=function(S,P){var L,R=0,F=S.length>>>1;for(L=0;L<F;L++){var D=S[2*L],O=S[2*L+1];D>=P[0]&&D<=P[2]&&O>=P[1]&&O<=P[3]&&R++}return R}(this.points,A),M=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(w,.33333)));m[0]=2/b,m[4]=2/k,m[6]=-2*A[0]/b-1,m[7]=-2*A[1]/k-1,this.offsetBuffer.bind(),x.bind(),x.attributes.position.pointer(),x.uniforms.matrix=m,x.uniforms.color=this.color,x.uniforms.borderColor=this.borderColor,x.uniforms.pointCloud=M<5,x.uniforms.pointSize=M,x.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),v&&(p[0]=255&y,p[1]=y>>8&255,p[2]=y>>16&255,p[3]=y>>24&255,this.pickBuffer.bind(),x.attributes.pickId.pointer(_.UNSIGNED_BYTE),x.uniforms.pickOffset=p,this.pickOffset=y);var T=_.getParameter(_.BLEND),E=_.getParameter(_.DITHER);return T&&!this.blend&&_.disable(_.BLEND),E&&_.disable(_.DITHER),_.drawArrays(_.POINTS,0,this.pointCount),T&&!this.blend&&_.enable(_.BLEND),E&&_.enable(_.DITHER),y+this.pointCount}),g.draw=g.unifiedDraw,g.drawPick=g.unifiedDraw,g.pick=function(y,v,x){var _=this.pickOffset,A=this.pointCount;if(x<_||x>=_+A)return null;var b=x-_,k=this.points;return{object:this,pointId:b,dataCoord:[k[2*b],k[2*b+1]]}}},{"./lib/shader":122,"gl-buffer":78,"gl-shader":132,"typedarray-pool":308}],124:[function(a,l,c){l.exports=function(i,s,u,h){var d,m,p,g,y,v=s[0],x=s[1],_=s[2],A=s[3],b=u[0],k=u[1],w=u[2],M=u[3];return(m=v*b+x*k+_*w+A*M)<0&&(m=-m,b=-b,k=-k,w=-w,M=-M),1-m>1e-6?(d=Math.acos(m),p=Math.sin(d),g=Math.sin((1-h)*d)/p,y=Math.sin(h*d)/p):(g=1-h,y=h),i[0]=g*v+y*b,i[1]=g*x+y*k,i[2]=g*_+y*w,i[3]=g*A+y*M,i}},{}],125:[function(a,l,c){l.exports=function(i){return i||i===0?i.toString():""}},{}],126:[function(a,l,c){var i=a("vectorize-text");l.exports=function(u,h,d){var m=s[h];if(m||(m=s[h]={}),u in m)return m[u];var p={textAlign:"center",textBaseline:"middle",lineHeight:1,font:h,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},g=i(u,p);p.triangles=!1;var y,v,x=i(u,p);if(d&&d!==1){for(y=0;y<g.positions.length;++y)for(v=0;v<g.positions[y].length;++v)g.positions[y][v]/=d;for(y=0;y<x.positions.length;++y)for(v=0;v<x.positions[y].length;++v)x.positions[y][v]/=d}var _=[[1/0,1/0],[-1/0,-1/0]],A=x.positions.length;for(y=0;y<A;++y){var b=x.positions[y];for(v=0;v<2;++v)_[0][v]=Math.min(_[0][v],b[v]),_[1][v]=Math.max(_[1][v],b[v])}return m[u]=[g,x,_]};var s={}},{"vectorize-text":311}],127:[function(a,l,c){var i=a("gl-shader"),s=a("glslify"),u=s([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-attribute vec3 position;
-attribute vec4 color;
-attribute vec2 glyph;
-attribute vec4 id;
-
-uniform vec4 highlightId;
-uniform float highlightScale;
-uniform mat4 model, view, projection;
-uniform vec3 clipBounds[2];
-
-varying vec4 interpColor;
-varying vec4 pickId;
-varying vec3 dataCoordinate;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
-
-    gl_Position = vec4(0,0,0,0);
-  } else {
-    float scale = 1.0;
-    if(distance(highlightId, id) < 0.0001) {
-      scale = highlightScale;
-    }
-
-    vec4 worldPosition = model * vec4(position, 1);
-    vec4 viewPosition = view * worldPosition;
-    viewPosition = viewPosition / viewPosition.w;
-    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));
-
-    gl_Position = clipPosition;
-    interpColor = color;
-    pickId = id;
-    dataCoordinate = position;
-  }
-}`]),h=s([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-attribute vec3 position;
-attribute vec4 color;
-attribute vec2 glyph;
-attribute vec4 id;
-
-uniform mat4 model, view, projection;
-uniform vec2 screenSize;
-uniform vec3 clipBounds[2];
-uniform float highlightScale, pixelRatio;
-uniform vec4 highlightId;
-
-varying vec4 interpColor;
-varying vec4 pickId;
-varying vec3 dataCoordinate;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
-
-    gl_Position = vec4(0,0,0,0);
-  } else {
-    float scale = pixelRatio;
-    if(distance(highlightId.bgr, id.bgr) < 0.001) {
-      scale *= highlightScale;
-    }
-
-    vec4 worldPosition = model * vec4(position, 1.0);
-    vec4 viewPosition = view * worldPosition;
-    vec4 clipPosition = projection * viewPosition;
-    clipPosition /= clipPosition.w;
-
-    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);
-    interpColor = color;
-    pickId = id;
-    dataCoordinate = position;
-  }
-}`]),d=s([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-attribute vec3 position;
-attribute vec4 color;
-attribute vec2 glyph;
-attribute vec4 id;
-
-uniform float highlightScale;
-uniform vec4 highlightId;
-uniform vec3 axes[2];
-uniform mat4 model, view, projection;
-uniform vec2 screenSize;
-uniform vec3 clipBounds[2];
-uniform float scale, pixelRatio;
-
-varying vec4 interpColor;
-varying vec4 pickId;
-varying vec3 dataCoordinate;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], position)) {
-
-    gl_Position = vec4(0,0,0,0);
-  } else {
-    float lscale = pixelRatio * scale;
-    if(distance(highlightId, id) < 0.0001) {
-      lscale *= highlightScale;
-    }
-
-    vec4 clipCenter   = projection * view * model * vec4(position, 1);
-    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;
-    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);
-
-    gl_Position = clipPosition;
-    interpColor = color;
-    pickId = id;
-    dataCoordinate = dataPosition;
-  }
-}
-`]),m=s([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 fragClipBounds[2];
-uniform float opacity;
-
-varying vec4 interpColor;
-varying vec3 dataCoordinate;
-
-void main() {
-  if (
-    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||
-    interpColor.a * opacity == 0.
-  ) discard;
-  gl_FragColor = interpColor * opacity;
-}
-`]),p=s([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 fragClipBounds[2];
-uniform float pickGroup;
-
-varying vec4 pickId;
-varying vec3 dataCoordinate;
-
-void main() {
-  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;
-
-  gl_FragColor = vec4(pickGroup, pickId.bgr);
-}`]),g=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],y={vertex:u,fragment:m,attributes:g},v={vertex:h,fragment:m,attributes:g},x={vertex:d,fragment:m,attributes:g},_={vertex:u,fragment:p,attributes:g},A={vertex:h,fragment:p,attributes:g},b={vertex:d,fragment:p,attributes:g};function k(w,M){var T=i(w,M),E=T.attributes;return E.position.location=0,E.color.location=1,E.glyph.location=2,E.id.location=3,T}c.createPerspective=function(w){return k(w,y)},c.createOrtho=function(w){return k(w,v)},c.createProject=function(w){return k(w,x)},c.createPickPerspective=function(w){return k(w,_)},c.createPickOrtho=function(w){return k(w,A)},c.createPickProject=function(w){return k(w,b)}},{"gl-shader":132,glslify:231}],128:[function(a,l,c){var i=a("is-string-blank"),s=a("gl-buffer"),u=a("gl-vao"),h=a("typedarray-pool"),d=a("gl-mat4/multiply"),m=a("./lib/shaders"),p=a("./lib/glyphs"),g=a("./lib/get-simple-string"),y=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function v(K,te){var Y=K[0],J=K[1],re=K[2],U=K[3];return K[0]=te[0]*Y+te[4]*J+te[8]*re+te[12]*U,K[1]=te[1]*Y+te[5]*J+te[9]*re+te[13]*U,K[2]=te[2]*Y+te[6]*J+te[10]*re+te[14]*U,K[3]=te[3]*Y+te[7]*J+te[11]*re+te[15]*U,K}function x(K,te,Y,J){return v(J,J),v(J,J),v(J,J)}function _(K,te){this.index=K,this.dataCoordinate=this.position=te}function A(K){return K===!0||K>1?1:K}function b(K,te,Y,J,re,U,V,H,ne,q,Q,ee){this.gl=K,this.pixelRatio=1,this.shader=te,this.orthoShader=Y,this.projectShader=J,this.pointBuffer=re,this.colorBuffer=U,this.glyphBuffer=V,this.idBuffer=H,this.vao=ne,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=q,this.pickOrthoShader=Q,this.pickProjectShader=ee,this.points=[],this._selectResult=new _(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}l.exports=function(K){var te=K.gl,Y=m.createPerspective(te),J=m.createOrtho(te),re=m.createProject(te),U=m.createPickPerspective(te),V=m.createPickOrtho(te),H=m.createPickProject(te),ne=s(te),q=s(te),Q=s(te),ee=s(te),ie=u(te,[{buffer:ne,size:3,type:te.FLOAT},{buffer:q,size:4,type:te.FLOAT},{buffer:Q,size:2,type:te.FLOAT},{buffer:ee,size:4,type:te.UNSIGNED_BYTE,normalized:!0}]),ae=new b(te,Y,J,re,ne,q,Q,ee,ie,U,V,H);return ae.update(K),ae};var k=b.prototype;k.pickSlots=1,k.setPickBase=function(K){this.pickId=K},k.isTransparent=function(){if(this.hasAlpha)return!0;for(var K=0;K<3;++K)if(this.axesProject[K]&&this.projectHasAlpha)return!0;return!1},k.isOpaque=function(){if(!this.hasAlpha)return!0;for(var K=0;K<3;++K)if(this.axesProject[K]&&!this.projectHasAlpha)return!0;return!1};var w=[0,0],M=[0,0,0],T=[0,0,0],E=[0,0,0,1],S=[0,0,0,1],P=y.slice(),L=[0,0,0],R=[[0,0,0],[0,0,0]];function F(K){return K[0]=K[1]=K[2]=0,K}function D(K,te){return K[0]=te[0],K[1]=te[1],K[2]=te[2],K[3]=1,K}function O(K,te,Y,J){return K[0]=te[0],K[1]=te[1],K[2]=te[2],K[Y]=J,K}function N(K,te,Y,J){var re,U=te.axesProject,V=te.gl,H=K.uniforms,ne=Y.model||y,q=Y.view||y,Q=Y.projection||y,ee=te.axesBounds,ie=function(we){for(var Ce=R,Fe=0;Fe<2;++Fe)for(var ze=0;ze<3;++ze)Ce[Fe][ze]=Math.max(Math.min(we[Fe][ze],1e8),-1e8);return Ce}(te.clipBounds);re=te.axes&&te.axes.lastCubeProps?te.axes.lastCubeProps.axis:[1,1,1],w[0]=2/V.drawingBufferWidth,w[1]=2/V.drawingBufferHeight,K.bind(),H.view=q,H.projection=Q,H.screenSize=w,H.highlightId=te.highlightId,H.highlightScale=te.highlightScale,H.clipBounds=ie,H.pickGroup=te.pickId/255,H.pixelRatio=J;for(var ae=0;ae<3;++ae)if(U[ae]){H.scale=te.projectScale[ae],H.opacity=te.projectOpacity[ae];for(var ue=P,le=0;le<16;++le)ue[le]=0;for(le=0;le<4;++le)ue[5*le]=1;ue[5*ae]=0,re[ae]<0?ue[12+ae]=ee[0][ae]:ue[12+ae]=ee[1][ae],d(ue,ne,ue),H.model=ue;var ge=(ae+1)%3,fe=(ae+2)%3,me=F(M),_e=F(T);me[ge]=1,_e[fe]=1;var Ae=x(0,0,0,D(E,me)),ke=x(0,0,0,D(S,_e));if(Math.abs(Ae[1])>Math.abs(ke[1])){var Le=Ae;Ae=ke,ke=Le,Le=me,me=_e,_e=Le;var de=ge;ge=fe,fe=de}Ae[0]<0&&(me[ge]=-1),ke[1]>0&&(_e[fe]=-1);var ve=0,Me=0;for(le=0;le<4;++le)ve+=Math.pow(ne[4*ge+le],2),Me+=Math.pow(ne[4*fe+le],2);me[ge]/=Math.sqrt(ve),_e[fe]/=Math.sqrt(Me),H.axes[0]=me,H.axes[1]=_e,H.fragClipBounds[0]=O(L,ie[0],ae,-1e8),H.fragClipBounds[1]=O(L,ie[1],ae,1e8),te.vao.bind(),te.vao.draw(V.TRIANGLES,te.vertexCount),te.lineWidth>0&&(V.lineWidth(te.lineWidth*J),te.vao.draw(V.LINES,te.lineVertexCount,te.vertexCount)),te.vao.unbind()}}var B=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function W(K,te,Y,J,re,U,V){var H=Y.gl;if((U===Y.projectHasAlpha||V)&&N(te,Y,J,re),U===Y.hasAlpha||V){K.bind();var ne=K.uniforms;ne.model=J.model||y,ne.view=J.view||y,ne.projection=J.projection||y,w[0]=2/H.drawingBufferWidth,w[1]=2/H.drawingBufferHeight,ne.screenSize=w,ne.highlightId=Y.highlightId,ne.highlightScale=Y.highlightScale,ne.fragClipBounds=B,ne.clipBounds=Y.axes.bounds,ne.opacity=Y.opacity,ne.pickGroup=Y.pickId/255,ne.pixelRatio=re,Y.vao.bind(),Y.vao.draw(H.TRIANGLES,Y.vertexCount),Y.lineWidth>0&&(H.lineWidth(Y.lineWidth*re),Y.vao.draw(H.LINES,Y.lineVertexCount,Y.vertexCount)),Y.vao.unbind()}}function G(K,te,Y,J){var re;re=Array.isArray(K)?te<K.length?K[te]:void 0:K,re=g(re);var U=!0;i(re)&&(re="▼",U=!1);var V=p(re,Y,J);return{mesh:V[0],lines:V[1],bounds:V[2],visible:U}}k.draw=function(K){W(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,K,this.pixelRatio,!1,!1)},k.drawTransparent=function(K){W(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,K,this.pixelRatio,!0,!1)},k.drawPick=function(K){W(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,K,1,!0,!0)},k.pick=function(K){if(!K||K.id!==this.pickId)return null;var te=K.value[2]+(K.value[1]<<8)+(K.value[0]<<16);if(te>=this.pointCount||te<0)return null;var Y=this.points[te],J=this._selectResult;J.index=te;for(var re=0;re<3;++re)J.position[re]=J.dataCoordinate[re]=Y[re];return J},k.highlight=function(K){if(K){var te=K.index,Y=255&te,J=te>>8&255,re=te>>16&255;this.highlightId=[Y/255,J/255,re/255,0]}else this.highlightId=[1,1,1,1]},k.update=function(K){if("perspective"in(K=K||{})&&(this.useOrtho=!K.perspective),"orthographic"in K&&(this.useOrtho=!!K.orthographic),"lineWidth"in K&&(this.lineWidth=K.lineWidth),"project"in K)if(Array.isArray(K.project))this.axesProject=K.project;else{var te=!!K.project;this.axesProject=[te,te,te]}if("projectScale"in K)if(Array.isArray(K.projectScale))this.projectScale=K.projectScale.slice();else{var Y=+K.projectScale;this.projectScale=[Y,Y,Y]}if(this.projectHasAlpha=!1,"projectOpacity"in K){Array.isArray(K.projectOpacity)?this.projectOpacity=K.projectOpacity.slice():(Y=+K.projectOpacity,this.projectOpacity=[Y,Y,Y]);for(var J=0;J<3;++J)this.projectOpacity[J]=A(this.projectOpacity[J]),this.projectOpacity[J]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in K&&(this.opacity=A(K.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var re,U,V=K.position,H=K.font||"normal",ne=K.alignment||[0,0];if(ne.length===2)re=ne[0],U=ne[1];else for(re=[],U=[],J=0;J<ne.length;++J)re[J]=ne[J][0],U[J]=ne[J][1];var q=[1/0,1/0,1/0],Q=[-1/0,-1/0,-1/0],ee=K.glyph,ie=K.color,ae=K.size,ue=K.angle,le=K.lineColor,ge=-1,fe=0,me=0,_e=0;if(V.length){_e=V.length;e:for(J=0;J<_e;++J){for(var Ae=V[J],ke=0;ke<3;++ke)if(isNaN(Ae[ke])||!isFinite(Ae[ke]))continue e;var Le=(nt=G(ee,J,H,this.pixelRatio)).mesh,de=nt.lines,ve=nt.bounds;fe+=3*Le.cells.length,me+=2*de.edges.length}}var Me=fe+me,we=h.mallocFloat(3*Me),Ce=h.mallocFloat(4*Me),Fe=h.mallocFloat(2*Me),ze=h.mallocUint32(Me);if(Me>0){var $e=0,Ke=fe,Re=[0,0,0,1],Ve=[0,0,0,1],We=Array.isArray(ie)&&Array.isArray(ie[0]),Ye=Array.isArray(le)&&Array.isArray(le[0]);e:for(J=0;J<_e;++J){for(ge+=1,Ae=V[J],ke=0;ke<3;++ke){if(isNaN(Ae[ke])||!isFinite(Ae[ke]))continue e;Q[ke]=Math.max(Q[ke],Ae[ke]),q[ke]=Math.min(q[ke],Ae[ke])}Le=(nt=G(ee,J,H,this.pixelRatio)).mesh,de=nt.lines,ve=nt.bounds;var nt,ft=nt.visible;if(ft)if(Array.isArray(ie)){if((yt=We?J<ie.length?ie[J]:[0,0,0,0]:ie).length===3){for(ke=0;ke<3;++ke)Re[ke]=yt[ke];Re[3]=1}else if(yt.length===4){for(ke=0;ke<4;++ke)Re[ke]=yt[ke];!this.hasAlpha&&yt[3]<1&&(this.hasAlpha=!0)}}else Re[0]=Re[1]=Re[2]=0,Re[3]=1;else Re=[1,1,1,0];if(ft)if(Array.isArray(le)){var yt;if((yt=Ye?J<le.length?le[J]:[0,0,0,0]:le).length===3){for(ke=0;ke<3;++ke)Ve[ke]=yt[ke];Ve[ke]=1}else if(yt.length===4){for(ke=0;ke<4;++ke)Ve[ke]=yt[ke];!this.hasAlpha&&yt[3]<1&&(this.hasAlpha=!0)}}else Ve[0]=Ve[1]=Ve[2]=0,Ve[3]=1;else Ve=[1,1,1,0];var Ot=.5;ft?Array.isArray(ae)?Ot=J<ae.length?+ae[J]:12:ae?Ot=+ae:this.useOrtho&&(Ot=12):Ot=0;var Tt=0;Array.isArray(ue)?Tt=J<ue.length?+ue[J]:0:ue&&(Tt=+ue);var at=Math.cos(Tt),et=Math.sin(Tt);for(Ae=V[J],ke=0;ke<3;++ke)Q[ke]=Math.max(Q[ke],Ae[ke]),q[ke]=Math.min(q[ke],Ae[ke]);var Lt=re,Wt=U;Lt=0,Array.isArray(re)?Lt=J<re.length?re[J]:0:re&&(Lt=re),Wt=0,Array.isArray(U)?Wt=J<U.length?U[J]:0:U&&(Wt=U);var Jt=[Lt*=Lt>0?1-ve[0][0]:Lt<0?1+ve[1][0]:1,Wt*=Wt>0?1-ve[0][1]:Wt<0?1+ve[1][1]:1],Be=Le.cells||[],Ge=Le.positions||[];for(ke=0;ke<Be.length;++ke)for(var kt=Be[ke],dt=0;dt<3;++dt){for(var Oe=0;Oe<3;++Oe)we[3*$e+Oe]=Ae[Oe];for(Oe=0;Oe<4;++Oe)Ce[4*$e+Oe]=Re[Oe];ze[$e]=ge;var Ie=Ge[kt[dt]];Fe[2*$e]=Ot*(at*Ie[0]-et*Ie[1]+Jt[0]),Fe[2*$e+1]=Ot*(et*Ie[0]+at*Ie[1]+Jt[1]),$e+=1}for(Be=de.edges,Ge=de.positions,ke=0;ke<Be.length;++ke)for(kt=Be[ke],dt=0;dt<2;++dt){for(Oe=0;Oe<3;++Oe)we[3*Ke+Oe]=Ae[Oe];for(Oe=0;Oe<4;++Oe)Ce[4*Ke+Oe]=Ve[Oe];ze[Ke]=ge,Ie=Ge[kt[dt]],Fe[2*Ke]=Ot*(at*Ie[0]-et*Ie[1]+Jt[0]),Fe[2*Ke+1]=Ot*(et*Ie[0]+at*Ie[1]+Jt[1]),Ke+=1}}}this.bounds=[q,Q],this.points=V,this.pointCount=V.length,this.vertexCount=fe,this.lineVertexCount=me,this.pointBuffer.update(we),this.colorBuffer.update(Ce),this.glyphBuffer.update(Fe),this.idBuffer.update(ze),h.free(we),h.free(Ce),h.free(Fe),h.free(ze)},k.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/get-simple-string":125,"./lib/glyphs":126,"./lib/shaders":127,"gl-buffer":78,"gl-mat4/multiply":100,"gl-vao":150,"is-string-blank":239,"typedarray-pool":308}],129:[function(a,l,c){var i=a("glslify");c.boxVertex=i([`precision mediump float;
-#define GLSLIFY 1
-
-attribute vec2 vertex;
-
-uniform vec2 cornerA, cornerB;
-
-void main() {
-  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);
-}
-`]),c.boxFragment=i([`precision mediump float;
-#define GLSLIFY 1
-
-uniform vec4 color;
-
-void main() {
-  gl_FragColor = color;
-}
-`])},{glslify:231}],130:[function(a,l,c){var i=a("gl-shader"),s=a("gl-buffer"),u=a("./lib/shaders");function h(m,p,g){this.plot=m,this.boxBuffer=p,this.boxShader=g,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}l.exports=function(m,p){var g=m.gl,y=s(g,[0,0,0,1,1,0,1,1]),v=i(g,u.boxVertex,u.boxFragment),x=new h(m,y,v);return x.update(p),m.addOverlay(x),x};var d=h.prototype;d.draw=function(){if(this.enabled){var m=this.plot,p=this.selectBox,g=this.borderWidth,y=(this.innerFill,this.innerColor),v=(this.outerFill,this.outerColor),x=this.borderColor,_=m.box,A=m.screenBox,b=m.dataBox,k=m.viewBox,w=m.pixelRatio,M=(p[0]-b[0])*(k[2]-k[0])/(b[2]-b[0])+k[0],T=(p[1]-b[1])*(k[3]-k[1])/(b[3]-b[1])+k[1],E=(p[2]-b[0])*(k[2]-k[0])/(b[2]-b[0])+k[0],S=(p[3]-b[1])*(k[3]-k[1])/(b[3]-b[1])+k[1];if(M=Math.max(M,k[0]),T=Math.max(T,k[1]),E=Math.min(E,k[2]),S=Math.min(S,k[3]),!(E<M||S<T)){_.bind();var P=A[2]-A[0],L=A[3]-A[1];if(this.outerFill&&(_.drawBox(0,0,P,T,v),_.drawBox(0,T,M,S,v),_.drawBox(0,S,P,L,v),_.drawBox(E,T,P,S,v)),this.innerFill&&_.drawBox(M,T,E,S,y),g>0){var R=g*w;_.drawBox(M-R,T-R,E+R,T+R,x),_.drawBox(M-R,S-R,E+R,S+R,x),_.drawBox(M-R,T-R,M+R,S+R,x),_.drawBox(E-R,T-R,E+R,S+R,x)}}}},d.update=function(m){m=m||{},this.innerFill=!!m.innerFill,this.outerFill=!!m.outerFill,this.innerColor=(m.innerColor||[0,0,0,.5]).slice(),this.outerColor=(m.outerColor||[0,0,0,.5]).slice(),this.borderColor=(m.borderColor||[0,0,0,1]).slice(),this.borderWidth=m.borderWidth||0,this.selectBox=(m.selectBox||this.selectBox).slice()},d.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":129,"gl-buffer":78,"gl-shader":132}],131:[function(a,l,c){l.exports=function(g,y){var v=y[0],x=y[1],_=i(g,v,x,{}),A=s.mallocUint8(v*x*4);return new m(g,_,A)};var i=a("gl-fbo"),s=a("typedarray-pool"),u=a("ndarray"),h=a("bit-twiddle").nextPow2;function d(g,y,v,x,_){this.coord=[g,y],this.id=v,this.value=x,this.distance=_}function m(g,y,v){this.gl=g,this.fbo=y,this.buffer=v,this._readTimeout=null;var x=this;this._readCallback=function(){x.gl&&(y.bind(),g.readPixels(0,0,y.shape[0],y.shape[1],g.RGBA,g.UNSIGNED_BYTE,x.buffer),x._readTimeout=null)}}var p=m.prototype;Object.defineProperty(p,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(g){if(this.gl){this.fbo.shape=g;var y=this.fbo.shape[0],v=this.fbo.shape[1];if(v*y*4>this.buffer.length){s.free(this.buffer);for(var x=this.buffer=s.mallocUint8(h(v*y*4)),_=0;_<v*y*4;++_)x[_]=255}return g}}}),p.begin=function(){var g=this.gl;this.shape,g&&(this.fbo.bind(),g.clearColor(1,1,1,1),g.clear(g.COLOR_BUFFER_BIT|g.DEPTH_BUFFER_BIT))},p.end=function(){var g=this.gl;g&&(g.bindFramebuffer(g.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},p.query=function(g,y,v){if(!this.gl)return null;var x=this.fbo.shape.slice();g|=0,y|=0,typeof v!="number"&&(v=1);var _=0|Math.min(Math.max(g-v,0),x[0]),A=0|Math.min(Math.max(g+v,0),x[0]),b=0|Math.min(Math.max(y-v,0),x[1]),k=0|Math.min(Math.max(y+v,0),x[1]);if(A<=_||k<=b)return null;var w=[A-_,k-b],M=u(this.buffer,[w[0],w[1],4],[4,4*x[0],1],4*(_+x[0]*b)),T=function(P,L,R){for(var F=1e8,D=-1,O=-1,N=P.shape[0],B=P.shape[1],W=0;W<N;W++)for(var G=0;G<B;G++){var K=P.get(W,G,0),te=P.get(W,G,1),Y=P.get(W,G,2),J=P.get(W,G,3);if(K<255||te<255||Y<255||J<255){var re=L-W,U=R-G,V=re*re+U*U;V<F&&(F=V,D=W,O=G)}}return[D,O,F]}(M.hi(w[0],w[1],1),v,v),E=T[0],S=T[1];return E<0||Math.pow(this.radius,2)<T[2]?null:new d(E+_|0,S+b|0,M.get(E,S,0),[M.get(E,S,1),M.get(E,S,2),M.get(E,S,3)],Math.sqrt(T[2]))},p.dispose=function(){this.gl&&(this.fbo.dispose(),s.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{"bit-twiddle":32,"gl-fbo":86,ndarray:259,"typedarray-pool":308}],132:[function(a,l,c){var i=a("./lib/create-uniforms"),s=a("./lib/create-attributes"),u=a("./lib/reflect"),h=a("./lib/shader-cache"),d=a("./lib/runtime-reflect"),m=a("./lib/GLError");function p(v){this.gl=v,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var g=p.prototype;function y(v,x){return v.name<x.name?-1:1}g.bind=function(){var v;this.program||this._relink();var x=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),_=this.gl.lastAttribCount;if(x>_)for(v=_;v<x;v++)this.gl.enableVertexAttribArray(v);else if(_>x)for(v=x;v<_;v++)this.gl.disableVertexAttribArray(v);this.gl.lastAttribCount=x,this.gl.useProgram(this.program)},g.dispose=function(){for(var v=this.gl.lastAttribCount,x=0;x<v;x++)this.gl.disableVertexAttribArray(x);this.gl.lastAttribCount=0,this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},g.update=function(v,x,_,A){if(!x||arguments.length===1){var b=v;v=b.vertex,x=b.fragment,_=b.uniforms,A=b.attributes}var k=this,w=k.gl,M=k._vref;k._vref=h.shader(w,w.VERTEX_SHADER,v),M&&M.dispose(),k.vertShader=k._vref.shader;var T=this._fref;if(k._fref=h.shader(w,w.FRAGMENT_SHADER,x),T&&T.dispose(),k.fragShader=k._fref.shader,!_||!A){var E=w.createProgram();if(w.attachShader(E,k.fragShader),w.attachShader(E,k.vertShader),w.linkProgram(E),!w.getProgramParameter(E,w.LINK_STATUS)){var S=w.getProgramInfoLog(E);throw new m(S,"Error linking program:"+S)}_=_||d.uniforms(w,E),A=A||d.attributes(w,E),w.deleteProgram(E)}(A=A.slice()).sort(y);var P,L=[],R=[],F=[];for(P=0;P<A.length;++P){var D=A[P];if(D.type.indexOf("mat")>=0){for(var O=0|D.type.charAt(D.type.length-1),N=new Array(O),B=0;B<O;++B)N[B]=F.length,R.push(D.name+"["+B+"]"),typeof D.location=="number"?F.push(D.location+B):Array.isArray(D.location)&&D.location.length===O&&typeof D.location[B]=="number"?F.push(0|D.location[B]):F.push(-1);L.push({name:D.name,type:D.type,locations:N})}else L.push({name:D.name,type:D.type,locations:[F.length]}),R.push(D.name),typeof D.location=="number"?F.push(0|D.location):F.push(-1)}var W=0;for(P=0;P<F.length;++P)if(F[P]<0){for(;F.indexOf(W)>=0;)W+=1;F[P]=W}var G=new Array(_.length);function K(){k.program=h.program(w,k._vref,k._fref,R,F);for(var te=0;te<_.length;++te)G[te]=w.getUniformLocation(k.program,_[te].name)}K(),k._relink=K,k.types={uniforms:u(_),attributes:u(A)},k.attributes=s(w,k,L,F),Object.defineProperty(k,"uniforms",i(w,k,_,G))},l.exports=function(v,x,_,A,b){var k=new p(v);return k.update(x,_,A,b),k}},{"./lib/GLError":133,"./lib/create-attributes":134,"./lib/create-uniforms":135,"./lib/reflect":136,"./lib/runtime-reflect":137,"./lib/shader-cache":138}],133:[function(a,l,c){function i(s,u,h){this.shortMessage=u||"",this.longMessage=h||"",this.rawError=s||"",this.message="gl-shader: "+(u||s||"")+(h?`
-`+h:""),this.stack=new Error().stack}i.prototype=new Error,i.prototype.name="GLError",i.prototype.constructor=i,l.exports=i},{}],134:[function(a,l,c){l.exports=function(p,g,y,v){for(var x={},_=0,A=y.length;_<A;++_){var b=y[_],k=b.name,w=b.type,M=b.locations;switch(w){case"bool":case"int":case"float":d(p,g,M[0],v,1,x,k);break;default:if(w.indexOf("vec")>=0){if((T=w.charCodeAt(w.length-1)-48)<2||T>4)throw new i("","Invalid data type for attribute "+k+": "+w);d(p,g,M[0],v,T,x,k)}else{if(!(w.indexOf("mat")>=0))throw new i("","Unknown data type for attribute "+k+": "+w);var T;if((T=w.charCodeAt(w.length-1)-48)<2||T>4)throw new i("","Invalid data type for attribute "+k+": "+w);m(p,g,M,v,T,x,k)}}}return x};var i=a("./GLError");function s(p,g,y,v,x,_){this._gl=p,this._wrapper=g,this._index=y,this._locations=v,this._dimension=x,this._constFunc=_}var u=s.prototype;u.pointer=function(p,g,y,v){var x=this._gl,_=this._locations[this._index];x.vertexAttribPointer(_,this._dimension,p||x.FLOAT,!!g,y||0,v||0),x.enableVertexAttribArray(_)},u.set=function(p,g,y,v){return this._constFunc(this._locations[this._index],p,g,y,v)},Object.defineProperty(u,"location",{get:function(){return this._locations[this._index]},set:function(p){return p!==this._locations[this._index]&&(this._locations[this._index]=0|p,this._wrapper.program=null),0|p}});var h=[function(p,g,y){return y.length===void 0?p.vertexAttrib1f(g,y):p.vertexAttrib1fv(g,y)},function(p,g,y,v){return y.length===void 0?p.vertexAttrib2f(g,y,v):p.vertexAttrib2fv(g,y)},function(p,g,y,v,x){return y.length===void 0?p.vertexAttrib3f(g,y,v,x):p.vertexAttrib3fv(g,y)},function(p,g,y,v,x,_){return y.length===void 0?p.vertexAttrib4f(g,y,v,x,_):p.vertexAttrib4fv(g,y)}];function d(p,g,y,v,x,_,A){var b=h[x],k=new s(p,g,y,v,x,b);Object.defineProperty(_,A,{set:function(w){return p.disableVertexAttribArray(v[y]),b(p,v[y],w),w},get:function(){return 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S;if((S=T.charCodeAt(T.length-1)-48)<2||S>4)throw new s("","Invalid uniform dimension type for matrix "+name+": "+T);return h(S*S,0)}throw new s("","Unknown uniform data type for "+name+": "+T)}}(p[k].type);var M}function x(A){var b;if(Array.isArray(A)){b=new Array(A.length);for(var k=0;k<A.length;++k)v(b,k,A[k])}else for(var w in b={},A)v(b,w,A[w]);return b}var _=i(p,!0);return{get:u(x(_)),set:y(_),enumerable:!0,configurable:!0}}},{"./GLError":133,"./reflect":136}],136:[function(a,l,c){l.exports=function(i,s){for(var u={},h=0;h<i.length;++h)for(var d=i[h].name.split("."),m=u,p=0;p<d.length;++p){var g=d[p].split("[");if(g.length>1){g[0]in m||(m[g[0]]=[]),m=m[g[0]];for(var y=1;y<g.length;++y){var v=parseInt(g[y]);y<g.length-1||p<d.length-1?(v in m||(y<g.length-1?m[v]=[]:m[v]={}),m=m[v]):m[v]=s?h:i[h].type}}else p<d.length-1?(g[0]in m||(m[g[0]]={}),m=m[g[0]]):m[g[0]]=s?h:i[h].type}return u}},{}],137:[function(a,l,c){c.uniforms=function(h,d){for(var m=h.getProgramParameter(d,h.ACTIVE_UNIFORMS),p=[],g=0;g<m;++g){var y=h.getActiveUniform(d,g);if(y){var v=u(h,y.type);if(y.size>1)for(var x=0;x<y.size;++x)p.push({name:y.name.replace("[0]","["+x+"]"),type:v});else p.push({name:y.name,type:v})}}return p},c.attributes=function(h,d){for(var m=h.getProgramParameter(d,h.ACTIVE_ATTRIBUTES),p=[],g=0;g<m;++g){var y=h.getActiveAttrib(d,g);y&&p.push({name:y.name,type:u(h,y.type)})}return p};var i={FLOAT:"float",FLOAT_VEC2:"vec2",FLOAT_VEC3:"vec3",FLOAT_VEC4:"vec4",INT:"int",INT_VEC2:"ivec2",INT_VEC3:"ivec3",INT_VEC4:"ivec4",BOOL:"bool",BOOL_VEC2:"bvec2",BOOL_VEC3:"bvec3",BOOL_VEC4:"bvec4",FLOAT_MAT2:"mat2",FLOAT_MAT3:"mat3",FLOAT_MAT4:"mat4",SAMPLER_2D:"sampler2D",SAMPLER_CUBE:"samplerCube"},s=null;function u(h,d){if(!s){var m=Object.keys(i);s={};for(var p=0;p<m.length;++p){var g=m[p];s[h[g]]=i[g]}}return s[d]}},{}],138:[function(a,l,c){c.shader=function(y,v,x){return g(y).getShaderReference(v,x)},c.program=function(y,v,x,_,A){return g(y).getProgram(v,x,_,A)};var i=a("./GLError"),s=a("gl-format-compiler-error"),u=new(typeof WeakMap>"u"?a("weakmap-shim"):WeakMap),h=0;function d(y,v,x,_,A,b,k){this.id=y,this.src=v,this.type=x,this.shader=_,this.count=b,this.programs=[],this.cache=k}function m(y){this.gl=y,this.shaders=[{},{}],this.programs={}}d.prototype.dispose=function(){if(--this.count==0){for(var y=this.cache,v=y.gl,x=this.programs,_=0,A=x.length;_<A;++_){var b=y.programs[x[_]];b&&(delete y.programs[_],v.deleteProgram(b))}v.deleteShader(this.shader),delete y.shaders[this.type===v.FRAGMENT_SHADER|0][this.src]}};var p=m.prototype;function g(y){var v=u.get(y);return v||(v=new m(y),u.set(y,v)),v}p.getShaderReference=function(y,v){var x=this.gl,_=this.shaders[y===x.FRAGMENT_SHADER|0],A=_[v];if(A&&x.isShader(A.shader))A.count+=1;else{var b=function(k,w,M){var T=k.createShader(w);if(k.shaderSource(T,M),k.compileShader(T),!k.getShaderParameter(T,k.COMPILE_STATUS)){var E=k.getShaderInfoLog(T);try{var S=s(E,M,w)}catch(P){throw console.warn("Failed to format compiler error: "+P),new i(E,`Error compiling shader:
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-#define GLSLIFY 1
-
-attribute vec3 position, color;
-attribute float weight;
-
-uniform mat4 model, view, projection;
-uniform vec3 coordinates[3];
-uniform vec4 colors[3];
-uniform vec2 screenShape;
-uniform float lineWidth;
-
-varying vec4 fragColor;
-
-void main() {
-  vec3 vertexPosition = mix(coordinates[0],
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-
-  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);
-  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;
-  vec2 delta = weight * clipOffset * screenShape;
-  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;
-
-  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);
-  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];
-}
-`]),h=i([`precision mediump float;
-#define GLSLIFY 1
-
-varying vec4 fragColor;
-
-void main() {
-  gl_FragColor = fragColor;
-}`]);l.exports=function(d){return s(d,u,h,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":132,glslify:231}],141:[function(a,l,c){var i=a("gl-buffer"),s=a("gl-vao"),u=a("./shaders/index");l.exports=function(v,x){var _=[];function A(T,E,S,P,L,R){var F=[T,E,S,0,0,0,1];F[P+3]=1,F[P]=L,_.push.apply(_,F),F[6]=-1,_.push.apply(_,F),F[P]=R,_.push.apply(_,F),_.push.apply(_,F),F[6]=1,_.push.apply(_,F),F[P]=L,_.push.apply(_,F)}A(0,0,0,0,0,1),A(0,0,0,1,0,1),A(0,0,0,2,0,1),A(1,0,0,1,-1,1),A(1,0,0,2,-1,1),A(0,1,0,0,-1,1),A(0,1,0,2,-1,1),A(0,0,1,0,-1,1),A(0,0,1,1,-1,1);var b=i(v,_),k=s(v,[{type:v.FLOAT,buffer:b,size:3,offset:0,stride:28},{type:v.FLOAT,buffer:b,size:3,offset:12,stride:28},{type:v.FLOAT,buffer:b,size:1,offset:24,stride:28}]),w=u(v);w.attributes.position.location=0,w.attributes.color.location=1,w.attributes.weight.location=2;var M=new d(v,b,k,w);return M.update(x),M};var h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(v,x,_,A){this.gl=v,this.buffer=x,this.vao=_,this.shader=A,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var m=d.prototype,p=[0,0,0],g=[0,0,0],y=[0,0];m.isTransparent=function(){return!1},m.drawTransparent=function(v){},m.draw=function(v){var x=this.gl,_=this.vao,A=this.shader;_.bind(),A.bind();var b,k=v.model||h,w=v.view||h,M=v.projection||h;this.axes&&(b=this.axes.lastCubeProps.axis);for(var T=p,E=g,S=0;S<3;++S)b&&b[S]<0?(T[S]=this.bounds[0][S],E[S]=this.bounds[1][S]):(T[S]=this.bounds[1][S],E[S]=this.bounds[0][S]);for(y[0]=x.drawingBufferWidth,y[1]=x.drawingBufferHeight,A.uniforms.model=k,A.uniforms.view=w,A.uniforms.projection=M,A.uniforms.coordinates=[this.position,T,E],A.uniforms.colors=this.colors,A.uniforms.screenShape=y,S=0;S<3;++S)A.uniforms.lineWidth=this.lineWidth[S]*this.pixelRatio,this.enabled[S]&&(_.draw(x.TRIANGLES,6,6*S),this.drawSides[S]&&_.draw(x.TRIANGLES,12,18+12*S));_.unbind()},m.update=function(v){v&&("bounds"in v&&(this.bounds=v.bounds),"position"in v&&(this.position=v.position),"lineWidth"in v&&(this.lineWidth=v.lineWidth),"colors"in v&&(this.colors=v.colors),"enabled"in v&&(this.enabled=v.enabled),"drawSides"in v&&(this.drawSides=v.drawSides))},m.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":140,"gl-buffer":78,"gl-vao":150}],142:[function(a,l,c){var i=a("glslify"),s=i([`precision highp float;
-
-precision highp float;
-#define GLSLIFY 1
-
-vec3 getOrthogonalVector(vec3 v) {
-  // Return up-vector for only-z vector.
-  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
-  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
-  // Assign z = 0, x = -b, y = a:
-  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
-  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
-    return normalize(vec3(-v.y, v.x, 0.0));
-  } else {
-    return normalize(vec3(0.0, v.z, -v.y));
-  }
-}
-
-// Calculate the tube vertex and normal at the given index.
-//
-// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.
-//
-// Each tube segment is made up of a ring of vertices.
-// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.
-// The indexes of tube segments run from 0 to 8.
-//
-vec3 getTubePosition(vec3 d, float index, out vec3 normal) {
-  float segmentCount = 8.0;
-
-  float angle = 2.0 * 3.14159 * (index / segmentCount);
-
-  vec3 u = getOrthogonalVector(d);
-  vec3 v = normalize(cross(u, d));
-
-  vec3 x = u * cos(angle) * length(d);
-  vec3 y = v * sin(angle) * length(d);
-  vec3 v3 = x + y;
-
-  normal = normalize(v3);
-
-  return v3;
-}
-
-attribute vec4 vector;
-attribute vec4 color, position;
-attribute vec2 uv;
-
-uniform float vectorScale, tubeScale;
-uniform mat4 model, view, projection, inverseModel;
-uniform vec3 eyePosition, lightPosition;
-
-varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
-varying vec4 f_color;
-varying vec2 f_uv;
-
-void main() {
-  // Scale the vector magnitude to stay constant with
-  // model & view changes.
-  vec3 normal;
-  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);
-  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
-
-  //Lighting geometry parameters
-  vec4 cameraCoordinate = view * tubePosition;
-  cameraCoordinate.xyz /= cameraCoordinate.w;
-  f_lightDirection = lightPosition - cameraCoordinate.xyz;
-  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;
-  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);
-
-  // vec4 m_position  = model * vec4(tubePosition, 1.0);
-  vec4 t_position  = view * tubePosition;
-  gl_Position      = projection * t_position;
-
-  f_color          = color;
-  f_data           = tubePosition.xyz;
-  f_position       = position.xyz;
-  f_uv             = uv;
-}
-`]),u=i([`#extension GL_OES_standard_derivatives : enable
-
-precision highp float;
-#define GLSLIFY 1
-
-float beckmannDistribution(float x, float roughness) {
-  float NdotH = max(x, 0.0001);
-  float cos2Alpha = NdotH * NdotH;
-  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
-  float roughness2 = roughness * roughness;
-  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
-  return exp(tan2Alpha / roughness2) / denom;
-}
-
-float cookTorranceSpecular(
-  vec3 lightDirection,
-  vec3 viewDirection,
-  vec3 surfaceNormal,
-  float roughness,
-  float fresnel) {
-
-  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);
-  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);
-
-  //Half angle vector
-  vec3 H = normalize(lightDirection + viewDirection);
-
-  //Geometric term
-  float NdotH = max(dot(surfaceNormal, H), 0.0);
-  float VdotH = max(dot(viewDirection, H), 0.000001);
-  float LdotH = max(dot(lightDirection, H), 0.000001);
-  float G1 = (2.0 * NdotH * VdotN) / VdotH;
-  float G2 = (2.0 * NdotH * LdotN) / LdotH;
-  float G = min(1.0, min(G1, G2));
-  
-  //Distribution term
-  float D = beckmannDistribution(NdotH, roughness);
-
-  //Fresnel term
-  float F = pow(1.0 - VdotN, fresnel);
-
-  //Multiply terms and done
-  return  G * F * D / max(3.14159265 * VdotN, 0.000001);
-}
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 clipBounds[2];
-uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;
-uniform sampler2D texture;
-
-varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;
-varying vec4 f_color;
-varying vec2 f_uv;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
-  vec3 N = normalize(f_normal);
-  vec3 L = normalize(f_lightDirection);
-  vec3 V = normalize(f_eyeDirection);
-
-  if(gl_FrontFacing) {
-    N = -N;
-  }
-
-  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));
-  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
-
-  vec4 surfaceColor = f_color * texture2D(texture, f_uv);
-  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
-
-  gl_FragColor = litColor * opacity;
-}
-`]),h=i([`precision highp float;
-
-precision highp float;
-#define GLSLIFY 1
-
-vec3 getOrthogonalVector(vec3 v) {
-  // Return up-vector for only-z vector.
-  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).
-  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.
-  // Assign z = 0, x = -b, y = a:
-  // a*-b + b*a + c*0 = -ba + ba + 0 = 0
-  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {
-    return normalize(vec3(-v.y, v.x, 0.0));
-  } else {
-    return normalize(vec3(0.0, v.z, -v.y));
-  }
-}
-
-// Calculate the tube vertex and normal at the given index.
-//
-// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.
-//
-// Each tube segment is made up of a ring of vertices.
-// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.
-// The indexes of tube segments run from 0 to 8.
-//
-vec3 getTubePosition(vec3 d, float index, out vec3 normal) {
-  float segmentCount = 8.0;
-
-  float angle = 2.0 * 3.14159 * (index / segmentCount);
-
-  vec3 u = getOrthogonalVector(d);
-  vec3 v = normalize(cross(u, d));
-
-  vec3 x = u * cos(angle) * length(d);
-  vec3 y = v * sin(angle) * length(d);
-  vec3 v3 = x + y;
-
-  normal = normalize(v3);
-
-  return v3;
-}
-
-attribute vec4 vector;
-attribute vec4 position;
-attribute vec4 id;
-
-uniform mat4 model, view, projection;
-uniform float tubeScale;
-
-varying vec3 f_position;
-varying vec4 f_id;
-
-void main() {
-  vec3 normal;
-  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);
-  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);
-
-  gl_Position = projection * view * tubePosition;
-  f_id        = id;
-  f_position  = position.xyz;
-}
-`]),d=i([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3  clipBounds[2];
-uniform float pickId;
-
-varying vec3 f_position;
-varying vec4 f_id;
-
-void main() {
-  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;
-
-  gl_FragColor = vec4(pickId, f_id.xyz);
-}`]);c.meshShader={vertex:s,fragment:u,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},c.pickShader={vertex:h,fragment:d,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:231}],143:[function(a,l,c){var i=a("gl-vec3"),s=a("gl-vec4"),u=["xyz","xzy","yxz","yzx","zxy","zyx"],h=function(v,x,_,A){for(var b=0,k=0;k<v.length;k++)for(var w=v[k].velocities,M=0;M<w.length;M++)b=Math.max(b,i.length(w[M]));var T=v.map(function(B){return function(W,G,K,te){for(var Y=W.points,J=W.velocities,re=W.divergences,U=[],V=[],H=[],ne=[],q=[],Q=[],ee=0,ie=0,ae=s.create(),ue=s.create(),le=0;le<Y.length;le++){var ge=Y[le],fe=J[le],me=re[le];G===0&&(me=.05*K),ie=i.length(fe)/te,ae=s.create(),i.copy(ae,fe),ae[3]=me;for(var _e=0;_e<8;_e++)q[_e]=[ge[0],ge[1],ge[2],_e];if(ne.length>0)for(_e=0;_e<8;_e++){var Ae=(_e+1)%8;U.push(ne[_e],q[_e],q[Ae],q[Ae],ne[Ae],ne[_e]),H.push(ue,ae,ae,ae,ue,ue),Q.push(ee,ie,ie,ie,ee,ee);var ke=U.length;V.push([ke-6,ke-5,ke-4],[ke-3,ke-2,ke-1])}var Le=ne;ne=q,q=Le;var de=ue;ue=ae,ae=de;var ve=ee;ee=ie,ie=ve}return{positions:U,cells:V,vectors:H,vertexIntensity:Q}}(B,_,A,b)}),E=[],S=[],P=[],L=[];for(k=0;k<T.length;k++){var R=T[k],F=E.length;for(E=E.concat(R.positions),P=P.concat(R.vectors),L=L.concat(R.vertexIntensity),M=0;M<R.cells.length;M++){var D=R.cells[M],O=[];S.push(O);for(var N=0;N<D.length;N++)O.push(D[N]+F)}}return{positions:E,cells:S,vectors:P,vertexIntensity:L,colormap:x}},d=function(v,x){var _,A=v.length;for(_=0;_<A;_++){var b=v[_];if(b===x)return _;if(b>x)return _-1}return _},m=function(v,x,_){return v<x?x:v>_?_:v},p=function(v){var x=1/0;v.sort(function(k,w){return k-w});for(var _=v.length,A=1;A<_;A++){var b=Math.abs(v[A]-v[A-1]);b<x&&(x=b)}return x};l.exports=function(v,x){var _=v.startingPositions,A=v.maxLength||1e3,b=v.tubeSize||1,k=v.absoluteTubeSize,w=v.gridFill||"+x+y+z",M={};w.indexOf("-x")!==-1&&(M.reversedX=!0),w.indexOf("-y")!==-1&&(M.reversedY=!0),w.indexOf("-z")!==-1&&(M.reversedZ=!0),M.filled=u.indexOf(w.replace(/-/g,"").replace(/\+/g,""));var T=v.getVelocity||function(le){return function(ge,fe,me){var _e=fe.vectors,Ae=fe.meshgrid,ke=ge[0],Le=ge[1],de=ge[2],ve=Ae[0].length,Me=Ae[1].length,we=Ae[2].length,Ce=d(Ae[0],ke),Fe=d(Ae[1],Le),ze=d(Ae[2],de),$e=Ce+1,Ke=Fe+1,Re=ze+1;if(Ce=m(Ce,0,ve-1),$e=m($e,0,ve-1),Fe=m(Fe,0,Me-1),Ke=m(Ke,0,Me-1),ze=m(ze,0,we-1),Re=m(Re,0,we-1),Ce<0||Fe<0||ze<0||$e>ve-1||Ke>Me-1||Re>we-1)return i.create();var Ve,We,Ye,nt,ft,yt,Ot=Ae[0][Ce],Tt=Ae[0][$e],at=Ae[1][Fe],et=Ae[1][Ke],Lt=Ae[2][ze],Wt=(ke-Ot)/(Tt-Ot),Jt=(Le-at)/(et-at),Be=(de-Lt)/(Ae[2][Re]-Lt);switch(isFinite(Wt)||(Wt=.5),isFinite(Jt)||(Jt=.5),isFinite(Be)||(Be=.5),me.reversedX&&(Ce=ve-1-Ce,$e=ve-1-$e),me.reversedY&&(Fe=Me-1-Fe,Ke=Me-1-Ke),me.reversedZ&&(ze=we-1-ze,Re=we-1-Re),me.filled){case 5:ft=ze,yt=Re,Ye=Fe*we,nt=Ke*we,Ve=Ce*we*Me,We=$e*we*Me;break;case 4:ft=ze,yt=Re,Ve=Ce*we,We=$e*we,Ye=Fe*we*ve,nt=Ke*we*ve;break;case 3:Ye=Fe,nt=Ke,ft=ze*Me,yt=Re*Me,Ve=Ce*Me*we,We=$e*Me*we;break;case 2:Ye=Fe,nt=Ke,Ve=Ce*Me,We=$e*Me,ft=ze*Me*ve,yt=Re*Me*ve;break;case 1:Ve=Ce,We=$e,ft=ze*ve,yt=Re*ve,Ye=Fe*ve*we,nt=Ke*ve*we;break;default:Ve=Ce,We=$e,Ye=Fe*ve,nt=Ke*ve,ft=ze*ve*Me,yt=Re*ve*Me}var Ge=_e[Ve+Ye+ft],kt=_e[Ve+Ye+yt],dt=_e[Ve+nt+ft],Oe=_e[Ve+nt+yt],Ie=_e[We+Ye+ft],Te=_e[We+Ye+yt],Pe=_e[We+nt+ft],qe=_e[We+nt+yt],rt=i.create(),lt=i.create(),ot=i.create(),At=i.create();i.lerp(rt,Ge,Ie,Wt),i.lerp(lt,kt,Te,Wt),i.lerp(ot,dt,Pe,Wt),i.lerp(At,Oe,qe,Wt);var wt=i.create(),$t=i.create();i.lerp(wt,rt,ot,Jt),i.lerp($t,lt,At,Jt);var Ut=i.create();return i.lerp(Ut,wt,$t,Be),Ut}(le,v,M)},E=v.getDivergence||function(le,ge){var fe=i.create(),me=1e-4;i.add(fe,le,[me,0,0]);var _e=T(fe);i.subtract(_e,_e,ge),i.scale(_e,_e,1/me),i.add(fe,le,[0,me,0]);var Ae=T(fe);i.subtract(Ae,Ae,ge),i.scale(Ae,Ae,1/me),i.add(fe,le,[0,0,me]);var ke=T(fe);return i.subtract(ke,ke,ge),i.scale(ke,ke,1/me),i.add(fe,_e,Ae),i.add(fe,fe,ke),fe},S=[],P=x[0][0],L=x[0][1],R=x[0][2],F=x[1][0],D=x[1][1],O=x[1][2],N=function(le){var ge=le[0],fe=le[1],me=le[2];return!(ge<P||ge>F||fe<L||fe>D||me<R||me>O)},B=10*i.distance(x[0],x[1])/A,W=B*B,G=1,K=0,te=_.length;te>1&&(G=function(le){for(var ge=[],fe=[],me=[],_e={},Ae={},ke={},Le=le.length,de=0;de<Le;de++){var ve=le[de],Me=ve[0],we=ve[1],Ce=ve[2];_e[Me]||(ge.push(Me),_e[Me]=!0),Ae[we]||(fe.push(we),Ae[we]=!0),ke[Ce]||(me.push(Ce),ke[Ce]=!0)}var Fe=p(ge),ze=p(fe),$e=p(me),Ke=Math.min(Fe,ze,$e);return isFinite(Ke)?Ke:1}(_));for(var Y=0;Y<te;Y++){var J=i.create();i.copy(J,_[Y]);var re=[J],U=[],V=T(J),H=J;U.push(V);var ne=[],q=E(J,V),Q=i.length(q);isFinite(Q)&&Q>K&&(K=Q),ne.push(Q),S.push({points:re,velocities:U,divergences:ne});for(var ee=0;ee<100*A&&re.length<A&&N(J);){ee++;var ie=i.clone(V),ae=i.squaredLength(ie);if(ae===0)break;ae>W&&i.scale(ie,ie,B/Math.sqrt(ae)),i.add(ie,ie,J),V=T(ie),i.squaredDistance(H,ie)-W>-1e-4*W&&(re.push(ie),H=ie,U.push(V),q=E(ie,V),Q=i.length(q),isFinite(Q)&&Q>K&&(K=Q),ne.push(Q)),J=ie}}var ue=h(S,v.colormap,K,G);return k?ue.tubeScale=k:(K===0&&(K=1),ue.tubeScale=.5*b*G/K),ue};var g=a("./lib/shaders"),y=a("gl-cone3d").createMesh;l.exports.createTubeMesh=function(v,x){return y(v,x,{shaders:g,traceType:"streamtube"})}},{"./lib/shaders":142,"gl-cone3d":79,"gl-vec3":169,"gl-vec4":205}],144:[function(a,l,c){var i=a("gl-shader"),s=a("glslify"),u=s([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec4 uv;
-attribute vec3 f;
-attribute vec3 normal;
-
-uniform vec3 objectOffset;
-uniform mat4 model, view, projection, inverseModel;
-uniform vec3 lightPosition, eyePosition;
-uniform sampler2D colormap;
-
-varying float value, kill;
-varying vec3 worldCoordinate;
-varying vec2 planeCoordinate;
-varying vec3 lightDirection, eyeDirection, surfaceNormal;
-varying vec4 vColor;
-
-void main() {
-  vec3 localCoordinate = vec3(uv.zw, f.x);
-  worldCoordinate = objectOffset + localCoordinate;
-  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);
-  vec4 clipPosition = projection * view * worldPosition;
-  gl_Position = clipPosition;
-  kill = f.y;
-  value = f.z;
-  planeCoordinate = uv.xy;
-
-  vColor = texture2D(colormap, vec2(value, value));
-
-  //Lighting geometry parameters
-  vec4 cameraCoordinate = view * worldPosition;
-  cameraCoordinate.xyz /= cameraCoordinate.w;
-  lightDirection = lightPosition - cameraCoordinate.xyz;
-  eyeDirection   = eyePosition - cameraCoordinate.xyz;
-  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);
-}
-`]),h=s([`precision highp float;
-#define GLSLIFY 1
-
-float beckmannDistribution(float x, float roughness) {
-  float NdotH = max(x, 0.0001);
-  float cos2Alpha = NdotH * NdotH;
-  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;
-  float roughness2 = roughness * roughness;
-  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;
-  return exp(tan2Alpha / roughness2) / denom;
-}
-
-float beckmannSpecular(
-  vec3 lightDirection,
-  vec3 viewDirection,
-  vec3 surfaceNormal,
-  float roughness) {
-  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);
-}
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec3 lowerBound, upperBound;
-uniform float contourTint;
-uniform vec4 contourColor;
-uniform sampler2D colormap;
-uniform vec3 clipBounds[2];
-uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;
-uniform float vertexColor;
-
-varying float value, kill;
-varying vec3 worldCoordinate;
-varying vec3 lightDirection, eyeDirection, surfaceNormal;
-varying vec4 vColor;
-
-void main() {
-  if (
-    kill > 0.0 ||
-    vColor.a == 0.0 ||
-    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)
-  ) discard;
-
-  vec3 N = normalize(surfaceNormal);
-  vec3 V = normalize(eyeDirection);
-  vec3 L = normalize(lightDirection);
-
-  if(gl_FrontFacing) {
-    N = -N;
-  }
-
-  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);
-  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);
-
-  //decide how to interpolate color — in vertex or in fragment
-  vec4 surfaceColor =
-    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +
-    step(.5, vertexColor) * vColor;
-
-  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);
-
-  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;
-}
-`]),d=s([`precision highp float;
-#define GLSLIFY 1
-
-attribute vec4 uv;
-attribute float f;
-
-uniform vec3 objectOffset;
-uniform mat3 permutation;
-uniform mat4 model, view, projection;
-uniform float height, zOffset;
-uniform sampler2D colormap;
-
-varying float value, kill;
-varying vec3 worldCoordinate;
-varying vec2 planeCoordinate;
-varying vec3 lightDirection, eyeDirection, surfaceNormal;
-varying vec4 vColor;
-
-void main() {
-  vec3 dataCoordinate = permutation * vec3(uv.xy, height);
-  worldCoordinate = objectOffset + dataCoordinate;
-  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);
-
-  vec4 clipPosition = projection * view * worldPosition;
-  clipPosition.z += zOffset;
-
-  gl_Position = clipPosition;
-  value = f + objectOffset.z;
-  kill = -1.0;
-  planeCoordinate = uv.zw;
-
-  vColor = texture2D(colormap, vec2(value, value));
-
-  //Don't do lighting for contours
-  surfaceNormal   = vec3(1,0,0);
-  eyeDirection    = vec3(0,1,0);
-  lightDirection  = vec3(0,0,1);
-}
-`]),m=s([`precision highp float;
-#define GLSLIFY 1
-
-bool outOfRange(float a, float b, float p) {
-  return ((p > max(a, b)) || 
-          (p < min(a, b)));
-}
-
-bool outOfRange(vec2 a, vec2 b, vec2 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y));
-}
-
-bool outOfRange(vec3 a, vec3 b, vec3 p) {
-  return (outOfRange(a.x, b.x, p.x) ||
-          outOfRange(a.y, b.y, p.y) ||
-          outOfRange(a.z, b.z, p.z));
-}
-
-bool outOfRange(vec4 a, vec4 b, vec4 p) {
-  return outOfRange(a.xyz, b.xyz, p.xyz);
-}
-
-uniform vec2 shape;
-uniform vec3 clipBounds[2];
-uniform float pickId;
-
-varying float value, kill;
-varying vec3 worldCoordinate;
-varying vec2 planeCoordinate;
-varying vec3 surfaceNormal;
-
-vec2 splitFloat(float v) {
-  float vh = 255.0 * v;
-  float upper = floor(vh);
-  float lower = fract(vh);
-  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);
-}
-
-void main() {
-  if ((kill > 0.0) ||
-      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;
-
-  vec2 ux = splitFloat(planeCoordinate.x / shape.x);
-  vec2 uy = splitFloat(planeCoordinate.y / shape.y);
-  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));
-}
-`]);c.createShader=function(p){var g=i(p,u,h,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return g.attributes.uv.location=0,g.attributes.f.location=1,g.attributes.normal.location=2,g},c.createPickShader=function(p){var g=i(p,u,m,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return g.attributes.uv.location=0,g.attributes.f.location=1,g.attributes.normal.location=2,g},c.createContourShader=function(p){var g=i(p,d,h,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return g.attributes.uv.location=0,g.attributes.f.location=1,g},c.createPickContourShader=function(p){var g=i(p,d,m,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return g.attributes.uv.location=0,g.attributes.f.location=1,g}},{"gl-shader":132,glslify:231}],145:[function(a,l,c){l.exports=function(V){var H=V.gl,ne=w(H),q=T(H),Q=M(H),ee=E(H),ie=s(H),ae=u(H,[{buffer:ie,size:4,stride:40,offset:0},{buffer:ie,size:3,stride:40,offset:16},{buffer:ie,size:3,stride:40,offset:28}]),ue=s(H),le=u(H,[{buffer:ue,size:4,stride:20,offset:0},{buffer:ue,size:1,stride:20,offset:16}]),ge=s(H),fe=u(H,[{buffer:ge,size:2,type:H.FLOAT}]),me=h(H,1,256,H.RGBA,H.UNSIGNED_BYTE);me.minFilter=H.LINEAR,me.magFilter=H.LINEAR;var _e=new F(H,[0,0],[[0,0,0],[0,0,0]],ne,q,ie,ae,me,Q,ee,ue,le,ge,fe,[0,0,0]),Ae={levels:[[],[],[]]};for(var ke in V)Ae[ke]=V[ke];return Ae.colormap=Ae.colormap||"jet",_e.update(Ae),_e};var i=a("bit-twiddle"),s=a("gl-buffer"),u=a("gl-vao"),h=a("gl-texture2d"),d=a("typedarray-pool"),m=a("colormap"),p=a("ndarray-ops"),g=a("ndarray-pack"),y=a("ndarray"),v=a("surface-nets"),x=a("gl-mat4/multiply"),_=a("gl-mat4/invert"),A=a("binary-search-bounds"),b=a("ndarray-gradient"),k=a("./lib/shaders"),w=k.createShader,M=k.createContourShader,T=k.createPickShader,E=k.createPickContourShader,S=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],L=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function R(V,H,ne,q,Q){this.position=V,this.index=H,this.uv=ne,this.level=q,this.dataCoordinate=Q}(function(){for(var V=0;V<3;++V){var H=L[V],ne=(V+2)%3;H[(V+1)%3+0]=1,H[ne+3]=1,H[V+6]=1}})();function F(V,H,ne,q,Q,ee,ie,ae,ue,le,ge,fe,me,_e,Ae){this.gl=V,this.shape=H,this.bounds=ne,this.objectOffset=Ae,this.intensityBounds=[],this._shader=q,this._pickShader=Q,this._coordinateBuffer=ee,this._vao=ie,this._colorMap=ae,this._contourShader=ue,this._contourPickShader=le,this._contourBuffer=ge,this._contourVAO=fe,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new R([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=me,this._dynamicVAO=_e,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[y(d.mallocFloat(1024),[0,0]),y(d.mallocFloat(1024),[0,0]),y(d.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var D=F.prototype;D.genColormap=function(V,H){var ne=!1,q=g([m({colormap:V,nshades:256,format:"rgba"}).map(function(Q,ee){var ie=H?function(ae,ue){if(!ue||!ue.length)return 1;for(var le=0;le<ue.length;++le){if(ue.length<2)return 1;if(ue[le][0]===ae)return ue[le][1];if(ue[le][0]>ae&&le>0){var ge=(ue[le][0]-ae)/(ue[le][0]-ue[le-1][0]);return ue[le][1]*(1-ge)+ge*ue[le-1][1]}}return 1}(ee/255,H):Q[3];return ie<1&&(ne=!0),[Q[0],Q[1],Q[2],255*ie]})]);return p.divseq(q,255),this.hasAlphaScale=ne,q},D.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},D.isOpaque=function(){return!this.isTransparent()},D.pickSlots=1,D.setPickBase=function(V){this.pickId=V};var O=[0,0,0],N={showSurface:!1,showContour:!1,projections:[S.slice(),S.slice(),S.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function B(V,H){var ne,q,Q,ee=H.axes&&H.axes.lastCubeProps.axis||O,ie=H.showSurface,ae=H.showContour;for(ne=0;ne<3;++ne)for(ie=ie||H.surfaceProject[ne],q=0;q<3;++q)ae=ae||H.contourProject[ne][q];for(ne=0;ne<3;++ne){var ue=N.projections[ne];for(q=0;q<16;++q)ue[q]=0;for(q=0;q<4;++q)ue[5*q]=1;ue[5*ne]=0,ue[12+ne]=H.axesBounds[+(ee[ne]>0)][ne],x(ue,V.model,ue);var le=N.clipBounds[ne];for(Q=0;Q<2;++Q)for(q=0;q<3;++q)le[Q][q]=V.clipBounds[Q][q];le[0][ne]=-1e8,le[1][ne]=1e8}return N.showSurface=ie,N.showContour=ae,N}var W={model:S,view:S,projection:S,inverseModel:S.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},G=S.slice(),K=[1,0,0,0,1,0,0,0,1];function te(V,H){V=V||{};var ne=this.gl;ne.disable(ne.CULL_FACE),this._colorMap.bind(0);var q=W;q.model=V.model||S,q.view=V.view||S,q.projection=V.projection||S,q.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],q.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],q.objectOffset=this.objectOffset,q.contourColor=this.contourColor[0],q.inverseModel=_(q.inverseModel,q.model);for(var Q=0;Q<2;++Q)for(var ee=q.clipBounds[Q],ie=0;ie<3;++ie)ee[ie]=Math.min(Math.max(this.clipBounds[Q][ie],-1e8),1e8);q.kambient=this.ambientLight,q.kdiffuse=this.diffuseLight,q.kspecular=this.specularLight,q.roughness=this.roughness,q.fresnel=this.fresnel,q.opacity=this.opacity,q.height=0,q.permutation=K,q.vertexColor=this.vertexColor;var ae=G;for(x(ae,q.view,q.model),x(ae,q.projection,ae),_(ae,ae),Q=0;Q<3;++Q)q.eyePosition[Q]=ae[12+Q]/ae[15];var ue=ae[15];for(Q=0;Q<3;++Q)ue+=this.lightPosition[Q]*ae[4*Q+3];for(Q=0;Q<3;++Q){var le=ae[12+Q];for(ie=0;ie<3;++ie)le+=ae[4*ie+Q]*this.lightPosition[ie];q.lightPosition[Q]=le/ue}var ge=B(q,this);if(ge.showSurface){for(this._shader.bind(),this._shader.uniforms=q,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(ne.TRIANGLES,this._vertexCount),Q=0;Q<3;++Q)this.surfaceProject[Q]&&this.vertexCount&&(this._shader.uniforms.model=ge.projections[Q],this._shader.uniforms.clipBounds=ge.clipBounds[Q],this._vao.draw(ne.TRIANGLES,this._vertexCount));this._vao.unbind()}if(ge.showContour){var fe=this._contourShader;q.kambient=1,q.kdiffuse=0,q.kspecular=0,q.opacity=1,fe.bind(),fe.uniforms=q;var me=this._contourVAO;for(me.bind(),Q=0;Q<3;++Q)for(fe.uniforms.permutation=L[Q],ne.lineWidth(this.contourWidth[Q]*this.pixelRatio),ie=0;ie<this.contourLevels[Q].length;++ie)ie===this.highlightLevel[Q]?(fe.uniforms.contourColor=this.highlightColor[Q],fe.uniforms.contourTint=this.highlightTint[Q]):ie!==0&&ie-1!==this.highlightLevel[Q]||(fe.uniforms.contourColor=this.contourColor[Q],fe.uniforms.contourTint=this.contourTint[Q]),this._contourCounts[Q][ie]&&(fe.uniforms.height=this.contourLevels[Q][ie],me.draw(ne.LINES,this._contourCounts[Q][ie],this._contourOffsets[Q][ie]));for(Q=0;Q<3;++Q)for(fe.uniforms.model=ge.projections[Q],fe.uniforms.clipBounds=ge.clipBounds[Q],ie=0;ie<3;++ie)if(this.contourProject[Q][ie]){fe.uniforms.permutation=L[ie],ne.lineWidth(this.contourWidth[ie]*this.pixelRatio);for(var _e=0;_e<this.contourLevels[ie].length;++_e)_e===this.highlightLevel[ie]?(fe.uniforms.contourColor=this.highlightColor[ie],fe.uniforms.contourTint=this.highlightTint[ie]):_e!==0&&_e-1!==this.highlightLevel[ie]||(fe.uniforms.contourColor=this.contourColor[ie],fe.uniforms.contourTint=this.contourTint[ie]),this._contourCounts[ie][_e]&&(fe.uniforms.height=this.contourLevels[ie][_e],me.draw(ne.LINES,this._contourCounts[ie][_e],this._contourOffsets[ie][_e]))}for(me.unbind(),(me=this._dynamicVAO).bind(),Q=0;Q<3;++Q)if(this._dynamicCounts[Q]!==0)for(fe.uniforms.model=q.model,fe.uniforms.clipBounds=q.clipBounds,fe.uniforms.permutation=L[Q],ne.lineWidth(this.dynamicWidth[Q]*this.pixelRatio),fe.uniforms.contourColor=this.dynamicColor[Q],fe.uniforms.contourTint=this.dynamicTint[Q],fe.uniforms.height=this.dynamicLevel[Q],me.draw(ne.LINES,this._dynamicCounts[Q],this._dynamicOffsets[Q]),ie=0;ie<3;++ie)this.contourProject[ie][Q]&&(fe.uniforms.model=ge.projections[ie],fe.uniforms.clipBounds=ge.clipBounds[ie],me.draw(ne.LINES,this._dynamicCounts[Q],this._dynamicOffsets[Q]));me.unbind()}}D.draw=function(V){return te.call(this,V,!1)},D.drawTransparent=function(V){return te.call(this,V,!0)};var Y={model:S,view:S,projection:S,inverseModel:S,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function J(V,H){return Array.isArray(V)?[H(V[0]),H(V[1]),H(V[2])]:[H(V),H(V),H(V)]}function re(V){return Array.isArray(V)?V.length===3?[V[0],V[1],V[2],1]:[V[0],V[1],V[2],V[3]]:[0,0,0,1]}function U(V){if(Array.isArray(V)){if(Array.isArray(V))return[re(V[0]),re(V[1]),re(V[2])];var H=re(V);return[H.slice(),H.slice(),H.slice()]}}D.drawPick=function(V){V=V||{};var H=this.gl;H.disable(H.CULL_FACE);var ne=Y;ne.model=V.model||S,ne.view=V.view||S,ne.projection=V.projection||S,ne.shape=this._field[2].shape,ne.pickId=this.pickId/255,ne.lowerBound=this.bounds[0],ne.upperBound=this.bounds[1],ne.objectOffset=this.objectOffset,ne.permutation=K;for(var q=0;q<2;++q)for(var Q=ne.clipBounds[q],ee=0;ee<3;++ee)Q[ee]=Math.min(Math.max(this.clipBounds[q][ee],-1e8),1e8);var ie=B(ne,this);if(ie.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=ne,this._vao.bind(),this._vao.draw(H.TRIANGLES,this._vertexCount),q=0;q<3;++q)this.surfaceProject[q]&&(this._pickShader.uniforms.model=ie.projections[q],this._pickShader.uniforms.clipBounds=ie.clipBounds[q],this._vao.draw(H.TRIANGLES,this._vertexCount));this._vao.unbind()}if(ie.showContour){var ae=this._contourPickShader;ae.bind(),ae.uniforms=ne;var ue=this._contourVAO;for(ue.bind(),ee=0;ee<3;++ee)for(H.lineWidth(this.contourWidth[ee]*this.pixelRatio),ae.uniforms.permutation=L[ee],q=0;q<this.contourLevels[ee].length;++q)this._contourCounts[ee][q]&&(ae.uniforms.height=this.contourLevels[ee][q],ue.draw(H.LINES,this._contourCounts[ee][q],this._contourOffsets[ee][q]));for(q=0;q<3;++q)for(ae.uniforms.model=ie.projections[q],ae.uniforms.clipBounds=ie.clipBounds[q],ee=0;ee<3;++ee)if(this.contourProject[q][ee]){ae.uniforms.permutation=L[ee],H.lineWidth(this.contourWidth[ee]*this.pixelRatio);for(var le=0;le<this.contourLevels[ee].length;++le)this._contourCounts[ee][le]&&(ae.uniforms.height=this.contourLevels[ee][le],ue.draw(H.LINES,this._contourCounts[ee][le],this._contourOffsets[ee][le]))}ue.unbind()}},D.pick=function(V){if(!V||V.id!==this.pickId)return null;var H=this._field[2].shape,ne=this._pickResult,q=H[0]*(V.value[0]+(V.value[2]>>4)/16)/255,Q=Math.floor(q),ee=q-Q,ie=H[1]*(V.value[1]+(15&V.value[2])/16)/255,ae=Math.floor(ie),ue=ie-ae;Q+=1,ae+=1;var le=ne.position;le[0]=le[1]=le[2]=0;for(var ge=0;ge<2;++ge)for(var fe=ge?ee:1-ee,me=0;me<2;++me)for(var _e=Q+ge,Ae=ae+me,ke=fe*(me?ue:1-ue),Le=0;Le<3;++Le)le[Le]+=this._field[Le].get(_e,Ae)*ke;for(var de=this._pickResult.level,ve=0;ve<3;++ve)if(de[ve]=A.le(this.contourLevels[ve],le[ve]),de[ve]<0)this.contourLevels[ve].length>0&&(de[ve]=0);else if(de[ve]<this.contourLevels[ve].length-1){var Me=this.contourLevels[ve][de[ve]],we=this.contourLevels[ve][de[ve]+1];Math.abs(Me-le[ve])>Math.abs(we-le[ve])&&(de[ve]+=1)}for(ne.index[0]=ee<.5?Q:Q+1,ne.index[1]=ue<.5?ae:ae+1,ne.uv[0]=q/H[0],ne.uv[1]=ie/H[1],Le=0;Le<3;++Le)ne.dataCoordinate[Le]=this._field[Le].get(ne.index[0],ne.index[1]);return ne},D.padField=function(V,H){var ne=H.shape.slice(),q=V.shape.slice();p.assign(V.lo(1,1).hi(ne[0],ne[1]),H),p.assign(V.lo(1).hi(ne[0],1),H.hi(ne[0],1)),p.assign(V.lo(1,q[1]-1).hi(ne[0],1),H.lo(0,ne[1]-1).hi(ne[0],1)),p.assign(V.lo(0,1).hi(1,ne[1]),H.hi(1)),p.assign(V.lo(q[0]-1,1).hi(1,ne[1]),H.lo(ne[0]-1)),V.set(0,0,H.get(0,0)),V.set(0,q[1]-1,H.get(0,ne[1]-1)),V.set(q[0]-1,0,H.get(ne[0]-1,0)),V.set(q[0]-1,q[1]-1,H.get(ne[0]-1,ne[1]-1))},D.update=function(V){V=V||{},this.objectOffset=V.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in V&&(this.contourWidth=J(V.contourWidth,Number)),"showContour"in V&&(this.showContour=J(V.showContour,Boolean)),"showSurface"in V&&(this.showSurface=!!V.showSurface),"contourTint"in V&&(this.contourTint=J(V.contourTint,Boolean)),"contourColor"in V&&(this.contourColor=U(V.contourColor)),"contourProject"in V&&(this.contourProject=J(V.contourProject,function(Kt){return J(Kt,Boolean)})),"surfaceProject"in V&&(this.surfaceProject=V.surfaceProject),"dynamicColor"in V&&(this.dynamicColor=U(V.dynamicColor)),"dynamicTint"in V&&(this.dynamicTint=J(V.dynamicTint,Number)),"dynamicWidth"in V&&(this.dynamicWidth=J(V.dynamicWidth,Number)),"opacity"in V&&(this.opacity=V.opacity),"opacityscale"in V&&(this.opacityscale=V.opacityscale),"colorBounds"in V&&(this.colorBounds=V.colorBounds),"vertexColor"in V&&(this.vertexColor=V.vertexColor?1:0),"colormap"in V&&this._colorMap.setPixels(this.genColormap(V.colormap,this.opacityscale));var H=V.field||V.coords&&V.coords[2]||null,ne=!1;if(H||(H=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in V||"coords"in V){var q=(H.shape[0]+2)*(H.shape[1]+2);q>this._field[2].data.length&&(d.freeFloat(this._field[2].data),this._field[2].data=d.mallocFloat(i.nextPow2(q))),this._field[2]=y(this._field[2].data,[H.shape[0]+2,H.shape[1]+2]),this.padField(this._field[2],H),this.shape=H.shape.slice();for(var Q=this.shape,ee=0;ee<2;++ee)this._field[2].size>this._field[ee].data.length&&(d.freeFloat(this._field[ee].data),this._field[ee].data=d.mallocFloat(this._field[2].size)),this._field[ee]=y(this._field[ee].data,[Q[0]+2,Q[1]+2]);if(V.coords){var ie=V.coords;if(!Array.isArray(ie)||ie.length!==3)throw new Error("gl-surface: invalid coordinates for x/y");for(ee=0;ee<2;++ee){var ae=ie[ee];for(me=0;me<2;++me)if(ae.shape[me]!==Q[me])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[ee],ae)}}else if(V.ticks){var ue=V.ticks;if(!Array.isArray(ue)||ue.length!==2)throw new Error("gl-surface: invalid ticks");for(ee=0;ee<2;++ee){var le=ue[ee];if((Array.isArray(le)||le.length)&&(le=y(le)),le.shape[0]!==Q[ee])throw new Error("gl-surface: invalid tick length");var ge=y(le.data,Q);ge.stride[ee]=le.stride[0],ge.stride[1^ee]=0,this.padField(this._field[ee],ge)}}else{for(ee=0;ee<2;++ee){var fe=[0,0];fe[ee]=1,this._field[ee]=y(this._field[ee].data,[Q[0]+2,Q[1]+2],fe,0)}this._field[0].set(0,0,0);for(var me=0;me<Q[0];++me)this._field[0].set(me+1,0,me);for(this._field[0].set(Q[0]+1,0,Q[0]-1),this._field[1].set(0,0,0),me=0;me<Q[1];++me)this._field[1].set(0,me+1,me);this._field[1].set(0,Q[1]+1,Q[1]-1)}var _e=this._field,Ae=y(d.mallocFloat(3*_e[2].size*2),[3,Q[0]+2,Q[1]+2,2]);for(ee=0;ee<3;++ee)b(Ae.pick(ee),_e[ee],"mirror");var ke=y(d.mallocFloat(3*_e[2].size),[Q[0]+2,Q[1]+2,3]);for(ee=0;ee<Q[0]+2;++ee)for(me=0;me<Q[1]+2;++me){var Le=Ae.get(0,ee,me,0),de=Ae.get(0,ee,me,1),ve=Ae.get(1,ee,me,0),Me=Ae.get(1,ee,me,1),we=Ae.get(2,ee,me,0),Ce=Ae.get(2,ee,me,1),Fe=ve*Ce-Me*we,ze=we*de-Ce*Le,$e=Le*Me-de*ve,Ke=Math.sqrt(Fe*Fe+ze*ze+$e*$e);Ke<1e-8?(Ke=Math.max(Math.abs(Fe),Math.abs(ze),Math.abs($e)))<1e-8?($e=1,ze=Fe=0,Ke=1):Ke=1/Ke:Ke=1/Math.sqrt(Ke),ke.set(ee,me,0,Fe*Ke),ke.set(ee,me,1,ze*Ke),ke.set(ee,me,2,$e*Ke)}d.free(Ae.data);var Re=[1/0,1/0,1/0],Ve=[-1/0,-1/0,-1/0],We=1/0,Ye=-1/0,nt=(Q[0]-1)*(Q[1]-1)*6,ft=d.mallocFloat(i.nextPow2(10*nt)),yt=0,Ot=0;for(ee=0;ee<Q[0]-1;++ee)e:for(me=0;me<Q[1]-1;++me){for(var Tt=0;Tt<2;++Tt)for(var at=0;at<2;++at)for(var et=0;et<3;++et){var Lt=this._field[et].get(1+ee+Tt,1+me+at);if(isNaN(Lt)||!isFinite(Lt))continue e}for(et=0;et<6;++et){var Wt=ee+P[et][0],Jt=me+P[et][1],Be=this._field[0].get(Wt+1,Jt+1),Ge=this._field[1].get(Wt+1,Jt+1);Lt=this._field[2].get(Wt+1,Jt+1),Fe=ke.get(Wt+1,Jt+1,0),ze=ke.get(Wt+1,Jt+1,1),$e=ke.get(Wt+1,Jt+1,2),V.intensity&&(kt=V.intensity.get(Wt,Jt));var kt=V.intensity?V.intensity.get(Wt,Jt):Lt+this.objectOffset[2];ft[yt++]=Wt,ft[yt++]=Jt,ft[yt++]=Be,ft[yt++]=Ge,ft[yt++]=Lt,ft[yt++]=0,ft[yt++]=kt,ft[yt++]=Fe,ft[yt++]=ze,ft[yt++]=$e,Re[0]=Math.min(Re[0],Be+this.objectOffset[0]),Re[1]=Math.min(Re[1],Ge+this.objectOffset[1]),Re[2]=Math.min(Re[2],Lt+this.objectOffset[2]),We=Math.min(We,kt),Ve[0]=Math.max(Ve[0],Be+this.objectOffset[0]),Ve[1]=Math.max(Ve[1],Ge+this.objectOffset[1]),Ve[2]=Math.max(Ve[2],Lt+this.objectOffset[2]),Ye=Math.max(Ye,kt),Ot+=1}}for(V.intensityBounds&&(We=+V.intensityBounds[0],Ye=+V.intensityBounds[1]),ee=6;ee<yt;ee+=10)ft[ee]=(ft[ee]-We)/(Ye-We);this._vertexCount=Ot,this._coordinateBuffer.update(ft.subarray(0,yt)),d.freeFloat(ft),d.free(ke.data),this.bounds=[Re,Ve],this.intensity=V.intensity||this._field[2],this.intensityBounds[0]===We&&this.intensityBounds[1]===Ye||(ne=!0),this.intensityBounds=[We,Ye]}if("levels"in V){var dt=V.levels;for(dt=Array.isArray(dt[0])?dt.slice():[[],[],dt],ee=0;ee<3;++ee)dt[ee]=dt[ee].slice(),dt[ee].sort(function(Kt,qt){return Kt-qt});for(ee=0;ee<3;++ee)for(me=0;me<dt[ee].length;++me)dt[ee][me]-=this.objectOffset[ee];e:for(ee=0;ee<3;++ee){if(dt[ee].length!==this.contourLevels[ee].length){ne=!0;break}for(me=0;me<dt[ee].length;++me)if(dt[ee][me]!==this.contourLevels[ee][me]){ne=!0;break e}}this.contourLevels=dt}if(ne){_e=this._field,Q=this.shape;for(var Oe=[],Ie=0;Ie<3;++Ie){var Te=this.contourLevels[Ie],Pe=[],qe=[],rt=[0,0,0];for(ee=0;ee<Te.length;++ee){var lt=v(this._field[Ie],Te[ee]);Pe.push(Oe.length/5|0),Ot=0;e:for(me=0;me<lt.cells.length;++me){var ot=lt.cells[me];for(et=0;et<2;++et){var At=lt.positions[ot[et]],wt=At[0],$t=0|Math.floor(wt),Ut=wt-$t,tt=At[1],bt=0|Math.floor(tt),Ft=tt-bt,Et=!1;t:for(var Pt=0;Pt<3;++Pt){rt[Pt]=0;var De=(Ie+Pt+1)%3;for(Tt=0;Tt<2;++Tt){var Je=Tt?Ut:1-Ut;for(Wt=0|Math.min(Math.max($t+Tt,0),Q[0]),at=0;at<2;++at){var st=at?Ft:1-Ft;if(Jt=0|Math.min(Math.max(bt+at,0),Q[1]),Lt=Pt<2?this._field[De].get(Wt,Jt):(this.intensity.get(Wt,Jt)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(Lt)||isNaN(Lt)){Et=!0;break t}var St=Je*st;rt[Pt]+=St*Lt}}}if(Et){if(et>0){for(var It=0;It<5;++It)Oe.pop();Ot-=1}continue e}Oe.push(rt[0],rt[1],At[0],At[1],rt[2]),Ot+=1}}qe.push(Ot)}this._contourOffsets[Ie]=Pe,this._contourCounts[Ie]=qe}var Zt=d.mallocFloat(Oe.length);for(ee=0;ee<Oe.length;++ee)Zt[ee]=Oe[ee];this._contourBuffer.update(Zt),d.freeFloat(Zt)}},D.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var V=0;V<3;++V)d.freeFloat(this._field[V].data)},D.highlight=function(V){var H,ne;if(!V)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(H=0;H<3;++H)this.enableHighlight[H]?this.highlightLevel[H]=V.level[H]:this.highlightLevel[H]=-1;for(ne=this.snapToData?V.dataCoordinate:V.position,H=0;H<3;++H)ne[H]-=this.objectOffset[H];if(this.enableDynamic[0]&&ne[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&ne[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&ne[2]!==this.dynamicLevel[2]){for(var q=0,Q=this.shape,ee=d.mallocFloat(12*Q[0]*Q[1]),ie=0;ie<3;++ie)if(this.enableDynamic[ie]){this.dynamicLevel[ie]=ne[ie];var ae=(ie+1)%3,ue=(ie+2)%3,le=this._field[ie],ge=this._field[ae],fe=this._field[ue],me=v(le,ne[ie]),_e=me.cells,Ae=me.positions;for(this._dynamicOffsets[ie]=q,H=0;H<_e.length;++H)for(var ke=_e[H],Le=0;Le<2;++Le){var de=Ae[ke[Le]],ve=+de[0],Me=0|ve,we=0|Math.min(Me+1,Q[0]),Ce=ve-Me,Fe=1-Ce,ze=+de[1],$e=0|ze,Ke=0|Math.min($e+1,Q[1]),Re=ze-$e,Ve=1-Re,We=Fe*Ve,Ye=Fe*Re,nt=Ce*Ve,ft=Ce*Re,yt=We*ge.get(Me,$e)+Ye*ge.get(Me,Ke)+nt*ge.get(we,$e)+ft*ge.get(we,Ke),Ot=We*fe.get(Me,$e)+Ye*fe.get(Me,Ke)+nt*fe.get(we,$e)+ft*fe.get(we,Ke);if(isNaN(yt)||isNaN(Ot)){Le&&(q-=1);break}ee[2*q+0]=yt,ee[2*q+1]=Ot,q+=1}this._dynamicCounts[ie]=q-this._dynamicOffsets[ie]}else this.dynamicLevel[ie]=NaN,this._dynamicCounts[ie]=0;this._dynamicBuffer.update(ee.subarray(0,2*q)),d.freeFloat(ee)}}},{"./lib/shaders":144,"binary-search-bounds":31,"bit-twiddle":32,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-texture2d":146,"gl-vao":150,ndarray:259,"ndarray-gradient":252,"ndarray-ops":254,"ndarray-pack":255,"surface-nets":302,"typedarray-pool":308}],146:[function(a,l,c){var i=a("ndarray"),s=a("ndarray-ops"),u=a("typedarray-pool");l.exports=function(T){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");if(h||p(T),typeof arguments[1]=="number")return k(T,arguments[1],arguments[2],arguments[3]||T.RGBA,arguments[4]||T.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return k(T,0|arguments[1][0],0|arguments[1][1],arguments[2]||T.RGBA,arguments[3]||T.UNSIGNED_BYTE);if(typeof arguments[1]=="object"){var E=arguments[1],S=g(E)?E:E.raw;if(S)return w(T,S,0|E.width,0|E.height,arguments[2]||T.RGBA,arguments[3]||T.UNSIGNED_BYTE);if(E.shape&&E.data&&E.stride)return M(T,E)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var h=null,d=null,m=null;function p(T){h=[T.LINEAR,T.NEAREST_MIPMAP_LINEAR,T.LINEAR_MIPMAP_NEAREST,T.LINEAR_MIPMAP_NEAREST],d=[T.NEAREST,T.LINEAR,T.NEAREST_MIPMAP_NEAREST,T.NEAREST_MIPMAP_LINEAR,T.LINEAR_MIPMAP_NEAREST,T.LINEAR_MIPMAP_LINEAR],m=[T.REPEAT,T.CLAMP_TO_EDGE,T.MIRRORED_REPEAT]}function g(T){return typeof HTMLCanvasElement<"u"&&T instanceof HTMLCanvasElement||typeof HTMLImageElement<"u"&&T instanceof HTMLImageElement||typeof HTMLVideoElement<"u"&&T instanceof HTMLVideoElement||typeof ImageData<"u"&&T instanceof ImageData}var y=function(T,E){s.muls(T,E,255)};function v(T,E,S){var P=T.gl,L=P.getParameter(P.MAX_TEXTURE_SIZE);if(E<0||E>L||S<0||S>L)throw new Error("gl-texture2d: Invalid texture size");return T._shape=[E,S],T.bind(),P.texImage2D(P.TEXTURE_2D,0,T.format,E,S,0,T.format,T.type,null),T._mipLevels=[0],T}function x(T,E,S,P,L,R){this.gl=T,this.handle=E,this.format=L,this.type=R,this._shape=[S,P],this._mipLevels=[0],this._magFilter=T.NEAREST,this._minFilter=T.NEAREST,this._wrapS=T.CLAMP_TO_EDGE,this._wrapT=T.CLAMP_TO_EDGE,this._anisoSamples=1;var F=this,D=[this._wrapS,this._wrapT];Object.defineProperties(D,[{get:function(){return F._wrapS},set:function(N){return F.wrapS=N}},{get:function(){return F._wrapT},set:function(N){return F.wrapT=N}}]),this._wrapVector=D;var O=[this._shape[0],this._shape[1]];Object.defineProperties(O,[{get:function(){return F._shape[0]},set:function(N){return F.width=N}},{get:function(){return F._shape[1]},set:function(N){return F.height=N}}]),this._shapeVector=O}var _=x.prototype;function A(T,E){return T.length===3?E[2]===1&&E[1]===T[0]*T[2]&&E[0]===T[2]:E[0]===1&&E[1]===T[0]}function b(T){var E=T.createTexture();return T.bindTexture(T.TEXTURE_2D,E),T.texParameteri(T.TEXTURE_2D,T.TEXTURE_MIN_FILTER,T.NEAREST),T.texParameteri(T.TEXTURE_2D,T.TEXTURE_MAG_FILTER,T.NEAREST),T.texParameteri(T.TEXTURE_2D,T.TEXTURE_WRAP_S,T.CLAMP_TO_EDGE),T.texParameteri(T.TEXTURE_2D,T.TEXTURE_WRAP_T,T.CLAMP_TO_EDGE),E}function k(T,E,S,P,L){var R=T.getParameter(T.MAX_TEXTURE_SIZE);if(E<0||E>R||S<0||S>R)throw new Error("gl-texture2d: Invalid texture shape");if(L===T.FLOAT&&!T.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var F=b(T);return T.texImage2D(T.TEXTURE_2D,0,P,E,S,0,P,L,null),new x(T,F,E,S,P,L)}function w(T,E,S,P,L,R){var F=b(T);return T.texImage2D(T.TEXTURE_2D,0,L,L,R,E),new x(T,F,S,P,L,R)}function M(T,E){var S=E.dtype,P=E.shape.slice(),L=T.getParameter(T.MAX_TEXTURE_SIZE);if(P[0]<0||P[0]>L||P[1]<0||P[1]>L)throw new Error("gl-texture2d: Invalid texture size");var R=A(P,E.stride.slice()),F=0;S==="float32"?F=T.FLOAT:S==="float64"?(F=T.FLOAT,R=!1,S="float32"):S==="uint8"?F=T.UNSIGNED_BYTE:(F=T.UNSIGNED_BYTE,R=!1,S="uint8");var D,O,N=0;if(P.length===2)N=T.LUMINANCE,P=[P[0],P[1],1],E=i(E.data,P,[E.stride[0],E.stride[1],1],E.offset);else{if(P.length!==3)throw new Error("gl-texture2d: Invalid shape for texture");if(P[2]===1)N=T.ALPHA;else if(P[2]===2)N=T.LUMINANCE_ALPHA;else if(P[2]===3)N=T.RGB;else{if(P[2]!==4)throw new Error("gl-texture2d: Invalid shape for pixel coords");N=T.RGBA}}F!==T.FLOAT||T.getExtension("OES_texture_float")||(F=T.UNSIGNED_BYTE,R=!1);var B=E.size;if(R)D=E.offset===0&&E.data.length===B?E.data:E.data.subarray(E.offset,E.offset+B);else{var W=[P[2],P[2]*P[0],1];O=u.malloc(B,S);var 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E=this._anisoSamples;if(this._anisoSamples=0|Math.max(T,1),E!==this._anisoSamples){var S=this.gl.getExtension("EXT_texture_filter_anisotropic");S&&this.gl.texParameterf(this.gl.TEXTURE_2D,S.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(T){if(this.bind(),m.indexOf(T)<0)throw new Error("gl-texture2d: Unknown wrap mode "+T);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,T),this._wrapS=T}},wrapT:{get:function(){return this._wrapT},set:function(T){if(this.bind(),m.indexOf(T)<0)throw new Error("gl-texture2d: Unknown wrap mode "+T);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,T),this._wrapT=T}},wrap:{get:function(){return this._wrapVector},set:function(T){if(Array.isArray(T)||(T=[T,T]),T.length!==2)throw new Error("gl-texture2d: Must specify wrap mode for rows and columns");for(var E=0;E<2;++E)if(m.indexOf(T[E])<0)throw new Error("gl-texture2d: Unknown wrap 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ne=K*k[T]+te*L[T]+Y*b[T];H[4*T+1]=J*k[T]+re*L[T]+U*b[T],H[4*T+2]=ne,H[4*T+3]=0}var q=H[1],Q=H[5],ee=H[9],ie=H[2],ae=H[6],ue=H[10],le=Q*ue-ee*ae,ge=ee*ie-q*ue,fe=q*ae-Q*ie,me=p(le,ge,fe);for(le/=me,ge/=me,fe/=me,H[0]=le,H[4]=ge,H[8]=fe,T=0;T<3;++T)V[T]=G[T]+H[2+4*T]*R;for(T=0;T<3;++T){S=0;for(var _e=0;_e<3;++_e)S+=H[T+4*_e]*V[_e];H[12+T]=-S}H[15]=1},x.getMatrix=function(A,b){this.recalcMatrix(A);var k=this.computedMatrix;if(b){for(var w=0;w<16;++w)b[w]=k[w];return b}return k};var _=[0,0,0];x.rotate=function(A,b,k,w){if(this.angle.move(A,b,k),w){this.recalcMatrix(A);var M=this.computedMatrix;_[0]=M[2],_[1]=M[6],_[2]=M[10];for(var T=this.computedUp,E=this.computedRight,S=this.computedToward,P=0;P<3;++P)M[4*P]=T[P],M[4*P+1]=E[P],M[4*P+2]=S[P];for(u(M,M,w,_),P=0;P<3;++P)T[P]=M[4*P],E[P]=M[4*P+1];this.up.set(A,T[0],T[1],T[2]),this.right.set(A,E[0],E[1],E[2])}},x.pan=function(A,b,k,w){b=b||0,k=k||0,w=w||0,this.recalcMatrix(A);var 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N,B,W=b[T],G=b[T+4],K=b[T+8],te=W*E+G*S+K*P,Y=p(W-=E*te,G-=S*te,K-=P*te),J=S*(K/=Y)-P*(G/=Y),re=P*(W/=Y)-E*K,U=E*G-S*W,V=p(J,re,U);if(J/=V,re/=V,U/=V,this.center.jump(A,de,ve,Me),this.radius.idle(A),this.up.jump(A,E,S,P),this.right.jump(A,W,G,K),M===2){var H=b[1],ne=b[5],q=b[9],Q=H*W+ne*G+q*K,ee=H*J+ne*re+q*U;N=le<0?-Math.PI/2:Math.PI/2,B=Math.atan2(ee,Q)}else{var ie=b[2],ae=b[6],ue=b[10],le=ie*E+ae*S+ue*P,ge=ie*W+ae*G+ue*K,fe=ie*J+ae*re+ue*U;N=Math.asin(g(le)),B=Math.atan2(fe,ge)}this.angle.jump(A,B,N),this.recalcMatrix(A);var me=b[2],_e=b[6],Ae=b[10],ke=this.computedMatrix;s(ke,b);var Le=ke[15],de=ke[12]/Le,ve=ke[13]/Le,Me=ke[14]/Le,we=Math.exp(this.computedRadius[0]);this.center.jump(A,de-me*we,ve-_e*we,Me-Ae*we)},x.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},x.idle=function(A){this.center.idle(A),this.up.idle(A),this.right.idle(A),this.radius.idle(A),this.angle.idle(A)},x.flush=function(A){this.center.flush(A),this.up.flush(A),this.right.flush(A),this.radius.flush(A),this.angle.flush(A)},x.setDistance=function(A,b){b>0&&this.radius.set(A,Math.log(b))},x.lookAt=function(A,b,k,w){this.recalcMatrix(A),b=b||this.computedEye,k=k||this.computedCenter;var M=(w=w||this.computedUp)[0],T=w[1],E=w[2],S=p(M,T,E);if(!(S<1e-6)){M/=S,T/=S,E/=S;var P=b[0]-k[0],L=b[1]-k[1],R=b[2]-k[2],F=p(P,L,R);if(!(F<1e-6)){P/=F,L/=F,R/=F;var D=this.computedRight,O=D[0],N=D[1],B=D[2],W=M*O+T*N+E*B,G=p(O-=W*M,N-=W*T,B-=W*E);if(!(G<.01&&(G=p(O=T*R-E*L,N=E*P-M*R,B=M*L-T*P))<1e-6)){O/=G,N/=G,B/=G,this.up.set(A,M,T,E),this.right.set(A,O,N,B),this.center.set(A,k[0],k[1],k[2]),this.radius.set(A,Math.log(F));var K=T*B-E*N,te=E*O-M*B,Y=M*N-T*O,J=p(K,te,Y),re=M*P+T*L+E*R,U=O*P+N*L+B*R,V=(K/=J)*P+(te/=J)*L+(Y/=J)*R,H=Math.asin(g(re)),ne=Math.atan2(V,U),q=this.angle._state,Q=q[q.length-1],ee=q[q.length-2];Q%=2*Math.PI;var ie=Math.abs(Q+2*Math.PI-ne),ae=Math.abs(Q-ne),ue=Math.abs(Q-2*Math.PI-ne);ie<ae&&(Q+=2*Math.PI),ue<ae&&(Q-=2*Math.PI),this.angle.jump(this.angle.lastT(),Q,ee),this.angle.set(A,ne,H)}}}}},{"filtered-vector":68,"gl-mat4/invert":98,"gl-mat4/rotate":103,"gl-vec3/cross":157,"gl-vec3/dot":162,"gl-vec3/normalize":179}],306:[function(a,l,c){l.exports=function(s,u,h){var d=s*u,m=i*s,p=m-(m-s),g=s-p,y=i*u,v=y-(y-u),x=u-v,_=g*x-(d-p*v-g*v-p*x);return h?(h[0]=_,h[1]=d,h):[_,d]};var i=+(Math.pow(2,27)+1)},{}],307:[function(a,l,c){l.exports=function(i,s,u){var h=i+s,d=h-i,m=s-d,p=i-(h-d);return u?(u[0]=p+m,u[1]=h,u):[p+m,h]}},{}],308:[function(a,l,c){(function(i){(function(){var s=a("bit-twiddle"),u=a("dup"),h=a("buffer").Buffer;i.__TYPEDARRAY_POOL||(i.__TYPEDARRAY_POOL={UINT8:u([32,0]),UINT16:u([32,0]),UINT32:u([32,0]),BIGUINT64:u([32,0]),INT8:u([32,0]),INT16:u([32,0]),INT32:u([32,0]),BIGINT64:u([32,0]),FLOAT:u([32,0]),DOUBLE:u([32,0]),DATA:u([32,0]),UINT8C:u([32,0]),BUFFER:u([32,0])});var d=typeof Uint8ClampedArray<"u",m=typeof BigUint64Array<"u",p=typeof BigInt64Array<"u",g=i.__TYPEDARRAY_POOL;g.UINT8C||(g.UINT8C=u([32,0])),g.BIGUINT64||(g.BIGUINT64=u([32,0])),g.BIGINT64||(g.BIGINT64=u([32,0])),g.BUFFER||(g.BUFFER=u([32,0]));var y=g.DATA,v=g.BUFFER;function x(O){if(O){var N=O.length||O.byteLength,B=s.log2(N);y[B].push(O)}}function _(O){O=s.nextPow2(O);var N=s.log2(O),B=y[N];return B.length>0?B.pop():new ArrayBuffer(O)}function A(O){return new Uint8Array(_(O),0,O)}function b(O){return new Uint16Array(_(2*O),0,O)}function k(O){return new Uint32Array(_(4*O),0,O)}function w(O){return new Int8Array(_(O),0,O)}function M(O){return new 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Wue=Object.freeze(Object.defineProperty({__proto__:null,loess:oS,regression:sS},Symbol.toStringTag,{value:"Module"})),Xh=11102230246251565e-32,Bl=134217729,Yue=(3+8*Xh)*Xh;function J4(e,n,t,o,f){let r,a,l,c,i=n[0],s=o[0],u=0,h=0;s>i==s>-i?(r=i,i=n[++u]):(r=s,s=o[++h]);let d=0;if(u<e&&h<t)for(s>i==s>-i?(a=i+r,l=r-(a-i),i=n[++u]):(a=s+r,l=r-(a-s),s=o[++h]),r=a,l!==0&&(f[d++]=l);u<e&&h<t;)s>i==s>-i?(a=r+i,c=a-r,l=r-(a-c)+(i-c),i=n[++u]):(a=r+s,c=a-r,l=r-(a-c)+(s-c),s=o[++h]),r=a,l!==0&&(f[d++]=l);for(;u<e;)a=r+i,c=a-r,l=r-(a-c)+(i-c),i=n[++u],r=a,l!==0&&(f[d++]=l);for(;h<t;)a=r+s,c=a-r,l=r-(a-c)+(s-c),s=o[++h],r=a,l!==0&&(f[d++]=l);return(r!==0||d===0)&&(f[d++]=r),d}function Xue(e,n){let t=n[0];for(let o=1;o<e;o++)t+=n[o];return t}function mx(e){return new Float64Array(e)}const Zue=(3+16*Xh)*Xh,Jue=(2+12*Xh)*Xh,Kue=(9+64*Xh)*Xh*Xh,Jg=mx(4),ED=mx(8),SD=mx(12),CD=mx(16),su=mx(4);function Que(e,n,t,o,f,r,a){let l,c,i,s,u,h,d,m,p,g,y,v,x,_,A,b,k,w;const M=e-f,T=t-f,E=n-r,S=o-r;_=M*S,h=Bl*M,d=h-(h-M),m=M-d,h=Bl*S,p=h-(h-S),g=S-p,A=m*g-(_-d*p-m*p-d*g),b=E*T,h=Bl*E,d=h-(h-E),m=E-d,h=Bl*T,p=h-(h-T),g=T-p,k=m*g-(b-d*p-m*p-d*g),y=A-k,u=A-y,Jg[0]=A-(y+u)+(u-k),v=_+y,u=v-_,x=_-(v-u)+(y-u),y=x-b,u=x-y,Jg[1]=x-(y+u)+(u-b),w=v+y,u=w-v,Jg[2]=v-(w-u)+(y-u),Jg[3]=w;let P=Xue(4,Jg),L=Jue*a;if(P>=L||-P>=L||(u=e-M,l=e-(M+u)+(u-f),u=t-T,i=t-(T+u)+(u-f),u=n-E,c=n-(E+u)+(u-r),u=o-S,s=o-(S+u)+(u-r),l===0&&c===0&&i===0&&s===0)||(L=Kue*a+Yue*Math.abs(P),P+=M*s+S*l-(E*i+T*c),P>=L||-P>=L))return P;_=l*S,h=Bl*l,d=h-(h-l),m=l-d,h=Bl*S,p=h-(h-S),g=S-p,A=m*g-(_-d*p-m*p-d*g),b=c*T,h=Bl*c,d=h-(h-c),m=c-d,h=Bl*T,p=h-(h-T),g=T-p,k=m*g-(b-d*p-m*p-d*g),y=A-k,u=A-y,su[0]=A-(y+u)+(u-k),v=_+y,u=v-_,x=_-(v-u)+(y-u),y=x-b,u=x-y,su[1]=x-(y+u)+(u-b),w=v+y,u=w-v,su[2]=v-(w-u)+(y-u),su[3]=w;const R=J4(4,Jg,4,su,ED);_=M*s,h=Bl*M,d=h-(h-M),m=M-d,h=Bl*s,p=h-(h-s),g=s-p,A=m*g-(_-d*p-m*p-d*g),b=E*i,h=Bl*E,d=h-(h-E),m=E-d,h=Bl*i,p=h-(h-i),g=i-p,k=m*g-(b-d*p-m*p-d*g),y=A-k,u=A-y,su[0]=A-(y+u)+(u-k),v=_+y,u=v-_,x=_-(v-u)+(y-u),y=x-b,u=x-y,su[1]=x-(y+u)+(u-b),w=v+y,u=w-v,su[2]=v-(w-u)+(y-u),su[3]=w;const F=J4(R,ED,4,su,SD);_=l*s,h=Bl*l,d=h-(h-l),m=l-d,h=Bl*s,p=h-(h-s),g=s-p,A=m*g-(_-d*p-m*p-d*g),b=c*i,h=Bl*c,d=h-(h-c),m=c-d,h=Bl*i,p=h-(h-i),g=i-p,k=m*g-(b-d*p-m*p-d*g),y=A-k,u=A-y,su[0]=A-(y+u)+(u-k),v=_+y,u=v-_,x=_-(v-u)+(y-u),y=x-b,u=x-y,su[1]=x-(y+u)+(u-b),w=v+y,u=w-v,su[2]=v-(w-u)+(y-u),su[3]=w;const D=J4(F,SD,4,su,CD);return CD[D-1]}function qb(e,n,t,o,f,r){const a=(n-r)*(t-f),l=(e-f)*(o-r),c=a-l;if(a===0||l===0||a>0!=l>0)return c;const i=Math.abs(a+l);return Math.abs(c)>=Zue*i?c:-Que(e,n,t,o,f,r,i)}const LD=Math.pow(2,-52),Hb=new Uint32Array(512);class $_{static from(n,t=ice,o=ace){const f=n.length,r=new Float64Array(f*2);for(let a=0;a<f;a++){const l=n[a];r[2*a]=t(l),r[2*a+1]=o(l)}return new $_(r)}constructor(n){const t=n.length>>1;if(t>0&&typeof n[0]!="number")throw new Error("Expected coords to contain numbers.");this.coords=n;const o=Math.max(2*t-5,0);this._triangles=new Uint32Array(o*3),this._halfedges=new Int32Array(o*3),this._hashSize=Math.ceil(Math.sqrt(t)),this._hullPrev=new Uint32Array(t),this._hullNext=new Uint32Array(t),this._hullTri=new Uint32Array(t),this._hullHash=new Int32Array(this._hashSize).fill(-1),this._ids=new Uint32Array(t),this._dists=new Float64Array(t),this.update()}update(){const{coords:n,_hullPrev:t,_hullNext:o,_hullTri:f,_hullHash:r}=this,a=n.length>>1;let l=1/0,c=1/0,i=-1/0,s=-1/0;for(let T=0;T<a;T++){const E=n[2*T],S=n[2*T+1];E<l&&(l=E),S<c&&(c=S),E>i&&(i=E),S>s&&(s=S),this._ids[T]=T}const u=(l+i)/2,h=(c+s)/2;let d=1/0,m,p,g;for(let T=0;T<a;T++){const E=K4(u,h,n[2*T],n[2*T+1]);E<d&&(m=T,d=E)}const y=n[2*m],v=n[2*m+1];d=1/0;for(let T=0;T<a;T++){if(T===m)continue;const 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T=0,E,S;T<this._ids.length;T++){const P=this._ids[T],L=n[2*P],R=n[2*P+1];if(T>0&&Math.abs(L-E)<=LD&&Math.abs(R-S)<=LD||(E=L,S=R,P===m||P===p||P===g))continue;let F=0;for(let W=0,G=this._hashKey(L,R);W<this._hashSize&&(F=r[(G+W)%this._hashSize],!(F!==-1&&F!==o[F]));W++);F=t[F];let D=F,O;for(;O=o[D],qb(L,R,n[2*D],n[2*D+1],n[2*O],n[2*O+1])>=0;)if(D=O,D===F){D=-1;break}if(D===-1)continue;let N=this._addTriangle(D,P,o[D],-1,-1,f[D]);f[P]=this._legalize(N+2),f[D]=N,M++;let B=o[D];for(;O=o[B],qb(L,R,n[2*B],n[2*B+1],n[2*O],n[2*O+1])<0;)N=this._addTriangle(B,P,O,f[P],-1,f[B]),f[P]=this._legalize(N+2),o[B]=B,M--,B=O;if(D===F)for(;O=t[D],qb(L,R,n[2*O],n[2*O+1],n[2*D],n[2*D+1])<0;)N=this._addTriangle(O,P,D,-1,f[D],f[O]),this._legalize(N+2),f[O]=N,o[D]=D,M--,D=O;this._hullStart=t[P]=D,o[D]=t[B]=P,o[P]=B,r[this._hashKey(L,R)]=P,r[this._hashKey(n[2*D],n[2*D+1])]=D}this.hull=new Uint32Array(M);for(let T=0,E=this._hullStart;T<M;T++)this.hull[T]=E,E=o[E];this.triangles=this._triangles.subarray(0,this.trianglesLen),this.halfedges=this._halfedges.subarray(0,this.trianglesLen)}_hashKey(n,t){return Math.floor(ece(n-this._cx,t-this._cy)*this._hashSize)%this._hashSize}_legalize(n){const{_triangles:t,_halfedges:o,coords:f}=this;let r=0,a=0;for(;;){const l=o[n],c=n-n%3;if(a=c+(n+2)%3,l===-1){if(r===0)break;n=Hb[--r];continue}const i=l-l%3,s=c+(n+1)%3,u=i+(l+2)%3,h=t[a],d=t[n],m=t[s],p=t[u];if(tce(f[2*h],f[2*h+1],f[2*d],f[2*d+1],f[2*m],f[2*m+1],f[2*p],f[2*p+1])){t[n]=p,t[l]=h;const y=o[u];if(y===-1){let x=this._hullStart;do{if(this._hullTri[x]===u){this._hullTri[x]=n;break}x=this._hullPrev[x]}while(x!==this._hullStart)}this._link(n,y),this._link(l,o[a]),this._link(a,u);const v=i+(l+1)%3;r<Hb.length&&(Hb[r++]=v)}else{if(r===0)break;n=Hb[--r]}}return a}_link(n,t){this._halfedges[n]=t,t!==-1&&(this._halfedges[t]=n)}_addTriangle(n,t,o,f,r,a){const l=this.trianglesLen;return 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l=(a+2)%f.length,c=(a+4)%f.length;(f[a]===f[l]&&f[l]===f[c]||f[a+1]===f[l+1]&&f[l+1]===f[c+1])&&(f.splice(l,2),a-=2)}return r}_project(n,t,o,f){let r=1/0,a,l,c;if(f<0){if(t<=this.ymin)return null;(a=(this.ymin-t)/f)<r&&(c=this.ymin,l=n+(r=a)*o)}else if(f>0){if(t>=this.ymax)return null;(a=(this.ymax-t)/f)<r&&(c=this.ymax,l=n+(r=a)*o)}if(o>0){if(n>=this.xmax)return null;(a=(this.xmax-n)/o)<r&&(l=this.xmax,c=t+(r=a)*f)}else if(o<0){if(n<=this.xmin)return null;(a=(this.xmin-n)/o)<r&&(l=this.xmin,c=t+(r=a)*f)}return[l,c]}_edgecode(n,t){return(n===this.xmin?1:n===this.xmax?2:0)|(t===this.ymin?4:t===this.ymax?8:0)}_regioncode(n,t){return(n<this.xmin?1:n>this.xmax?2:0)|(t<this.ymin?4:t>this.ymax?8:0)}};const sce=2*Math.PI,Kg=Math.pow;function lce(e){return e[0]}function uce(e){return e[1]}function cce(e){const{triangles:n,coords:t}=e;for(let o=0;o<n.length;o+=3){const f=2*n[o],r=2*n[o+1],a=2*n[o+2];if((t[a]-t[f])*(t[r+1]-t[f+1])-(t[r]-t[f])*(t[a+1]-t[f+1])>1e-10)return!1}return!0}function 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o=this.halfedges=this._delaunator.halfedges,f=this.hull=this._delaunator.hull,r=this.triangles=this._delaunator.triangles,a=this.inedges.fill(-1),l=this._hullIndex.fill(-1);for(let c=0,i=o.length;c<i;++c){const s=r[c%3===2?c-2:c+1];(o[c]===-1||a[s]===-1)&&(a[s]=c)}for(let c=0,i=f.length;c<i;++c)l[f[c]]=c;f.length<=2&&f.length>0&&(this.triangles=new Int32Array(3).fill(-1),this.halfedges=new Int32Array(3).fill(-1),this.triangles[0]=f[0],a[f[0]]=1,f.length===2&&(a[f[1]]=0,this.triangles[1]=f[1],this.triangles[2]=f[1]))}voronoi(n){return new oce(this,n)}*neighbors(n){const{inedges:t,hull:o,_hullIndex:f,halfedges:r,triangles:a,collinear:l}=this;if(l){const u=l.indexOf(n);u>0&&(yield l[u-1]),u<l.length-1&&(yield l[u+1]);return}const c=t[n];if(c===-1)return;let i=c,s=-1;do{if(yield s=a[i],i=i%3===2?i-2:i+1,a[i]!==n)return;if(i=r[i],i===-1){const u=o[(f[n]+1)%o.length];u!==s&&(yield u);return}}while(i!==c)}find(n,t,o=0){if(n=+n,n!==n||(t=+t,t!==t))return-1;const f=o;let 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Q4=Math.PI/180,$y=1<<11>>5,T2=1<<11;function yce(){var e=[256,256],n,t,o,f,r,a,l,c=Kj,i=[],s=Math.random,u={};u.layout=function(){for(var m=h(Qd()),p=Ace((e[0]>>5)*e[1]),g=null,y=i.length,v=-1,x=[],_=i.map(b=>({text:n(b),font:t(b),style:f(b),weight:r(b),rotate:a(b),size:~~(o(b)+1e-14),padding:l(b),xoff:0,yoff:0,x1:0,y1:0,x0:0,y0:0,hasText:!1,sprite:null,datum:b})).sort((b,k)=>k.size-b.size);++v<y;){var A=_[v];A.x=e[0]*(s()+.5)>>1,A.y=e[1]*(s()+.5)>>1,vce(m,A,_,v),A.hasText&&d(p,A,g)&&(x.push(A),g?bce(g,A):g=[{x:A.x+A.x0,y:A.y+A.y0},{x:A.x+A.x1,y:A.y+A.y1}],A.x-=e[0]>>1,A.y-=e[1]>>1)}return x};function h(m){m.width=m.height=1;var p=Math.sqrt(m.getContext("2d").getImageData(0,0,1,1).data.length>>2);m.width=($y<<5)/p,m.height=T2/p;var g=m.getContext("2d");return g.fillStyle=g.strokeStyle="red",g.textAlign="center",{context:g,ratio:p}}function d(m,p,g){for(var 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o=this,f=o.counts||(o.counts={}),r=d$(n);let a,l,c;return r!=null&&(e=o.scope,a=f[r]),a?t&&t.field&&yO(e,a.agg.params,t):(c={groupby:e.fieldRef(n,"key"),pulse:Hi(o.output)},t&&t.field&&yO(e,c,t),l=e.add(JU(c)),a=e.add(Af({pulse:Hi(l)})),a={agg:l,ref:Hi(a)},r!=null&&(f[r]=a)),a.ref},tuplesRef(){return Hi(this.values)},extentRef(e,n){return py(e,this,"extent","extent",n,!1)},domainRef(e,n){return py(e,this,"domain","values",n,!1)},valuesRef(e,n,t){return py(e,this,"vals","values",n,t||!0)},lookupRef(e,n){return py(e,this,"lookup","tupleindex",n,!1)},indataRef(e,n){return py(e,this,"indata","tupleindex",n,!0,!0)}};function qge(e,n,t){const o=e.from.facet,f=o.name,r=D2(o,n);let a;o.name||Pr("Facet must have a name: "+ri(o)),o.data||Pr("Facet must reference a data set: "+ri(o)),o.field?a=n.add(e$({field:n.fieldRef(o.field),pulse:r})):o.groupby?a=n.add(E0e({key:n.keyRef(o.groupby),group:Hi(n.proxy(t.parent)),pulse:r})):Pr("Facet must specify groupby or field: "+ri(o));const l=n.fork(),c=l.add(Af()),i=l.add(sg({pulse:Hi(c)}));l.addData(f,new U0(l,c,c,i)),l.addSignal("parent",null),a.params.subflow={$subflow:l.parse(e).toRuntime()}}function Hge(e,n,t){const o=n.add(e$({pulse:t.pulse})),f=n.fork();f.add(sg()),f.addSignal("parent",null),o.params.subflow={$subflow:f.parse(e).toRuntime()}}function p$(e,n,t){const o=e.remove,f=e.insert,r=e.toggle,a=e.modify,l=e.values,c=n.add(X_()),i="if("+e.trigger+',modify("'+t+'",'+[f,o,r,a,l].map(u=>u??"null").join(",")+"),0)",s=ch(i,n);c.update=s.$expr,c.params=s.$params}function L3(e,n){const t=f$(e),o=e.type===E3,f=e.from&&e.from.facet,r=e.overlap;let a=e.layout||t===LS||t===CS,l,c,i,s,u,h,d;const m=t===SS||a||f,p=Vge(e.from,o,n);c=n.add(T0e({key:p.key||(e.key?Lv(e.key):void 0),pulse:p.pulse,clean:!o}));const g=Hi(c);c=i=n.add(Af({pulse:g})),c=n.add(O0e({markdef:Fge(e),interactive:Rge(e.interactive,n),clip:Ige(e.clip,n),context:{$context:!0},groups:n.lookup(),parent:n.signals.parent?n.signalRef("parent"):null,index:n.markpath(),pulse:Hi(c)}));const y=Hi(c);c=s=n.add(QU(XU(e.encode,e.type,t,e.style,n,{mod:!1,pulse:y}))),c.params.parent=n.encode(),e.transform&&e.transform.forEach(b=>{const k=jS(b,n),w=k.metadata;(w.generates||w.changes)&&Pr("Mark transforms should not generate new data."),w.nomod||(s.params.mod=!0),k.params.pulse=Hi(c),n.add(c=k)}),e.sort&&(c=n.add(U0e({sort:n.compareRef(e.sort),pulse:Hi(c)})));const v=Hi(c);(f||a)&&(a=n.add(n$({layout:n.objectProperty(e.layout),legends:n.legends,mark:y,pulse:v})),h=Hi(a));const x=n.add(KU({mark:y,pulse:h||v}));d=Hi(x),o&&(m&&(l=n.operators,l.pop(),a&&l.pop()),n.pushState(v,h||d,g),f?qge(e,n,p):m?Hge(e,n,p):n.parse(e),n.popState(),m&&(a&&l.push(a),l.push(x))),r&&(d=Gge(r,d,n));const _=n.add(t$({pulse:d})),A=n.add(sg({pulse:Hi(_)},void 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t=e.type||cO;return!e.type&&Xge(e)===1&&(e.fill||e.stroke)&&(t=zM(n)?L2:sk(n)?gT:cO),t!==L2?t:sk(n)?gT:L2}function Xge(e){return FS.reduce((n,t)=>n+(e[t]?1:0),0)}function Zge(e,n,t){const o={enter:{},update:{}};return Ol(o,{orient:e("orient"),offset:e("offset"),padding:e("padding"),titlePadding:e("titlePadding"),cornerRadius:e("cornerRadius"),fill:e("fillColor"),stroke:e("strokeColor"),strokeWidth:t.strokeWidth,strokeDash:t.strokeDash,x:e("legendX"),y:e("legendY"),format:n.format,formatType:n.formatType}),o}function Jge(e,n,t){const o=T0(vO("size",e,t)),f=T0(vO("strokeWidth",e,t)),r=T0(Kge(t[1].encode,n,_x));return ch(`max(ceil(sqrt(${o})+${f}),${r})`,n)}function vO(e,n,t){return n[e]?`scale("${n[e]}",datum)`:u$(e,t[0].encode)}function Kge(e,n,t){return u$("fontSize",e)||vge("fontSize",n,t)}const Qge=`item.orient==="${y1}"?-90:item.orient==="${v1}"?90:0`;function eme(e,n){e=Li(e)?{text:e}:e;const t=Xu(e,n.config.title),o=e.encode||{},f=o.group||{},r=f.name||void 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Ol(a,{text:r,align:{signal:"item.mark.group.align"},angle:{signal:"item.mark.group.angle"},limit:{signal:"item.mark.group.limit"},baseline:"top",dx:n("dx"),dy:n("dy"),fill:n("color"),font:n("font"),fontSize:n("fontSize"),fontStyle:n("fontStyle"),fontWeight:n("fontWeight"),lineHeight:n("lineHeight")},{align:n("align"),angle:n("angle"),baseline:n("baseline")}),dc({type:lg,role:Jpe,style:uge,from:o,encode:a},t)}function ime(e,n,t,o){const f={value:0},r=e.subtitle,a={enter:{opacity:f},update:{opacity:{value:1}},exit:{opacity:f}};return Ol(a,{text:r,align:{signal:"item.mark.group.align"},angle:{signal:"item.mark.group.angle"},limit:{signal:"item.mark.group.limit"},baseline:"top",dx:n("dx"),dy:n("dy"),fill:n("subtitleColor"),font:n("subtitleFont"),fontSize:n("subtitleFontSize"),fontStyle:n("subtitleFontStyle"),fontWeight:n("subtitleFontWeight"),lineHeight:n("subtitleLineHeight")},{align:n("align"),angle:n("angle"),baseline:n("baseline")}),dc({type:lg,role:Kpe,style:cge,from:o,encode:a},t)}function ame(e,n){const t=[];e.transform&&e.transform.forEach(o=>{t.push(jS(o,n))}),e.on&&e.on.forEach(o=>{p$(o,n,e.name)}),n.addDataPipeline(e.name,ome(e,n,t))}function ome(e,n,t){const o=[];let f=null,r=!1,a=!1,l,c,i,s,u;for(e.values?Qs(e.values)||S2(e.format)?(o.push(xO(n,e)),o.push(f=Xp())):o.push(f=Xp({$ingest:e.values,$format:e.format})):e.url?S2(e.url)||S2(e.format)?(o.push(xO(n,e)),o.push(f=Xp())):o.push(f=Xp({$request:e.url,$format:e.format})):e.source&&(f=l=Ti(e.source).map(h=>Hi(n.getData(h).output)),o.push(null)),c=0,i=t.length;c<i;++c)s=t[c],u=s.metadata,!f&&!u.source&&o.push(f=Xp()),o.push(s),u.generates&&(a=!0),u.modifies&&!a&&(r=!0),u.source?f=s:u.changes&&(f=null);return l&&(i=l.length-1,o[0]=B0e({derive:r,pulse:i?l:l[0]}),(r||i)&&o.splice(1,0,Xp())),f||o.push(Xp()),o.push(sg({})),o}function Xp(e){const n=Af({},e);return n.metadata={source:!0},n}function xO(e,n){return D0e({url:n.url?e.property(n.url):void 0,async:n.async?e.property(n.async):void 0,values:n.values?e.property(n.values):void 0,format:e.objectProperty(n.format)})}const 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(${pm(n)}) : `:"")+"(null)"}),pm=e=>Qs(e)?e.signal:e==null?null:ri(e),pme=(e,n)=>n===0?0:Qs(e)?{signal:`(${e.signal}) * ${n}`}:{value:e*n},vm=(e,n)=>{const t=e.signal;return t&&t.endsWith("(null)")?{signal:t.slice(0,-6)+n.signal}:e};function Qg(e,n,t,o){let f;if(n&&Yi(n,e))return n[e];if(Yi(t,e))return t[e];if(e.startsWith("title")){switch(e){case"titleColor":f="fill";break;case"titleFont":case"titleFontSize":case"titleFontWeight":f=e[5].toLowerCase()+e.slice(6)}return o[IS][f]}else if(e.startsWith("label")){switch(e){case"labelColor":f="fill";break;case"labelFont":case"labelFontSize":f=e[5].toLowerCase()+e.slice(6)}return o[_x][f]}return null}function _O(e){const n={};for(const t of e)if(t)for(const o in t)n[o]=1;return Object.keys(n)}function gme(e,n){var t=n.config,o=t.style,f=t.axis,r=n.scaleType(e.scale)==="band"&&t.axisBand,a=e.orient,l,c,i;if(Qs(a)){const u=_O([t.axisX,t.axisY]),h=_O([t.axisTop,t.axisBottom,t.axisLeft,t.axisRight]);l={};for(i of 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d={enter:s={opacity:$a},update:h={opacity:Ec},exit:u={opacity:$a}};Ol(d,{stroke:r("gridColor"),strokeCap:r("gridCap"),strokeDash:r("gridDash"),strokeDashOffset:r("gridDashOffset"),strokeOpacity:r("gridOpacity"),strokeWidth:r("gridWidth")});const m={scale:e.scale,field:Mc,band:f.band,extra:f.extra,offset:f.offset,round:r("tickRound")},p=cl(a,{signal:"height"},{signal:"width"}),g=l?{scale:l,range:0,mult:c,offset:i}:{value:0,offset:i},y=l?{scale:l,range:1,mult:c,offset:i}:Ea(p,{mult:c,offset:i});return s.x=h.x=cl(a,m,g),s.y=h.y=pf(a,m,g),s.x2=h.x2=pf(a,y),s.y2=h.y2=cl(a,y),u.x=cl(a,m),u.y=pf(a,m),dc({type:RS,role:jpe,key:Mc,from:o,encode:d},t)}function vme(e,n){if(n!==1)if(!Si(e))e=Qs(n)?{signal:`(${n.signal}) * (${e||0})`}:n*(e||0);else{let t=e=Ea({},e);for(;t.mult!=null;)if(Si(t.mult))t=t.mult=Ea({},t.mult);else return t.mult=Qs(n)?{signal:`(${t.mult}) * (${n.signal})`}:t.mult*n,e;t.mult=n}return e}function xme(e,n,t,o,f,r){const a=Xu(e,n),l=e.orient,c=D3(l,-1,1);let i,s,u;const h={enter:i={opacity:$a},update:u={opacity:Ec},exit:s={opacity:$a}};Ol(h,{stroke:a("tickColor"),strokeCap:a("tickCap"),strokeDash:a("tickDash"),strokeDashOffset:a("tickDashOffset"),strokeOpacity:a("tickOpacity"),strokeWidth:a("tickWidth")});const d=ul(f);d.mult=c;const m={scale:e.scale,field:Mc,band:r.band,extra:r.extra,offset:r.offset,round:a("tickRound")};return u.y=i.y=cl(l,$a,m),u.y2=i.y2=cl(l,d),s.x=cl(l,m),u.x=i.x=pf(l,$a,m),u.x2=i.x2=pf(l,d),s.y=pf(l,m),dc({type:RS,role:$pe,key:Mc,from:o,encode:h},t)}function uA(e,n,t,o,f){return{signal:'flush(range("'+e+'"), scale("'+e+'", datum.value), '+n+","+t+","+o+","+f+")"}}function bme(e,n,t,o,f,r){const a=Xu(e,n),l=e.orient,c=e.scale,i=D3(l,-1,1),s=T0(a("labelFlush")),u=T0(a("labelFlushOffset")),h=a("labelAlign"),d=a("labelBaseline");let m=s===0||!!s,p;const g=ul(f);g.mult=i,g.offset=ul(a("labelPadding")||0),g.offset.mult=i;const y={scale:c,field:Mc,band:.5,offset:c$(r.offset,a("labelOffset"))},v=cl(l,m?uA(c,s,'"left"','"right"','"center"'):{value:"center"},sme(l,"left","right")),x=cl(l,m$(l,"bottom","top"),m?uA(c,s,'"top"','"bottom"','"middle"'):{value:"middle"}),_=uA(c,s,`-(${u})`,u,0);m=m&&u;const A={opacity:$a,x:cl(l,y,g),y:pf(l,y,g)},b={enter:A,update:p={opacity:Ec,text:{field:PS},x:A.x,y:A.y,align:v,baseline:x},exit:{opacity:$a,x:A.x,y:A.y}};Ol(b,{dx:!h&&m?cl(l,_):null,dy:!d&&m?pf(l,_):null}),Ol(b,{angle:a("labelAngle"),fill:a("labelColor"),fillOpacity:a("labelOpacity"),font:a("labelFont"),fontSize:a("labelFontSize"),fontWeight:a("labelFontWeight"),fontStyle:a("labelFontStyle"),limit:a("labelLimit"),lineHeight:a("labelLineHeight")},{align:h,baseline:d});const k=a("labelBound");let w=a("labelOverlap");return w=w||k?{separation:a("labelSeparation"),method:w,order:"datum.index",bound:k?{scale:c,orient:l,tolerance:k}:null}:void 0,p.align!==v&&(p.align=vm(p.align,v)),p.baseline!==x&&(p.baseline=vm(p.baseline,x)),dc({type:lg,role:Upe,style:_x,key:Mc,from:o,encode:b,overlap:w},t)}function _me(e,n,t,o){const f=Xu(e,n),r=e.orient,a=D3(r,-1,1);let l,c;const i={enter:l={opacity:$a,anchor:ul(f("titleAnchor",null)),align:{signal:zS}},update:c=Ea({},l,{opacity:Ec,text:ul(e.title)}),exit:{opacity:$a}},s={signal:`lerp(range("${e.scale}"), ${S3(0,1,.5)})`};return c.x=cl(r,s),c.y=pf(r,s),l.angle=cl(r,$a,pme(a,90)),l.baseline=cl(r,m$(r,dp,kf),{value:dp}),c.angle=l.angle,c.baseline=l.baseline,Ol(i,{fill:f("titleColor"),fillOpacity:f("titleOpacity"),font:f("titleFont"),fontSize:f("titleFontSize"),fontStyle:f("titleFontStyle"),fontWeight:f("titleFontWeight"),limit:f("titleLimit"),lineHeight:f("titleLineHeight")},{align:f("titleAlign"),angle:f("titleAngle"),baseline:f("titleBaseline")}),wme(f,r,i,t),i.update.align=vm(i.update.align,l.align),i.update.angle=vm(i.update.angle,l.angle),i.update.baseline=vm(i.update.baseline,l.baseline),dc({type:lg,role:Vpe,style:IS,from:o,encode:i},t)}function wme(e,n,t,o){const f=(l,c)=>l!=null?(t.update[c]=vm(ul(l),t.update[c]),!1):!om(c,o),r=f(e("titleX"),"x"),a=f(e("titleY"),"y");t.enter.auto=a===r?ul(a):cl(n,ul(a),ul(r))}function Ame(e,n){const t=gme(e,n),o=e.encode||{},f=o.axis||{},r=f.name||void 0,a=f.interactive,l=f.style,c=Xu(e,t),i=xge(c),s={scale:e.scale,ticks:!!c("ticks"),labels:!!c("labels"),grid:!!c("grid"),domain:!!c("domain"),title:e.title!=null},u=Hi(n.add(Af({},[s]))),h=Hi(n.add(k0e({scale:n.scaleRef(e.scale),extra:n.property(i.extra),count:n.objectProperty(e.tickCount),values:n.objectProperty(e.values),minstep:n.property(e.tickMinStep),formatType:n.property(e.formatType),formatSpecifier:n.property(e.format)}))),d=[];let m;return s.grid&&d.push(yme(e,t,o.grid,h,i)),s.ticks&&(m=c("tickSize"),d.push(xme(e,t,o.ticks,h,m,i))),s.labels&&(m=s.ticks?m:0,d.push(bme(e,t,o.labels,h,m,i))),s.domain&&d.push(mme(e,t,o.domain,u)),s.title&&d.push(_me(e,t,o.title,u)),L3(Ov({role:Npe,from:u,encode:m1(kme(c,e),f,wx),marks:d,aria:c("aria"),description:c("description"),zindex:c("zindex"),name:r,interactive:a,style:l}),n)}function kme(e,n){const t={enter:{},update:{}};return Ol(t,{orient:e("orient"),offset:e("offset")||0,position:nf(n.position,0),titlePadding:e("titlePadding"),minExtent:e("minExtent"),maxExtent:e("maxExtent"),range:{signal:`abs(span(range("${n.scale}")))`},translate:e("translate"),format:n.format,formatType:n.formatType}),t}function v$(e,n,t){const o=Ti(e.signals),f=Ti(e.scales);return t||o.forEach(r=>ZU(r,n)),Ti(e.projections).forEach(r=>nge(r,n)),f.forEach(r=>q0e(r,n)),Ti(e.data).forEach(r=>ame(r,n)),f.forEach(r=>H0e(r,n)),(t||o).forEach(r=>A0e(r,n)),Ti(e.axes).forEach(r=>Ame(r,n)),Ti(e.marks).forEach(r=>L3(r,n)),Ti(e.legends).forEach(r=>Wge(r,n)),e.title&&eme(e.title,n),n.parseLambdas(),n}const Tme=e=>m1({enter:{x:{value:0},y:{value:0}},update:{width:{signal:"width"},height:{signal:"height"}}},e);function Mme(e,n){const t=n.config,o=Hi(n.root=n.add(X_())),f=Eme(e,t);f.forEach(i=>ZU(i,n)),n.description=e.description||t.description,n.eventConfig=t.events,n.legends=n.objectProperty(t.legend&&t.legend.layout),n.locale=t.locale;const r=n.add(Af()),a=n.add(QU(XU(Tme(e.encode),E3,CS,e.style,n,{pulse:Hi(r)}))),l=n.add(n$({layout:n.objectProperty(e.layout),legends:n.legends,autosize:n.signalRef("autosize"),mark:o,pulse:Hi(a)}));n.operators.pop(),n.pushState(Hi(a),Hi(l),null),v$(e,n,f),n.operators.push(l);let c=n.add(KU({mark:o,pulse:Hi(l)}));return c=n.add(t$({pulse:Hi(c)})),c=n.add(sg({pulse:Hi(c)})),n.addData("root",new U0(n,r,r,c)),n}function gy(e,n){return n&&n.signal?{name:e,update:n.signal}:{name:e,value:n}}function Eme(e,n){const t=a=>nf(e[a],n[a]),o=[gy("background",t("background")),gy("autosize",Fpe(t("autosize"))),gy("padding",zpe(t("padding"))),gy("width",t("width")||0),gy("height",t("height")||0)],f=o.reduce((a,l)=>(a[l.name]=l,a),{}),r={};return Ti(e.signals).forEach(a=>{Yi(f,a.name)?a=Ea(f[a.name],a):o.push(a),r[a.name]=a}),Ti(n.signals).forEach(a=>{!Yi(r,a.name)&&!Yi(f,a.name)&&o.push(a)}),o}function x$(e,n){this.config=e||{},this.options=n||{},this.bindings=[],this.field={},this.signals={},this.lambdas={},this.scales={},this.events={},this.data={},this.streams=[],this.updates=[],this.operators=[],this.eventConfig=null,this.locale=null,this._id=0,this._subid=0,this._nextsub=[0],this._parent=[],this._encode=[],this._lookup=[],this._markpath=[]}function AO(e){this.config=e.config,this.options=e.options,this.legends=e.legends,this.field=Object.create(e.field),this.signals=Object.create(e.signals),this.lambdas=Object.create(e.lambdas),this.scales=Object.create(e.scales),this.events=Object.create(e.events),this.data=Object.create(e.data),this.streams=[],this.updates=[],this.operators=[],this._id=0,this._subid=++e._nextsub[0],this._nextsub=e._nextsub,this._parent=e._parent.slice(),this._encode=e._encode.slice(),this._lookup=e._lookup.slice(),this._markpath=e._markpath}x$.prototype=AO.prototype={parse(e){return v$(e,this)},fork(){return new AO(this)},isSubscope(){return this._subid>0},toRuntime(){return this.finish(),{description:this.description,operators:this.operators,streams:this.streams,updates:this.updates,bindings:this.bindings,eventConfig:this.eventConfig,locale:this.locale}},id(){return(this._subid?this._subid+":":0)+this._id++},add(e){return this.operators.push(e),e.id=this.id(),e.refs&&(e.refs.forEach(n=>{n.$ref=e.id}),e.refs=null),e},proxy(e){const n=e instanceof cT?Hi(e):e;return this.add(N0e({value:n}))},addStream(e){return this.streams.push(e),e.id=this.id(),e},addUpdate(e){return this.updates.push(e),e},finish(){let e,n;this.root&&(this.root.root=!0);for(e in this.signals)this.signals[e].signal=e;for(e in this.scales)this.scales[e].scale=e;function t(o,f,r){let a,l;o&&(a=o.data||(o.data={}),l=a[f]||(a[f]=[]),l.push(r))}for(e in this.data){n=this.data[e],t(n.input,e,"input"),t(n.output,e,"output"),t(n.values,e,"values");for(const o in n.index)t(n.index[o],e,"index:"+o)}return this},pushState(e,n,t){this._encode.push(Hi(this.add(sg({pulse:e})))),this._parent.push(n),this._lookup.push(t?Hi(this.proxy(t)):null),this._markpath.push(-1)},popState(){this._encode.pop(),this._parent.pop(),this._lookup.pop(),this._markpath.pop()},parent(){return qa(this._parent)},encode(){return qa(this._encode)},lookup(){return qa(this._lookup)},markpath(){const e=this._markpath;return++e[e.length-1]},fieldRef(e,n){if(Li(e))return Lv(e,n);e.signal||Pr("Unsupported field reference: "+ri(e));const t=e.signal;let o=this.field[t];if(!o){const f={name:this.signalRef(t)};n&&(f.as=n),this.field[t]=o=Hi(this.add(S0e(f)))}return o},compareRef(e){let n=!1;const t=r=>Qs(r)?(n=!0,this.signalRef(r.signal)):d0e(r)?(n=!0,this.exprRef(r.expr)):r,o=Ti(e.field).map(t),f=Ti(e.order).map(t);return n?Hi(this.add(uO({fields:o,orders:f}))):sO(o,f)},keyRef(e,n){let t=!1;const o=r=>Qs(r)?(t=!0,Hi(f[r.signal])):r,f=this.signals;return e=Ti(e).map(o),t?Hi(this.add(C0e({fields:e,flat:n}))):u0e(e,n)},sortRef(e){if(!e)return e;const n=T3(e.op,e.field),t=e.order||c0e;return t.signal?Hi(this.add(uO({fields:n,orders:this.signalRef(t.signal)}))):sO(n,t)},event(e,n){const t=e+":"+n;if(!this.events[t]){const o=this.id();this.streams.push({id:o,source:e,type:n}),this.events[t]=o}return this.events[t]},hasOwnSignal(e){return Yi(this.signals,e)},addSignal(e,n){this.hasOwnSignal(e)&&Pr("Duplicate signal name: "+ri(e));const t=n instanceof cT?n:this.add(X_(n));return this.signals[e]=t},getSignal(e){return this.signals[e]||Pr("Unrecognized signal name: "+ri(e)),this.signals[e]},signalRef(e){return this.signals[e]?Hi(this.signals[e]):(Yi(this.lambdas,e)||(this.lambdas[e]=this.add(X_(null))),Hi(this.lambdas[e]))},parseLambdas(){const e=Object.keys(this.lambdas);for(let n=0,t=e.length;n<t;++n){const o=e[n],f=ch(o,this),r=this.lambdas[o];r.params=f.$params,r.update=f.$expr}},property(e){return 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Try moving the lookup into a second, layered view?`}const iye="The same selection must be used to override scale domains in a layered view.",aye='Interval selections should be initialized using "x", "y", "longitude", or "latitude" keys.';function oye(e){return`Unknown repeated value "${e}".`}function DO(e){return`The "columns" property cannot be used when "${e}" has nested row/column.`}const sye="Axes cannot be shared in concatenated or repeated views yet (https://github.com/vega/vega-lite/issues/2415).";function lye(e){return`Unrecognized parse "${e}".`}function OO(e,n,t){return`An ancestor parsed field "${e}" as ${t} but a child wants to parse the field as ${n}.`}const uye="Attempt to add the same child twice.";function cye(e){return`Ignoring an invalid transform: ${$o(e)}.`}const fye='If "from.fields" is not specified, "as" has to be a string that specifies the key to be used for the data from the secondary source.';function PO(e){return`Config.customFormatTypes is not true, thus custom format type and format for channel ${e} are dropped.`}function hye(e){const{parentProjection:n,projection:t}=e;return`Layer's shared projection ${$o(n)} is overridden by a child projection ${$o(t)}.`}const dye="Arc marks uses theta channel rather than angle, replacing angle with theta.";function pye(e){return`${e}Offset dropped because ${e} is continuous`}function gye(e){return`There is no ${e} encoding. Replacing ${e}Offset encoding as ${e}.`}function mye(e,n,t){return`Channel ${e} is a ${n}. Converted to {value: ${$o(t)}}.`}function eV(e){return`Invalid field type "${e}".`}function yye(e,n){return`Invalid field type "${e}" for aggregate: "${n}", using "quantitative" instead.`}function vye(e){return`Invalid aggregation operator "${e}".`}function tV(e,n){const{fill:t,stroke:o}=n;return`Dropping color ${e} as the plot also has ${t&&o?"fill and stroke":t?"fill":"stroke"}.`}function xye(e){return`Position range does not support relative band size for ${e}.`}function _T(e,n){return`Dropping ${$o(e)} from channel "${n}" since it does not contain any data field, datum, value, or signal.`}const bye="Line marks cannot encode size with a non-groupby field. You may want to use trail marks instead.";function U3(e,n,t){return`${e} dropped as it is incompatible with "${n}"${t?` when ${t}`:""}.`}function _ye(e){return`${e} encoding has no scale, so specified scale is ignored.`}function wye(e){return`${e}-encoding is dropped as ${e} is not a valid encoding channel.`}function Aye(e){return`${e} encoding should be discrete (ordinal / nominal / binned).`}function kye(e){return`${e} encoding should be discrete (ordinal / nominal / binned) or use a discretizing scale (e.g. threshold).`}function Tye(e){return`Facet encoding dropped as ${e.join(" and ")} ${e.length>1?"are":"is"} also specified.`}function fA(e,n){return`Using discrete channel "${e}" to encode "${n}" field can be misleading as it does not encode ${n==="ordinal"?"order":"magnitude"}.`}function Mye(e){return`The ${e} for range marks cannot be an expression`}function Eye(e,n){return`Line mark is for continuous lines and thus cannot be used with ${e&&n?"x2 and y2":e?"x2":"y2"}. We will use the rule mark (line segments) instead.`}function Sye(e,n){return`Specified orient "${e}" overridden with "${n}".`}function Cye(e){return`Cannot use the scale property "${e}" with non-color channel.`}function Lye(e){return`Cannot use the relative band size with ${e} scale.`}function Dye(e){return`Using unaggregated domain with raw field has no effect (${$o(e)}).`}function Oye(e){return`Unaggregated domain not applicable for "${e}" since it produces values outside the origin domain of the source data.`}function Pye(e){return`Unaggregated domain is currently unsupported for log scale (${$o(e)}).`}function Iye(e){return`Cannot apply size to non-oriented mark "${e}".`}function Fye(e,n,t){return`Channel "${e}" does not work with "${n}" scale. We are using "${t}" scale instead.`}function Rye(e,n){return`FieldDef does not work with "${e}" scale. We are using "${n}" scale instead.`}function nV(e,n,t){return`${t}-scale's "${n}" is dropped as it does not work with ${e} scale.`}function rV(e){return`The step for "${e}" is dropped because the ${e==="width"?"x":"y"} is continuous.`}function zye(e,n,t,o){return`Conflicting ${n.toString()} property "${e.toString()}" (${$o(t)} and ${$o(o)}). Using ${$o(t)}.`}function Nye(e,n,t,o){return`Conflicting ${n.toString()} property "${e.toString()}" (${$o(t)} and ${$o(o)}). Using the union of the two domains.`}function Bye(e){return`Setting the scale to be independent for "${e}" means we also have to set the guide (axis or legend) to be independent.`}function jye(e){return`Dropping sort property ${$o(e)} as unioned domains only support boolean or op "count", "min", and "max".`}const IO="Domains that should be unioned has conflicting sort properties. Sort will be set to true.",Uye="Detected faceted independent scales that union domain of multiple fields from different data sources. 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gV(e)&&e.timeUnit?{...e,timeUnit:hl(e.timeUnit)}:e}const Ex={quantitative:"quantitative",ordinal:"ordinal",temporal:"temporal",nominal:"nominal",geojson:"geojson"};function _ve(e){return e==="quantitative"||e==="temporal"}function yV(e){return e==="ordinal"||e==="nominal"}const G0=Ex.quantitative,x8=Ex.ordinal,Wm=Ex.temporal,b8=Ex.nominal,_1=Ex.geojson;function wve(e){if(e)switch(e=e.toLowerCase(),e){case"q":case G0:return"quantitative";case"t":case Wm:return"temporal";case"o":case x8:return"ordinal";case"n":case b8:return"nominal";case _1:return"geojson"}}const 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Dve={type:1,domain:1,domainMax:1,domainMin:1,domainMid:1,align:1,range:1,rangeMax:1,rangeMin:1,scheme:1,bins:1,reverse:1,round:1,clamp:1,nice:1,base:1,exponent:1,constant:1,interpolate:1,zero:1,padding:1,paddingInner:1,paddingOuter:1},{type:GTe,domain:WTe,range:YTe,rangeMax:XTe,rangeMin:ZTe,scheme:JTe,...Ove}=Dve,Pve=Zr(Ove);function AT(e,n){switch(n){case"type":case"domain":case"reverse":case"range":return!0;case"scheme":case"interpolate":return!["point","band","identity"].includes(e);case"bins":return!["point","band","identity","ordinal"].includes(e);case"round":return ff(e)||e==="band"||e==="point";case"padding":case"rangeMin":case"rangeMax":return ff(e)||["point","band"].includes(e);case"paddingOuter":case"align":return["point","band"].includes(e);case"paddingInner":return e==="band";case"domainMax":case"domainMid":case"domainMin":case"clamp":return ff(e);case"nice":return ff(e)||e==="quantize"||e==="threshold";case"exponent":return e==="pow";case"base":return e==="log";case"constant":return e==="symlog";case"zero":return pc(e)&&!ja(["log","time","utc","threshold","quantile"],e)}}function AV(e,n){switch(n){case"interpolate":case"scheme":case"domainMid":return bm(e)?void 0:Cye(n);case"align":case"type":case"bins":case"domain":case"domainMax":case"domainMin":case"range":case"base":case"exponent":case"constant":case"nice":case"padding":case"paddingInner":case"paddingOuter":case"rangeMax":case"rangeMin":case"reverse":case"round":case"clamp":case"zero":return}}function Ive(e,n){return ja([x8,b8],n)?e===void 0||ml(e):n===Wm?ja([Uu.TIME,Uu.UTC,void 0],e):n===G0?bV(e)||Ym(e)||e===void 0:!0}function Fve(e,n,t=!1){if(!gd(e))return!1;switch(e){case ts:case dl:case Ap:case x1:case Ic:case Mf:return ff(n)||n==="band"?!0:n==="point"?!t:!1;case dd:case Mp:case pd:case kp:case Tp:case ug:return ff(n)||Ym(n)||ja(["band","point","ordinal"],n);case Gu:case xh:case bh:return n!=="band";case Ep:case Wu:return n==="ordinal"||Ym(n)}}const Tu={arc:"arc",area:"area",bar:"bar",image:"image",line:"line",point:"point",rect:"rect",rule:"rule",text:"text",tick:"tick",trail:"trail",circle:"circle",square:"square",geoshape:"geoshape"},kV=Tu.arc,V3=Tu.area,q3=Tu.bar,Rve=Tu.image,H3=Tu.line,G3=Tu.point,zve=Tu.rect,Q_=Tu.rule,TV=Tu.text,_8=Tu.tick,Nve=Tu.trail,w8=Tu.circle,A8=Tu.square,MV=Tu.geoshape;function Lp(e){return["line","area","trail"].includes(e)}function k8(e){return["rect","bar","image","arc"].includes(e)}const Bve=new Set(Zr(Tu));function fh(e){return e.type}const 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f={...t,...t.tooltipFormat},r={},a=o?"datum.datum":"datum",l=[];function c(s,u){const h=cg(u),d=Au(s)?s:{...s,type:e[h].type},m=d.title||O8(d,f),p=Ti(m).join(", ");let g;if(pl(u)){const y=u==="x"?"x2":"y2",v=hh(e[y]);if(Ml(d.bin)&&v){const x=hi(d,{expr:a}),_=hi(v,{expr:a}),{format:A,formatType:b}=rw(d);g=Sx(x,_,A,b,f),r[y]=!0}}if((pl(u)||u===Ic||u===Mf)&&n&&n.fieldChannel===u&&n.offset==="normalize"){const{format:y,formatType:v}=rw(d);g=S8({fieldOrDatumDef:d,format:y,formatType:v,expr:a,config:f,normalizeStack:!0}).signal}g??(g=i5(d,f,a).signal),l.push({channel:u,key:p,value:g})}F8(e,(s,u)=>{ni(s)?c(s,u):Z3(s)&&c(s.condition,u)});const i={};for(const{channel:s,key:u,value:h}of l)!r[s]&&!i[u]&&(i[u]=h);return i}function oP(e,n,t,{reactiveGeom:o}={}){const f=Uq(e,n,t,{reactiveGeom:o}),r=pp(f).map(([a,l])=>`"${a}": ${l}`);return r.length>0?{signal:`{${r.join(", ")}}`}:void 0}function A2e(e){const{markDef:n,config:t}=e,o=uo("aria",n,t);return 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Ywe(e){if(e.hasProjection){const n=Iu(e.specifiedProjection),t=!(n&&(n.scale!=null||n.translate!=null)),o=t?[e.getSizeSignalRef("width"),e.getSizeSignalRef("height")]:void 0,f=t?Xwe(e):void 0,r=new LH(e.projectionName(!0),{...Iu(e.config.projection)??{},...n??{}},o,f);return r.get("type")||r.set("type","equalEarth",!1),r}}function Xwe(e){const n=[],{encoding:t}=e;for(const o of[[Sf,Ef],[Oc,Cf]])(Ks(t[o[0]])||Ks(t[o[1]]))&&n.push({signal:e.getName(`geojson_${n.length}`)});return e.channelHasField(Wu)&&e.typedFieldDef(Wu).type===_1&&n.push({signal:e.getName(`geojson_${n.length}`)}),n.length===0&&n.push(e.requestDataName(Uo.Main)),n}function Zwe(e,n){const t=WS(Wwe,f=>!!(!Yi(e.explicit,f)&&!Yi(n.explicit,f)||Yi(e.explicit,f)&&Yi(n.explicit,f)&&Xf(e.get(f),n.get(f))));if(Xf(e.size,n.size)){if(t)return e;if(Xf(e.explicit,{}))return n;if(Xf(n.explicit,{}))return e}return null}function Jwe(e){if(e.children.length===0)return;let n;for(const o of e.children)DH(o);const t=WS(e.children,o=>{const 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o,f;e3e(e)?o=Li(e.as)?[e.as,`${e.as}_end`]:[e.as[0],e.as[1]]:o=[hi(e,{forAs:!0}),hi(e,{binSuffix:"end",forAs:!0})];const r={...J3(n,void 0)},a=OH(r,e.field),{signal:l,extentSignal:c}=Qwe(t,a);if(j3(r.extent)){const s=r.extent;f=cH(t,s.param,s),delete r.extent}const i={bin:r,field:e.field,as:[o],...l?{signal:l}:{},...c?{extentSignal:c}:{},...f?{span:f}:{}};return{key:a,binComponent:i}}class ih extends wo{clone(){return new ih(null,la(this.bins))}constructor(n,t){super(n),this.bins=t}static makeFromEncoding(n,t){const o=t.reduceFieldDef((f,r,a)=>{if(Au(r)&&Vo(r.bin)){const{key:l,binComponent:c}=MP(r,r.bin,t);f[l]={...c,...f[l],...Kwe(t,r,a,t.config)}}return f},{});return Mo(o)?null:new ih(n,o)}static makeFromTransform(n,t,o){const{key:f,binComponent:r}=MP(t,t.bin,o);return new ih(n,{[f]:r})}merge(n,t){for(const o of Zr(n.bins))o in this.bins?(t(n.bins[o].signal,this.bins[o].signal),this.bins[o].as=Zf([...this.bins[o].as,...n.bins[o].as],Ba)):this.bins[o]=n.bins[o];for(const o of n.children)n.removeChild(o),o.parent=this;n.remove()}producedFields(){return new Set(Dl(this.bins).map(n=>n.as).flat(2))}dependentFields(){return new Set(Dl(this.bins).map(n=>n.field))}hash(){return`Bin ${Ba(this.bins)}`}assemble(){return Dl(this.bins).flatMap(n=>{const t=[],[o,...f]=n.as,{extent:r,...a}=n.bin,l={type:"bin",field:Dc(n.field),as:o,signal:n.signal,...j3(r)?{extent:null}:{extent:r},...n.span?{span:{signal:`span(${n.span})`}}:{},...a};!r&&n.extentSignal&&(t.push({type:"extent",field:Dc(n.field),signal:n.extentSignal}),l.extent={signal:n.extentSignal}),t.push(l);for(const c of f)for(let i=0;i<2;i++)t.push({type:"formula",expr:hi({field:o[i]},{expr:"datum"}),as:c[i]});return n.formula&&t.push({type:"formula",expr:n.formula,as:n.formulaAs}),t})}}function t3e(e,n,t,o){const f=As(o)?o.encoding[_h(n)]:void 0;if(Au(t)&&As(o)&&NV(t,f,o.markDef,o.config))e.add(hi(t,{})),e.add(hi(t,{suffix:"end"})),t.bin&&Dx(t,n)&&e.add(hi(t,{binSuffix:"range"}));else if(P$(n)){const r=O$(n);e.add(o.getName(r))}else e.add(hi(t));return pg(t)&&Lve(t.scale?.range)&&e.add(t.scale.range.field),e}function n3e(e,n){for(const t of Zr(n)){const o=n[t];for(const f of Zr(o))t in e?e[t][f]=new Set([...e[t][f]??[],...o[f]]):e[t]={[f]:o[f]}}}class gf extends wo{clone(){return new gf(null,new Set(this.dimensions),la(this.measures))}constructor(n,t,o){super(n),this.dimensions=t,this.measures=o}get groupBy(){return this.dimensions}static makeFromEncoding(n,t){let o=!1;t.forEachFieldDef(a=>{a.aggregate&&(o=!0)});const f={},r=new Set;return!o||(t.forEachFieldDef((a,l)=>{const{aggregate:c,field:i}=a;if(c)if(c==="count")f["*"]??(f["*"]={}),f["*"].count=new Set([hi(a,{forAs:!0})]);else{if(rd(c)||Sp(c)){const s=rd(c)?"argmin":"argmax",u=c[s];f[u]??(f[u]={}),f[u][s]=new Set([hi({op:s,field:u},{forAs:!0})])}else f[i]??(f[i]={}),f[i][c]=new Set([hi(a,{forAs:!0})]);gd(l)&&t.scaleDomain(l)==="unaggregated"&&(f[i]??(f[i]={}),f[i].min=new Set([hi({field:i,aggregate:"min"},{forAs:!0})]),f[i].max=new Set([hi({field:i,aggregate:"max"},{forAs:!0})]))}else t3e(r,l,a,t)}),r.size+Zr(f).length===0)?null:new gf(n,r,f)}static makeFromTransform(n,t){const o=new Set,f={};for(const r of t.aggregate){const{op:a,field:l,as:c}=r;a&&(a==="count"?(f["*"]??(f["*"]={}),f["*"].count=new Set([c||hi(r,{forAs:!0})])):(f[l]??(f[l]={}),f[l][a]=new Set([c||hi(r,{forAs:!0})])))}for(const r of t.groupby??[])o.add(r);return o.size+Zr(f).length===0?null:new gf(n,o,f)}merge(n){return k$(this.dimensions,n.dimensions)?(n3e(this.measures,n.measures),!0):(tve("different dimensions, cannot merge"),!1)}addDimensions(n){n.forEach(this.dimensions.add,this.dimensions)}dependentFields(){return new Set([...this.dimensions,...Zr(this.measures)])}producedFields(){const n=new Set;for(const t of Zr(this.measures))for(const o of Zr(this.measures[t])){const f=this.measures[t][o];f.size===0?n.add(`${o}_${t}`):f.forEach(n.add,n)}return n}hash(){return`Aggregate ${Ba({dimensions:this.dimensions,measures:this.measures})}`}assemble(){const n=[],t=[],o=[];for(const r of Zr(this.measures))for(const a of Zr(this.measures[r]))for(const l of this.measures[r][a])o.push(l),n.push(a),t.push(r==="*"?null:Dc(r));return{type:"aggregate",groupby:[...this.dimensions].map(Dc),ops:n,fields:t,as:o}}}class T1 extends wo{constructor(n,t,o,f){super(n),this.model=t,this.name=o,this.data=f;for(const r of Tc){const a=t.facet[r];if(a){const{bin:l,sort:c}=a;this[r]={name:t.getName(`${r}_domain`),fields:[hi(a),...Vo(l)?[hi(a,{binSuffix:"end"})]:[]],...nh(c)?{sortField:c}:zr(c)?{sortIndexField:t1(a,r)}:{}}}}this.childModel=t.child}hash(){let n="Facet";for(const t of Tc)this[t]&&(n+=` ${t.charAt(0)}:${Ba(this[t])}`);return n}get fields(){const n=[];for(const t of Tc)this[t]?.fields&&n.push(...this[t].fields);return n}dependentFields(){const n=new Set(this.fields);for(const t of Tc)this[t]&&(this[t].sortField&&n.add(this[t].sortField.field),this[t].sortIndexField&&n.add(this[t].sortIndexField));return n}producedFields(){return new Set}getSource(){return this.name}getChildIndependentFieldsWithStep(){const n={};for(const t of wh){const o=this.childModel.component.scales[t];if(o&&!o.merged){const f=o.get("type"),r=o.get("range");if(ml(f)&&Cp(r)){const a=h5(this.childModel,t),l=cC(a);l?n[t]=l:ei(s8(t))}}}return n}assembleRowColumnHeaderData(n,t,o){const f={row:"y",column:"x",facet:void 0}[n],r=[],a=[],l=[];f&&o&&o[f]&&(t?(r.push(`distinct_${o[f]}`),a.push("max")):(r.push(o[f]),a.push("distinct")),l.push(`distinct_${o[f]}`));const{sortField:c,sortIndexField:i}=this[n];if(c){const{op:s=W3,field:u}=c;r.push(u),a.push(s),l.push(hi(c,{forAs:!0}))}else i&&(r.push(i),a.push("max"),l.push(i));return{name:this[n].name,source:t??this.data,transform:[{type:"aggregate",groupby:this[n].fields,...r.length?{fields:r,ops:a,as:l}:{}}]}}assembleFacetHeaderData(n){const{columns:t}=this.model.layout,{layoutHeaders:o}=this.model.component,f=[],r={};for(const c of nC){for(const i of rC){const s=(o[c]&&o[c][i])??[];for(const u of s)if(u.axes?.length>0){r[c]=!0;break}}if(r[c]){const i=`length(data("${this.facet.name}"))`,s=c==="row"?t?{signal:`ceil(${i} / ${t})`}:1:t?{signal:`min(${i}, ${t})`}:{signal:i};f.push({name:`${this.facet.name}_${c}`,transform:[{type:"sequence",start:0,stop:s}]})}}const{row:a,column:l}=r;return(a||l)&&f.unshift(this.assembleRowColumnHeaderData("facet",null,n)),f}assemble(){const n=[];let t=null;const o=this.getChildIndependentFieldsWithStep(),{column:f,row:r,facet:a}=this;if(f&&r&&(o.x||o.y)){t=`cross_${this.column.name}_${this.row.name}`;const l=[].concat(o.x??[],o.y??[]),c=l.map(()=>"distinct");n.push({name:t,source:this.data,transform:[{type:"aggregate",groupby:this.fields,fields:l,ops:c}]})}for(const l of[Jh,Zh])this[l]&&n.push(this.assembleRowColumnHeaderData(l,t,o));if(a){const l=this.assembleFacetHeaderData(o);l&&n.push(...l)}return n}}function EP(e){return e.startsWith("'")&&e.endsWith("'")||e.startsWith('"')&&e.endsWith('"')?e.slice(1,-1):e}function r3e(e,n){const t=ZS(e);if(n==="number")return`toNumber(${t})`;if(n==="boolean")return`toBoolean(${t})`;if(n==="string")return`toString(${t})`;if(n==="date")return`toDate(${t})`;if(n==="flatten")return t;if(n.startsWith("date:")){const o=EP(n.slice(5,n.length));return`timeParse(${t},'${o}')`}else if(n.startsWith("utc:")){const o=EP(n.slice(4,n.length));return`utcParse(${t},'${o}')`}else return ei(lye(n)),null}function i3e(e){const n={};return O2(e.filter,t=>{if(gV(t)){let o=null;f8(t)?o=ac(t.equal):d8(t)?o=ac(t.lte):h8(t)?o=ac(t.lt):p8(t)?o=ac(t.gt):g8(t)?o=ac(t.gte):m8(t)?o=t.range[0]:y8(t)&&(o=(t.oneOf??t.in)[0]),o&&(hg(o)?n[t.field]="date":Eo(o)?n[t.field]="number":Li(o)&&(n[t.field]="string")),t.timeUnit&&(n[t.field]="date")}}),n}function a3e(e){const n={};function t(o){Jm(o)?n[o.field]="date":o.type==="quantitative"&&j1e(o.aggregate)?n[o.field]="number":qm(o.field)>1?o.field in n||(n[o.field]="flatten"):pg(o)&&nh(o.sort)&&qm(o.sort.field)>1&&(o.sort.field in n||(n[o.sort.field]="flatten"))}if((As(e)||mf(e))&&e.forEachFieldDef((o,f)=>{if(Au(o))t(o);else{const r=cg(f),a=e.fieldDef(r);t({...o,type:a.type})}}),As(e)){const{mark:o,markDef:f,encoding:r}=e;if(Lp(o)&&!e.encoding.order){const a=f.orient==="horizontal"?"y":"x",l=r[a];ni(l)&&l.type==="quantitative"&&!(l.field in n)&&(n[l.field]="number")}}return n}function o3e(e){const n={};if(As(e)&&e.component.selection)for(const t of Zr(e.component.selection)){const o=e.component.selection[t];for(const f of o.project.items)!f.channel&&qm(f.field)>1&&(n[f.field]="flatten")}return n}class Gl extends wo{clone(){return new Gl(null,la(this._parse))}constructor(n,t){super(n),this._parse=t}hash(){return`Parse ${Ba(this._parse)}`}static makeExplicit(n,t,o){let f={};const r=t.data;return!tp(r)&&r?.format?.parse&&(f=r.format.parse),this.makeWithAncestors(n,f,{},o)}static makeWithAncestors(n,t,o,f){for(const l of Zr(o)){const c=f.getWithExplicit(l);c.value!==void 0&&(c.explicit||c.value===o[l]||c.value==="derived"||o[l]==="flatten"?delete o[l]:ei(OO(l,o[l],c.value)))}for(const l of Zr(t)){const c=f.get(l);c!==void 0&&(c===t[l]?delete t[l]:ei(OO(l,t[l],c)))}const r=new yd(t,o);f.copyAll(r);const a={};for(const l of Zr(r.combine())){const c=r.get(l);c!==null&&(a[l]=c)}return Zr(a).length===0||f.parseNothing?null:new Gl(n,a)}get parse(){return this._parse}merge(n){this._parse={...this._parse,...n.parse},n.remove()}assembleFormatParse(){const n={};for(const t of Zr(this._parse)){const o=this._parse[t];qm(t)===1&&(n[t]=o)}return n}producedFields(){return new Set(Zr(this._parse))}dependentFields(){return new Set(Zr(this._parse))}assembleTransforms(n=!1){return Zr(this._parse).filter(t=>n?qm(t)>1:!0).map(t=>{const o=r3e(t,this._parse[t]);return o?{type:"formula",expr:o,as:JS(t)}:null}).filter(t=>t!==null)}}class xp extends wo{clone(){return new xp(null)}constructor(n){super(n)}dependentFields(){return new Set}producedFields(){return new Set([_f])}hash(){return"Identifier"}assemble(){return{type:"identifier",as:_f}}}class zx extends wo{clone(){return new zx(null,this.params)}constructor(n,t){super(n),this.params=t}dependentFields(){return new Set}producedFields(){}hash(){return`Graticule ${Ba(this.params)}`}assemble(){return{type:"graticule",...this.params===!0?{}:this.params}}}class Nx extends wo{clone(){return new Nx(null,this.params)}constructor(n,t){super(n),this.params=t}dependentFields(){return new Set}producedFields(){return new Set([this.params.as??"data"])}hash(){return`Hash ${Ba(this.params)}`}assemble(){return{type:"sequence",...this.params}}}class Q0 extends wo{constructor(n){super(null),n??(n={name:"source"});let t;if(tp(n)||(t=n.format?{...ju(n.format,["parse"])}:{}),Fv(n))this._data={values:n.values};else if(Km(n)){if(this._data={url:n.url},!t.type){let o=/(?:\.([^.]+))?$/.exec(n.url)[1];ja(["json","csv","tsv","dsv","topojson"],o)||(o="json"),t.type=o}}else Cq(n)?this._data={values:[{type:"Sphere"}]}:(Eq(n)||tp(n))&&(this._data={});this._generator=tp(n),n.name&&(this._name=n.name),t&&!Mo(t)&&(this._data.format=t)}dependentFields(){return new Set}producedFields(){}get data(){return this._data}hasName(){return!!this._name}get isGenerator(){return this._generator}get dataName(){return this._name}set dataName(n){this._name=n}set parent(n){throw new Error("Source nodes have to be roots.")}remove(){throw new Error("Source nodes are roots and cannot be removed.")}hash(){throw new Error("Cannot hash sources")}assemble(){return{name:this._name,...this._data,transform:[]}}}var SP=globalThis&&globalThis.__classPrivateFieldSet||function(e,n,t,o,f){if(o==="m")throw new TypeError("Private method is not writable");if(o==="a"&&!f)throw new TypeError("Private accessor was defined without a setter");if(typeof n=="function"?e!==n||!f:!n.has(e))throw new TypeError("Cannot write private member to an object whose class did not declare it");return o==="a"?f.call(e,t):f?f.value=t:n.set(e,t),t},s3e=globalThis&&globalThis.__classPrivateFieldGet||function(e,n,t,o){if(t==="a"&&!o)throw new TypeError("Private accessor was defined without a getter");if(typeof n=="function"?e!==n||!o:!n.has(e))throw new TypeError("Cannot read private member from an object whose class did not declare it");return t==="m"?o:t==="a"?o.call(e):o?o.value:n.get(e)},Gy;function sC(e){return e instanceof Q0||e instanceof zx||e instanceof Nx}class lC{constructor(){Gy.set(this,void 0),SP(this,Gy,!1,"f")}setModified(){SP(this,Gy,!0,"f")}get modifiedFlag(){return s3e(this,Gy,"f")}}Gy=new WeakMap;class mg extends lC{getNodeDepths(n,t,o){o.set(n,t);for(const f of n.children)this.getNodeDepths(f,t+1,o);return o}optimize(n){const o=[...this.getNodeDepths(n,0,new Map).entries()].sort((f,r)=>r[1]-f[1]);for(const f of o)this.run(f[0]);return this.modifiedFlag}}class uC extends lC{optimize(n){this.run(n);for(const t of n.children)this.optimize(t);return this.modifiedFlag}}class l3e extends uC{mergeNodes(n,t){const o=t.shift();for(const f of t)n.removeChild(f),f.parent=o,f.remove()}run(n){const t=n.children.map(f=>f.hash()),o={};for(let f=0;f<t.length;f++)o[t[f]]===void 0?o[t[f]]=[n.children[f]]:o[t[f]].push(n.children[f]);for(const f of Zr(o))o[f].length>1&&(this.setModified(),this.mergeNodes(n,o[f]))}}class u3e extends uC{constructor(n){super(),this.requiresSelectionId=n&&K8(n)}run(n){n instanceof xp&&(this.requiresSelectionId&&(sC(n.parent)||n.parent instanceof gf||n.parent instanceof Gl)||(this.setModified(),n.remove()))}}class c3e extends lC{optimize(n){return this.run(n,new Set),this.modifiedFlag}run(n,t){let o=new Set;n instanceof rh&&(o=n.producedFields(),YS(o,t)&&(this.setModified(),n.removeFormulas(t),n.producedFields.length===0&&n.remove()));for(const f of n.children)this.run(f,new Set([...t,...o]))}}class f3e extends uC{constructor(){super()}run(n){n instanceof vu&&!n.isRequired()&&(this.setModified(),n.remove())}}class h3e extends mg{run(n){if(!sC(n)&&!(n.numChildren()>1)){for(const t of n.children)if(t instanceof Gl)if(n instanceof Gl)this.setModified(),n.merge(t);else{if(XS(n.producedFields(),t.dependentFields()))continue;this.setModified(),t.swapWithParent()}}}}class d3e extends mg{run(n){const t=[...n.children],o=n.children.filter(f=>f instanceof Gl);if(n.numChildren()>1&&o.length>=1){const f={},r=new Set;for(const a of o){const l=a.parse;for(const c of Zr(l))c in f?f[c]!==l[c]&&r.add(c):f[c]=l[c]}for(const a of r)delete f[a];if(!Mo(f)){this.setModified();const a=new Gl(n,f);for(const l of t){if(l instanceof Gl)for(const c of Zr(f))delete l.parse[c];n.removeChild(l),l.parent=a,l instanceof Gl&&Zr(l.parse).length===0&&l.remove()}}}}}class p3e extends mg{run(n){n instanceof vu||n.numChildren()>0||n instanceof T1||n instanceof Q0||(this.setModified(),n.remove())}}class g3e extends mg{run(n){const t=n.children.filter(f=>f instanceof rh),o=t.pop();for(const f of t)this.setModified(),o.merge(f)}}class m3e extends mg{run(n){const t=n.children.filter(f=>f instanceof gf),o={};for(const f of t){const r=Ba(f.groupBy);r in o||(o[r]=[]),o[r].push(f)}for(const f of Zr(o)){const r=o[f];if(r.length>1){const a=r.pop();for(const l of r)a.merge(l)&&(n.removeChild(l),l.parent=a,l.remove(),this.setModified())}}}}class y3e extends mg{constructor(n){super(),this.model=n}run(n){const t=!(sC(n)||n instanceof k1||n instanceof Gl||n instanceof xp),o=[],f=[];for(const r of n.children)r instanceof ih&&(t&&!XS(n.producedFields(),r.dependentFields())?o.push(r):f.push(r));if(o.length>0){const r=o.pop();for(const a of o)r.merge(a,this.model.renameSignal.bind(this.model));this.setModified(),n instanceof ih?n.merge(r,this.model.renameSignal.bind(this.model)):r.swapWithParent()}if(f.length>1){const r=f.pop();for(const a of f)r.merge(a,this.model.renameSignal.bind(this.model));this.setModified()}}}class v3e extends mg{run(n){const t=[...n.children];if(!$0(t,a=>a instanceof vu)||n.numChildren()<=1)return;const f=[];let r;for(const a of t)if(a instanceof vu){let l=a;for(;l.numChildren()===1;){const[c]=l.children;if(c instanceof vu)l=c;else break}f.push(...l.children),r?(n.removeChild(a),a.parent=r.parent,r.parent.removeChild(r),r.parent=l,this.setModified()):r=l}else f.push(a);if(f.length){this.setModified();for(const a of f)a.parent.removeChild(a),a.parent=r}}}class yg extends wo{clone(){return new yg(null,la(this.transform))}constructor(n,t){super(n),this.transform=t}addDimensions(n){this.transform.groupby=Zf(this.transform.groupby.concat(n),t=>t)}dependentFields(){const n=new Set;return this.transform.groupby&&this.transform.groupby.forEach(n.add,n),this.transform.joinaggregate.map(t=>t.field).filter(t=>t!==void 0).forEach(n.add,n),n}producedFields(){return new Set(this.transform.joinaggregate.map(this.getDefaultName))}getDefaultName(n){return n.as??hi(n)}hash(){return`JoinAggregateTransform ${Ba(this.transform)}`}assemble(){const n=[],t=[],o=[];for(const r of this.transform.joinaggregate)t.push(r.op),o.push(this.getDefaultName(r)),n.push(r.field===void 0?null:r.field);const f=this.transform.groupby;return{type:"joinaggregate",as:o,ops:t,fields:n,...f!==void 0?{groupby:f}:{}}}}function x3e(e){return e.stack.stackBy.reduce((n,t)=>{const o=t.fieldDef,f=hi(o);return f&&n.push(f),n},[])}function b3e(e){return zr(e)&&e.every(n=>Li(n))&&e.length>1}class Qh extends wo{clone(){return new Qh(null,la(this._stack))}constructor(n,t){super(n),this._stack=t}static makeFromTransform(n,t){const{stack:o,groupby:f,as:r,offset:a="zero"}=t,l=[],c=[];if(t.sort!==void 0)for(const u of t.sort)l.push(u.field),c.push(zs(u.order,"ascending"));const i={field:l,order:c};let s;return b3e(r)?s=r:Li(r)?s=[r,`${r}_end`]:s=[`${t.stack}_start`,`${t.stack}_end`],new Qh(n,{dimensionFieldDefs:[],stackField:o,groupby:f,offset:a,sort:i,facetby:[],as:s})}static makeFromEncoding(n,t){const o=t.stack,{encoding:f}=t;if(!o)return null;const{groupbyChannels:r,fieldChannel:a,offset:l,impute:c}=o,i=r.map(d=>{const m=f[d];return hh(m)}).filter(d=>!!d),s=x3e(t),u=t.encoding.order;let h;if(zr(u)||ni(u))h=X$(u);else{const d=BV(u)?u.sort:a==="y"?"descending":"ascending";h=s.reduce((m,p)=>(m.field.push(p),m.order.push(d),m),{field:[],order:[]})}return new Qh(n,{dimensionFieldDefs:i,stackField:t.vgField(a),facetby:[],stackby:s,sort:h,offset:l,impute:c,as:[t.vgField(a,{suffix:"start",forAs:!0}),t.vgField(a,{suffix:"end",forAs:!0})]})}get stack(){return this._stack}addDimensions(n){this._stack.facetby.push(...n)}dependentFields(){const n=new Set;return n.add(this._stack.stackField),this.getGroupbyFields().forEach(n.add,n),this._stack.facetby.forEach(n.add,n),this._stack.sort.field.forEach(n.add,n),n}producedFields(){return new Set(this._stack.as)}hash(){return`Stack ${Ba(this._stack)}`}getGroupbyFields(){const{dimensionFieldDefs:n,impute:t,groupby:o}=this._stack;return n.length>0?n.map(f=>f.bin?t?[hi(f,{binSuffix:"mid"})]:[hi(f,{}),hi(f,{binSuffix:"end"})]:[hi(f)]).flat():o??[]}assemble(){const n=[],{facetby:t,dimensionFieldDefs:o,stackField:f,stackby:r,sort:a,offset:l,impute:c,as:i}=this._stack;if(c)for(const s of o){const{bandPosition:u=.5,bin:h}=s;if(h){const d=hi(s,{expr:"datum"}),m=hi(s,{expr:"datum",binSuffix:"end"});n.push({type:"formula",expr:`${u}*${d}+${1-u}*${m}`,as:hi(s,{binSuffix:"mid",forAs:!0})})}n.push({type:"impute",field:f,groupby:[...r,...t],key:hi(s,{binSuffix:"mid"}),method:"value",value:0})}return n.push({type:"stack",groupby:[...this.getGroupbyFields(),...t],field:f,sort:a,as:i,offset:l}),n}}class M1 extends wo{clone(){return new M1(null,la(this.transform))}constructor(n,t){super(n),this.transform=t}addDimensions(n){this.transform.groupby=Zf(this.transform.groupby.concat(n),t=>t)}dependentFields(){const n=new Set;return(this.transform.groupby??[]).forEach(n.add,n),(this.transform.sort??[]).forEach(t=>n.add(t.field)),this.transform.window.map(t=>t.field).filter(t=>t!==void 0).forEach(n.add,n),n}producedFields(){return new Set(this.transform.window.map(this.getDefaultName))}getDefaultName(n){return n.as??hi(n)}hash(){return`WindowTransform ${Ba(this.transform)}`}assemble(){const n=[],t=[],o=[],f=[];for(const u of this.transform.window)t.push(u.op),o.push(this.getDefaultName(u)),f.push(u.param===void 0?null:u.param),n.push(u.field===void 0?null:u.field);const r=this.transform.frame,a=this.transform.groupby;if(r&&r[0]===null&&r[1]===null&&t.every(u=>a8(u)))return{type:"joinaggregate",as:o,ops:t,fields:n,...a!==void 0?{groupby:a}:{}};const l=[],c=[];if(this.transform.sort!==void 0)for(const u of this.transform.sort)l.push(u.field),c.push(u.order??"ascending");const i={field:l,order:c},s=this.transform.ignorePeers;return{type:"window",params:f,as:o,ops:t,fields:n,sort:i,...s!==void 0?{ignorePeers:s}:{},...a!==void 0?{groupby:a}:{},...r!==void 0?{frame:r}:{}}}}function _3e(e){function n(t){if(!(t instanceof T1)){const o=t.clone();if(o instanceof vu){const f=UT+o.getSource();o.setSource(f),e.model.component.data.outputNodes[f]=o}else(o instanceof gf||o instanceof Qh||o instanceof M1||o instanceof yg)&&o.addDimensions(e.fields);for(const f of t.children.flatMap(n))f.parent=o;return[o]}return t.children.flatMap(n)}return n}function jT(e){if(e instanceof T1)if(e.numChildren()===1&&!(e.children[0]instanceof vu)){const n=e.children[0];(n instanceof gf||n instanceof Qh||n instanceof M1||n instanceof yg)&&n.addDimensions(e.fields),n.swapWithParent(),jT(e)}else{const n=e.model.component.data.main;PH(n);const t=_3e(e),o=e.children.map(t).flat();for(const f of o)f.parent=n}else e.children.map(jT)}function PH(e){if(e instanceof vu&&e.type===Uo.Main&&e.numChildren()===1){const n=e.children[0];n instanceof T1||(n.swapWithParent(),PH(e))}}const UT="scale_",e2=5;function $T(e){for(const n of e){for(const t of n.children)if(t.parent!==n)return!1;if(!$T(n.children))return!1}return!0}function Jc(e,n){let t=!1;for(const o of n)t=e.optimize(o)||t;return t}function CP(e,n,t){let o=e.sources,f=!1;return f=Jc(new f3e,o)||f,f=Jc(new u3e(n),o)||f,o=o.filter(r=>r.numChildren()>0),f=Jc(new p3e,o)||f,o=o.filter(r=>r.numChildren()>0),t||(f=Jc(new h3e,o)||f,f=Jc(new y3e(n),o)||f,f=Jc(new c3e,o)||f,f=Jc(new d3e,o)||f,f=Jc(new m3e,o)||f,f=Jc(new g3e,o)||f,f=Jc(new l3e,o)||f,f=Jc(new v3e,o)||f),e.sources=o,f}function w3e(e,n){$T(e.sources);let t=0,o=0;for(let f=0;f<e2&&CP(e,n,!0);f++)t++;e.sources.map(jT);for(let f=0;f<e2&&CP(e,n,!1);f++)o++;$T(e.sources),Math.max(t,o)===e2&&ei(`Maximum optimization runs(${e2}) reached.`)}class $u{constructor(n){Object.defineProperty(this,"signal",{enumerable:!0,get:n})}static fromName(n,t){return new $u(()=>n(t))}}function IH(e){As(e)?A3e(e):k3e(e)}function A3e(e){const n=e.component.scales;for(const t of Zr(n)){const o=M3e(e,t);if(n[t].setWithExplicit("domains",o),S3e(e,t),e.component.data.isFaceted){let r=e;for(;!mf(r)&&r.parent;)r=r.parent;if(r.component.resolve.scale[t]==="shared")for(const l of o.value)Yh(l)&&(l.data=UT+l.data.replace(UT,""))}}}function k3e(e){for(const t of e.children)IH(t);const n=e.component.scales;for(const t of Zr(n)){let o,f=null;for(const r of e.children){const a=r.component.scales[t];if(a){o===void 0?o=a.getWithExplicit("domains"):o=mp(o,a.getWithExplicit("domains"),"domains","scale",VT);const l=a.get("selectionExtent");f&&l&&f.param!==l.param&&ei(iye),f=l}}n[t].setWithExplicit("domains",o),f&&n[t].set("selectionExtent",f,!0)}}function T3e(e,n,t,o){if(e==="unaggregated"){const{valid:f,reason:r}=LP(n,t);if(!f){ei(r);return}}else if(e===void 0&&o.useUnaggregatedDomain){const{valid:f}=LP(n,t);if(f)return"unaggregated"}return e}function M3e(e,n){const t=e.getScaleComponent(n).get("type"),{encoding:o}=e,f=T3e(e.scaleDomain(n),e.typedFieldDef(n),t,e.config.scale);return f!==e.scaleDomain(n)&&(e.specifiedScales[n]={...e.specifiedScales[n],domain:f}),n==="x"&&Ks(o.x2)?Ks(o.x)?mp(Ld(t,f,e,"x"),Ld(t,f,e,"x2"),"domain","scale",VT):Ld(t,f,e,"x2"):n==="y"&&Ks(o.y2)?Ks(o.y)?mp(Ld(t,f,e,"y"),Ld(t,f,e,"y2"),"domain","scale",VT):Ld(t,f,e,"y2"):Ld(t,f,e,n)}function E3e(e,n,t){return e.map(o=>({signal:`{data: ${K3(o,{timeUnit:t,type:n})}}`}))}function _A(e,n,t){const o=hl(t)?.unit;return n==="temporal"||o?E3e(e,n,o):[e]}function Ld(e,n,t,o){const{encoding:f}=t,r=Ks(f[o]),{type:a}=r,l=r.timeUnit;if(Cve(n)){const u=Ld(e,void 0,t,o),h=_A(n.unionWith,a,l);return qf([...h,...u.value])}else{if(ji(n))return qf([n]);if(n&&n!=="unaggregated"&&!wV(n))return qf(_A(n,a,l))}const c=t.stack;if(c&&o===c.fieldChannel){if(c.offset==="normalize")return nc([[0,1]]);const u=t.requestDataName(Uo.Main);return nc([{data:u,field:t.vgField(o,{suffix:"start"})},{data:u,field:t.vgField(o,{suffix:"end"})}])}const i=gd(o)&&ni(r)?C3e(t,o,e):void 0;if(Ah(r)){const u=_A([r.datum],a,l);return nc(u)}const s=r;if(n==="unaggregated"){const u=t.requestDataName(Uo.Main),{field:h}=r;return nc([{data:u,field:hi({field:h,aggregate:"min"})},{data:u,field:hi({field:h,aggregate:"max"})}])}else if(Vo(s.bin)){if(ml(e))return nc(e==="bin-ordinal"?[]:[{data:Pv(i)?t.requestDataName(Uo.Main):t.requestDataName(Uo.Raw),field:t.vgField(o,Dx(s,o)?{binSuffix:"range"}:{}),sort:i===!0||!Si(i)?{field:t.vgField(o,{}),op:"min"}:i}]);{const{bin:u}=s;if(Vo(u)){const h=oC(t,s.field,u);return nc([new $u(()=>{const d=t.getSignalName(h);return`[${d}.start, ${d}.stop]`})])}else return nc([{data:t.requestDataName(Uo.Main),field:t.vgField(o,{})}])}}else if(s.timeUnit&&ja(["time","utc"],e)&&NV(s,As(t)?t.encoding[_h(o)]:void 0,t.markDef,t.config)){const u=t.requestDataName(Uo.Main);return nc([{data:u,field:t.vgField(o)},{data:u,field:t.vgField(o,{suffix:"end"})}])}else return nc(i?[{data:Pv(i)?t.requestDataName(Uo.Main):t.requestDataName(Uo.Raw),field:t.vgField(o),sort:i}]:[{data:t.requestDataName(Uo.Main),field:t.vgField(o)}])}function wA(e,n){const{op:t,field:o,order:f}=e;return{op:t??(n?"sum":W3),...o?{field:Dc(o)}:{},...f?{order:f}:{}}}function S3e(e,n){const t=e.component.scales[n],o=e.specifiedScales[n].domain,f=e.fieldDef(n)?.bin,r=wV(o)&&o,a=fg(f)&&j3(f.extent)&&f.extent;(r||a)&&t.set("selectionExtent",r??a,!0)}function C3e(e,n,t){if(!ml(t))return;const o=e.fieldDef(n),f=o.sort;if(FV(f))return{op:"min",field:t1(o,n),order:"ascending"};const{stack:r}=e,a=r?new Set([...r.groupbyFields,...r.stackBy.map(l=>l.fieldDef.field)]):void 0;if(nh(f)){const l=r&&!a.has(f.field);return wA(f,l)}else if(IV(f)){const{encoding:l,order:c}=f,i=e.fieldDef(l),{aggregate:s,field:u}=i,h=r&&!a.has(u);if(rd(s)||Sp(s))return wA({field:hi(i),order:c},h);if(a8(s)||!s)return wA({op:s,field:u,order:c},h)}else{if(f==="descending")return{op:"min",field:e.vgField(n),order:"descending"};if(ja(["ascending",void 0],f))return!0}}function LP(e,n){const{aggregate:t,type:o}=e;return t?Li(t)&&!$1e.has(t)?{valid:!1,reason:Oye(t)}:o==="quantitative"&&n==="log"?{valid:!1,reason:Pye(e)}:{valid:!0}:{valid:!1,reason:Dye(e)}}function VT(e,n,t,o){return e.explicit&&n.explicit&&ei(Nye(t,o,e.value,n.value)),{explicit:e.explicit,value:[...e.value,...n.value]}}function L3e(e){const n=Zf(e.map(a=>{if(Yh(a)){const{sort:l,...c}=a;return c}return a}),Ba),t=Zf(e.map(a=>{if(Yh(a)){const l=a.sort;return l!==void 0&&!Pv(l)&&("op"in l&&l.op==="count"&&delete l.field,l.order==="ascending"&&delete l.order),l}}).filter(a=>a!==void 0),Ba);if(n.length===0)return;if(n.length===1){const a=e[0];if(Yh(a)&&t.length>0){let l=t[0];if(t.length>1){ei(IO);const c=t.filter(i=>Si(i)&&"op"in i&&i.op!=="min");t.every(i=>Si(i)&&"op"in i)&&c.length===1?l=c[0]:l=!0}else if(Si(l)&&"field"in l){const c=l.field;a.field===c&&(l=l.order?{order:l.order}:!0)}return{...a,sort:l}}return a}const o=Zf(t.map(a=>Pv(a)||!("op"in a)||Li(a.op)&&a.op in N1e?a:(ei(jye(a)),!0)),Ba);let f;o.length===1?f=o[0]:o.length>1&&(ei(IO),f=!0);const r=Zf(e.map(a=>Yh(a)?a.data:null),a=>a);return r.length===1&&r[0]!==null?{data:r[0],fields:n.map(l=>l.field),...f?{sort:f}:{}}:{fields:n,...f?{sort:f}:{}}}function cC(e){if(Yh(e)&&Li(e.field))return e.field;if(V1e(e)){let n;for(const t of e.fields)if(Yh(t)&&Li(t.field)){if(!n)n=t.field;else if(n!==t.field)return ei(Uye),n}return ei($ye),n}else if(q1e(e)){ei(Vye);const n=e.fields[0];return Li(n)?n:void 0}}function h5(e,n){const o=e.component.scales[n].get("domains").map(f=>(Yh(f)&&(f.data=e.lookupDataSource(f.data)),f));return L3e(o)}function FH(e){return E1(e)||fC(e)?e.children.reduce((n,t)=>n.concat(FH(t)),DP(e)):DP(e)}function DP(e){return Zr(e.component.scales).reduce((n,t)=>{const o=e.component.scales[t];if(o.merged)return n;const f=o.combine(),{name:r,type:a,selectionExtent:l,domains:c,range:i,reverse:s,...u}=f,h=D3e(f.range,r,t,e),d=h5(e,t),m=l?m2e(e,l,o,d):null;return n.push({name:r,type:a,...d?{domain:d}:{},...m?{domainRaw:m}:{},range:h,...s!==void 0?{reverse:s}:{},...u}),n},[])}function D3e(e,n,t,o){if(pl(t)){if(Cp(e))return{step:{signal:`${n}_step`}}}else if(Si(e)&&Yh(e))return{...e,data:o.lookupDataSource(e.data)};return e}class RH extends yd{constructor(n,t){super({},{name:n}),this.merged=!1,this.setWithExplicit("type",t)}domainDefinitelyIncludesZero(){return this.get("zero")!==!1?!0:$0(this.get("domains"),n=>zr(n)&&n.length===2&&n[0]<=0&&n[1]>=0)}}const O3e=["range","scheme"];function P3e(e){const n=e.component.scales;for(const t of B3){const o=n[t];if(!o)continue;const f=I3e(t,e);o.setWithExplicit("range",f)}}function OP(e,n){const t=e.fieldDef(n);if(t?.bin){const{bin:o,field:f}=t,r=Yu(n),a=e.getName(r);if(Si(o)&&o.binned&&o.step!==void 0)return new $u(()=>{const l=e.scaleName(n),c=`(domain("${l}")[1] - domain("${l}")[0]) / ${o.step}`;return`${e.getSignalName(a)} / (${c})`});if(Vo(o)){const l=oC(e,f,o);return new $u(()=>{const c=e.getSignalName(l),i=`(${c}.stop - ${c}.start) / ${c}.step`;return`${e.getSignalName(a)} / (${i})`})}}}function I3e(e,n){const t=n.specifiedScales[e],{size:o}=n,r=n.getScaleComponent(e).get("type");for(const u of O3e)if(t[u]!==void 0){const h=AT(r,u),d=AV(e,u);if(!h)ei(nV(r,u,e));else if(d)ei(d);else switch(u){case"range":{const m=t.range;if(zr(m)){if(pl(e))return qf(m.map(p=>{if(p==="width"||p==="height"){const g=n.getName(p),y=n.getSignalName.bind(n);return $u.fromName(y,g)}return p}))}else if(Si(m))return qf({data:n.requestDataName(Uo.Main),field:m.field,sort:{op:"min",field:n.vgField(e)}});return qf(m)}case"scheme":return qf(F3e(t[u]))}}const a=e===ts||e==="xOffset"?"width":"height",l=o[a];if(dh(l)){if(pl(e))if(ml(r)){const u=zH(l,n,e);if(u)return qf({step:u})}else ei(rV(a));else if(b1(e)){const u=e===Ap?"x":"y";if(n.getScaleComponent(u).get("type")==="band"){const m=NH(l,r);if(m)return qf(m)}}}const{rangeMin:c,rangeMax:i}=t,s=R3e(e,n);return(c!==void 0||i!==void 0)&&AT(r,"rangeMin")&&zr(s)&&s.length===2?qf([c??s[0],i??s[1]]):nc(s)}function F3e(e){return Sve(e)?{scheme:e.name,...ju(e,["name"])}:{scheme:e}}function R3e(e,n){const{size:t,config:o,mark:f,encoding:r}=n,a=n.getSignalName.bind(n),{type:l}=Ks(r[e]),i=n.getScaleComponent(e).get("type"),{domain:s,domainMid:u}=n.specifiedScales[e];switch(e){case ts:case dl:{if(ja(["point","band"],i)){const m=BH(e,t,o.view);if(dh(m))return{step:zH(m,n,e)}}const h=Yu(e),d=n.getName(h);return e===dl&&pc(i)?[$u.fromName(a,d),0]:[0,$u.fromName(a,d)]}case Ap:case x1:return z3e(e,n,i);case dd:{const h=n.component.scales[e].get("zero"),d=jH(f,h,o),m=j3e(f,t,n,o);return Ym(i)?B3e(d,m,N3e(i,o,s,e)):[d,m]}case Ic:return[0,Math.PI*2];case ug:return[0,360];case Mf:return[0,new $u(()=>{const h=n.getSignalName("width"),d=n.getSignalName("height");return`min(${h},${d})/2`})];case Mp:return[o.scale.minStrokeWidth,o.scale.maxStrokeWidth];case Ep:return[[1,0],[4,2],[2,1],[1,1],[1,2,4,2]];case Wu:return"symbol";case Gu:case xh:case bh:return i==="ordinal"?l==="nominal"?"category":"ordinal":u!==void 0?"diverging":f==="rect"||f==="geoshape"?"heatmap":"ramp";case pd:case kp:case Tp:return[o.scale.minOpacity,o.scale.maxOpacity]}}function zH(e,n,t){const{encoding:o}=n,f=n.getScaleComponent(t),r=t8(t),a=o[r];if(pq({step:e,offsetIsDiscrete:fa(a)&&yV(a.type)})==="offset"&&ZV(o,r)){const c=n.getScaleComponent(r);let s=`domain('${n.scaleName(r)}').length`;if(c.get("type")==="band"){const h=c.get("paddingInner")??c.get("padding")??0,d=c.get("paddingOuter")??c.get("padding")??0;s=`bandspace(${s}, ${h}, ${d})`}const u=f.get("paddingInner")??f.get("padding");return{signal:`${e.step} * ${s} / (1-${Y1e(u)})`}}else return e.step}function NH(e,n){if(pq({step:e,offsetIsDiscrete:ml(n)})==="offset")return{step:e.step}}function z3e(e,n,t){const o=e===Ap?"x":"y",r=n.getScaleComponent(o).get("type"),a=n.scaleName(o);if(r==="band"){const l=BH(o,n.size,n.config.view);if(dh(l)){const c=NH(l,t);if(c)return c}return[0,{signal:`bandwidth('${a}')`}]}else{const l=n.encoding[o];if(ni(l)&&l.timeUnit){const c=dV(l.timeUnit,s=>`scale('${a}', ${s})`),i=n.config.scale.bandWithNestedOffsetPaddingInner;if(i){const s=ji(i)?`${i.signal}/2`:`${i/2}`,u=ji(i)?`(1 - ${i.signal}/2)`:`${1-i/2}`;return[{signal:`${s} * (${c})`},{signal:`${u} * (${c})`}]}return[0,{signal:c}]}return w$(`Cannot use ${e} scale if ${o} scale is not discrete.`)}}function BH(e,n,t){const o=e===ts?"width":"height",f=n[o];return f||sw(t,o)}function N3e(e,n,t,o){switch(e){case"quantile":return n.scale.quantileCount;case"quantize":return n.scale.quantizeCount;case"threshold":return t!==void 0&&zr(t)?t.length+1:(ei(Kye(o)),3)}}function B3e(e,n,t){const o=()=>{const f=cf(n),r=cf(e),a=`(${f} - ${r}) / (${t} - 1)`;return`sequence(${r}, ${f} + ${a}, ${a})`};return ji(n)?new $u(o):{signal:o()}}function jH(e,n,t){if(n)return ji(n)?{signal:`${n.signal} ? 0 : ${jH(e,!1,t)}`}:0;switch(e){case"bar":case"tick":return t.scale.minBandSize;case"line":case"trail":case"rule":return t.scale.minStrokeWidth;case"text":return t.scale.minFontSize;case"point":case"square":case"circle":return t.scale.minSize}throw new Error(U3("size",e))}const PP=.95;function j3e(e,n,t,o){const f={x:OP(t,"x"),y:OP(t,"y")};switch(e){case"bar":case"tick":{if(o.scale.maxBandSize!==void 0)return o.scale.maxBandSize;const r=IP(n,f,o.view);return Eo(r)?r-1:new $u(()=>`${r.signal} - 1`)}case"line":case"trail":case"rule":return o.scale.maxStrokeWidth;case"text":return o.scale.maxFontSize;case"point":case"square":case"circle":{if(o.scale.maxSize)return o.scale.maxSize;const r=IP(n,f,o.view);return Eo(r)?Math.pow(PP*r,2):new $u(()=>`pow(${PP} * ${r.signal}, 2)`)}}throw new Error(U3("size",e))}function IP(e,n,t){const o=dh(e.width)?e.width.step:ow(t,"width"),f=dh(e.height)?e.height.step:ow(t,"height");return n.x||n.y?new $u(()=>`min(${[n.x?n.x.signal:o,n.y?n.y.signal:f].join(", ")})`):Math.min(o,f)}function UH(e,n){As(e)?U3e(e,n):VH(e,n)}function U3e(e,n){const t=e.component.scales,{config:o,encoding:f,markDef:r,specifiedScales:a}=e;for(const l of Zr(t)){const c=a[l],i=t[l],s=e.getScaleComponent(l),u=Ks(f[l]),h=c[n],d=s.get("type"),m=s.get("padding"),p=s.get("paddingInner"),g=AT(d,n),y=AV(l,n);if(h!==void 0&&(g?y&&ei(y):ei(nV(d,n,l))),g&&y===void 0)if(h!==void 0){const v=u.timeUnit,x=u.type;switch(n){case"domainMax":case"domainMin":hg(c[n])||x==="temporal"||v?i.set(n,{signal:K3(c[n],{type:x,timeUnit:v})},!0):i.set(n,c[n],!0);break;default:i.copyKeyFromObject(n,c)}}else{const v=n in FP?FP[n]({model:e,channel:l,fieldOrDatumDef:u,scaleType:d,scalePadding:m,scalePaddingInner:p,domain:c.domain,domainMin:c.domainMin,domainMax:c.domainMax,markDef:r,config:o,hasNestedOffsetScale:TT(f,l),hasSecondaryRangeChannel:!!f[_h(l)]}):o.scale[n];v!==void 0&&i.set(n,v,!1)}}}const FP={bins:({model:e,fieldOrDatumDef:n})=>ni(n)?$3e(e,n):void 0,interpolate:({channel:e,fieldOrDatumDef:n})=>V3e(e,n.type),nice:({scaleType:e,channel:n,domain:t,domainMin:o,domainMax:f,fieldOrDatumDef:r})=>q3e(e,n,t,o,f,r),padding:({channel:e,scaleType:n,fieldOrDatumDef:t,markDef:o,config:f})=>H3e(e,n,f.scale,t,o,f.bar),paddingInner:({scalePadding:e,channel:n,markDef:t,scaleType:o,config:f,hasNestedOffsetScale:r})=>G3e(e,n,t.type,o,f.scale,r),paddingOuter:({scalePadding:e,channel:n,scaleType:t,scalePaddingInner:o,config:f,hasNestedOffsetScale:r})=>W3e(e,n,t,o,f.scale,r),reverse:({fieldOrDatumDef:e,scaleType:n,channel:t,config:o})=>{const f=ni(e)?e.sort:void 0;return Y3e(n,f,t,o.scale)},zero:({channel:e,fieldOrDatumDef:n,domain:t,markDef:o,scaleType:f,config:r,hasSecondaryRangeChannel:a})=>X3e(e,n,t,o,f,r.scale,a)};function $H(e){As(e)?P3e(e):VH(e,"range")}function VH(e,n){const t=e.component.scales;for(const o of e.children)n==="range"?$H(o):UH(o,n);for(const o of Zr(t)){let f;for(const r of e.children){const a=r.component.scales[o];if(a){const l=a.getWithExplicit(n);f=mp(f,l,n,"scale",Mq((c,i)=>{switch(n){case"range":return c.step&&i.step?c.step-i.step:0}return 0}))}}t[o].setWithExplicit(n,f)}}function $3e(e,n){const t=n.bin;if(Vo(t)){const o=oC(e,n.field,t);return new $u(()=>e.getSignalName(o))}else if(Ml(t)&&fg(t)&&t.step!==void 0)return{step:t.step}}function V3e(e,n){if(ja([Gu,xh,bh],e)&&n!=="nominal")return"hcl"}function q3e(e,n,t,o,f,r){if(!(hh(r)?.bin||zr(t)||f!=null||o!=null||ja([Uu.TIME,Uu.UTC],e)))return pl(n)?!0:void 0}function H3e(e,n,t,o,f,r){if(pl(e)){if(ff(n)){if(t.continuousPadding!==void 0)return t.continuousPadding;const{type:a,orient:l}=f;if(a==="bar"&&!(ni(o)&&(o.bin||o.timeUnit))&&(l==="vertical"&&e==="x"||l==="horizontal"&&e==="y"))return r.continuousBandSize}if(n===Uu.POINT)return t.pointPadding}}function G3e(e,n,t,o,f,r=!1){if(e===void 0){if(pl(n)){const{bandPaddingInner:a,barBandPaddingInner:l,rectBandPaddingInner:c,bandWithNestedOffsetPaddingInner:i}=f;return r?i:zs(a,t==="bar"?l:c)}else if(b1(n)&&o===Uu.BAND)return f.offsetBandPaddingInner}}function W3e(e,n,t,o,f,r=!1){if(e===void 0){if(pl(n)){const{bandPaddingOuter:a,bandWithNestedOffsetPaddingOuter:l}=f;if(r)return l;if(t===Uu.BAND)return zs(a,ji(o)?{signal:`${o.signal}/2`}:o/2)}else if(b1(n)){if(t===Uu.POINT)return .5;if(t===Uu.BAND)return f.offsetBandPaddingOuter}}}function Y3e(e,n,t,o){if(t==="x"&&o.xReverse!==void 0)return pc(e)&&n==="descending"?ji(o.xReverse)?{signal:`!${o.xReverse.signal}`}:!o.xReverse:o.xReverse;if(pc(e)&&n==="descending")return!0}function X3e(e,n,t,o,f,r,a){if(!!t&&t!=="unaggregated"&&pc(f)){if(zr(t)){const c=t[0],i=t[t.length-1];if(c<=0&&i>=0)return!0}return!1}if(e==="size"&&n.type==="quantitative"&&!Ym(f))return!0;if(!(ni(n)&&n.bin)&&ja([...wh,...L1e],e)){const{orient:c,type:i}=o;return ja(["bar","area","line","trail"],i)&&(c==="horizontal"&&e==="y"||c==="vertical"&&e==="x")?!1:ja(["bar","area"],i)&&!a?!0:r?.zero}return!1}function Z3e(e,n,t,o,f=!1){const r=J3e(n,t,o,f),{type:a}=e;return gd(n)?a!==void 0?Fve(n,a)?ni(t)&&!Ive(a,t.type)?(ei(Rye(a,r)),r):a:(ei(Fye(n,a,r)),r):r:null}function J3e(e,n,t,o){switch(n.type){case"nominal":case"ordinal":{if(bm(e)||cA(e)==="discrete")return e==="shape"&&n.type==="ordinal"&&ei(fA(e,"ordinal")),"ordinal";if(pl(e)||b1(e)){if(ja(["rect","bar","image","rule"],t.type)||o)return"band"}else if(t.type==="arc"&&e in i8)return"band";const f=t[Yu(e)];return W0(f)||Zm(n)&&n.axis?.tickBand?"band":"point"}case"temporal":return bm(e)?"time":cA(e)==="discrete"?(ei(fA(e,"temporal")),"ordinal"):ni(n)&&n.timeUnit&&hl(n.timeUnit).utc?"utc":"time";case"quantitative":return bm(e)?ni(n)&&Vo(n.bin)?"bin-ordinal":"linear":cA(e)==="discrete"?(ei(fA(e,"quantitative")),"ordinal"):"linear";case"geojson":return}throw new Error(eV(n.type))}function K3e(e,{ignoreRange:n}={}){qH(e),IH(e);for(const t of Pve)UH(e,t);n||$H(e)}function qH(e){As(e)?e.component.scales=Q3e(e):e.component.scales=t5e(e)}function Q3e(e){const{encoding:n,mark:t,markDef:o}=e,f={};for(const r of B3){const a=Ks(n[r]);if(a&&t===MV&&r===Wu&&a.type===_1)continue;let l=a&&a.scale;if(b1(r)){const c=N$(r);if(!TT(n,c)){l&&ei(_ye(r));continue}}if(a&&l!==null&&l!==!1){l??(l={});const c=TT(n,r),i=Z3e(l,r,a,o,c);f[r]=new RH(e.scaleName(`${r}`,!0),{value:i,explicit:l.type===i})}}return f}const e5e=Mq((e,n)=>RO(e)-RO(n));function t5e(e){var n;const t=e.component.scales={},o={},f=e.component.resolve;for(const r of e.children){qH(r);for(const a of Zr(r.component.scales))if((n=f.scale)[a]??(n[a]=_H(a,e)),f.scale[a]==="shared"){const l=o[a],c=r.component.scales[a].getWithExplicit("type");l?Ave(l.value,c.value)?o[a]=mp(l,c,"type","scale",e5e):(f.scale[a]="independent",delete o[a]):o[a]=c}}for(const r of Zr(o)){const a=e.scaleName(r,!0),l=o[r];t[r]=new RH(a,l);for(const c of e.children){const i=c.component.scales[r];i&&(c.renameScale(i.get("name"),a),i.merged=!0)}}return t}class AA{constructor(){this.nameMap={}}rename(n,t){this.nameMap[n]=t}has(n){return this.nameMap[n]!==void 0}get(n){for(;this.nameMap[n]&&n!==this.nameMap[n];)n=this.nameMap[n];return n}}function As(e){return e?.type==="unit"}function mf(e){return e?.type==="facet"}function fC(e){return e?.type==="concat"}function E1(e){return e?.type==="layer"}class hC{constructor(n,t,o,f,r,a,l){this.type=t,this.parent=o,this.config=r,this.correctDataNames=c=>(c.from?.data&&(c.from.data=this.lookupDataSource(c.from.data)),c.from?.facet?.data&&(c.from.facet.data=this.lookupDataSource(c.from.facet.data)),c),this.parent=o,this.config=r,this.view=Iu(l),this.name=n.name??f,this.title=Id(n.title)?{text:n.title}:n.title?Iu(n.title):void 0,this.scaleNameMap=o?o.scaleNameMap:new AA,this.projectionNameMap=o?o.projectionNameMap:new AA,this.signalNameMap=o?o.signalNameMap:new AA,this.data=n.data,this.description=n.description,this.transforms=Vbe(n.transform??[]),this.layout=t==="layer"||t==="unit"?{}:Jxe(n,t,r),this.component={data:{sources:o?o.component.data.sources:[],outputNodes:o?o.component.data.outputNodes:{},outputNodeRefCounts:o?o.component.data.outputNodeRefCounts:{},isFaceted:Y3(n)||o?.component.data.isFaceted&&n.data===void 0},layoutSize:new yd,layoutHeaders:{row:{},column:{},facet:{}},mark:null,resolve:{scale:{},axis:{},legend:{},...a?la(a):{}},selection:null,scales:null,projection:null,axes:{},legends:{}}}get width(){return this.getSizeSignalRef("width")}get height(){return this.getSizeSignalRef("height")}parse(){this.parseScale(),this.parseLayoutSize(),this.renameTopLevelLayoutSizeSignal(),this.parseSelections(),this.parseProjection(),this.parseData(),this.parseAxesAndHeaders(),this.parseLegends(),this.parseMarkGroup()}parseScale(){K3e(this)}parseProjection(){DH(this)}renameTopLevelLayoutSizeSignal(){this.getName("width")!=="width"&&this.renameSignal(this.getName("width"),"width"),this.getName("height")!=="height"&&this.renameSignal(this.getName("height"),"height")}parseLegends(){MH(this)}assembleEncodeFromView(n){const{style:t,...o}=n,f={};for(const r of Zr(o)){const a=o[r];a!==void 0&&(f[r]=Yo(a))}return f}assembleGroupEncodeEntry(n){let t={};return this.view&&(t=this.assembleEncodeFromView(this.view)),!n&&(this.description&&(t.description=Yo(this.description)),this.type==="unit"||this.type==="layer")?{width:this.getSizeSignalRef("width"),height:this.getSizeSignalRef("height"),...t??{}}:Mo(t)?void 0:t}assembleLayout(){if(!this.layout)return;const{spacing:n,...t}=this.layout,{component:o,config:f}=this,r=wwe(o.layoutHeaders,f);return{padding:n,...this.assembleDefaultLayout(),...t,...r?{titleBand:r}:{}}}assembleDefaultLayout(){return{}}assembleHeaderMarks(){const{layoutHeaders:n}=this.component;let t=[];for(const o of Tc)n[o].title&&t.push(mwe(this,o));for(const o of nC)t=t.concat(ywe(this,o));return t}assembleAxes(){return rwe(this.component.axes,this.config)}assembleLegends(){return SH(this)}assembleProjections(){return Hwe(this)}assembleTitle(){const{encoding:n,...t}=this.title??{},o={...H$(this.config.title).nonMarkTitleProperties,...t,...n?{encode:{update:n}}:{}};if(o.text)return ja(["unit","layer"],this.type)?ja(["middle",void 0],o.anchor)&&(o.frame??(o.frame="group")):o.anchor??(o.anchor="start"),Mo(o)?void 0:o}assembleGroup(n=[]){const t={};n=n.concat(this.assembleSignals()),n.length>0&&(t.signals=n);const o=this.assembleLayout();o&&(t.layout=o),t.marks=[].concat(this.assembleHeaderMarks(),this.assembleMarks());const f=!this.parent||mf(this.parent)?FH(this):[];f.length>0&&(t.scales=f);const r=this.assembleAxes();r.length>0&&(t.axes=r);const a=this.assembleLegends();return a.length>0&&(t.legends=a),t}getName(n){return es((this.name?`${this.name}_`:"")+n)}getDataName(n){return this.getName(Uo[n].toLowerCase())}requestDataName(n){const t=this.getDataName(n),o=this.component.data.outputNodeRefCounts;return o[t]=(o[t]||0)+1,t}getSizeSignalRef(n){if(mf(this.parent)){const t=xH(n),o=N3(t),f=this.component.scales[o];if(f&&!f.merged){const r=f.get("type"),a=f.get("range");if(ml(r)&&Cp(a)){const l=f.get("name"),c=h5(this,o),i=cC(c);if(i){const s=hi({aggregate:"distinct",field:i},{expr:"datum"});return{signal:vH(l,f,s)}}else return ei(s8(o)),null}}}return{signal:this.signalNameMap.get(this.getName(n))}}lookupDataSource(n){const t=this.component.data.outputNodes[n];return t?t.getSource():n}getSignalName(n){return this.signalNameMap.get(n)}renameSignal(n,t){this.signalNameMap.rename(n,t)}renameScale(n,t){this.scaleNameMap.rename(n,t)}renameProjection(n,t){this.projectionNameMap.rename(n,t)}scaleName(n,t){if(t)return this.getName(n);if(F$(n)&&gd(n)&&this.component.scales[n]||this.scaleNameMap.has(this.getName(n)))return this.scaleNameMap.get(this.getName(n))}projectionName(n){if(n)return this.getName("projection");if(this.component.projection&&!this.component.projection.merged||this.projectionNameMap.has(this.getName("projection")))return this.projectionNameMap.get(this.getName("projection"))}getScaleComponent(n){if(!this.component.scales)throw new Error("getScaleComponent cannot be called before parseScale(). Make sure you have called parseScale or use parseUnitModelWithScale().");const t=this.component.scales[n];return t&&!t.merged?t:this.parent?this.parent.getScaleComponent(n):void 0}getSelectionComponent(n,t){let o=this.component.selection[n];if(!o&&this.parent&&(o=this.parent.getSelectionComponent(n,t)),!o)throw new Error(Q1e(t));return o}hasAxisOrientSignalRef(){return this.component.axes.x?.some(n=>n.hasOrientSignalRef())||this.component.axes.y?.some(n=>n.hasOrientSignalRef())}}class HH extends hC{vgField(n,t={}){const o=this.fieldDef(n);if(o)return hi(o,t)}reduceFieldDef(n,t){return Mxe(this.getMapping(),(o,f,r)=>{const a=hh(f);return a?n(o,a,r):o},t)}forEachFieldDef(n,t){F8(this.getMapping(),(o,f)=>{const r=hh(o);r&&n(r,f)},t)}}class d5 extends wo{clone(){return new d5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t,this.transform=la(t);const o=this.transform.as??[void 0,void 0];this.transform.as=[o[0]??"value",o[1]??"density"],t.groupby&&t.minsteps==null&&t.maxsteps==null&&t.steps==null&&(this.transform.steps=200)}dependentFields(){return new Set([this.transform.density,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`DensityTransform ${Ba(this.transform)}`}assemble(){const{density:n,...t}=this.transform;return{type:"kde",field:n,...t}}}class p5 extends wo{clone(){return new p5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t,this.transform=la(t)}dependentFields(){return new Set([this.transform.extent])}producedFields(){return new Set([])}hash(){return`ExtentTransform ${Ba(this.transform)}`}assemble(){const{extent:n,param:t}=this.transform;return{type:"extent",field:n,signal:t}}}class Nv extends wo{clone(){return new Nv(null,{...this.filter})}constructor(n,t){super(n),this.filter=t}static make(n,t){const{config:o,mark:f,markDef:r}=t;if(uo("invalid",r,o)!=="filter")return null;const l=t.reduceFieldDef((c,i,s)=>{const u=gd(s)&&t.getScaleComponent(s);if(u){const h=u.get("type");pc(h)&&i.aggregate!=="count"&&!Lp(f)&&(c[i.field]=i)}return c},{});return Zr(l).length?new Nv(n,l):null}dependentFields(){return new Set(Zr(this.filter))}producedFields(){return new Set}hash(){return`FilterInvalid ${Ba(this.filter)}`}assemble(){const n=Zr(this.filter).reduce((t,o)=>{const f=this.filter[o],r=hi(f,{expr:"datum"});return f!==null&&(f.type==="temporal"?t.push(`(isDate(${r}) || (isValid(${r}) && isFinite(+${r})))`):f.type==="quantitative"&&(t.push(`isValid(${r})`),t.push(`isFinite(+${r})`))),t},[]);return n.length>0?{type:"filter",expr:n.join(" && ")}:null}}class g5 extends wo{clone(){return new g5(this.parent,la(this.transform))}constructor(n,t){super(n),this.transform=t,this.transform=la(t);const{flatten:o,as:f=[]}=this.transform;this.transform.as=o.map((r,a)=>f[a]??r)}dependentFields(){return new Set(this.transform.flatten)}producedFields(){return new Set(this.transform.as)}hash(){return`FlattenTransform ${Ba(this.transform)}`}assemble(){const{flatten:n,as:t}=this.transform;return{type:"flatten",fields:n,as:t}}}class m5 extends wo{clone(){return new m5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t,this.transform=la(t);const o=this.transform.as??[void 0,void 0];this.transform.as=[o[0]??"key",o[1]??"value"]}dependentFields(){return new Set(this.transform.fold)}producedFields(){return new Set(this.transform.as)}hash(){return`FoldTransform ${Ba(this.transform)}`}assemble(){const{fold:n,as:t}=this.transform;return{type:"fold",fields:n,as:t}}}class Am extends wo{clone(){return new Am(null,la(this.fields),this.geojson,this.signal)}static parseAll(n,t){if(t.component.projection&&!t.component.projection.isFit)return n;let o=0;for(const f of[[Sf,Ef],[Oc,Cf]]){const r=f.map(a=>{const l=Ks(t.encoding[a]);return ni(l)?l.field:Ah(l)?{expr:`${l.datum}`}:bf(l)?{expr:`${l.value}`}:void 0});(r[0]||r[1])&&(n=new Am(n,r,null,t.getName(`geojson_${o++}`)))}if(t.channelHasField(Wu)){const f=t.typedFieldDef(Wu);f.type===_1&&(n=new Am(n,null,f.field,t.getName(`geojson_${o++}`)))}return n}constructor(n,t,o,f){super(n),this.fields=t,this.geojson=o,this.signal=f}dependentFields(){const n=(this.fields??[]).filter(Li);return new Set([...this.geojson?[this.geojson]:[],...n])}producedFields(){return new Set}hash(){return`GeoJSON ${this.geojson} ${this.signal} ${Ba(this.fields)}`}assemble(){return[...this.geojson?[{type:"filter",expr:`isValid(datum["${this.geojson}"])`}]:[],{type:"geojson",...this.fields?{fields:this.fields}:{},...this.geojson?{geojson:this.geojson}:{},signal:this.signal}]}}class Bv extends wo{clone(){return new Bv(null,this.projection,la(this.fields),la(this.as))}constructor(n,t,o,f){super(n),this.projection=t,this.fields=o,this.as=f}static parseAll(n,t){if(!t.projectionName())return n;for(const o of[[Sf,Ef],[Oc,Cf]]){const f=o.map(a=>{const l=Ks(t.encoding[a]);return ni(l)?l.field:Ah(l)?{expr:`${l.datum}`}:bf(l)?{expr:`${l.value}`}:void 0}),r=o[0]===Oc?"2":"";(f[0]||f[1])&&(n=new Bv(n,t.projectionName(),f,[t.getName(`x${r}`),t.getName(`y${r}`)]))}return n}dependentFields(){return new Set(this.fields.filter(Li))}producedFields(){return new Set(this.as)}hash(){return`Geopoint ${this.projection} ${Ba(this.fields)} ${Ba(this.as)}`}assemble(){return{type:"geopoint",projection:this.projection,fields:this.fields,as:this.as}}}class C0 extends wo{clone(){return new C0(null,la(this.transform))}constructor(n,t){super(n),this.transform=t}dependentFields(){return new Set([this.transform.impute,this.transform.key,...this.transform.groupby??[]])}producedFields(){return new Set([this.transform.impute])}processSequence(n){const{start:t=0,stop:o,step:f}=n;return{signal:`sequence(${[t,o,...f?[f]:[]].join(",")})`}}static makeFromTransform(n,t){return new C0(n,t)}static makeFromEncoding(n,t){const o=t.encoding,f=o.x,r=o.y;if(ni(f)&&ni(r)){const a=f.impute?f:r.impute?r:void 0;if(a===void 0)return;const l=f.impute?r:r.impute?f:void 0,{method:c,value:i,frame:s,keyvals:u}=a.impute,h=KV(t.mark,o);return new C0(n,{impute:a.field,key:l.field,...c?{method:c}:{},...i!==void 0?{value:i}:{},...s?{frame:s}:{},...u!==void 0?{keyvals:u}:{},...h.length?{groupby:h}:{}})}return null}hash(){return`Impute ${Ba(this.transform)}`}assemble(){const{impute:n,key:t,keyvals:o,method:f,groupby:r,value:a,frame:l=[null,null]}=this.transform,c={type:"impute",field:n,key:t,...o?{keyvals:kbe(o)?this.processSequence(o):o}:{},method:"value",...r?{groupby:r}:{},value:!f||f==="value"?a:null};if(f&&f!=="value"){const i={type:"window",as:[`imputed_${n}_value`],ops:[f],fields:[n],frame:l,ignorePeers:!1,...r?{groupby:r}:{}},s={type:"formula",expr:`datum.${n} === null ? datum.imputed_${n}_value : datum.${n}`,as:n};return[c,i,s]}else return[c]}}class y5 extends wo{clone(){return new y5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t,this.transform=la(t);const o=this.transform.as??[void 0,void 0];this.transform.as=[o[0]??t.on,o[1]??t.loess]}dependentFields(){return new Set([this.transform.loess,this.transform.on,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`LoessTransform ${Ba(this.transform)}`}assemble(){const{loess:n,on:t,...o}=this.transform;return{type:"loess",x:t,y:n,...o}}}class jv extends wo{clone(){return new jv(null,la(this.transform),this.secondary)}constructor(n,t,o){super(n),this.transform=t,this.secondary=o}static make(n,t,o,f){const r=t.component.data.sources,{from:a}=o;let l=null;if(Tbe(a)){let c=YH(a.data,r);c||(c=new Q0(a.data),r.push(c));const i=t.getName(`lookup_${f}`);l=new vu(c,i,Uo.Lookup,t.component.data.outputNodeRefCounts),t.component.data.outputNodes[i]=l}else if(Mbe(a)){const c=a.param;o={as:c,...o};let i;try{i=t.getSelectionComponent(es(c),c)}catch{throw new Error(nye(c))}if(l=i.materialized,!l)throw new Error(rye(c))}return new jv(n,o,l.getSource())}dependentFields(){return new Set([this.transform.lookup])}producedFields(){return new Set(this.transform.as?Ti(this.transform.as):this.transform.from.fields)}hash(){return`Lookup ${Ba({transform:this.transform,secondary:this.secondary})}`}assemble(){let n;if(this.transform.from.fields)n={values:this.transform.from.fields,...this.transform.as?{as:Ti(this.transform.as)}:{}};else{let t=this.transform.as;Li(t)||(ei(fye),t="_lookup"),n={as:[t]}}return{type:"lookup",from:this.secondary,key:this.transform.from.key,fields:[this.transform.lookup],...n,...this.transform.default?{default:this.transform.default}:{}}}}class v5 extends wo{clone(){return new v5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t,this.transform=la(t);const o=this.transform.as??[void 0,void 0];this.transform.as=[o[0]??"prob",o[1]??"value"]}dependentFields(){return new Set([this.transform.quantile,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`QuantileTransform ${Ba(this.transform)}`}assemble(){const{quantile:n,...t}=this.transform;return{type:"quantile",field:n,...t}}}class x5 extends wo{clone(){return new x5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t,this.transform=la(t);const o=this.transform.as??[void 0,void 0];this.transform.as=[o[0]??t.on,o[1]??t.regression]}dependentFields(){return new Set([this.transform.regression,this.transform.on,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`RegressionTransform ${Ba(this.transform)}`}assemble(){const{regression:n,on:t,...o}=this.transform;return{type:"regression",x:t,y:n,...o}}}class b5 extends wo{clone(){return new b5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t}addDimensions(n){this.transform.groupby=Zf((this.transform.groupby??[]).concat(n),t=>t)}producedFields(){}dependentFields(){return new Set([this.transform.pivot,this.transform.value,...this.transform.groupby??[]])}hash(){return`PivotTransform ${Ba(this.transform)}`}assemble(){const{pivot:n,value:t,groupby:o,limit:f,op:r}=this.transform;return{type:"pivot",field:n,value:t,...f!==void 0?{limit:f}:{},...r!==void 0?{op:r}:{},...o!==void 0?{groupby:o}:{}}}}class _5 extends wo{clone(){return new _5(null,la(this.transform))}constructor(n,t){super(n),this.transform=t}dependentFields(){return new Set}producedFields(){return new Set}hash(){return`SampleTransform ${Ba(this.transform)}`}assemble(){return{type:"sample",size:this.transform.sample}}}function GH(e){let n=0;function t(o,f){if(o instanceof Q0&&!o.isGenerator&&!Km(o.data)&&(e.push(f),f={name:null,source:f.name,transform:[]}),o instanceof Gl&&(o.parent instanceof 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vu?o.setSource(f.name):(f.name||(f.name=`data_${n++}`),o.setSource(f.name),o.numChildren()===1&&(e.push(f),f={name:null,source:f.name,transform:[]}))),o.numChildren()){case 0:o instanceof vu&&(!f.source||f.transform.length>0)&&e.push(f);break;case 1:t(o.children[0],f);break;default:{f.name||(f.name=`data_${n++}`);let r=f.name;!f.source||f.transform.length>0?e.push(f):r=f.source;for(const a of o.children)t(a,{name:null,source:r,transform:[]});break}}}return t}function n5e(e){const n=[],t=GH(n);for(const o of e.children)t(o,{source:e.name,name:null,transform:[]});return n}function r5e(e,n){const t=[],o=GH(t);let f=0;for(const a of e.sources){a.hasName()||(a.dataName=`source_${f++}`);const l=a.assemble();o(a,l)}for(const a of t)a.transform.length===0&&delete a.transform;let r=0;for(const[a,l]of t.entries())(l.transform??[]).length===0&&!l.source&&t.splice(r++,0,t.splice(a,1)[0]);for(const a of t)for(const l of 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diff --git a/spaces/DaleChen/AutoGPT/autogpt/chat.py b/spaces/DaleChen/AutoGPT/autogpt/chat.py
deleted file mode 100644
index 1f6bca96eb216c667656b50f131006b83c681065..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/chat.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import time
-
-from openai.error import RateLimitError
-
-from autogpt import token_counter
-from autogpt.config import Config
-from autogpt.llm_utils import create_chat_completion
-from autogpt.logs import logger
-
-cfg = Config()
-
-
-def create_chat_message(role, content):
-    """
-    Create a chat message with the given role and content.
-
-    Args:
-    role (str): The role of the message sender, e.g., "system", "user", or "assistant".
-    content (str): The content of the message.
-
-    Returns:
-    dict: A dictionary containing the role and content of the message.
-    """
-    return {"role": role, "content": content}
-
-
-def generate_context(prompt, relevant_memory, full_message_history, model):
-    current_context = [
-        create_chat_message("system", prompt),
-        create_chat_message(
-            "system", f"The current time and date is {time.strftime('%c')}"
-        ),
-        create_chat_message(
-            "system",
-            f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
-        ),
-    ]
-
-    # Add messages from the full message history until we reach the token limit
-    next_message_to_add_index = len(full_message_history) - 1
-    insertion_index = len(current_context)
-    # Count the currently used tokens
-    current_tokens_used = token_counter.count_message_tokens(current_context, model)
-    return (
-        next_message_to_add_index,
-        current_tokens_used,
-        insertion_index,
-        current_context,
-    )
-
-
-# TODO: Change debug from hardcode to argument
-def chat_with_ai(
-    prompt, user_input, full_message_history, permanent_memory, token_limit
-):
-    """Interact with the OpenAI API, sending the prompt, user input, message history,
-    and permanent memory."""
-    while True:
-        try:
-            """
-            Interact with the OpenAI API, sending the prompt, user input,
-                message history, and permanent memory.
-
-            Args:
-                prompt (str): The prompt explaining the rules to the AI.
-                user_input (str): The input from the user.
-                full_message_history (list): The list of all messages sent between the
-                    user and the AI.
-                permanent_memory (Obj): The memory object containing the permanent
-                  memory.
-                token_limit (int): The maximum number of tokens allowed in the API call.
-
-            Returns:
-            str: The AI's response.
-            """
-            model = cfg.fast_llm_model  # TODO: Change model from hardcode to argument
-            # Reserve 1000 tokens for the response
-
-            logger.debug(f"Token limit: {token_limit}")
-            send_token_limit = token_limit - 1000
-
-            relevant_memory = (
-                ""
-                if len(full_message_history) == 0
-                else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
-            )
-
-            logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
-
-            (
-                next_message_to_add_index,
-                current_tokens_used,
-                insertion_index,
-                current_context,
-            ) = generate_context(prompt, relevant_memory, full_message_history, model)
-
-            while current_tokens_used > 2500:
-                # remove memories until we are under 2500 tokens
-                relevant_memory = relevant_memory[:-1]
-                (
-                    next_message_to_add_index,
-                    current_tokens_used,
-                    insertion_index,
-                    current_context,
-                ) = generate_context(
-                    prompt, relevant_memory, full_message_history, model
-                )
-
-            current_tokens_used += token_counter.count_message_tokens(
-                [create_chat_message("user", user_input)], model
-            )  # Account for user input (appended later)
-
-            while next_message_to_add_index >= 0:
-                # print (f"CURRENT TOKENS USED: {current_tokens_used}")
-                message_to_add = full_message_history[next_message_to_add_index]
-
-                tokens_to_add = token_counter.count_message_tokens(
-                    [message_to_add], model
-                )
-                if current_tokens_used + tokens_to_add > send_token_limit:
-                    break
-
-                # Add the most recent message to the start of the current context,
-                #  after the two system prompts.
-                current_context.insert(
-                    insertion_index, full_message_history[next_message_to_add_index]
-                )
-
-                # Count the currently used tokens
-                current_tokens_used += tokens_to_add
-
-                # Move to the next most recent message in the full message history
-                next_message_to_add_index -= 1
-
-            # Append user input, the length of this is accounted for above
-            current_context.extend([create_chat_message("user", user_input)])
-
-            # Calculate remaining tokens
-            tokens_remaining = token_limit - current_tokens_used
-            # assert tokens_remaining >= 0, "Tokens remaining is negative.
-            # This should never happen, please submit a bug report at
-            #  https://www.github.com/Torantulino/Auto-GPT"
-
-            # Debug print the current context
-            logger.debug(f"Token limit: {token_limit}")
-            logger.debug(f"Send Token Count: {current_tokens_used}")
-            logger.debug(f"Tokens remaining for response: {tokens_remaining}")
-            logger.debug("------------ CONTEXT SENT TO AI ---------------")
-            for message in current_context:
-                # Skip printing the prompt
-                if message["role"] == "system" and message["content"] == prompt:
-                    continue
-                logger.debug(f"{message['role'].capitalize()}: {message['content']}")
-                logger.debug("")
-            logger.debug("----------- END OF CONTEXT ----------------")
-
-            # TODO: use a model defined elsewhere, so that model can contain
-            # temperature and other settings we care about
-            assistant_reply = create_chat_completion(
-                model=model,
-                messages=current_context,
-                max_tokens=tokens_remaining,
-            )
-
-            # Update full message history
-            full_message_history.append(create_chat_message("user", user_input))
-            full_message_history.append(
-                create_chat_message("assistant", assistant_reply)
-            )
-
-            return assistant_reply
-        except RateLimitError:
-            # TODO: When we switch to langchain, this is built in
-            print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
-            time.sleep(10)
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/postprocessing/post_process.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/postprocessing/post_process.py
deleted file mode 100644
index c58d894d58d6ed1e90fc1c35d85b55acb24a3125..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/postprocessing/post_process.py
+++ /dev/null
@@ -1,34 +0,0 @@
-""" 
-@Date: 2021/10/08
-@description:
-"""
-import numpy as np
-import cv2
-
-from postprocessing.dula.layout import fit_layout
-from postprocessing.dula.layout_old import fit_layout_old
-from utils.conversion import depth2xyz, xyz2depth
-
-
-def post_process(b_depth, type_name='manhattan', need_cube=False):
-    plan_y = 1
-    b_xyz = depth2xyz(b_depth, plan_y)
-
-    b_processed_xyz = []
-    for xyz in b_xyz:
-        if type_name == 'manhattan':
-            processed_xz = fit_layout(floor_xz=xyz[..., ::2], need_cube=need_cube, show=False)
-        elif type_name == 'manhattan_old':
-            processed_xz = fit_layout_old(floor_xz=xyz[..., ::2], need_cube=need_cube, show=False)
-        elif type_name == 'atalanta':
-            processed_xz = cv2.approxPolyDP(xyz[..., ::2].astype(np.float32), 0.1, False)[:, 0, :]
-        else:
-            raise NotImplementedError("Unknown post-processing type")
-
-        if need_cube:
-            assert len(processed_xz) == 4
-
-        processed_xyz = np.insert(processed_xz, 1, plan_y, axis=1)
-        b_processed_xyz.append(processed_xyz)
-
-    return np.array(b_processed_xyz)
\ No newline at end of file
diff --git a/spaces/Denevan/BingAI/README.md b/spaces/Denevan/BingAI/README.md
deleted file mode 100644
index 918acef7b8464a56d793eb16119e4812092a8b90..0000000000000000000000000000000000000000
--- a/spaces/Denevan/BingAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: BingAI
-emoji: 📉
-colorFrom: yellow
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DragGan/DragGan/torch_utils/training_stats.py b/spaces/DragGan/DragGan/torch_utils/training_stats.py
deleted file mode 100644
index 5de4134f1943e7c3104bbc926b2abaf828626525..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/torch_utils/training_stats.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-#----------------------------------------------------------------------------
-
-_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype   = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype  = torch.float64 # Data type to use for the internal counters.
-_rank           = 0             # Rank of the current process.
-_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
-_sync_called    = False         # Has _sync() been called yet?
-_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-
-#----------------------------------------------------------------------------
-
-def init_multiprocessing(rank, sync_device):
-    r"""Initializes `torch_utils.training_stats` for collecting statistics
-    across multiple processes.
-
-    This function must be called after
-    `torch.distributed.init_process_group()` and before `Collector.update()`.
-    The call is not necessary if multi-process collection is not needed.
-
-    Args:
-        rank:           Rank of the current process.
-        sync_device:    PyTorch device to use for inter-process
-                        communication, or None to disable multi-process
-                        collection. Typically `torch.device('cuda', rank)`.
-    """
-    global _rank, _sync_device
-    assert not _sync_called
-    _rank = rank
-    _sync_device = sync_device
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def report(name, value):
-    r"""Broadcasts the given set of scalars to all interested instances of
-    `Collector`, across device and process boundaries.
-
-    This function is expected to be extremely cheap and can be safely
-    called from anywhere in the training loop, loss function, or inside a
-    `torch.nn.Module`.
-
-    Warning: The current implementation expects the set of unique names to
-    be consistent across processes. Please make sure that `report()` is
-    called at least once for each unique name by each process, and in the
-    same order. If a given process has no scalars to broadcast, it can do
-    `report(name, [])` (empty list).
-
-    Args:
-        name:   Arbitrary string specifying the name of the statistic.
-                Averages are accumulated separately for each unique name.
-        value:  Arbitrary set of scalars. Can be a list, tuple,
-                NumPy array, PyTorch tensor, or Python scalar.
-
-    Returns:
-        The same `value` that was passed in.
-    """
-    if name not in _counters:
-        _counters[name] = dict()
-
-    elems = torch.as_tensor(value)
-    if elems.numel() == 0:
-        return value
-
-    elems = elems.detach().flatten().to(_reduce_dtype)
-    moments = torch.stack([
-        torch.ones_like(elems).sum(),
-        elems.sum(),
-        elems.square().sum(),
-    ])
-    assert moments.ndim == 1 and moments.shape[0] == _num_moments
-    moments = moments.to(_counter_dtype)
-
-    device = moments.device
-    if device not in _counters[name]:
-        _counters[name][device] = torch.zeros_like(moments)
-    _counters[name][device].add_(moments)
-    return value
-
-#----------------------------------------------------------------------------
-
-def report0(name, value):
-    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
-    but ignores any scalars provided by the other processes.
-    See `report()` for further details.
-    """
-    report(name, value if _rank == 0 else [])
-    return value
-
-#----------------------------------------------------------------------------
-
-class Collector:
-    r"""Collects the scalars broadcasted by `report()` and `report0()` and
-    computes their long-term averages (mean and standard deviation) over
-    user-defined periods of time.
-
-    The averages are first collected into internal counters that are not
-    directly visible to the user. They are then copied to the user-visible
-    state as a result of calling `update()` and can then be queried using
-    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
-    internal counters for the next round, so that the user-visible state
-    effectively reflects averages collected between the last two calls to
-    `update()`.
-
-    Args:
-        regex:          Regular expression defining which statistics to
-                        collect. The default is to collect everything.
-        keep_previous:  Whether to retain the previous averages if no
-                        scalars were collected on a given round
-                        (default: True).
-    """
-    def __init__(self, regex='.*', keep_previous=True):
-        self._regex = re.compile(regex)
-        self._keep_previous = keep_previous
-        self._cumulative = dict()
-        self._moments = dict()
-        self.update()
-        self._moments.clear()
-
-    def names(self):
-        r"""Returns the names of all statistics broadcasted so far that
-        match the regular expression specified at construction time.
-        """
-        return [name for name in _counters if self._regex.fullmatch(name)]
-
-    def update(self):
-        r"""Copies current values of the internal counters to the
-        user-visible state and resets them for the next round.
-
-        If `keep_previous=True` was specified at construction time, the
-        operation is skipped for statistics that have received no scalars
-        since the last update, retaining their previous averages.
-
-        This method performs a number of GPU-to-CPU transfers and one
-        `torch.distributed.all_reduce()`. It is intended to be called
-        periodically in the main training loop, typically once every
-        N training steps.
-        """
-        if not self._keep_previous:
-            self._moments.clear()
-        for name, cumulative in _sync(self.names()):
-            if name not in self._cumulative:
-                self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-            delta = cumulative - self._cumulative[name]
-            self._cumulative[name].copy_(cumulative)
-            if float(delta[0]) != 0:
-                self._moments[name] = delta
-
-    def _get_delta(self, name):
-        r"""Returns the raw moments that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        assert self._regex.fullmatch(name)
-        if name not in self._moments:
-            self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        return self._moments[name]
-
-    def num(self, name):
-        r"""Returns the number of scalars that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        return int(delta[0])
-
-    def mean(self, name):
-        r"""Returns the mean of the scalars that were accumulated for the
-        given statistic between the last two calls to `update()`, or NaN if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0:
-            return float('nan')
-        return float(delta[1] / delta[0])
-
-    def std(self, name):
-        r"""Returns the standard deviation of the scalars that were
-        accumulated for the given statistic between the last two calls to
-        `update()`, or NaN if no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
-            return float('nan')
-        if int(delta[0]) == 1:
-            return float(0)
-        mean = float(delta[1] / delta[0])
-        raw_var = float(delta[2] / delta[0])
-        return np.sqrt(max(raw_var - np.square(mean), 0))
-
-    def as_dict(self):
-        r"""Returns the averages accumulated between the last two calls to
-        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
-            dnnlib.EasyDict(
-                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
-                ...
-            )
-        """
-        stats = dnnlib.EasyDict()
-        for name in self.names():
-            stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
-        return stats
-
-    def __getitem__(self, name):
-        r"""Convenience getter.
-        `collector[name]` is a synonym for `collector.mean(name)`.
-        """
-        return self.mean(name)
-
-#----------------------------------------------------------------------------
-
-def _sync(names):
-    r"""Synchronize the global cumulative counters across devices and
-    processes. Called internally by `Collector.update()`.
-    """
-    if len(names) == 0:
-        return []
-    global _sync_called
-    _sync_called = True
-
-    # Collect deltas within current rank.
-    deltas = []
-    device = _sync_device if _sync_device is not None else torch.device('cpu')
-    for name in names:
-        delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
-        for counter in _counters[name].values():
-            delta.add_(counter.to(device))
-            counter.copy_(torch.zeros_like(counter))
-        deltas.append(delta)
-    deltas = torch.stack(deltas)
-
-    # Sum deltas across ranks.
-    if _sync_device is not None:
-        torch.distributed.all_reduce(deltas)
-
-    # Update cumulative values.
-    deltas = deltas.cpu()
-    for idx, name in enumerate(names):
-        if name not in _cumulative:
-            _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        _cumulative[name].add_(deltas[idx])
-
-    # Return name-value pairs.
-    return [(name, _cumulative[name]) for name in names]
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Duskfallcrew/Duskfallcrew-Osenayan_Mix/app.py b/spaces/Duskfallcrew/Duskfallcrew-Osenayan_Mix/app.py
deleted file mode 100644
index 0fd663daea0fc544b5ef185e424f8ee81aed7e1e..0000000000000000000000000000000000000000
--- a/spaces/Duskfallcrew/Duskfallcrew-Osenayan_Mix/app.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Duskfallcrew/Osenayan_Mix").launch()
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>Osenayan Mix</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/Duskfallcrew/Osenayan_Mix">Osenayan Mix</a> Stable Diffusion model.  We stream a lot of our testing on  <a href="https://www.twitch.tv/duskfallcrew"> Twitch </a>. Any chance you can spare a coffee or three? <a href="https://ko-fi.com/DUSKFALLcrew">Ko-Fi Anyone?</a>. Request image gens via our <a href="https://www.pixiv.net/en/users/70748346"> Pixiv</a>. Hang with us on discord: <a href="https://discord.gg/Da7s8d3KJ7"> Earth & Dusk Discord </a>. No tokens are required. <br>
-              
-            </div>
-        """
-    )
\ No newline at end of file
diff --git a/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/schedules/schedule_1x.py b/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/schedules/schedule_1x.py
deleted file mode 100644
index 1c01d3df3d9169fee87ffeaa4e0fb60ac3f07b66..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/schedules/schedule_1x.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step',
-                 warmup='linear',
-                 warmup_iters=500,
-                 warmup_ratio=0.001,
-                 step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/spaces/Emanuel/porttagger/app.py b/spaces/Emanuel/porttagger/app.py
deleted file mode 100644
index 45e4862dd8dd9025e1314519dcea2f6b2b3edda4..0000000000000000000000000000000000000000
--- a/spaces/Emanuel/porttagger/app.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import logging
-import os
-from pathlib import Path
-from typing import List, Tuple
-
-import gradio as gr
-import pandas as pd
-import spacy
-import torch
-from dante_tokenizer import DanteTokenizer
-from transformers import AutoModelForTokenClassification, AutoTokenizer
-
-from preprocessing import expand_contractions
-
-try:
-    nlp = spacy.load("pt_core_news_sm")
-except Exception:
-    os.system("python -m spacy download pt_core_news_sm")
-    nlp = spacy.load("pt_core_news_sm")
-dt_tokenizer = DanteTokenizer()
-
-default_model = "News"
-model_choices = {
-    "News": "Emanuel/porttagger-news-base",
-    "Tweets (stock market)": "Emanuel/porttagger-tweets-base",
-    "Oil and Gas (academic texts)": "Emanuel/porttagger-oilgas-base",
-    "Multigenre": "Emanuel/porttagger-base",
-}
-pre_tokenizers = {
-    "News": nlp,
-    "Tweets (stock market)": dt_tokenizer.tokenize,
-    "Oil and Gas (academic texts)": nlp,
-    "Multigenre": nlp,
-}
-logger = logging.getLogger()
-logger.setLevel(logging.DEBUG)
-
-class MyApp:
-    def __init__(self) -> None:
-        self.model = None
-        self.tokenizer = None
-        self.pre_tokenizer = None
-        self.load_model()
-
-    def load_model(self, model_name: str = default_model):
-        if model_name not in model_choices.keys():
-            logger.error("Selected model is not supported, resetting to the default model.")
-            model_name = default_model
-        self.model = AutoModelForTokenClassification.from_pretrained(model_choices[model_name])
-        self.tokenizer = AutoTokenizer.from_pretrained(model_choices[model_name])
-        self.pre_tokenizer = pre_tokenizers[model_name]
-
-myapp = MyApp()
-
-def predict(text, logger=None) -> Tuple[List[str], List[str]]:
-    doc = myapp.pre_tokenizer(text)
-    tokens = [token.text if not isinstance(token, str) else token for token in doc]
-
-    logger.info("Starting predictions for sentence: {}".format(text))
-    print("Using model {}".format(myapp.model.config.__dict__["_name_or_path"]))
-
-    input_tokens = myapp.tokenizer(
-        tokens,
-        return_tensors="pt",
-        is_split_into_words=True,
-        return_offsets_mapping=True,
-        return_special_tokens_mask=True,
-    )
-    output = myapp.model(input_tokens["input_ids"])
-
-    i_token = 0
-    labels = []
-    scores = []
-    for off, is_special_token, pred in zip(
-        input_tokens["offset_mapping"][0],
-        input_tokens["special_tokens_mask"][0],
-        output.logits[0],
-    ):
-        if is_special_token or off[0] > 0:
-            continue
-        label = myapp.model.config.__dict__["id2label"][int(pred.argmax(axis=-1))]
-        if logger is not None:
-            logger.info("{}, {}, {}".format(off, tokens[i_token], label))
-        labels.append(label)
-        scores.append(
-            "{:.2f}".format(100 * float(torch.softmax(pred, dim=-1).detach().max()))
-        )
-        i_token += 1
-
-    return tokens, labels, scores
-
-
-def text_analysis(text):
-    text = expand_contractions(text)
-    tokens, labels, scores = predict(text, logger)
-    if len(labels) != len(tokens):
-        m = len(tokens) - len(labels)
-        labels += [None] * m
-        scores += [0] * m
-    pos_count = pd.DataFrame(
-        {
-            "token": tokens,
-            "tag": labels,
-            "confidence": scores,
-        }
-    )
-    pos_tokens = []
-    for token, label in zip(tokens, labels):
-        pos_tokens.extend([(token, label), (" ", None)])
-
-    output_highlighted.update(visible=True)
-    output_df.update(visible=True)
-
-    return {
-        output_highlighted: output_highlighted.update(visible=True, value=(pos_tokens)),
-        output_df: output_df.update(visible=True, value=pos_count),
-    }
-
-
-def batch_analysis(input_file):
-    text = open(input_file.name, encoding="utf-8").read()
-    text = text.split("\n")
-    name = Path(input_file.name).stem
-    sents = []
-    for sent in text:
-        sub_sents = nlp(sent).sents
-        sub_sents = [str(_sent).strip() for _sent in sub_sents]
-        sents += sub_sents
-    conllu_output = []
-
-    for i, sent in enumerate(sents):
-        sent = expand_contractions(sent)
-        conllu_output.append("# sent_id = {}-{}\n".format(name, i + 1))
-        conllu_output.append("# text = {}\n".format(sent))
-        tokens, labels, scores = predict(sent, logger)
-        for j, (token, label) in enumerate(zip(tokens, labels)):
-            conllu_output.append(
-                "{}\t{}\t_\t{}".format(j + 1, token, label) + "\t_" * 5 + "\n"
-            )
-        conllu_output.append("\n")
-
-    output_filename = "output.conllu"
-    with open(output_filename, "w") as out_f:
-        out_f.writelines(conllu_output)
-
-    return {output_file: output_file.update(visible=True, value=output_filename)}
-
-
-css = open("style.css").read()
-top_html = open("top.html").read()
-bottom_html = open("bottom.html").read()
-
-with gr.Blocks(css=css) as demo:
-    gr.HTML(top_html)
-    select_model = gr.Dropdown(choices=list(model_choices.keys()), label="Tagger model", value=default_model)
-    select_model.change(myapp.load_model, inputs=[select_model])
-    with gr.Tab("Single sentence"):
-        text = gr.Textbox(placeholder="Enter your text here...", label="Input")
-        examples = gr.Examples(
-            examples=[
-                [
-                    "A população não poderia ter acesso a relatórios que explicassem, por exemplo, os motivos exatos de atrasos em obras de linhas e estações."
-                ],
-                [
-                    "Filme 'Star Wars : Os Últimos Jedi' ganha trailer definitivo; assista."
-                ],
-            ],
-            inputs=[text],
-            label="Select an example",
-        )
-        output_highlighted = gr.HighlightedText(label="Colorful output", visible=False)
-        output_df = gr.Dataframe(label="Tabular output", visible=False)
-        submit_btn = gr.Button("Tag it")
-        submit_btn.click(
-            fn=text_analysis, inputs=text, outputs=[output_highlighted, output_df]
-        )
-    with gr.Tab("Multiple sentences"):
-        gr.HTML(
-            """
-        <p align="justify"">
-        &emsp;Upload a plain text file with sentences in it. 
-        Find below an example of what we expect the content of the file to look like. 
-        Sentences are automatically split by spaCy's sentencizer. 
-        To force an explicit segmentation, manually separate the sentences using a new line for each one.</p>
-        """
-        )
-        gr.Markdown(
-            """
-        ```
-        Então ele hesitou, quase como se estivesse surpreso com as próprias palavras, e recitou:
-        – Vá e não tornes a pecar!
-        Baley, sorrindo de repente, pegou no cotovelo de R. Daneel e eles saíram juntos pela porta.
-        ```
-        """
-        )
-        input_file = gr.File(label="Upload your input file here...")
-        output_file = gr.File(label="Tagged file", visible=False)
-        submit_btn_batch = gr.Button("Tag it")
-        submit_btn_batch.click(
-            fn=batch_analysis, inputs=input_file, outputs=output_file
-        )
-
-    gr.HTML(bottom_html)
-
-
-demo.launch(debug=True)
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/textrecog/crnn/crnn_academic_dataset.py b/spaces/EuroPython2022/mmocr-demo/configs/textrecog/crnn/crnn_academic_dataset.py
deleted file mode 100644
index b8288cb5a1cb48ddc6b32e988b45305e01e76df5..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/textrecog/crnn/crnn_academic_dataset.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py', '../../_base_/recog_models/crnn.py',
-    '../../_base_/recog_pipelines/crnn_pipeline.py',
-    '../../_base_/recog_datasets/MJ_train.py',
-    '../../_base_/recog_datasets/academic_test.py',
-    '../../_base_/schedules/schedule_adadelta_5e.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-data = dict(
-    samples_per_gpu=64,
-    workers_per_gpu=4,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
-
-cudnn_benchmark = True
diff --git a/spaces/ExpUnGeD404/Bamber/Dockerfile b/spaces/ExpUnGeD404/Bamber/Dockerfile
deleted file mode 100644
index eef259fa372a804549fb0af0913718a13344da34..0000000000000000000000000000000000000000
--- a/spaces/ExpUnGeD404/Bamber/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]
diff --git a/spaces/Forbu14/LoiLibreQA/utils.py b/spaces/Forbu14/LoiLibreQA/utils.py
deleted file mode 100644
index b4e7ef2ed0f4856f69db36d621d09be0307827d5..0000000000000000000000000000000000000000
--- a/spaces/Forbu14/LoiLibreQA/utils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import numpy as np
-import openai
-import os
-import random
-import string
-
-
-def is_climate_change_related(sentence: str, classifier) -> bool:
-    """_summary_
-    Args:
-        sentence (str): your sentence to classify
-        classifier (_type_): zero shot hugging face pipeline classifier
-    Returns:
-        bool: is_climate_change_related or not
-    """
-    results = classifier(
-        sequences=sentence,
-        candidate_labels=["climate change related", "non climate change related"],
-    )
-    print(f" ## Result from is climate change related {results}")
-    return results["labels"][np.argmax(results["scores"])] == "climate change related"
-
-
-def make_pairs(lst):
-    """From a list of even lenght, make tupple pairs
-    Args:
-        lst (list): a list of even lenght
-    Returns:
-        list: the list as tupple pairs
-    """
-    assert not (l := len(lst) % 2), f"your list is of lenght {l} which is not even"
-    return [(lst[i], lst[i + 1]) for i in range(0, len(lst), 2)]
-
-
-def set_openai_api_key(text):
-    """Set the api key and return chain.If no api_key, then None is returned.
-    To do : add raise error & Warning message
-    Args:
-        text (str): openai api key
-    Returns:
-        str: Result of connection
-    """
-    openai.api_key = os.environ["api_key"]
-
-    if text.startswith("sk-") and len(text) > 10:
-        openai.api_key = text
-    return f"You're all set: this is your api key: {openai.api_key}"
-
-
-def create_user_id(length):
-    """Create user_id
-    Args:
-        length (int): length of user id
-    Returns:
-        str: String to id user
-    """
-    letters = string.ascii_lowercase
-    user_id = "".join(random.choice(letters) for i in range(length))
-    return user_id
-
-
-def to_completion(messages):
-    s = []
-    for message in messages:
-        s.append(f"<|im_start|>{message['role']}\n{message['content']}<|im_end|>")
-    s.append("<|im_start|>assistant\n")
-    return "\n".join(s)
diff --git a/spaces/GAIR/Factool/factool/knowledge_qa/tool.py b/spaces/GAIR/Factool/factool/knowledge_qa/tool.py
deleted file mode 100644
index 1aa0614ae733d8c69de1ff2b8fa564ce2946d53f..0000000000000000000000000000000000000000
--- a/spaces/GAIR/Factool/factool/knowledge_qa/tool.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import asyncio
-from factool.knowledge_qa.google_serper import GoogleSerperAPIWrapper
-from factool.utils.openai_wrapper import OpenAIEmbed
-import json
-import os
-import numpy as np
-import jsonlines
-import pdb
-
-class google_search():
-    def __init__(self, snippet_cnt):
-        self.serper = GoogleSerperAPIWrapper(snippet_cnt=snippet_cnt)
-
-    async def run(self, queries):
-        return await self.serper.run(queries)
-
-class local_search():
-    def __init__(self, snippet_cnt, data_link, embedding_link=None):
-        self.snippet_cnt = snippet_cnt
-        self.data_link = data_link
-        self.embedding_link = embedding_link
-        self.openai_embed = OpenAIEmbed()
-        self.data = None
-        self.embedding = None
-        asyncio.run(self.init_async())
-        
-    
-    async def init_async(self):
-        print("init local search")
-        self.load_data_by_link()
-        if self.embedding_link is None:
-            await self.calculate_embedding()
-        else:
-            self.load_embedding_by_link()
-        print("loaded data and embedding")
-
-    def add_suffix_to_json_filename(self, filename):
-        base_name, extension = os.path.splitext(filename)
-        return base_name + '_embed' + extension
-
-    def load_data_by_link(self):
-        #load data from json link
-        self.data = []
-        #self.data = json.load(open(self.data_link, 'r'))
-        with jsonlines.open(self.data_link) as reader:
-            for obj in reader:
-                self.data.append(obj['text'])
-
-    def load_embedding_by_link(self):
-        self.embedding = []
-        #self.embedding = json.load(open(self.embedding_link, 'r'))
-        with jsonlines.open(self.embedding_link) as reader:
-            for obj in reader:
-                self.embedding.append(obj)
-    
-    def save_embeddings(self):
-        #json.dump(self.embedding, open(self.add_suffix_to_json_filename(self.data_link), 'w'))
-        with jsonlines.open(self.add_suffix_to_json_filename(self.data_link), mode='w') as writer:
-            writer.write_all(self.embedding)
-
-    async def calculate_embedding(self):
-        result = await self.openai_embed.process_batch(self.data,retry=3)
-        self.embedding = [emb["data"][0]["embedding"] for emb in result]
-        self.save_embeddings()
-
-    async def search(self, query):
-        result = await self.openai_embed.create_embedding(query)
-        query_embed = result["data"][0]["embedding"]
-        dot_product = np.dot(self.embedding, query_embed)
-        sorted_indices = np.argsort(dot_product)[::-1]
-        top_k_indices = sorted_indices[:self.snippet_cnt]
-        return [{"content":self.data[i],"source":"local"} for i in top_k_indices]
-
-    
-    async def run(self, queries):
-        flattened_queries = []
-        for sublist in queries:
-            if sublist is None:
-                sublist = ['None', 'None']
-            for item in sublist:
-                flattened_queries.append(item)
-        
-        snippets = await asyncio.gather(*[self.search(query) for query in flattened_queries])
-        snippets_split = [snippets[i] + snippets[i+1] for i in range(0, len(snippets), 2)]
-        return snippets_split
-
-
-
-
-
-
diff --git a/spaces/GitMylo/bark-voice-cloning/app.py b/spaces/GitMylo/bark-voice-cloning/app.py
deleted file mode 100644
index 4382649733cfacbc1267ca3253d4533fd4454325..0000000000000000000000000000000000000000
--- a/spaces/GitMylo/bark-voice-cloning/app.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import math
-import os.path
-import uuid
-
-import gradio
-import numpy
-import torch
-
-from hubert.hubert_manager import HuBERTManager
-from hubert.pre_kmeans_hubert import CustomHubert
-from hubert.customtokenizer import CustomTokenizer
-from encodec import EncodecModel
-from encodec.utils import convert_audio
-
-
-hubert_model = CustomHubert(HuBERTManager.make_sure_hubert_installed())
-tokenizer_model = CustomTokenizer.load_from_checkpoint(
-    HuBERTManager.make_sure_tokenizer_installed(model='quantifier_V1_hubert_base_ls960_23.pth'),
-    map_location=torch.device('cpu')
-)
-encodec_model = EncodecModel.encodec_model_24khz()
-
-
-
-def clone(audio, *args):
-    sr, wav = audio
-
-    wav = torch.tensor(wav)
-
-    if wav.dtype == torch.int16:
-        wav = wav.float() / 32767.0
-
-    if len(wav.shape) == 2:
-        if wav.shape[0] == 2:  # Stereo to mono if needed
-            wav = wav.mean(0, keepdim=True)
-        if wav.shape[1] == 2:
-            wav = wav.mean(1, keepdim=False).unsqueeze(-1)
-
-    wav = wav[-int(sr*20):]  # Take only the last 20 seconds
-
-    wav = wav.reshape(1, -1)  # Reshape from gradio style to HuBERT shape. (N, 1) to (1, N)
-
-    semantic_vectors = hubert_model.forward(wav, input_sample_hz=sr)
-    semantic_tokens = tokenizer_model.get_token(semantic_vectors)
-
-    encodec_model.set_target_bandwidth(6.0)
-    wav = convert_audio(wav, sr, encodec_model.sample_rate, 1)
-    wav = wav.unsqueeze(0)
-
-    with torch.no_grad():
-        encoded_frames = encodec_model.encode(wav)
-
-    codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()  # [B, n_q, T]
-
-    if not os.path.isdir('data/speakers'):
-        os.makedirs('data/speakers')
-
-    file_path = f'data/speakers/{uuid.uuid4().hex}.npz'
-
-    numpy.savez(
-        file_path,
-        semantic_prompt=semantic_tokens,
-        fine_prompt=codes,
-        coarse_prompt=codes[:2, :]
-    )
-
-    return file_path
-
-
-
-iface = gradio.interface.Interface(fn=clone, inputs=[
-    'audio',
-    gradio.Markdown(
-        '''
-        # Bark text to speech voice cloning
-        [Model](https://huggingface.co/GitMylo/bark-voice-cloning/), [Model GitHub](https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer), [Webui GitHub](https://github.com/gitmylo/audio-webui)
-
-        For faster creation of voice clones [Duplicate this space](https://huggingface.co/spaces/GitMylo/bark-voice-cloning?duplicate=true)
-
-        Uploaded audio files get cut to 20 seconds in order to keep it fast for everyone. Only the last 20 seconds will be used. (Bark only uses the last 14 seconds anyway)
-
-        ## Tips for better cloning
-        ### Make sure these things are **NOT** in your voice input: (in no particular order)
-        * Noise (You can use a noise remover before)
-        * Music (There are also music remover tools) (Unless you want music in the background)
-        * A cut-off at the end (This will cause it to try and continue on the generation)
-        * Under 1 second of training data (i personally suggest around 10 seconds for good potential, but i've had great results with 5 seconds as well.)
-
-        ### What makes for good prompt audio? (in no particular order)
-        * Clearly spoken
-        * No weird background noises
-        * Only one speaker
-        * Audio which ends after a sentence ends
-        * Regular/common voice (They usually have more success, it's still capable of cloning complex voices, but not as good at it)
-        * Around 10 seconds of data
-        ''')
-], outputs='file')
-iface.launch()
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
deleted file mode 100644
index cd78b6df320aea7b23412b2f734e8684f84b9822..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './retinanet_r50_fpn_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/transforms.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/transforms.py
deleted file mode 100644
index caed51d89ffc1259d0b086954f03c3d4c0749cf2..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/transforms.py
+++ /dev/null
@@ -1,1811 +0,0 @@
-import copy
-import inspect
-
-import mmcv
-import numpy as np
-from numpy import random
-
-from mmdet.core import PolygonMasks
-from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
-from ..builder import PIPELINES
-
-try:
-    from imagecorruptions import corrupt
-except ImportError:
-    corrupt = None
-
-try:
-    import albumentations
-    from albumentations import Compose
-except ImportError:
-    albumentations = None
-    Compose = None
-
-
-@PIPELINES.register_module()
-class Resize(object):
-    """Resize images & bbox & mask.
-
-    This transform resizes the input image to some scale. Bboxes and masks are
-    then resized with the same scale factor. If the input dict contains the key
-    "scale", then the scale in the input dict is used, otherwise the specified
-    scale in the init method is used. If the input dict contains the key
-    "scale_factor" (if MultiScaleFlipAug does not give img_scale but
-    scale_factor), the actual scale will be computed by image shape and
-    scale_factor.
-
-    `img_scale` can either be a tuple (single-scale) or a list of tuple
-    (multi-scale). There are 3 multiscale modes:
-
-    - ``ratio_range is not None``: randomly sample a ratio from the ratio \
-      range and multiply it with the image scale.
-    - ``ratio_range is None`` and ``multiscale_mode == "range"``: randomly \
-      sample a scale from the multiscale range.
-    - ``ratio_range is None`` and ``multiscale_mode == "value"``: randomly \
-      sample a scale from multiple scales.
-
-    Args:
-        img_scale (tuple or list[tuple]): Images scales for resizing.
-        multiscale_mode (str): Either "range" or "value".
-        ratio_range (tuple[float]): (min_ratio, max_ratio)
-        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
-            image.
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-        backend (str): Image resize backend, choices are 'cv2' and 'pillow'.
-            These two backends generates slightly different results. Defaults
-            to 'cv2'.
-        override (bool, optional): Whether to override `scale` and
-            `scale_factor` so as to call resize twice. Default False. If True,
-            after the first resizing, the existed `scale` and `scale_factor`
-            will be ignored so the second resizing can be allowed.
-            This option is a work-around for multiple times of resize in DETR.
-            Defaults to False.
-    """
-
-    def __init__(self,
-                 img_scale=None,
-                 multiscale_mode='range',
-                 ratio_range=None,
-                 keep_ratio=True,
-                 bbox_clip_border=True,
-                 backend='cv2',
-                 override=False):
-        if img_scale is None:
-            self.img_scale = None
-        else:
-            if isinstance(img_scale, list):
-                self.img_scale = img_scale
-            else:
-                self.img_scale = [img_scale]
-            assert mmcv.is_list_of(self.img_scale, tuple)
-
-        if ratio_range is not None:
-            # mode 1: given a scale and a range of image ratio
-            assert len(self.img_scale) == 1
-        else:
-            # mode 2: given multiple scales or a range of scales
-            assert multiscale_mode in ['value', 'range']
-
-        self.backend = backend
-        self.multiscale_mode = multiscale_mode
-        self.ratio_range = ratio_range
-        self.keep_ratio = keep_ratio
-        # TODO: refactor the override option in Resize
-        self.override = override
-        self.bbox_clip_border = bbox_clip_border
-
-    @staticmethod
-    def random_select(img_scales):
-        """Randomly select an img_scale from given candidates.
-
-        Args:
-            img_scales (list[tuple]): Images scales for selection.
-
-        Returns:
-            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``, \
-                where ``img_scale`` is the selected image scale and \
-                ``scale_idx`` is the selected index in the given candidates.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple)
-        scale_idx = np.random.randint(len(img_scales))
-        img_scale = img_scales[scale_idx]
-        return img_scale, scale_idx
-
-    @staticmethod
-    def random_sample(img_scales):
-        """Randomly sample an img_scale when ``multiscale_mode=='range'``.
-
-        Args:
-            img_scales (list[tuple]): Images scale range for sampling.
-                There must be two tuples in img_scales, which specify the lower
-                and upper bound of image scales.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(img_scale, None)``, where \
-                ``img_scale`` is sampled scale and None is just a placeholder \
-                to be consistent with :func:`random_select`.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
-        img_scale_long = [max(s) for s in img_scales]
-        img_scale_short = [min(s) for s in img_scales]
-        long_edge = np.random.randint(
-            min(img_scale_long),
-            max(img_scale_long) + 1)
-        short_edge = np.random.randint(
-            min(img_scale_short),
-            max(img_scale_short) + 1)
-        img_scale = (long_edge, short_edge)
-        return img_scale, None
-
-    @staticmethod
-    def random_sample_ratio(img_scale, ratio_range):
-        """Randomly sample an img_scale when ``ratio_range`` is specified.
-
-        A ratio will be randomly sampled from the range specified by
-        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
-        generate sampled scale.
-
-        Args:
-            img_scale (tuple): Images scale base to multiply with ratio.
-            ratio_range (tuple[float]): The minimum and maximum ratio to scale
-                the ``img_scale``.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(scale, None)``, where \
-                ``scale`` is sampled ratio multiplied with ``img_scale`` and \
-                None is just a placeholder to be consistent with \
-                :func:`random_select`.
-        """
-
-        assert isinstance(img_scale, tuple) and len(img_scale) == 2
-        min_ratio, max_ratio = ratio_range
-        assert min_ratio <= max_ratio
-        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
-        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
-        return scale, None
-
-    def _random_scale(self, results):
-        """Randomly sample an img_scale according to ``ratio_range`` and
-        ``multiscale_mode``.
-
-        If ``ratio_range`` is specified, a ratio will be sampled and be
-        multiplied with ``img_scale``.
-        If multiple scales are specified by ``img_scale``, a scale will be
-        sampled according to ``multiscale_mode``.
-        Otherwise, single scale will be used.
-
-        Args:
-            results (dict): Result dict from :obj:`dataset`.
-
-        Returns:
-            dict: Two new keys 'scale` and 'scale_idx` are added into \
-                ``results``, which would be used by subsequent pipelines.
-        """
-
-        if self.ratio_range is not None:
-            scale, scale_idx = self.random_sample_ratio(
-                self.img_scale[0], self.ratio_range)
-        elif len(self.img_scale) == 1:
-            scale, scale_idx = self.img_scale[0], 0
-        elif self.multiscale_mode == 'range':
-            scale, scale_idx = self.random_sample(self.img_scale)
-        elif self.multiscale_mode == 'value':
-            scale, scale_idx = self.random_select(self.img_scale)
-        else:
-            raise NotImplementedError
-
-        results['scale'] = scale
-        results['scale_idx'] = scale_idx
-
-    def _resize_img(self, results):
-        """Resize images with ``results['scale']``."""
-        for key in results.get('img_fields', ['img']):
-            if self.keep_ratio:
-                img, scale_factor = mmcv.imrescale(
-                    results[key],
-                    results['scale'],
-                    return_scale=True,
-                    backend=self.backend)
-                # the w_scale and h_scale has minor difference
-                # a real fix should be done in the mmcv.imrescale in the future
-                new_h, new_w = img.shape[:2]
-                h, w = results[key].shape[:2]
-                w_scale = new_w / w
-                h_scale = new_h / h
-            else:
-                img, w_scale, h_scale = mmcv.imresize(
-                    results[key],
-                    results['scale'],
-                    return_scale=True,
-                    backend=self.backend)
-            results[key] = img
-
-            scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
-                                    dtype=np.float32)
-            results['img_shape'] = img.shape
-            # in case that there is no padding
-            results['pad_shape'] = img.shape
-            results['scale_factor'] = scale_factor
-            results['keep_ratio'] = self.keep_ratio
-
-    def _resize_bboxes(self, results):
-        """Resize bounding boxes with ``results['scale_factor']``."""
-        for key in results.get('bbox_fields', []):
-            bboxes = results[key] * results['scale_factor']
-            if self.bbox_clip_border:
-                img_shape = results['img_shape']
-                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
-                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
-            results[key] = bboxes
-
-    def _resize_masks(self, results):
-        """Resize masks with ``results['scale']``"""
-        for key in results.get('mask_fields', []):
-            if results[key] is None:
-                continue
-            if self.keep_ratio:
-                results[key] = results[key].rescale(results['scale'])
-            else:
-                results[key] = results[key].resize(results['img_shape'][:2])
-
-    def _resize_seg(self, results):
-        """Resize semantic segmentation map with ``results['scale']``."""
-        for key in results.get('seg_fields', []):
-            if self.keep_ratio:
-                gt_seg = mmcv.imrescale(
-                    results[key],
-                    results['scale'],
-                    interpolation='nearest',
-                    backend=self.backend)
-            else:
-                gt_seg = mmcv.imresize(
-                    results[key],
-                    results['scale'],
-                    interpolation='nearest',
-                    backend=self.backend)
-            results['gt_semantic_seg'] = gt_seg
-
-    def __call__(self, results):
-        """Call function to resize images, bounding boxes, masks, semantic
-        segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', \
-                'keep_ratio' keys are added into result dict.
-        """
-
-        if 'scale' not in results:
-            if 'scale_factor' in results:
-                img_shape = results['img'].shape[:2]
-                scale_factor = results['scale_factor']
-                assert isinstance(scale_factor, float)
-                results['scale'] = tuple(
-                    [int(x * scale_factor) for x in img_shape][::-1])
-            else:
-                self._random_scale(results)
-        else:
-            if not self.override:
-                assert 'scale_factor' not in results, (
-                    'scale and scale_factor cannot be both set.')
-            else:
-                results.pop('scale')
-                if 'scale_factor' in results:
-                    results.pop('scale_factor')
-                self._random_scale(results)
-
-        self._resize_img(results)
-        self._resize_bboxes(results)
-        self._resize_masks(results)
-        self._resize_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(img_scale={self.img_scale}, '
-        repr_str += f'multiscale_mode={self.multiscale_mode}, '
-        repr_str += f'ratio_range={self.ratio_range}, '
-        repr_str += f'keep_ratio={self.keep_ratio}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomFlip(object):
-    """Flip the image & bbox & mask.
-
-    If the input dict contains the key "flip", then the flag will be used,
-    otherwise it will be randomly decided by a ratio specified in the init
-    method.
-
-    When random flip is enabled, ``flip_ratio``/``direction`` can either be a
-    float/string or tuple of float/string. There are 3 flip modes:
-
-    - ``flip_ratio`` is float, ``direction`` is string: the image will be
-        ``direction``ly flipped with probability of ``flip_ratio`` .
-        E.g., ``flip_ratio=0.5``, ``direction='horizontal'``,
-        then image will be horizontally flipped with probability of 0.5.
-    - ``flip_ratio`` is float, ``direction`` is list of string: the image wil
-        be ``direction[i]``ly flipped with probability of
-        ``flip_ratio/len(direction)``.
-        E.g., ``flip_ratio=0.5``, ``direction=['horizontal', 'vertical']``,
-        then image will be horizontally flipped with probability of 0.25,
-        vertically with probability of 0.25.
-    - ``flip_ratio`` is list of float, ``direction`` is list of string:
-        given ``len(flip_ratio) == len(direction)``, the image wil
-        be ``direction[i]``ly flipped with probability of ``flip_ratio[i]``.
-        E.g., ``flip_ratio=[0.3, 0.5]``, ``direction=['horizontal',
-        'vertical']``, then image will be horizontally flipped with probability
-         of 0.3, vertically with probability of 0.5
-
-    Args:
-        flip_ratio (float | list[float], optional): The flipping probability.
-            Default: None.
-        direction(str | list[str], optional): The flipping direction. Options
-            are 'horizontal', 'vertical', 'diagonal'. Default: 'horizontal'.
-            If input is a list, the length must equal ``flip_ratio``. Each
-            element in ``flip_ratio`` indicates the flip probability of
-            corresponding direction.
-    """
-
-    def __init__(self, flip_ratio=None, direction='horizontal'):
-        if isinstance(flip_ratio, list):
-            assert mmcv.is_list_of(flip_ratio, float)
-            assert 0 <= sum(flip_ratio) <= 1
-        elif isinstance(flip_ratio, float):
-            assert 0 <= flip_ratio <= 1
-        elif flip_ratio is None:
-            pass
-        else:
-            raise ValueError('flip_ratios must be None, float, '
-                             'or list of float')
-        self.flip_ratio = flip_ratio
-
-        valid_directions = ['horizontal', 'vertical', 'diagonal']
-        if isinstance(direction, str):
-            assert direction in valid_directions
-        elif isinstance(direction, list):
-            assert mmcv.is_list_of(direction, str)
-            assert set(direction).issubset(set(valid_directions))
-        else:
-            raise ValueError('direction must be either str or list of str')
-        self.direction = direction
-
-        if isinstance(flip_ratio, list):
-            assert len(self.flip_ratio) == len(self.direction)
-
-    def bbox_flip(self, bboxes, img_shape, direction):
-        """Flip bboxes horizontally.
-
-        Args:
-            bboxes (numpy.ndarray): Bounding boxes, shape (..., 4*k)
-            img_shape (tuple[int]): Image shape (height, width)
-            direction (str): Flip direction. Options are 'horizontal',
-                'vertical'.
-
-        Returns:
-            numpy.ndarray: Flipped bounding boxes.
-        """
-
-        assert bboxes.shape[-1] % 4 == 0
-        flipped = bboxes.copy()
-        if direction == 'horizontal':
-            w = img_shape[1]
-            flipped[..., 0::4] = w - bboxes[..., 2::4]
-            flipped[..., 2::4] = w - bboxes[..., 0::4]
-        elif direction == 'vertical':
-            h = img_shape[0]
-            flipped[..., 1::4] = h - bboxes[..., 3::4]
-            flipped[..., 3::4] = h - bboxes[..., 1::4]
-        elif direction == 'diagonal':
-            w = img_shape[1]
-            h = img_shape[0]
-            flipped[..., 0::4] = w - bboxes[..., 2::4]
-            flipped[..., 1::4] = h - bboxes[..., 3::4]
-            flipped[..., 2::4] = w - bboxes[..., 0::4]
-            flipped[..., 3::4] = h - bboxes[..., 1::4]
-        else:
-            raise ValueError(f"Invalid flipping direction '{direction}'")
-        return flipped
-
-    def __call__(self, results):
-        """Call function to flip bounding boxes, masks, semantic segmentation
-        maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Flipped results, 'flip', 'flip_direction' keys are added \
-                into result dict.
-        """
-
-        if 'flip' not in results:
-            if isinstance(self.direction, list):
-                # None means non-flip
-                direction_list = self.direction + [None]
-            else:
-                # None means non-flip
-                direction_list = [self.direction, None]
-
-            if isinstance(self.flip_ratio, list):
-                non_flip_ratio = 1 - sum(self.flip_ratio)
-                flip_ratio_list = self.flip_ratio + [non_flip_ratio]
-            else:
-                non_flip_ratio = 1 - self.flip_ratio
-                # exclude non-flip
-                single_ratio = self.flip_ratio / (len(direction_list) - 1)
-                flip_ratio_list = [single_ratio] * (len(direction_list) -
-                                                    1) + [non_flip_ratio]
-
-            cur_dir = np.random.choice(direction_list, p=flip_ratio_list)
-
-            results['flip'] = cur_dir is not None
-        if 'flip_direction' not in results:
-            results['flip_direction'] = cur_dir
-        if results['flip']:
-            # flip image
-            for key in results.get('img_fields', ['img']):
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction'])
-            # flip bboxes
-            for key in results.get('bbox_fields', []):
-                results[key] = self.bbox_flip(results[key],
-                                              results['img_shape'],
-                                              results['flip_direction'])
-            # flip masks
-            for key in results.get('mask_fields', []):
-                results[key] = results[key].flip(results['flip_direction'])
-
-            # flip segs
-            for key in results.get('seg_fields', []):
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction'])
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(flip_ratio={self.flip_ratio})'
-
-
-@PIPELINES.register_module()
-class Pad(object):
-    """Pad the image & mask.
-
-    There are two padding modes: (1) pad to a fixed size and (2) pad to the
-    minimum size that is divisible by some number.
-    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
-
-    Args:
-        size (tuple, optional): Fixed padding size.
-        size_divisor (int, optional): The divisor of padded size.
-        pad_val (float, optional): Padding value, 0 by default.
-    """
-
-    def __init__(self, size=None, size_divisor=None, pad_val=0):
-        self.size = size
-        self.size_divisor = size_divisor
-        self.pad_val = pad_val
-        # only one of size and size_divisor should be valid
-        assert size is not None or size_divisor is not None
-        assert size is None or size_divisor is None
-
-    def _pad_img(self, results):
-        """Pad images according to ``self.size``."""
-        for key in results.get('img_fields', ['img']):
-            if self.size is not None:
-                padded_img = mmcv.impad(
-                    results[key], shape=self.size, pad_val=self.pad_val)
-            elif self.size_divisor is not None:
-                padded_img = mmcv.impad_to_multiple(
-                    results[key], self.size_divisor, pad_val=self.pad_val)
-            results[key] = padded_img
-        results['pad_shape'] = padded_img.shape
-        results['pad_fixed_size'] = self.size
-        results['pad_size_divisor'] = self.size_divisor
-
-    def _pad_masks(self, results):
-        """Pad masks according to ``results['pad_shape']``."""
-        pad_shape = results['pad_shape'][:2]
-        for key in results.get('mask_fields', []):
-            results[key] = results[key].pad(pad_shape, pad_val=self.pad_val)
-
-    def _pad_seg(self, results):
-        """Pad semantic segmentation map according to
-        ``results['pad_shape']``."""
-        for key in results.get('seg_fields', []):
-            results[key] = mmcv.impad(
-                results[key], shape=results['pad_shape'][:2])
-
-    def __call__(self, results):
-        """Call function to pad images, masks, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Updated result dict.
-        """
-        self._pad_img(results)
-        self._pad_masks(results)
-        self._pad_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(size={self.size}, '
-        repr_str += f'size_divisor={self.size_divisor}, '
-        repr_str += f'pad_val={self.pad_val})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Normalize(object):
-    """Normalize the image.
-
-    Added key is "img_norm_cfg".
-
-    Args:
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB,
-            default is true.
-    """
-
-    def __init__(self, mean, std, to_rgb=True):
-        self.mean = np.array(mean, dtype=np.float32)
-        self.std = np.array(std, dtype=np.float32)
-        self.to_rgb = to_rgb
-
-    def __call__(self, results):
-        """Call function to normalize images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Normalized results, 'img_norm_cfg' key is added into
-                result dict.
-        """
-        for key in results.get('img_fields', ['img']):
-            results[key] = mmcv.imnormalize(results[key], self.mean, self.std,
-                                            self.to_rgb)
-        results['img_norm_cfg'] = dict(
-            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCrop(object):
-    """Random crop the image & bboxes & masks.
-
-    The absolute `crop_size` is sampled based on `crop_type` and `image_size`,
-    then the cropped results are generated.
-
-    Args:
-        crop_size (tuple): The relative ratio or absolute pixels of
-            height and width.
-        crop_type (str, optional): one of "relative_range", "relative",
-            "absolute", "absolute_range". "relative" randomly crops
-            (h * crop_size[0], w * crop_size[1]) part from an input of size
-            (h, w). "relative_range" uniformly samples relative crop size from
-            range [crop_size[0], 1] and [crop_size[1], 1] for height and width
-            respectively. "absolute" crops from an input with absolute size
-            (crop_size[0], crop_size[1]). "absolute_range" uniformly samples
-            crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w
-            in range [crop_size[0], min(w, crop_size[1])]. Default "absolute".
-        allow_negative_crop (bool, optional): Whether to allow a crop that does
-            not contain any bbox area. Default False.
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-
-    Note:
-        - If the image is smaller than the absolute crop size, return the
-            original image.
-        - The keys for bboxes, labels and masks must be aligned. That is,
-          `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and
-          `gt_bboxes_ignore` corresponds to `gt_labels_ignore` and
-          `gt_masks_ignore`.
-        - If the crop does not contain any gt-bbox region and
-          `allow_negative_crop` is set to False, skip this image.
-    """
-
-    def __init__(self,
-                 crop_size,
-                 crop_type='absolute',
-                 allow_negative_crop=False,
-                 bbox_clip_border=True):
-        if crop_type not in [
-                'relative_range', 'relative', 'absolute', 'absolute_range'
-        ]:
-            raise ValueError(f'Invalid crop_type {crop_type}.')
-        if crop_type in ['absolute', 'absolute_range']:
-            assert crop_size[0] > 0 and crop_size[1] > 0
-            assert isinstance(crop_size[0], int) and isinstance(
-                crop_size[1], int)
-        else:
-            assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1
-        self.crop_size = crop_size
-        self.crop_type = crop_type
-        self.allow_negative_crop = allow_negative_crop
-        self.bbox_clip_border = bbox_clip_border
-        # The key correspondence from bboxes to labels and masks.
-        self.bbox2label = {
-            'gt_bboxes': 'gt_labels',
-            'gt_bboxes_ignore': 'gt_labels_ignore'
-        }
-        self.bbox2mask = {
-            'gt_bboxes': 'gt_masks',
-            'gt_bboxes_ignore': 'gt_masks_ignore'
-        }
-
-    def _crop_data(self, results, crop_size, allow_negative_crop):
-        """Function to randomly crop images, bounding boxes, masks, semantic
-        segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-            crop_size (tuple): Expected absolute size after cropping, (h, w).
-            allow_negative_crop (bool): Whether to allow a crop that does not
-                contain any bbox area. Default to False.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-        assert crop_size[0] > 0 and crop_size[1] > 0
-        for key in results.get('img_fields', ['img']):
-            img = results[key]
-            margin_h = max(img.shape[0] - crop_size[0], 0)
-            margin_w = max(img.shape[1] - crop_size[1], 0)
-            offset_h = np.random.randint(0, margin_h + 1)
-            offset_w = np.random.randint(0, margin_w + 1)
-            crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]
-            crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]
-
-            # crop the image
-            img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
-            img_shape = img.shape
-            results[key] = img
-        results['img_shape'] = img_shape
-
-        # crop bboxes accordingly and clip to the image boundary
-        for key in results.get('bbox_fields', []):
-            # e.g. gt_bboxes and gt_bboxes_ignore
-            bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
-                                   dtype=np.float32)
-            bboxes = results[key] - bbox_offset
-            if self.bbox_clip_border:
-                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
-                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
-            valid_inds = (bboxes[:, 2] > bboxes[:, 0]) & (
-                bboxes[:, 3] > bboxes[:, 1])
-            # If the crop does not contain any gt-bbox area and
-            # allow_negative_crop is False, skip this image.
-            if (key == 'gt_bboxes' and not valid_inds.any()
-                    and not allow_negative_crop):
-                return None
-            results[key] = bboxes[valid_inds, :]
-            # label fields. e.g. gt_labels and gt_labels_ignore
-            label_key = self.bbox2label.get(key)
-            if label_key in results:
-                results[label_key] = results[label_key][valid_inds]
-
-            # mask fields, e.g. gt_masks and gt_masks_ignore
-            mask_key = self.bbox2mask.get(key)
-            if mask_key in results:
-                results[mask_key] = results[mask_key][
-                    valid_inds.nonzero()[0]].crop(
-                        np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))
-
-        # crop semantic seg
-        for key in results.get('seg_fields', []):
-            results[key] = results[key][crop_y1:crop_y2, crop_x1:crop_x2]
-
-        return results
-
-    def _get_crop_size(self, image_size):
-        """Randomly generates the absolute crop size based on `crop_type` and
-        `image_size`.
-
-        Args:
-            image_size (tuple): (h, w).
-
-        Returns:
-            crop_size (tuple): (crop_h, crop_w) in absolute pixels.
-        """
-        h, w = image_size
-        if self.crop_type == 'absolute':
-            return (min(self.crop_size[0], h), min(self.crop_size[1], w))
-        elif self.crop_type == 'absolute_range':
-            assert self.crop_size[0] <= self.crop_size[1]
-            crop_h = np.random.randint(
-                min(h, self.crop_size[0]),
-                min(h, self.crop_size[1]) + 1)
-            crop_w = np.random.randint(
-                min(w, self.crop_size[0]),
-                min(w, self.crop_size[1]) + 1)
-            return crop_h, crop_w
-        elif self.crop_type == 'relative':
-            crop_h, crop_w = self.crop_size
-            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
-        elif self.crop_type == 'relative_range':
-            crop_size = np.asarray(self.crop_size, dtype=np.float32)
-            crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size)
-            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
-
-    def __call__(self, results):
-        """Call function to randomly crop images, bounding boxes, masks,
-        semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-        image_size = results['img'].shape[:2]
-        crop_size = self._get_crop_size(image_size)
-        results = self._crop_data(results, crop_size, self.allow_negative_crop)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(crop_size={self.crop_size}, '
-        repr_str += f'crop_type={self.crop_type}, '
-        repr_str += f'allow_negative_crop={self.allow_negative_crop}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class SegRescale(object):
-    """Rescale semantic segmentation maps.
-
-    Args:
-        scale_factor (float): The scale factor of the final output.
-        backend (str): Image rescale backend, choices are 'cv2' and 'pillow'.
-            These two backends generates slightly different results. Defaults
-            to 'cv2'.
-    """
-
-    def __init__(self, scale_factor=1, backend='cv2'):
-        self.scale_factor = scale_factor
-        self.backend = backend
-
-    def __call__(self, results):
-        """Call function to scale the semantic segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with semantic segmentation map scaled.
-        """
-
-        for key in results.get('seg_fields', []):
-            if self.scale_factor != 1:
-                results[key] = mmcv.imrescale(
-                    results[key],
-                    self.scale_factor,
-                    interpolation='nearest',
-                    backend=self.backend)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
-
-
-@PIPELINES.register_module()
-class PhotoMetricDistortion(object):
-    """Apply photometric distortion to image sequentially, every transformation
-    is applied with a probability of 0.5. The position of random contrast is in
-    second or second to last.
-
-    1. random brightness
-    2. random contrast (mode 0)
-    3. convert color from BGR to HSV
-    4. random saturation
-    5. random hue
-    6. convert color from HSV to BGR
-    7. random contrast (mode 1)
-    8. randomly swap channels
-
-    Args:
-        brightness_delta (int): delta of brightness.
-        contrast_range (tuple): range of contrast.
-        saturation_range (tuple): range of saturation.
-        hue_delta (int): delta of hue.
-    """
-
-    def __init__(self,
-                 brightness_delta=32,
-                 contrast_range=(0.5, 1.5),
-                 saturation_range=(0.5, 1.5),
-                 hue_delta=18):
-        self.brightness_delta = brightness_delta
-        self.contrast_lower, self.contrast_upper = contrast_range
-        self.saturation_lower, self.saturation_upper = saturation_range
-        self.hue_delta = hue_delta
-
-    def __call__(self, results):
-        """Call function to perform photometric distortion on images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images distorted.
-        """
-
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        img = results['img']
-        assert img.dtype == np.float32, \
-            'PhotoMetricDistortion needs the input image of dtype np.float32,'\
-            ' please set "to_float32=True" in "LoadImageFromFile" pipeline'
-        # random brightness
-        if random.randint(2):
-            delta = random.uniform(-self.brightness_delta,
-                                   self.brightness_delta)
-            img += delta
-
-        # mode == 0 --> do random contrast first
-        # mode == 1 --> do random contrast last
-        mode = random.randint(2)
-        if mode == 1:
-            if random.randint(2):
-                alpha = random.uniform(self.contrast_lower,
-                                       self.contrast_upper)
-                img *= alpha
-
-        # convert color from BGR to HSV
-        img = mmcv.bgr2hsv(img)
-
-        # random saturation
-        if random.randint(2):
-            img[..., 1] *= random.uniform(self.saturation_lower,
-                                          self.saturation_upper)
-
-        # random hue
-        if random.randint(2):
-            img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
-            img[..., 0][img[..., 0] > 360] -= 360
-            img[..., 0][img[..., 0] < 0] += 360
-
-        # convert color from HSV to BGR
-        img = mmcv.hsv2bgr(img)
-
-        # random contrast
-        if mode == 0:
-            if random.randint(2):
-                alpha = random.uniform(self.contrast_lower,
-                                       self.contrast_upper)
-                img *= alpha
-
-        # randomly swap channels
-        if random.randint(2):
-            img = img[..., random.permutation(3)]
-
-        results['img'] = img
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(\nbrightness_delta={self.brightness_delta},\n'
-        repr_str += 'contrast_range='
-        repr_str += f'{(self.contrast_lower, self.contrast_upper)},\n'
-        repr_str += 'saturation_range='
-        repr_str += f'{(self.saturation_lower, self.saturation_upper)},\n'
-        repr_str += f'hue_delta={self.hue_delta})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Expand(object):
-    """Random expand the image & bboxes.
-
-    Randomly place the original image on a canvas of 'ratio' x original image
-    size filled with mean values. The ratio is in the range of ratio_range.
-
-    Args:
-        mean (tuple): mean value of dataset.
-        to_rgb (bool): if need to convert the order of mean to align with RGB.
-        ratio_range (tuple): range of expand ratio.
-        prob (float): probability of applying this transformation
-    """
-
-    def __init__(self,
-                 mean=(0, 0, 0),
-                 to_rgb=True,
-                 ratio_range=(1, 4),
-                 seg_ignore_label=None,
-                 prob=0.5):
-        self.to_rgb = to_rgb
-        self.ratio_range = ratio_range
-        if to_rgb:
-            self.mean = mean[::-1]
-        else:
-            self.mean = mean
-        self.min_ratio, self.max_ratio = ratio_range
-        self.seg_ignore_label = seg_ignore_label
-        self.prob = prob
-
-    def __call__(self, results):
-        """Call function to expand images, bounding boxes.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images, bounding boxes expanded
-        """
-
-        if random.uniform(0, 1) > self.prob:
-            return results
-
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        img = results['img']
-
-        h, w, c = img.shape
-        ratio = random.uniform(self.min_ratio, self.max_ratio)
-        # speedup expand when meets large image
-        if np.all(self.mean == self.mean[0]):
-            expand_img = np.empty((int(h * ratio), int(w * ratio), c),
-                                  img.dtype)
-            expand_img.fill(self.mean[0])
-        else:
-            expand_img = np.full((int(h * ratio), int(w * ratio), c),
-                                 self.mean,
-                                 dtype=img.dtype)
-        left = int(random.uniform(0, w * ratio - w))
-        top = int(random.uniform(0, h * ratio - h))
-        expand_img[top:top + h, left:left + w] = img
-
-        results['img'] = expand_img
-        # expand bboxes
-        for key in results.get('bbox_fields', []):
-            results[key] = results[key] + np.tile(
-                (left, top), 2).astype(results[key].dtype)
-
-        # expand masks
-        for key in results.get('mask_fields', []):
-            results[key] = results[key].expand(
-                int(h * ratio), int(w * ratio), top, left)
-
-        # expand segs
-        for key in results.get('seg_fields', []):
-            gt_seg = results[key]
-            expand_gt_seg = np.full((int(h * ratio), int(w * ratio)),
-                                    self.seg_ignore_label,
-                                    dtype=gt_seg.dtype)
-            expand_gt_seg[top:top + h, left:left + w] = gt_seg
-            results[key] = expand_gt_seg
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, to_rgb={self.to_rgb}, '
-        repr_str += f'ratio_range={self.ratio_range}, '
-        repr_str += f'seg_ignore_label={self.seg_ignore_label})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class MinIoURandomCrop(object):
-    """Random crop the image & bboxes, the cropped patches have minimum IoU
-    requirement with original image & bboxes, the IoU threshold is randomly
-    selected from min_ious.
-
-    Args:
-        min_ious (tuple): minimum IoU threshold for all intersections with
-        bounding boxes
-        min_crop_size (float): minimum crop's size (i.e. h,w := a*h, a*w,
-        where a >= min_crop_size).
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-
-    Note:
-        The keys for bboxes, labels and masks should be paired. That is, \
-        `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and \
-        `gt_bboxes_ignore` to `gt_labels_ignore` and `gt_masks_ignore`.
-    """
-
-    def __init__(self,
-                 min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
-                 min_crop_size=0.3,
-                 bbox_clip_border=True):
-        # 1: return ori img
-        self.min_ious = min_ious
-        self.sample_mode = (1, *min_ious, 0)
-        self.min_crop_size = min_crop_size
-        self.bbox_clip_border = bbox_clip_border
-        self.bbox2label = {
-            'gt_bboxes': 'gt_labels',
-            'gt_bboxes_ignore': 'gt_labels_ignore'
-        }
-        self.bbox2mask = {
-            'gt_bboxes': 'gt_masks',
-            'gt_bboxes_ignore': 'gt_masks_ignore'
-        }
-
-    def __call__(self, results):
-        """Call function to crop images and bounding boxes with minimum IoU
-        constraint.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images and bounding boxes cropped, \
-                'img_shape' key is updated.
-        """
-
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        img = results['img']
-        assert 'bbox_fields' in results
-        boxes = [results[key] for key in results['bbox_fields']]
-        boxes = np.concatenate(boxes, 0)
-        h, w, c = img.shape
-        while True:
-            mode = random.choice(self.sample_mode)
-            self.mode = mode
-            if mode == 1:
-                return results
-
-            min_iou = mode
-            for i in range(50):
-                new_w = random.uniform(self.min_crop_size * w, w)
-                new_h = random.uniform(self.min_crop_size * h, h)
-
-                # h / w in [0.5, 2]
-                if new_h / new_w < 0.5 or new_h / new_w > 2:
-                    continue
-
-                left = random.uniform(w - new_w)
-                top = random.uniform(h - new_h)
-
-                patch = np.array(
-                    (int(left), int(top), int(left + new_w), int(top + new_h)))
-                # Line or point crop is not allowed
-                if patch[2] == patch[0] or patch[3] == patch[1]:
-                    continue
-                overlaps = bbox_overlaps(
-                    patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
-                if len(overlaps) > 0 and overlaps.min() < min_iou:
-                    continue
-
-                # center of boxes should inside the crop img
-                # only adjust boxes and instance masks when the gt is not empty
-                if len(overlaps) > 0:
-                    # adjust boxes
-                    def is_center_of_bboxes_in_patch(boxes, patch):
-                        center = (boxes[:, :2] + boxes[:, 2:]) / 2
-                        mask = ((center[:, 0] > patch[0]) *
-                                (center[:, 1] > patch[1]) *
-                                (center[:, 0] < patch[2]) *
-                                (center[:, 1] < patch[3]))
-                        return mask
-
-                    mask = is_center_of_bboxes_in_patch(boxes, patch)
-                    if not mask.any():
-                        continue
-                    for key in results.get('bbox_fields', []):
-                        boxes = results[key].copy()
-                        mask = is_center_of_bboxes_in_patch(boxes, patch)
-                        boxes = boxes[mask]
-                        if self.bbox_clip_border:
-                            boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
-                            boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
-                        boxes -= np.tile(patch[:2], 2)
-
-                        results[key] = boxes
-                        # labels
-                        label_key = self.bbox2label.get(key)
-                        if label_key in results:
-                            results[label_key] = results[label_key][mask]
-
-                        # mask fields
-                        mask_key = self.bbox2mask.get(key)
-                        if mask_key in results:
-                            results[mask_key] = results[mask_key][
-                                mask.nonzero()[0]].crop(patch)
-                # adjust the img no matter whether the gt is empty before crop
-                img = img[patch[1]:patch[3], patch[0]:patch[2]]
-                results['img'] = img
-                results['img_shape'] = img.shape
-
-                # seg fields
-                for key in results.get('seg_fields', []):
-                    results[key] = results[key][patch[1]:patch[3],
-                                                patch[0]:patch[2]]
-                return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(min_ious={self.min_ious}, '
-        repr_str += f'min_crop_size={self.min_crop_size}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Corrupt(object):
-    """Corruption augmentation.
-
-    Corruption transforms implemented based on
-    `imagecorruptions <https://github.com/bethgelab/imagecorruptions>`_.
-
-    Args:
-        corruption (str): Corruption name.
-        severity (int, optional): The severity of corruption. Default: 1.
-    """
-
-    def __init__(self, corruption, severity=1):
-        self.corruption = corruption
-        self.severity = severity
-
-    def __call__(self, results):
-        """Call function to corrupt image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images corrupted.
-        """
-
-        if corrupt is None:
-            raise RuntimeError('imagecorruptions is not installed')
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        results['img'] = corrupt(
-            results['img'].astype(np.uint8),
-            corruption_name=self.corruption,
-            severity=self.severity)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(corruption={self.corruption}, '
-        repr_str += f'severity={self.severity})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Albu(object):
-    """Albumentation augmentation.
-
-    Adds custom transformations from Albumentations library.
-    Please, visit `https://albumentations.readthedocs.io`
-    to get more information.
-
-    An example of ``transforms`` is as followed:
-
-    .. code-block::
-
-        [
-            dict(
-                type='ShiftScaleRotate',
-                shift_limit=0.0625,
-                scale_limit=0.0,
-                rotate_limit=0,
-                interpolation=1,
-                p=0.5),
-            dict(
-                type='RandomBrightnessContrast',
-                brightness_limit=[0.1, 0.3],
-                contrast_limit=[0.1, 0.3],
-                p=0.2),
-            dict(type='ChannelShuffle', p=0.1),
-            dict(
-                type='OneOf',
-                transforms=[
-                    dict(type='Blur', blur_limit=3, p=1.0),
-                    dict(type='MedianBlur', blur_limit=3, p=1.0)
-                ],
-                p=0.1),
-        ]
-
-    Args:
-        transforms (list[dict]): A list of albu transformations
-        bbox_params (dict): Bbox_params for albumentation `Compose`
-        keymap (dict): Contains {'input key':'albumentation-style key'}
-        skip_img_without_anno (bool): Whether to skip the image if no ann left
-            after aug
-    """
-
-    def __init__(self,
-                 transforms,
-                 bbox_params=None,
-                 keymap=None,
-                 update_pad_shape=False,
-                 skip_img_without_anno=False):
-        if Compose is None:
-            raise RuntimeError('albumentations is not installed')
-
-        # Args will be modified later, copying it will be safer
-        transforms = copy.deepcopy(transforms)
-        if bbox_params is not None:
-            bbox_params = copy.deepcopy(bbox_params)
-        if keymap is not None:
-            keymap = copy.deepcopy(keymap)
-        self.transforms = transforms
-        self.filter_lost_elements = False
-        self.update_pad_shape = update_pad_shape
-        self.skip_img_without_anno = skip_img_without_anno
-
-        # A simple workaround to remove masks without boxes
-        if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params
-                and 'filter_lost_elements' in bbox_params):
-            self.filter_lost_elements = True
-            self.origin_label_fields = bbox_params['label_fields']
-            bbox_params['label_fields'] = ['idx_mapper']
-            del bbox_params['filter_lost_elements']
-
-        self.bbox_params = (
-            self.albu_builder(bbox_params) if bbox_params else None)
-        self.aug = Compose([self.albu_builder(t) for t in self.transforms],
-                           bbox_params=self.bbox_params)
-
-        if not keymap:
-            self.keymap_to_albu = {
-                'img': 'image',
-                'gt_masks': 'masks',
-                'gt_bboxes': 'bboxes'
-            }
-        else:
-            self.keymap_to_albu = keymap
-        self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
-
-    def albu_builder(self, cfg):
-        """Import a module from albumentations.
-
-        It inherits some of :func:`build_from_cfg` logic.
-
-        Args:
-            cfg (dict): Config dict. It should at least contain the key "type".
-
-        Returns:
-            obj: The constructed object.
-        """
-
-        assert isinstance(cfg, dict) and 'type' in cfg
-        args = cfg.copy()
-
-        obj_type = args.pop('type')
-        if mmcv.is_str(obj_type):
-            if albumentations is None:
-                raise RuntimeError('albumentations is not installed')
-            obj_cls = getattr(albumentations, obj_type)
-        elif inspect.isclass(obj_type):
-            obj_cls = obj_type
-        else:
-            raise TypeError(
-                f'type must be a str or valid type, but got {type(obj_type)}')
-
-        if 'transforms' in args:
-            args['transforms'] = [
-                self.albu_builder(transform)
-                for transform in args['transforms']
-            ]
-
-        return obj_cls(**args)
-
-    @staticmethod
-    def mapper(d, keymap):
-        """Dictionary mapper. Renames keys according to keymap provided.
-
-        Args:
-            d (dict): old dict
-            keymap (dict): {'old_key':'new_key'}
-        Returns:
-            dict: new dict.
-        """
-
-        updated_dict = {}
-        for k, v in zip(d.keys(), d.values()):
-            new_k = keymap.get(k, k)
-            updated_dict[new_k] = d[k]
-        return updated_dict
-
-    def __call__(self, results):
-        # dict to albumentations format
-        results = self.mapper(results, self.keymap_to_albu)
-        # TODO: add bbox_fields
-        if 'bboxes' in results:
-            # to list of boxes
-            if isinstance(results['bboxes'], np.ndarray):
-                results['bboxes'] = [x for x in results['bboxes']]
-            # add pseudo-field for filtration
-            if self.filter_lost_elements:
-                results['idx_mapper'] = np.arange(len(results['bboxes']))
-
-        # TODO: Support mask structure in albu
-        if 'masks' in results:
-            if isinstance(results['masks'], PolygonMasks):
-                raise NotImplementedError(
-                    'Albu only supports BitMap masks now')
-            ori_masks = results['masks']
-            if albumentations.__version__ < '0.5':
-                results['masks'] = results['masks'].masks
-            else:
-                results['masks'] = [mask for mask in results['masks'].masks]
-
-        results = self.aug(**results)
-
-        if 'bboxes' in results:
-            if isinstance(results['bboxes'], list):
-                results['bboxes'] = np.array(
-                    results['bboxes'], dtype=np.float32)
-            results['bboxes'] = results['bboxes'].reshape(-1, 4)
-
-            # filter label_fields
-            if self.filter_lost_elements:
-
-                for label in self.origin_label_fields:
-                    results[label] = np.array(
-                        [results[label][i] for i in results['idx_mapper']])
-                if 'masks' in results:
-                    results['masks'] = np.array(
-                        [results['masks'][i] for i in results['idx_mapper']])
-                    results['masks'] = ori_masks.__class__(
-                        results['masks'], results['image'].shape[0],
-                        results['image'].shape[1])
-
-                if (not len(results['idx_mapper'])
-                        and self.skip_img_without_anno):
-                    return None
-
-        if 'gt_labels' in results:
-            if isinstance(results['gt_labels'], list):
-                results['gt_labels'] = np.array(results['gt_labels'])
-            results['gt_labels'] = results['gt_labels'].astype(np.int64)
-
-        # back to the original format
-        results = self.mapper(results, self.keymap_back)
-
-        # update final shape
-        if self.update_pad_shape:
-            results['pad_shape'] = results['img'].shape
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__ + f'(transforms={self.transforms})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCenterCropPad(object):
-    """Random center crop and random around padding for CornerNet.
-
-    This operation generates randomly cropped image from the original image and
-    pads it simultaneously. Different from :class:`RandomCrop`, the output
-    shape may not equal to ``crop_size`` strictly. We choose a random value
-    from ``ratios`` and the output shape could be larger or smaller than
-    ``crop_size``. The padding operation is also different from :class:`Pad`,
-    here we use around padding instead of right-bottom padding.
-
-    The relation between output image (padding image) and original image:
-
-    .. code:: text
-
-                        output image
-
-               +----------------------------+
-               |          padded area       |
-        +------|----------------------------|----------+
-        |      |         cropped area       |          |
-        |      |         +---------------+  |          |
-        |      |         |    .   center |  |          | original image
-        |      |         |        range  |  |          |
-        |      |         +---------------+  |          |
-        +------|----------------------------|----------+
-               |          padded area       |
-               +----------------------------+
-
-    There are 5 main areas in the figure:
-
-    - output image: output image of this operation, also called padding
-      image in following instruction.
-    - original image: input image of this operation.
-    - padded area: non-intersect area of output image and original image.
-    - cropped area: the overlap of output image and original image.
-    - center range: a smaller area where random center chosen from.
-      center range is computed by ``border`` and original image's shape
-      to avoid our random center is too close to original image's border.
-
-    Also this operation act differently in train and test mode, the summary
-    pipeline is listed below.
-
-    Train pipeline:
-
-    1. Choose a ``random_ratio`` from ``ratios``, the shape of padding image
-       will be ``random_ratio * crop_size``.
-    2. Choose a ``random_center`` in center range.
-    3. Generate padding image with center matches the ``random_center``.
-    4. Initialize the padding image with pixel value equals to ``mean``.
-    5. Copy the cropped area to padding image.
-    6. Refine annotations.
-
-    Test pipeline:
-
-    1. Compute output shape according to ``test_pad_mode``.
-    2. Generate padding image with center matches the original image
-       center.
-    3. Initialize the padding image with pixel value equals to ``mean``.
-    4. Copy the ``cropped area`` to padding image.
-
-    Args:
-        crop_size (tuple | None): expected size after crop, final size will
-            computed according to ratio. Requires (h, w) in train mode, and
-            None in test mode.
-        ratios (tuple): random select a ratio from tuple and crop image to
-            (crop_size[0] * ratio) * (crop_size[1] * ratio).
-            Only available in train mode.
-        border (int): max distance from center select area to image border.
-            Only available in train mode.
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB.
-        test_mode (bool): whether involve random variables in transform.
-            In train mode, crop_size is fixed, center coords and ratio is
-            random selected from predefined lists. In test mode, crop_size
-            is image's original shape, center coords and ratio is fixed.
-        test_pad_mode (tuple): padding method and padding shape value, only
-            available in test mode. Default is using 'logical_or' with
-            127 as padding shape value.
-
-            - 'logical_or': final_shape = input_shape | padding_shape_value
-            - 'size_divisor': final_shape = int(
-              ceil(input_shape / padding_shape_value) * padding_shape_value)
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-    """
-
-    def __init__(self,
-                 crop_size=None,
-                 ratios=(0.9, 1.0, 1.1),
-                 border=128,
-                 mean=None,
-                 std=None,
-                 to_rgb=None,
-                 test_mode=False,
-                 test_pad_mode=('logical_or', 127),
-                 bbox_clip_border=True):
-        if test_mode:
-            assert crop_size is None, 'crop_size must be None in test mode'
-            assert ratios is None, 'ratios must be None in test mode'
-            assert border is None, 'border must be None in test mode'
-            assert isinstance(test_pad_mode, (list, tuple))
-            assert test_pad_mode[0] in ['logical_or', 'size_divisor']
-        else:
-            assert isinstance(crop_size, (list, tuple))
-            assert crop_size[0] > 0 and crop_size[1] > 0, (
-                'crop_size must > 0 in train mode')
-            assert isinstance(ratios, (list, tuple))
-            assert test_pad_mode is None, (
-                'test_pad_mode must be None in train mode')
-
-        self.crop_size = crop_size
-        self.ratios = ratios
-        self.border = border
-        # We do not set default value to mean, std and to_rgb because these
-        # hyper-parameters are easy to forget but could affect the performance.
-        # Please use the same setting as Normalize for performance assurance.
-        assert mean is not None and std is not None and to_rgb is not None
-        self.to_rgb = to_rgb
-        self.input_mean = mean
-        self.input_std = std
-        if to_rgb:
-            self.mean = mean[::-1]
-            self.std = std[::-1]
-        else:
-            self.mean = mean
-            self.std = std
-        self.test_mode = test_mode
-        self.test_pad_mode = test_pad_mode
-        self.bbox_clip_border = bbox_clip_border
-
-    def _get_border(self, border, size):
-        """Get final border for the target size.
-
-        This function generates a ``final_border`` according to image's shape.
-        The area between ``final_border`` and ``size - final_border`` is the
-        ``center range``. We randomly choose center from the ``center range``
-        to avoid our random center is too close to original image's border.
-        Also ``center range`` should be larger than 0.
-
-        Args:
-            border (int): The initial border, default is 128.
-            size (int): The width or height of original image.
-        Returns:
-            int: The final border.
-        """
-        k = 2 * border / size
-        i = pow(2, np.ceil(np.log2(np.ceil(k))) + (k == int(k)))
-        return border // i
-
-    def _filter_boxes(self, patch, boxes):
-        """Check whether the center of each box is in the patch.
-
-        Args:
-            patch (list[int]): The cropped area, [left, top, right, bottom].
-            boxes (numpy array, (N x 4)): Ground truth boxes.
-
-        Returns:
-            mask (numpy array, (N,)): Each box is inside or outside the patch.
-        """
-        center = (boxes[:, :2] + boxes[:, 2:]) / 2
-        mask = (center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) * (
-            center[:, 0] < patch[2]) * (
-                center[:, 1] < patch[3])
-        return mask
-
-    def _crop_image_and_paste(self, image, center, size):
-        """Crop image with a given center and size, then paste the cropped
-        image to a blank image with two centers align.
-
-        This function is equivalent to generating a blank image with ``size``
-        as its shape. Then cover it on the original image with two centers (
-        the center of blank image and the random center of original image)
-        aligned. The overlap area is paste from the original image and the
-        outside area is filled with ``mean pixel``.
-
-        Args:
-            image (np array, H x W x C): Original image.
-            center (list[int]): Target crop center coord.
-            size (list[int]): Target crop size. [target_h, target_w]
-
-        Returns:
-            cropped_img (np array, target_h x target_w x C): Cropped image.
-            border (np array, 4): The distance of four border of
-                ``cropped_img`` to the original image area, [top, bottom,
-                left, right]
-            patch (list[int]): The cropped area, [left, top, right, bottom].
-        """
-        center_y, center_x = center
-        target_h, target_w = size
-        img_h, img_w, img_c = image.shape
-
-        x0 = max(0, center_x - target_w // 2)
-        x1 = min(center_x + target_w // 2, img_w)
-        y0 = max(0, center_y - target_h // 2)
-        y1 = min(center_y + target_h // 2, img_h)
-        patch = np.array((int(x0), int(y0), int(x1), int(y1)))
-
-        left, right = center_x - x0, x1 - center_x
-        top, bottom = center_y - y0, y1 - center_y
-
-        cropped_center_y, cropped_center_x = target_h // 2, target_w // 2
-        cropped_img = np.zeros((target_h, target_w, img_c), dtype=image.dtype)
-        for i in range(img_c):
-            cropped_img[:, :, i] += self.mean[i]
-        y_slice = slice(cropped_center_y - top, cropped_center_y + bottom)
-        x_slice = slice(cropped_center_x - left, cropped_center_x + right)
-        cropped_img[y_slice, x_slice, :] = image[y0:y1, x0:x1, :]
-
-        border = np.array([
-            cropped_center_y - top, cropped_center_y + bottom,
-            cropped_center_x - left, cropped_center_x + right
-        ],
-                          dtype=np.float32)
-
-        return cropped_img, border, patch
-
-    def _train_aug(self, results):
-        """Random crop and around padding the original image.
-
-        Args:
-            results (dict): Image infomations in the augment pipeline.
-
-        Returns:
-            results (dict): The updated dict.
-        """
-        img = results['img']
-        h, w, c = img.shape
-        boxes = results['gt_bboxes']
-        while True:
-            scale = random.choice(self.ratios)
-            new_h = int(self.crop_size[0] * scale)
-            new_w = int(self.crop_size[1] * scale)
-            h_border = self._get_border(self.border, h)
-            w_border = self._get_border(self.border, w)
-
-            for i in range(50):
-                center_x = random.randint(low=w_border, high=w - w_border)
-                center_y = random.randint(low=h_border, high=h - h_border)
-
-                cropped_img, border, patch = self._crop_image_and_paste(
-                    img, [center_y, center_x], [new_h, new_w])
-
-                mask = self._filter_boxes(patch, boxes)
-                # if image do not have valid bbox, any crop patch is valid.
-                if not mask.any() and len(boxes) > 0:
-                    continue
-
-                results['img'] = cropped_img
-                results['img_shape'] = cropped_img.shape
-                results['pad_shape'] = cropped_img.shape
-
-                x0, y0, x1, y1 = patch
-
-                left_w, top_h = center_x - x0, center_y - y0
-                cropped_center_x, cropped_center_y = new_w // 2, new_h // 2
-
-                # crop bboxes accordingly and clip to the image boundary
-                for key in results.get('bbox_fields', []):
-                    mask = self._filter_boxes(patch, results[key])
-                    bboxes = results[key][mask]
-                    bboxes[:, 0:4:2] += cropped_center_x - left_w - x0
-                    bboxes[:, 1:4:2] += cropped_center_y - top_h - y0
-                    if self.bbox_clip_border:
-                        bboxes[:, 0:4:2] = np.clip(bboxes[:, 0:4:2], 0, new_w)
-                        bboxes[:, 1:4:2] = np.clip(bboxes[:, 1:4:2], 0, new_h)
-                    keep = (bboxes[:, 2] > bboxes[:, 0]) & (
-                        bboxes[:, 3] > bboxes[:, 1])
-                    bboxes = bboxes[keep]
-                    results[key] = bboxes
-                    if key in ['gt_bboxes']:
-                        if 'gt_labels' in results:
-                            labels = results['gt_labels'][mask]
-                            labels = labels[keep]
-                            results['gt_labels'] = labels
-                        if 'gt_masks' in results:
-                            raise NotImplementedError(
-                                'RandomCenterCropPad only supports bbox.')
-
-                # crop semantic seg
-                for key in results.get('seg_fields', []):
-                    raise NotImplementedError(
-                        'RandomCenterCropPad only supports bbox.')
-                return results
-
-    def _test_aug(self, results):
-        """Around padding the original image without cropping.
-
-        The padding mode and value are from ``test_pad_mode``.
-
-        Args:
-            results (dict): Image infomations in the augment pipeline.
-
-        Returns:
-            results (dict): The updated dict.
-        """
-        img = results['img']
-        h, w, c = img.shape
-        results['img_shape'] = img.shape
-        if self.test_pad_mode[0] in ['logical_or']:
-            target_h = h | self.test_pad_mode[1]
-            target_w = w | self.test_pad_mode[1]
-        elif self.test_pad_mode[0] in ['size_divisor']:
-            divisor = self.test_pad_mode[1]
-            target_h = int(np.ceil(h / divisor)) * divisor
-            target_w = int(np.ceil(w / divisor)) * divisor
-        else:
-            raise NotImplementedError(
-                'RandomCenterCropPad only support two testing pad mode:'
-                'logical-or and size_divisor.')
-
-        cropped_img, border, _ = self._crop_image_and_paste(
-            img, [h // 2, w // 2], [target_h, target_w])
-        results['img'] = cropped_img
-        results['pad_shape'] = cropped_img.shape
-        results['border'] = border
-        return results
-
-    def __call__(self, results):
-        img = results['img']
-        assert img.dtype == np.float32, (
-            'RandomCenterCropPad needs the input image of dtype np.float32,'
-            ' please set "to_float32=True" in "LoadImageFromFile" pipeline')
-        h, w, c = img.shape
-        assert c == len(self.mean)
-        if self.test_mode:
-            return self._test_aug(results)
-        else:
-            return self._train_aug(results)
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(crop_size={self.crop_size}, '
-        repr_str += f'ratios={self.ratios}, '
-        repr_str += f'border={self.border}, '
-        repr_str += f'mean={self.input_mean}, '
-        repr_str += f'std={self.input_std}, '
-        repr_str += f'to_rgb={self.to_rgb}, '
-        repr_str += f'test_mode={self.test_mode}, '
-        repr_str += f'test_pad_mode={self.test_pad_mode}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class CutOut(object):
-    """CutOut operation.
-
-    Randomly drop some regions of image used in
-    `Cutout <https://arxiv.org/abs/1708.04552>`_.
-
-    Args:
-        n_holes (int | tuple[int, int]): Number of regions to be dropped.
-            If it is given as a list, number of holes will be randomly
-            selected from the closed interval [`n_holes[0]`, `n_holes[1]`].
-        cutout_shape (tuple[int, int] | list[tuple[int, int]]): The candidate
-            shape of dropped regions. It can be `tuple[int, int]` to use a
-            fixed cutout shape, or `list[tuple[int, int]]` to randomly choose
-            shape from the list.
-        cutout_ratio (tuple[float, float] | list[tuple[float, float]]): The
-            candidate ratio of dropped regions. It can be `tuple[float, float]`
-            to use a fixed ratio or `list[tuple[float, float]]` to randomly
-            choose ratio from the list. Please note that `cutout_shape`
-            and `cutout_ratio` cannot be both given at the same time.
-        fill_in (tuple[float, float, float] | tuple[int, int, int]): The value
-            of pixel to fill in the dropped regions. Default: (0, 0, 0).
-    """
-
-    def __init__(self,
-                 n_holes,
-                 cutout_shape=None,
-                 cutout_ratio=None,
-                 fill_in=(0, 0, 0)):
-
-        assert (cutout_shape is None) ^ (cutout_ratio is None), \
-            'Either cutout_shape or cutout_ratio should be specified.'
-        assert (isinstance(cutout_shape, (list, tuple))
-                or isinstance(cutout_ratio, (list, tuple)))
-        if isinstance(n_holes, tuple):
-            assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1]
-        else:
-            n_holes = (n_holes, n_holes)
-        self.n_holes = n_holes
-        self.fill_in = fill_in
-        self.with_ratio = cutout_ratio is not None
-        self.candidates = cutout_ratio if self.with_ratio else cutout_shape
-        if not isinstance(self.candidates, list):
-            self.candidates = [self.candidates]
-
-    def __call__(self, results):
-        """Call function to drop some regions of image."""
-        h, w, c = results['img'].shape
-        n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1)
-        for _ in range(n_holes):
-            x1 = np.random.randint(0, w)
-            y1 = np.random.randint(0, h)
-            index = np.random.randint(0, len(self.candidates))
-            if not self.with_ratio:
-                cutout_w, cutout_h = self.candidates[index]
-            else:
-                cutout_w = int(self.candidates[index][0] * w)
-                cutout_h = int(self.candidates[index][1] * h)
-
-            x2 = np.clip(x1 + cutout_w, 0, w)
-            y2 = np.clip(y1 + cutout_h, 0, h)
-            results['img'][y1:y2, x1:x2, :] = self.fill_in
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(n_holes={self.n_holes}, '
-        repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio
-                     else f'cutout_shape={self.candidates}, ')
-        repr_str += f'fill_in={self.fill_in})'
-        return repr_str
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/dmnet_r50-d8.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/dmnet_r50-d8.py
deleted file mode 100644
index d22ba52640bebd805b3b8d07025e276dfb023759..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/dmnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='DMHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        filter_sizes=(1, 3, 5, 7),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/schedules/schedule_20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/schedules/schedule_20k.py
deleted file mode 100644
index bf780a1b6f6521833c6a5859675147824efa599d..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/schedules/schedule_20k.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
-# runtime settings
-runner = dict(type='IterBasedRunner', max_iters=20000)
-checkpoint_config = dict(by_epoch=False, interval=2000)
-evaluation = dict(interval=2000, metric='mIoU')
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
deleted file mode 100644
index 2221b202d6c53c4b04f2431d3344379cbfe06dd7..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/__init__.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/__init__.py
deleted file mode 100644
index 662aae3c0022845bd173b71f48c4765767eef3e1..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from .ann_head import ANNHead
-from .apc_head import APCHead
-from .aspp_head import ASPPHead
-from .cc_head import CCHead
-from .da_head import DAHead
-from .dm_head import DMHead
-from .dnl_head import DNLHead
-from .ema_head import EMAHead
-from .enc_head import EncHead
-from .fcn_head import FCNHead
-from .fpn_head import FPNHead
-from .gc_head import GCHead
-from .lraspp_head import LRASPPHead
-from .nl_head import NLHead
-from .ocr_head import OCRHead
-from .point_head import PointHead
-from .psa_head import PSAHead
-from .psp_head import PSPHead
-from .sep_aspp_head import DepthwiseSeparableASPPHead
-from .sep_fcn_head import DepthwiseSeparableFCNHead
-from .uper_head import UPerHead
-
-__all__ = [
-    'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
-    'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
-    'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
-    'PointHead', 'APCHead', 'DMHead', 'LRASPPHead'
-]
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/quantization/vq.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/quantization/vq.py
deleted file mode 100644
index aa57bea59db95ddae35e0657f723ca3a29ee943b..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/quantization/vq.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
-    """Residual Vector Quantizer.
-
-    Args:
-        dimension (int): Dimension of the codebooks.
-        n_q (int): Number of residual vector quantizers used.
-        q_dropout (bool): Random quantizer drop out at train time.
-        bins (int): Codebook size.
-        decay (float): Decay for exponential moving average over the codebooks.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
-            for orthogonal regularization.
-    """
-    def __init__(
-        self,
-        dimension: int = 256,
-        n_q: int = 8,
-        q_dropout: bool = False,
-        bins: int = 1024,
-        decay: float = 0.99,
-        kmeans_init: bool = True,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        self.max_n_q = n_q
-        self.n_q = n_q
-        self.q_dropout = q_dropout
-        self.dimension = dimension
-        self.bins = bins
-        self.decay = decay
-        self.kmeans_init = kmeans_init
-        self.kmeans_iters = kmeans_iters
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-        self.vq = ResidualVectorQuantization(
-            dim=self.dimension,
-            codebook_size=self.bins,
-            num_quantizers=self.n_q,
-            decay=self.decay,
-            kmeans_init=self.kmeans_init,
-            kmeans_iters=self.kmeans_iters,
-            threshold_ema_dead_code=self.threshold_ema_dead_code,
-            orthogonal_reg_weight=self.orthogonal_reg_weight,
-            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
-            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
-            channels_last=False
-        )
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        n_q = self.n_q
-        if self.training and self.q_dropout:
-            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
-        bw_per_q = math.log2(self.bins) * frame_rate / 1000
-        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        bw = torch.tensor(n_q * bw_per_q).to(x)
-        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified frame rate at the given bandwidth.
-        The RVQ encode method sets the appropriate number of quantizer to use
-        and returns indices for each quantizer.
-        """
-        n_q = self.n_q
-        codes = self.vq.encode(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        return codes
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation."""
-        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
-        codes = codes.transpose(0, 1)
-        quantized = self.vq.decode(codes)
-        return quantized
-
-    @property
-    def total_codebooks(self):
-        return self.max_n_q
-
-    @property
-    def num_codebooks(self):
-        return self.n_q
-
-    def set_num_codebooks(self, n: int):
-        assert n > 0 and n <= self.max_n_q
-        self.n_q = n
diff --git a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/modules/test_conv.py b/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/modules/test_conv.py
deleted file mode 100644
index 28fbc4f1a0ebaf41b56947b767958ae696e75eec..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/modules/test_conv.py
+++ /dev/null
@@ -1,203 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-import math
-import random
-
-import pytest
-import torch
-from torch import nn
-
-from audiocraft.modules import (
-    NormConv1d,
-    NormConvTranspose1d,
-    StreamableConv1d,
-    StreamableConvTranspose1d,
-    pad1d,
-    unpad1d,
-)
-
-
-def test_get_extra_padding_for_conv1d():
-    # TODO: Implement me!
-    pass
-
-
-def test_pad1d_zeros():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='constant', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='constant', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='constant', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='constant', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='constant', value=0.)
-
-
-def test_pad1d_reflect():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='reflect', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='reflect', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='reflect', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='reflect', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='reflect', value=0.)
-
-
-def test_unpad1d():
-    x = torch.randn(1, 1, 20)
-
-    u1 = unpad1d(x, (5, 5))
-    assert u1.shape[-1] == 10
-    u2 = unpad1d(x, (0, 5))
-    assert u2.shape[-1] == 15
-    u3 = unpad1d(x, (5, 0))
-    assert u3.shape[-1] == 15
-    u4 = unpad1d(x, (0, 0))
-    assert u4.shape[-1] == x.shape[-1]
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, 0))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (0, -1))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, -1))
-
-
-class TestNormConv1d:
-
-    def test_norm_conv1d_modules(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = int((T - kernel_size) / stride + 1)
-        wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm')
-        gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm')
-        nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none')
-
-        assert isinstance(wn_conv.norm, nn.Identity)
-        assert isinstance(wn_conv.conv, nn.Conv1d)
-
-        assert isinstance(gn_conv.norm, nn.GroupNorm)
-        assert isinstance(gn_conv.conv, nn.Conv1d)
-
-        assert isinstance(nn_conv.norm, nn.Identity)
-        assert isinstance(nn_conv.conv, nn.Conv1d)
-
-        for conv_layer in [wn_conv, gn_conv, nn_conv]:
-            out = conv_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestNormConvTranspose1d:
-
-    def test_normalizations(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1
-
-        wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm')
-        gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm')
-        nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none')
-
-        assert isinstance(wn_convtr.norm, nn.Identity)
-        assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(gn_convtr.norm, nn.GroupNorm)
-        assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(nn_convtr.norm, nn.Identity)
-        assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d)
-
-        for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]:
-            out = convtr_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConv1d:
-
-    def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation):
-        # StreamableConv1d internally pads to make sure that the last window is full
-        padding_total = (kernel_size - 1) * dilation - (stride - 1)
-        n_frames = (length - kernel_size + padding_total) / stride + 1
-        ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-        return ideal_length // stride
-
-    def test_streamable_conv1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-        C_out = 1
-
-        # conv params are [(kernel_size, stride, dilation)]
-        conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)]
-        for causal, (kernel_size, stride, dilation) in product([False, True], conv_params):
-            expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation)
-            sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal)
-            out = sconv(t0)
-            assert isinstance(out, torch.Tensor)
-            print(list(out.shape), [N, C_out, expected_out_length])
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConvTranspose1d:
-
-    def get_streamable_convtr1d_output_length(self, length, kernel_size, stride):
-        padding_total = (kernel_size - stride)
-        return (length - 1) * stride - padding_total + (kernel_size - 1) + 1
-
-    def test_streamable_convtr1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out = 1
-
-        with pytest.raises(AssertionError):
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2)
-
-        # causal params are [(causal, trim_right)]
-        causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)]
-        # conv params are [(kernel_size, stride)]
-        conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)]
-        for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params):
-            expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride)
-            sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride,
-                                                causal=causal, trim_right_ratio=trim_right_ratio)
-            out = sconvtr(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
diff --git a/spaces/Guochun/THUDM-chatglm2-6b/app.py b/spaces/Guochun/THUDM-chatglm2-6b/app.py
deleted file mode 100644
index 178500883f421fa82a74ed826246337066c7194a..0000000000000000000000000000000000000000
--- a/spaces/Guochun/THUDM-chatglm2-6b/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/THUDM/chatglm2-6b").launch()
\ No newline at end of file
diff --git a/spaces/Hallucinate/demo/ldm/models/diffusion/plms.py b/spaces/Hallucinate/demo/ldm/models/diffusion/plms.py
deleted file mode 100644
index 78eeb1003aa45d27bdbfc6b4a1d7ccbff57cd2e3..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/diffusion/plms.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-
-
-class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        if ddim_eta != 0:
-            raise ValueError('ddim_eta must be 0 for PLMS')
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for PLMS sampling is {size}')
-
-        samples, intermediates = self.plms_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def plms_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running PLMS Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
-        old_eps = []
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      old_eps=old_eps, t_next=ts_next)
-            img, pred_x0, e_t = outs
-            old_eps.append(e_t)
-            if len(old_eps) >= 4:
-                old_eps.pop(0)
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
-        b, *_, device = *x.shape, x.device
-
-        def get_model_output(x, t):
-            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-                e_t = self.model.apply_model(x, t, c)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t] * 2)
-                c_in = torch.cat([unconditional_conditioning, c])
-                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-            if score_corrector is not None:
-                assert self.model.parameterization == "eps"
-                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-            return e_t
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-        def get_x_prev_and_pred_x0(e_t, index):
-            # select parameters corresponding to the currently considered timestep
-            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-            # current prediction for x_0
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-            if quantize_denoised:
-                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-            # direction pointing to x_t
-            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
-                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-            return x_prev, pred_x0
-
-        e_t = get_model_output(x, t)
-        if len(old_eps) == 0:
-            # Pseudo Improved Euler (2nd order)
-            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-            e_t_next = get_model_output(x_prev, t_next)
-            e_t_prime = (e_t + e_t_next) / 2
-        elif len(old_eps) == 1:
-            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (3 * e_t - old_eps[-1]) / 2
-        elif len(old_eps) == 2:
-            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-        elif len(old_eps) >= 3:
-            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-        return x_prev, pred_x0, e_t
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/model_utils.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/model_utils.py
deleted file mode 100644
index 732d66b1d5f695151c26d29eb7f6b53179c269f1..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/model_utils.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import List, Optional
-
-import torch
-from torch import Tensor
-
-
-@torch.jit.script
-def script_skip_tensor_list(x: List[Tensor], mask):
-    res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x]
-    outputs = []
-    for i, t in enumerate(res):
-        if t.numel() != 0:
-            outputs.append(t)
-        else:
-            outputs.append(x[i])
-    return outputs
-
-
-@torch.jit.script
-def script_skip_tensor(x: Tensor, mask):
-    # None case
-    if x.size(0) == 0:
-        return x
-    res = x[mask] if x.size(0) == mask.size(0) else x[:, mask]
-    if res.numel() == 0:
-        return x
-    else:
-        return res
-
-
-@torch.jit.script
-def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int):
-    """
-    Expand 2D/3D tensor on dim=1
-    """
-    if x is None:
-        return None
-
-    assert x.dim() == 2 or x.dim() == 3
-    assert trg_dim >= x.size(1), (trg_dim, x.size())
-    if trg_dim == x.size(1):
-        return x
-
-    dims = [x.size(0), trg_dim - x.size(1)]
-    if x.dim() == 3:
-        dims.append(x.size(2))
-    x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1)
-
-    return x
-
-
-@torch.jit.script
-def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor:
-    return x if x is not None else y
-
-
-@torch.jit.script
-def fill_tensors(
-    x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int
-) -> Optional[Tensor]:
-    """
-    Filling tensor x with y at masked positions (dim=0).
-    """
-    if x is None or x.size()[0] == 0 or y is None:
-        return x
-    assert x.dim() == y.dim() and mask.size(0) == x.size(0)
-    assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
-
-    n_selected = mask.sum()
-    if n_selected == 0:
-        return x
-    assert n_selected == y.size(0)
-    if n_selected == x.size(0):
-        return y
-
-    if x.size(1) < y.size(1):
-        x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
-        x[mask] = y
-    elif x.size(1) > y.size(1):
-        x[mask] = torch.tensor(padding_idx).type_as(x)
-        if x.dim() == 2:
-            x[mask, : y.size(1)] = y
-        else:
-            x[mask, : y.size(1), :] = y
-    else:
-        x[mask] = y
-    return x
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/cpu_adam.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/cpu_adam.py
deleted file mode 100644
index b2f893aeda69ee1741e5e3af406ff4182b6f2416..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/cpu_adam.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-from collections.abc import Collection
-from dataclasses import dataclass, field
-from typing import List
-
-import torch
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim import FairseqOptimizer, register_optimizer
-from omegaconf import II, DictConfig
-
-
-try:
-    import deepspeed
-    has_deepspeed = True
-except ImportError as e:
-    has_deepspeed = False
-
-
-def _get_cpu_adam():
-    try:
-        from deepspeed.ops.op_builder import CPUAdamBuilder
-        return CPUAdamBuilder().load()
-    except ImportError:
-        # fbcode
-        from deepspeed.ops.adam import DeepSpeedCPUAdam as ds_opt_adam
-        return ds_opt_adam
-
-@dataclass
-class FairseqCPUAdamConfig(FairseqDataclass):
-    adam_betas: str = field(
-        default="(0.9, 0.999)", metadata={"help": "betas for Adam optimizer"}
-    )
-    adam_eps: float = field(
-        default=1e-8, metadata={"help": "epsilon for Adam optimizer"}
-    )
-    weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
-    fp16_adam_stats: bool = field(
-        default=False, metadata={"help": "use FP16 stats (with automatic scaling)"}
-    )
-    # TODO common vars below in parent
-    lr: List[float] = II("optimization.lr")
-
-
-@register_optimizer("cpu_adam", dataclass=FairseqCPUAdamConfig)
-class FairseqCPUAdam(FairseqOptimizer):
-    """Adam optimizer for fairseq, optimized for CPU tensors.
-
-    Important note: this optimizer corresponds to the "AdamW" variant of
-    Adam in its weight decay behavior. As such, it is most closely
-    analogous to torch.optim.AdamW from PyTorch.
-    """
-
-    def __init__(self, cfg: DictConfig, params):
-        super().__init__(cfg)
-        self._optimizer = CPUAdam(params, **self.optimizer_config)
-
-    @property
-    def optimizer_config(self):
-        """
-        Return a kwarg dictionary that will be used to override optimizer
-        args stored in checkpoints. This allows us to load a checkpoint and
-        resume training using a different set of optimizer args, e.g., with a
-        different learning rate.
-        """
-        return {
-            "lr": self.cfg.lr[0]
-            if isinstance(self.cfg.lr, Collection)
-            else self.cfg.lr,
-            "betas": eval(self.cfg.adam_betas),
-            "eps": self.cfg.adam_eps,
-            "weight_decay": self.cfg.weight_decay,
-            "use_fp16_stats": self.cfg.fp16_adam_stats,
-        }
-
-
-class CPUAdam(torch.optim.Optimizer):
-
-    optimizer_id = 0
-
-    def __init__(
-        self,
-        params,
-        lr=1e-3,
-        bias_correction=True,
-        betas=(0.9, 0.999),
-        eps=1e-8,
-        weight_decay=0,
-        use_fp16_stats=False,
-    ):
-        defaults = {
-            "lr": lr,
-            "bias_correction": bias_correction,
-            "betas": betas,
-            "eps": eps,
-            "weight_decay": weight_decay,
-        }
-        super().__init__(params, defaults)
-
-        self.use_fp16_stats = use_fp16_stats
-        self.FLOAT16_MAX = 65504.0
-
-        if not has_deepspeed:
-            raise ImportError("Please install DeepSpeed: pip install deepspeed")
-
-        self.opt_id = CPUAdam.optimizer_id
-        CPUAdam.optimizer_id = CPUAdam.optimizer_id + 1
-
-        self.ds_opt_adam = _get_cpu_adam()
-        adamw_mode = True
-        self.ds_opt_adam.create_adam(
-            self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode
-        )
-
-    @property
-    def supports_memory_efficient_fp16(self):
-        return True
-
-    @property
-    def supports_flat_params(self):
-        return True
-
-    @torch.no_grad()
-    def step(self, closure=None):
-        loss = None
-        if closure is not None:
-            with torch.enable_grad():
-                loss = closure()
-
-        torch.cuda.synchronize()
-
-        for group_id, group in enumerate(self.param_groups):
-            for param_id, p in enumerate(group["params"]):
-                if p.grad is None:
-                    continue
-
-                state = self.state[p]
-                if len(state) == 0:
-                    state["step"] = 0
-                    dtype = torch.float16 if self.use_fp16_stats else p.data.dtype
-                    # gradient momentums
-                    state["exp_avg"] = torch.zeros_like(
-                        p.data, dtype=dtype, device="cpu"
-                    )
-                    # gradient variances
-                    state["exp_avg_sq"] = torch.zeros_like(
-                        p.data, dtype=dtype, device="cpu"
-                    )
-                    if self.use_fp16_stats:
-                        assert torch.is_floating_point(p.data)
-                        state["exp_avg_scale"] = 1.0
-                        state["exp_avg_sq_scale"] = 1.0
-
-                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
-
-                p_data_bak = p.data  # backup of the original data pointer
-
-                p.data = p.data.to(dtype=torch.float32, device="cpu")
-                p.grad.data = p.grad.data.to(dtype=torch.float32, device="cpu")
-
-                if self.use_fp16_stats:
-                    exp_avg = exp_avg.float() * state["exp_avg_scale"]
-                    exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"]
-
-                state["step"] += 1
-                beta1, beta2 = group["betas"]
-
-                self.ds_opt_adam.adam_update(
-                    self.opt_id,
-                    state["step"],
-                    group["lr"],
-                    beta1,
-                    beta2,
-                    group["eps"],
-                    group["weight_decay"],
-                    group["bias_correction"],
-                    p.data,
-                    p.grad.data,
-                    exp_avg,
-                    exp_avg_sq,
-                )
-
-                if p_data_bak.data_ptr() != p.data.data_ptr():
-                    p_data_bak.copy_(p.data)
-                    p.data = p_data_bak
-
-                if self.use_fp16_stats:
-
-                    def inf_norm(t):
-                        return torch.norm(t, float("inf"))
-
-                    # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py
-                    state["exp_avg_scale"], state["exp_avg_sq_scale"] = (
-                        1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX,
-                        1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX,
-                    )
-                    state["exp_avg"], state["exp_avg_sq"] = (
-                        (exp_avg / state["exp_avg_scale"]).half(),
-                        (exp_avg_sq / state["exp_avg_sq_scale"]).half(),
-                    )
-
-        return loss
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq_cli/train.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq_cli/train.py
deleted file mode 100644
index 83475873138c5d1bac288c234afb6b4a1a7882d7..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq_cli/train.py
+++ /dev/null
@@ -1,514 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Train a new model on one or across multiple GPUs.
-"""
-
-import argparse
-import logging
-import math
-import os
-import sys
-from typing import Dict, Optional, Any, List, Tuple, Callable
-
-# We need to setup root logger before importing any fairseq libraries.
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("fairseq_cli.train")
-
-import numpy as np
-import torch
-from fairseq import (
-    checkpoint_utils,
-    options,
-    quantization_utils,
-    tasks,
-    utils,
-)
-from fairseq.data import iterators, data_utils
-from fairseq.data.plasma_utils import PlasmaStore
-from fairseq.dataclass.configs import FairseqConfig
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils
-from fairseq.file_io import PathManager
-from fairseq.logging import meters, metrics, progress_bar
-from fairseq.model_parallel.megatron_trainer import MegatronTrainer
-from fairseq.trainer import Trainer
-from omegaconf import DictConfig, OmegaConf
-
-
-
-
-def main(cfg: FairseqConfig) -> None:
-    if isinstance(cfg, argparse.Namespace):
-        cfg = convert_namespace_to_omegaconf(cfg)
-
-    utils.import_user_module(cfg.common)
-
-    if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg:
-        # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
-        logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg))
-
-    assert (
-        cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
-    ), "Must specify batch size either with --max-tokens or --batch-size"
-    metrics.reset()
-
-    if cfg.common.log_file is not None:
-        handler = logging.FileHandler(filename=cfg.common.log_file)
-        logger.addHandler(handler)
-
-    np.random.seed(cfg.common.seed)
-    utils.set_torch_seed(cfg.common.seed)
-
-    if distributed_utils.is_master(cfg.distributed_training):
-        checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir)
-
-    # Print args
-    logger.info(cfg)
-
-    if cfg.checkpoint.write_checkpoints_asynchronously:
-        try:
-            import iopath  # noqa: F401
-        except ImportError:
-            logging.exception(
-                "Asynchronous checkpoint writing is specified but iopath is "
-                "not installed: `pip install iopath`"
-            )
-            return
-
-    # Setup task, e.g., translation, language modeling, etc.
-    task = tasks.setup_task(cfg.task)
-
-    assert cfg.criterion, "Please specify criterion to train a model"
-
-    # Build model and criterion
-    if cfg.distributed_training.ddp_backend == "fully_sharded":
-        with fsdp_enable_wrap(cfg.distributed_training):
-            model = fsdp_wrap(task.build_model(cfg.model))
-    else:
-        model = task.build_model(cfg.model)
-    criterion = task.build_criterion(cfg.criterion)
-    logger.info(model)
-    logger.info("task: {}".format(task.__class__.__name__))
-    logger.info("model: {}".format(model.__class__.__name__))
-    logger.info("criterion: {}".format(criterion.__class__.__name__))
-    logger.info(
-        "num. shared model params: {:,} (num. trained: {:,})".format(
-            sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)),
-            sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad)
-        )
-    )
-
-    logger.info(
-        "num. expert model params: {} (num. trained: {})".format(
-            sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)),
-            sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad),
-        )
-    )
-
-    # Load valid dataset (we load training data below, based on the latest checkpoint)
-    # We load the valid dataset AFTER building the model
-    data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg)
-    if cfg.dataset.combine_valid_subsets:
-        task.load_dataset("valid", combine=True, epoch=1)
-    else:
-        for valid_sub_split in cfg.dataset.valid_subset.split(","):
-            task.load_dataset(valid_sub_split, combine=False, epoch=1)
-
-    # (optionally) Configure quantization
-    if cfg.common.quantization_config_path is not None:
-        quantizer = quantization_utils.Quantizer(
-            config_path=cfg.common.quantization_config_path,
-            max_epoch=cfg.optimization.max_epoch,
-            max_update=cfg.optimization.max_update,
-        )
-    else:
-        quantizer = None
-
-    # Build trainer
-    if cfg.common.model_parallel_size == 1:
-        trainer = Trainer(cfg, task, model, criterion, quantizer)
-    else:
-        trainer = MegatronTrainer(cfg, task, model, criterion)
-    logger.info(
-        "training on {} devices (GPUs/TPUs)".format(
-            cfg.distributed_training.distributed_world_size
-        )
-    )
-    logger.info(
-        "max tokens per device = {} and max sentences per device = {}".format(
-            cfg.dataset.max_tokens,
-            cfg.dataset.batch_size,
-        )
-    )
-
-    # Load the latest checkpoint if one is available and restore the
-    # corresponding train iterator
-    extra_state, epoch_itr = checkpoint_utils.load_checkpoint(
-        cfg.checkpoint,
-        trainer,
-        # don't cache epoch iterators for sharded datasets
-        disable_iterator_cache=task.has_sharded_data("train"),
-    )
-    if cfg.common.tpu:
-        import torch_xla.core.xla_model as xm
-        xm.rendezvous("load_checkpoint")  # wait for all workers
-
-    max_epoch = cfg.optimization.max_epoch or math.inf
-    lr = trainer.get_lr()
-
-    train_meter = meters.StopwatchMeter()
-    train_meter.start()
-    while epoch_itr.next_epoch_idx <= max_epoch:
-        if lr <= cfg.optimization.stop_min_lr:
-            logger.info(
-                f"stopping training because current learning rate ({lr}) is smaller "
-                "than or equal to minimum learning rate "
-                f"(--stop-min-lr={cfg.optimization.stop_min_lr})"
-            )
-            break
-
-        # train for one epoch
-        valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
-        if should_stop:
-            break
-
-        # only use first validation loss to update the learning rate
-        lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
-
-        epoch_itr = trainer.get_train_iterator(
-            epoch_itr.next_epoch_idx,
-            # sharded data: get train iterator for next epoch
-            load_dataset=task.has_sharded_data("train"),
-            # don't cache epoch iterators for sharded datasets
-            disable_iterator_cache=task.has_sharded_data("train"),
-        )
-    train_meter.stop()
-    logger.info("done training in {:.1f} seconds".format(train_meter.sum))
-
-    # ioPath implementation to wait for all asynchronous file writes to complete.
-    if cfg.checkpoint.write_checkpoints_asynchronously:
-        logger.info(
-            "ioPath PathManager waiting for all asynchronous checkpoint "
-            "writes to finish."
-        )
-        PathManager.async_close()
-        logger.info("ioPath PathManager finished waiting.")
-
-
-def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
-    # skip check if no validation was done in the current epoch
-    if valid_loss is None:
-        return False
-    if cfg.checkpoint.patience <= 0:
-        return False
-
-    def is_better(a, b):
-        return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b
-
-    prev_best = getattr(should_stop_early, "best", None)
-    if prev_best is None or is_better(valid_loss, prev_best):
-        should_stop_early.best = valid_loss
-        should_stop_early.num_runs = 0
-        return False
-    else:
-        should_stop_early.num_runs += 1
-        if should_stop_early.num_runs >= cfg.checkpoint.patience:
-            logger.info(
-                "early stop since valid performance hasn't improved for last {} runs".format(
-                    cfg.checkpoint.patience
-                )
-            )
-            return True
-        else:
-            return False
-
-
-@metrics.aggregate("train")
-def train(
-    cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
-) -> Tuple[List[Optional[float]], bool]:
-    """Train the model for one epoch and return validation losses."""
-    # Initialize data iterator
-    itr = epoch_itr.next_epoch_itr(
-        fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
-        shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
-    )
-    update_freq = (
-        cfg.optimization.update_freq[epoch_itr.epoch - 1]
-        if epoch_itr.epoch <= len(cfg.optimization.update_freq)
-        else cfg.optimization.update_freq[-1]
-    )
-    itr = iterators.GroupedIterator(itr, update_freq)
-    if cfg.common.tpu:
-        itr = utils.tpu_data_loader(itr)
-    progress = progress_bar.progress_bar(
-        itr,
-        log_format=cfg.common.log_format,
-        log_file=cfg.common.log_file,
-        log_interval=cfg.common.log_interval,
-        epoch=epoch_itr.epoch,
-        tensorboard_logdir=(
-            cfg.common.tensorboard_logdir
-            if distributed_utils.is_master(cfg.distributed_training)
-            else None
-        ),
-        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
-        wandb_project=(
-            cfg.common.wandb_project
-            if distributed_utils.is_master(cfg.distributed_training)
-            else None
-        ),
-        wandb_run_name=os.environ.get(
-            "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
-        ),
-        azureml_logging=(
-            cfg.common.azureml_logging
-            if distributed_utils.is_master(cfg.distributed_training)
-            else False
-        ),
-    )
-    progress.update_config(_flatten_config(cfg))
-
-    trainer.begin_epoch(epoch_itr.epoch)
-
-    valid_subsets = cfg.dataset.valid_subset.split(",")
-    should_stop = False
-    num_updates = trainer.get_num_updates()
-    logger.info("Start iterating over samples")
-    for i, samples in enumerate(progress):
-        with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function(
-            "train_step-%d" % i
-        ):
-            log_output = trainer.train_step(samples)
-
-        if log_output is not None:  # not OOM, overflow, ...
-            # log mid-epoch stats
-            num_updates = trainer.get_num_updates()
-            if num_updates % cfg.common.log_interval == 0:
-                stats = get_training_stats(metrics.get_smoothed_values("train_inner"))
-                progress.log(stats, tag="train_inner", step=num_updates)
-
-                # reset mid-epoch stats after each log interval
-                # the end-of-epoch stats will still be preserved
-                metrics.reset_meters("train_inner")
-
-        end_of_epoch = not itr.has_next()
-        valid_losses, should_stop = validate_and_save(
-            cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch
-        )
-
-        if should_stop:
-            break
-
-    # log end-of-epoch stats
-    logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch))
-    stats = get_training_stats(metrics.get_smoothed_values("train"))
-    progress.print(stats, tag="train", step=num_updates)
-
-    # reset epoch-level meters
-    metrics.reset_meters("train")
-    return valid_losses, should_stop
-
-
-def _flatten_config(cfg: DictConfig):
-    config = OmegaConf.to_container(cfg)
-    # remove any legacy Namespaces and replace with a single "args"
-    namespace = None
-    for k, v in list(config.items()):
-        if isinstance(v, argparse.Namespace):
-            namespace = v
-            del config[k]
-    if namespace is not None:
-        config["args"] = vars(namespace)
-    return config
-
-
-def validate_and_save(
-    cfg: DictConfig,
-    trainer: Trainer,
-    task: tasks.FairseqTask,
-    epoch_itr,
-    valid_subsets: List[str],
-    end_of_epoch: bool,
-) -> Tuple[List[Optional[float]], bool]:
-    num_updates = trainer.get_num_updates()
-    max_update = cfg.optimization.max_update or math.inf
-
-    # Stopping conditions (and an additional one based on validation loss later
-    # on)
-    should_stop = False
-    if num_updates >= max_update:
-        should_stop = True
-        logger.info(
-            f"Stopping training due to "
-            f"num_updates: {num_updates} >= max_update: {max_update}"
-        )
-
-    training_time_hours = trainer.cumulative_training_time() / (60 * 60)
-    if (
-        cfg.optimization.stop_time_hours > 0
-        and training_time_hours > cfg.optimization.stop_time_hours
-    ):
-        should_stop = True
-        logger.info(
-            f"Stopping training due to "
-            f"cumulative_training_time: {training_time_hours} > "
-            f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
-        )
-
-    do_save = (
-        (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0)
-        or should_stop
-        or (
-            cfg.checkpoint.save_interval_updates > 0
-            and num_updates > 0
-            and num_updates % cfg.checkpoint.save_interval_updates == 0
-            and num_updates >= cfg.dataset.validate_after_updates
-        )
-    )
-    do_validate = (
-        (not end_of_epoch and do_save)  # validate during mid-epoch saves
-        or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0)
-        or should_stop
-        or (
-            cfg.dataset.validate_interval_updates > 0
-            and num_updates > 0
-            and num_updates % cfg.dataset.validate_interval_updates == 0
-        )
-    ) and not cfg.dataset.disable_validation and num_updates >= cfg.dataset.validate_after_updates
-
-    # Validate
-    valid_losses = [None]
-    if do_validate:
-        valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets)
-
-    should_stop |= should_stop_early(cfg, valid_losses[0])
-
-    # Save checkpoint
-    if do_save or should_stop:
-        checkpoint_utils.save_checkpoint(
-            cfg.checkpoint, trainer, epoch_itr, valid_losses[0]
-        )
-
-    return valid_losses, should_stop
-
-
-def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
-    stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0)
-    return stats
-
-
-def validate(
-    cfg: DictConfig,
-    trainer: Trainer,
-    task: tasks.FairseqTask,
-    epoch_itr,
-    subsets: List[str],
-) -> List[Optional[float]]:
-    """Evaluate the model on the validation set(s) and return the losses."""
-
-    if cfg.dataset.fixed_validation_seed is not None:
-        # set fixed seed for every validation
-        utils.set_torch_seed(cfg.dataset.fixed_validation_seed)
-
-    trainer.begin_valid_epoch(epoch_itr.epoch)
-    valid_losses = []
-    for subset in subsets:
-        logger.info('begin validation on "{}" subset'.format(subset))
-
-        # Initialize data iterator
-        itr = trainer.get_valid_iterator(subset).next_epoch_itr(
-            shuffle=False, set_dataset_epoch=False  # use a fixed valid set
-        )
-        if cfg.common.tpu:
-            itr = utils.tpu_data_loader(itr)
-        progress = progress_bar.progress_bar(
-            itr,
-            log_format=cfg.common.log_format,
-            log_interval=cfg.common.log_interval,
-            epoch=epoch_itr.epoch,
-            prefix=f"valid on '{subset}' subset",
-            tensorboard_logdir=(
-                cfg.common.tensorboard_logdir
-                if distributed_utils.is_master(cfg.distributed_training)
-                else None
-            ),
-            default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
-            wandb_project=(
-                cfg.common.wandb_project
-                if distributed_utils.is_master(cfg.distributed_training)
-                else None
-            ),
-            wandb_run_name=os.environ.get(
-                "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
-            ),
-        )
-
-        # create a new root metrics aggregator so validation metrics
-        # don't pollute other aggregators (e.g., train meters)
-        with metrics.aggregate(new_root=True) as agg:
-            for i, sample in enumerate(progress):
-                if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps:
-                    break
-                trainer.valid_step(sample)
-
-        # log validation stats
-        stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values())
-
-        if hasattr(task, "post_validate"):
-            task.post_validate(trainer.get_model(), stats, agg)
-
-        progress.print(stats, tag=subset, step=trainer.get_num_updates())
-
-        valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
-    return valid_losses
-
-
-def get_valid_stats(
-    cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
-) -> Dict[str, Any]:
-    stats["num_updates"] = trainer.get_num_updates()
-    if hasattr(checkpoint_utils.save_checkpoint, "best"):
-        key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
-        best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
-        stats[key] = best_function(
-            checkpoint_utils.save_checkpoint.best,
-            stats[cfg.checkpoint.best_checkpoint_metric],
-        )
-    return stats
-
-
-def cli_main(
-    modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
-) -> None:
-    parser = options.get_training_parser()
-    args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
-
-    cfg = convert_namespace_to_omegaconf(args)
-
-    if cfg.common.use_plasma_view:
-        server = PlasmaStore(path=cfg.common.plasma_path)
-        logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}")
-
-    if args.profile:
-        with torch.cuda.profiler.profile():
-            with torch.autograd.profiler.emit_nvtx():
-                distributed_utils.call_main(cfg, main)
-    else:
-        distributed_utils.call_main(cfg, main)
-
-    # if cfg.common.use_plasma_view:
-    #     server.server.kill()
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/text/cleaners.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/text/cleaners.py
deleted file mode 100644
index a7d4e029baa436e88e4d68090e886afdd998a68d..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/text/cleaners.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import re
-
-from unidecode import unidecode
-from .numbers import normalize_numbers
-
-
-
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r"\s+")
-
-def lowercase(text):
-    return text.lower()
-
-def collapse_whitespace(text):
-    return re.sub(_whitespace_re, " ", text)
-
-def basic_indic_cleaners(text):
-    """Basic pipeline that collapses whitespace without transliteration."""
-    text = collapse_whitespace(text)
-    return text
-
-
-def english_cleaner(text):
-    text = text.lower().replace('‘','\'').replace('’','\'')
-    return text
-
-
-def lowercase(text):
-    return text.lower()
-
-def convert_to_ascii(text):
-    return unidecode(text)
-
-def expand_numbers(text):
-    return normalize_numbers(text)
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'missus'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-  ('pvt', 'private'),
-  ('rs', 'Rupees')
-]]
-
-
-
-
-
-
-def english_cleaners(text):
-    '''Pipeline for English text, including number and abbreviation expansion.'''
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = expand_numbers(text)
-    text = expand_abbreviations(text)
-    text = collapse_whitespace(text)
-    return text
diff --git a/spaces/HopeMan/Claude/README.md b/spaces/HopeMan/Claude/README.md
deleted file mode 100644
index ca3553344fa3a379d7686165233589684ae6217a..0000000000000000000000000000000000000000
--- a/spaces/HopeMan/Claude/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Claude
-emoji: 📚
-colorFrom: gray
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Hydrangea/computing/README.md b/spaces/Hydrangea/computing/README.md
deleted file mode 100644
index f18d50955c94fcaff3272a067cb4837a66637c9c..0000000000000000000000000000000000000000
--- a/spaces/Hydrangea/computing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Computing
-emoji: 🐨
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ICML2022/OFA/fairseq/examples/adaptive_span/adaptive_span_loss.py b/spaces/ICML2022/OFA/fairseq/examples/adaptive_span/adaptive_span_loss.py
deleted file mode 100644
index 056245807e5f8d313a8ad5be68aea4e285f4f580..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/adaptive_span/adaptive_span_loss.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass
-
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import register_criterion
-from fairseq.criterions.cross_entropy import CrossEntropyCriterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class AdaptiveSpanCriterionConfig(FairseqDataclass):
-    sentence_avg: bool = II("optimization.sentence_avg")
-
-
-@register_criterion("adaptive_span_loss", dataclass=AdaptiveSpanCriterionConfig)
-class AdaptiveSpanCriterion(CrossEntropyCriterion):
-    def __init__(self, task, sentence_avg):
-        super().__init__(task, sentence_avg)
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss here is summed, different from the adaptive span code
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        net_output = model(**sample["net_input"])
-        loss, aux_loss, avg_span, max_span = self.compute_loss(
-            model, net_output, sample, reduce=reduce
-        )
-        sample_size = (
-            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
-        )
-        loss /= sample_size
-        total_loss = loss + aux_loss
-        sample_size = 1
-
-        logging_output = {
-            "loss": loss.data,
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-            "total_loss": total_loss.data,
-            "avg_span": avg_span * sample_size,
-            "max_span": max_span * sample_size,
-        }
-        return total_loss, sample_size, logging_output
-
-    def compute_loss(self, model, net_output, sample, reduce=True):
-        loss, _ = super().compute_loss(model, net_output, sample, reduce)
-        aux_loss = model.get_aux_loss()
-        avg_span = model.get_current_avg_span()
-        max_span = model.get_current_max_span()
-        return loss, aux_loss, avg_span, max_span
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-        total_loss_sum = sum(log.get("total_loss", 0) for log in logging_outputs)
-        avg_span_sum = sum(log.get("avg_span", 0) for log in logging_outputs)
-        max_span_sum = sum(log.get("max_span", 0) for log in logging_outputs)
-
-        # we divide by log(2) to convert the loss from base e to base 2
-        metrics.log_scalar(
-            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
-        )
-        metrics.log_scalar("avg_span", avg_span_sum / sample_size, sample_size, round=3)
-        metrics.log_scalar("max_span", max_span_sum / sample_size, sample_size, round=3)
-        # total loss contains the L1 norm on adaptive-span
-        metrics.log_scalar(
-            "total_loss",
-            total_loss_sum / sample_size / math.log(2),
-            sample_size,
-            round=3,
-        )
-        if sample_size != ntokens:
-            metrics.log_scalar(
-                "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
-            )
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
-            )
-        else:
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
-            )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/Illumotion/Koboldcpp/Makefile b/spaces/Illumotion/Koboldcpp/Makefile
deleted file mode 100644
index 695a943eafb3708e79a8d0f516f7c9e05878db75..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/Makefile
+++ /dev/null
@@ -1,506 +0,0 @@
-default: koboldcpp_default koboldcpp_failsafe koboldcpp_openblas koboldcpp_noavx2 koboldcpp_clblast koboldcpp_cublas koboldcpp_hipblas
-tools: quantize_gpt2 quantize_gptj quantize_llama quantize_neox quantize_mpt
-dev: koboldcpp_openblas
-dev2: koboldcpp_clblast
-
-
-ifndef UNAME_S
-UNAME_S := $(shell uname -s)
-endif
-
-ifndef UNAME_P
-UNAME_P := $(shell uname -p)
-endif
-
-ifndef UNAME_M
-UNAME_M := $(shell uname -m)
-endif
-
-ifneq ($(shell grep -e "Arch Linux" -e "ID_LIKE=arch" /etc/os-release 2>/dev/null),)
-ARCH_ADD = -lcblas
-endif
-
-
-# Mac OS + Arm can report x86_64
-# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
-ifeq ($(UNAME_S),Darwin)
-	ifneq ($(UNAME_P),arm)
-		SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
-		ifeq ($(SYSCTL_M),1)
-			# UNAME_P := arm
-			# UNAME_M := arm64
-			warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
-		endif
-	endif
-endif
-
-#
-# Compile flags
-#
-
-# keep standard at C11 and C++11
-CFLAGS   = -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE
-CXXFLAGS = -I. -I./common -I./include -I./include/CL -I./otherarch -I./otherarch/tools -Ofast -DNDEBUG -std=c++11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE
-LDFLAGS  =
-
-ifndef LLAMA_NO_K_QUANTS
-CFLAGS   += -DGGML_USE_K_QUANTS
-CXXFLAGS += -DGGML_USE_K_QUANTS
-endif
-
-# these are used on windows, to build some libraries with extra old device compatibility
-SIMPLECFLAGS =
-FULLCFLAGS =
-NONECFLAGS =
-
-OPENBLAS_FLAGS = -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
-CLBLAST_FLAGS = -DGGML_USE_CLBLAST
-FAILSAFE_FLAGS = -DUSE_FAILSAFE
-ifdef LLAMA_CUBLAS
-	CUBLAS_FLAGS = -DGGML_USE_CUBLAS
-else
-	CUBLAS_FLAGS =
-endif
-CUBLASLD_FLAGS =
-CUBLAS_OBJS =
-
-#lets try enabling everything
-CFLAGS   += -pthread -s
-CXXFLAGS += -pthread -s -Wno-multichar -Wno-write-strings
-
-# OS specific
-# TODO: support Windows
-ifeq ($(UNAME_S),Linux)
-	CFLAGS   += -pthread
-	CXXFLAGS += -pthread
-endif
-
-ifeq ($(UNAME_S),Darwin)
-	CFLAGS   += -pthread
-	CXXFLAGS += -pthread
-endif
-ifeq ($(UNAME_S),FreeBSD)
-	CFLAGS   += -pthread
-	CXXFLAGS += -pthread
-endif
-ifeq ($(UNAME_S),NetBSD)
-	CFLAGS   += -pthread
-	CXXFLAGS += -pthread
-endif
-ifeq ($(UNAME_S),OpenBSD)
-	CFLAGS   += -pthread
-	CXXFLAGS += -pthread
-endif
-ifeq ($(UNAME_S),Haiku)
-	CFLAGS   += -pthread
-	CXXFLAGS += -pthread
-endif
-
-ifdef LLAMA_GPROF
-	CFLAGS   += -pg
-	CXXFLAGS += -pg
-endif
-ifdef LLAMA_PERF
-	CFLAGS   += -DGGML_PERF
-	CXXFLAGS += -DGGML_PERF
-endif
-
-# Architecture specific
-# TODO: probably these flags need to be tweaked on some architectures
-#       feel free to update the Makefile for your architecture and send a pull request or issue
-ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
-	# Use all CPU extensions that are available:
-# old library NEEDS mf16c to work. so we must build with it. new one doesnt
-	ifeq ($(OS),Windows_NT)
-		CFLAGS +=
-		NONECFLAGS +=
-# -mno-sse3
-		SIMPLECFLAGS += -mavx -msse3
-		FULLCFLAGS += -mavx2 -msse3 -mfma -mf16c -mavx
-	else
-# if not on windows, they are clearly building it themselves, so lets just use whatever is supported
-		CFLAGS += -march=native -mtune=native
-	endif
-endif
-ifneq ($(filter ppc64%,$(UNAME_M)),)
-	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
-	ifneq (,$(findstring POWER9,$(POWER9_M)))
-		CFLAGS   += -mcpu=power9
-		CXXFLAGS += -mcpu=power9
-	endif
-	# Require c++23's std::byteswap for big-endian support.
-	ifeq ($(UNAME_M),ppc64)
-		CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
-	endif
-endif
-ifndef LLAMA_NO_ACCELERATE
-	# Mac M1 - include Accelerate framework.
-	# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time).
-	ifeq ($(UNAME_S),Darwin)
-		CFLAGS  += -DGGML_USE_ACCELERATE
-		LDFLAGS += -framework Accelerate
-	endif
-endif
-
-# it is recommended to use the CMAKE file to build for cublas if you can - will likely work better
-ifdef LLAMA_CUBLAS
-	CUBLAS_FLAGS = -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
-	CUBLASLD_FLAGS = -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
-	CUBLAS_OBJS = ggml-cuda.o ggml_v2-cuda.o ggml_v2-cuda-legacy.o
-	NVCC      = nvcc
-	NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
-ifdef CUDA_DOCKER_ARCH
-	NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
-else
-	NVCCFLAGS += -arch=native
-endif # CUDA_DOCKER_ARCH
-ifdef LLAMA_CUDA_FORCE_DMMV
-	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
-endif # LLAMA_CUDA_FORCE_DMMV
-ifdef LLAMA_CUDA_DMMV_X
-	NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
-else
-	NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
-endif # LLAMA_CUDA_DMMV_X
-ifdef LLAMA_CUDA_MMV_Y
-	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
-else ifdef LLAMA_CUDA_DMMV_Y
-	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
-else
-	NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
-endif # LLAMA_CUDA_MMV_Y
-ifdef LLAMA_CUDA_F16
-	NVCCFLAGS += -DGGML_CUDA_F16
-endif # LLAMA_CUDA_F16
-ifdef LLAMA_CUDA_DMMV_F16
-	NVCCFLAGS += -DGGML_CUDA_F16
-endif # LLAMA_CUDA_DMMV_F16
-ifdef LLAMA_CUDA_KQUANTS_ITER
-	NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
-else
-	NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
-endif
-ifdef LLAMA_CUDA_MMQ_Y
-	NVCCFLAGS += -DGGML_CUDA_MMQ_Y=$(LLAMA_CUDA_MMQ_Y)
-else
-	NVCCFLAGS += -DGGML_CUDA_MMQ_Y=64
-endif # LLAMA_CUDA_MMQ_Y
-#ifdef LLAMA_CUDA_CUBLAS
-#	NVCCFLAGS += -DGGML_CUDA_CUBLAS
-#endif # LLAMA_CUDA_CUBLAS
-ifdef LLAMA_CUDA_CCBIN
-	NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
-endif
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
-	$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
-ggml_v2-cuda.o: otherarch/ggml_v2-cuda.cu otherarch/ggml_v2-cuda.h
-	$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
-ggml_v2-cuda-legacy.o: otherarch/ggml_v2-cuda-legacy.cu otherarch/ggml_v2-cuda-legacy.h
-	$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
-endif # LLAMA_CUBLAS
-
-ifdef LLAMA_HIPBLAS
-	ROCM_PATH	?= /opt/rocm
-	CC         := $(ROCM_PATH)/llvm/bin/clang
-	CXX        := $(ROCM_PATH)/llvm/bin/clang++
-	GPU_TARGETS ?= gfx803 gfx900 gfx906 gfx908 gfx90a gfx1030 gfx1100 $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
-	LLAMA_CUDA_DMMV_X ?= 32
-	LLAMA_CUDA_MMV_Y ?= 2
-	LLAMA_CUDA_KQUANTS_ITER ?= 2
-	HIPFLAGS   += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS $(shell $(ROCM_PATH)/bin/hipconfig -C)
-ifdef LLAMA_CUDA_FORCE_DMMV
-	HIPFLAGS 	+= -DGGML_CUDA_FORCE_DMMV
-endif # LLAMA_CUDA_FORCE_DMMV
-	HIPLDFLAGS    += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib -lhipblas -lamdhip64 -lrocblas
-	HIP_OBJS       += ggml-cuda.o ggml_v2-cuda.o ggml_v2-cuda-legacy.o
-ggml-cuda.o: HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) \
-						-DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) \
-                        -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) \
-                        -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
-ggml_v2-cuda.o: HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) \
-						-DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) \
-                        -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) \
-                        -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
-ggml_v2-cuda-legacy.o: HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) \
-						-DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) \
-                        -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) \
-                        -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
-	$(CXX) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
-ggml_v2-cuda.o: otherarch/ggml_v2-cuda.cu otherarch/ggml_v2-cuda.h
-	$(CXX) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
-ggml_v2-cuda-legacy.o: otherarch/ggml_v2-cuda-legacy.cu otherarch/ggml_v2-cuda-legacy.h
-	$(CXX) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
-endif # LLAMA_HIPBLAS
-
-
-
-ifdef LLAMA_METAL
-	CFLAGS   += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
-	CXXFLAGS += -DGGML_USE_METAL
-	LDFLAGS  += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
-	OBJS     += ggml-metal.o
-
-ggml-metal.o: ggml-metal.m ggml-metal.h
-	$(CC) $(CFLAGS) -c $< -o $@
-endif # LLAMA_METAL
-
-ifneq ($(filter aarch64%,$(UNAME_M)),)
-	# Apple M1, M2, etc.
-	# Raspberry Pi 3, 4, Zero 2 (64-bit)
-	CFLAGS +=
-	CXXFLAGS +=
-endif
-ifneq ($(filter armv6%,$(UNAME_M)),)
-	# Raspberry Pi 1, Zero
-	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
-endif
-ifneq ($(filter armv7%,$(UNAME_M)),)
-	# Raspberry Pi 2
-	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
-endif
-ifneq ($(filter armv8%,$(UNAME_M)),)
-	# Raspberry Pi 3, 4, Zero 2 (32-bit)
-	CFLAGS += -mfp16-format=ieee -mno-unaligned-access
-endif
-
-CCV := $(shell $(CC) --version | head -n 1)
-CXXV := $(shell $(CXX) --version | head -n 1)
-
-DEFAULT_BUILD =
-FAILSAFE_BUILD =
-OPENBLAS_BUILD =
-NOAVX2_BUILD =
-CLBLAST_BUILD =
-CUBLAS_BUILD =
-HIPBLAS_BUILD =
-
-ifeq ($(OS),Windows_NT)
-	DEFAULT_BUILD = $(CXX) $(CXXFLAGS)  $^ -shared -o $@.dll $(LDFLAGS)
-	FAILSAFE_BUILD = $(CXX) $(CXXFLAGS) $^ -shared -o $@.dll $(LDFLAGS)
-	OPENBLAS_BUILD = $(CXX) $(CXXFLAGS) $^ lib/libopenblas.lib -shared -o $@.dll $(LDFLAGS)
-	NOAVX2_BUILD = $(CXX) $(CXXFLAGS) $^ -shared -o $@.dll $(LDFLAGS)
-	CLBLAST_BUILD = $(CXX) $(CXXFLAGS) $^ lib/OpenCL.lib lib/clblast.lib -shared -o $@.dll $(LDFLAGS)
-
-	ifdef LLAMA_CUBLAS
-		CUBLAS_BUILD = $(CXX) $(CXXFLAGS) $(CUBLAS_FLAGS) $^ -shared -o $@.dll $(CUBLASLD_FLAGS) $(LDFLAGS)
-	endif
-	ifdef LLAMA_HIPBLAS
-		HIPBLAS_BUILD = $(CXX) $(CXXFLAGS) $(HIPFLAGS) $^ -shared -o $@.dll $(HIPLDFLAGS) $(LDFLAGS)
-	endif
-else
-	DEFAULT_BUILD = $(CXX) $(CXXFLAGS)  $^ -shared -o $@.so $(LDFLAGS)
-
-	ifdef LLAMA_OPENBLAS
-	OPENBLAS_BUILD = $(CXX) $(CXXFLAGS) $^ $(ARCH_ADD) -lopenblas -shared -o $@.so $(LDFLAGS)
-	endif
-	ifdef LLAMA_CLBLAST
-		ifeq ($(UNAME_S),Darwin)
-			CLBLAST_BUILD = $(CXX) $(CXXFLAGS) $^ -lclblast -framework OpenCL $(ARCH_ADD) -lopenblas -shared -o $@.so $(LDFLAGS)
-		else
-			CLBLAST_BUILD = $(CXX) $(CXXFLAGS) $^ -lclblast -lOpenCL $(ARCH_ADD) -lopenblas -shared -o $@.so $(LDFLAGS)
-		endif
-	endif
-	ifdef LLAMA_CUBLAS
-		CUBLAS_BUILD = $(CXX) $(CXXFLAGS) $(CUBLAS_FLAGS) $^ -shared -o $@.so $(CUBLASLD_FLAGS) $(LDFLAGS)
-	endif
-	ifdef LLAMA_HIPBLAS
-		HIPBLAS_BUILD = $(CXX) $(CXXFLAGS) $(HIPFLAGS) $^ -shared -o $@.so $(HIPLDFLAGS) $(LDFLAGS)
-	endif
-
-	ifndef LLAMA_OPENBLAS
-	ifndef LLAMA_CLBLAST
-	ifndef LLAMA_CUBLAS
-	ifndef LLAMA_HIPBLAS
-	OPENBLAS_BUILD = @echo 'Your OS $(OS) does not appear to be Windows. For faster speeds, install and link a BLAS library. Set LLAMA_OPENBLAS=1 to compile with OpenBLAS support or LLAMA_CLBLAST=1 to compile with ClBlast support. This is just a reminder, not an error.'
-	endif
-	endif
-	endif
-	endif
-endif
-
-
-
-#
-# Print build information
-#
-
-$(info I llama.cpp build info: )
-$(info I UNAME_S:  $(UNAME_S))
-$(info I UNAME_P:  $(UNAME_P))
-$(info I UNAME_M:  $(UNAME_M))
-$(info I CFLAGS:   $(CFLAGS))
-$(info I CXXFLAGS: $(CXXFLAGS))
-$(info I LDFLAGS:  $(LDFLAGS))
-$(info I CC:       $(CCV))
-$(info I CXX:      $(CXXV))
-$(info )
-
-#
-# Build library
-#
-
-ggml.o: ggml.c ggml.h ggml-cuda.h k_quants.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) -c $< -o $@
-ggml_openblas.o: ggml.c ggml.h ggml-cuda.h k_quants.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(OPENBLAS_FLAGS) -c $< -o $@
-ggml_failsafe.o: ggml.c ggml.h ggml-cuda.h k_quants.h
-	$(CC)  $(CFLAGS) $(NONECFLAGS) -c $< -o $@
-ggml_noavx2.o: ggml.c ggml.h ggml-cuda.h k_quants.h
-	$(CC)  $(CFLAGS) $(SIMPLECFLAGS) -c $< -o $@
-ggml_clblast.o: ggml.c ggml.h ggml-cuda.h k_quants.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(CLBLAST_FLAGS) -c $< -o $@
-ggml_cublas.o: ggml.c ggml.h ggml-cuda.h k_quants.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(CUBLAS_FLAGS) $(HIPFLAGS) -c $< -o $@
-
-#quants K
-KQ1 =
-KQ2 =
-KQ3 =
-ifndef LLAMA_NO_K_QUANTS
-KQ1 = k_quants.o
-KQ2 = k_quants_noavx2.o
-KQ3 = k_quants_failsafe.o
-k_quants.o: k_quants.c k_quants.h ggml.h ggml-cuda.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) -c $< -o $@
-k_quants_noavx2.o: k_quants.c k_quants.h ggml.h ggml-cuda.h
-	$(CC)  $(CFLAGS) $(SIMPLECFLAGS) -c $< -o $@
-k_quants_failsafe.o: k_quants.c k_quants.h ggml.h ggml-cuda.h
-	$(CC)  $(CFLAGS) $(NONECFLAGS) -c $< -o $@
-endif # LLAMA_NO_K_QUANTS
-
-#there's no intrinsics or special gpu ops used here, so we can have a universal object
-ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
-	$(CC)  $(CFLAGS) -c $< -o $@
-
-#version 2 libs
-ggml_v2.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) -c $< -o $@
-ggml_v2_openblas.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(OPENBLAS_FLAGS) -c $< -o $@
-ggml_v2_failsafe.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
-	$(CC)  $(CFLAGS) $(NONECFLAGS) -c $< -o $@
-ggml_v2_noavx2.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
-	$(CC)  $(CFLAGS) $(SIMPLECFLAGS) -c $< -o $@
-ggml_v2_clblast.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(CLBLAST_FLAGS) -c $< -o $@
-ggml_v2_cublas.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(CUBLAS_FLAGS) $(HIPFLAGS) -c $< -o $@
-
-#extreme old version compat
-ggml_v1.o: otherarch/ggml_v1.c otherarch/ggml_v1.h
-	$(CC)  $(CFLAGS) $(FULLCFLAGS) -c $< -o $@
-ggml_v1_failsafe.o: otherarch/ggml_v1.c otherarch/ggml_v1.h
-	$(CC)  $(CFLAGS) $(NONECFLAGS) -c $< -o $@
-
-#opencl
-ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
-	$(CXX) $(CXXFLAGS) $(CLBLAST_FLAGS) -c $< -o $@
-ggml_v2-opencl.o: otherarch/ggml_v2-opencl.cpp otherarch/ggml_v2-opencl.h
-	$(CXX) $(CXXFLAGS) $(CLBLAST_FLAGS) -c $< -o $@
-ggml_v2-opencl-legacy.o: otherarch/ggml_v2-opencl-legacy.c otherarch/ggml_v2-opencl-legacy.h
-	$(CC) $(CFLAGS) -c $< -o $@
-
-# intermediate objects
-llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h otherarch/llama-util.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-common.o: common/common.cpp common/common.h common/log.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-console.o: common/console.cpp common/console.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-expose.o: expose.cpp expose.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-
-# idiotic "for easier compilation"
-GPTTYPE_ADAPTER = gpttype_adapter.cpp otherarch/llama_v2.cpp otherarch/llama_v3.cpp llama.cpp otherarch/utils.cpp otherarch/gptj_v1.cpp otherarch/gptj_v2.cpp otherarch/gptj_v3.cpp otherarch/gpt2_v1.cpp otherarch/gpt2_v2.cpp otherarch/gpt2_v3.cpp otherarch/rwkv_v2.cpp otherarch/rwkv_v3.cpp otherarch/neox_v2.cpp otherarch/neox_v3.cpp otherarch/mpt_v3.cpp ggml.h ggml-cuda.h llama.h otherarch/llama-util.h
-gpttype_adapter_failsafe.o: $(GPTTYPE_ADAPTER)
-	$(CXX) $(CXXFLAGS) $(FAILSAFE_FLAGS) -c $< -o $@
-gpttype_adapter.o: $(GPTTYPE_ADAPTER)
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-gpttype_adapter_clblast.o: $(GPTTYPE_ADAPTER)
-	$(CXX) $(CXXFLAGS) $(CLBLAST_FLAGS) -c $< -o $@
-gpttype_adapter_cublas.o: $(GPTTYPE_ADAPTER)
-	$(CXX) $(CXXFLAGS) $(CUBLAS_FLAGS) $(HIPFLAGS) -c $< -o $@
-
-clean:
-	rm -vf *.o main quantize_llama quantize_gpt2 quantize_gptj quantize_neox quantize_mpt quantize-stats perplexity embedding benchmark-matmult save-load-state gguf gguf.exe main.exe quantize_llama.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe quantize_mpt.exe koboldcpp_default.dll koboldcpp_openblas.dll koboldcpp_failsafe.dll koboldcpp_noavx2.dll koboldcpp_clblast.dll koboldcpp_cublas.dll koboldcpp_hipblas.dll koboldcpp_default.so koboldcpp_openblas.so koboldcpp_failsafe.so koboldcpp_noavx2.so koboldcpp_clblast.so koboldcpp_cublas.so koboldcpp_hipblas.so
-
-main: examples/main/main.cpp build-info.h ggml.o $(KQ1) ggml-alloc.o llama.o common.o console.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-	@echo
-	@echo '====  Run ./main -h for help.  ===='
-	@echo
-
-gguf: examples/gguf/gguf.cpp build-info.h ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-
-
-#generated libraries
-koboldcpp_default: ggml.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter.o $(KQ1) ggml-alloc.o grammar-parser.o $(OBJS)
-	$(DEFAULT_BUILD)
-
-ifdef OPENBLAS_BUILD
-koboldcpp_openblas: ggml_openblas.o ggml_v2_openblas.o ggml_v1.o expose.o common.o gpttype_adapter.o $(KQ1) ggml-alloc.o grammar-parser.o $(OBJS)
-	$(OPENBLAS_BUILD)
-else
-koboldcpp_openblas:
-	$(DONOTHING)
-endif
-
-ifdef FAILSAFE_BUILD
-koboldcpp_failsafe: ggml_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o $(KQ3) ggml-alloc.o grammar-parser.o $(OBJS)
-	$(FAILSAFE_BUILD)
-else
-koboldcpp_failsafe:
-	$(DONOTHING)
-endif
-
-ifdef NOAVX2_BUILD
-koboldcpp_noavx2: ggml_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o $(KQ2) ggml-alloc.o grammar-parser.o $(OBJS)
-	$(NOAVX2_BUILD)
-else
-koboldcpp_noavx2:
-	$(DONOTHING)
-endif
-
-ifdef CLBLAST_BUILD
-koboldcpp_clblast: ggml_clblast.o ggml_v2_clblast.o ggml_v1.o expose.o common.o gpttype_adapter_clblast.o ggml-opencl.o ggml_v2-opencl.o ggml_v2-opencl-legacy.o $(KQ1) ggml-alloc.o grammar-parser.o $(OBJS)
-	$(CLBLAST_BUILD)
-else
-koboldcpp_clblast:
-	$(DONOTHING)
-endif
-
-ifdef CUBLAS_BUILD
-koboldcpp_cublas: ggml_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o $(KQ1) ggml-alloc.o grammar-parser.o $(CUBLAS_OBJS) $(OBJS)
-	$(CUBLAS_BUILD)
-else
-koboldcpp_cublas:
-	$(DONOTHING)
-endif
-
-ifdef HIPBLAS_BUILD
-koboldcpp_hipblas: ggml_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o $(KQ1) ggml-alloc.o grammar-parser.o $(HIP_OBJS) $(OBJS)
-	$(HIPBLAS_BUILD)
-else
-koboldcpp_hipblas:
-	$(DONOTHING)
-endif
-
-# tools
-quantize_llama: examples/quantize/quantize.cpp ggml.o llama.o $(KQ1) ggml-alloc.o
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gptj: ggml.o llama.o $(KQ1) ggml-alloc.o otherarch/tools/gptj_quantize.cpp otherarch/tools/common-ggml.cpp
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gpt2: ggml.o llama.o $(KQ1) ggml-alloc.o otherarch/tools/gpt2_quantize.cpp otherarch/tools/common-ggml.cpp
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_neox: ggml.o llama.o $(KQ1) ggml-alloc.o otherarch/tools/neox_quantize.cpp otherarch/tools/common-ggml.cpp
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_mpt: ggml.o llama.o $(KQ1) ggml-alloc.o otherarch/tools/mpt_quantize.cpp otherarch/tools/common-ggml.cpp
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-
-
-build-info.h:
-	$(DONOTHING)
diff --git a/spaces/Jamel887/Rvc-tio887/lib/infer_pack/models_onnx.py b/spaces/Jamel887/Rvc-tio887/lib/infer_pack/models_onnx.py
deleted file mode 100644
index 963e67b29f828e9fdd096397952054fe77cf3d10..0000000000000000000000000000000000000000
--- a/spaces/Jamel887/Rvc-tio887/lib/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,819 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMsNSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        version,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        self.speaker_map = None
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/Joabutt/test/index.html b/spaces/Joabutt/test/index.html
deleted file mode 100644
index 918e851d9dd1baf9e4fb4f067fd979d432472161..0000000000000000000000000000000000000000
--- a/spaces/Joabutt/test/index.html
+++ /dev/null
@@ -1,24 +0,0 @@
-<!DOCTYPE html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>
-				You can modify this app directly by editing <i>index.html</i> in the
-				Files and versions tab.
-			</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank"
-					>Spaces documentation</a
-				>.
-			</p>
-		</div>
-	</body>
-</html>
diff --git a/spaces/Kreaols/ChuanhuChatGPT/modules/overwrites.py b/spaces/Kreaols/ChuanhuChatGPT/modules/overwrites.py
deleted file mode 100644
index e029f4a50285c64dcb286a34cb1c3b2680880e05..0000000000000000000000000000000000000000
--- a/spaces/Kreaols/ChuanhuChatGPT/modules/overwrites.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from __future__ import annotations
-import logging
-
-from typing import List, Tuple
-from gradio_client import utils as client_utils
-from gradio import utils
-import inspect
-
-from modules.presets import *
-from modules.index_func import *
-
-
-def postprocess(
-        self,
-        y: List[List[str | Tuple[str] | Tuple[str, str] | None] | Tuple],
-    ) -> List[List[str | Dict | None]]:
-        """
-        Parameters:
-            y: List of lists representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.  It can also be a tuple whose first element is a string filepath or URL to an image/video/audio, and second (optional) element is the alt text, in which case the media file is displayed. It can also be None, in which case that message is not displayed.
-        Returns:
-            List of lists representing the message and response. Each message and response will be a string of HTML, or a dictionary with media information. Or None if the message is not to be displayed.
-        """
-        if y is None:
-            return []
-        processed_messages = []
-        for message_pair in y:
-            assert isinstance(
-                message_pair, (tuple, list)
-            ), f"Expected a list of lists or list of tuples. Received: {message_pair}"
-            assert (
-                len(message_pair) == 2
-            ), f"Expected a list of lists of length 2 or list of tuples of length 2. Received: {message_pair}"
-
-            processed_messages.append(
-                [
-                    self._postprocess_chat_messages(message_pair[0], "user"),
-                    self._postprocess_chat_messages(message_pair[1], "bot"),
-                ]
-            )
-        return processed_messages
-
-def postprocess_chat_messages(
-        self, chat_message: str | tuple | list | None, role: str
-    ) -> str | dict | None:
-        if chat_message is None:
-            return None
-        elif isinstance(chat_message, (tuple, list)):
-            file_uri = chat_message[0]
-            if utils.validate_url(file_uri):
-                filepath = file_uri
-            else:
-                filepath = self.make_temp_copy_if_needed(file_uri)
-
-            mime_type = client_utils.get_mimetype(filepath)
-            return {
-                "name": filepath,
-                "mime_type": mime_type,
-                "alt_text": chat_message[1] if len(chat_message) > 1 else None,
-                "data": None,  # These last two fields are filled in by the frontend
-                "is_file": True,
-            }
-        elif isinstance(chat_message, str):
-            # chat_message = inspect.cleandoc(chat_message)
-            # escape html spaces
-            # chat_message = chat_message.replace(" ", "&nbsp;")
-            if role == "bot":
-                chat_message = convert_bot_before_marked(chat_message)
-            elif role == "user":
-                chat_message = convert_user_before_marked(chat_message)
-            return chat_message
-        else:
-            raise ValueError(f"Invalid message for Chatbot component: {chat_message}")
-
-with open("./assets/custom.js", "r", encoding="utf-8") as f, \
-    open("./assets/external-scripts.js", "r", encoding="utf-8") as f1:
-    customJS = f.read()
-    externalScripts = f1.read()
-
-
-def reload_javascript():
-    print("Reloading javascript...")
-    js = f'<script>{customJS}</script><script async>{externalScripts}</script>'
-    # if render_latex:
-    #     js += """\"""
-    def template_response(*args, **kwargs):
-        res = GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gr.routes.templates.TemplateResponse = template_response
-
-GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse
\ No newline at end of file
diff --git a/spaces/KyanChen/RSPrompter/mmdet/engine/schedulers/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/engine/schedulers/__init__.py
deleted file mode 100644
index 01261646fa8255c643e86ba0517019760a50d387..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/engine/schedulers/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .quadratic_warmup import (QuadraticWarmupLR, QuadraticWarmupMomentum,
-                               QuadraticWarmupParamScheduler)
-
-__all__ = [
-    'QuadraticWarmupParamScheduler', 'QuadraticWarmupMomentum',
-    'QuadraticWarmupLR'
-]
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/faster_rcnn.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/faster_rcnn.py
deleted file mode 100644
index 36109e3200a2d8e7d8a1032f7028e47a7699fb6a..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/faster_rcnn.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.registry import MODELS
-from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
-from .two_stage import TwoStageDetector
-
-
-@MODELS.register_module()
-class FasterRCNN(TwoStageDetector):
-    """Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
-
-    def __init__(self,
-                 backbone: ConfigType,
-                 rpn_head: ConfigType,
-                 roi_head: ConfigType,
-                 train_cfg: ConfigType,
-                 test_cfg: ConfigType,
-                 neck: OptConfigType = None,
-                 data_preprocessor: OptConfigType = None,
-                 init_cfg: OptMultiConfig = None) -> None:
-        super().__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            init_cfg=init_cfg,
-            data_preprocessor=data_preprocessor)
diff --git a/spaces/LaynzKunz/Model-RCV/vc_infer_pipeline.py b/spaces/LaynzKunz/Model-RCV/vc_infer_pipeline.py
deleted file mode 100644
index 167dc0d4ac25f75e0b1ce96cab5688b51b349ee7..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Model-RCV/vc_infer_pipeline.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import numpy as np, parselmouth, torch, pdb, sys, os
-from time import time as ttime
-import torch.nn.functional as F
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
-from scipy import signal
-from functools import lru_cache
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class VC(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        inp_f0=None,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-        elif f0_method == "crepe":
-            model = "full"
-            # Pick a batch size that doesn't cause memory errors on your gpu
-            batch_size = 512
-            # Compute pitch using first gpu
-            audio = torch.tensor(np.copy(x))[None].float()
-            f0, pd = torchcrepe.predict(
-                audio,
-                self.sr,
-                self.window,
-                f0_min,
-                f0_max,
-                model,
-                batch_size=batch_size,
-                device=self.device,
-                return_periodicity=True,
-            )
-            pd = torchcrepe.filter.median(pd, 3)
-            f0 = torchcrepe.filter.mean(f0, 3)
-            f0[pd < 0.1] = 0
-            f0 = f0[0].cpu().numpy()
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                print("loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "rmvpe.pt", is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-        protect,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = feats.clone()
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch != None and pitchf != None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
-                0, 2, 1
-            )
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch != None and pitchf != None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        # file_big_npy,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=None,
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps":
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                        protect,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                    protect,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
\ No newline at end of file
diff --git a/spaces/LeeroyVonJenkins/hard-hat-detection/README.md b/spaces/LeeroyVonJenkins/hard-hat-detection/README.md
deleted file mode 100644
index 3191a36ab7677823a09aae4ae68d64d268db4200..0000000000000000000000000000000000000000
--- a/spaces/LeeroyVonJenkins/hard-hat-detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hard Hat Detection
-emoji: ⚡
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/LeoLeoLeo1/ChuanhuChatGPT/chatgpt - macOS.command b/spaces/LeoLeoLeo1/ChuanhuChatGPT/chatgpt - macOS.command
deleted file mode 100644
index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000
--- a/spaces/LeoLeoLeo1/ChuanhuChatGPT/chatgpt - macOS.command	
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.
\ No newline at end of file
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/studies/contrib/fractal.py b/spaces/Lianjd/stock_dashboard/backtrader/studies/contrib/fractal.py
deleted file mode 100644
index c18714236ff1cecb4e1966d466073e1e5476d614..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/studies/contrib/fractal.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-# (based on backtrader from Daniel Rodriguez)
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-
-import backtrader as bt
-
-
-__all__ = ['Fractal']
-
-
-class Fractal(bt.ind.PeriodN):
-    '''
-    References:
-        [Ref 1] http://www.investopedia.com/articles/trading/06/fractals.asp
-
-    '''
-    lines = ('fractal_bearish', 'fractal_bullish')
-
-    plotinfo = dict(subplot=False, plotlinelabels=False, plot=True)
-
-    plotlines = dict(
-        fractal_bearish=dict(marker='^', markersize=4.0, color='lightblue',
-                             fillstyle='full', ls=''),
-        fractal_bullish=dict(marker='v', markersize=4.0, color='lightblue',
-                             fillstyle='full', ls='')
-    )
-    params = (
-        ('period', 5),
-        ('bardist', 0.015),  # distance to max/min in absolute perc
-        ('shift_to_potential_fractal', 2),
-    )
-
-    def next(self):
-        # A bearish turning point occurs when there is a pattern with the
-        # highest high in the middle and two lower highs on each side. [Ref 1]
-
-        last_five_highs = self.data.high.get(size=self.p.period)
-        max_val = max(last_five_highs)
-        max_idx = last_five_highs.index(max_val)
-
-        if max_idx == self.p.shift_to_potential_fractal:
-            self.lines.fractal_bearish[-2] = max_val * (1 + self.p.bardist)
-
-        # A bullish turning point occurs when there is a pattern with the
-        # lowest low in the middle and two higher lowers on each side. [Ref 1]
-        last_five_lows = self.data.low.get(size=self.p.period)
-        min_val = min(last_five_lows)
-        min_idx = last_five_lows.index(min_val)
-
-        if min_idx == self.p.shift_to_potential_fractal:
-            self.l.fractal_bullish[-2] = min_val * (1 - self.p.bardist)
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/global.d.ts b/spaces/LuxOAI/ChatGpt-Web/app/global.d.ts
deleted file mode 100644
index bd1c062def60f16d3eaa34560dd27408df851ec8..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/global.d.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-declare module "*.jpg";
-declare module "*.png";
-declare module "*.woff2";
-declare module "*.woff";
-declare module "*.ttf";
-declare module "*.scss" {
-  const content: Record<string, string>;
-  export default content;
-}
-
-declare module "*.svg";
diff --git a/spaces/MKFMIKU/Bi-Noising.Diffusion/app.py b/spaces/MKFMIKU/Bi-Noising.Diffusion/app.py
deleted file mode 100644
index 0f9b2d4d265f93b17ed29962925616186cd25ed1..0000000000000000000000000000000000000000
--- a/spaces/MKFMIKU/Bi-Noising.Diffusion/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-import os
-import torch
-from diffusion import DiffusionPipeline
-
-auth_token = os.environ.get("API_TOKEN") or True
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = DiffusionPipeline(device)
-
-def predict(input, diffusion_step, binoising_step, grid_size):
-    for output in pipe(input, diffusion_step, binoising_step, grid_size):
-        yield output[0], output[1]
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-
-css = '''
-.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
-#image_upload{min-height:256px}
-#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 256px}
-#mask_radio .gr-form{background:transparent; border: none}
-#word_mask{margin-top: .75em !important}
-#word_mask textarea:disabled{opacity: 0.3}
-.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
-.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
-.dark .footer {border-color: #303030}
-.dark .footer>p {background: #0b0f19}
-.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
-#image_upload .touch-none{display: flex}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-'''
-
-image_blocks = gr.Blocks(css=css)
-with image_blocks as demo:
-    gr.HTML(read_content("header.html"))
-    with gr.Group():
-        with gr.Box():
-            with gr.Row():
-                with gr.Column():
-                    image = gr.Image(source='upload', elem_id="image_upload", type="pil", image_mode="L", label="Gray Image").style(height=256)
-
-                    diffusion_step = gr.Slider(minimum=10, maximum=200, step=5, value=50, label="Diffusion Time Step")
-                    binoising_step = gr.Slider(minimum=1, maximum=50, step=1, value=50, label="Bi-Noising Start Step")
-                    grid_size = gr.Slider(minimum=1, maximum=16, step=1, value=2, label="Grid Size")
-
-                    with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
-                        btn = gr.Button("Colorization").style(
-                            margin=False,
-                            full_width=True,
-                        )
-                with gr.Column():
-                    diffusion_result = gr.Image(elem_id="output-simg", label="Diffusion Result").style(height=256)
-                    bidiffusion_result = gr.Image(elem_id="output-bimg", label="Bi-Noising Diffsuion Result").style(height=256)
-
-                # with gr.Column():
-
-
-            with gr.Row():
-                gr.Examples(examples=[
-                    'examples/00015.jpg',
-                    'examples/00065.jpg'
-                ], inputs=[image])
-
-            btn.click(fn=predict, inputs=[image, diffusion_step, binoising_step, grid_size], outputs=[diffusion_result, bidiffusion_result])
-
-image_blocks.queue()
-image_blocks.launch(enable_queue=True, share=False, debug=False)
diff --git a/spaces/MMYang/microsoft-BioGPT-Large/app.py b/spaces/MMYang/microsoft-BioGPT-Large/app.py
deleted file mode 100644
index fc639d08afd8af5d583dfded7b1b1a6384f6e7dc..0000000000000000000000000000000000000000
--- a/spaces/MMYang/microsoft-BioGPT-Large/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/microsoft/BioGPT-Large").launch()
\ No newline at end of file
diff --git a/spaces/Madhes/GradioLangChainBota/README.md b/spaces/Madhes/GradioLangChainBota/README.md
deleted file mode 100644
index 97dc0b004b2cc48300ca53c617792458e03402c9..0000000000000000000000000000000000000000
--- a/spaces/Madhes/GradioLangChainBota/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GradioLangChainBota
-emoji: 😻
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Manjushri/MusicGen/tests/modules/test_conv.py b/spaces/Manjushri/MusicGen/tests/modules/test_conv.py
deleted file mode 100644
index 28fbc4f1a0ebaf41b56947b767958ae696e75eec..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/tests/modules/test_conv.py
+++ /dev/null
@@ -1,203 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-import math
-import random
-
-import pytest
-import torch
-from torch import nn
-
-from audiocraft.modules import (
-    NormConv1d,
-    NormConvTranspose1d,
-    StreamableConv1d,
-    StreamableConvTranspose1d,
-    pad1d,
-    unpad1d,
-)
-
-
-def test_get_extra_padding_for_conv1d():
-    # TODO: Implement me!
-    pass
-
-
-def test_pad1d_zeros():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='constant', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='constant', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='constant', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='constant', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='constant', value=0.)
-
-
-def test_pad1d_reflect():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='reflect', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='reflect', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='reflect', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='reflect', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='reflect', value=0.)
-
-
-def test_unpad1d():
-    x = torch.randn(1, 1, 20)
-
-    u1 = unpad1d(x, (5, 5))
-    assert u1.shape[-1] == 10
-    u2 = unpad1d(x, (0, 5))
-    assert u2.shape[-1] == 15
-    u3 = unpad1d(x, (5, 0))
-    assert u3.shape[-1] == 15
-    u4 = unpad1d(x, (0, 0))
-    assert u4.shape[-1] == x.shape[-1]
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, 0))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (0, -1))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, -1))
-
-
-class TestNormConv1d:
-
-    def test_norm_conv1d_modules(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = int((T - kernel_size) / stride + 1)
-        wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm')
-        gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm')
-        nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none')
-
-        assert isinstance(wn_conv.norm, nn.Identity)
-        assert isinstance(wn_conv.conv, nn.Conv1d)
-
-        assert isinstance(gn_conv.norm, nn.GroupNorm)
-        assert isinstance(gn_conv.conv, nn.Conv1d)
-
-        assert isinstance(nn_conv.norm, nn.Identity)
-        assert isinstance(nn_conv.conv, nn.Conv1d)
-
-        for conv_layer in [wn_conv, gn_conv, nn_conv]:
-            out = conv_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestNormConvTranspose1d:
-
-    def test_normalizations(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1
-
-        wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm')
-        gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm')
-        nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none')
-
-        assert isinstance(wn_convtr.norm, nn.Identity)
-        assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(gn_convtr.norm, nn.GroupNorm)
-        assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(nn_convtr.norm, nn.Identity)
-        assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d)
-
-        for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]:
-            out = convtr_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConv1d:
-
-    def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation):
-        # StreamableConv1d internally pads to make sure that the last window is full
-        padding_total = (kernel_size - 1) * dilation - (stride - 1)
-        n_frames = (length - kernel_size + padding_total) / stride + 1
-        ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-        return ideal_length // stride
-
-    def test_streamable_conv1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-        C_out = 1
-
-        # conv params are [(kernel_size, stride, dilation)]
-        conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)]
-        for causal, (kernel_size, stride, dilation) in product([False, True], conv_params):
-            expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation)
-            sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal)
-            out = sconv(t0)
-            assert isinstance(out, torch.Tensor)
-            print(list(out.shape), [N, C_out, expected_out_length])
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConvTranspose1d:
-
-    def get_streamable_convtr1d_output_length(self, length, kernel_size, stride):
-        padding_total = (kernel_size - stride)
-        return (length - 1) * stride - padding_total + (kernel_size - 1) + 1
-
-    def test_streamable_convtr1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out = 1
-
-        with pytest.raises(AssertionError):
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2)
-
-        # causal params are [(causal, trim_right)]
-        causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)]
-        # conv params are [(kernel_size, stride)]
-        conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)]
-        for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params):
-            expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride)
-            sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride,
-                                                causal=causal, trim_right_ratio=trim_right_ratio)
-            out = sconvtr(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/inference/__init__.py b/spaces/MashiroSA/sovits-emu-voice-transform/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Meena/table-question-answering-space/app/__init__.py b/spaces/Meena/table-question-answering-space/app/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/pixel_group.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/pixel_group.py
deleted file mode 100644
index 2143c75f835a467c802fc3c37ecd3ac0f85bcda4..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/pixel_group.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['pixel_group'])
-
-
-def pixel_group(score, mask, embedding, kernel_label, kernel_contour,
-                kernel_region_num, distance_threshold):
-    """Group pixels into text instances, which is widely used text detection
-    methods.
-
-    Arguments:
-        score (np.array or Tensor): The foreground score with size hxw.
-        mask (np.array or Tensor): The foreground mask with size hxw.
-        embedding (np.array or Tensor): The embedding with size hxwxc to
-            distinguish instances.
-        kernel_label (np.array or Tensor): The instance kernel index with
-            size hxw.
-        kernel_contour (np.array or Tensor): The kernel contour with size hxw.
-        kernel_region_num (int): The instance kernel region number.
-        distance_threshold (float): The embedding distance threshold between
-            kernel and pixel in one instance.
-
-    Returns:
-        pixel_assignment (List[List[float]]): The instance coordinate list.
-            Each element consists of averaged confidence, pixel number, and
-            coordinates (x_i, y_i for all pixels) in order.
-    """
-    assert isinstance(score, (torch.Tensor, np.ndarray))
-    assert isinstance(mask, (torch.Tensor, np.ndarray))
-    assert isinstance(embedding, (torch.Tensor, np.ndarray))
-    assert isinstance(kernel_label, (torch.Tensor, np.ndarray))
-    assert isinstance(kernel_contour, (torch.Tensor, np.ndarray))
-    assert isinstance(kernel_region_num, int)
-    assert isinstance(distance_threshold, float)
-
-    if isinstance(score, np.ndarray):
-        score = torch.from_numpy(score)
-    if isinstance(mask, np.ndarray):
-        mask = torch.from_numpy(mask)
-    if isinstance(embedding, np.ndarray):
-        embedding = torch.from_numpy(embedding)
-    if isinstance(kernel_label, np.ndarray):
-        kernel_label = torch.from_numpy(kernel_label)
-    if isinstance(kernel_contour, np.ndarray):
-        kernel_contour = torch.from_numpy(kernel_contour)
-
-    if torch.__version__ == 'parrots':
-        label = ext_module.pixel_group(
-            score,
-            mask,
-            embedding,
-            kernel_label,
-            kernel_contour,
-            kernel_region_num=kernel_region_num,
-            distance_threshold=distance_threshold)
-        label = label.tolist()
-        label = label[0]
-        list_index = kernel_region_num
-        pixel_assignment = []
-        for x in range(kernel_region_num):
-            pixel_assignment.append(
-                np.array(
-                    label[list_index:list_index + int(label[x])],
-                    dtype=np.float))
-            list_index = list_index + int(label[x])
-    else:
-        pixel_assignment = ext_module.pixel_group(score, mask, embedding,
-                                                  kernel_label, kernel_contour,
-                                                  kernel_region_num,
-                                                  distance_threshold)
-    return pixel_assignment
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py
deleted file mode 100644
index 60437756ceedf06055ec349df69a25465738d3f0..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-from .lr_updater import annealing_cos, annealing_linear, format_param
-
-
-class MomentumUpdaterHook(Hook):
-
-    def __init__(self,
-                 by_epoch=True,
-                 warmup=None,
-                 warmup_iters=0,
-                 warmup_ratio=0.9):
-        # validate the "warmup" argument
-        if warmup is not None:
-            if warmup not in ['constant', 'linear', 'exp']:
-                raise ValueError(
-                    f'"{warmup}" is not a supported type for warming up, valid'
-                    ' types are "constant" and "linear"')
-        if warmup is not None:
-            assert warmup_iters > 0, \
-                '"warmup_iters" must be a positive integer'
-            assert 0 < warmup_ratio <= 1.0, \
-                '"warmup_momentum" must be in range (0,1]'
-
-        self.by_epoch = by_epoch
-        self.warmup = warmup
-        self.warmup_iters = warmup_iters
-        self.warmup_ratio = warmup_ratio
-
-        self.base_momentum = []  # initial momentum for all param groups
-        self.regular_momentum = [
-        ]  # expected momentum if no warming up is performed
-
-    def _set_momentum(self, runner, momentum_groups):
-        if isinstance(runner.optimizer, dict):
-            for k, optim in runner.optimizer.items():
-                for param_group, mom in zip(optim.param_groups,
-                                            momentum_groups[k]):
-                    if 'momentum' in param_group.keys():
-                        param_group['momentum'] = mom
-                    elif 'betas' in param_group.keys():
-                        param_group['betas'] = (mom, param_group['betas'][1])
-        else:
-            for param_group, mom in zip(runner.optimizer.param_groups,
-                                        momentum_groups):
-                if 'momentum' in param_group.keys():
-                    param_group['momentum'] = mom
-                elif 'betas' in param_group.keys():
-                    param_group['betas'] = (mom, param_group['betas'][1])
-
-    def get_momentum(self, runner, base_momentum):
-        raise NotImplementedError
-
-    def get_regular_momentum(self, runner):
-        if isinstance(runner.optimizer, dict):
-            momentum_groups = {}
-            for k in runner.optimizer.keys():
-                _momentum_group = [
-                    self.get_momentum(runner, _base_momentum)
-                    for _base_momentum in self.base_momentum[k]
-                ]
-                momentum_groups.update({k: _momentum_group})
-            return momentum_groups
-        else:
-            return [
-                self.get_momentum(runner, _base_momentum)
-                for _base_momentum in self.base_momentum
-            ]
-
-    def get_warmup_momentum(self, cur_iters):
-
-        def _get_warmup_momentum(cur_iters, regular_momentum):
-            if self.warmup == 'constant':
-                warmup_momentum = [
-                    _momentum / self.warmup_ratio
-                    for _momentum in self.regular_momentum
-                ]
-            elif self.warmup == 'linear':
-                k = (1 - cur_iters / self.warmup_iters) * (1 -
-                                                           self.warmup_ratio)
-                warmup_momentum = [
-                    _momentum / (1 - k) for _momentum in self.regular_mom
-                ]
-            elif self.warmup == 'exp':
-                k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
-                warmup_momentum = [
-                    _momentum / k for _momentum in self.regular_mom
-                ]
-            return warmup_momentum
-
-        if isinstance(self.regular_momentum, dict):
-            momentum_groups = {}
-            for key, regular_momentum in self.regular_momentum.items():
-                momentum_groups[key] = _get_warmup_momentum(
-                    cur_iters, regular_momentum)
-            return momentum_groups
-        else:
-            return _get_warmup_momentum(cur_iters, self.regular_momentum)
-
-    def before_run(self, runner):
-        # NOTE: when resuming from a checkpoint,
-        # if 'initial_momentum' is not saved,
-        # it will be set according to the optimizer params
-        if isinstance(runner.optimizer, dict):
-            self.base_momentum = {}
-            for k, optim in runner.optimizer.items():
-                for group in optim.param_groups:
-                    if 'momentum' in group.keys():
-                        group.setdefault('initial_momentum', group['momentum'])
-                    else:
-                        group.setdefault('initial_momentum', group['betas'][0])
-                _base_momentum = [
-                    group['initial_momentum'] for group in optim.param_groups
-                ]
-                self.base_momentum.update({k: _base_momentum})
-        else:
-            for group in runner.optimizer.param_groups:
-                if 'momentum' in group.keys():
-                    group.setdefault('initial_momentum', group['momentum'])
-                else:
-                    group.setdefault('initial_momentum', group['betas'][0])
-            self.base_momentum = [
-                group['initial_momentum']
-                for group in runner.optimizer.param_groups
-            ]
-
-    def before_train_epoch(self, runner):
-        if not self.by_epoch:
-            return
-        self.regular_mom = self.get_regular_momentum(runner)
-        self._set_momentum(runner, self.regular_mom)
-
-    def before_train_iter(self, runner):
-        cur_iter = runner.iter
-        if not self.by_epoch:
-            self.regular_mom = self.get_regular_momentum(runner)
-            if self.warmup is None or cur_iter >= self.warmup_iters:
-                self._set_momentum(runner, self.regular_mom)
-            else:
-                warmup_momentum = self.get_warmup_momentum(cur_iter)
-                self._set_momentum(runner, warmup_momentum)
-        elif self.by_epoch:
-            if self.warmup is None or cur_iter > self.warmup_iters:
-                return
-            elif cur_iter == self.warmup_iters:
-                self._set_momentum(runner, self.regular_mom)
-            else:
-                warmup_momentum = self.get_warmup_momentum(cur_iter)
-                self._set_momentum(runner, warmup_momentum)
-
-
-@HOOKS.register_module()
-class StepMomentumUpdaterHook(MomentumUpdaterHook):
-    """Step momentum scheduler with min value clipping.
-
-    Args:
-        step (int | list[int]): Step to decay the momentum. If an int value is
-            given, regard it as the decay interval. If a list is given, decay
-            momentum at these steps.
-        gamma (float, optional): Decay momentum ratio. Default: 0.5.
-        min_momentum (float, optional): Minimum momentum value to keep. If
-            momentum after decay is lower than this value, it will be clipped
-            accordingly. If None is given, we don't perform lr clipping.
-            Default: None.
-    """
-
-    def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
-        if isinstance(step, list):
-            assert mmcv.is_list_of(step, int)
-            assert all([s > 0 for s in step])
-        elif isinstance(step, int):
-            assert step > 0
-        else:
-            raise TypeError('"step" must be a list or integer')
-        self.step = step
-        self.gamma = gamma
-        self.min_momentum = min_momentum
-        super(StepMomentumUpdaterHook, self).__init__(**kwargs)
-
-    def get_momentum(self, runner, base_momentum):
-        progress = runner.epoch if self.by_epoch else runner.iter
-
-        # calculate exponential term
-        if isinstance(self.step, int):
-            exp = progress // self.step
-        else:
-            exp = len(self.step)
-            for i, s in enumerate(self.step):
-                if progress < s:
-                    exp = i
-                    break
-
-        momentum = base_momentum * (self.gamma**exp)
-        if self.min_momentum is not None:
-            # clip to a minimum value
-            momentum = max(momentum, self.min_momentum)
-        return momentum
-
-
-@HOOKS.register_module()
-class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
-
-    def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
-        assert (min_momentum is None) ^ (min_momentum_ratio is None)
-        self.min_momentum = min_momentum
-        self.min_momentum_ratio = min_momentum_ratio
-        super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
-
-    def get_momentum(self, runner, base_momentum):
-        if self.by_epoch:
-            progress = runner.epoch
-            max_progress = runner.max_epochs
-        else:
-            progress = runner.iter
-            max_progress = runner.max_iters
-        if self.min_momentum_ratio is not None:
-            target_momentum = base_momentum * self.min_momentum_ratio
-        else:
-            target_momentum = self.min_momentum
-        return annealing_cos(base_momentum, target_momentum,
-                             progress / max_progress)
-
-
-@HOOKS.register_module()
-class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
-    """Cyclic momentum Scheduler.
-
-    Implement the cyclical momentum scheduler policy described in
-    https://arxiv.org/pdf/1708.07120.pdf
-
-    This momentum scheduler usually used together with the CyclicLRUpdater
-    to improve the performance in the 3D detection area.
-
-    Attributes:
-        target_ratio (tuple[float]): Relative ratio of the lowest momentum and
-            the highest momentum to the initial momentum.
-        cyclic_times (int): Number of cycles during training
-        step_ratio_up (float): The ratio of the increasing process of momentum
-            in  the total cycle.
-        by_epoch (bool): Whether to update momentum by epoch.
-    """
-
-    def __init__(self,
-                 by_epoch=False,
-                 target_ratio=(0.85 / 0.95, 1),
-                 cyclic_times=1,
-                 step_ratio_up=0.4,
-                 **kwargs):
-        if isinstance(target_ratio, float):
-            target_ratio = (target_ratio, target_ratio / 1e5)
-        elif isinstance(target_ratio, tuple):
-            target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
-                if len(target_ratio) == 1 else target_ratio
-        else:
-            raise ValueError('target_ratio should be either float '
-                             f'or tuple, got {type(target_ratio)}')
-
-        assert len(target_ratio) == 2, \
-            '"target_ratio" must be list or tuple of two floats'
-        assert 0 <= step_ratio_up < 1.0, \
-            '"step_ratio_up" must be in range [0,1)'
-
-        self.target_ratio = target_ratio
-        self.cyclic_times = cyclic_times
-        self.step_ratio_up = step_ratio_up
-        self.momentum_phases = []  # init momentum_phases
-        # currently only support by_epoch=False
-        assert not by_epoch, \
-            'currently only support "by_epoch" = False'
-        super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
-
-    def before_run(self, runner):
-        super(CyclicMomentumUpdaterHook, self).before_run(runner)
-        # initiate momentum_phases
-        # total momentum_phases are separated as up and down
-        max_iter_per_phase = runner.max_iters // self.cyclic_times
-        iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
-        self.momentum_phases.append(
-            [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
-        self.momentum_phases.append([
-            iter_up_phase, max_iter_per_phase, max_iter_per_phase,
-            self.target_ratio[0], self.target_ratio[1]
-        ])
-
-    def get_momentum(self, runner, base_momentum):
-        curr_iter = runner.iter
-        for (start_iter, end_iter, max_iter_per_phase, start_ratio,
-             end_ratio) in self.momentum_phases:
-            curr_iter %= max_iter_per_phase
-            if start_iter <= curr_iter < end_iter:
-                progress = curr_iter - start_iter
-                return annealing_cos(base_momentum * start_ratio,
-                                     base_momentum * end_ratio,
-                                     progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
-    """OneCycle momentum Scheduler.
-
-    This momentum scheduler usually used together with the OneCycleLrUpdater
-    to improve the performance.
-
-    Args:
-        base_momentum (float or list): Lower momentum boundaries in the cycle
-            for each parameter group. Note that momentum is cycled inversely
-            to learning rate; at the peak of a cycle, momentum is
-            'base_momentum' and learning rate is 'max_lr'.
-            Default: 0.85
-        max_momentum (float or list): Upper momentum boundaries in the cycle
-            for each parameter group. Functionally,
-            it defines the cycle amplitude (max_momentum - base_momentum).
-            Note that momentum is cycled inversely
-            to learning rate; at the start of a cycle, momentum is
-            'max_momentum' and learning rate is 'base_lr'
-            Default: 0.95
-        pct_start (float): The percentage of the cycle (in number of steps)
-            spent increasing the learning rate.
-            Default: 0.3
-        anneal_strategy (str): {'cos', 'linear'}
-            Specifies the annealing strategy: 'cos' for cosine annealing,
-            'linear' for linear annealing.
-            Default: 'cos'
-        three_phase (bool): If three_phase is True, use a third phase of the
-            schedule to annihilate the learning rate according to
-            final_div_factor instead of modifying the second phase (the first
-            two phases will be symmetrical about the step indicated by
-            pct_start).
-            Default: False
-    """
-
-    def __init__(self,
-                 base_momentum=0.85,
-                 max_momentum=0.95,
-                 pct_start=0.3,
-                 anneal_strategy='cos',
-                 three_phase=False,
-                 **kwargs):
-        # validate by_epoch, currently only support by_epoch=False
-        if 'by_epoch' not in kwargs:
-            kwargs['by_epoch'] = False
-        else:
-            assert not kwargs['by_epoch'], \
-                'currently only support "by_epoch" = False'
-        if not isinstance(base_momentum, (float, list, dict)):
-            raise ValueError('base_momentum must be the type among of float,'
-                             'list or dict.')
-        self._base_momentum = base_momentum
-        if not isinstance(max_momentum, (float, list, dict)):
-            raise ValueError('max_momentum must be the type among of float,'
-                             'list or dict.')
-        self._max_momentum = max_momentum
-        # validate pct_start
-        if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
-            raise ValueError('Expected float between 0 and 1 pct_start, but '
-                             f'got {pct_start}')
-        self.pct_start = pct_start
-        # validate anneal_strategy
-        if anneal_strategy not in ['cos', 'linear']:
-            raise ValueError('anneal_strategy must by one of "cos" or '
-                             f'"linear", instead got {anneal_strategy}')
-        elif anneal_strategy == 'cos':
-            self.anneal_func = annealing_cos
-        elif anneal_strategy == 'linear':
-            self.anneal_func = annealing_linear
-        self.three_phase = three_phase
-        self.momentum_phases = []  # init momentum_phases
-        super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
-
-    def before_run(self, runner):
-        if isinstance(runner.optimizer, dict):
-            for k, optim in runner.optimizer.items():
-                if ('momentum' not in optim.defaults
-                        and 'betas' not in optim.defaults):
-                    raise ValueError('optimizer must support momentum with'
-                                     'option enabled')
-                self.use_beta1 = 'betas' in optim.defaults
-                _base_momentum = format_param(k, optim, self._base_momentum)
-                _max_momentum = format_param(k, optim, self._max_momentum)
-                for group, b_momentum, m_momentum in zip(
-                        optim.param_groups, _base_momentum, _max_momentum):
-                    if self.use_beta1:
-                        _, beta2 = group['betas']
-                        group['betas'] = (m_momentum, beta2)
-                    else:
-                        group['momentum'] = m_momentum
-                    group['base_momentum'] = b_momentum
-                    group['max_momentum'] = m_momentum
-        else:
-            optim = runner.optimizer
-            if ('momentum' not in optim.defaults
-                    and 'betas' not in optim.defaults):
-                raise ValueError('optimizer must support momentum with'
-                                 'option enabled')
-            self.use_beta1 = 'betas' in optim.defaults
-            k = type(optim).__name__
-            _base_momentum = format_param(k, optim, self._base_momentum)
-            _max_momentum = format_param(k, optim, self._max_momentum)
-            for group, b_momentum, m_momentum in zip(optim.param_groups,
-                                                     _base_momentum,
-                                                     _max_momentum):
-                if self.use_beta1:
-                    _, beta2 = group['betas']
-                    group['betas'] = (m_momentum, beta2)
-                else:
-                    group['momentum'] = m_momentum
-                group['base_momentum'] = b_momentum
-                group['max_momentum'] = m_momentum
-
-        if self.three_phase:
-            self.momentum_phases.append({
-                'end_iter':
-                float(self.pct_start * runner.max_iters) - 1,
-                'start_momentum':
-                'max_momentum',
-                'end_momentum':
-                'base_momentum'
-            })
-            self.momentum_phases.append({
-                'end_iter':
-                float(2 * self.pct_start * runner.max_iters) - 2,
-                'start_momentum':
-                'base_momentum',
-                'end_momentum':
-                'max_momentum'
-            })
-            self.momentum_phases.append({
-                'end_iter': runner.max_iters - 1,
-                'start_momentum': 'max_momentum',
-                'end_momentum': 'max_momentum'
-            })
-        else:
-            self.momentum_phases.append({
-                'end_iter':
-                float(self.pct_start * runner.max_iters) - 1,
-                'start_momentum':
-                'max_momentum',
-                'end_momentum':
-                'base_momentum'
-            })
-            self.momentum_phases.append({
-                'end_iter': runner.max_iters - 1,
-                'start_momentum': 'base_momentum',
-                'end_momentum': 'max_momentum'
-            })
-
-    def _set_momentum(self, runner, momentum_groups):
-        if isinstance(runner.optimizer, dict):
-            for k, optim in runner.optimizer.items():
-                for param_group, mom in zip(optim.param_groups,
-                                            momentum_groups[k]):
-                    if 'momentum' in param_group.keys():
-                        param_group['momentum'] = mom
-                    elif 'betas' in param_group.keys():
-                        param_group['betas'] = (mom, param_group['betas'][1])
-        else:
-            for param_group, mom in zip(runner.optimizer.param_groups,
-                                        momentum_groups):
-                if 'momentum' in param_group.keys():
-                    param_group['momentum'] = mom
-                elif 'betas' in param_group.keys():
-                    param_group['betas'] = (mom, param_group['betas'][1])
-
-    def get_momentum(self, runner, param_group):
-        curr_iter = runner.iter
-        start_iter = 0
-        for i, phase in enumerate(self.momentum_phases):
-            end_iter = phase['end_iter']
-            if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
-                pct = (curr_iter - start_iter) / (end_iter - start_iter)
-                momentum = self.anneal_func(
-                    param_group[phase['start_momentum']],
-                    param_group[phase['end_momentum']], pct)
-                break
-            start_iter = end_iter
-        return momentum
-
-    def get_regular_momentum(self, runner):
-        if isinstance(runner.optimizer, dict):
-            momentum_groups = {}
-            for k, optim in runner.optimizer.items():
-                _momentum_group = [
-                    self.get_momentum(runner, param_group)
-                    for param_group in optim.param_groups
-                ]
-                momentum_groups.update({k: _momentum_group})
-            return momentum_groups
-        else:
-            momentum_groups = []
-            for param_group in runner.optimizer.param_groups:
-                momentum_groups.append(self.get_momentum(runner, param_group))
-            return momentum_groups
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/pipelines/transforms.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/pipelines/transforms.py
deleted file mode 100644
index 94e869b252ef6d8b43604add2bbc02f034614bfb..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/pipelines/transforms.py
+++ /dev/null
@@ -1,889 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import deprecated_api_warning, is_tuple_of
-from numpy import random
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class Resize(object):
-    """Resize images & seg.
-
-    This transform resizes the input image to some scale. If the input dict
-    contains the key "scale", then the scale in the input dict is used,
-    otherwise the specified scale in the init method is used.
-
-    ``img_scale`` can be None, a tuple (single-scale) or a list of tuple
-    (multi-scale). There are 4 multiscale modes:
-
-    - ``ratio_range is not None``:
-    1. When img_scale is None, img_scale is the shape of image in results
-        (img_scale = results['img'].shape[:2]) and the image is resized based
-        on the original size. (mode 1)
-    2. When img_scale is a tuple (single-scale), randomly sample a ratio from
-        the ratio range and multiply it with the image scale. (mode 2)
-
-    - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a
-    scale from the a range. (mode 3)
-
-    - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a
-    scale from multiple scales. (mode 4)
-
-    Args:
-        img_scale (tuple or list[tuple]): Images scales for resizing.
-        multiscale_mode (str): Either "range" or "value".
-        ratio_range (tuple[float]): (min_ratio, max_ratio)
-        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
-            image.
-    """
-
-    def __init__(self,
-                 img_scale=None,
-                 multiscale_mode='range',
-                 ratio_range=None,
-                 keep_ratio=True):
-        if img_scale is None:
-            self.img_scale = None
-        else:
-            if isinstance(img_scale, list):
-                self.img_scale = img_scale
-            else:
-                self.img_scale = [img_scale]
-            assert mmcv.is_list_of(self.img_scale, tuple)
-
-        if ratio_range is not None:
-            # mode 1: given img_scale=None and a range of image ratio
-            # mode 2: given a scale and a range of image ratio
-            assert self.img_scale is None or len(self.img_scale) == 1
-        else:
-            # mode 3 and 4: given multiple scales or a range of scales
-            assert multiscale_mode in ['value', 'range']
-
-        self.multiscale_mode = multiscale_mode
-        self.ratio_range = ratio_range
-        self.keep_ratio = keep_ratio
-
-    @staticmethod
-    def random_select(img_scales):
-        """Randomly select an img_scale from given candidates.
-
-        Args:
-            img_scales (list[tuple]): Images scales for selection.
-
-        Returns:
-            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
-                where ``img_scale`` is the selected image scale and
-                ``scale_idx`` is the selected index in the given candidates.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple)
-        scale_idx = np.random.randint(len(img_scales))
-        img_scale = img_scales[scale_idx]
-        return img_scale, scale_idx
-
-    @staticmethod
-    def random_sample(img_scales):
-        """Randomly sample an img_scale when ``multiscale_mode=='range'``.
-
-        Args:
-            img_scales (list[tuple]): Images scale range for sampling.
-                There must be two tuples in img_scales, which specify the lower
-                and upper bound of image scales.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(img_scale, None)``, where
-                ``img_scale`` is sampled scale and None is just a placeholder
-                to be consistent with :func:`random_select`.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
-        img_scale_long = [max(s) for s in img_scales]
-        img_scale_short = [min(s) for s in img_scales]
-        long_edge = np.random.randint(
-            min(img_scale_long),
-            max(img_scale_long) + 1)
-        short_edge = np.random.randint(
-            min(img_scale_short),
-            max(img_scale_short) + 1)
-        img_scale = (long_edge, short_edge)
-        return img_scale, None
-
-    @staticmethod
-    def random_sample_ratio(img_scale, ratio_range):
-        """Randomly sample an img_scale when ``ratio_range`` is specified.
-
-        A ratio will be randomly sampled from the range specified by
-        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
-        generate sampled scale.
-
-        Args:
-            img_scale (tuple): Images scale base to multiply with ratio.
-            ratio_range (tuple[float]): The minimum and maximum ratio to scale
-                the ``img_scale``.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(scale, None)``, where
-                ``scale`` is sampled ratio multiplied with ``img_scale`` and
-                None is just a placeholder to be consistent with
-                :func:`random_select`.
-        """
-
-        assert isinstance(img_scale, tuple) and len(img_scale) == 2
-        min_ratio, max_ratio = ratio_range
-        assert min_ratio <= max_ratio
-        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
-        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
-        return scale, None
-
-    def _random_scale(self, results):
-        """Randomly sample an img_scale according to ``ratio_range`` and
-        ``multiscale_mode``.
-
-        If ``ratio_range`` is specified, a ratio will be sampled and be
-        multiplied with ``img_scale``.
-        If multiple scales are specified by ``img_scale``, a scale will be
-        sampled according to ``multiscale_mode``.
-        Otherwise, single scale will be used.
-
-        Args:
-            results (dict): Result dict from :obj:`dataset`.
-
-        Returns:
-            dict: Two new keys 'scale` and 'scale_idx` are added into
-                ``results``, which would be used by subsequent pipelines.
-        """
-
-        if self.ratio_range is not None:
-            if self.img_scale is None:
-                h, w = results['img'].shape[:2]
-                scale, scale_idx = self.random_sample_ratio((w, h),
-                                                            self.ratio_range)
-            else:
-                scale, scale_idx = self.random_sample_ratio(
-                    self.img_scale[0], self.ratio_range)
-        elif len(self.img_scale) == 1:
-            scale, scale_idx = self.img_scale[0], 0
-        elif self.multiscale_mode == 'range':
-            scale, scale_idx = self.random_sample(self.img_scale)
-        elif self.multiscale_mode == 'value':
-            scale, scale_idx = self.random_select(self.img_scale)
-        else:
-            raise NotImplementedError
-
-        results['scale'] = scale
-        results['scale_idx'] = scale_idx
-
-    def _resize_img(self, results):
-        """Resize images with ``results['scale']``."""
-        if self.keep_ratio:
-            img, scale_factor = mmcv.imrescale(
-                results['img'], results['scale'], return_scale=True)
-            # the w_scale and h_scale has minor difference
-            # a real fix should be done in the mmcv.imrescale in the future
-            new_h, new_w = img.shape[:2]
-            h, w = results['img'].shape[:2]
-            w_scale = new_w / w
-            h_scale = new_h / h
-        else:
-            img, w_scale, h_scale = mmcv.imresize(
-                results['img'], results['scale'], return_scale=True)
-        scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
-                                dtype=np.float32)
-        results['img'] = img
-        results['img_shape'] = img.shape
-        results['pad_shape'] = img.shape  # in case that there is no padding
-        results['scale_factor'] = scale_factor
-        results['keep_ratio'] = self.keep_ratio
-
-    def _resize_seg(self, results):
-        """Resize semantic segmentation map with ``results['scale']``."""
-        for key in results.get('seg_fields', []):
-            if self.keep_ratio:
-                gt_seg = mmcv.imrescale(
-                    results[key], results['scale'], interpolation='nearest')
-            else:
-                gt_seg = mmcv.imresize(
-                    results[key], results['scale'], interpolation='nearest')
-            results[key] = gt_seg
-
-    def __call__(self, results):
-        """Call function to resize images, bounding boxes, masks, semantic
-        segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
-                'keep_ratio' keys are added into result dict.
-        """
-
-        if 'scale' not in results:
-            self._random_scale(results)
-        self._resize_img(results)
-        self._resize_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(img_scale={self.img_scale}, '
-                     f'multiscale_mode={self.multiscale_mode}, '
-                     f'ratio_range={self.ratio_range}, '
-                     f'keep_ratio={self.keep_ratio})')
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomFlip(object):
-    """Flip the image & seg.
-
-    If the input dict contains the key "flip", then the flag will be used,
-    otherwise it will be randomly decided by a ratio specified in the init
-    method.
-
-    Args:
-        prob (float, optional): The flipping probability. Default: None.
-        direction(str, optional): The flipping direction. Options are
-            'horizontal' and 'vertical'. Default: 'horizontal'.
-    """
-
-    @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip')
-    def __init__(self, prob=None, direction='horizontal'):
-        self.prob = prob
-        self.direction = direction
-        if prob is not None:
-            assert prob >= 0 and prob <= 1
-        assert direction in ['horizontal', 'vertical']
-
-    def __call__(self, results):
-        """Call function to flip bounding boxes, masks, semantic segmentation
-        maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Flipped results, 'flip', 'flip_direction' keys are added into
-                result dict.
-        """
-
-        if 'flip' not in results:
-            flip = True if np.random.rand() < self.prob else False
-            results['flip'] = flip
-        if 'flip_direction' not in results:
-            results['flip_direction'] = self.direction
-        if results['flip']:
-            # flip image
-            results['img'] = mmcv.imflip(
-                results['img'], direction=results['flip_direction'])
-
-            # flip segs
-            for key in results.get('seg_fields', []):
-                # use copy() to make numpy stride positive
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction']).copy()
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(prob={self.prob})'
-
-
-@PIPELINES.register_module()
-class Pad(object):
-    """Pad the image & mask.
-
-    There are two padding modes: (1) pad to a fixed size and (2) pad to the
-    minimum size that is divisible by some number.
-    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
-
-    Args:
-        size (tuple, optional): Fixed padding size.
-        size_divisor (int, optional): The divisor of padded size.
-        pad_val (float, optional): Padding value. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-    """
-
-    def __init__(self,
-                 size=None,
-                 size_divisor=None,
-                 pad_val=0,
-                 seg_pad_val=255):
-        self.size = size
-        self.size_divisor = size_divisor
-        self.pad_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        # only one of size and size_divisor should be valid
-        assert size is not None or size_divisor is not None
-        assert size is None or size_divisor is None
-
-    def _pad_img(self, results):
-        """Pad images according to ``self.size``."""
-        if self.size is not None:
-            padded_img = mmcv.impad(
-                results['img'], shape=self.size, pad_val=self.pad_val)
-        elif self.size_divisor is not None:
-            padded_img = mmcv.impad_to_multiple(
-                results['img'], self.size_divisor, pad_val=self.pad_val)
-        results['img'] = padded_img
-        results['pad_shape'] = padded_img.shape
-        results['pad_fixed_size'] = self.size
-        results['pad_size_divisor'] = self.size_divisor
-
-    def _pad_seg(self, results):
-        """Pad masks according to ``results['pad_shape']``."""
-        for key in results.get('seg_fields', []):
-            results[key] = mmcv.impad(
-                results[key],
-                shape=results['pad_shape'][:2],
-                pad_val=self.seg_pad_val)
-
-    def __call__(self, results):
-        """Call function to pad images, masks, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Updated result dict.
-        """
-
-        self._pad_img(results)
-        self._pad_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \
-                    f'pad_val={self.pad_val})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Normalize(object):
-    """Normalize the image.
-
-    Added key is "img_norm_cfg".
-
-    Args:
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB,
-            default is true.
-    """
-
-    def __init__(self, mean, std, to_rgb=True):
-        self.mean = np.array(mean, dtype=np.float32)
-        self.std = np.array(std, dtype=np.float32)
-        self.to_rgb = to_rgb
-
-    def __call__(self, results):
-        """Call function to normalize images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Normalized results, 'img_norm_cfg' key is added into
-                result dict.
-        """
-
-        results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
-                                          self.to_rgb)
-        results['img_norm_cfg'] = dict(
-            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \
-                    f'{self.to_rgb})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Rerange(object):
-    """Rerange the image pixel value.
-
-    Args:
-        min_value (float or int): Minimum value of the reranged image.
-            Default: 0.
-        max_value (float or int): Maximum value of the reranged image.
-            Default: 255.
-    """
-
-    def __init__(self, min_value=0, max_value=255):
-        assert isinstance(min_value, float) or isinstance(min_value, int)
-        assert isinstance(max_value, float) or isinstance(max_value, int)
-        assert min_value < max_value
-        self.min_value = min_value
-        self.max_value = max_value
-
-    def __call__(self, results):
-        """Call function to rerange images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-        Returns:
-            dict: Reranged results.
-        """
-
-        img = results['img']
-        img_min_value = np.min(img)
-        img_max_value = np.max(img)
-
-        assert img_min_value < img_max_value
-        # rerange to [0, 1]
-        img = (img - img_min_value) / (img_max_value - img_min_value)
-        # rerange to [min_value, max_value]
-        img = img * (self.max_value - self.min_value) + self.min_value
-        results['img'] = img
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class CLAHE(object):
-    """Use CLAHE method to process the image.
-
-    See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
-    Graphics Gems, 1994:474-485.` for more information.
-
-    Args:
-        clip_limit (float): Threshold for contrast limiting. Default: 40.0.
-        tile_grid_size (tuple[int]): Size of grid for histogram equalization.
-            Input image will be divided into equally sized rectangular tiles.
-            It defines the number of tiles in row and column. Default: (8, 8).
-    """
-
-    def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
-        assert isinstance(clip_limit, (float, int))
-        self.clip_limit = clip_limit
-        assert is_tuple_of(tile_grid_size, int)
-        assert len(tile_grid_size) == 2
-        self.tile_grid_size = tile_grid_size
-
-    def __call__(self, results):
-        """Call function to Use CLAHE method process images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        for i in range(results['img'].shape[2]):
-            results['img'][:, :, i] = mmcv.clahe(
-                np.array(results['img'][:, :, i], dtype=np.uint8),
-                self.clip_limit, self.tile_grid_size)
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(clip_limit={self.clip_limit}, '\
-                    f'tile_grid_size={self.tile_grid_size})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCrop(object):
-    """Random crop the image & seg.
-
-    Args:
-        crop_size (tuple): Expected size after cropping, (h, w).
-        cat_max_ratio (float): The maximum ratio that single category could
-            occupy.
-    """
-
-    def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
-        assert crop_size[0] > 0 and crop_size[1] > 0
-        self.crop_size = crop_size
-        self.cat_max_ratio = cat_max_ratio
-        self.ignore_index = ignore_index
-
-    def get_crop_bbox(self, img):
-        """Randomly get a crop bounding box."""
-        margin_h = max(img.shape[0] - self.crop_size[0], 0)
-        margin_w = max(img.shape[1] - self.crop_size[1], 0)
-        offset_h = np.random.randint(0, margin_h + 1)
-        offset_w = np.random.randint(0, margin_w + 1)
-        crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
-        crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
-
-        return crop_y1, crop_y2, crop_x1, crop_x2
-
-    def crop(self, img, crop_bbox):
-        """Crop from ``img``"""
-        crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
-        img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
-        return img
-
-    def __call__(self, results):
-        """Call function to randomly crop images, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-
-        img = results['img']
-        crop_bbox = self.get_crop_bbox(img)
-        if self.cat_max_ratio < 1.:
-            # Repeat 10 times
-            for _ in range(10):
-                seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
-                labels, cnt = np.unique(seg_temp, return_counts=True)
-                cnt = cnt[labels != self.ignore_index]
-                if len(cnt) > 1 and np.max(cnt) / np.sum(
-                        cnt) < self.cat_max_ratio:
-                    break
-                crop_bbox = self.get_crop_bbox(img)
-
-        # crop the image
-        img = self.crop(img, crop_bbox)
-        img_shape = img.shape
-        results['img'] = img
-        results['img_shape'] = img_shape
-
-        # crop semantic seg
-        for key in results.get('seg_fields', []):
-            results[key] = self.crop(results[key], crop_bbox)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(crop_size={self.crop_size})'
-
-
-@PIPELINES.register_module()
-class RandomRotate(object):
-    """Rotate the image & seg.
-
-    Args:
-        prob (float): The rotation probability.
-        degree (float, tuple[float]): Range of degrees to select from. If
-            degree is a number instead of tuple like (min, max),
-            the range of degree will be (``-degree``, ``+degree``)
-        pad_val (float, optional): Padding value of image. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-        center (tuple[float], optional): Center point (w, h) of the rotation in
-            the source image. If not specified, the center of the image will be
-            used. Default: None.
-        auto_bound (bool): Whether to adjust the image size to cover the whole
-            rotated image. Default: False
-    """
-
-    def __init__(self,
-                 prob,
-                 degree,
-                 pad_val=0,
-                 seg_pad_val=255,
-                 center=None,
-                 auto_bound=False):
-        self.prob = prob
-        assert prob >= 0 and prob <= 1
-        if isinstance(degree, (float, int)):
-            assert degree > 0, f'degree {degree} should be positive'
-            self.degree = (-degree, degree)
-        else:
-            self.degree = degree
-        assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
-                                      f'tuple of (min, max)'
-        self.pal_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        self.center = center
-        self.auto_bound = auto_bound
-
-    def __call__(self, results):
-        """Call function to rotate image, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Rotated results.
-        """
-
-        rotate = True if np.random.rand() < self.prob else False
-        degree = np.random.uniform(min(*self.degree), max(*self.degree))
-        if rotate:
-            # rotate image
-            results['img'] = mmcv.imrotate(
-                results['img'],
-                angle=degree,
-                border_value=self.pal_val,
-                center=self.center,
-                auto_bound=self.auto_bound)
-
-            # rotate segs
-            for key in results.get('seg_fields', []):
-                results[key] = mmcv.imrotate(
-                    results[key],
-                    angle=degree,
-                    border_value=self.seg_pad_val,
-                    center=self.center,
-                    auto_bound=self.auto_bound,
-                    interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(prob={self.prob}, ' \
-                    f'degree={self.degree}, ' \
-                    f'pad_val={self.pal_val}, ' \
-                    f'seg_pad_val={self.seg_pad_val}, ' \
-                    f'center={self.center}, ' \
-                    f'auto_bound={self.auto_bound})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RGB2Gray(object):
-    """Convert RGB image to grayscale image.
-
-    This transform calculate the weighted mean of input image channels with
-    ``weights`` and then expand the channels to ``out_channels``. When
-    ``out_channels`` is None, the number of output channels is the same as
-    input channels.
-
-    Args:
-        out_channels (int): Expected number of output channels after
-            transforming. Default: None.
-        weights (tuple[float]): The weights to calculate the weighted mean.
-            Default: (0.299, 0.587, 0.114).
-    """
-
-    def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
-        assert out_channels is None or out_channels > 0
-        self.out_channels = out_channels
-        assert isinstance(weights, tuple)
-        for item in weights:
-            assert isinstance(item, (float, int))
-        self.weights = weights
-
-    def __call__(self, results):
-        """Call function to convert RGB image to grayscale image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with grayscale image.
-        """
-        img = results['img']
-        assert len(img.shape) == 3
-        assert img.shape[2] == len(self.weights)
-        weights = np.array(self.weights).reshape((1, 1, -1))
-        img = (img * weights).sum(2, keepdims=True)
-        if self.out_channels is None:
-            img = img.repeat(weights.shape[2], axis=2)
-        else:
-            img = img.repeat(self.out_channels, axis=2)
-
-        results['img'] = img
-        results['img_shape'] = img.shape
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(out_channels={self.out_channels}, ' \
-                    f'weights={self.weights})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class AdjustGamma(object):
-    """Using gamma correction to process the image.
-
-    Args:
-        gamma (float or int): Gamma value used in gamma correction.
-            Default: 1.0.
-    """
-
-    def __init__(self, gamma=1.0):
-        assert isinstance(gamma, float) or isinstance(gamma, int)
-        assert gamma > 0
-        self.gamma = gamma
-        inv_gamma = 1.0 / gamma
-        self.table = np.array([(i / 255.0)**inv_gamma * 255
-                               for i in np.arange(256)]).astype('uint8')
-
-    def __call__(self, results):
-        """Call function to process the image with gamma correction.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        results['img'] = mmcv.lut_transform(
-            np.array(results['img'], dtype=np.uint8), self.table)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(gamma={self.gamma})'
-
-
-@PIPELINES.register_module()
-class SegRescale(object):
-    """Rescale semantic segmentation maps.
-
-    Args:
-        scale_factor (float): The scale factor of the final output.
-    """
-
-    def __init__(self, scale_factor=1):
-        self.scale_factor = scale_factor
-
-    def __call__(self, results):
-        """Call function to scale the semantic segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with semantic segmentation map scaled.
-        """
-        for key in results.get('seg_fields', []):
-            if self.scale_factor != 1:
-                results[key] = mmcv.imrescale(
-                    results[key], self.scale_factor, interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
-
-
-@PIPELINES.register_module()
-class PhotoMetricDistortion(object):
-    """Apply photometric distortion to image sequentially, every transformation
-    is applied with a probability of 0.5. The position of random contrast is in
-    second or second to last.
-
-    1. random brightness
-    2. random contrast (mode 0)
-    3. convert color from BGR to HSV
-    4. random saturation
-    5. random hue
-    6. convert color from HSV to BGR
-    7. random contrast (mode 1)
-
-    Args:
-        brightness_delta (int): delta of brightness.
-        contrast_range (tuple): range of contrast.
-        saturation_range (tuple): range of saturation.
-        hue_delta (int): delta of hue.
-    """
-
-    def __init__(self,
-                 brightness_delta=32,
-                 contrast_range=(0.5, 1.5),
-                 saturation_range=(0.5, 1.5),
-                 hue_delta=18):
-        self.brightness_delta = brightness_delta
-        self.contrast_lower, self.contrast_upper = contrast_range
-        self.saturation_lower, self.saturation_upper = saturation_range
-        self.hue_delta = hue_delta
-
-    def convert(self, img, alpha=1, beta=0):
-        """Multiple with alpha and add beat with clip."""
-        img = img.astype(np.float32) * alpha + beta
-        img = np.clip(img, 0, 255)
-        return img.astype(np.uint8)
-
-    def brightness(self, img):
-        """Brightness distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                beta=random.uniform(-self.brightness_delta,
-                                    self.brightness_delta))
-        return img
-
-    def contrast(self, img):
-        """Contrast distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                alpha=random.uniform(self.contrast_lower, self.contrast_upper))
-        return img
-
-    def saturation(self, img):
-        """Saturation distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :, 1] = self.convert(
-                img[:, :, 1],
-                alpha=random.uniform(self.saturation_lower,
-                                     self.saturation_upper))
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def hue(self, img):
-        """Hue distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :,
-                0] = (img[:, :, 0].astype(int) +
-                      random.randint(-self.hue_delta, self.hue_delta)) % 180
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def __call__(self, results):
-        """Call function to perform photometric distortion on images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images distorted.
-        """
-
-        img = results['img']
-        # random brightness
-        img = self.brightness(img)
-
-        # mode == 0 --> do random contrast first
-        # mode == 1 --> do random contrast last
-        mode = random.randint(2)
-        if mode == 1:
-            img = self.contrast(img)
-
-        # random saturation
-        img = self.saturation(img)
-
-        # random hue
-        img = self.hue(img)
-
-        # random contrast
-        if mode == 0:
-            img = self.contrast(img)
-
-        results['img'] = img
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(brightness_delta={self.brightness_delta}, '
-                     f'contrast_range=({self.contrast_lower}, '
-                     f'{self.contrast_upper}), '
-                     f'saturation_range=({self.saturation_lower}, '
-                     f'{self.saturation_upper}), '
-                     f'hue_delta={self.hue_delta})')
-        return repr_str
diff --git a/spaces/MirageML/sjc/sd1/ldm/modules/losses/vqperceptual.py b/spaces/MirageML/sjc/sd1/ldm/modules/losses/vqperceptual.py
deleted file mode 100644
index f69981769e4bd5462600458c4fcf26620f7e4306..0000000000000000000000000000000000000000
--- a/spaces/MirageML/sjc/sd1/ldm/modules/losses/vqperceptual.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from einops import repeat
-
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
-from taming.modules.losses.lpips import LPIPS
-from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
-
-
-def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
-    assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
-    loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
-    loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
-    loss_real = (weights * loss_real).sum() / weights.sum()
-    loss_fake = (weights * loss_fake).sum() / weights.sum()
-    d_loss = 0.5 * (loss_real + loss_fake)
-    return d_loss
-
-def adopt_weight(weight, global_step, threshold=0, value=0.):
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-def measure_perplexity(predicted_indices, n_embed):
-    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
-    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
-    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
-    avg_probs = encodings.mean(0)
-    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
-    cluster_use = torch.sum(avg_probs > 0)
-    return perplexity, cluster_use
-
-def l1(x, y):
-    return torch.abs(x-y)
-
-
-def l2(x, y):
-    return torch.pow((x-y), 2)
-
-
-class VQLPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
-                 pixel_loss="l1"):
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        assert perceptual_loss in ["lpips", "clips", "dists"]
-        assert pixel_loss in ["l1", "l2"]
-        self.codebook_weight = codebook_weight
-        self.pixel_weight = pixelloss_weight
-        if perceptual_loss == "lpips":
-            print(f"{self.__class__.__name__}: Running with LPIPS.")
-            self.perceptual_loss = LPIPS().eval()
-        else:
-            raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
-        self.perceptual_weight = perceptual_weight
-
-        if pixel_loss == "l1":
-            self.pixel_loss = l1
-        else:
-            self.pixel_loss = l2
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ndf=disc_ndf
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-        self.n_classes = n_classes
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
-        if not exists(codebook_loss):
-            codebook_loss = torch.tensor([0.]).to(inputs.device)
-        #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss
-        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        nll_loss = torch.mean(nll_loss)
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/p_loss".format(split): p_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            if predicted_indices is not None:
-                assert self.n_classes is not None
-                with torch.no_grad():
-                    perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
-                log[f"{split}/perplexity"] = perplexity
-                log[f"{split}/cluster_usage"] = cluster_usage
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
diff --git a/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/model/SurfaceClassifier.py b/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/model/SurfaceClassifier.py
deleted file mode 100644
index af5afe4fdd4767f72549df258e5b67dea6ac671d..0000000000000000000000000000000000000000
--- a/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/model/SurfaceClassifier.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class SurfaceClassifier(nn.Module):
-    def __init__(self, filter_channels, num_views=1, no_residual=True, last_op=None):
-        super(SurfaceClassifier, self).__init__()
-
-        self.filters = []
-        self.num_views = num_views
-        self.no_residual = no_residual
-        filter_channels = filter_channels
-        self.last_op = last_op
-
-        if self.no_residual:
-            for l in range(0, len(filter_channels) - 1):
-                self.filters.append(nn.Conv1d(
-                    filter_channels[l],
-                    filter_channels[l + 1],
-                    1))
-                self.add_module("conv%d" % l, self.filters[l])
-        else:
-            for l in range(0, len(filter_channels) - 1):
-                if 0 != l:
-                    self.filters.append(
-                        nn.Conv1d(
-                            filter_channels[l] + filter_channels[0],
-                            filter_channels[l + 1],
-                            1))
-                else:
-                    self.filters.append(nn.Conv1d(
-                        filter_channels[l],
-                        filter_channels[l + 1],
-                        1))
-
-                self.add_module("conv%d" % l, self.filters[l])
-
-    def forward(self, feature):
-        '''
-
-        :param feature: list of [BxC_inxHxW] tensors of image features
-        :param xy: [Bx3xN] tensor of (x,y) coodinates in the image plane
-        :return: [BxC_outxN] tensor of features extracted at the coordinates
-        '''
-
-        y = feature
-        tmpy = feature
-        for i, f in enumerate(self.filters):
-            if self.no_residual:
-                y = self._modules['conv' + str(i)](y)
-            else:
-                y = self._modules['conv' + str(i)](
-                    y if i == 0
-                    else torch.cat([y, tmpy], 1)
-                )
-            if i != len(self.filters) - 1:
-                y = F.leaky_relu(y)
-
-            if self.num_views > 1 and i == len(self.filters) // 2:
-                y = y.view(
-                    -1, self.num_views, y.shape[1], y.shape[2]
-                ).mean(dim=1)
-                tmpy = feature.view(
-                    -1, self.num_views, feature.shape[1], feature.shape[2]
-                ).mean(dim=1)
-
-        if self.last_op:
-            y = self.last_op(y)
-
-        return y
diff --git a/spaces/MohitGupta/Eng2Indic_Translitration/transliteration/__metadata.py b/spaces/MohitGupta/Eng2Indic_Translitration/transliteration/__metadata.py
deleted file mode 100644
index 5becc17c04a9e3ad1c2a15f53252b7bb5a7517e7..0000000000000000000000000000000000000000
--- a/spaces/MohitGupta/Eng2Indic_Translitration/transliteration/__metadata.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "1.0.0"
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/detectors/mmdet_wrapper.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/detectors/mmdet_wrapper.py
deleted file mode 100644
index 1d6be8caa6469ab2da2e55eb1f645f9129037490..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/detectors/mmdet_wrapper.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, List, Optional, Tuple, Union
-
-import cv2
-import torch
-from mmdet.structures import DetDataSample
-from mmdet.structures import SampleList as MMDET_SampleList
-from mmdet.structures.mask import bitmap_to_polygon
-from mmengine.model import BaseModel
-from mmengine.structures import InstanceData
-
-from mmocr.registry import MODELS
-from mmocr.utils.bbox_utils import bbox2poly
-from mmocr.utils.typing_utils import DetSampleList
-
-ForwardResults = Union[Dict[str, torch.Tensor], List[DetDataSample],
-                       Tuple[torch.Tensor], torch.Tensor]
-
-
-@MODELS.register_module()
-class MMDetWrapper(BaseModel):
-    """A wrapper of MMDet's model.
-
-    Args:
-        cfg (dict): The config of the model.
-        text_repr_type (str): The boundary encoding type 'poly' or 'quad'.
-            Defaults to 'poly'.
-    """
-
-    def __init__(self, cfg: Dict, text_repr_type: str = 'poly') -> None:
-        data_preprocessor = cfg.pop('data_preprocessor')
-        data_preprocessor.update(_scope_='mmdet')
-        super().__init__(data_preprocessor=data_preprocessor, init_cfg=None)
-        cfg['_scope_'] = 'mmdet'
-        self.wrapped_model = MODELS.build(cfg)
-        self.text_repr_type = text_repr_type
-
-    def forward(self,
-                inputs: torch.Tensor,
-                data_samples: Optional[Union[DetSampleList,
-                                             MMDET_SampleList]] = None,
-                mode: str = 'tensor',
-                **kwargs) -> ForwardResults:
-        """The unified entry for a forward process in both training and test.
-
-        The method works in three modes: "tensor", "predict" and "loss":
-
-        - "tensor": Forward the whole network and return tensor or tuple of
-        tensor without any post-processing, same as a common nn.Module.
-        - "predict": Forward and return the predictions, which are fully
-        processed to a list of :obj:`DetDataSample`.
-        - "loss": Forward and return a dict of losses according to the given
-        inputs and data samples.
-
-        Note that this method doesn't handle either back propagation or
-        parameter update, which are supposed to be done in :meth:`train_step`.
-
-        Args:
-            inputs (torch.Tensor): The input tensor with shape
-                (N, C, ...) in general.
-            data_samples (list[:obj:`DetDataSample`] or
-                list[:obj:`TextDetDataSample`]): The annotation data of every
-                sample. When in "predict" mode, it should be a list of
-                :obj:`TextDetDataSample`. Otherwise they are
-                :obj:`DetDataSample`s. Defaults to None.
-            mode (str): Running mode. Defaults to 'tensor'.
-
-        Returns:
-            The return type depends on ``mode``.
-
-            - If ``mode="tensor"``, return a tensor or a tuple of tensor.
-            - If ``mode="predict"``, return a list of :obj:`TextDetDataSample`.
-            - If ``mode="loss"``, return a dict of tensor.
-        """
-        if mode == 'predict':
-            ocr_data_samples = data_samples
-            data_samples = []
-            for i in range(len(ocr_data_samples)):
-                data_samples.append(
-                    DetDataSample(metainfo=ocr_data_samples[i].metainfo))
-
-        results = self.wrapped_model.forward(inputs, data_samples, mode,
-                                             **kwargs)
-
-        if mode == 'predict':
-            results = self.adapt_predictions(results, ocr_data_samples)
-
-        return results
-
-    def adapt_predictions(self, data: MMDET_SampleList,
-                          data_samples: DetSampleList) -> DetSampleList:
-        """Convert Instance datas from MMDet into MMOCR's format.
-
-        Args:
-            data: (list[DetDataSample]): Detection results of the
-                input images. Each DetDataSample usually contain
-                'pred_instances'. And the ``pred_instances`` usually
-                contains following keys.
-                - scores (Tensor): Classification scores, has a shape
-                    (num_instance, )
-                - labels (Tensor): Labels of bboxes, has a shape
-                    (num_instances, ).
-                - bboxes (Tensor): Has a shape (num_instances, 4),
-                    the last dimension 4 arrange as (x1, y1, x2, y2).
-                - masks (Tensor, Optional): Has a shape (num_instances, H, W).
-            data_samples (list[:obj:`TextDetDataSample`]): The annotation data
-                of every samples.
-
-        Returns:
-            list[TextDetDataSample]: A list of N datasamples containing ground
-                truth and prediction results.
-                The polygon results are saved in
-                ``TextDetDataSample.pred_instances.polygons``
-                The confidence scores are saved in
-                ``TextDetDataSample.pred_instances.scores``.
-        """
-        for i, det_data_sample in enumerate(data):
-            data_samples[i].pred_instances = InstanceData()
-            # convert mask to polygons if mask exists
-            if 'masks' in det_data_sample.pred_instances.keys():
-                masks = det_data_sample.pred_instances.masks.cpu().numpy()
-                polygons = []
-                scores = []
-                for mask_idx, mask in enumerate(masks):
-                    contours, _ = bitmap_to_polygon(mask)
-                    polygons += [contour.reshape(-1) for contour in contours]
-                    scores += [
-                        det_data_sample.pred_instances.scores[mask_idx].cpu()
-                    ] * len(contours)
-                # filter invalid polygons
-                filterd_polygons = []
-                keep_idx = []
-                for poly_idx, polygon in enumerate(polygons):
-                    if len(polygon) < 6:
-                        continue
-                    filterd_polygons.append(polygon)
-                    keep_idx.append(poly_idx)
-                # convert by text_repr_type
-                if self.text_repr_type == 'quad':
-                    for j, poly in enumerate(filterd_polygons):
-                        rect = cv2.minAreaRect(poly)
-                        vertices = cv2.boxPoints(rect)
-                        poly = vertices.flatten()
-                        filterd_polygons[j] = poly
-
-                data_samples[i].pred_instances.polygons = filterd_polygons
-                data_samples[i].pred_instances.scores = torch.FloatTensor(
-                    scores)[keep_idx]
-            else:
-                bboxes = det_data_sample.pred_instances.bboxes.cpu().numpy()
-                polygons = [bbox2poly(bbox) for bbox in bboxes]
-                data_samples[i].pred_instances.polygons = polygons
-                data_samples[i].pred_instances.scores = torch.FloatTensor(
-                    det_data_sample.pred_instances.scores.cpu())
-
-        return data_samples
diff --git a/spaces/Msp/Document_Parser/app.py b/spaces/Msp/Document_Parser/app.py
deleted file mode 100644
index 58215fe7f54ceb0a818850e72ca47b9af5ef0d16..0000000000000000000000000000000000000000
--- a/spaces/Msp/Document_Parser/app.py
+++ /dev/null
@@ -1,419 +0,0 @@
-import os
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-from PIL import Image, ImageDraw
-import traceback
-
-import gradio as gr
-
-import torch
-from docquery import pipeline
-from docquery.document import load_document, ImageDocument
-from docquery.ocr_reader import get_ocr_reader
-
-
-def ensure_list(x):
-    if isinstance(x, list):
-        return x
-    else:
-        return [x]
-
-
-CHECKPOINTS = {
-    "LayoutLMv1 🦉": "impira/layoutlm-document-qa",
-    "LayoutLMv1 for Invoices 💸": "impira/layoutlm-invoices",
-    "Donut 🍩": "naver-clova-ix/donut-base-finetuned-docvqa",
-}
-
-PIPELINES = {}
-
-
-def construct_pipeline(task, model):
-    global PIPELINES
-    if model in PIPELINES:
-        return PIPELINES[model]
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    ret = pipeline(task=task, model=CHECKPOINTS[model], device=device)
-    PIPELINES[model] = ret
-    return ret
-
-
-def run_pipeline(model, question, document, top_k):
-    pipeline = construct_pipeline("document-question-answering", model)
-    return pipeline(question=question, **document.context, top_k=top_k)
-
-
-# TODO: Move into docquery
-# TODO: Support words past the first page (or window?)
-def lift_word_boxes(document, page):
-    return document.context["image"][page][1]
-
-
-def expand_bbox(word_boxes):
-    if len(word_boxes) == 0:
-        return None
-
-    min_x, min_y, max_x, max_y = zip(*[x[1] for x in word_boxes])
-    min_x, min_y, max_x, max_y = [min(min_x), min(min_y), max(max_x), max(max_y)]
-    return [min_x, min_y, max_x, max_y]
-
-
-# LayoutLM boxes are normalized to 0, 1000
-def normalize_bbox(box, width, height, padding=0.005):
-    min_x, min_y, max_x, max_y = [c / 1000 for c in box]
-    if padding != 0:
-        min_x = max(0, min_x - padding)
-        min_y = max(0, min_y - padding)
-        max_x = min(max_x + padding, 1)
-        max_y = min(max_y + padding, 1)
-    return [min_x * width, min_y * height, max_x * width, max_y * height]
-
-
-examples = [
-    [
-        "invoice.png",
-        "What is the invoice number?",
-    ],
-    [
-        "contract.jpeg",
-        "What is the purchase amount?",
-    ],
-    [
-        "statement.png",
-        "What are net sales for 2020?",
-    ],
-    
-]
-
-question_files = {
-    "What are net sales for 2020?": "statement.pdf",
-    "How many likes does the space have?": "https://huggingface.co/spaces/impira/docquery",
-    "What is the title of post number 5?": "https://news.ycombinator.com",
-}
-
-
-def process_path(path):
-    error = None
-    if path:
-        try:
-            document = load_document(path)
-            return (
-                document,
-                gr.update(visible=True, value=document.preview),
-                gr.update(visible=True),
-                gr.update(visible=False, value=None),
-                gr.update(visible=False, value=None),
-                None,
-            )
-        except Exception as e:
-            traceback.print_exc()
-            error = str(e)
-    return (
-        None,
-        gr.update(visible=False, value=None),
-        gr.update(visible=False),
-        gr.update(visible=False, value=None),
-        gr.update(visible=False, value=None),
-        gr.update(visible=True, value=error) if error is not None else None,
-        None,
-    )
-
-
-def process_upload(file):
-    if file:
-        return process_path(file.name)
-    else:
-        return (
-            None,
-            gr.update(visible=False, value=None),
-            gr.update(visible=False),
-            gr.update(visible=False, value=None),
-            gr.update(visible=False, value=None),
-            None,
-        )
-
-
-colors = ["#64A087", "green", "black"]
-
-
-def process_question(question, document, model=list(CHECKPOINTS.keys())[0]):
-    if not question or document is None:
-        return None, None, None
-
-    text_value = None
-    predictions = run_pipeline(model, question, document, 3)
-    pages = [x.copy().convert("RGB") for x in document.preview]
-    for i, p in enumerate(ensure_list(predictions)):
-        if i == 0:
-            text_value = p["answer"]
-        else:
-            # Keep the code around to produce multiple boxes, but only show the top
-            # prediction for now
-            break
-
-        if "word_ids" in p:
-            image = pages[p["page"]]
-            draw = ImageDraw.Draw(image, "RGBA")
-            word_boxes = lift_word_boxes(document, p["page"])
-            x1, y1, x2, y2 = normalize_bbox(
-                expand_bbox([word_boxes[i] for i in p["word_ids"]]),
-                image.width,
-                image.height,
-            )
-            draw.rectangle(((x1, y1), (x2, y2)), fill=(0, 255, 0, int(0.4 * 255)))
-
-    return (
-        gr.update(visible=True, value=pages),
-        gr.update(visible=True, value=predictions),
-        gr.update(
-            visible=True,
-            value=text_value,
-        ),
-    )
-
-
-def load_example_document(img, question, model):
-    if img is not None:
-        if question in question_files:
-            document = load_document(question_files[question])
-        else:
-            document = ImageDocument(Image.fromarray(img), get_ocr_reader())
-        preview, answer, answer_text = process_question(question, document, model)
-        return document, question, preview, gr.update(visible=True), answer, answer_text
-    else:
-        return None, None, None, gr.update(visible=False), None, None
-
-
-CSS = """
-#question input {
-    font-size: 16px;
-}
-#url-textbox {
-    padding: 0 !important;
-}
-#short-upload-box .w-full {
-    min-height: 10rem !important;
-}
-/* I think something like this can be used to re-shape
- * the table
- */
-/*
-.gr-samples-table tr {
-    display: inline;
-}
-.gr-samples-table .p-2 {
-    width: 100px;
-}
-*/
-#select-a-file {
-    width: 100%;
-}
-#file-clear {
-    padding-top: 2px !important;
-    padding-bottom: 2px !important;
-    padding-left: 8px !important;
-    padding-right: 8px !important;
-	margin-top: 10px;
-}
-.gradio-container .gr-button-primary {
-    background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%);
-    border: 1px solid #B0DCCC;
-    border-radius: 8px;
-    color: #1B8700;
-}
-.gradio-container.dark button#submit-button {
-    background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%);
-    border: 1px solid #B0DCCC;
-    border-radius: 8px;
-    color: #1B8700
-}
-
-table.gr-samples-table tr td {
-    border: none;
-    outline: none;
-}
-
-table.gr-samples-table tr td:first-of-type {
-    width: 0%;
-}
-
-div#short-upload-box div.absolute {
-    display: none !important;
-}
-
-gradio-app > div > div > div > div.w-full > div, .gradio-app > div > div > div > div.w-full > div {
-    gap: 0px 2%;
-}
-
-gradio-app div div div div.w-full, .gradio-app div div div div.w-full {
-    gap: 0px;
-}
-
-gradio-app h2, .gradio-app h2 {
-    padding-top: 10px;
-}
-
-#answer {
-    overflow-y: scroll;
-    color: white;
-    background: #666;
-    border-color: #666;
-    font-size: 20px;
-    font-weight: bold;
-}
-
-#answer span {
-    color: white;
-}
-
-#answer textarea {
-    color:white;
-    background: #777;
-    border-color: #777;
-    font-size: 18px;
-}
-
-#url-error input {
-    color: red;
-}
-"""
-
-with gr.Blocks(css=CSS) as demo:
-    gr.Markdown("# Document Parser: Document Parser Engine")
-    gr.Markdown(
-        "Document_Parser is built on top of DocQuery library)"
-        " uses LayoutLMv1 fine-tuned on DocVQA, a document visual question"
-        " answering dataset, as well as SQuAD, which boosts its English-language comprehension."
-       
-    )
-
-    document = gr.Variable()
-    example_question = gr.Textbox(visible=False)
-    example_image = gr.Image(visible=False)
-
-    with gr.Row(equal_height=True):
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## 1. Select a file", elem_id="select-a-file")
-                img_clear_button = gr.Button(
-                    "Clear", variant="secondary", elem_id="file-clear", visible=False
-                )
-            image = gr.Gallery(visible=False)
-            with gr.Row(equal_height=True):
-                with gr.Column():
-                    with gr.Row():
-                        url = gr.Textbox(
-                            show_label=False,
-                            placeholder="URL",
-                            lines=1,
-                            max_lines=1,
-                            elem_id="url-textbox",
-                        )
-                        submit = gr.Button("Get")
-                    url_error = gr.Textbox(
-                        visible=False,
-                        elem_id="url-error",
-                        max_lines=1,
-                        interactive=False,
-                        label="Error",
-                    )
-            gr.Markdown("— or —")
-            upload = gr.File(label=None, interactive=True, elem_id="short-upload-box")
-            gr.Examples(
-                examples=examples,
-                inputs=[example_image, example_question],
-            )
-
-        with gr.Column() as col:
-            gr.Markdown("## 2. Ask a question")
-            question = gr.Textbox(
-                label="Question",
-                placeholder="e.g. What is the invoice number?",
-                lines=1,
-                max_lines=1,
-            )
-            model = gr.Radio(
-                choices=list(CHECKPOINTS.keys()),
-                value=list(CHECKPOINTS.keys())[0],
-                label="Model",
-            )
-
-            with gr.Row():
-                clear_button = gr.Button("Clear", variant="secondary")
-                submit_button = gr.Button(
-                    "Submit", variant="primary", elem_id="submit-button"
-                )
-            with gr.Column():
-                output_text = gr.Textbox(
-                    label="Top Answer", visible=False, elem_id="answer"
-                )
-                output = gr.JSON(label="Output", visible=False)
-
-    for cb in [img_clear_button, clear_button]:
-        cb.click(
-            lambda _: (
-                gr.update(visible=False, value=None),
-                None,
-                gr.update(visible=False, value=None),
-                gr.update(visible=False, value=None),
-                gr.update(visible=False),
-                None,
-                None,
-                None,
-                gr.update(visible=False, value=None),
-                None,
-            ),
-            inputs=clear_button,
-            outputs=[
-                image,
-                document,
-                output,
-                output_text,
-                img_clear_button,
-                example_image,
-                upload,
-                url,
-                url_error,
-                question,
-            ],
-        )
-
-    upload.change(
-        fn=process_upload,
-        inputs=[upload],
-        outputs=[document, image, img_clear_button, output, output_text, url_error],
-    )
-    submit.click(
-        fn=process_path,
-        inputs=[url],
-        outputs=[document, image, img_clear_button, output, output_text, url_error],
-    )
-
-    question.submit(
-        fn=process_question,
-        inputs=[question, document, model],
-        outputs=[image, output, output_text],
-    )
-
-    submit_button.click(
-        process_question,
-        inputs=[question, document, model],
-        outputs=[image, output, output_text],
-    )
-
-    model.change(
-        process_question,
-        inputs=[question, document, model],
-        outputs=[image, output, output_text],
-    )
-
-    example_image.change(
-        fn=load_example_document,
-        inputs=[example_image, example_question, model],
-        outputs=[document, question, image, img_clear_button, output, output_text],
-    )
-
-if __name__ == "__main__":
-    demo.launch(enable_queue=False)
diff --git a/spaces/NATSpeech/PortaSpeech/utils/audio/pitch/utils.py b/spaces/NATSpeech/PortaSpeech/utils/audio/pitch/utils.py
deleted file mode 100644
index 238b8022185753a7d4d9d674d189a99050c29b6f..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/utils/audio/pitch/utils.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import numpy as np
-import torch
-
-
-def to_lf0(f0):
-    f0[f0 < 1.0e-5] = 1.0e-6
-    lf0 = f0.log() if isinstance(f0, torch.Tensor) else np.log(f0)
-    lf0[f0 < 1.0e-5] = - 1.0E+10
-    return lf0
-
-
-def to_f0(lf0):
-    f0 = np.where(lf0 <= 0, 0.0, np.exp(lf0))
-    return f0.flatten()
-
-
-def f0_to_coarse(f0, f0_bin=256, f0_max=900.0, f0_min=50.0):
-    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-    is_torch = isinstance(f0, torch.Tensor)
-    f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
-
-    f0_mel[f0_mel <= 1] = 1
-    f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
-    f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(int)
-    assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min(), f0.min(), f0.max())
-    return f0_coarse
-
-
-def coarse_to_f0(f0_coarse, f0_bin=256, f0_max=900.0, f0_min=50.0):
-    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-    uv = f0_coarse == 1
-    f0 = f0_mel_min + (f0_coarse - 1) * (f0_mel_max - f0_mel_min) / (f0_bin - 2)
-    f0 = ((f0 / 1127).exp() - 1) * 700
-    f0[uv] = 0
-    return f0
-
-
-def norm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100):
-    is_torch = isinstance(f0, torch.Tensor)
-    if pitch_norm == 'standard':
-        f0 = (f0 - f0_mean) / f0_std
-    if pitch_norm == 'log':
-        f0 = torch.log2(f0 + 1e-8) if is_torch else np.log2(f0 + 1e-8)
-    if uv is not None:
-        f0[uv > 0] = 0
-    return f0
-
-
-def norm_interp_f0(f0, pitch_norm='log', f0_mean=None, f0_std=None):
-    is_torch = isinstance(f0, torch.Tensor)
-    if is_torch:
-        device = f0.device
-        f0 = f0.data.cpu().numpy()
-    uv = f0 == 0
-    f0 = norm_f0(f0, uv, pitch_norm, f0_mean, f0_std)
-    if sum(uv) == len(f0):
-        f0[uv] = 0
-    elif sum(uv) > 0:
-        f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
-    if is_torch:
-        uv = torch.FloatTensor(uv)
-        f0 = torch.FloatTensor(f0)
-        f0 = f0.to(device)
-        uv = uv.to(device)
-    return f0, uv
-
-
-def denorm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100, pitch_padding=None, min=50, max=900):
-    is_torch = isinstance(f0, torch.Tensor)
-    if pitch_norm == 'standard':
-        f0 = f0 * f0_std + f0_mean
-    if pitch_norm == 'log':
-        f0 = 2 ** f0
-    f0 = f0.clamp(min=min, max=max) if is_torch else np.clip(f0, a_min=min, a_max=max)
-    if uv is not None:
-        f0[uv > 0] = 0
-    if pitch_padding is not None:
-        f0[pitch_padding] = 0
-    return f0
diff --git a/spaces/NSect/multitrack-midi-music-generator/string_to_notes.py b/spaces/NSect/multitrack-midi-music-generator/string_to_notes.py
deleted file mode 100644
index ba1d9525678b3e0ae270e8f2e06e9be443a2c112..0000000000000000000000000000000000000000
--- a/spaces/NSect/multitrack-midi-music-generator/string_to_notes.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from typing import Optional
-
-from note_seq.protobuf.music_pb2 import NoteSequence
-from note_seq.constants import STANDARD_PPQ
-
-
-def token_sequence_to_note_sequence(
-    token_sequence: str,
-    qpm: float = 120.0,
-    use_program: bool = True,
-    use_drums: bool = True,
-    instrument_mapper: Optional[dict] = None,
-    only_piano: bool = False,
-) -> NoteSequence:
-    """
-    Converts a sequence of tokens into a sequence of notes.
-
-    Args:
-        token_sequence (str): The sequence of tokens to convert.
-        qpm (float, optional): The quarter notes per minute. Defaults to 120.0.
-        use_program (bool, optional): Whether to use program. Defaults to True.
-        use_drums (bool, optional): Whether to use drums. Defaults to True.
-        instrument_mapper (Optional[dict], optional): The instrument mapper. Defaults to None.
-        only_piano (bool, optional): Whether to only use piano. Defaults to False.
-
-    Returns:
-        NoteSequence: The resulting sequence of notes.
-    """
-    if isinstance(token_sequence, str):
-        token_sequence = token_sequence.split()
-
-    note_sequence = empty_note_sequence(qpm)
-
-    # Compute note and bar lengths based on the provided QPM
-    note_length_16th = 0.25 * 60 / qpm
-    bar_length = 4.0 * 60 / qpm
-
-    # Render all notes.
-    current_program = 1
-    current_is_drum = False
-    current_instrument = 0
-    track_count = 0
-    for _, token in enumerate(token_sequence):
-        if token == "PIECE_START":
-            pass
-        elif token == "PIECE_END":
-            break
-        elif token == "TRACK_START":
-            current_bar_index = 0
-            track_count += 1
-            pass
-        elif token == "TRACK_END":
-            pass
-        elif token == "KEYS_START":
-            pass
-        elif token == "KEYS_END":
-            pass
-        elif token.startswith("KEY="):
-            pass
-        elif token.startswith("INST"):
-            instrument = token.split("=")[-1]
-            if instrument != "DRUMS" and use_program:
-                if instrument_mapper is not None:
-                    if instrument in instrument_mapper:
-                        instrument = instrument_mapper[instrument]
-                current_program = int(instrument)
-                current_instrument = track_count
-                current_is_drum = False
-            if instrument == "DRUMS" and use_drums:
-                current_instrument = 0
-                current_program = 0
-                current_is_drum = True
-        elif token == "BAR_START":
-            current_time = current_bar_index * bar_length
-            current_notes = {}
-        elif token == "BAR_END":
-            current_bar_index += 1
-            pass
-        elif token.startswith("NOTE_ON"):
-            pitch = int(token.split("=")[-1])
-            note = note_sequence.notes.add()
-            note.start_time = current_time
-            note.end_time = current_time + 4 * note_length_16th
-            note.pitch = pitch
-            note.instrument = current_instrument
-            note.program = current_program
-            note.velocity = 80
-            note.is_drum = current_is_drum
-            current_notes[pitch] = note
-        elif token.startswith("NOTE_OFF"):
-            pitch = int(token.split("=")[-1])
-            if pitch in current_notes:
-                note = current_notes[pitch]
-                note.end_time = current_time
-        elif token.startswith("TIME_DELTA"):
-            delta = float(token.split("=")[-1]) * note_length_16th
-            current_time += delta
-        elif token.startswith("DENSITY="):
-            pass
-        elif token == "[PAD]":
-            pass
-        else:
-            pass
-
-    # Make the instruments right.
-    instruments_drums = []
-    for note in note_sequence.notes:
-        pair = [note.program, note.is_drum]
-        if pair not in instruments_drums:
-            instruments_drums += [pair]
-        note.instrument = instruments_drums.index(pair)
-
-    if only_piano:
-        for note in note_sequence.notes:
-            if not note.is_drum:
-                note.instrument = 0
-                note.program = 0
-
-    return note_sequence
-
-
-def empty_note_sequence(qpm: float = 120.0, total_time: float = 0.0) -> NoteSequence:
-    """
-    Creates an empty note sequence.
-
-    Args:
-        qpm (float, optional): The quarter notes per minute. Defaults to 120.0.
-        total_time (float, optional): The total time. Defaults to 0.0.
-
-    Returns:
-        NoteSequence: The empty note sequence.
-    """
-    note_sequence = NoteSequence()
-    note_sequence.tempos.add().qpm = qpm
-    note_sequence.ticks_per_quarter = STANDARD_PPQ
-    note_sequence.total_time = total_time
-    return note_sequence
diff --git a/spaces/Namit2111/ChatGpt_Detector/README.md b/spaces/Namit2111/ChatGpt_Detector/README.md
deleted file mode 100644
index 185f5334a8ec9e7a7bc825b07b385bb98496dd97..0000000000000000000000000000000000000000
--- a/spaces/Namit2111/ChatGpt_Detector/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChatGpt Detector
-emoji: 🏃
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NimaBoscarino/climategan/tests/test_trainer.py b/spaces/NimaBoscarino/climategan/tests/test_trainer.py
deleted file mode 100644
index b84153c169aab557a3aee937ea13429c62a72ec0..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/tests/test_trainer.py
+++ /dev/null
@@ -1,384 +0,0 @@
-print("Imports...", end="", flush=True)
-
-import sys
-from pathlib import Path
-
-sys.path.append(str(Path(__file__).resolve().parent.parent))
-
-import atexit
-import logging
-from argparse import ArgumentParser
-from copy import deepcopy
-
-import comet_ml
-import climategan
-from comet_ml.api import API
-from climategan.trainer import Trainer
-from climategan.utils import get_comet_rest_api_key
-
-logging.basicConfig()
-logging.getLogger().setLevel(logging.ERROR)
-import traceback
-
-print("Done.")
-
-
-def set_opts(opts, str_nested_key, value):
-    """
-    Changes an opts with nested keys:
-    set_opts(addict.Dict(), "a.b.c", 2) == Dict({"a":{"b": {"c": 2}}})
-
-    Args:
-        opts (addict.Dict): opts whose values should be changed
-        str_nested_key (str): nested keys joined on "."
-        value (any): value to set to the nested keys of opts
-    """
-    keys = str_nested_key.split(".")
-    o = opts
-    for k in keys[:-1]:
-        o = o[k]
-    o[keys[-1]] = value
-
-
-def set_conf(opts, conf):
-    """
-    Updates opts according to a test scenario's configuration dict.
-    Ignores all keys starting with "__" which are used for the scenario
-    but outside the opts
-
-    Args:
-        opts (addict.Dict): trainer options
-        conf (dict): scenario's configuration
-    """
-    for k, v in conf.items():
-        if k.startswith("__"):
-            continue
-        set_opts(opts, k, v)
-
-
-class bcolors:
-    HEADER = "\033[95m"
-    OKBLUE = "\033[94m"
-    OKGREEN = "\033[92m"
-    WARNING = "\033[93m"
-    FAIL = "\033[91m"
-    ENDC = "\033[0m"
-    BOLD = "\033[1m"
-    UNDERLINE = "\033[4m"
-
-
-class Colors:
-    def _r(self, key, *args):
-        return f"{key}{' '.join(args)}{bcolors.ENDC}"
-
-    def ob(self, *args):
-        return self._r(bcolors.OKBLUE, *args)
-
-    def w(self, *args):
-        return self._r(bcolors.WARNING, *args)
-
-    def og(self, *args):
-        return self._r(bcolors.OKGREEN, *args)
-
-    def f(self, *args):
-        return self._r(bcolors.FAIL, *args)
-
-    def b(self, *args):
-        return self._r(bcolors.BOLD, *args)
-
-    def u(self, *args):
-        return self._r(bcolors.UNDERLINE, *args)
-
-
-def comet_handler(exp, api):
-    def sub_handler():
-        p = Colors()
-        print()
-        print(p.b(p.w("Deleting comet experiment")))
-        api.delete_experiment(exp.get_key())
-
-    return sub_handler
-
-
-def print_start(desc):
-    p = Colors()
-    cdesc = p.b(p.ob(desc))
-    title = "|  " + cdesc + "  |"
-    line = "-" * (len(desc) + 6)
-    print(f"{line}\n{title}\n{line}")
-
-
-def print_end(desc=None, ok=None):
-    p = Colors()
-    if ok and desc is None:
-        desc = "Done"
-        cdesc = p.b(p.og(desc))
-    elif not ok and desc is None:
-        desc = "! Fail !"
-        cdesc = p.b(p.f(desc))
-    elif desc is not None:
-        cdesc = p.b(p.og(desc))
-    else:
-        desc = "Unknown"
-        cdesc = desc
-
-    title = "|  " + cdesc + "  |"
-    line = "-" * (len(desc) + 6)
-    print(f"{line}\n{title}\n{line}\n")
-
-
-def delete_on_exit(exp):
-    """
-    Registers a callback to delete the comet exp at program exit
-
-    Args:
-        exp (comet_ml.Experiment): The exp to delete
-    """
-    rest_api_key = get_comet_rest_api_key()
-    api = API(api_key=rest_api_key)
-    atexit.register(comet_handler(exp, api))
-
-
-if __name__ == "__main__":
-
-    # -----------------------------
-    # -----  Parse Arguments  -----
-    # -----------------------------
-    parser = ArgumentParser()
-    parser.add_argument("--no_delete", action="store_true", default=False)
-    parser.add_argument("--no_end_to_end", action="store_true", default=False)
-    parser.add_argument("--include", "-i", nargs="+", default=[])
-    parser.add_argument("--exclude", "-e", nargs="+", default=[])
-    args = parser.parse_args()
-
-    assert not (args.include and args.exclude), "Choose 1: include XOR exclude"
-
-    include = set(int(i) for i in args.include)
-    exclude = set(int(i) for i in args.exclude)
-    if include:
-        print("Including exclusively tests", " ".join(args.include))
-    if exclude:
-        print("Excluding tests", " ".join(args.exclude))
-
-    # --------------------------------------
-    # -----  Create global experiment  -----
-    # --------------------------------------
-    print("Creating comet Experiment...", end="", flush=True)
-    global_exp = comet_ml.Experiment(
-        project_name="climategan-test", display_summary_level=0
-    )
-    print("Done.")
-
-    if not args.no_delete:
-        delete_on_exit(global_exp)
-
-    # prompt util for colors
-    prompt = Colors()
-
-    # -------------------------------------
-    # -----  Base Test Scenario Opts  -----
-    # -------------------------------------
-    print("Loading opts...", end="", flush=True)
-    base_opts = climategan.utils.load_opts()
-    base_opts.data.check_samples = False
-    base_opts.train.fid.n_images = 5
-    base_opts.comet.display_size = 5
-    base_opts.tasks = ["m", "s", "d"]
-    base_opts.domains = ["r", "s"]
-    base_opts.data.loaders.num_workers = 4
-    base_opts.data.loaders.batch_size = 2
-    base_opts.data.max_samples = 9
-    base_opts.train.epochs = 1
-    if isinstance(base_opts.data.transforms[-1].new_size, int):
-        base_opts.data.transforms[-1].new_size = 256
-    else:
-        base_opts.data.transforms[-1].new_size.default = 256
-    print("Done.")
-
-    # --------------------------------------
-    # -----  Configure Test Scenarios  -----
-    # --------------------------------------
-
-    # override any nested key in opts
-    # create scenario-specific variables with __key
-    # ALWAYS specify a __doc key to describe your scenario
-    test_scenarios = [
-        {"__use_comet": False, "__doc": "MSD no exp", "__verbose": 1},  # 0
-        {"__doc": "MSD with exp"},  # 1
-        {
-            "__doc": "MSD no exp upsample_featuremaps",  # 2
-            "__use_comet": False,
-            "gen.d.upsample_featuremaps": True,
-            "gen.s.upsample_featuremaps": True,
-        },
-        {"tasks": ["p"], "domains": ["rf"], "__doc": "Painter"},  # 3
-        {
-            "__doc": "M no exp low level feats",  # 4
-            "__use_comet": False,
-            "gen.m.use_low_level_feats": True,
-            "gen.m.use_dada": False,
-            "tasks": ["m"],
-        },
-        {
-            "__doc": "MSD no exp deeplabv2",  # 5
-            "__use_comet": False,
-            "gen.encoder.architecture": "deeplabv2",
-            "gen.s.architecture": "deeplabv2",
-        },
-        {
-            "__doc": "MSDP no End-to-end",  # 6
-            "domains": ["rf", "r", "s"],
-            "tasks": ["m", "s", "d", "p"],
-        },
-        {
-            "__doc": "MSDP inference only no exp",  # 7
-            "__inference": True,
-            "__use_comet": False,
-            "domains": ["rf", "r", "s"],
-            "tasks": ["m", "s", "d", "p"],
-        },
-        {
-            "__doc": "MSDP with End-to-end",  # 8
-            "__pl4m": True,
-            "domains": ["rf", "r", "s"],
-            "tasks": ["m", "s", "d", "p"],
-        },
-        {
-            "__doc": "Kitti pretrain",  # 9
-            "train.epochs": 2,
-            "train.kitti.pretrain": True,
-            "train.kitti.epochs": 1,
-            "domains": ["kitti", "r", "s"],
-            "train.kitti.batch_size": 2,
-        },
-        {"__doc": "Depth Dada archi", "gen.d.architecture": "dada"},  # 10
-        {
-            "__doc": "Depth Base archi",
-            "gen.d.architecture": "base",
-            "gen.m.use_dada": False,
-            "gen.s.use_dada": False,
-        },  # 11
-        {
-            "__doc": "Depth Base Classification",  # 12
-            "gen.d.architecture": "base",
-            "gen.d.classify.enable": True,
-            "gen.m.use_dada": False,
-            "gen.s.use_dada": False,
-        },
-        {
-            "__doc": "MSD Resnet V3+ backbone",
-            "gen.deeplabv3.backbone": "resnet",
-        },  # 13
-        {
-            "__use_comet": False,
-            "__doc": "MSD SPADE 12 (without x)",
-            "__verbose": 1,
-            "gen.m.use_spade": True,
-            "gen.m.spade.cond_nc": 12,
-        },  # 14
-        {
-            "__use_comet": False,
-            "__doc": "MSD SPADE 15 (with x)",
-            "__verbose": 1,
-            "gen.m.use_spade": True,
-            "gen.m.spade.cond_nc": 15,
-        },  # 15
-        {
-            "__use_comet": False,
-            "__doc": "Painter With Diff Augment",
-            "__verbose": 1,
-            "domains": ["rf"],
-            "tasks": ["p"],
-            "gen.p.diff_aug.use": True,
-        },  # 15
-        {
-            "__use_comet": False,
-            "__doc": "MSD DADA_s",
-            "__verbose": 1,
-            "gen.s.use_dada": True,
-            "gen.m.use_dada": False,
-        },  # 16
-        {
-            "__use_comet": False,
-            "__doc": "MSD DADA_ms",
-            "__verbose": 1,
-            "gen.s.use_dada": True,
-            "gen.m.use_dada": True,
-        },  # 17
-    ]
-
-    n_confs = len(test_scenarios)
-
-    fails = []
-    successes = []
-
-    # --------------------------------
-    # -----  Run Test Scenarios  -----
-    # --------------------------------
-
-    for test_idx, conf in enumerate(test_scenarios):
-        if test_idx in exclude or (include and test_idx not in include):
-            reason = (
-                "because it is in exclude"
-                if test_idx in exclude
-                else "because it is not in include"
-            )
-            print("Ignoring test", test_idx, reason)
-            continue
-
-        # copy base scenario opts
-        test_opts = deepcopy(base_opts)
-        # update with scenario configuration
-        set_conf(test_opts, conf)
-
-        # print scenario description
-        print_start(
-            f"[{test_idx}/{n_confs - 1}] "
-            + conf.get("__doc", "WARNING: no __doc for test scenario")
-        )
-        print()
-
-        comet = conf.get("__use_comet", True)
-        pl4m = conf.get("__pl4m", False)
-        inference = conf.get("__inference", False)
-        verbose = conf.get("__verbose", 0)
-
-        # set (or not) experiment
-        test_exp = None
-        if comet:
-            test_exp = global_exp
-
-        try:
-            # create trainer
-            trainer = Trainer(
-                opts=test_opts,
-                verbose=verbose,
-                comet_exp=test_exp,
-            )
-            trainer.functional_test_mode()
-
-            # set (or not) painter loss for masker (= end-to-end)
-            if pl4m:
-                trainer.use_pl4m = True
-
-            # test training procedure
-            trainer.setup(inference=inference)
-            if not inference:
-                trainer.train()
-
-            successes.append(test_idx)
-            ok = True
-        except Exception as e:
-            print(e)
-            print(traceback.format_exc())
-            fails.append(test_idx)
-            ok = False
-        finally:
-            print_end(ok=ok)
-
-    print_end(desc="     -----   Summary   -----     ")
-    if len(fails) == 0:
-        print("•• All scenarios were successful")
-    else:
-        print(f"•• {len(successes)}/{len(test_scenarios)} successful tests")
-        print(f"•• Failed test indices: {', '.join(map(str, fails))}")
diff --git a/spaces/NoamSiegel/gpt-workouts/app.py b/spaces/NoamSiegel/gpt-workouts/app.py
deleted file mode 100644
index f2a69218ea5cc7eddb0368aa50aa4a80955362bf..0000000000000000000000000000000000000000
--- a/spaces/NoamSiegel/gpt-workouts/app.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import openai
-import gradio as gr
-import os
-
-# Set up OpenAI API credentials
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-# function to generate a workout plan based on user inputs
-def generate_workout(
-			age,
-			weight,
-			height,
-			sex,
-			goal,
-			equipment,
-			years,
-			split,
-			days,
-			time,
-			rest,
-			cardio,
-			warm_up,
-			stretching
-		):
-
-	age = int(age)
-	
-	if warm_up:
-		warm_up = "Wants all their workout to include a warm-up"
-	else:
-		warm_up = "Doesn't want their workout to have a warm-up "
-	if stretching:
-		stretching = "Wants all their workouts to include stretching"
-	else:
-		stretching = "Doesn't want a portion of their workout to be stretching"
-
-	prompt = f"""
-	Individual_Exercise_Time = (Sets * Reps)
-	Total_Rest_Time = (Sets * Rest Time)
-	Total_Exercise_Time = Sum(Individual_Exercise_Time) + Total_Rest_Time
-	Total_Workout_Time = Total_Exercise_Time + Warm-Up Time + Stretching Time + Cardio Time
-	
-	Create a workout plan for an individual where you take into account that:
-	- Workout Time is {time} minutes or less
-	- They are {age} years old
-	- They weight {weight} pounds
-	- They are {height} inches high
-	- They are a {sex}
-	- Their primary goal is {goal}
-	- They have access to {equipment}
-	- They have {years} years of experience weight lifting
-	- They need a {split} workout split
-	- They want to work out {days} a week
-	- They want at least {rest} seconds of rest between sets
-	- They wants to add a {cardio} to each workout day
-	- {warm_up}
-	- {stretching}
-
-	For each exercise, write down the approximate time it will take. Do the same for each workout day. If "Total_Workout_Time" > {time}, reduce the number of exercises accordingly.
-
-	Format the workout plan for easy reading by separating each workout day by week day.
-	"""
-    
-	response = openai.Completion.create(
-		engine="text-davinci-003",
-		prompt=prompt,
-		temperature=0.5,
-		n=1,
-		max_tokens=1000,
-	)
-
-	plan = response.choices[0].text.strip()
-	# plan = plan + "\n\n" + prompt
-    
-	return plan
-
-# Gradio interface
-with gr.Blocks() as main:
-	gr.Markdown("""
-		# Neural Network Workout Planner
-		Generate a unique, specifically tailored workouts plan using OpenAI's GPT API. If enough people [show interest](https://www.linkedin.com/in/noam-siegel/), I will update to have these features:
-		""")
-	with gr.Tab("The Workout Plan"):
-		with gr.Row():
-			with gr.Column():
-				
-				gr.Markdown("#### Physical Information")
-				with gr.Row():
-					age    = gr.Number(label="Age (years)") #1 age
-					weight = gr.Number(label="Weight (lbs)") #2 weight
-					height = gr.Number(label="Height (inches)") # 3 height
-				with gr.Row():
-					sex = gr.Dropdown( #4 sex
-						label="Biological Sex",
-						choices=["Male", "Female"]
-						)
-				
-				gr.Markdown("#### Background Information")
-				with gr.Row():
-					goal = gr.Dropdown( #5 goal
-						label="What is your primary goal?",
-						choices=[
-							"Hypertrophy (muscle growth)", 
-							"Power lifting (strength increase)", 
-							"Flexibility and mobility",
-							])
-					equipment = gr.Dropdown( #6 equipment
-						label="What equipment do you have access to?",
-						choices=[
-							"A Home Gym",
-							"A Commercial Gym",
-							"No Equipment"
-	       				]
-					)
-				
-				# What is the title for this?
-				with gr.Row():
-					years = gr.Dropdown( # 7 years
-						label = "How many years have you been working out?",
-						choices = [
-							"0 - 1 years",
-							"1 - 2 years",
-							"2-4 years",
-							"Over 4 years"
-						]
-					)
-					split = gr.Dropdown( #8 split
-						label="What is your preferred training split?",
-						choices=[
-							"Full body", 
-							"Upper-lower", 
-							"Push-pull", 
-							"Body-part", 
-							"Compound exercises", 
-							"Olympic lifts",
-							"No preference"
-						]
-					)
-				
-				gr.Markdown("##### Workout Information")
-				with gr.Column():
-					days = gr.Slider(1, 7, value=3, step=1, label="How many days per week do you want to work out?") #9  days
-					time = gr.Slider(30, 120, value=60, step=15, label="How many minutes do you have per workout?") # 10 time
-					rest = gr.Slider(30, 180, value=90, step=15, label="How many seconds of rest time do you want in between sets?") # 11 rest
-				
-				gr.Markdown("##### Additonal Information")
-				with gr.Column():
-					cardio = gr.Dropdown( # 12 cardio
-						label="Cardio options",
-						choices=[
-							"No cardio",
-							"Low Intensity Interval Training (LIIT)",
-							"Medium Intensity Interval Training (MIIT)",
-							"High Intensity Interval Training (HIIT)" 
-						]
-			  		)
-					warm_up = gr.Checkbox(label="Include warm-up") # 13 warm up
-					stretching = gr.Checkbox(label="Include stretching session") # 14 stretching
-
-				submit_btn = gr.Button(value="Create my plan")
-			
-			# output textbox
-			with gr.Column():
-				gr.Markdown("### Generated Plan")
-				plan = gr.Textbox("Fill out your preferences first!")
-				# gr.Button(label="Add feedback")
-	
-	with gr.Tab("Explanation"):
-		gr.Markdown("""
-			### How it works
-			1. Each Gradio block converts your custom workout preferences into variables
-			2. Upon clicking the "Submit" button, the variables transformed into a prompt
-			3. The prompt is then sent into OpenAI's ChatGPT Davinci API
-			4. ChatGPT's response is returned in the text box
-
-			### Tools used:
-			- [OpenAI ChatGPT APIs]()
-			- [Gradio]()			
-			""")
-
-	submit_btn.click(
-		generate_workout, 
-		inputs=[
-			age,
-			weight,
-			height,
-			sex,
-			goal,
-			equipment,
-			years,
-			split,
-			days,
-			time,
-			rest,
-			cardio,
-			warm_up,
-			stretching,
-			],
-		outputs=[
-			plan
-		]
-	)
-
-main.launch()
\ No newline at end of file
diff --git a/spaces/Norod78/Hebrew-GPT-Neo-Small/README.md b/spaces/Norod78/Hebrew-GPT-Neo-Small/README.md
deleted file mode 100644
index aa711f1b708e0f0e637bdc9d4b557cabe2eb0349..0000000000000000000000000000000000000000
--- a/spaces/Norod78/Hebrew-GPT-Neo-Small/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Hebrew GPT Neo (Small)
-emoji: 📚
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-app_file: app.py
-pinned: true
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/multilingual_denoising.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/multilingual_denoising.py
deleted file mode 100644
index d1c914917feb5165aad7482cd1377f5f65b21635..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/multilingual_denoising.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-
-import numpy as np
-from fairseq.data import (
-    AppendTokenDataset,
-    ConcatDataset,
-    DenoisingDataset,
-    Dictionary,
-    PrependTokenDataset,
-    ResamplingDataset,
-    SortDataset,
-    TokenBlockDataset,
-    data_utils,
-)
-from fairseq.data.encoders.utils import get_whole_word_mask
-from fairseq.tasks import register_task
-
-from .denoising import DenoisingTask
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_task("multilingual_denoising")
-class MultilingualDenoisingTask(DenoisingTask):
-    @staticmethod
-    def add_args(parser):
-        DenoisingTask.add_args(parser)
-        parser.add_argument(
-            "--multilang-sampling-alpha",
-            type=float,
-            default=1.0,
-            help="smoothing alpha for sample ratios across multiple datasets",
-        )
-        parser.add_argument("--add-lang-token", default=False, action="store_true")
-        parser.add_argument(
-            "--langs", type=str, help="language ids we are considering", default=None
-        )
-        parser.add_argument(
-            "--no-whole-word-mask-langs",
-            type=str,
-            default="",
-            metavar="N",
-            help="languages without spacing between words dont support whole word masking",
-        )
-
-    @classmethod
-    def setup_task(cls, args, **kwargs):
-        """Setup the task."""
-        paths = args.data.split(":")
-        assert len(paths) > 0
-        dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt"))
-
-        data_path = paths[0]
-        if args.langs is None:
-            languages = sorted(
-                [
-                    name
-                    for name in os.listdir(data_path)
-                    if os.path.isdir(os.path.join(data_path, name))
-                ]
-            )
-        else:
-            languages = args.langs.split(",")
-
-        if args.add_lang_token:
-            for lang in languages:
-                dictionary.add_symbol("[{}]".format(lang))
-
-        logger.info("dictionary: {} types".format(len(dictionary)))
-        if not hasattr(args, "shuffle_instance"):
-            args.shuffle_instance = False
-        return cls(args, dictionary)
-
-    def __init__(self, args, dictionary):
-        super().__init__(args, dictionary)
-        self.dictionary = dictionary
-        self.seed = args.seed
-
-        # add mask token
-        self.mask_idx = self.dictionary.add_symbol("<mask>")
-        self.langs = args.langs
-        self.args = args
-
-    def _get_sample_prob(self, dataset_lens):
-        """
-        Get smoothed sampling porbability by languages. This helps low resource
-        languages by upsampling them.
-        """
-        prob = dataset_lens / dataset_lens.sum()
-        smoothed_prob = prob ** self.args.multilang_sampling_alpha
-        smoothed_prob = smoothed_prob / smoothed_prob.sum()
-        return smoothed_prob
-
-    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
-        """Load a given dataset split.
-
-        Args:
-            split (str): name of the split (e.g., train, valid, test)
-        """
-        paths = self.args.data.split(":")
-        assert len(paths) > 0
-        data_path = paths[(epoch - 1) % len(paths)]
-        split_path = os.path.join(data_path, split)
-
-        if self.langs is None:
-            languages = sorted(
-                [
-                    name
-                    for name in os.listdir(data_path)
-                    if os.path.isdir(os.path.join(data_path, name))
-                ]
-            )
-        else:
-            languages = self.langs.split(",")
-            for name in languages:
-                p = os.path.join(data_path, name)
-                assert os.path.exists(p), "data not found: {}".format(p)
-
-        logger.info("Training on {0} languages: {1}".format(len(languages), languages))
-        logger.info(
-            "Language to id mapping: ", {lang: id for id, lang in enumerate(languages)}
-        )
-
-        mask_whole_words = get_whole_word_mask(self.args, self.dictionary)
-        language_without_segmentations = self.args.no_whole_word_mask_langs.split(",")
-        lang_datasets = []
-        for language in languages:
-            split_path = os.path.join(data_path, language, split)
-
-            dataset = data_utils.load_indexed_dataset(
-                split_path,
-                self.source_dictionary,
-                self.args.dataset_impl,
-                combine=combine,
-            )
-            if dataset is None:
-                raise FileNotFoundError(
-                    "Dataset not found: {} ({})".format(split, split_path)
-                )
-
-            end_token = (
-                self.source_dictionary.index("[{}]".format(language))
-                if self.args.add_lang_token
-                else self.source_dictionary.eos()
-            )
-
-            # create continuous blocks of tokens
-            dataset = TokenBlockDataset(
-                dataset,
-                dataset.sizes,
-                self.args.tokens_per_sample - 2,  # one less for <s>
-                pad=self.source_dictionary.pad(),
-                eos=end_token,
-                break_mode=self.args.sample_break_mode,
-            )
-            logger.info("loaded {} blocks from: {}".format(len(dataset), split_path))
-
-            # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT)
-            dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())
-            dataset = AppendTokenDataset(dataset, end_token)
-
-            lang_mask_whole_words = (
-                mask_whole_words
-                if language not in language_without_segmentations
-                else None
-            )
-            lang_dataset = DenoisingDataset(
-                dataset,
-                dataset.sizes,
-                self.dictionary,
-                self.mask_idx,
-                lang_mask_whole_words,
-                shuffle=self.args.shuffle_instance,
-                seed=self.seed,
-                args=self.args,
-                eos=None
-                if not self.args.add_lang_token
-                else self.source_dictionary.index("[{}]".format(language)),
-            )
-            lang_datasets.append(lang_dataset)
-
-        dataset_lengths = np.array(
-            [len(d) for d in lang_datasets],
-            dtype=float,
-        )
-        logger.info(
-            "loaded total {} blocks for all languages".format(
-                int(dataset_lengths.sum()),
-            )
-        )
-        if split == self.args.train_subset:
-            # For train subset, additionally up or down sample languages.
-            sample_probs = self._get_sample_prob(dataset_lengths)
-            logger.info(
-                "Sample probability by language: {}".format(
-                    {
-                        lang: "{0:.4f}".format(sample_probs[id])
-                        for id, lang in enumerate(languages)
-                    }
-                )
-            )
-            size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths
-            logger.info(
-                "Up/Down Sampling ratio by language: {}".format(
-                    {
-                        lang: "{0:.2f}".format(size_ratio[id])
-                        for id, lang in enumerate(languages)
-                    }
-                )
-            )
-
-            resampled_lang_datasets = [
-                ResamplingDataset(
-                    lang_datasets[i],
-                    size_ratio=size_ratio[i],
-                    seed=self.args.seed,
-                    epoch=epoch,
-                    replace=size_ratio[i] >= 1.0,
-                )
-                for i, d in enumerate(lang_datasets)
-            ]
-            dataset = ConcatDataset(
-                resampled_lang_datasets,
-            )
-        else:
-            dataset = ConcatDataset(lang_datasets)
-            lang_splits = [split]
-            for lang_id, lang_dataset in enumerate(lang_datasets):
-                split_name = split + "_" + languages[lang_id]
-                lang_splits.append(split_name)
-                self.datasets[split_name] = lang_dataset
-
-            if split in self.args.valid_subset:
-                self.args.valid_subset = self.args.valid_subset.replace(
-                    split, ",".join(lang_splits)
-                )
-
-        with data_utils.numpy_seed(self.args.seed + epoch):
-            shuffle = np.random.permutation(len(dataset))
-
-        self.datasets[split] = SortDataset(
-            dataset,
-            sort_order=[
-                shuffle,
-                dataset.sizes,
-            ],
-        )
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/multilingual_fairseq_gen.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/multilingual_fairseq_gen.sh
deleted file mode 100644
index 65aa322d7daaa428015de98abe4664a6a4164bfd..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/multilingual_fairseq_gen.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-lang_pairs="en-fr,en-cs,fr-en,cs-en"
-path_2_data=$1 # <path to data>
-lang_list=$2 # <path to a file which contains list of languages separted by new lines>
-model=$3  # <path to a trained model>
-source_lang=cs
-target_lang=en
-
-fairseq-generate "$path_2_data" \
-  --path "$model" \
-  --task translation_multi_simple_epoch \
-  --gen-subset test \
-  --source-lang "$source_lang" \
-  --target-lang "$target_lang" \
-  --sacrebleu --remove-bpe 'sentencepiece'\
-  --batch-size 32 \
-  --encoder-langtok "src" \
-  --decoder-langtok \
-  --lang-dict "$lang_list" \
-  --lang-pairs "$lang_pairs"
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/sparse_transformer_sentence_encoder.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/sparse_transformer_sentence_encoder.py
deleted file mode 100644
index f41ec09327fe80b50d20674e7482794ce45c531c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/sparse_transformer_sentence_encoder.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch.nn as nn
-from fairseq.modules import TransformerSentenceEncoder
-from fairseq.modules.sparse_transformer_sentence_encoder_layer import (
-    SparseTransformerSentenceEncoderLayer,
-)
-
-
-class SparseTransformerSentenceEncoder(TransformerSentenceEncoder):
-    """
-    Sparse implementation of the TransformerSentenceEncoder
-    - see SparseMultiheadAttention
-    """
-
-    def __init__(
-        self,
-        padding_idx: int,
-        vocab_size: int,
-        num_encoder_layers: int = 6,
-        embedding_dim: int = 768,
-        ffn_embedding_dim: int = 3072,
-        num_attention_heads: int = 8,
-        dropout: float = 0.1,
-        attention_dropout: float = 0.1,
-        activation_dropout: float = 0.1,
-        max_seq_len: int = 256,
-        num_segments: int = 2,
-        use_position_embeddings: bool = True,
-        offset_positions_by_padding: bool = True,
-        encoder_normalize_before: bool = False,
-        apply_bert_init: bool = False,
-        activation_fn: str = "relu",
-        learned_pos_embedding: bool = True,
-        embed_scale: float = None,
-        freeze_embeddings: bool = False,
-        n_trans_layers_to_freeze: int = 0,
-        export: bool = False,
-        is_bidirectional: bool = True,
-        stride: int = 32,
-        expressivity: int = 8,
-    ) -> None:
-
-        super().__init__(
-            padding_idx,
-            vocab_size,
-            num_encoder_layers,
-            embedding_dim,
-            ffn_embedding_dim,
-            num_attention_heads,
-            dropout,
-            attention_dropout,
-            activation_dropout,
-            max_seq_len,
-            num_segments,
-            use_position_embeddings,
-            offset_positions_by_padding,
-            encoder_normalize_before,
-            apply_bert_init,
-            activation_fn,
-            learned_pos_embedding,
-            embed_scale,
-            freeze_embeddings,
-            n_trans_layers_to_freeze,
-            export,
-        )
-
-        self.layers = nn.ModuleList(
-            [
-                SparseTransformerSentenceEncoderLayer(
-                    embedding_dim=self.embedding_dim,
-                    ffn_embedding_dim=ffn_embedding_dim,
-                    num_attention_heads=num_attention_heads,
-                    dropout=dropout,
-                    attention_dropout=attention_dropout,
-                    activation_dropout=activation_dropout,
-                    activation_fn=activation_fn,
-                    export=export,
-                    is_bidirectional=is_bidirectional,
-                    stride=stride,
-                    expressivity=expressivity,
-                )
-                for _ in range(num_encoder_layers)
-            ]
-        )
-
-        def freeze_module_params(m):
-            if m is not None:
-                for p in m.parameters():
-                    p.requires_grad = False
-
-        for layer in range(n_trans_layers_to_freeze):
-            freeze_module_params(self.layers[layer])
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py
deleted file mode 100644
index 9cf121081fbde2f5085ed380f0841649d143a4be..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py
+++ /dev/null
@@ -1,192 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import collections
-import contextlib
-import wave
-
-try:
-    import webrtcvad
-except ImportError:
-    raise ImportError("Please install py-webrtcvad: pip install webrtcvad")
-import argparse
-import os
-import logging
-from tqdm import tqdm
-
-AUDIO_SUFFIX = '.wav'
-FS_MS = 30
-SCALE = 6e-5
-THRESHOLD = 0.3
-
-
-def read_wave(path):
-    """Reads a .wav file.
-    Takes the path, and returns (PCM audio data, sample rate).
-    """
-    with contextlib.closing(wave.open(path, 'rb')) as wf:
-        num_channels = wf.getnchannels()
-        assert num_channels == 1
-        sample_width = wf.getsampwidth()
-        assert sample_width == 2
-        sample_rate = wf.getframerate()
-        assert sample_rate in (8000, 16000, 32000, 48000)
-        pcm_data = wf.readframes(wf.getnframes())
-        return pcm_data, sample_rate
-
-
-def write_wave(path, audio, sample_rate):
-    """Writes a .wav file.
-    Takes path, PCM audio data, and sample rate.
-    """
-    with contextlib.closing(wave.open(path, 'wb')) as wf:
-        wf.setnchannels(1)
-        wf.setsampwidth(2)
-        wf.setframerate(sample_rate)
-        wf.writeframes(audio)
-
-
-class Frame(object):
-    """Represents a "frame" of audio data."""
-    def __init__(self, bytes, timestamp, duration):
-        self.bytes = bytes
-        self.timestamp = timestamp
-        self.duration = duration
-
-
-def frame_generator(frame_duration_ms, audio, sample_rate):
-    """Generates audio frames from PCM audio data.
-    Takes the desired frame duration in milliseconds, the PCM data, and
-    the sample rate.
-    Yields Frames of the requested duration.
-    """
-    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
-    offset = 0
-    timestamp = 0.0
-    duration = (float(n) / sample_rate) / 2.0
-    while offset + n < len(audio):
-        yield Frame(audio[offset:offset + n], timestamp, duration)
-        timestamp += duration
-        offset += n
-
-
-def vad_collector(sample_rate, frame_duration_ms,
-                  padding_duration_ms, vad, frames):
-    """Filters out non-voiced audio frames.
-    Given a webrtcvad.Vad and a source of audio frames, yields only
-    the voiced audio.
-    Uses a padded, sliding window algorithm over the audio frames.
-    When more than 90% of the frames in the window are voiced (as
-    reported by the VAD), the collector triggers and begins yielding
-    audio frames. Then the collector waits until 90% of the frames in
-    the window are unvoiced to detrigger.
-    The window is padded at the front and back to provide a small
-    amount of silence or the beginnings/endings of speech around the
-    voiced frames.
-    Arguments:
-    sample_rate - The audio sample rate, in Hz.
-    frame_duration_ms - The frame duration in milliseconds.
-    padding_duration_ms - The amount to pad the window, in milliseconds.
-    vad - An instance of webrtcvad.Vad.
-    frames - a source of audio frames (sequence or generator).
-    Returns: A generator that yields PCM audio data.
-    """
-    num_padding_frames = int(padding_duration_ms / frame_duration_ms)
-    # We use a deque for our sliding window/ring buffer.
-    ring_buffer = collections.deque(maxlen=num_padding_frames)
-    # We have two states: TRIGGERED and NOTTRIGGERED. We start in the
-    # NOTTRIGGERED state.
-    triggered = False
-
-    voiced_frames = []
-    for frame in frames:
-        is_speech = vad.is_speech(frame.bytes, sample_rate)
-
-        #  sys.stdout.write('1' if is_speech else '0')
-        if not triggered:
-            ring_buffer.append((frame, is_speech))
-            num_voiced = len([f for f, speech in ring_buffer if speech])
-            # If we're NOTTRIGGERED and more than 90% of the frames in
-            # the ring buffer are voiced frames, then enter the
-            # TRIGGERED state.
-            if num_voiced > 0.9 * ring_buffer.maxlen:
-                triggered = True
-                # We want to yield all the audio we see from now until
-                # we are NOTTRIGGERED, but we have to start with the
-                # audio that's already in the ring buffer.
-                for f, _ in ring_buffer:
-                    voiced_frames.append(f)
-                ring_buffer.clear()
-        else:
-            # We're in the TRIGGERED state, so collect the audio data
-            # and add it to the ring buffer.
-            voiced_frames.append(frame)
-            ring_buffer.append((frame, is_speech))
-            num_unvoiced = len([f for f, speech in ring_buffer if not speech])
-            # If more than 90% of the frames in the ring buffer are
-            # unvoiced, then enter NOTTRIGGERED and yield whatever
-            # audio we've collected.
-            if num_unvoiced > 0.9 * ring_buffer.maxlen:
-                triggered = False
-                yield [b''.join([f.bytes for f in voiced_frames]),
-                       voiced_frames[0].timestamp, voiced_frames[-1].timestamp]
-                ring_buffer.clear()
-                voiced_frames = []
-    # If we have any leftover voiced audio when we run out of input,
-    # yield it.
-    if voiced_frames:
-        yield [b''.join([f.bytes for f in voiced_frames]),
-               voiced_frames[0].timestamp, voiced_frames[-1].timestamp]
-
-
-def main(args):
-    # create output folder
-    try:
-        cmd = f"mkdir -p {args.out_path}"
-        os.system(cmd)
-    except Exception:
-        logging.error("Can not create output folder")
-        exit(-1)
-
-    # build vad object
-    vad = webrtcvad.Vad(int(args.agg))
-    # iterating over wavs in dir
-    for file in tqdm(os.listdir(args.in_path)):
-        if file.endswith(AUDIO_SUFFIX):
-            audio_inpath = os.path.join(args.in_path, file)
-            audio_outpath = os.path.join(args.out_path, file)
-            audio, sample_rate = read_wave(audio_inpath)
-            frames = frame_generator(FS_MS, audio, sample_rate)
-            frames = list(frames)
-            segments = vad_collector(sample_rate, FS_MS, 300, vad, frames)
-            merge_segments = list()
-            timestamp_start = 0.0
-            timestamp_end = 0.0
-            # removing start, end, and long sequences of sils
-            for i, segment in enumerate(segments):
-                merge_segments.append(segment[0])
-                if i and timestamp_start:
-                    sil_duration = segment[1] - timestamp_end
-                    if sil_duration > THRESHOLD:
-                        merge_segments.append(int(THRESHOLD / SCALE)*(b'\x00'))
-                    else:
-                        merge_segments.append(int((sil_duration / SCALE))*(b'\x00'))
-                timestamp_start = segment[1]
-                timestamp_end = segment[2]
-            segment = b''.join(merge_segments)
-            write_wave(audio_outpath, segment, sample_rate)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Apply vad to a file of fils.')
-    parser.add_argument('in_path', type=str, help='Path to the input files')
-    parser.add_argument('out_path', type=str,
-                        help='Path to save the processed files')
-    parser.add_argument('--agg', type=int, default=3,
-                        help='The level of aggressiveness of the VAD: [0-3]')
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh
deleted file mode 100644
index c10a6b8809b77bca2b2c02df8b8702725bdd51c7..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/bin/bash
-
-split="dev_other"
-ref_txt=""  # ground truth transcript path
-psd_txt=""  # pseudo transcript path
-get_best_wer=true
-dec_name="decode"
-graph_name="graph"
-kenlm_path=/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o6.bin
-phonemize_lexicon=""
-
-. ./cmd.sh
-. ./path.sh
-. parse_options.sh
-. /private/home/wnhsu/unsup_asr/fairseq-py-unsup/env.sh
-
-exp_root=$1
-
-set -eu
-
-if [ ! -z $ref_txt ] && $get_best_wer; then
-  echo "==== WER w.r.t. real transcript (select based on unsupervised metric)"
-  for x in $exp_root/*/${dec_name}_${split}*; do
-    lang=$(dirname $x)/$graph_name
-
-    for tra in $x/scoring/*.tra; do
-      cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:\<UNK\>::g' > $tra.txt
-      python local/unsup_select.py $psd_txt $tra.txt \
-        --kenlm_path $kenlm_path --gt_tra $ref_txt --phonemize \
-        --phonemize_lexicon "$phonemize_lexicon"
-    done | grep "score=" | sed 's/=/ /g' | sed 's/;//g' | sort -k3n | head -n1
-  done
-fi
-
-
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/distributed/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/distributed/__init__.py
deleted file mode 100644
index d0b96b734c4b5e7cd5d295238d0764c05093dc27..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/distributed/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .distributed_timeout_wrapper import DistributedTimeoutWrapper
-from .fully_sharded_data_parallel import fsdp_enable_wrap, fsdp_wrap, FullyShardedDataParallel
-from .legacy_distributed_data_parallel import LegacyDistributedDataParallel
-from .module_proxy_wrapper import ModuleProxyWrapper
-from .tpu_distributed_data_parallel import TPUDistributedDataParallel
-
-
-__all__ = [
-    "DistributedTimeoutWrapper",
-    "fsdp_enable_wrap",
-    "fsdp_wrap",
-    "FullyShardedDataParallel",
-    "LegacyDistributedDataParallel",
-    "ModuleProxyWrapper",
-    "TPUDistributedDataParallel",
-]
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/vis.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/vis.py
deleted file mode 100644
index c2910b4ef8c61efee72dabd0531a9b669ec8bf98..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/vis.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import numpy as np
-from skimage import io
-from skimage.segmentation import mark_boundaries
-
-
-def save_item_for_vis(item, out_file):
-    mask = item['mask'] > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    img = mark_boundaries(np.transpose(item['image'], (1, 2, 0)),
-                          mask,
-                          color=(1., 0., 0.),
-                          outline_color=(1., 1., 1.),
-                          mode='thick')
-
-    if 'inpainted' in item:
-        inp_img = mark_boundaries(np.transpose(item['inpainted'], (1, 2, 0)),
-                                  mask,
-                                  color=(1., 0., 0.),
-                                  mode='outer')
-        img = np.concatenate((img, inp_img), axis=1)
-
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
-
-
-def save_mask_for_sidebyside(item, out_file):
-    mask = item['mask']# > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    mask = np.clip(mask * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, mask)
-
-def save_img_for_sidebyside(item, out_file):
-    img = np.transpose(item['image'], (1, 2, 0))
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
\ No newline at end of file
diff --git a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/README_en.md b/spaces/Osborn-bh/ChatGLM3-6B-Osborn/README_en.md
deleted file mode 100644
index 495789d21dbfc21c00e84961629e2ae290d9323f..0000000000000000000000000000000000000000
--- a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/README_en.md
+++ /dev/null
@@ -1,243 +0,0 @@
-# ChatGLM3
-
-<p align="center">
-🤗 <a href="https://huggingface.co/THUDM/chatglm3-6b" target="_blank">HF Repo</a> • 🤖 <a href="https://modelscope.cn/models/ZhipuAI/chatglm3-6b" target="_blank">ModelScope</a>  • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br>
-</p>
-<p align="center">
-    👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-25ti5uohv-A_hs~am_D3Q8XPZMpj7wwQ" target="_blank">Slack</a> and <a href="resources/WECHAT.md" target="_blank">WeChat</a>
-</p>
-<p align="center">
-📍Experience the larger-scale ChatGLM model at <a href="https://www.chatglm.cn">chatglm.cn</a>
-</p>
-
-## Introduction
-
-ChatGLM3 is a new generation of pre-trained dialogue models jointly released by Zhipu AI and Tsinghua KEG. ChatGLM3-6B is the open-source model in the ChatGLM3 series, maintaining many excellent features of the first two generations such as smooth dialogue and low deployment threshold, while introducing the following features:
-
-1. **Stronger Base Model:** The base model of ChatGLM3-6B, ChatGLM3-6B-Base, adopts a more diverse training dataset, more sufficient training steps, and a more reasonable training strategy. Evaluations on datasets from various perspectives such as semantics, mathematics, reasoning, code, and knowledge show that **ChatGLM3-6B-Base has the strongest performance among base models below 10B**.
-
-2. **More Complete Function Support:** ChatGLM3-6B adopts a newly designed [Prompt format](PROMPT_en.md), supporting multi-turn dialogues as usual. It also natively supports [tool invocation](tool_using/README_en.md) (Function Call), code execution (Code Interpreter), and Agent tasks in complex scenarios.
-
-3. **More Comprehensive Open-source Series:** In addition to the dialogue model [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b), the basic model [ChatGLM3-6B-Base](https://huggingface.co/THUDM/chatglm3-6b-base), and the long-text dialogue model [ChatGLM3-6B-32K](https://huggingface.co/THUDM/chatglm3-6b-32k) have also been open-sourced. All these weights are **fully open** for academic research, and **free commercial use is also allowed** after registration via a [questionnaire](https://open.bigmodel.cn/mla/form).
-
------
-
-The ChatGLM3 open-source model aims to promote the development of large-model technology together with the open-source community. Developers and everyone are earnestly requested to comply with the [open-source protocol](MODEL_LICENSE), and not to use the open-source models, codes, and derivatives for any purposes that might harm the nation and society, and for any services that have not been evaluated and filed for safety. Currently, no applications, including web, Android, Apple iOS, and Windows App, have been developed based on the **ChatGLM3 open-source model** by our project team.
-
-Although every effort has been made to ensure the compliance and accuracy of the data at various stages of model training, due to the smaller scale of the ChatGLM3-6B model and the influence of probabilistic randomness factors, the accuracy of output content cannot be guaranteed. The model output is also easily misled by user input. **This project does not assume risks and liabilities caused by data security, public opinion risks, or any misleading, abuse, dissemination, and improper use of open-source models and codes.**
-
-## Model List
-
-| Model | Seq Length |                                                              Download                                                               
-| :---: |:---------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------:
-| ChatGLM3-6B | 8k |      [HuggingFace](https://huggingface.co/THUDM/chatglm3-6b) \| [ModelScope](https://modelscope.cn/models/ZhipuAI/chatglm3-6b)      
-| ChatGLM3-6B-Base | 8k | [HuggingFace](https://huggingface.co/THUDM/chatglm3-6b-base) \| [ModelScope](https://modelscope.cn/models/ZhipuAI/chatglm3-6b-base) 
-| ChatGLM3-6B-32K | 32k |                                   [HuggingFace](https://huggingface.co/THUDM/chatglm3-6b-32k) \| [ModelScope](https://modelscope.cn/models/ZhipuAI/chatglm3-6b-32k)                                    
-
-## Projects
-Open source projects that accelerate ChatGLM3:
-* [chatglm.cpp](https://github.com/li-plus/chatglm.cpp): Real-time inference on your laptop accelerated by quantization, similar to llama.cpp.
-
-## Evaluation Results
-
-### Typical Tasks
-
-We selected 8 typical Chinese-English datasets and conducted performance tests on the ChatGLM3-6B (base) version.
-
-| Model            | GSM8K | MATH | BBH  | MMLU | C-Eval | CMMLU | MBPP | AGIEval |
-|------------------|:-----:|:----:|:----:|:----:|:------:|:-----:|:----:|:-------:|
-| ChatGLM2-6B-Base | 32.4  | 6.5  | 33.7 | 47.9 |  51.7  | 50.0  |  -   |    -    |
-| Best Baseline    | 52.1  | 13.1 | 45.0 | 60.1 |  63.5  | 62.2  | 47.5 |  45.8   |
-| ChatGLM3-6B-Base | 72.3  | 25.7 | 66.1 | 61.4 |  69.0  | 67.5  | 52.4 |  53.7   |
-> "Best Baseline" refers to the pre-trained models that perform best on the corresponding datasets with model parameters below 10B, excluding models that are trained specifically for a single task and do not maintain general capabilities.
-
-> In the tests of ChatGLM3-6B-Base, BBH used a 3-shot test, GSM8K and MATH that require inference used a 0-shot CoT test, MBPP used a 0-shot generation followed by running test cases to calculate Pass@1, and other multiple-choice type datasets all used a 0-shot test.
-
-We have conducted manual evaluation tests on ChatGLM3-6B-32K in multiple long-text application scenarios. Compared with the second-generation model, its effect has improved by more than 50% on average. In applications such as paper reading, document summarization, and financial report analysis, this improvement is particularly significant. In addition, we also tested the model on the LongBench evaluation set, and the specific results are shown in the table below.
-
-| Model                |  Average |  Summary | Single-Doc QA |  Multi-Doc QA | Code | Few-shot | Synthetic | 
-|----------------------|:-----:|:----:|:----:|:----:|:------:|:-----:|:-----:|
-| ChatGLM2-6B-32K   |  41.5 | 24.8 | 37.6 | 34.7 |  52.8  |  51.3 | 47.7 | 
-| ChatGLM3-6B-32K   |  50.2 | 26.6 | 45.8 | 46.1 |  56.2  |  61.2 | 65 |
-
-
-## How to Use
-
-### Environment Installation
-First, you need to download this repository:
-```shell
-git clone https://github.com/THUDM/ChatGLM3
-cd ChatGLM3
-```
-
-Then use pip to install the dependencies:
-```
-pip install -r requirements.txt
-```
-It is recommended to use version `4.30.2` for the `transformers` library, and version 2.0 or above for `torch`, to achieve the best inference performance.
-
-### Integrated Demo
-
-We provide an integrated demo that incorporates the following three functionalities. Please refer to [Integrated Demo](composite_demo/README_en.md) for how to run it.
-
-- Chat: Dialogue mode, where you can interact with the model.
-- Tool: Tool mode, where in addition to dialogue, the model can also perform other operations using tools.
-    ![tool](resources/tool.png)
-- Code Interpreter: Code interpreter mode, where the model can execute code in a Jupyter environment and obtain results to complete complex tasks.
-    ![code](resources/heart.png)
-
-### Usage 
-
-The ChatGLM model can be called to start a conversation using the following code:
-
-```python
->>> from transformers import AutoTokenizer, AutoModel
->>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True)
->>> model = AutoModel.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True, device='cuda')
->>> model = model.eval()
->>> response, history = model.chat(tokenizer, "Hello", history=[])
->>> print(response)
-Hello 👋! I'm ChatGLM3-6B, the artificial intelligence assistant, nice to meet you. Feel free to ask me any questions.
->>> response, history = model.chat(tokenizer, "What should I do if I can't sleep at night", history=history)
->>> print(response)
-If you're having trouble sleeping at night, here are a few suggestions that might help:
-
-1. Create a relaxing sleep environment: Make sure your bedroom is cool, quiet, and dark. Consider using earplugs, a white noise machine, or a fan to help create an optimal environment.
-2. Establish a bedtime routine: Try to go to bed and wake up at the same time every day, even on weekends. A consistent routine can help regulate your body's internal clock.
-3. Avoid stimulating activities before bedtime: Avoid using electronic devices, watching TV, or engaging in stimulating activities like exercise or puzzle-solving, as these can interfere with your ability to fall asleep.
-4. Limit caffeine and alcohol: Avoid consuming caffeine and alcohol close to bedtime, as these can disrupt your sleep patterns.
-5. Practice relaxation techniques: Try meditation, deep breathing, or progressive muscle relaxation to help calm your mind and body before sleep.
-6. Consider taking a warm bath or shower: A warm bath or shower can help relax your muscles and promote sleep.
-7. Get some fresh air: Make sure to get some fresh air during the day, as lack of vitamin D can interfere with sleep quality.
-
-If you continue to have difficulty sleeping, consult with a healthcare professional for further guidance and support.
-```
-
-#### Load Model Locally
-The above code will automatically download the model implementation and parameters by `transformers`. The complete model implementation is available on [Hugging Face Hub](https://huggingface.co/THUDM/chatglm3-6b). If your network environment is poor, downloading model parameters might take a long time or even fail. In this case, you can first download the model to your local machine, and then load it from there.
-
-To download the model from Hugging Face Hub, you need to [install Git LFS](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage) first, then run
-```Shell
-git clone https://huggingface.co/THUDM/chatglm3-6b
-```
-
-If the download from HuggingFace is slow, you can also download it from [ModelScope](https://modelscope.cn/models/ZhipuAI/chatglm3-6b).
-
-### Web-based Dialogue Demo
-![web-demo](resources/web-demo.gif)
-You can launch a web-based demo using Gradio with the following command:
-```shell
-python web_demo.py
-```
-
-![web-demo](resources/web-demo2.png)
-
-You can launch a web-based demo using Streamlit with the following command:
-```shell
-streamlit run web_demo2.py
-```
-
-The web-based demo will run a Web Server and output an address. You can use it by opening the output address in a browser. Based on tests, the web-based demo using Streamlit runs more smoothly.
-
-### Command Line Dialogue Demo
-
-![cli-demo](resources/cli-demo.png)
-
-Run [cli_demo.py](cli_demo.py) in the repository:
-
-```shell
-python cli_demo.py
-```
-
-The program will interact in the command line, enter instructions in the command line and hit enter to generate a response. Enter `clear` to clear the dialogue history, enter `stop` to terminate the program.
-
-### API Deployment
-Thanks to [@xusenlinzy](https://github.com/xusenlinzy) for implementing the OpenAI format streaming API deployment, which can serve as the backend for any ChatGPT-based application, such as [ChatGPT-Next-Web](https://github.com/Yidadaa/ChatGPT-Next-Web). You can deploy it by running [openai_api.py](openai_api.py) in the repository:
-```shell
-python openai_api.py
-```
-The example code for API calls is as follows:
-```python
-import openai
-if __name__ == "__main__":
-    openai.api_base = "http://localhost:8000/v1"
-    openai.api_key = "none"
-    for chunk in openai.ChatCompletion.create(
-        model="chatglm3-6b",
-        messages=[
-            {"role": "user", "content": "你好"}
-        ],
-        stream=True
-    ):
-        if hasattr(chunk.choices[0].delta, "content"):
-            print(chunk.choices[0].delta.content, end="", flush=True)
-```
-
-### Tool Invocation
-
-For methods of tool invocation, please refer to [Tool Invocation](tool_using/README_en.md).
-
-## Low-Cost Deployment
-
-### Model Quantization
-
-By default, the model is loaded with FP16 precision, running the above code requires about 13GB of VRAM. If your GPU's VRAM is limited, you can try loading the model quantitatively, as follows:
-
-```python
-model = AutoModel.from_pretrained("THUDM/chatglm3-6b",trust_remote_code=True).quantize(4).cuda()
-```
-
-Model quantization will bring some performance loss. Through testing, ChatGLM3-6B can still perform natural and smooth generation under 4-bit quantization.
-
-### CPU Deployment
-
-If you don't have GPU hardware, you can also run inference on the CPU, but the inference speed will be slower. The usage is as follows (requires about 32GB of memory):
-
-```python
-model = AutoModel.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True).float()
-```
-
-### Mac Deployment
-
-For Macs equipped with Apple Silicon or AMD GPUs, the MPS backend can be used to run ChatGLM3-6B on the GPU. Refer to Apple's [official instructions](https://developer.apple.com/metal/pytorch) to install PyTorch-Nightly (the correct version number should be 2.x.x.dev2023xxxx, not 2.x.x).
-
-Currently, only [loading the model locally](README_en.md#load-model-locally) is supported on MacOS. Change the model loading in the code to load locally and use the MPS backend:
-
-```python
-model = AutoModel.from_pretrained("your local path", trust_remote_code=True).to('mps')
-```
-
-Loading the half-precision ChatGLM3-6B model requires about 13GB of memory. Machines with smaller memory (such as a 16GB memory MacBook Pro) will use virtual memory on the hard disk when there is insufficient free memory, resulting in a significant slowdown in inference speed.
-
-### Multi-GPU Deployment
-
-If you have multiple GPUs, but each GPU's VRAM size is not enough to accommodate the complete model, then the model can be split across multiple GPUs. First, install accelerate: `pip install accelerate`, and then load the model through the following methods:
-
-```python
-from utils import load_model_on_gpus
-model = load_model_on_gpus("THUDM/chatglm3-6b", num_gpus=2)
-```
-
-This allows the model to be deployed on two GPUs for inference. You can change `num_gpus` to the number of GPUs you want to use. It is evenly split by default, but you can also pass the `device_map` parameter to specify it yourself.
-
-## Citation
-
-If you find our work helpful, please consider citing the following papers.
-
-```
-@article{zeng2022glm,
-  title={Glm-130b: An open bilingual pre-trained model},
-  author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
-  journal={arXiv preprint arXiv:2210.02414},
-  year={2022}
-}
-```
-```
-@inproceedings{du2022glm,
-  title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
-  author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
-  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
-  pages={320--335},
-  year={2022}
-}
-```
diff --git a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/composite_demo/client.py b/spaces/Osborn-bh/ChatGLM3-6B-Osborn/composite_demo/client.py
deleted file mode 100644
index 07f84962bb8331e8d6f7f280f0ce82ba6755524d..0000000000000000000000000000000000000000
--- a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/composite_demo/client.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Iterable
-import os
-from typing import Any, Protocol
-
-from huggingface_hub.inference._text_generation import TextGenerationStreamResponse, Token
-import streamlit as st
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-from conversation import Conversation
-
-TOOL_PROMPT = 'Answer the following questions as best as you can. You have access to the following tools:'
-
-MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
-
-@st.cache_resource
-def get_client() -> Client:
-    client = HFClient(MODEL_PATH)
-    return client
-
-class Client(Protocol):
-    def generate_stream(self,
-        system: str | None,
-        tools: list[dict] | None,
-        history: list[Conversation],
-        **parameters: Any
-    ) -> Iterable[TextGenerationStreamResponse]:
-        ...
-
-def stream_chat(self, tokenizer, query: str, history: list[tuple[str, str]] = None, role: str = "user",
-                    past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8,
-                    logits_processor=None, return_past_key_values=False, **kwargs):
-        
-    from transformers.generation.logits_process import LogitsProcessor
-    from transformers.generation.utils import LogitsProcessorList
-
-    class InvalidScoreLogitsProcessor(LogitsProcessor):
-        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-            if torch.isnan(scores).any() or torch.isinf(scores).any():
-                scores.zero_()
-                scores[..., 5] = 5e4
-            return scores
-
-    if history is None:
-        history = []
-    if logits_processor is None:
-        logits_processor = LogitsProcessorList()
-    logits_processor.append(InvalidScoreLogitsProcessor())
-    eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
-                    tokenizer.get_command("<|observation|>")]
-    gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
-                    "temperature": temperature, "logits_processor": logits_processor, **kwargs}
-    if past_key_values is None:
-        inputs = tokenizer.build_chat_input(query, history=history, role=role)
-    else:
-        inputs = tokenizer.build_chat_input(query, role=role)
-    inputs = inputs.to(self.device)
-    if past_key_values is not None:
-        past_length = past_key_values[0][0].shape[0]
-        if self.transformer.pre_seq_len is not None:
-            past_length -= self.transformer.pre_seq_len
-        inputs.position_ids += past_length
-        attention_mask = inputs.attention_mask
-        attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
-        inputs['attention_mask'] = attention_mask
-    history.append({"role": role, "content": query})
-    for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
-                                        eos_token_id=eos_token_id, return_past_key_values=return_past_key_values,
-                                        **gen_kwargs):
-        if return_past_key_values:
-            outputs, past_key_values = outputs
-        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
-        response = tokenizer.decode(outputs)
-        if response and response[-1] != "�":
-            new_history = history
-            if return_past_key_values:
-                yield response, new_history, past_key_values
-            else:
-                yield response, new_history
-
-class HFClient(Client):
-    def __init__(self, model_path: str):
-        self.model_path = model_path
-        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-        self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(
-            'cuda' if torch.cuda.is_available() else
-            'mps' if torch.backends.mps.is_available() else
-            'cpu'
-        )
-        self.model = self.model.eval()
-
-    def generate_stream(self,
-        system: str | None,
-        tools: list[dict] | None,
-        history: list[Conversation],
-        **parameters: Any
-    ) -> Iterable[TextGenerationStreamResponse]:
-        chat_history = [{
-            'role': 'system',
-            'content': system if not tools else TOOL_PROMPT,
-        }]
-
-        if tools:
-            chat_history[0]['tools'] = tools
-
-        for conversation in history[:-1]:
-            chat_history.append({
-                'role': str(conversation.role).removeprefix('<|').removesuffix('|>'),
-                'content': conversation.content,
-            })
-        
-        query = history[-1].content
-        role = str(history[-1].role).removeprefix('<|').removesuffix('|>')
-
-        text = ''
-        
-        for new_text, _ in stream_chat(self.model,
-            self.tokenizer,
-            query,
-            chat_history,
-            role,
-            **parameters,
-        ):
-            word = new_text.removeprefix(text)
-            word_stripped = word.strip()
-            text = new_text
-            yield TextGenerationStreamResponse(
-                generated_text=text,
-                token=Token(
-                    id=0,
-                    logprob=0,
-                    text=word,
-                    special=word_stripped.startswith('<|') and word_stripped.endswith('|>'),
-                )
-            )
diff --git a/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/optimization/fake_face_bayesian_optimization.py b/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/optimization/fake_face_bayesian_optimization.py
deleted file mode 100644
index 9f7cf4f8b5694377f2c03e5f705ebc202ef75348..0000000000000000000000000000000000000000
--- a/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/optimization/fake_face_bayesian_optimization.py
+++ /dev/null
@@ -1,175 +0,0 @@
-from fake_face_detection.utils.generation import PI_generate_sample as generate_sample
-from fake_face_detection.utils.acquisitions import PI_acquisition as acquisition
-from fake_face_detection.utils.sampling import get_random_samples
-from sklearn.gaussian_process import GaussianProcessRegressor
-from functools import partial
-from typing import *
-import pandas as pd
-import numpy as np
-import string
-import random
-import pickle
-import os
-
-letters = string.ascii_letters + string.digits
-
-class SimpleBayesianOptimizationForFakeReal:
-    
-    def __init__(self, objective: Callable, search_spaces: dict, maximize: bool = True, random_kwargs: dict = {}, kwargs: dict = {}, checkpoint: str = "data/trials/checkpoint.txt"):
-        
-        # recuperate the optimization strategy
-        self.maximize = maximize
-        
-        # checkpoint where the data and score will be saved
-        self.checkpoint = checkpoint
-        
-        # initialize the search spaces
-        self.search_spaces = search_spaces
-        
-        # recuperate the random kwargs
-        self.random_kwargs = random_kwargs
-        
-        # initialize the objective function
-        self.objective = objective
-        
-        # initialize the kwargs
-        self.kwargs = kwargs
-        
-        # initialize the model
-        self.model = GaussianProcessRegressor()
-        
-        # initialize the random kwargs with a random values
-        random_kwargs = {key: value + ''.join(random.choice(letters) for i in range(7)) for key, value in self.random_kwargs.items()}
-        
-        # add random kwargs to the kwargs
-        self.kwargs.update(random_kwargs)
-        
-        # recuperate random sample
-        config = get_random_samples(search_spaces)
-        
-        if os.path.exists(self.checkpoint):
-            
-            with open(self.checkpoint, 'rb') as f:
-            
-                pickler = pickle.Unpickler(f)
-                
-                checkpoint = pickler.load()
-                
-                self.data = checkpoint['data']
-                
-                self.scores = checkpoint['scores']
-                
-                self.model = checkpoint['model']
-                
-                self.current_trial = checkpoint['trial']
-                
-                print(f"Checkpoint loaded at trial {self.current_trial}")
-        
-        else:
-            
-            # add config to kwargs
-            self.kwargs['config'] = config
-            
-            # calculate the first score
-            score = self.objective(**self.kwargs)
-            
-            # initialize the input data
-            self.data = [list(config.values())]
-            
-            # initialize the scores
-            self.scores = [[score]]
-            
-            # fit the model with the input data and the target
-            self.model.fit(self.data, self.scores)
-            
-            # initialize the number of trials to zero
-            self.current_trial = 0
-            
-            with open(self.checkpoint, 'wb') as f:
-                
-                pickler = pickle.Pickler(f)
-                
-                checkpoint = {
-                    'data': self.data,
-                    'scores': self.scores,
-                    'model': self.model,
-                    'trial': self.current_trial
-                }
-                
-                pickler.dump(checkpoint)
-    
-    def optimize(self, n_trials: int = 50, n_tests: int = 100):
-        """Finding the best hyperparameters with the Bayesian Optimization
-
-        Args:
-            n_trials (int, optional): The number of trials. Defaults to 50.
-            n_tests (int, optional): The number of random samples to test for each trial. Defaults to 100.
-        """
-        
-        # let us make multiple trials in order to find the best params
-        for trial in range(self.current_trial + 1, self.current_trial + n_trials + 1):
-            
-            # let us generate new samples with the acquisition and the surrogate functions
-            new_sample = generate_sample(self.data, self.model, self.search_spaces, n_tests, maximize = self.maximize)
-            config = {key: new_sample[i] for i, key in enumerate(self.search_spaces)}
-            
-            # recuperate a new score
-            new_score = self.get_score(config)
-            
-            # let us add the new sample, target and score to their lists
-            self.data.append(new_sample)
-            
-            self.scores.append([new_score])
-            
-            # let us train again the model
-            self.model.fit(self.data, self.scores)
-            
-            # recuperate the current trial
-            self.current_trial = trial
-        
-            with open(self.checkpoint, 'wb') as f:
-                
-                pickler = pickle.Pickler(f)
-                
-                checkpoint = {
-                    'data': self.data,
-                    'scores': self.scores,
-                    'model': self.model,
-                    'trial': self.current_trial
-                }
-                
-                pickler.dump(checkpoint)
-    
-    def get_score(self, config: dict):
-        
-        # add random seed (since we have always the same problem of randomizing the seed)
-        random.seed(None)
-        
-        # initialize the random kwargs with a random values
-        random_kwargs = {key: value + ''.join(random.choice(letters) for i in range(7)) for key, value in self.random_kwargs.items()}
-        print(random_kwargs)
-        # add random kwargs to the kwargs
-        self.kwargs.update(random_kwargs)
-        
-        # add config to kwargs
-        self.kwargs['config'] = config
-        
-        # calculate the first score
-        new_score = self.objective(**self.kwargs)
-        
-        return new_score
-        
-    def get_results(self):
-        """Recuperate the generated samples and the scores
-
-        Returns:
-            pd.DataFrame: A data frame containing the results
-        """
-        # let us return the results as a data frame
-        data = {key: np.array(self.data, dtype = object)[:, i] for i, key in enumerate(self.search_spaces)}
-        
-        data.update({'score': np.array(self.scores)[:, 0]})
-        
-        return pd.DataFrame(data)
-        
-        
diff --git a/spaces/PAIR/Text2Video-Zero/app_canny_db.py b/spaces/PAIR/Text2Video-Zero/app_canny_db.py
deleted file mode 100644
index 1d0129ae9c13a57f6c5c50cd8e0cb6e1a7af9fc7..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/app_canny_db.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import gradio as gr
-from model import Model
-import gradio_utils
-import os
-on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR"
-
-
-examples = [
-    ['Anime DB', "woman1", "Portrait of detailed 1girl, feminine, soldier cinematic shot on canon 5d ultra realistic skin intricate clothes accurate hands Rory Lewis Artgerm WLOP Jeremy Lipking Jane Ansell studio lighting"],
-    ['Arcane DB', "woman1", "Oil painting of a beautiful girl arcane style, masterpiece, a high-quality, detailed, and professional photo"],
-    ['GTA-5 DB', "man1", "gtav style"],
-    ['GTA-5 DB', "woman3", "gtav style"],
-    ['Avatar DB', "woman2", "oil painting of a beautiful girl avatar style"],
-]
-
-
-def load_db_model(evt: gr.SelectData):
-    db_name = gradio_utils.get_db_name_from_id(evt.index)
-    return db_name
-
-
-def canny_select(evt: gr.SelectData):
-    canny_name = gradio_utils.get_canny_name_from_id(evt.index)
-    return canny_name
-
-
-def create_demo(model: Model):
-
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown(
-                '## Text, Canny-Edge and DreamBooth Conditional Video Generation')
-        with gr.Row():
-            gr.HTML(
-                """
-                <div style="text-align: left; auto;">
-                <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem">
-                    Description: Our current release supports only four predefined DreamBooth models and four "motion edges". So you must choose one DreamBooth model and one "motion edges" shown below, or use the examples. The keywords <b>1girl</b>, <b>arcane style</b>, <b>gtav</b>, and <b>avatar style</b> correspond to the models from left to right.
-                </h3>
-                </div>
-                """)
-        with gr.Row():
-            with gr.Column():
-                # input_video_path = gr.Video(source='upload', format="mp4", visible=False)
-                gr.Markdown("## Selection")
-                db_text_field = gr.Markdown('DB Model: **Anime DB** ')
-                canny_text_field = gr.Markdown('Motion: **woman1**')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    watermark = gr.Radio(["Picsart AI Research", "Text2Video-Zero",
-                                         "None"], label="Watermark", value='Picsart AI Research')
-                    chunk_size = gr.Slider(
-                        label="Chunk size", minimum=2, maximum=16, value=2, step=1, visible=not on_huggingspace,
-                        info="Number of frames processed at once. Reduce for lower memory usage.")
-                    merging_ratio = gr.Slider(
-                        label="Merging ratio", minimum=0.0, maximum=0.9, step=0.1, value=0.0, visible=not on_huggingspace,
-                        info="Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference).")
-            with gr.Column():
-                result = gr.Image(label="Generated Video").style(height=400)
-
-        with gr.Row():
-            gallery_db = gr.Gallery(label="Db models", value=[('__assets__/db_files/anime.jpg', "anime"), ('__assets__/db_files/arcane.jpg', "Arcane"), (
-                '__assets__/db_files/gta.jpg', "GTA-5 (Man)"), ('__assets__/db_files/avatar.jpg', "Avatar DB")]).style(grid=[4], height=50)
-        with gr.Row():
-            gallery_canny = gr.Gallery(label="Motions", value=[('__assets__/db_files/woman1.gif', "woman1"), ('__assets__/db_files/woman2.gif', "woman2"), (
-                '__assets__/db_files/man1.gif', "man1"), ('__assets__/db_files/woman3.gif', "woman3")]).style(grid=[4], height=50)
-
-        db_selection = gr.Textbox(label="DB Model", visible=False)
-        canny_selection = gr.Textbox(
-            label="One of the above defined motions", visible=False)
-
-        gallery_db.select(load_db_model, None, db_selection)
-        gallery_canny.select(canny_select, None, canny_selection)
-
-        db_selection.change(on_db_selection_update, None, db_text_field)
-        canny_selection.change(on_canny_selection_update,
-                               None, canny_text_field)
-
-        inputs = [
-            db_selection,
-            canny_selection,
-            prompt,
-            chunk_size,
-            watermark,
-            merging_ratio,
-        ]
-
-        gr.Examples(examples=examples,
-                    inputs=inputs,
-                    outputs=result,
-                    fn=model.process_controlnet_canny_db,
-                    # cache_examples=on_huggingspace,
-                    cache_examples=False,
-                    )
-
-        run_button.click(fn=model.process_controlnet_canny_db,
-                         inputs=inputs,
-                         outputs=result,)
-    return demo
-
-
-def on_db_selection_update(evt: gr.EventData):
-
-    return f"DB model: **{evt._data}**"
-
-
-def on_canny_selection_update(evt: gr.EventData):
-    return f"Motion: **{evt._data}**"
diff --git a/spaces/PaddlePaddle/photo2cartoon/app.py b/spaces/PaddlePaddle/photo2cartoon/app.py
deleted file mode 100644
index fceb167b07ca54f80e7542bbd2b1eeaaf538d44b..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/photo2cartoon/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import os
-os.system("hub install Photo2Cartoon==1.0.0")
-import gradio as gr
-import paddlehub as hub
-import cv2
-from pathlib import Path
-
-model = hub.Module(name='Photo2Cartoon')
-
-def inference(image):
-    result = model.Cartoon_GEN(
-    images=[cv2.imread(image.name)],
-    paths=None,
-    batch_size=4,
-    output_dir='output',
-    visualization=True,
-    use_gpu=False)
-    return './output/result_0.png'
-    
-title = "Photo2Cartoon"
-description = "Gradio demo for Photo2cartoon. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://github.com/minivision-ai/photo2cartoon' target='_blank'>Github Repo</a></p>"
-iface = gr.Interface(inference, inputs=gr.inputs.Image(type="file",source="webcam"), outputs=gr.outputs.Image(type="file"),enable_queue=True,title=title,article=article,description=description)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/json_utils/json_fix_llm.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/json_utils/json_fix_llm.py
deleted file mode 100644
index 869aed125cfb8cd7a69ed02eeb389cc72a3e296b..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/json_utils/json_fix_llm.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance
-of the ChatGPT API or LLM models."""
-from __future__ import annotations
-
-import contextlib
-import json
-from typing import Any, Dict
-
-from colorama import Fore
-from regex import regex
-
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_general import correct_json
-from autogpt.llm_utils import call_ai_function
-from autogpt.logs import logger
-from autogpt.speech import say_text
-
-JSON_SCHEMA = """
-{
-    "command": {
-        "name": "command name",
-        "args": {
-            "arg name": "value"
-        }
-    },
-    "thoughts":
-    {
-        "text": "thought",
-        "reasoning": "reasoning",
-        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-        "criticism": "constructive self-criticism",
-        "speak": "thoughts summary to say to user"
-    }
-}
-"""
-
-CFG = Config()
-
-
-def auto_fix_json(json_string: str, schema: str) -> str:
-    """Fix the given JSON string to make it parseable and fully compliant with
-        the provided schema using GPT-3.
-
-    Args:
-        json_string (str): The JSON string to fix.
-        schema (str): The schema to use to fix the JSON.
-    Returns:
-        str: The fixed JSON string.
-    """
-    # Try to fix the JSON using GPT:
-    function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
-    args = [f"'''{json_string}'''", f"'''{schema}'''"]
-    description_string = (
-        "This function takes a JSON string and ensures that it"
-        " is parseable and fully compliant with the provided schema. If an object"
-        " or field specified in the schema isn't contained within the correct JSON,"
-        " it is omitted. The function also escapes any double quotes within JSON"
-        " string values to ensure that they are valid. If the JSON string contains"
-        " any None or NaN values, they are replaced with null before being parsed."
-    )
-
-    # If it doesn't already start with a "`", add one:
-    if not json_string.startswith("`"):
-        json_string = "```json\n" + json_string + "\n```"
-    result_string = call_ai_function(
-        function_string, args, description_string, model=CFG.fast_llm_model
-    )
-    logger.debug("------------ JSON FIX ATTEMPT ---------------")
-    logger.debug(f"Original JSON: {json_string}")
-    logger.debug("-----------")
-    logger.debug(f"Fixed JSON: {result_string}")
-    logger.debug("----------- END OF FIX ATTEMPT ----------------")
-
-    try:
-        json.loads(result_string)  # just check the validity
-        return result_string
-    except json.JSONDecodeError:  # noqa: E722
-        # Get the call stack:
-        # import traceback
-        # call_stack = traceback.format_exc()
-        # print(f"Failed to fix JSON: '{json_string}' "+call_stack)
-        return "failed"
-
-
-def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]:
-    """Fix the given JSON string to make it parseable and fully compliant with two techniques.
-
-    Args:
-        json_string (str): The JSON string to fix.
-
-    Returns:
-        str: The fixed JSON string.
-    """
-
-    # Parse and print Assistant response
-    assistant_reply_json = fix_and_parse_json(assistant_reply)
-    if assistant_reply_json == {}:
-        assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
-            assistant_reply
-        )
-
-    if assistant_reply_json != {}:
-        return assistant_reply_json
-
-    logger.error(
-        "Error: The following AI output couldn't be converted to a JSON:\n",
-        assistant_reply,
-    )
-    if CFG.speak_mode:
-        say_text("I have received an invalid JSON response from the OpenAI API.")
-
-    return {}
-
-
-def fix_and_parse_json(
-    json_to_load: str, try_to_fix_with_gpt: bool = True
-) -> Dict[Any, Any]:
-    """Fix and parse JSON string
-
-    Args:
-        json_to_load (str): The JSON string.
-        try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
-            Defaults to True.
-
-    Returns:
-        str or dict[Any, Any]: The parsed JSON.
-    """
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = json_to_load.replace("\t", "")
-        return json.loads(json_to_load)
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = correct_json(json_to_load)
-        return json.loads(json_to_load)
-    # Let's do something manually:
-    # sometimes GPT responds with something BEFORE the braces:
-    # "I'm sorry, I don't understand. Please try again."
-    # {"text": "I'm sorry, I don't understand. Please try again.",
-    #  "confidence": 0.0}
-    # So let's try to find the first brace and then parse the rest
-    #  of the string
-    try:
-        brace_index = json_to_load.index("{")
-        maybe_fixed_json = json_to_load[brace_index:]
-        last_brace_index = maybe_fixed_json.rindex("}")
-        maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
-        return json.loads(maybe_fixed_json)
-    except (json.JSONDecodeError, ValueError) as e:
-        return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
-
-
-def try_ai_fix(
-    try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
-) -> Dict[Any, Any]:
-    """Try to fix the JSON with the AI
-
-    Args:
-        try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
-        exception (Exception): The exception that was raised.
-        json_to_load (str): The JSON string to load.
-
-    Raises:
-        exception: If try_to_fix_with_gpt is False.
-
-    Returns:
-        str or dict[Any, Any]: The JSON string or dictionary.
-    """
-    if not try_to_fix_with_gpt:
-        raise exception
-    if CFG.debug_mode:
-        logger.warn(
-            "Warning: Failed to parse AI output, attempting to fix."
-            "\n If you see this warning frequently, it's likely that"
-            " your prompt is confusing the AI. Try changing it up"
-            " slightly."
-        )
-    # Now try to fix this up using the ai_functions
-    ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA)
-
-    if ai_fixed_json != "failed":
-        return json.loads(ai_fixed_json)
-    # This allows the AI to react to the error message,
-    #   which usually results in it correcting its ways.
-    # logger.error("Failed to fix AI output, telling the AI.")
-    return {}
-
-
-def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
-    if CFG.speak_mode and CFG.debug_mode:
-        say_text(
-            "I have received an invalid JSON response from the OpenAI API. "
-            "Trying to fix it now."
-        )
-        logger.error("Attempting to fix JSON by finding outermost brackets\n")
-
-    try:
-        json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
-        json_match = json_pattern.search(json_string)
-
-        if json_match:
-            # Extract the valid JSON object from the string
-            json_string = json_match.group(0)
-            logger.typewriter_log(
-                title="Apparently json was fixed.", title_color=Fore.GREEN
-            )
-            if CFG.speak_mode and CFG.debug_mode:
-                say_text("Apparently json was fixed.")
-        else:
-            return {}
-
-    except (json.JSONDecodeError, ValueError):
-        if CFG.debug_mode:
-            logger.error(f"Error: Invalid JSON: {json_string}\n")
-        if CFG.speak_mode:
-            say_text("Didn't work. I will have to ignore this response then.")
-        logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
-        json_string = {}
-
-    return fix_and_parse_json(json_string)
diff --git a/spaces/Pengyey/bingo-chuchu/src/components/voice.tsx b/spaces/Pengyey/bingo-chuchu/src/components/voice.tsx
deleted file mode 100644
index ab886394487445e4b0675770b76096bba0e61b0e..0000000000000000000000000000000000000000
--- a/spaces/Pengyey/bingo-chuchu/src/components/voice.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import React, { useEffect } from 'react'
-import { useSetAtom } from 'jotai'
-import { useBing } from '@/lib/hooks/use-bing'
-import Image from 'next/image'
-import VoiceIcon from '@/assets/images/voice.svg'
-import VoiceButton from './ui/voice'
-import { SR } from '@/lib/bots/bing/sr'
-import { voiceListenAtom } from '@/state'
-
-const sr = new SR(['发送', '清空', '退出'])
-
-const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick<ReturnType<typeof useBing>, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => {
-  const setListen = useSetAtom(voiceListenAtom)
-  useEffect(() => {
-    if (sr.listening) return
-    sr.transcript = !isSpeaking
-  }, [isSpeaking])
-
-  useEffect(() => {
-    sr.onchange = (msg: string, command?: string) => {
-      switch (command) {
-        case '退出':
-          sr.stop()
-          break;
-        case '发送':
-          sendMessage(input)
-        case '清空':
-          setInput('')
-          break;
-        default:
-          setInput(input + msg)
-      }
-    }
-  }, [input, setInput, sendMessage])
-
-  const switchSR = (enable: boolean = false) => {
-    setListen(enable)
-    if (enable) {
-      sr.start()
-    } else {
-      sr.stop()
-    }
-  }
-
-  return sr.listening ? (
-    <VoiceButton onClick={() => switchSR(false)} />
-  ) : (
-    <Image alt="start voice" src={VoiceIcon} width={24} className="-mt-0.5" onClick={() => switchSR(true)} />
-  )
-};
-
-export default Voice;
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/__init__.py
deleted file mode 100644
index 378a0068432a371af364de9d73785901c0f83383..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/__init__.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# flake8: noqa
-# Copyright (c) OpenMMLab. All rights reserved.
-from .config import Config, ConfigDict, DictAction
-from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
-                   has_method, import_modules_from_strings, is_list_of,
-                   is_method_overridden, is_seq_of, is_str, is_tuple_of,
-                   iter_cast, list_cast, requires_executable, requires_package,
-                   slice_list, to_1tuple, to_2tuple, to_3tuple, to_4tuple,
-                   to_ntuple, tuple_cast)
-from .path import (check_file_exist, fopen, is_filepath, mkdir_or_exist,
-                   scandir, symlink)
-from .progressbar import (ProgressBar, track_iter_progress,
-                          track_parallel_progress, track_progress)
-from .testing import (assert_attrs_equal, assert_dict_contains_subset,
-                      assert_dict_has_keys, assert_is_norm_layer,
-                      assert_keys_equal, assert_params_all_zeros,
-                      check_python_script)
-from .timer import Timer, TimerError, check_time
-from .version_utils import digit_version, get_git_hash
-
-try:
-    import torch
-except ImportError:
-    __all__ = [
-        'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
-        'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
-        'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
-        'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
-        'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
-        'track_progress', 'track_iter_progress', 'track_parallel_progress',
-        'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
-        'digit_version', 'get_git_hash', 'import_modules_from_strings',
-        'assert_dict_contains_subset', 'assert_attrs_equal',
-        'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
-        'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
-        'is_method_overridden', 'has_method'
-    ]
-else:
-    from .env import collect_env
-    from .logging import get_logger, print_log
-    from .parrots_jit import jit, skip_no_elena
-    from .parrots_wrapper import (
-        TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
-        PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
-        _AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
-        _MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
-    from .registry import Registry, build_from_cfg
-    from .trace import is_jit_tracing
-    __all__ = [
-        'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
-        'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
-        'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
-        'check_prerequisites', 'requires_package', 'requires_executable',
-        'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
-        'symlink', 'scandir', 'ProgressBar', 'track_progress',
-        'track_iter_progress', 'track_parallel_progress', 'Registry',
-        'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
-        '_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
-        '_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
-        'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
-        'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
-        'deprecated_api_warning', 'digit_version', 'get_git_hash',
-        'import_modules_from_strings', 'jit', 'skip_no_elena',
-        'assert_dict_contains_subset', 'assert_attrs_equal',
-        'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
-        'assert_params_all_zeros', 'check_python_script',
-        'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
-        '_get_cuda_home', 'has_method'
-    ]
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/resolvelib/compat/collections_abc.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/resolvelib/compat/collections_abc.py
deleted file mode 100644
index 1becc5093c5ab8e196bb9fee415e2381e7158fc3..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/resolvelib/compat/collections_abc.py
+++ /dev/null
@@ -1,6 +0,0 @@
-__all__ = ["Mapping", "Sequence"]
-
-try:
-    from collections.abc import Mapping, Sequence
-except ImportError:
-    from collections import Mapping, Sequence
diff --git a/spaces/Realcat/image-matching-webui/third_party/DKM/dkm/train/train.py b/spaces/Realcat/image-matching-webui/third_party/DKM/dkm/train/train.py
deleted file mode 100644
index b580221f56a2667784836f0237955cc75131b88c..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DKM/dkm/train/train.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from tqdm import tqdm
-from dkm.utils.utils import to_cuda
-
-
-def train_step(train_batch, model, objective, optimizer, **kwargs):
-    optimizer.zero_grad()
-    out = model(train_batch)
-    l = objective(out, train_batch)
-    l.backward()
-    optimizer.step()
-    return {"train_out": out, "train_loss": l.item()}
-
-
-def train_k_steps(
-    n_0, k, dataloader, model, objective, optimizer, lr_scheduler, progress_bar=True
-):
-    for n in tqdm(range(n_0, n_0 + k), disable=not progress_bar):
-        batch = next(dataloader)
-        model.train(True)
-        batch = to_cuda(batch)
-        train_step(
-            train_batch=batch,
-            model=model,
-            objective=objective,
-            optimizer=optimizer,
-            lr_scheduler=lr_scheduler,
-            n=n,
-        )
-        lr_scheduler.step()
-
-
-def train_epoch(
-    dataloader=None,
-    model=None,
-    objective=None,
-    optimizer=None,
-    lr_scheduler=None,
-    epoch=None,
-):
-    model.train(True)
-    print(f"At epoch {epoch}")
-    for batch in tqdm(dataloader, mininterval=5.0):
-        batch = to_cuda(batch)
-        train_step(
-            train_batch=batch, model=model, objective=objective, optimizer=optimizer
-        )
-    lr_scheduler.step()
-    return {
-        "model": model,
-        "optimizer": optimizer,
-        "lr_scheduler": lr_scheduler,
-        "epoch": epoch,
-    }
-
-
-def train_k_epochs(
-    start_epoch, end_epoch, dataloader, model, objective, optimizer, lr_scheduler
-):
-    for epoch in range(start_epoch, end_epoch + 1):
-        train_epoch(
-            dataloader=dataloader,
-            model=model,
-            objective=objective,
-            optimizer=optimizer,
-            lr_scheduler=lr_scheduler,
-            epoch=epoch,
-        )
diff --git a/spaces/Reeve/Ohayou_Face/models/mtcnn/mtcnn_pytorch/src/box_utils.py b/spaces/Reeve/Ohayou_Face/models/mtcnn/mtcnn_pytorch/src/box_utils.py
deleted file mode 100644
index 1e8081b73639a7d70e4391b3d45417569550ddc6..0000000000000000000000000000000000000000
--- a/spaces/Reeve/Ohayou_Face/models/mtcnn/mtcnn_pytorch/src/box_utils.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import numpy as np
-from PIL import Image
-
-
-def nms(boxes, overlap_threshold=0.5, mode='union'):
-    """Non-maximum suppression.
-
-    Arguments:
-        boxes: a float numpy array of shape [n, 5],
-            where each row is (xmin, ymin, xmax, ymax, score).
-        overlap_threshold: a float number.
-        mode: 'union' or 'min'.
-
-    Returns:
-        list with indices of the selected boxes
-    """
-
-    # if there are no boxes, return the empty list
-    if len(boxes) == 0:
-        return []
-
-    # list of picked indices
-    pick = []
-
-    # grab the coordinates of the bounding boxes
-    x1, y1, x2, y2, score = [boxes[:, i] for i in range(5)]
-
-    area = (x2 - x1 + 1.0) * (y2 - y1 + 1.0)
-    ids = np.argsort(score)  # in increasing order
-
-    while len(ids) > 0:
-
-        # grab index of the largest value
-        last = len(ids) - 1
-        i = ids[last]
-        pick.append(i)
-
-        # compute intersections
-        # of the box with the largest score
-        # with the rest of boxes
-
-        # left top corner of intersection boxes
-        ix1 = np.maximum(x1[i], x1[ids[:last]])
-        iy1 = np.maximum(y1[i], y1[ids[:last]])
-
-        # right bottom corner of intersection boxes
-        ix2 = np.minimum(x2[i], x2[ids[:last]])
-        iy2 = np.minimum(y2[i], y2[ids[:last]])
-
-        # width and height of intersection boxes
-        w = np.maximum(0.0, ix2 - ix1 + 1.0)
-        h = np.maximum(0.0, iy2 - iy1 + 1.0)
-
-        # intersections' areas
-        inter = w * h
-        if mode == 'min':
-            overlap = inter / np.minimum(area[i], area[ids[:last]])
-        elif mode == 'union':
-            # intersection over union (IoU)
-            overlap = inter / (area[i] + area[ids[:last]] - inter)
-
-        # delete all boxes where overlap is too big
-        ids = np.delete(
-            ids,
-            np.concatenate([[last], np.where(overlap > overlap_threshold)[0]])
-        )
-
-    return pick
-
-
-def convert_to_square(bboxes):
-    """Convert bounding boxes to a square form.
-
-    Arguments:
-        bboxes: a float numpy array of shape [n, 5].
-
-    Returns:
-        a float numpy array of shape [n, 5],
-            squared bounding boxes.
-    """
-
-    square_bboxes = np.zeros_like(bboxes)
-    x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
-    h = y2 - y1 + 1.0
-    w = x2 - x1 + 1.0
-    max_side = np.maximum(h, w)
-    square_bboxes[:, 0] = x1 + w * 0.5 - max_side * 0.5
-    square_bboxes[:, 1] = y1 + h * 0.5 - max_side * 0.5
-    square_bboxes[:, 2] = square_bboxes[:, 0] + max_side - 1.0
-    square_bboxes[:, 3] = square_bboxes[:, 1] + max_side - 1.0
-    return square_bboxes
-
-
-def calibrate_box(bboxes, offsets):
-    """Transform bounding boxes to be more like true bounding boxes.
-    'offsets' is one of the outputs of the nets.
-
-    Arguments:
-        bboxes: a float numpy array of shape [n, 5].
-        offsets: a float numpy array of shape [n, 4].
-
-    Returns:
-        a float numpy array of shape [n, 5].
-    """
-    x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
-    w = x2 - x1 + 1.0
-    h = y2 - y1 + 1.0
-    w = np.expand_dims(w, 1)
-    h = np.expand_dims(h, 1)
-
-    # this is what happening here:
-    # tx1, ty1, tx2, ty2 = [offsets[:, i] for i in range(4)]
-    # x1_true = x1 + tx1*w
-    # y1_true = y1 + ty1*h
-    # x2_true = x2 + tx2*w
-    # y2_true = y2 + ty2*h
-    # below is just more compact form of this
-
-    # are offsets always such that
-    # x1 < x2 and y1 < y2 ?
-
-    translation = np.hstack([w, h, w, h]) * offsets
-    bboxes[:, 0:4] = bboxes[:, 0:4] + translation
-    return bboxes
-
-
-def get_image_boxes(bounding_boxes, img, size=24):
-    """Cut out boxes from the image.
-
-    Arguments:
-        bounding_boxes: a float numpy array of shape [n, 5].
-        img: an instance of PIL.Image.
-        size: an integer, size of cutouts.
-
-    Returns:
-        a float numpy array of shape [n, 3, size, size].
-    """
-
-    num_boxes = len(bounding_boxes)
-    width, height = img.size
-
-    [dy, edy, dx, edx, y, ey, x, ex, w, h] = correct_bboxes(bounding_boxes, width, height)
-    img_boxes = np.zeros((num_boxes, 3, size, size), 'float32')
-
-    for i in range(num_boxes):
-        img_box = np.zeros((h[i], w[i], 3), 'uint8')
-
-        img_array = np.asarray(img, 'uint8')
-        img_box[dy[i]:(edy[i] + 1), dx[i]:(edx[i] + 1), :] = \
-            img_array[y[i]:(ey[i] + 1), x[i]:(ex[i] + 1), :]
-
-        # resize
-        img_box = Image.fromarray(img_box)
-        img_box = img_box.resize((size, size), Image.BILINEAR)
-        img_box = np.asarray(img_box, 'float32')
-
-        img_boxes[i, :, :, :] = _preprocess(img_box)
-
-    return img_boxes
-
-
-def correct_bboxes(bboxes, width, height):
-    """Crop boxes that are too big and get coordinates
-    with respect to cutouts.
-
-    Arguments:
-        bboxes: a float numpy array of shape [n, 5],
-            where each row is (xmin, ymin, xmax, ymax, score).
-        width: a float number.
-        height: a float number.
-
-    Returns:
-        dy, dx, edy, edx: a int numpy arrays of shape [n],
-            coordinates of the boxes with respect to the cutouts.
-        y, x, ey, ex: a int numpy arrays of shape [n],
-            corrected ymin, xmin, ymax, xmax.
-        h, w: a int numpy arrays of shape [n],
-            just heights and widths of boxes.
-
-        in the following order:
-            [dy, edy, dx, edx, y, ey, x, ex, w, h].
-    """
-
-    x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)]
-    w, h = x2 - x1 + 1.0, y2 - y1 + 1.0
-    num_boxes = bboxes.shape[0]
-
-    # 'e' stands for end
-    # (x, y) -> (ex, ey)
-    x, y, ex, ey = x1, y1, x2, y2
-
-    # we need to cut out a box from the image.
-    # (x, y, ex, ey) are corrected coordinates of the box
-    # in the image.
-    # (dx, dy, edx, edy) are coordinates of the box in the cutout
-    # from the image.
-    dx, dy = np.zeros((num_boxes,)), np.zeros((num_boxes,))
-    edx, edy = w.copy() - 1.0, h.copy() - 1.0
-
-    # if box's bottom right corner is too far right
-    ind = np.where(ex > width - 1.0)[0]
-    edx[ind] = w[ind] + width - 2.0 - ex[ind]
-    ex[ind] = width - 1.0
-
-    # if box's bottom right corner is too low
-    ind = np.where(ey > height - 1.0)[0]
-    edy[ind] = h[ind] + height - 2.0 - ey[ind]
-    ey[ind] = height - 1.0
-
-    # if box's top left corner is too far left
-    ind = np.where(x < 0.0)[0]
-    dx[ind] = 0.0 - x[ind]
-    x[ind] = 0.0
-
-    # if box's top left corner is too high
-    ind = np.where(y < 0.0)[0]
-    dy[ind] = 0.0 - y[ind]
-    y[ind] = 0.0
-
-    return_list = [dy, edy, dx, edx, y, ey, x, ex, w, h]
-    return_list = [i.astype('int32') for i in return_list]
-
-    return return_list
-
-
-def _preprocess(img):
-    """Preprocessing step before feeding the network.
-
-    Arguments:
-        img: a float numpy array of shape [h, w, c].
-
-    Returns:
-        a float numpy array of shape [1, c, h, w].
-    """
-    img = img.transpose((2, 0, 1))
-    img = np.expand_dims(img, 0)
-    img = (img - 127.5) * 0.0078125
-    return img
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/engine/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/engine/__init__.py
deleted file mode 100644
index 3193b7f664e19ce2458d81c836597fa22e4bb082..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/engine/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .test import (collect_results_cpu, collect_results_gpu, multi_gpu_test,
-                   single_gpu_test)
-
-__all__ = [
-    'collect_results_cpu', 'collect_results_gpu', 'multi_gpu_test',
-    'single_gpu_test'
-]
diff --git a/spaces/Rongjiehuang/GenerSpeech/modules/GenerSpeech/task/dataset.py b/spaces/Rongjiehuang/GenerSpeech/modules/GenerSpeech/task/dataset.py
deleted file mode 100644
index 0b1f6fa2c596a23b9bc49ad470c1723d3597d738..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/GenerSpeech/modules/GenerSpeech/task/dataset.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-from tasks.base_task import data_loader
-from tasks.tts.fs2 import FastSpeech2Task
-from tasks.tts.dataset_utils import FastSpeechDataset, BaseTTSDataset
-import glob
-import importlib
-from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse
-from inference.base_tts_infer import load_data_preprocessor
-from data_gen.tts.emotion import inference as EmotionEncoder
-from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance
-from data_gen.tts.emotion.inference import preprocess_wav
-from tqdm import tqdm
-from utils.hparams import hparams
-from data_gen.tts.data_gen_utils import build_phone_encoder, build_word_encoder
-import random
-import torch
-import torch.optim
-import torch.nn.functional as F
-import torch.utils.data
-from utils.indexed_datasets import IndexedDataset
-from resemblyzer import VoiceEncoder
-import torch.distributions
-import numpy as np
-import utils
-import os
-
-
-
-class GenerSpeech_dataset(BaseTTSDataset):
-    def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None):
-        super().__init__(prefix, shuffle, test_items, test_sizes, data_dir)
-        self.f0_mean, self.f0_std = hparams.get('f0_mean', None), hparams.get('f0_std', None)
-        if prefix == 'valid':
-            indexed_ds = IndexedDataset(f'{self.data_dir}/train')
-            sizes = np.load(f'{self.data_dir}/train_lengths.npy')
-            index = [i for i in range(len(indexed_ds))]
-            random.shuffle(index)
-            index = index[:300]
-            self.sizes = sizes[index]
-            self.indexed_ds = []
-            for i in index:
-                self.indexed_ds.append(indexed_ds[i])
-            self.avail_idxs = list(range(len(self.sizes)))
-            if hparams['min_frames'] > 0:
-                self.avail_idxs = [x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']]
-            self.sizes = [self.sizes[i] for i in self.avail_idxs]
-
-        if prefix == 'test' and hparams['test_input_dir'] != '':
-            self.preprocessor, self.preprocess_args = load_data_preprocessor()
-            self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir'])
-            self.avail_idxs = [i for i, _ in enumerate(self.sizes)]
-
-
-    def load_test_inputs(self, test_input_dir):
-        inp_wav_paths = sorted(glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3'))
-        binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer')
-        pkg = ".".join(binarizer_cls.split(".")[:-1])
-        cls_name = binarizer_cls.split(".")[-1]
-        binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
-
-        phone_encoder = build_phone_encoder(hparams['binary_data_dir'])
-        word_encoder = build_word_encoder(hparams['binary_data_dir'])
-        voice_encoder = VoiceEncoder().cuda()
-
-        encoder = [phone_encoder, word_encoder]
-        sizes = []
-        items = []
-        EmotionEncoder.load_model(hparams['emotion_encoder_path'])
-        preprocessor, preprocess_args = self.preprocessor, self.preprocess_args
-
-        for wav_fn in tqdm(inp_wav_paths):
-            item_name = wav_fn[len(test_input_dir) + 1:].replace("/", "_")
-            spk_id = emotion = 0
-            item2tgfn = wav_fn.replace('.wav', '.TextGrid') # prepare textgrid alignment
-            txtpath = wav_fn.replace('.wav', '.txt')  # prepare text
-            with open(txtpath, 'r') as f:
-                text_raw = f.readlines()
-                f.close()
-            ph, txt = preprocessor.txt_to_ph(preprocessor.txt_processor, text_raw[0], preprocess_args)
-
-            item = binarizer_cls.process_item(item_name, ph, txt, item2tgfn, wav_fn, spk_id, emotion, encoder, hparams['binarization_args'])
-            item['emo_embed'] = Embed_utterance(preprocess_wav(item['wav_fn']))
-            item['spk_embed'] = voice_encoder.embed_utterance(item['wav'])
-            items.append(item)
-            sizes.append(item['len'])
-        return items, sizes
-
-    def _get_item(self, index):
-        if hasattr(self, 'avail_idxs') and self.avail_idxs is not None:
-            index = self.avail_idxs[index]
-        if self.indexed_ds is None:
-            self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
-        return self.indexed_ds[index]
-
-    def __getitem__(self, index):
-        hparams = self.hparams
-        item = self._get_item(index)
-        assert len(item['mel']) == self.sizes[index], (len(item['mel']), self.sizes[index])
-        max_frames = hparams['max_frames']
-        spec = torch.Tensor(item['mel'])[:max_frames]
-        max_frames = spec.shape[0] // hparams['frames_multiple'] * hparams['frames_multiple']
-        spec = spec[:max_frames]
-        phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']])
-        sample = {
-            "id": index,
-            "item_name": item['item_name'],
-            "text": item['txt'],
-            "txt_token": phone,
-            "mel": spec,
-            "mel_nonpadding": spec.abs().sum(-1) > 0,
-        }
-        spec = sample['mel']
-        T = spec.shape[0]
-        sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if 'mel2ph' in item else None
-        if hparams['use_pitch_embed']:
-            assert 'f0' in item
-            if hparams.get('normalize_pitch', False):
-                f0 = item["f0"]
-                if len(f0 > 0) > 0 and f0[f0 > 0].std() > 0:
-                    f0[f0 > 0] = (f0[f0 > 0] - f0[f0 > 0].mean()) / f0[f0 > 0].std() * hparams['f0_std'] + \
-                                 hparams['f0_mean']
-                    f0[f0 > 0] = f0[f0 > 0].clip(min=60, max=500)
-                pitch = f0_to_coarse(f0)
-                pitch = torch.LongTensor(pitch[:max_frames])
-            else:
-                pitch = torch.LongTensor(item.get("pitch"))[:max_frames] if "pitch" in item else None
-            f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
-            uv = torch.FloatTensor(uv)
-            f0 = torch.FloatTensor(f0)
-        else:
-            f0 = uv = torch.zeros_like(mel2ph)
-            pitch = None
-        sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch
-        sample["spk_embed"] = torch.Tensor(item['spk_embed'])
-        sample["emotion"] = item['emotion']
-        sample["emo_embed"] = torch.Tensor(item['emo_embed'])
-
-        if hparams.get('use_word', False):
-            sample["ph_words"] = item["ph_words"]
-            sample["word_tokens"] = torch.LongTensor(item["word_tokens"])
-            sample["mel2word"] = torch.LongTensor(item.get("mel2word"))[:max_frames]
-            sample["ph2word"] = torch.LongTensor(item['ph2word'][:hparams['max_input_tokens']])
-        return sample
-
-    def collater(self, samples):
-        if len(samples) == 0:
-            return {}
-        hparams = self.hparams
-        id = torch.LongTensor([s['id'] for s in samples])
-        item_names = [s['item_name'] for s in samples]
-        text = [s['text'] for s in samples]
-        txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0)
-        mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
-        txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples])
-        mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
-
-        batch = {
-            'id': id,
-            'item_name': item_names,
-            'nsamples': len(samples),
-            'text': text,
-            'txt_tokens': txt_tokens,
-            'txt_lengths': txt_lengths,
-            'mels': mels,
-            'mel_lengths': mel_lengths,
-        }
-
-        f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
-        pitch = utils.collate_1d([s['pitch'] for s in samples]) if samples[0]['pitch'] is not None else None
-        uv = utils.collate_1d([s['uv'] for s in samples])
-        mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) if samples[0]['mel2ph'] is not None else None
-        batch.update({
-            'mel2ph': mel2ph,
-            'pitch': pitch,
-            'f0': f0,
-            'uv': uv,
-        })
-        spk_embed = torch.stack([s['spk_embed'] for s in samples])
-        batch['spk_embed'] = spk_embed
-        emo_embed = torch.stack([s['emo_embed'] for s in samples])
-        batch['emo_embed'] = emo_embed
-
-        if hparams.get('use_word', False):
-            ph_words = [s['ph_words'] for s in samples]
-            batch['ph_words'] = ph_words
-            word_tokens = utils.collate_1d([s['word_tokens'] for s in samples], 0)
-            batch['word_tokens'] = word_tokens
-            mel2word = utils.collate_1d([s['mel2word'] for s in samples], 0)
-            batch['mel2word'] = mel2word
-            ph2word = utils.collate_1d([s['ph2word'] for s in samples], 0)
-            batch['ph2word'] = ph2word
-        return batch
\ No newline at end of file
diff --git a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/ops/fma.py b/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/ops/fma.py
deleted file mode 100644
index 2eeac58a626c49231e04122b93e321ada954c5d3..0000000000000000000000000000000000000000
--- a/spaces/Rothfeld/stable-diffusion-mat-outpainting-primer/torch_utils/ops/fma.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
-
-import torch
-
-#----------------------------------------------------------------------------
-
-def fma(a, b, c): # => a * b + c
-    return _FusedMultiplyAdd.apply(a, b, c)
-
-#----------------------------------------------------------------------------
-
-class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
-    @staticmethod
-    def forward(ctx, a, b, c): # pylint: disable=arguments-differ
-        out = torch.addcmul(c, a, b)
-        ctx.save_for_backward(a, b)
-        ctx.c_shape = c.shape
-        return out
-
-    @staticmethod
-    def backward(ctx, dout): # pylint: disable=arguments-differ
-        a, b = ctx.saved_tensors
-        c_shape = ctx.c_shape
-        da = None
-        db = None
-        dc = None
-
-        if ctx.needs_input_grad[0]:
-            da = _unbroadcast(dout * b, a.shape)
-
-        if ctx.needs_input_grad[1]:
-            db = _unbroadcast(dout * a, b.shape)
-
-        if ctx.needs_input_grad[2]:
-            dc = _unbroadcast(dout, c_shape)
-
-        return da, db, dc
-
-#----------------------------------------------------------------------------
-
-def _unbroadcast(x, shape):
-    extra_dims = x.ndim - len(shape)
-    assert extra_dims >= 0
-    dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
-    if len(dim):
-        x = x.sum(dim=dim, keepdim=True)
-    if extra_dims:
-        x = x.reshape(-1, *x.shape[extra_dims+1:])
-    assert x.shape == shape
-    return x
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Ryukijano/jax-diffusers-event-canny-coyo1m/app.py b/spaces/Ryukijano/jax-diffusers-event-canny-coyo1m/app.py
deleted file mode 100644
index 07bb6439ef3df3074148ab01313165d031f39808..0000000000000000000000000000000000000000
--- a/spaces/Ryukijano/jax-diffusers-event-canny-coyo1m/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/jax-diffusers-event/canny-coyo1m").launch()
\ No newline at end of file
diff --git a/spaces/SamerKharboush/chatGPT-Sam-Turbo/assets/custom.css b/spaces/SamerKharboush/chatGPT-Sam-Turbo/assets/custom.css
deleted file mode 100644
index 7a08a3878ec520619245eef7704b66f6f2b5f5ed..0000000000000000000000000000000000000000
--- a/spaces/SamerKharboush/chatGPT-Sam-Turbo/assets/custom.css
+++ /dev/null
@@ -1,239 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* 覆盖gradio的页脚信息QAQ */
-footer {
-    display: none !important;
-}
-#footer{
-    text-align: center;
-}
-#footer div{
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.85;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-
-/* usage_display */
-#usage_display {
-    position: relative;
-    margin: 0;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius);
-    background: var(--block-background-fill);
-    width: 100%;
-    line-height: var(--line-sm);
-    min-height: 2em;
-}
-#usage_display p, #usage_display span {
-    margin: 0;
-    padding: .5em 1em;
-    font-size: .85em;
-    color: var(--body-text-color-subdued);
-}
-.progress-bar {
-    background-color: var(--input-background-fill);;
-    margin: 0 1em;
-    height: 20px;
-    border-radius: 10px;
-    overflow: hidden;
-}
-.progress {
-    background-color: var(--block-title-background-fill);;
-    height: 100%;
-    border-radius: 10px;
-    text-align: right;
-    transition: width 0.5s ease-in-out;
-}
-.progress-text {
-    /* color: white; */
-    color: var(--color-accent) !important;
-    font-size: 1em !important;
-    font-weight: bold;
-    padding-right: 10px;
-    line-height: 20px;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-@media (prefers-color-scheme: light) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-light) !important;
-        color: #000000 !important;
-    }
-    [data-testid = "bot"] {
-        background-color: ##b2a361 !important;
-    }
-    [data-testid = "user"] {
-        background-color: #95EC69 !important;
-    }
-}
-/* 暗色 */
-@media (prefers-color-scheme: dark) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-dark) !important;
-        color: #FFFFFF !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #b2a361 !important;
-    }
-    [data-testid = "user"] {
-        background-color: #26B561 !important;
-    }
-    body {
-        background-color: var(--neutral-950) !important;
-    }
-}
-
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/SantoshKumar/06-SD-SL-AI-Image-Music-Video-UI-UX/app.py b/spaces/SantoshKumar/06-SD-SL-AI-Image-Music-Video-UI-UX/app.py
deleted file mode 100644
index 0f4298365bc4f58d285202fb9442e12805d2db95..0000000000000000000000000000000000000000
--- a/spaces/SantoshKumar/06-SD-SL-AI-Image-Music-Video-UI-UX/app.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import streamlit as st      
-import gradio as gr
-import IPython
-import streamlit as st
-import streamlit.components.v1 as components
-from IPython.display import IFrame
-
-src=''   # URL parameter to change the iframe url
-def SetIframeURL(option_selected):
-    if (option_selected=='Collager'):
-        src='https://www.artbreeder.com/'
-    if (option_selected=='Midjourney'):
-        src='https://www.midjourney.com/'
-    if (option_selected=='DreamStudio'):
-        src='https://beta.dreamstudio.ai/'
-    if (option_selected=='NightCafe'):
-        src='https://creator.nightcafe.studio/'
-    if (option_selected=='RunwayML'):
-        src='https://app.runwayml.com/'
-    if (option_selected=='ArtFromTextandImages'):
-        src='https://huggingface.co/spaces/awacke1/Art-from-Text-and-Images'   
-    if (option_selected=='Boomy'):
-        src='https://boomy.com/'   
-    
-    width = st.sidebar.slider("Width", 200, 1500, 800, 100)
-    height = st.sidebar.slider("Height", 200, 1500, 900, 100)
-    st.components.v1.iframe(src, width, height, scrolling=True)
-
-try:
-    options = ['Midjourney', 'RunwayML', 'Boomy']
-    query_params = st.experimental_get_query_params()
-    query_option = query_params['option'][0] #throws an exception when visiting http://host:port
-    option_selected = st.sidebar.selectbox('Pick option', options, index=options.index(query_option))
-    if option_selected:
-        st.experimental_set_query_params(option=option_selected)
-        SetIframeURL(option_selected)
-except: 
-    options = ['Midjourney', 'RunwayML', 'Boomy']
-    st.experimental_set_query_params(option=options[1]) # defaults to 1
-    query_params = st.experimental_get_query_params()
-    query_option = query_params['option'][0]
-    option_selected = st.sidebar.selectbox('Pick option', options, index=options.index(query_option))
-    if option_selected:
-        st.experimental_set_query_params(option=option_selected)
-        SetIframeURL(option_selected)
\ No newline at end of file
diff --git a/spaces/Shibe/sahil2801-replit-code-instruct-glaive/README.md b/spaces/Shibe/sahil2801-replit-code-instruct-glaive/README.md
deleted file mode 100644
index 05ac22700fcc2018959d63fa1b3e0a6ebdd1fff7..0000000000000000000000000000000000000000
--- a/spaces/Shibe/sahil2801-replit-code-instruct-glaive/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sahil2801 Replit Code Instruct Glaive
-emoji: 🦀
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/traitlets.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/traitlets.py
deleted file mode 100644
index 2f979fa727127d12b7f66fa169c05778fae4801e..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/traitlets.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from warnings import warn
-
-warn("IPython.utils.traitlets has moved to a top-level traitlets package.", stacklevel=2)
-
-from traitlets import *
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/colorama/tests/winterm_test.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/colorama/tests/winterm_test.py
deleted file mode 100644
index d0955f9e608377940f0d548576964f2fcf3caf48..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/colorama/tests/winterm_test.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import sys
-from unittest import TestCase, main, skipUnless
-
-try:
-    from unittest.mock import Mock, patch
-except ImportError:
-    from mock import Mock, patch
-
-from ..winterm import WinColor, WinStyle, WinTerm
-
-
-class WinTermTest(TestCase):
-
-    @patch('colorama.winterm.win32')
-    def testInit(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 7 + 6 * 16 + 8
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        self.assertEqual(term._fore, 7)
-        self.assertEqual(term._back, 6)
-        self.assertEqual(term._style, 8)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testGetAttrs(self):
-        term = WinTerm()
-
-        term._fore = 0
-        term._back = 0
-        term._style = 0
-        self.assertEqual(term.get_attrs(), 0)
-
-        term._fore = WinColor.YELLOW
-        self.assertEqual(term.get_attrs(), WinColor.YELLOW)
-
-        term._back = WinColor.MAGENTA
-        self.assertEqual(
-            term.get_attrs(),
-            WinColor.YELLOW + WinColor.MAGENTA * 16)
-
-        term._style = WinStyle.BRIGHT
-        self.assertEqual(
-            term.get_attrs(),
-            WinColor.YELLOW + WinColor.MAGENTA * 16 + WinStyle.BRIGHT)
-
-    @patch('colorama.winterm.win32')
-    def testResetAll(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 1 + 2 * 16 + 8
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-
-        term.set_console = Mock()
-        term._fore = -1
-        term._back = -1
-        term._style = -1
-
-        term.reset_all()
-
-        self.assertEqual(term._fore, 1)
-        self.assertEqual(term._back, 2)
-        self.assertEqual(term._style, 8)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testFore(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._fore = 0
-
-        term.fore(5)
-
-        self.assertEqual(term._fore, 5)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testBack(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._back = 0
-
-        term.back(5)
-
-        self.assertEqual(term._back, 5)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testStyle(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._style = 0
-
-        term.style(22)
-
-        self.assertEqual(term._style, 22)
-        self.assertEqual(term.set_console.called, True)
-
-    @patch('colorama.winterm.win32')
-    def testSetConsole(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 0
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        term.windll = Mock()
-
-        term.set_console()
-
-        self.assertEqual(
-            mockWin32.SetConsoleTextAttribute.call_args,
-            ((mockWin32.STDOUT, term.get_attrs()), {})
-        )
-
-    @patch('colorama.winterm.win32')
-    def testSetConsoleOnStderr(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 0
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        term.windll = Mock()
-
-        term.set_console(on_stderr=True)
-
-        self.assertEqual(
-            mockWin32.SetConsoleTextAttribute.call_args,
-            ((mockWin32.STDERR, term.get_attrs()), {})
-        )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/url_3d/mesh_url.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/url_3d/mesh_url.py
deleted file mode 100644
index 70f32eb55817fc338989ebedf8f0c0041f5939f1..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/url_3d/mesh_url.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from typing import TYPE_CHECKING, Any, Dict, Optional, TypeVar
-
-import numpy as np
-from pydantic import parse_obj_as
-
-from docarray.typing.proto_register import _register_proto
-from docarray.typing.tensor.ndarray import NdArray
-from docarray.typing.url.url_3d.url_3d import Url3D
-
-if TYPE_CHECKING:
-    from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
-
-T = TypeVar('T', bound='Mesh3DUrl')
-
-
-@_register_proto(proto_type_name='mesh_url')
-class Mesh3DUrl(Url3D):
-    """
-    URL to a file containing 3D mesh information.
-    Can be remote (web) URL, or a local file path.
-    """
-
-    def load(
-        self: T,
-        skip_materials: bool = True,
-        trimesh_args: Optional[Dict[str, Any]] = None,
-    ) -> 'VerticesAndFaces':
-        """
-        Load the data from the url into a [`VerticesAndFaces`][docarray.documents.VerticesAndFaces]
-        object containing vertices and faces information.
-
-        ---
-
-        ```python
-        from docarray import BaseDoc
-
-        from docarray.typing import Mesh3DUrl, NdArray
-
-
-        class MyDoc(BaseDoc):
-            mesh_url: Mesh3DUrl
-
-
-        doc = MyDoc(mesh_url="https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj")
-
-        tensors = doc.mesh_url.load()
-        assert isinstance(tensors.vertices, NdArray)
-        assert isinstance(tensors.faces, NdArray)
-        ```
-
-
-        :param skip_materials: Skip materials if True, else skip.
-        :param trimesh_args: dictionary of additional arguments for `trimesh.load()`
-            or `trimesh.load_remote()`.
-        :return: VerticesAndFaces object containing vertices and faces information.
-        """
-        from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
-
-        if not trimesh_args:
-            trimesh_args = {}
-        mesh = self._load_trimesh_instance(
-            force='mesh', skip_materials=skip_materials, **trimesh_args
-        )
-
-        vertices = parse_obj_as(NdArray, mesh.vertices.view(np.ndarray))
-        faces = parse_obj_as(NdArray, mesh.faces.view(np.ndarray))
-
-        return VerticesAndFaces(vertices=vertices, faces=faces)
-
-    def display(self) -> None:
-        """
-        Plot mesh from url.
-        This loads the Trimesh instance of the 3D mesh, and then displays it.
-        """
-        from IPython.display import display
-
-        mesh = self._load_trimesh_instance(skip_materials=False)
-        display(mesh.show())
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/utils/_internal/compress.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/utils/_internal/compress.py
deleted file mode 100644
index 51b4860fe4ae8996d1249247ad08bddd3f288731..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/utils/_internal/compress.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from typing import IO, TYPE_CHECKING, Callable, Optional
-
-from docarray.utils._internal.misc import import_library
-
-
-def _compress_bytes(data: bytes, algorithm: Optional[str] = None) -> bytes:
-    if algorithm == 'lz4':
-        if TYPE_CHECKING:
-            from lz4 import frame
-        else:
-            lz4 = import_library('lz4', raise_error=True)  # noqa: F841
-            from lz4 import frame
-
-        data = frame.compress(data)
-    elif algorithm == 'bz2':
-        import bz2
-
-        data = bz2.compress(data)
-    elif algorithm == 'lzma':
-        import lzma
-
-        data = lzma.compress(data)
-    elif algorithm == 'zlib':
-        import zlib
-
-        data = zlib.compress(data)
-    elif algorithm == 'gzip':
-        import gzip
-
-        data = gzip.compress(data)
-    return data
-
-
-def _decompress_bytes(data: bytes, algorithm: Optional[str] = None) -> bytes:
-    if algorithm == 'lz4':
-        if TYPE_CHECKING:
-            from lz4 import frame
-        else:
-            lz4 = import_library('lz4', raise_error=True)  # noqa: F841
-            from lz4 import frame
-
-        data = frame.decompress(data)
-    elif algorithm == 'bz2':
-        import bz2
-
-        data = bz2.decompress(data)
-    elif algorithm == 'lzma':
-        import lzma
-
-        data = lzma.decompress(data)
-    elif algorithm == 'zlib':
-        import zlib
-
-        data = zlib.decompress(data)
-    elif algorithm == 'gzip':
-        import gzip
-
-        data = gzip.decompress(data)
-    return data
-
-
-def _get_compress_ctx(algorithm: Optional[str] = None) -> Optional[Callable]:
-    if algorithm == 'lz4':
-        if TYPE_CHECKING:
-            from lz4 import frame
-        else:
-            lz4 = import_library('lz4', raise_error=True)  # noqa: F841
-            from lz4 import frame
-
-        def _fun(x: IO[bytes]):
-            return frame.LZ4FrameFile(x, 'wb')
-
-        compress_ctx = _fun
-    elif algorithm == 'gzip':
-        import gzip
-
-        def _fun(x: IO[bytes]):
-            return gzip.GzipFile(fileobj=x, mode='wb')
-
-        compress_ctx = _fun
-    elif algorithm == 'bz2':
-        import bz2
-
-        def _fun(x: IO[bytes]):
-            return bz2.BZ2File(filename=x, mode='wb')
-
-        compress_ctx = _fun
-    elif algorithm == 'lzma':
-        import lzma
-
-        def _fun(x: IO[bytes]):
-            return lzma.LZMAFile(filename=x, mode='wb')
-
-        compress_ctx = _fun
-    else:
-        compress_ctx = None
-    return compress_ctx
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/evaluation/lvis_evaluation.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/evaluation/lvis_evaluation.py
deleted file mode 100644
index 7d712ef262789edb85392cb54577c3a6b15e223e..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/evaluation/lvis_evaluation.py
+++ /dev/null
@@ -1,380 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import json
-import logging
-import os
-import pickle
-from collections import OrderedDict
-import torch
-
-import annotator.oneformer.detectron2.utils.comm as comm
-from annotator.oneformer.detectron2.config import CfgNode
-from annotator.oneformer.detectron2.data import MetadataCatalog
-from annotator.oneformer.detectron2.structures import Boxes, BoxMode, pairwise_iou
-from annotator.oneformer.detectron2.utils.file_io import PathManager
-from annotator.oneformer.detectron2.utils.logger import create_small_table
-
-from .coco_evaluation import instances_to_coco_json
-from .evaluator import DatasetEvaluator
-
-
-class LVISEvaluator(DatasetEvaluator):
-    """
-    Evaluate object proposal and instance detection/segmentation outputs using
-    LVIS's metrics and evaluation API.
-    """
-
-    def __init__(
-        self,
-        dataset_name,
-        tasks=None,
-        distributed=True,
-        output_dir=None,
-        *,
-        max_dets_per_image=None,
-    ):
-        """
-        Args:
-            dataset_name (str): name of the dataset to be evaluated.
-                It must have the following corresponding metadata:
-                "json_file": the path to the LVIS format annotation
-            tasks (tuple[str]): tasks that can be evaluated under the given
-                configuration. A task is one of "bbox", "segm".
-                By default, will infer this automatically from predictions.
-            distributed (True): if True, will collect results from all ranks for evaluation.
-                Otherwise, will evaluate the results in the current process.
-            output_dir (str): optional, an output directory to dump results.
-            max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP
-                This limit, by default of the LVIS dataset, is 300.
-        """
-        from lvis import LVIS
-
-        self._logger = logging.getLogger(__name__)
-
-        if tasks is not None and isinstance(tasks, CfgNode):
-            self._logger.warn(
-                "COCO Evaluator instantiated using config, this is deprecated behavior."
-                " Please pass in explicit arguments instead."
-            )
-            self._tasks = None  # Infering it from predictions should be better
-        else:
-            self._tasks = tasks
-
-        self._distributed = distributed
-        self._output_dir = output_dir
-        self._max_dets_per_image = max_dets_per_image
-
-        self._cpu_device = torch.device("cpu")
-
-        self._metadata = MetadataCatalog.get(dataset_name)
-        json_file = PathManager.get_local_path(self._metadata.json_file)
-        self._lvis_api = LVIS(json_file)
-        # Test set json files do not contain annotations (evaluation must be
-        # performed using the LVIS evaluation server).
-        self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0
-
-    def reset(self):
-        self._predictions = []
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN).
-                It is a list of dict. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name", "image_id".
-            outputs: the outputs of a LVIS model. It is a list of dicts with key
-                "instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            if "instances" in output:
-                instances = output["instances"].to(self._cpu_device)
-                prediction["instances"] = instances_to_coco_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            self._predictions.append(prediction)
-
-    def evaluate(self):
-        if self._distributed:
-            comm.synchronize()
-            predictions = comm.gather(self._predictions, dst=0)
-            predictions = list(itertools.chain(*predictions))
-
-            if not comm.is_main_process():
-                return
-        else:
-            predictions = self._predictions
-
-        if len(predictions) == 0:
-            self._logger.warning("[LVISEvaluator] Did not receive valid predictions.")
-            return {}
-
-        if self._output_dir:
-            PathManager.mkdirs(self._output_dir)
-            file_path = os.path.join(self._output_dir, "instances_predictions.pth")
-            with PathManager.open(file_path, "wb") as f:
-                torch.save(predictions, f)
-
-        self._results = OrderedDict()
-        if "proposals" in predictions[0]:
-            self._eval_box_proposals(predictions)
-        if "instances" in predictions[0]:
-            self._eval_predictions(predictions)
-        # Copy so the caller can do whatever with results
-        return copy.deepcopy(self._results)
-
-    def _tasks_from_predictions(self, predictions):
-        for pred in predictions:
-            if "segmentation" in pred:
-                return ("bbox", "segm")
-        return ("bbox",)
-
-    def _eval_predictions(self, predictions):
-        """
-        Evaluate predictions. Fill self._results with the metrics of the tasks.
-
-        Args:
-            predictions (list[dict]): list of outputs from the model
-        """
-        self._logger.info("Preparing results in the LVIS format ...")
-        lvis_results = list(itertools.chain(*[x["instances"] for x in predictions]))
-        tasks = self._tasks or self._tasks_from_predictions(lvis_results)
-
-        # LVIS evaluator can be used to evaluate results for COCO dataset categories.
-        # In this case `_metadata` variable will have a field with COCO-specific category mapping.
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            reverse_id_mapping = {
-                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
-            }
-            for result in lvis_results:
-                result["category_id"] = reverse_id_mapping[result["category_id"]]
-        else:
-            # unmap the category ids for LVIS (from 0-indexed to 1-indexed)
-            for result in lvis_results:
-                result["category_id"] += 1
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "lvis_instances_results.json")
-            self._logger.info("Saving results to {}".format(file_path))
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(lvis_results))
-                f.flush()
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating predictions ...")
-        for task in sorted(tasks):
-            res = _evaluate_predictions_on_lvis(
-                self._lvis_api,
-                lvis_results,
-                task,
-                max_dets_per_image=self._max_dets_per_image,
-                class_names=self._metadata.get("thing_classes"),
-            )
-            self._results[task] = res
-
-    def _eval_box_proposals(self, predictions):
-        """
-        Evaluate the box proposals in predictions.
-        Fill self._results with the metrics for "box_proposals" task.
-        """
-        if self._output_dir:
-            # Saving generated box proposals to file.
-            # Predicted box_proposals are in XYXY_ABS mode.
-            bbox_mode = BoxMode.XYXY_ABS.value
-            ids, boxes, objectness_logits = [], [], []
-            for prediction in predictions:
-                ids.append(prediction["image_id"])
-                boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy())
-                objectness_logits.append(prediction["proposals"].objectness_logits.numpy())
-
-            proposal_data = {
-                "boxes": boxes,
-                "objectness_logits": objectness_logits,
-                "ids": ids,
-                "bbox_mode": bbox_mode,
-            }
-            with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f:
-                pickle.dump(proposal_data, f)
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating bbox proposals ...")
-        res = {}
-        areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
-        for limit in [100, 1000]:
-            for area, suffix in areas.items():
-                stats = _evaluate_box_proposals(predictions, self._lvis_api, area=area, limit=limit)
-                key = "AR{}@{:d}".format(suffix, limit)
-                res[key] = float(stats["ar"].item() * 100)
-        self._logger.info("Proposal metrics: \n" + create_small_table(res))
-        self._results["box_proposals"] = res
-
-
-# inspired from Detectron:
-# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa
-def _evaluate_box_proposals(dataset_predictions, lvis_api, thresholds=None, area="all", limit=None):
-    """
-    Evaluate detection proposal recall metrics. This function is a much
-    faster alternative to the official LVIS API recall evaluation code. However,
-    it produces slightly different results.
-    """
-    # Record max overlap value for each gt box
-    # Return vector of overlap values
-    areas = {
-        "all": 0,
-        "small": 1,
-        "medium": 2,
-        "large": 3,
-        "96-128": 4,
-        "128-256": 5,
-        "256-512": 6,
-        "512-inf": 7,
-    }
-    area_ranges = [
-        [0**2, 1e5**2],  # all
-        [0**2, 32**2],  # small
-        [32**2, 96**2],  # medium
-        [96**2, 1e5**2],  # large
-        [96**2, 128**2],  # 96-128
-        [128**2, 256**2],  # 128-256
-        [256**2, 512**2],  # 256-512
-        [512**2, 1e5**2],
-    ]  # 512-inf
-    assert area in areas, "Unknown area range: {}".format(area)
-    area_range = area_ranges[areas[area]]
-    gt_overlaps = []
-    num_pos = 0
-
-    for prediction_dict in dataset_predictions:
-        predictions = prediction_dict["proposals"]
-
-        # sort predictions in descending order
-        # TODO maybe remove this and make it explicit in the documentation
-        inds = predictions.objectness_logits.sort(descending=True)[1]
-        predictions = predictions[inds]
-
-        ann_ids = lvis_api.get_ann_ids(img_ids=[prediction_dict["image_id"]])
-        anno = lvis_api.load_anns(ann_ids)
-        gt_boxes = [
-            BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) for obj in anno
-        ]
-        gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4)  # guard against no boxes
-        gt_boxes = Boxes(gt_boxes)
-        gt_areas = torch.as_tensor([obj["area"] for obj in anno])
-
-        if len(gt_boxes) == 0 or len(predictions) == 0:
-            continue
-
-        valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
-        gt_boxes = gt_boxes[valid_gt_inds]
-
-        num_pos += len(gt_boxes)
-
-        if len(gt_boxes) == 0:
-            continue
-
-        if limit is not None and len(predictions) > limit:
-            predictions = predictions[:limit]
-
-        overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)
-
-        _gt_overlaps = torch.zeros(len(gt_boxes))
-        for j in range(min(len(predictions), len(gt_boxes))):
-            # find which proposal box maximally covers each gt box
-            # and get the iou amount of coverage for each gt box
-            max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-            # find which gt box is 'best' covered (i.e. 'best' = most iou)
-            gt_ovr, gt_ind = max_overlaps.max(dim=0)
-            assert gt_ovr >= 0
-            # find the proposal box that covers the best covered gt box
-            box_ind = argmax_overlaps[gt_ind]
-            # record the iou coverage of this gt box
-            _gt_overlaps[j] = overlaps[box_ind, gt_ind]
-            assert _gt_overlaps[j] == gt_ovr
-            # mark the proposal box and the gt box as used
-            overlaps[box_ind, :] = -1
-            overlaps[:, gt_ind] = -1
-
-        # append recorded iou coverage level
-        gt_overlaps.append(_gt_overlaps)
-    gt_overlaps = (
-        torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32)
-    )
-    gt_overlaps, _ = torch.sort(gt_overlaps)
-
-    if thresholds is None:
-        step = 0.05
-        thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
-    recalls = torch.zeros_like(thresholds)
-    # compute recall for each iou threshold
-    for i, t in enumerate(thresholds):
-        recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
-    # ar = 2 * np.trapz(recalls, thresholds)
-    ar = recalls.mean()
-    return {
-        "ar": ar,
-        "recalls": recalls,
-        "thresholds": thresholds,
-        "gt_overlaps": gt_overlaps,
-        "num_pos": num_pos,
-    }
-
-
-def _evaluate_predictions_on_lvis(
-    lvis_gt, lvis_results, iou_type, max_dets_per_image=None, class_names=None
-):
-    """
-    Args:
-        iou_type (str):
-        max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP
-            This limit, by default of the LVIS dataset, is 300.
-        class_names (None or list[str]): if provided, will use it to predict
-            per-category AP.
-
-    Returns:
-        a dict of {metric name: score}
-    """
-    metrics = {
-        "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
-        "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
-    }[iou_type]
-
-    logger = logging.getLogger(__name__)
-
-    if len(lvis_results) == 0:  # TODO: check if needed
-        logger.warn("No predictions from the model!")
-        return {metric: float("nan") for metric in metrics}
-
-    if iou_type == "segm":
-        lvis_results = copy.deepcopy(lvis_results)
-        # When evaluating mask AP, if the results contain bbox, LVIS API will
-        # use the box area as the area of the instance, instead of the mask area.
-        # This leads to a different definition of small/medium/large.
-        # We remove the bbox field to let mask AP use mask area.
-        for c in lvis_results:
-            c.pop("bbox", None)
-
-    if max_dets_per_image is None:
-        max_dets_per_image = 300  # Default for LVIS dataset
-
-    from lvis import LVISEval, LVISResults
-
-    logger.info(f"Evaluating with max detections per image = {max_dets_per_image}")
-    lvis_results = LVISResults(lvis_gt, lvis_results, max_dets=max_dets_per_image)
-    lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
-    lvis_eval.run()
-    lvis_eval.print_results()
-
-    # Pull the standard metrics from the LVIS results
-    results = lvis_eval.get_results()
-    results = {metric: float(results[metric] * 100) for metric in metrics}
-    logger.info("Evaluation results for {}: \n".format(iou_type) + create_small_table(results))
-    return results
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/matcher.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/matcher.py
deleted file mode 100644
index 2504d17a4f9707d7cdd8d47a6cb5a2faf3c397fd..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/matcher.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import List
-import torch
-
-from annotator.oneformer.detectron2.layers import nonzero_tuple
-
-
-# TODO: the name is too general
-class Matcher(object):
-    """
-    This class assigns to each predicted "element" (e.g., a box) a ground-truth
-    element. Each predicted element will have exactly zero or one matches; each
-    ground-truth element may be matched to zero or more predicted elements.
-
-    The matching is determined by the MxN match_quality_matrix, that characterizes
-    how well each (ground-truth, prediction)-pair match each other. For example,
-    if the elements are boxes, this matrix may contain box intersection-over-union
-    overlap values.
-
-    The matcher returns (a) a vector of length N containing the index of the
-    ground-truth element m in [0, M) that matches to prediction n in [0, N).
-    (b) a vector of length N containing the labels for each prediction.
-    """
-
-    def __init__(
-        self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False
-    ):
-        """
-        Args:
-            thresholds (list): a list of thresholds used to stratify predictions
-                into levels.
-            labels (list): a list of values to label predictions belonging at
-                each level. A label can be one of {-1, 0, 1} signifying
-                {ignore, negative class, positive class}, respectively.
-            allow_low_quality_matches (bool): if True, produce additional matches
-                for predictions with maximum match quality lower than high_threshold.
-                See set_low_quality_matches_ for more details.
-
-            For example,
-                thresholds = [0.3, 0.5]
-                labels = [0, -1, 1]
-                All predictions with iou < 0.3 will be marked with 0 and
-                thus will be considered as false positives while training.
-                All predictions with 0.3 <= iou < 0.5 will be marked with -1 and
-                thus will be ignored.
-                All predictions with 0.5 <= iou will be marked with 1 and
-                thus will be considered as true positives.
-        """
-        # Add -inf and +inf to first and last position in thresholds
-        thresholds = thresholds[:]
-        assert thresholds[0] > 0
-        thresholds.insert(0, -float("inf"))
-        thresholds.append(float("inf"))
-        # Currently torchscript does not support all + generator
-        assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])])
-        assert all([l in [-1, 0, 1] for l in labels])
-        assert len(labels) == len(thresholds) - 1
-        self.thresholds = thresholds
-        self.labels = labels
-        self.allow_low_quality_matches = allow_low_quality_matches
-
-    def __call__(self, match_quality_matrix):
-        """
-        Args:
-            match_quality_matrix (Tensor[float]): an MxN tensor, containing the
-                pairwise quality between M ground-truth elements and N predicted
-                elements. All elements must be >= 0 (due to the us of `torch.nonzero`
-                for selecting indices in :meth:`set_low_quality_matches_`).
-
-        Returns:
-            matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
-                ground-truth index in [0, M)
-            match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
-                whether a prediction is a true or false positive or ignored
-        """
-        assert match_quality_matrix.dim() == 2
-        if match_quality_matrix.numel() == 0:
-            default_matches = match_quality_matrix.new_full(
-                (match_quality_matrix.size(1),), 0, dtype=torch.int64
-            )
-            # When no gt boxes exist, we define IOU = 0 and therefore set labels
-            # to `self.labels[0]`, which usually defaults to background class 0
-            # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds
-            default_match_labels = match_quality_matrix.new_full(
-                (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
-            )
-            return default_matches, default_match_labels
-
-        assert torch.all(match_quality_matrix >= 0)
-
-        # match_quality_matrix is M (gt) x N (predicted)
-        # Max over gt elements (dim 0) to find best gt candidate for each prediction
-        matched_vals, matches = match_quality_matrix.max(dim=0)
-
-        match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)
-
-        for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
-            low_high = (matched_vals >= low) & (matched_vals < high)
-            match_labels[low_high] = l
-
-        if self.allow_low_quality_matches:
-            self.set_low_quality_matches_(match_labels, match_quality_matrix)
-
-        return matches, match_labels
-
-    def set_low_quality_matches_(self, match_labels, match_quality_matrix):
-        """
-        Produce additional matches for predictions that have only low-quality matches.
-        Specifically, for each ground-truth G find the set of predictions that have
-        maximum overlap with it (including ties); for each prediction in that set, if
-        it is unmatched, then match it to the ground-truth G.
-
-        This function implements the RPN assignment case (i) in Sec. 3.1.2 of
-        :paper:`Faster R-CNN`.
-        """
-        # For each gt, find the prediction with which it has highest quality
-        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
-        # Find the highest quality match available, even if it is low, including ties.
-        # Note that the matches qualities must be positive due to the use of
-        # `torch.nonzero`.
-        _, pred_inds_with_highest_quality = nonzero_tuple(
-            match_quality_matrix == highest_quality_foreach_gt[:, None]
-        )
-        # If an anchor was labeled positive only due to a low-quality match
-        # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B.
-        # This follows the implementation in Detectron, and is found to have no significant impact.
-        match_labels[pred_inds_with_highest_quality] = 1
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/roi_align.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/roi_align.py
deleted file mode 100644
index 0755aefc66e67233ceae0f4b77948301c443e9fb..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/roi_align.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import deprecated_api_warning, ext_loader
-
-ext_module = ext_loader.load_ext('_ext',
-                                 ['roi_align_forward', 'roi_align_backward'])
-
-
-class RoIAlignFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, rois, output_size, spatial_scale, sampling_ratio,
-                 pool_mode, aligned):
-        from ..onnx import is_custom_op_loaded
-        has_custom_op = is_custom_op_loaded()
-        if has_custom_op:
-            return g.op(
-                'mmcv::MMCVRoiAlign',
-                input,
-                rois,
-                output_height_i=output_size[0],
-                output_width_i=output_size[1],
-                spatial_scale_f=spatial_scale,
-                sampling_ratio_i=sampling_ratio,
-                mode_s=pool_mode,
-                aligned_i=aligned)
-        else:
-            from torch.onnx.symbolic_opset9 import sub, squeeze
-            from torch.onnx.symbolic_helper import _slice_helper
-            from torch.onnx import TensorProtoDataType
-            # batch_indices = rois[:, 0].long()
-            batch_indices = _slice_helper(
-                g, rois, axes=[1], starts=[0], ends=[1])
-            batch_indices = squeeze(g, batch_indices, 1)
-            batch_indices = g.op(
-                'Cast', batch_indices, to_i=TensorProtoDataType.INT64)
-            # rois = rois[:, 1:]
-            rois = _slice_helper(g, rois, axes=[1], starts=[1], ends=[5])
-            if aligned:
-                # rois -= 0.5/spatial_scale
-                aligned_offset = g.op(
-                    'Constant',
-                    value_t=torch.tensor([0.5 / spatial_scale],
-                                         dtype=torch.float32))
-                rois = sub(g, rois, aligned_offset)
-            # roi align
-            return g.op(
-                'RoiAlign',
-                input,
-                rois,
-                batch_indices,
-                output_height_i=output_size[0],
-                output_width_i=output_size[1],
-                spatial_scale_f=spatial_scale,
-                sampling_ratio_i=max(0, sampling_ratio),
-                mode_s=pool_mode)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                rois,
-                output_size,
-                spatial_scale=1.0,
-                sampling_ratio=0,
-                pool_mode='avg',
-                aligned=True):
-        ctx.output_size = _pair(output_size)
-        ctx.spatial_scale = spatial_scale
-        ctx.sampling_ratio = sampling_ratio
-        assert pool_mode in ('max', 'avg')
-        ctx.pool_mode = 0 if pool_mode == 'max' else 1
-        ctx.aligned = aligned
-        ctx.input_shape = input.size()
-
-        assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!'
-
-        output_shape = (rois.size(0), input.size(1), ctx.output_size[0],
-                        ctx.output_size[1])
-        output = input.new_zeros(output_shape)
-        if ctx.pool_mode == 0:
-            argmax_y = input.new_zeros(output_shape)
-            argmax_x = input.new_zeros(output_shape)
-        else:
-            argmax_y = input.new_zeros(0)
-            argmax_x = input.new_zeros(0)
-
-        ext_module.roi_align_forward(
-            input,
-            rois,
-            output,
-            argmax_y,
-            argmax_x,
-            aligned_height=ctx.output_size[0],
-            aligned_width=ctx.output_size[1],
-            spatial_scale=ctx.spatial_scale,
-            sampling_ratio=ctx.sampling_ratio,
-            pool_mode=ctx.pool_mode,
-            aligned=ctx.aligned)
-
-        ctx.save_for_backward(rois, argmax_y, argmax_x)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        rois, argmax_y, argmax_x = ctx.saved_tensors
-        grad_input = grad_output.new_zeros(ctx.input_shape)
-        # complex head architecture may cause grad_output uncontiguous.
-        grad_output = grad_output.contiguous()
-        ext_module.roi_align_backward(
-            grad_output,
-            rois,
-            argmax_y,
-            argmax_x,
-            grad_input,
-            aligned_height=ctx.output_size[0],
-            aligned_width=ctx.output_size[1],
-            spatial_scale=ctx.spatial_scale,
-            sampling_ratio=ctx.sampling_ratio,
-            pool_mode=ctx.pool_mode,
-            aligned=ctx.aligned)
-        return grad_input, None, None, None, None, None, None
-
-
-roi_align = RoIAlignFunction.apply
-
-
-class RoIAlign(nn.Module):
-    """RoI align pooling layer.
-
-    Args:
-        output_size (tuple): h, w
-        spatial_scale (float): scale the input boxes by this number
-        sampling_ratio (int): number of inputs samples to take for each
-            output sample. 0 to take samples densely for current models.
-        pool_mode (str, 'avg' or 'max'): pooling mode in each bin.
-        aligned (bool): if False, use the legacy implementation in
-            MMDetection. If True, align the results more perfectly.
-        use_torchvision (bool): whether to use roi_align from torchvision.
-
-    Note:
-        The implementation of RoIAlign when aligned=True is modified from
-        https://github.com/facebookresearch/detectron2/
-
-        The meaning of aligned=True:
-
-        Given a continuous coordinate c, its two neighboring pixel
-        indices (in our pixel model) are computed by floor(c - 0.5) and
-        ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete
-        indices [0] and [1] (which are sampled from the underlying signal
-        at continuous coordinates 0.5 and 1.5). But the original roi_align
-        (aligned=False) does not subtract the 0.5 when computing
-        neighboring pixel indices and therefore it uses pixels with a
-        slightly incorrect alignment (relative to our pixel model) when
-        performing bilinear interpolation.
-
-        With `aligned=True`,
-        we first appropriately scale the ROI and then shift it by -0.5
-        prior to calling roi_align. This produces the correct neighbors;
-
-        The difference does not make a difference to the model's
-        performance if ROIAlign is used together with conv layers.
-    """
-
-    @deprecated_api_warning(
-        {
-            'out_size': 'output_size',
-            'sample_num': 'sampling_ratio'
-        },
-        cls_name='RoIAlign')
-    def __init__(self,
-                 output_size,
-                 spatial_scale=1.0,
-                 sampling_ratio=0,
-                 pool_mode='avg',
-                 aligned=True,
-                 use_torchvision=False):
-        super(RoIAlign, self).__init__()
-
-        self.output_size = _pair(output_size)
-        self.spatial_scale = float(spatial_scale)
-        self.sampling_ratio = int(sampling_ratio)
-        self.pool_mode = pool_mode
-        self.aligned = aligned
-        self.use_torchvision = use_torchvision
-
-    def forward(self, input, rois):
-        """
-        Args:
-            input: NCHW images
-            rois: Bx5 boxes. First column is the index into N.\
-                The other 4 columns are xyxy.
-        """
-        if self.use_torchvision:
-            from torchvision.ops import roi_align as tv_roi_align
-            if 'aligned' in tv_roi_align.__code__.co_varnames:
-                return tv_roi_align(input, rois, self.output_size,
-                                    self.spatial_scale, self.sampling_ratio,
-                                    self.aligned)
-            else:
-                if self.aligned:
-                    rois -= rois.new_tensor([0.] +
-                                            [0.5 / self.spatial_scale] * 4)
-                return tv_roi_align(input, rois, self.output_size,
-                                    self.spatial_scale, self.sampling_ratio)
-        else:
-            return roi_align(input, rois, self.output_size, self.spatial_scale,
-                             self.sampling_ratio, self.pool_mode, self.aligned)
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(output_size={self.output_size}, '
-        s += f'spatial_scale={self.spatial_scale}, '
-        s += f'sampling_ratio={self.sampling_ratio}, '
-        s += f'pool_mode={self.pool_mode}, '
-        s += f'aligned={self.aligned}, '
-        s += f'use_torchvision={self.use_torchvision})'
-        return s
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/losses/__init__.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/losses/__init__.py
deleted file mode 100644
index beca72045694273d63465bac2f27dbc6672271db..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/losses/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .accuracy import Accuracy, accuracy
-from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
-                                 cross_entropy, mask_cross_entropy)
-from .dice_loss import DiceLoss
-from .lovasz_loss import LovaszLoss
-from .utils import reduce_loss, weight_reduce_loss, weighted_loss
-
-__all__ = [
-    'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
-    'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
-    'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss'
-]
diff --git a/spaces/Surfrider/surfnet/app.py b/spaces/Surfrider/surfnet/app.py
deleted file mode 100644
index d81e830b851dfdb5054c53ec0be34d5e7a239118..0000000000000000000000000000000000000000
--- a/spaces/Surfrider/surfnet/app.py
+++ /dev/null
@@ -1,421 +0,0 @@
-from webbrowser import get
-import gradio as gr
-import os
-import os.path as op
-
-import json
-from typing import Dict, List, Tuple
-import torch
-import numpy as np
-
-import datetime
-import logging
-import warnings
-
-# imports for gps & map
-import osmnx as ox
-import folium
-import pandas as pd
-import geopandas
-import codecs
-from tracking.gps import *
-
-# imports for tracking
-from plasticorigins.tools.files import download_from_url, create_unique_folder, load_trash_icons
-from plasticorigins.tools.video_readers import IterableFrameReader, SimpleVideoReader
-from plasticorigins.detection.centernet.networks.mobilenet import get_mobilenet_v3_small
-
-from plasticorigins.detection.yolo import load_model, predict_yolo
-from plasticorigins.detection.detect import detect
-from plasticorigins.tracking.postprocess_and_count_tracks import filter_tracks, postprocess_for_api, count_objects
-from plasticorigins.tracking.utils import get_detections_for_video, write_tracking_results_to_file, read_tracking_results, generate_video_with_annotations
-from plasticorigins.tracking.track_video import track_video
-from plasticorigins.tracking.trackers import get_tracker
-
-logger = logging.getLogger()
-logger.setLevel(logging.DEBUG)
-ch = logging.StreamHandler()
-ch.setLevel(logging.DEBUG)
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-ch.setFormatter(formatter)
-logger.addHandler(ch)
-
-class DotDict(dict):
-    """dot.notation access to dictionary attributes"""
-    __getattr__ = dict.get
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-id_categories = {
-    0: 'Fragment',    #'Sheet / tarp / plastic bag / fragment',
-    1: 'Insulating',  #'Insulating material',
-    2: 'Bottle',      #'Bottle-shaped',
-    3: 'Can',         #'Can-shaped',
-    4: 'Drum',
-    5: 'Packaging',   #'Other packaging',
-    6: 'Tire',
-    7: 'Fishing net', #'Fishing net / cord',
-    8: 'Easily namable',
-    9: 'Unclear',
-    10: 'Fragment',
-    11: 'Fragment'
-}
-
-
-config_track = DotDict({
-    "yolo_conf_thrld": 0.35,
-    "yolo_iou_thrld": 0.5,
-
-    "confidence_threshold": 0.004, # for the tracking part
-    "detection_threshold": 0.3, # for centernet
-    "downsampling_factor": 4,
-    "noise_covariances_path": "data/tracking_parameters",
-    "output_shape": (960,544),
-    "size": 768,
-    "skip_frames": 3, #3
-    "arch": "mobilenet_v3_small",
-    "device": "cpu",
-    "detection_batch_size": 1,
-    "display": 0,
-    "kappa": 4, #4
-    "tau": 3, #4
-    "max_length": 240,
-    "downscale_output":2
-})
-
-
-logger.info('---Yolo model...')
-# Yolo has warning problems, so we set an env variable to remove it
-os.environ["VERBOSE"] = "False"
-URL_MODEL = "https://github.com/surfriderfoundationeurope/surfnet/releases/download/v01.2023/yolo_latest.pt"
-FILE_MODEL = "yolov5.pt"
-model_path = download_from_url(URL_MODEL, FILE_MODEL, "./models", logger)
-model_yolo = load_model(model_path, config_track.device, config_track.yolo_conf_thrld, config_track.yolo_iou_thrld)
-
-logger.info('---Centernet model...')
-URL_MODEL = "https://partage.imt.fr/index.php/s/sJi22N6gedN6T4q/download"
-FILE_MODEL = "mobilenet_v3_pretrained.pth"
-model_path = download_from_url(URL_MODEL, FILE_MODEL, "./models", logger)
-model = get_mobilenet_v3_small(num_layers=0, heads={'hm': 1}, head_conv=256)
-checkpoint = torch.load(model_path, map_location="cpu")
-model.load_state_dict(checkpoint['model'], strict=True)
-
-
-URL_DEMO1 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_niv15.mp4"
-FILE_DEMO1 = "video_niv15.mp4"
-download_from_url(URL_DEMO1, FILE_DEMO1, "./data/", logger)
-video1_path = op.join("./data", FILE_DEMO1)
-URL_DEMO2 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_midouze15.mp4"
-FILE_DEMO2 = "video_midouze15.mp4"
-download_from_url(URL_DEMO2, FILE_DEMO2, "./data/", logger)
-video2_path = op.join("./data", FILE_DEMO2)
-URL_DEMO3 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_antoine15.mp4"
-FILE_DEMO3 = "video_antoine15.mp4"
-download_from_url(URL_DEMO3, FILE_DEMO3, "./data/", logger)
-video3_path = op.join("./data", FILE_DEMO3)
-JSON_FILE_PATH = "data/"
-
-video_json_correspondance = {
-    video1_path : JSON_FILE_PATH+"gavepau.json",
-    video2_path : JSON_FILE_PATH+"midouze.json",
-    video3_path : JSON_FILE_PATH+"gavepau.json"
-}
-
-labels2icons = load_trash_icons("./data/icons/")
-
-def track(args):
-    device = torch.device("cpu")
-
-    engine = get_tracker('EKF')
-
-    detector = None
-    # centernet version
-    if args.model_type == "yolo":
-        logger.info("---Using Yolo")
-        detector = lambda frame: predict_yolo(model_yolo, frame, size=config_track.size)
-    elif args.model_type == "centernet":
-        logger.info("---Using Centernet")
-        detector = lambda frame: detect(frame, threshold=args.detection_threshold, model=model)
-
-
-    transition_variance = np.load(op.join(args.noise_covariances_path, 'transition_variance.npy'))
-    observation_variance = np.load(op.join(args.noise_covariances_path, 'observation_variance.npy'))
-
-    logger.info(f'---Processing {args.video_path}')
-    reader = IterableFrameReader(video_filename=args.video_path,
-                                skip_frames=args.skip_frames,
-                                output_shape=args.output_shape,
-                                progress_bar=True,
-                                preload=False,
-                                max_frame=args.max_length)
-
-
-    input_shape = reader.input_shape
-    output_shape = reader.output_shape
-
-    detections = []
-    logger.info('---Detecting...')
-    if args.model_type == "yolo":
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore")
-
-            for frame in reader:
-                detections.append(detector(frame))
-    elif args.model_type == "centernet":
-        detections = get_detections_for_video(reader, detector, batch_size=args.detection_batch_size, device=device)
-
-    logger.info('---Tracking...')
-    display = None
-    results = track_video(reader, iter(detections), args, engine, transition_variance, observation_variance, display, is_yolo=args.model_type=="yolo")
-    reader.video.release()
-
-    # store unfiltered results
-    datestr = datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')
-    output_filename = op.splitext(args.video_path)[0] + "_" + datestr + '_unfiltered.txt'
-    coord_mapping = reader.get_inv_mapping(args.downsampling_factor)
-    write_tracking_results_to_file(
-        results,
-        coord_mapping,  # Scale the output back to original video size
-        output_filename=output_filename,
-    )
-    logger.info('---Filtering...')
-
-    # read from the file
-    results = read_tracking_results(output_filename)
-    filtered_results = filter_tracks(results, config_track.kappa, config_track.tau)
-    # store filtered results
-    output_filename = op.splitext(args.video_path)[0] + "_" + datestr + '_filtered.txt'
-    write_tracking_results_to_file(
-        filtered_results,
-        lambda x, y: (x, y),  # No scaling, already scaled!
-        output_filename=output_filename,
-    )
-
-    return filtered_results
-
-
-def run_model(video_path, model_type, seconds, skip, tau, kappa, gps_file):
-    logger.info('---video filename: '+ video_path)
-
-    # launch the tracking
-    config_track.video_path = video_path
-    config_track.model_type = model_type
-    config_track.skip_frames = int(skip)
-    config_track.tau = int(tau)
-    config_track.kappa = int(kappa)
-    config_track.max_length = int(seconds)*24
-
-    out_folder = create_unique_folder("/tmp/", "output")
-    output_path = op.join(out_folder, "video.mp4")
-    filtered_results = track(config_track)
-
-    # postprocess
-    logger.info('---Postprocessing...')
-    output_json_path = op.join(out_folder, "output.json")
-    output_json = postprocess_for_api(filtered_results, id_categories)
-    with open(output_json_path, 'w') as f_out:
-        json.dump(output_json, f_out)
-
-    # build video output
-    logger.info('---Generating new video...')
-    reader = IterableFrameReader(video_filename=config_track.video_path,
-                                skip_frames=0,
-                                progress_bar=True,
-                                preload=False,
-                                max_frame=config_track.max_length)
-
-    # Get GPS Data
-    video_duration = reader.total_num_frames / reader.fps
-    gps_data = get_filled_gps(gps_file, video_duration)
-
-    # Generate new video
-    generate_video_with_annotations(reader, output_json, output_path,
-                                    config_track.skip_frames, config_track.downscale_output, 
-                                    logger, gps_data=gps_data, labels2icons=labels2icons)
-    output_label = count_objects(output_json, id_categories)
-
-
-    # Get Plastic Map
-    map_frame = None # default value in case no GPS file
-    if gps_data is not None:
-        logger.info('---Creating Plastic Map...')
-        # Get Trash Prediction
-        with open(output_json_path) as json_file:
-            predictions = json.load(json_file)
-            trash_df = get_df_prediction(predictions, reader.fps)
-        if len(trash_df) != 0 :
-            # Get Trash prediction alongside GPS data
-            trash_gps_df = get_trash_gps_df(trash_df,gps_data)
-            trash_gps_geo_df = get_trash_gps_geo_df(trash_gps_df)
-            # Create Map
-            center_lat = trash_gps_df.iloc[0]['Latitude']
-            center_long = trash_gps_df.iloc[0]['Longitude']
-            map_path = get_plastic_map(center_lat,center_long,trash_gps_geo_df,out_folder)
-            html_content = codecs.open(map_path, 'r')
-            map_html = html_content.read()
-            map_frame =  f"""<iframe style="width: 100%; height: 480px" name="result" allow="midi; geolocation; microphone; camera;
-        display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-        allow-scripts allow-same-origin allow-popups
-        allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-        allowpaymentrequest="" frameborder="0" srcdoc='{map_html}'></iframe>"""
-
-    logger.info('---Surfnet End processing...')
-
-    return output_path, map_frame, output_label, output_json_path
-
-
-def run_model_simple(video_path):
-    """ Simplified version with less options
-    """
-    gps_file = video_json_correspondance.get(video_path)
-    model_type, seconds, skip, tau, kappa = "yolo", 10, 3, 3, 4
-
-    output_path, map_frame, output_label, output_json_path = run_model(video_path, model_type, seconds, skip, tau, kappa, gps_file)
-
-    return output_path, map_frame, output_label
-
-
-def get_filled_gps(file_obj, video_duration)->list:
-    """Get a filled GPS point list from Plastic Origin mobile GPS JSON track
-    Args:
-        file_obj: a file_obj from gradio input File type
-        video_duration: in seconds
-    Returns:
-        gps_data (list): the GPS filled data as a list
-    """
-
-    if file_obj is not None:
-        json_data = parse_json(file_obj)
-        json_data_list = get_json_gps_list(json_data)
-        gps_data = fill_gps(json_data_list, video_duration)
-        return gps_data
-    else:
-        return None
-
-
-def get_plastic_map(center_lat,center_long,trash_gps_gdf,out_folder)->str:
-    """Get the map with plastic trash detection
-    Args:
-        center_lat (float): latitude to center map
-        center_long (float): longitude to center map
-        trash_gps_gdf (DataFrame): trash & gps geo dataframe
-        out_folder (str): folder to save html map
-    Returns:
-        map_html_path (str): full path to html map
-    """
-
-    m = folium.Map([center_lat, center_long], zoom_start=16)
-    locs = zip(trash_gps_gdf.geometry.y,trash_gps_gdf.geometry.x)
-    labels = list(trash_gps_gdf['label'])
-    i = 0
-    for location in locs:
-        folium.CircleMarker(location=location).add_child(folium.Popup(labels[i])).add_to(m)
-        i = i + 1
-        map_html_path = op.join(out_folder,"plasticmap.html")
-    m.save(map_html_path)
-    return map_html_path
-
-
-model_type = gr.inputs.Dropdown(choices=["centernet", "yolo"], type="value", default="yolo", label="model")
-skip_slider = gr.inputs.Slider(minimum=0, maximum=15, step=1, default=3, label="skip frames")
-tau_slider = gr.inputs.Slider(minimum=1, maximum=7, step=1, default=3, label="tau")
-kappa_slider = gr.inputs.Slider(minimum=1, maximum=7, step=1, default=4, label="kappa")
-seconds_num = gr.inputs.Number(default=10, label="seconds")
-gps_in = gr.inputs.File(type="file", label="GPS Upload", optional=True)
-
-demo = gr.Blocks()
-title = "Surfnet AI Demo"
-with demo:
-    with gr.Tabs():
-        with gr.TabItem("The Project"):
-            gr.HTML(""" <!DOCTYPE html>
-<html lang="en-us">
-  <head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
-    <title>Surfnet AI Demo</title>
-  </head>
-
-  <body>
-    <h1 style="text-align: center;"> <b>Welcome to the Surfnet demo, the algo that tracks Plastic Pollution</b> 🌊</h1>
-            <p style="text-align: center;"> We all dream about swimming in clear blue waters and walking
-              bare-footed on a beautiful white sand beach. But our dream is
-              threatened. Plastics are invading every corner of the earth,
-              from remote alpine lakes to the deepest oceanic trench.
-               Thankfully, there are many things we can do.🤝
-              <b>Plastic Origins</b>, a citizen science project from <a href="https://surfrider.eu" target = "_blank"><b><u>Surfrider Europe</u></b></a>,
-              using artificial intelligence to map river plastic pollution, is one of them.
-               This demo is here for you to test the AI model we use to detect and count litter items on riverbanks.
-            </p>
-            <br>
-                <p style="text-align: center;">
-      ❓ Read more on <a href="https://plasticorigins.eu" target = "_blank"> <b><u>www.plasticorigins.eu</u></b></a>
-         <br>
-         💻 Join the dev team on <a href="https://github.com/surfriderfoundationeurope/The-Plastic-Origins-Project" target = "_blank"> <b><u>Github</u></b></a>
-         <br>
-         🏷️ Help us label images on <a href="https://www.trashroulette.com/#/" target = "_blank"> <b><u>www.trashroulette.com</u></b></a>
-          <br>
-          <br>
-             <p style="text-align: center">
-      📧 contact :
-        <br>
-        <a href="mailto:plasticorigins@surfrider.eu"> <b><u>plasticorigins@surfrider.eu</u></b></a>
-    </p>
-          </div>
-  </body>
-</html>""")
-        with gr.TabItem("Surfnet AI"):
-            gr.HTML(""" <!DOCTYPE html>
-<html lang="en">
-<head>
-<title>Left Side Panel</title>
-<meta charset="utf-8">
-</head>
-
-<body>
-   <p style="text-align: center;">
-        <b>Surfnet</b> is an AI model that detects trash on riverbanks.
-         We use it to map river plastic pollution and act to reduce the introduction of litter into the environment.
-         Developed & Maintain by a bunch of amazing volunteers from the NGO Surfrider Foundation Europe.
-         It takes a minute or two to generate the outputs, be patient!
-    </p>
-</body>
-</html>""" )
-            #video_in = gr.inputs.Video(type="mp4", source="upload", label="Video Upload", optional=False)
-
-            gr.Interface(fn=run_model_simple, inputs=["playable_video"],
-                        outputs=["playable_video","html","label"],
-                        examples=[[video1_path],
-                                  [video2_path],
-                                  [video3_path]],
-                        description="Upload a video, or select one below.",
-                        theme="huggingface",
-                        allow_screenshot=False, allow_flagging="never")
-
-        with gr.TabItem("Advanced Options"):
-            gr.HTML(""" <!DOCTYPE html>
-<html lang="en">
-<head>
-<title>Left Side Panel</title>
-<meta charset="utf-8">
-</head>
-
-<body>
-   <p style="text-align: center;">
-        <b>Advanced options</b> You may choose your own videos and parameters to run the AI: the model type (Yolov5 or Centernet), the sampling period of the video (skip frames), tracking parameters such as tau and kappa, the number of seconds you want to process, and the GPS file.
-    </p>
-</body>
-</html>""" )
-
-            #video_in = gr.inputs.Video(type="mp4", source="upload", label="Video Upload", optional=False)
-
-            gr.Interface(fn=run_model, inputs=["playable_video", model_type, seconds_num, skip_slider, tau_slider, kappa_slider,gps_in],
-                        outputs=["playable_video","html","label", "file"],
-                        examples=[[video1_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"gavepau.json"],
-                                  [video2_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"midouze.json"],
-                                  [video3_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"gavepau.json"]],
-                        description="Upload a video, optionnaly a GPS file and you'll get Plastic detection on river.",
-                        theme="huggingface",
-                        allow_screenshot=False, allow_flagging="never")
-
-demo.launch()
diff --git a/spaces/TILK/UrgencyBot/README.md b/spaces/TILK/UrgencyBot/README.md
deleted file mode 100644
index 86c0525502fbba9af4e8d7d7be2dbf9c16bf3b14..0000000000000000000000000000000000000000
--- a/spaces/TILK/UrgencyBot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: UrgencyBot
-emoji: 💻
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/_functools.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/_functools.py
deleted file mode 100644
index e7053bac12fdb7b2cc50448f88318cd93f62cc0e..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/_functools.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import functools
-
-
-# from jaraco.functools 3.5
-def pass_none(func):
-    """
-    Wrap func so it's not called if its first param is None
-
-    >>> print_text = pass_none(print)
-    >>> print_text('text')
-    text
-    >>> print_text(None)
-    """
-
-    @functools.wraps(func)
-    def wrapper(param, *args, **kwargs):
-        if param is not None:
-            return func(param, *args, **kwargs)
-
-    return wrapper
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/wheel.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/wheel.py
deleted file mode 100644
index 850e43cd01005c5d63ed08a35ad860858b74dce1..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/wheel.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""Wheels support."""
-
-import email
-import itertools
-import functools
-import os
-import posixpath
-import re
-import zipfile
-import contextlib
-
-from distutils.util import get_platform
-
-import setuptools
-from setuptools.extern.packaging.version import Version as parse_version
-from setuptools.extern.packaging.tags import sys_tags
-from setuptools.extern.packaging.utils import canonicalize_name
-from setuptools.command.egg_info import write_requirements, _egg_basename
-from setuptools.archive_util import _unpack_zipfile_obj
-
-
-WHEEL_NAME = re.compile(
-    r"""^(?P<project_name>.+?)-(?P<version>\d.*?)
-    ((-(?P<build>\d.*?))?-(?P<py_version>.+?)-(?P<abi>.+?)-(?P<platform>.+?)
-    )\.whl$""",
-    re.VERBOSE).match
-
-NAMESPACE_PACKAGE_INIT = \
-    "__import__('pkg_resources').declare_namespace(__name__)\n"
-
-
-@functools.lru_cache(maxsize=None)
-def _get_supported_tags():
-    # We calculate the supported tags only once, otherwise calling
-    # this method on thousands of wheels takes seconds instead of
-    # milliseconds.
-    return {(t.interpreter, t.abi, t.platform) for t in sys_tags()}
-
-
-def unpack(src_dir, dst_dir):
-    '''Move everything under `src_dir` to `dst_dir`, and delete the former.'''
-    for dirpath, dirnames, filenames in os.walk(src_dir):
-        subdir = os.path.relpath(dirpath, src_dir)
-        for f in filenames:
-            src = os.path.join(dirpath, f)
-            dst = os.path.join(dst_dir, subdir, f)
-            os.renames(src, dst)
-        for n, d in reversed(list(enumerate(dirnames))):
-            src = os.path.join(dirpath, d)
-            dst = os.path.join(dst_dir, subdir, d)
-            if not os.path.exists(dst):
-                # Directory does not exist in destination,
-                # rename it and prune it from os.walk list.
-                os.renames(src, dst)
-                del dirnames[n]
-    # Cleanup.
-    for dirpath, dirnames, filenames in os.walk(src_dir, topdown=True):
-        assert not filenames
-        os.rmdir(dirpath)
-
-
-@contextlib.contextmanager
-def disable_info_traces():
-    """
-    Temporarily disable info traces.
-    """
-    from distutils import log
-    saved = log.set_threshold(log.WARN)
-    try:
-        yield
-    finally:
-        log.set_threshold(saved)
-
-
-class Wheel:
-
-    def __init__(self, filename):
-        match = WHEEL_NAME(os.path.basename(filename))
-        if match is None:
-            raise ValueError('invalid wheel name: %r' % filename)
-        self.filename = filename
-        for k, v in match.groupdict().items():
-            setattr(self, k, v)
-
-    def tags(self):
-        '''List tags (py_version, abi, platform) supported by this wheel.'''
-        return itertools.product(
-            self.py_version.split('.'),
-            self.abi.split('.'),
-            self.platform.split('.'),
-        )
-
-    def is_compatible(self):
-        '''Is the wheel compatible with the current platform?'''
-        return next((True for t in self.tags() if t in _get_supported_tags()), False)
-
-    def egg_name(self):
-        return _egg_basename(
-            self.project_name,
-            self.version,
-            platform=(None if self.platform == 'any' else get_platform()),
-        ) + ".egg"
-
-    def get_dist_info(self, zf):
-        # find the correct name of the .dist-info dir in the wheel file
-        for member in zf.namelist():
-            dirname = posixpath.dirname(member)
-            if (dirname.endswith('.dist-info') and
-                    canonicalize_name(dirname).startswith(
-                        canonicalize_name(self.project_name))):
-                return dirname
-        raise ValueError("unsupported wheel format. .dist-info not found")
-
-    def install_as_egg(self, destination_eggdir):
-        '''Install wheel as an egg directory.'''
-        with zipfile.ZipFile(self.filename) as zf:
-            self._install_as_egg(destination_eggdir, zf)
-
-    def _install_as_egg(self, destination_eggdir, zf):
-        dist_basename = '%s-%s' % (self.project_name, self.version)
-        dist_info = self.get_dist_info(zf)
-        dist_data = '%s.data' % dist_basename
-        egg_info = os.path.join(destination_eggdir, 'EGG-INFO')
-
-        self._convert_metadata(zf, destination_eggdir, dist_info, egg_info)
-        self._move_data_entries(destination_eggdir, dist_data)
-        self._fix_namespace_packages(egg_info, destination_eggdir)
-
-    @staticmethod
-    def _convert_metadata(zf, destination_eggdir, dist_info, egg_info):
-        import pkg_resources
-
-        def get_metadata(name):
-            with zf.open(posixpath.join(dist_info, name)) as fp:
-                value = fp.read().decode('utf-8')
-                return email.parser.Parser().parsestr(value)
-
-        wheel_metadata = get_metadata('WHEEL')
-        # Check wheel format version is supported.
-        wheel_version = parse_version(wheel_metadata.get('Wheel-Version'))
-        wheel_v1 = (
-            parse_version('1.0') <= wheel_version < parse_version('2.0dev0')
-        )
-        if not wheel_v1:
-            raise ValueError(
-                'unsupported wheel format version: %s' % wheel_version)
-        # Extract to target directory.
-        _unpack_zipfile_obj(zf, destination_eggdir)
-        # Convert metadata.
-        dist_info = os.path.join(destination_eggdir, dist_info)
-        dist = pkg_resources.Distribution.from_location(
-            destination_eggdir, dist_info,
-            metadata=pkg_resources.PathMetadata(destination_eggdir, dist_info),
-        )
-
-        # Note: Evaluate and strip markers now,
-        # as it's difficult to convert back from the syntax:
-        # foobar; "linux" in sys_platform and extra == 'test'
-        def raw_req(req):
-            req.marker = None
-            return str(req)
-        install_requires = list(map(raw_req, dist.requires()))
-        extras_require = {
-            extra: [
-                req
-                for req in map(raw_req, dist.requires((extra,)))
-                if req not in install_requires
-            ]
-            for extra in dist.extras
-        }
-        os.rename(dist_info, egg_info)
-        os.rename(
-            os.path.join(egg_info, 'METADATA'),
-            os.path.join(egg_info, 'PKG-INFO'),
-        )
-        setup_dist = setuptools.Distribution(
-            attrs=dict(
-                install_requires=install_requires,
-                extras_require=extras_require,
-            ),
-        )
-        with disable_info_traces():
-            write_requirements(
-                setup_dist.get_command_obj('egg_info'),
-                None,
-                os.path.join(egg_info, 'requires.txt'),
-            )
-
-    @staticmethod
-    def _move_data_entries(destination_eggdir, dist_data):
-        """Move data entries to their correct location."""
-        dist_data = os.path.join(destination_eggdir, dist_data)
-        dist_data_scripts = os.path.join(dist_data, 'scripts')
-        if os.path.exists(dist_data_scripts):
-            egg_info_scripts = os.path.join(
-                destination_eggdir, 'EGG-INFO', 'scripts')
-            os.mkdir(egg_info_scripts)
-            for entry in os.listdir(dist_data_scripts):
-                # Remove bytecode, as it's not properly handled
-                # during easy_install scripts install phase.
-                if entry.endswith('.pyc'):
-                    os.unlink(os.path.join(dist_data_scripts, entry))
-                else:
-                    os.rename(
-                        os.path.join(dist_data_scripts, entry),
-                        os.path.join(egg_info_scripts, entry),
-                    )
-            os.rmdir(dist_data_scripts)
-        for subdir in filter(os.path.exists, (
-            os.path.join(dist_data, d)
-            for d in ('data', 'headers', 'purelib', 'platlib')
-        )):
-            unpack(subdir, destination_eggdir)
-        if os.path.exists(dist_data):
-            os.rmdir(dist_data)
-
-    @staticmethod
-    def _fix_namespace_packages(egg_info, destination_eggdir):
-        namespace_packages = os.path.join(
-            egg_info, 'namespace_packages.txt')
-        if os.path.exists(namespace_packages):
-            with open(namespace_packages) as fp:
-                namespace_packages = fp.read().split()
-            for mod in namespace_packages:
-                mod_dir = os.path.join(destination_eggdir, *mod.split('.'))
-                mod_init = os.path.join(mod_dir, '__init__.py')
-                if not os.path.exists(mod_dir):
-                    os.mkdir(mod_dir)
-                if not os.path.exists(mod_init):
-                    with open(mod_init, 'w') as fp:
-                        fp.write(NAMESPACE_PACKAGE_INIT)
diff --git a/spaces/Tetel/secondbing/SydneyGPT/main.py b/spaces/Tetel/secondbing/SydneyGPT/main.py
deleted file mode 100644
index 8dd056ac3a870fee1be113fabbf9617240825f85..0000000000000000000000000000000000000000
--- a/spaces/Tetel/secondbing/SydneyGPT/main.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from EdgeGPT import main as EdgeGPTMain
-
-import SydneyGPTUtils
-
-
-def main() -> None:
-    EdgeGPTMain.main()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Thafx/sdrv1_4/README.md b/spaces/Thafx/sdrv1_4/README.md
deleted file mode 100644
index 5891b6282652495041b1b75d33a6c1110043283f..0000000000000000000000000000000000000000
--- a/spaces/Thafx/sdrv1_4/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: Realistic Vision v1.4
-emoji: ⚡
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: true
-duplicated_from: Thafx/sdrv1_3
-tags:
-  - stable-diffusion
-  - stable-diffusion-diffusers
-  - text-to-image
-  - realistic-vision
-models:
-  - SG161222/Realistic_Vision_V1.4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Tirendaz/NER-Demo/README.md b/spaces/Tirendaz/NER-Demo/README.md
deleted file mode 100644
index 42df8b330f76b1a42e598325088a823e4d99a8f7..0000000000000000000000000000000000000000
--- a/spaces/Tirendaz/NER-Demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NER Demo
-emoji: 😻
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Tristan/static-rlhf-interface/utils.py b/spaces/Tristan/static-rlhf-interface/utils.py
deleted file mode 100644
index c75b14b26d9ea8f8cd3f495d51761c54d708931d..0000000000000000000000000000000000000000
--- a/spaces/Tristan/static-rlhf-interface/utils.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import subprocess
-
-from huggingface_hub.repository import _lfs_log_progress
-
-
-def force_git_push(
-    repo,
-):
-    """
-    force a simple git push
-    Blocking. Will return url to commit on remote
-    repo.
-    """
-    command = "git push --force"
-
-    try:
-        with _lfs_log_progress():
-            process = subprocess.Popen(
-                command.split(),
-                stderr=subprocess.PIPE,
-                stdout=subprocess.PIPE,
-                encoding="utf-8",
-                cwd=repo.local_dir,
-            )
-
-            stdout, stderr = process.communicate()
-            return_code = process.poll()
-            process.kill()
-
-            if len(stderr):
-                print(stderr)
-
-            if return_code:
-                raise subprocess.CalledProcessError(return_code, process.args, output=stdout, stderr=stderr)
-
-    except subprocess.CalledProcessError as exc:
-        raise EnvironmentError(exc.stderr)
-
-    return repo.git_head_commit_url()
diff --git a/spaces/TusharGoel/LayoutLM-DocVQA/README.md b/spaces/TusharGoel/LayoutLM-DocVQA/README.md
deleted file mode 100644
index 41da33fdf94bb94293385236f855408cca3989b9..0000000000000000000000000000000000000000
--- a/spaces/TusharGoel/LayoutLM-DocVQA/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TusharGoel LayoutLM Finetuned DocVQA
-emoji: 🌍
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/USERNAME0/abcdefghi/app.py b/spaces/USERNAME0/abcdefghi/app.py
deleted file mode 100644
index 75f7066c991f7a9442c2e4cbe7d8f7dc024bc39c..0000000000000000000000000000000000000000
--- a/spaces/USERNAME0/abcdefghi/app.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import streamlit as st
-
-a = st.number_input('A', 0, 10)
-b = st.number_input('B', 0, 10)
-st.write('A+B=', a+b)
\ No newline at end of file
diff --git a/spaces/VIPLab/Track-Anything/tracker/inference/__init__.py b/spaces/VIPLab/Track-Anything/tracker/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Visgift/nyami/README.md b/spaces/Visgift/nyami/README.md
deleted file mode 100644
index 743109f64e37ccac8d01035a544b648c881ca9d0..0000000000000000000000000000000000000000
--- a/spaces/Visgift/nyami/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Nyami
-emoji: 📈
-colorFrom: indigo
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/VishnuSaiTeja/RogerStaff/README.md b/spaces/VishnuSaiTeja/RogerStaff/README.md
deleted file mode 100644
index 66a7d1a65ef60b11421ae94312aa0a1c9f2f0559..0000000000000000000000000000000000000000
--- a/spaces/VishnuSaiTeja/RogerStaff/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RogerStaff
-emoji: 🔥
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/WKTSHNN/simplify_color_values/app.py b/spaces/WKTSHNN/simplify_color_values/app.py
deleted file mode 100644
index 3b65c48c1997c98dc9a30b56eff9097512366580..0000000000000000000000000000000000000000
--- a/spaces/WKTSHNN/simplify_color_values/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-from PIL import Image
-import numpy as np
-
-
-def brightness(image):
-
-    # 変数名の設定
-    tmp_dir = "./tmp" 
-    img_filename= "image"
-    ext = image.split('.')[1]
-    
-    
-    #画像取り込み（白黒）
-    im_gray = np.array(Image.open(f'{image}').convert('L'), np.float64)
-    
-    
-    #9分割
-    im_gray_copy = im_gray.copy()
-    for num in range(im_gray_copy.shape[0]):
-        
-        target = im_gray_copy[num]
-        target[(target>=216.75)&(target<=255)] = 229.5
-        target[(target>=191.25)&(target<216.75)] = 204
-        target[(target>=165.75)&(target<191.25)] = 178.5
-        target[(target>=140.25)&(target<165.75)] = 153
-        target[(target>=114.75)&(target<140.25)] = 127.5
-        target[(target>=89.25)&(target<114.75)] = 102
-        target[(target>=63.75)&(target<89.25)] = 76.5
-        target[(target>=38.25)&(target<63.75)] = 51
-        target[(target>=0)&(target<38.25)] = 25.5
-    
-    pil_img = Image.fromarray(im_gray_copy.astype(np.uint8))
-    
-    
-    #5分割
-    im_gray_copy2 = im_gray.copy()
-    for num2 in range(im_gray_copy2.shape[0]):
-    
-        target = im_gray_copy2[num2]
-        target[(target>=204)&(target<=255)] = 229.5
-        target[(target>=153)&(target<204)] = 178.5
-        target[(target>=102)&(target<153)] = 127.5
-        target[(target>=51)&(target<102)] = 76.5
-        target[(target>=0)&(target<51)] = 25.5
-    
-    pil_img2 = Image.fromarray(im_gray_copy2.astype(np.uint8))
-    
-    
-    #3分割
-    im_gray_copy3 = im_gray.copy()
-    for num3 in range(im_gray_copy2.shape[0]):
-    
-        target = im_gray_copy3[num3]
-        target[(target>=178.5)&(target<=255)] = 229.5
-        target[(target>=76.5)&(target<178.5)] = 127.5
-        target[(target>=0)&(target<76.5)] = 25.5
-    
-    pil_img3 = Image.fromarray(im_gray_copy3.astype(np.uint8))
-    
-    
-    #明部・中部・暗部の割合を求める
-    target2 = im_gray_copy3
-    area_bright = np.count_nonzero((target2>=178.5)&(target2<=255))
-    area_middle = np.count_nonzero((target2>=76.5)&(target2<178.5))
-    area_dark = np.count_nonzero((target2>=0)&(target2<76.5))
-    
-    ratio_bright = round(area_bright/target2.size*100)
-    ratio_middle = round(area_middle/target2.size*100)
-    ratio_dark = round(area_dark/target2.size*100)
-
-    
-    return f'{ratio_bright}％', f'{ratio_middle}％', f'{ratio_dark}％', pil_img, pil_img2, pil_img3
-
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("**This tool simplifies the colors in your image into 3-color, 5-color, and 9-color monochrome.**")
-    gr.Markdown("Start selecting your picture and then click **Run** to see the output.")
-    with gr.Row():
-        inp = gr.Image(label="select your picture", type='filepath')
-        
-        with gr.Column(scale=1):
-            outputs1 = gr.Textbox(label='明部 / bright tone')
-            outputs2 = gr.Textbox(label='中間調 / middle tone')
-            outputs3 = gr.Textbox(label='暗部 / dark tone')
-    
-    with gr.Row():
-        outputs4 = gr.Image(label='9-color')
-        outputs5 = gr.Image(label='5-color')
-        outputs6 = gr.Image(label='3-color')
-        
-    btn = gr.Button("Run")
-    btn.click(fn=brightness, inputs=inp, outputs=[outputs1, outputs2, outputs3, outputs4, outputs5, outputs6])
-
-    gr.Markdown("**Description**")
-    gr.Markdown("In paint software, brightness is represented by numbers ranging from 0 (black) to 100 (white). In the case of monochrome images, brightness directly corresponds to variations in shades of gray. We divide this brightness range as shown in the diagram. By grouping specific brightness ranges into a single color, we aim to simplify the brightness levels.")
-    gr.HTML("<img src='/file=color value.jpg'>") 
-
-demo.launch()
-
-
diff --git a/spaces/XzJosh/nanami-Bert-VITS2/server.py b/spaces/XzJosh/nanami-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nanami-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    print([f"{p}{t}" for p, t in zip(phone, tone)])
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
-    bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
-    with torch.no_grad():
-        x_tst=phones.to(dev).unsqueeze(0)
-        tones=tones.to(dev).unsqueeze(0)
-        lang_ids=lang_ids.to(dev).unsqueeze(0)
-        bert = bert.to(dev).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        return audio
-
-def replace_punctuation(text, i=2):
-    punctuation = "，。？！"
-    for char in punctuation:
-        text = text.replace(char, char * i)
-    return text
-
-def wav2(i, o, format):
-    inp = avopen(i, 'rb')
-    out = avopen(o, 'wb', format=format)
-    if format == "ogg": format = "libvorbis"
-
-    ostream = out.add_stream(format)
-
-    for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame): out.mux(p)
-
-    for p in ostream.encode(None): out.mux(p)
-
-    out.close()
-    inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
-    if request.method == 'GET':
-        try:
-            speaker = request.args.get('speaker')
-            text = request.args.get('text').replace("/n","")
-            sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
-            noise = float(request.args.get("noise", 0.5))
-            noisew = float(request.args.get("noisew", 0.6))
-            length = float(request.args.get("length", 1.2))
-            if length >= 2:
-                return "Too big length"
-            if len(text) >=200:
-                return "Too long text"
-            fmt = request.args.get("format", "wav")
-            if None in (speaker, text):
-                return "Missing Parameter"
-            if fmt not in ("mp3", "wav", "ogg"):
-                return "Invalid Format"
-        except:
-            return "Invalid Parameter"
-
-        with torch.no_grad():
-            audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
-        with BytesIO() as wav:
-            wavfile.write(wav, hps.data.sampling_rate, audio)
-            torch.cuda.empty_cache()
-            if fmt == "wav":
-                return Response(wav.getvalue(), mimetype="audio/wav")
-            wav.seek(0, 0)
-            with BytesIO() as ofp:
-                wav2(wav, ofp, fmt)
-                return Response(
-                    ofp.getvalue(),
-                    mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
-                )
diff --git a/spaces/YanzBotz/YanzBotz-Models/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/YanzBotz/YanzBotz-Models/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/YanzBotz/YanzBotz-Models/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/YotamNitzan/domain-expansion/torch_utils/misc.py b/spaces/YotamNitzan/domain-expansion/torch_utils/misc.py
deleted file mode 100644
index 7829f4d9f168557ce8a9a6dec289aa964234cb8c..0000000000000000000000000000000000000000
--- a/spaces/YotamNitzan/domain-expansion/torch_utils/misc.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-
-class suppress_tracer_warnings(warnings.catch_warnings):
-    def __enter__(self):
-        super().__enter__()
-        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
-        return self
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
-    # Print table.
-    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-    print()
-    for row in rows:
-        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
-    print()
-    return outputs
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Yusin/docker_test/Dockerfile b/spaces/Yusin/docker_test/Dockerfile
deleted file mode 100644
index 16fe92d72e0e5cb97a32e86609171953bd114c32..0000000000000000000000000000000000000000
--- a/spaces/Yusin/docker_test/Dockerfile
+++ /dev/null
@@ -1,52 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-FROM ubuntu:22.04
-
-RUN apt-get update
-
-RUN DEBIAN_FRONTEND=noninteractive apt-get install -y \
-    sudo \
-    build-essential \
-    gettext \
-    autoconf \
-    automake \
-    libproxy-dev \
-    libxml2-dev \
-    libtool \
-    vpnc-scripts \
-    pkg-config \
-    libgnutls28-dev \
-    git \
-    expect
-
-RUN apt-get update \
-  && apt-get install -y python3-pip python3-dev \
-  && cd /usr/local/bin \
-  && ln -s -f /usr/bin/python3 python \
-  && pip3 --no-cache-dir install --upgrade pip \
-  && rm -rf /var/lib/apt/lists/*
-
-RUN git clone https://github.com/openconnect/openconnect.git
-
-WORKDIR /openconnect
-RUN ./autogen.sh
-RUN ./configure
-RUN make
-
-COPY requirements.txt /openconnect/requirements.txt
-RUN python -m pip install -r /openconnect/requirements.txt
-
-USER root
-
-RUN mkdir -p /var/run/vpnc
-RUN chmod -R 777 /var/run/vpnc
-
-RUN cat /dev/net/tun
-
-RUN mkdir -p /dev/net
-RUN mknod /dev/net/tun c 10 200
-RUN chmod 600 /dev/net/tun
-
-COPY aberconnect.sh /openconnect
-RUN chmod +x aberconnect.sh
-ENTRYPOINT ["./aberconnect.sh"]
diff --git a/spaces/Yuzu22/rvc-models/infer_pack/models_onnx.py b/spaces/Yuzu22/rvc-models/infer_pack/models_onnx.py
deleted file mode 100644
index 3cdae2f7f8591a1e43b1d8520baa37b7e9744d72..0000000000000000000000000000000000000000
--- a/spaces/Yuzu22/rvc-models/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/Zengyf-CVer/Gradio-YOLOv8-Det/__init__.py b/spaces/Zengyf-CVer/Gradio-YOLOv8-Det/__init__.py
deleted file mode 100644
index a8a98f8df29d8f62fbd9b9cd15ab5192dc96467c..0000000000000000000000000000000000000000
--- a/spaces/Zengyf-CVer/Gradio-YOLOv8-Det/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-__author__ = "曾逸夫（Zeng Yifu）"
-__email__ = "zyfiy1314@163.com"
diff --git a/spaces/achimoraites/TextClassification-roberta-base_ag_news/app.py b/spaces/achimoraites/TextClassification-roberta-base_ag_news/app.py
deleted file mode 100644
index d7b438f0e0725558eee1870329b5d4f4282553b8..0000000000000000000000000000000000000000
--- a/spaces/achimoraites/TextClassification-roberta-base_ag_news/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/achimoraites/roberta-base_ag_news").launch()
\ No newline at end of file
diff --git a/spaces/adorp/ControlNet-v1-1-duplicate/app_scribble_interactive.py b/spaces/adorp/ControlNet-v1-1-duplicate/app_scribble_interactive.py
deleted file mode 100644
index 3550bf67211d14febd1a48cbafdbb523383d60f9..0000000000000000000000000000000000000000
--- a/spaces/adorp/ControlNet-v1-1-duplicate/app_scribble_interactive.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-import numpy as np
-
-from utils import randomize_seed_fn
-
-
-def create_canvas(w, h):
-    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                canvas_width = gr.Slider(label='Canvas width',
-                                         minimum=256,
-                                         maximum=512,
-                                         value=512,
-                                         step=1)
-                canvas_height = gr.Slider(label='Canvas height',
-                                          minimum=256,
-                                          maximum=512,
-                                          value=512,
-                                          step=1)
-                create_button = gr.Button('Open drawing canvas!')
-                image = gr.Image(tool='sketch', brush_radius=10)
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-
-        create_button.click(fn=create_canvas,
-                            inputs=[canvas_width, canvas_height],
-                            outputs=image,
-                            queue=False)
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='scribble')
-    demo = create_demo(model.process_scribble_interactive)
-    demo.queue().launch()
diff --git a/spaces/akhaliq/Holistic/app.py b/spaces/akhaliq/Holistic/app.py
deleted file mode 100644
index d871fc9229840b805634a3c00af26b16038b63b0..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Holistic/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import mediapipe as mp
-import gradio as gr
-import cv2
-import torch
-# Images
-torch.hub.download_url_to_file('https://i.imgur.com/9koaC6b.png', 'pose.png')
-
-import mediapipe as mp
-mp_holistic = mp.solutions.holistic
-
-# Prepare DrawingSpec for drawing the face landmarks later.
-mp_drawing = mp.solutions.drawing_utils 
-drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
-
-def inference(image):
-    # Run MediaPipe Holistic and draw pose landmarks.
-    with mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.5, model_complexity=2) as holistic:
-        # Convert the BGR image to RGB and process it with MediaPipe Pose.
-        results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-    
-        # Print nose coordinates.
-        image_hight, image_width, _ = image.shape
-        if results.pose_landmarks:
-          print(
-            f'Nose coordinates: ('
-            f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
-            f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_hight})'
-          )
-    
-        # Draw pose landmarks.
-        annotated_image = image.copy()
-        mp_drawing.draw_landmarks(annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-        mp_drawing.draw_landmarks(annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-        mp_drawing.draw_landmarks(
-            image=annotated_image, 
-            landmark_list=results.face_landmarks, 
-            connections=mp_holistic.FACEMESH_TESSELATION,
-            landmark_drawing_spec=drawing_spec,
-            connection_drawing_spec=drawing_spec)
-        mp_drawing.draw_landmarks(
-            image=annotated_image, 
-            landmark_list=results.pose_landmarks, 
-            connections=mp_holistic.POSE_CONNECTIONS,
-            landmark_drawing_spec=drawing_spec,
-            connection_drawing_spec=drawing_spec)
-        return annotated_image
-    
-
-title = "Holistic Tracking"
-description = "Gradio demo for Holistic Tracking, Simultaneous and semantically consistent tracking of 33 pose, 21 per-hand, and 468 facial landmarks. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html'>MediaPipe Holistic — Simultaneous Face, Hand and Pose Prediction, on Device</a> | <a href='https://github.com/google/mediapipe'>Github Repo</a></p>"
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(label="Input")], 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article, 
-    examples=[
-   ["pose.png"]    
-              ]
-    ).launch()
\ No newline at end of file
diff --git a/spaces/akhaliq/JoJoGAN/e4e/models/stylegan2/__init__.py b/spaces/akhaliq/JoJoGAN/e4e/models/stylegan2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/Utils/distributed.py b/spaces/akhaliq/SummerTime/model/third_party/HMNet/Utils/distributed.py
deleted file mode 100644
index 979c66822b499aa49dfebcdb6e639dc70966fae6..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/Utils/distributed.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import os
-import torch
-
-
-def distributed(opt, is_nocuda):
-    cluster = opt["cluster"]
-    world_size = 1
-    local_size = 1
-    rank = 0
-    local_rank = 0
-    is_master = True
-    run = None
-
-    if is_nocuda or not torch.cuda.is_available():
-        device = torch.device("cpu")
-        n_gpu = 0
-    else:
-        if "OMPI_COMM_WORLD_SIZE" in os.environ:
-            world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"])
-            local_size = int(os.environ["OMPI_COMM_WORLD_LOCAL_SIZE"])
-            rank = int(os.environ["OMPI_COMM_WORLD_RANK"])
-            local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
-            is_master = rank == 0
-
-        torch.cuda.set_device(local_rank)
-        device = torch.device("cuda", local_rank)
-        n_gpu = 1
-        # the following assumes that all processes run on a single node
-        if torch.distributed.is_available() and world_size > 1:
-            # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-            os.environ["WORLD_SIZE"] = str(world_size)
-            os.environ["RANK"] = str(rank)
-            os.environ["MASTER_ADDR"] = "127.0.0.1"
-            os.environ["MASTER_PORT"] = (
-                opt["master_port"] if "master_port" in opt else "35551"
-            )
-            torch.distributed.init_process_group(
-                backend="nccl"
-            )  # using environment variable initialization
-            print("Distributed package is available. Process group initialized.")
-
-    return device, n_gpu, world_size, local_size, rank, local_rank, is_master, run
diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/template_single_spk/voc1/local/data_prep.sh b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/template_single_spk/voc1/local/data_prep.sh
deleted file mode 100644
index b2044d8f4bc045ec1052547a2657bbc0ff35fce4..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/template_single_spk/voc1/local/data_prep.sh
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/bin/bash
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-# shellcheck disable=SC1091
-. ./path.sh || exit 1;
-
-fs=22050
-num_dev=100
-num_eval=100
-train_set="train_nodev"
-dev_set="dev"
-eval_set="eval"
-shuffle=false
-
-# shellcheck disable=SC1091
-. utils/parse_options.sh || exit 1;
-
-db_root=$1
-data_dir=$2
-
-# check arguments
-if [ $# != 2 ]; then
-    echo "Usage: $0 [Options] <db_root> <data_dir>"
-    echo ""
-    echo "Options:"
-    echo "    --num_dev: number of development uttreances (default=250)."
-    echo "    --num_eval: number of evaluation uttreances (default=250)."
-    echo "    --train_set: name of train set (default=train_nodev)."
-    echo "    --dev_set: name of dev set (default=dev)."
-    echo "    --eval_set: name of eval set (default=eval)."
-    echo "    --shuffle: whether to perform shuffle in making dev / eval set (default=false)."
-    exit 1
-fi
-
-set -euo pipefail
-
-[ ! -e "${data_dir}/all" ] && mkdir -p "${data_dir}/all"
-
-# set filenames
-scp="${data_dir}/all/wav.scp"
-
-# check file existence
-[ -e "${scp}" ] && rm "${scp}"
-
-# make all scp
-find "${db_root}" -follow -name "*.wav" | sort | while read -r filename; do
-    id=$(basename "${filename}" | sed -e "s/\.[^\.]*$//g")
-    echo "${id} cat ${filename} | sox -t wav - -c 1 -b 16 -t wav - rate ${fs} |" >> "${scp}"
-done
-
-# split
-num_all=$(wc -l < "${scp}")
-num_deveval=$((num_dev + num_eval))
-num_train=$((num_all - num_deveval))
-if [ ${num_eval} -ne 0 ]; then
-    utils/split_data.sh \
-        --num_first "${num_train}" \
-        --num_second "${num_deveval}" \
-        --shuffle "${shuffle}" \
-        "${data_dir}/all" \
-        "${data_dir}/${train_set}" \
-        "${data_dir}/deveval"
-    utils/split_data.sh \
-        --num_first "${num_dev}" \
-        --num_second "${num_eval}" \
-        --shuffle "${shuffle}" \
-        "${data_dir}/deveval" \
-        "${data_dir}/${dev_set}" \
-        "${data_dir}/${eval_set}"
-else
-    utils/split_data.sh \
-        --num_first "${num_train}" \
-        --num_second "${num_deveval}" \
-        --shuffle "${shuffle}" \
-        "${data_dir}/all" \
-        "${data_dir}/${train_set}" \
-        "${data_dir}/${dev_set}"
-    cp -r "${data_dir}/${dev_set}" "${data_dir}/${eval_set}"
-fi
-
-# remove tmp directories
-rm -rf "${data_dir}/all"
-rm -rf "${data_dir}/deveval"
-
-echo "Successfully prepared data."
diff --git a/spaces/ali-ghamdan/realesrgan-models/docs/anime_comparisons_CN.md b/spaces/ali-ghamdan/realesrgan-models/docs/anime_comparisons_CN.md
deleted file mode 100644
index 43ba58344ed9554d5b30e2815d1b7d4ab8bc503f..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/realesrgan-models/docs/anime_comparisons_CN.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# 动漫视频模型比较
-
-[English](anime_comparisons.md) **|** [简体中文](anime_comparisons_CN.md)
-
-## 更新
-
-- 2022/04/24: 发布 **AnimeVideo-v3**. 主要做了以下更新：
-  - **更自然**
-  - **更少瑕疵**
-  - **颜色保持得更好**
-  - **更好的纹理恢复**
-  - **虚化背景处理**
-
-## 比较
-
-我们将 RealESRGAN-AnimeVideo-v3 与以下方法进行了比较。我们的 RealESRGAN-AnimeVideo-v3 可以以更快的推理速度获得更好的结果。
-
-- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). 超参数: `tile=0`, `noiselevel=2`
-- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN): 我们使用了[20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode)版本, 超参: `cache_mode=0`, `tile=0`, `alpha=1`.
-- 我们的 RealESRGAN-AnimeVideo-v3
-
-## 结果
-
-您可能需要**放大**以比较详细信息, 或者**单击图像**以查看完整尺寸。 请注意下面表格的图片是从原图里裁剪patch并且resize后的结果，您可以从
-[Google Drive](https://drive.google.com/drive/folders/1bc_Hje1Nqop9NDkUvci2VACSjL7HZMRp?usp=sharing) 里下载原始的输入和输出。
-
-**更自然的结果，更好的虚化背景恢复**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
-| :---: | :---:        |     :---:      |  :---:      |
-|![157083983-bec52c67-9a5e-4eed-afef-01fe6cd2af85_patch](https://user-images.githubusercontent.com/11482921/164452769-5d8cb4f8-1708-42d2-b941-f44a6f136feb.png) | ![](https://user-images.githubusercontent.com/11482921/164452767-c825cdec-f721-4ff1-aef1-fec41f146c4c.png) | ![](https://user-images.githubusercontent.com/11482921/164452755-3be50895-e3d4-432d-a7b9-9085c2a8e771.png) | ![](https://user-images.githubusercontent.com/11482921/164452771-be300656-379a-4323-a755-df8025a8c451.png) |
-|![a0010_patch](https://user-images.githubusercontent.com/11482921/164454047-22eeb493-3fa9-4142-9fc2-6f2a1c074cd5.png) | ![](https://user-images.githubusercontent.com/11482921/164454046-d5e79f8f-00a0-4b55-bc39-295d0d69747a.png) | ![](https://user-images.githubusercontent.com/11482921/164454040-87886b11-9d08-48bd-862f-0d4aed72eb19.png) | ![](https://user-images.githubusercontent.com/11482921/164454055-73dc9f02-286e-4d5c-8f70-c13742e08f42.png) |
-|![00000044_patch](https://user-images.githubusercontent.com/11482921/164451232-bacf64fc-e55a-44db-afbb-6b31ab0f8973.png) | ![](https://user-images.githubusercontent.com/11482921/164451318-f309b61a-75b8-4b74-b5f3-595725f1cf0b.png) | ![](https://user-images.githubusercontent.com/11482921/164451348-994f8a35-adbe-4a4b-9c61-feaa294af06a.png) | ![](https://user-images.githubusercontent.com/11482921/164451361-9b7d376e-6f75-4648-b752-542b44845d1c.png) |
-
-**更少瑕疵，更好的细节纹理**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
-| :---: | :---:        |     :---:      |  :---:      |
-|![00000053_patch](https://user-images.githubusercontent.com/11482921/164448411-148a7e5c-cfcd-4504-8bc7-e318eb883bb6.png) | ![](https://user-images.githubusercontent.com/11482921/164448633-dfc15224-b6d2-4403-a3c9-4bb819979364.png) | ![](https://user-images.githubusercontent.com/11482921/164448771-0d359509-5293-4d4c-8e3c-86a2a314ea88.png) | ![](https://user-images.githubusercontent.com/11482921/164448848-1a4ff99e-075b-4458-9db7-2c89e8160aa0.png) |
-|![Disney_v4_22_018514_s2_patch](https://user-images.githubusercontent.com/11482921/164451898-83311cdf-bd3e-450f-b9f6-34d7fea3ab79.png) | ![](https://user-images.githubusercontent.com/11482921/164451894-6c56521c-6561-40d6-a3a5-8dde2c167b8a.png) | ![](https://user-images.githubusercontent.com/11482921/164451888-af9b47e3-39dc-4f3e-b0d7-d372d8191e2a.png) | ![](https://user-images.githubusercontent.com/11482921/164451901-31ca4dd4-9847-4baa-8cde-ad50f4053dcf.png) |
-|![Japan_v2_0_007261_s2_patch](https://user-images.githubusercontent.com/11482921/164454578-73c77392-77de-49c5-b03c-c36631723192.png) | ![](https://user-images.githubusercontent.com/11482921/164454574-b1ede5f0-4520-4eaa-8f59-086751a34e62.png) | ![](https://user-images.githubusercontent.com/11482921/164454567-4cb3fdd8-6a2d-4016-85b2-a305a8ff80e4.png) | ![](https://user-images.githubusercontent.com/11482921/164454583-7f243f20-eca3-4500-ac43-eb058a4a101a.png) |
-|![huluxiongdi_2_patch](https://user-images.githubusercontent.com/11482921/164453482-0726c842-337e-40ec-bf6c-f902ee956a8b.png) | ![](https://user-images.githubusercontent.com/11482921/164453480-71d5e091-5bfa-4c77-9c57-4e37f66ca0a3.png) | ![](https://user-images.githubusercontent.com/11482921/164453468-c295d3c9-3661-45f0-9ecd-406a1877f76e.png) | ![](https://user-images.githubusercontent.com/11482921/164453486-3091887c-587c-450e-b6fe-905cb518d57e.png) |
-
-**其他更好的结果**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
-| :---: | :---:        |     :---:      |  :---:      |
-|![Japan_v2_1_128525_s1_patch](https://user-images.githubusercontent.com/11482921/164454933-67697f7c-b6ef-47dc-bfca-822a78af8acf.png) | ![](https://user-images.githubusercontent.com/11482921/164454931-9450de7c-f0b3-4638-9c1e-0668e0c41ef0.png) | ![](https://user-images.githubusercontent.com/11482921/164454926-ed746976-786d-41c5-8a83-7693cd774c3a.png) | ![](https://user-images.githubusercontent.com/11482921/164454936-8abdf0f0-fb30-40eb-8281-3b46c0bcb9ae.png) |
-|![tianshuqitan_2_patch](https://user-images.githubusercontent.com/11482921/164456948-807c1476-90b6-4507-81da-cb986d01600c.png) | ![](https://user-images.githubusercontent.com/11482921/164456943-25e89de9-d7e5-4f61-a2e1-96786af6ae9e.png) | ![](https://user-images.githubusercontent.com/11482921/164456954-b468c447-59f5-4594-9693-3683e44ba3e6.png) | ![](https://user-images.githubusercontent.com/11482921/164456957-640f910c-3b04-407c-ac20-044d72e19735.png) |
-|![00000051_patch](https://user-images.githubusercontent.com/11482921/164456044-e9a6b3fa-b24e-4eb7-acf9-1f7746551b1e.png) ![00000051_patch](https://user-images.githubusercontent.com/11482921/164456421-b67245b0-767d-4250-9105-80bbe507ecfc.png) | ![](https://user-images.githubusercontent.com/11482921/164456040-85763cf2-cb28-4ba3-abb6-1dbb48c55713.png) ![](https://user-images.githubusercontent.com/11482921/164456419-59cf342e-bc1e-4044-868c-e1090abad313.png) | ![](https://user-images.githubusercontent.com/11482921/164456031-4244bb7b-8649-4e01-86f4-40c2099c5afd.png) ![](https://user-images.githubusercontent.com/11482921/164456411-b6afcbe9-c054-448d-a6df-96d3ba3047f8.png) | ![](https://user-images.githubusercontent.com/11482921/164456035-12e270be-fd52-46d4-b18a-3d3b680731fe.png) ![](https://user-images.githubusercontent.com/11482921/164456417-dcaa8b62-f497-427d-b2d2-f390f1200fb9.png) |
-|![00000099_patch](https://user-images.githubusercontent.com/11482921/164455312-6411b6e1-5823-4131-a4b0-a6be8a9ae89f.png) | ![](https://user-images.githubusercontent.com/11482921/164455310-f2b99646-3a22-47a4-805b-dc451ac86ddb.png) | ![](https://user-images.githubusercontent.com/11482921/164455294-35471b42-2826-4451-b7ec-6de01344954c.png) | ![](https://user-images.githubusercontent.com/11482921/164455305-fa4c9758-564a-4081-8b4e-f11057a0404d.png) |
-|![00000016_patch](https://user-images.githubusercontent.com/11482921/164455672-447353c9-2da2-4fcb-ba4a-7dd6b94c19c1.png) | ![](https://user-images.githubusercontent.com/11482921/164455669-df384631-baaa-42f8-9150-40f658471558.png) | ![](https://user-images.githubusercontent.com/11482921/164455657-68006bf0-138d-4981-aaca-8aa927d2f78a.png) | ![](https://user-images.githubusercontent.com/11482921/164455664-0342b93e-a62a-4b36-a90e-7118f3f1e45d.png) |
-
-## 推理速度比较
-
-### PyTorch
-
-请注意，我们只报告了**模型推理**的时间, 而忽略了读写硬盘的时间.
-
-| GPU | 输入尺寸 | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
-| :---: | :---:         |  :---:        |     :---:      |  :---:      |
-| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
-| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
-| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
-
-### ncnn
-
-- [ ] TODO
diff --git a/spaces/aliabid94/AutoGPT/autogpt/memory/redismem.py b/spaces/aliabid94/AutoGPT/autogpt/memory/redismem.py
deleted file mode 100644
index 082a812c5362cc9f19e35bf1bb10269b558f7724..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/memory/redismem.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""Redis memory provider."""
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import redis
-from colorama import Fore, Style
-from redis.commands.search.field import TextField, VectorField
-from redis.commands.search.indexDefinition import IndexDefinition, IndexType
-from redis.commands.search.query import Query
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-SCHEMA = [
-    TextField("data"),
-    VectorField(
-        "embedding",
-        "HNSW",
-        {"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
-    ),
-]
-
-
-class RedisMemory(MemoryProviderSingleton):
-    def __init__(self, cfg):
-        """
-        Initializes the Redis memory provider.
-
-        Args:
-            cfg: The config object.
-
-        Returns: None
-        """
-        redis_host = cfg.redis_host
-        redis_port = cfg.redis_port
-        redis_password = cfg.redis_password
-        self.dimension = 1536
-        self.redis = redis.Redis(
-            host=redis_host,
-            port=redis_port,
-            password=redis_password,
-            db=0,  # Cannot be changed
-        )
-        self.cfg = cfg
-
-        # Check redis connection
-        try:
-            self.redis.ping()
-        except redis.ConnectionError as e:
-            logger.typewriter_log(
-                "FAILED TO CONNECT TO REDIS",
-                Fore.RED,
-                Style.BRIGHT + str(e) + Style.RESET_ALL,
-            )
-            logger.double_check(
-                "Please ensure you have setup and configured Redis properly for use. "
-                + f"You can check out {Fore.CYAN + Style.BRIGHT}"
-                f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
-                " to ensure you've set up everything correctly."
-            )
-            exit(1)
-
-        if cfg.wipe_redis_on_start:
-            self.redis.flushall()
-        try:
-            self.redis.ft(f"{cfg.memory_index}").create_index(
-                fields=SCHEMA,
-                definition=IndexDefinition(
-                    prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
-                ),
-            )
-        except Exception as e:
-            print("Error creating Redis search index: ", e)
-        existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
-        self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
-
-    def add(self, data: str) -> str:
-        """
-        Adds a data point to the memory.
-
-        Args:
-            data: The data to add.
-
-        Returns: Message indicating that the data has been added.
-        """
-        if "Command Error:" in data:
-            return ""
-        vector = create_embedding_with_ada(data)
-        vector = np.array(vector).astype(np.float32).tobytes()
-        data_dict = {b"data": data, "embedding": vector}
-        pipe = self.redis.pipeline()
-        pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
-        _text = (
-            f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
-        )
-        self.vec_num += 1
-        pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
-        pipe.execute()
-        return _text
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: The most relevant data.
-        """
-        return self.get_relevant(data, 1)
-
-    def clear(self) -> str:
-        """
-        Clears the redis server.
-
-        Returns: A message indicating that the memory has been cleared.
-        """
-        self.redis.flushall()
-        return "Obliviated"
-
-    def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
-        """
-        Returns all the data in the memory that is relevant to the given data.
-        Args:
-            data: The data to compare to.
-            num_relevant: The number of relevant data to return.
-
-        Returns: A list of the most relevant data.
-        """
-        query_embedding = create_embedding_with_ada(data)
-        base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
-        query = (
-            Query(base_query)
-            .return_fields("data", "vector_score")
-            .sort_by("vector_score")
-            .dialect(2)
-        )
-        query_vector = np.array(query_embedding).astype(np.float32).tobytes()
-
-        try:
-            results = self.redis.ft(f"{self.cfg.memory_index}").search(
-                query, query_params={"vector": query_vector}
-            )
-        except Exception as e:
-            print("Error calling Redis search: ", e)
-            return None
-        return [result.data for result in results.docs]
-
-    def get_stats(self):
-        """
-        Returns: The stats of the memory index.
-        """
-        return self.redis.ft(f"{self.cfg.memory_index}").info()
diff --git a/spaces/allknowingroger/Image-Models-Test115/README.md b/spaces/allknowingroger/Image-Models-Test115/README.md
deleted file mode 100644
index c03258a816da426ad2dc3ef0b0577e1b419d7515..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test115/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-duplicated_from: allknowingroger/Image-Models-Test114
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Lockchat.py b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Lockchat.py
deleted file mode 100644
index 1bce74035403bf8615e68ccfcc9deb7e0151817a..0000000000000000000000000000000000000000
--- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Lockchat.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-url = 'http://supertest.lockchat.app'
-model = ['gpt-4', 'gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-
-    payload = {
-        "temperature": 0.7,
-        "messages": messages,
-        "model": model,
-        "stream": True,
-    }
-    headers = {
-        "user-agent": "ChatX/39 CFNetwork/1408.0.4 Darwin/22.5.0",
-    }
-    response = requests.post("http://supertest.lockchat.app/v1/chat/completions", 
-                            json=payload, headers=headers, stream=True)
-    for token in response.iter_lines():
-        if b'The model: `gpt-4` does not exist' in token:
-            print('error, retrying...')
-            _create_completion(model=model, messages=messages, stream=stream, temperature=temperature, **kwargs)
-        if b"content" in token:
-            token = json.loads(token.decode('utf-8').split('data: ')[1])['choices'][0]['delta'].get('content')
-            if token: yield (token)
-            
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/ui_extensions.py b/spaces/aodianyun/stable-diffusion-webui/modules/ui_extensions.py
deleted file mode 100644
index 12f395cef3a6e1e0ad28d1577c0208794b897335..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/ui_extensions.py
+++ /dev/null
@@ -1,354 +0,0 @@
-import json
-import os.path
-import shutil
-import sys
-import time
-import traceback
-
-import git
-
-import gradio as gr
-import html
-import shutil
-import errno
-
-from modules import extensions, shared, paths
-from modules.call_queue import wrap_gradio_gpu_call
-
-available_extensions = {"extensions": []}
-
-
-def check_access():
-    assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
-
-
-def apply_and_restart(disable_list, update_list):
-    check_access()
-
-    disabled = json.loads(disable_list)
-    assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
-
-    update = json.loads(update_list)
-    assert type(update) == list, f"wrong update_list data for apply_and_restart: {update_list}"
-
-    update = set(update)
-
-    for ext in extensions.extensions:
-        if ext.name not in update:
-            continue
-
-        try:
-            ext.fetch_and_reset_hard()
-        except Exception:
-            print(f"Error getting updates for {ext.name}:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-
-    shared.opts.disabled_extensions = disabled
-    shared.opts.save(shared.config_filename)
-
-    shared.state.interrupt()
-    shared.state.need_restart = True
-
-
-def check_updates(id_task, disable_list):
-    check_access()
-
-    disabled = json.loads(disable_list)
-    assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
-
-    exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
-    shared.state.job_count = len(exts)
-
-    for ext in exts:
-        shared.state.textinfo = ext.name
-
-        try:
-            ext.check_updates()
-        except Exception:
-            print(f"Error checking updates for {ext.name}:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-
-        shared.state.nextjob()
-
-    return extension_table(), ""
-
-
-def extension_table():
-    code = f"""<!-- {time.time()} -->
-    <table id="extensions">
-        <thead>
-            <tr>
-                <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
-                <th>URL</th>
-                <th><abbr title="Extension version">Version</abbr></th>
-                <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
-            </tr>
-        </thead>
-        <tbody>
-    """
-
-    for ext in extensions.extensions:
-        remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
-
-        if ext.can_update:
-            ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
-        else:
-            ext_status = ext.status
-
-        code += f"""
-            <tr>
-                <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
-                <td>{remote}</td>
-                <td>{ext.version}</td>
-                <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
-            </tr>
-    """
-
-    code += """
-        </tbody>
-    </table>
-    """
-
-    return code
-
-
-def normalize_git_url(url):
-    if url is None:
-        return ""
-
-    url = url.replace(".git", "")
-    return url
-
-
-def install_extension_from_url(dirname, url):
-    check_access()
-
-    assert url, 'No URL specified'
-
-    if dirname is None or dirname == "":
-        *parts, last_part = url.split('/')
-        last_part = normalize_git_url(last_part)
-
-        dirname = last_part
-
-    target_dir = os.path.join(extensions.extensions_dir, dirname)
-    assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
-
-    normalized_url = normalize_git_url(url)
-    assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
-
-    tmpdir = os.path.join(paths.data_path, "tmp", dirname)
-
-    try:
-        shutil.rmtree(tmpdir, True)
-
-        repo = git.Repo.clone_from(url, tmpdir)
-        repo.remote().fetch()
-
-        try:
-            os.rename(tmpdir, target_dir)
-        except OSError as err:
-            # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
-            # Shouldn't cause any new issues at least but we probably want to handle it there too.
-            if err.errno == errno.EXDEV:
-                # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
-                # Since we can't use a rename, do the slower but more versitile shutil.move()
-                shutil.move(tmpdir, target_dir)
-            else:
-                # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.
-                raise(err)
-
-        import launch
-        launch.run_extension_installer(target_dir)
-
-        extensions.list_extensions()
-        return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
-    finally:
-        shutil.rmtree(tmpdir, True)
-
-
-def install_extension_from_index(url, hide_tags, sort_column):
-    ext_table, message = install_extension_from_url(None, url)
-
-    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
-
-    return code, ext_table, message
-
-
-def refresh_available_extensions(url, hide_tags, sort_column):
-    global available_extensions
-
-    import urllib.request
-    with urllib.request.urlopen(url) as response:
-        text = response.read()
-
-    available_extensions = json.loads(text)
-
-    code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)
-
-    return url, code, gr.CheckboxGroup.update(choices=tags), ''
-
-
-def refresh_available_extensions_for_tags(hide_tags, sort_column):
-    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
-
-    return code, ''
-
-
-sort_ordering = [
-    # (reverse, order_by_function)
-    (True, lambda x: x.get('added', 'z')),
-    (False, lambda x: x.get('added', 'z')),
-    (False, lambda x: x.get('name', 'z')),
-    (True, lambda x: x.get('name', 'z')),
-    (False, lambda x: 'z'),
-]
-
-
-def refresh_available_extensions_from_data(hide_tags, sort_column):
-    extlist = available_extensions["extensions"]
-    installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
-
-    tags = available_extensions.get("tags", {})
-    tags_to_hide = set(hide_tags)
-    hidden = 0
-
-    code = f"""<!-- {time.time()} -->
-    <table id="available_extensions">
-        <thead>
-            <tr>
-                <th>Extension</th>
-                <th>Description</th>
-                <th>Action</th>
-            </tr>
-        </thead>
-        <tbody>
-    """
-
-    sort_reverse, sort_function = sort_ordering[sort_column if 0 <= sort_column < len(sort_ordering) else 0]
-
-    for ext in sorted(extlist, key=sort_function, reverse=sort_reverse):
-        name = ext.get("name", "noname")
-        added = ext.get('added', 'unknown')
-        url = ext.get("url", None)
-        description = ext.get("description", "")
-        extension_tags = ext.get("tags", [])
-
-        if url is None:
-            continue
-
-        existing = installed_extension_urls.get(normalize_git_url(url), None)
-        extension_tags = extension_tags + ["installed"] if existing else extension_tags
-
-        if len([x for x in extension_tags if x in tags_to_hide]) > 0:
-            hidden += 1
-            continue
-
-        install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
-
-        tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
-
-        code += f"""
-            <tr>
-                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
-                <td>{html.escape(description)}<p class="info"><span class="date_added">Added: {html.escape(added)}</span></p></td>
-                <td>{install_code}</td>
-            </tr>
-        
-        """
-
-        for tag in [x for x in extension_tags if x not in tags]:
-            tags[tag] = tag
-
-    code += """
-        </tbody>
-    </table>
-    """
-
-    if hidden > 0:
-        code += f"<p>Extension hidden: {hidden}</p>"
-
-    return code, list(tags)
-
-
-def create_ui():
-    import modules.ui
-
-    with gr.Blocks(analytics_enabled=False) as ui:
-        with gr.Tabs(elem_id="tabs_extensions") as tabs:
-            with gr.TabItem("Installed"):
-
-                with gr.Row(elem_id="extensions_installed_top"):
-                    apply = gr.Button(value="Apply and restart UI", variant="primary")
-                    check = gr.Button(value="Check for updates")
-                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
-                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
-
-                info = gr.HTML()
-                extensions_table = gr.HTML(lambda: extension_table())
-
-                apply.click(
-                    fn=apply_and_restart,
-                    _js="extensions_apply",
-                    inputs=[extensions_disabled_list, extensions_update_list],
-                    outputs=[],
-                )
-
-                check.click(
-                    fn=wrap_gradio_gpu_call(check_updates, extra_outputs=[gr.update()]),
-                    _js="extensions_check",
-                    inputs=[info, extensions_disabled_list],
-                    outputs=[extensions_table, info],
-                )
-
-            with gr.TabItem("Available"):
-                with gr.Row():
-                    refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
-                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
-                    extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
-                    install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
-
-                with gr.Row():
-                    hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
-                    sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
-
-                install_result = gr.HTML()
-                available_extensions_table = gr.HTML()
-
-                refresh_available_extensions_button.click(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
-                    inputs=[available_extensions_index, hide_tags, sort_column],
-                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
-                )
-
-                install_extension_button.click(
-                    fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
-                    inputs=[extension_to_install, hide_tags, sort_column],
-                    outputs=[available_extensions_table, extensions_table, install_result],
-                )
-
-                hide_tags.change(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
-                    inputs=[hide_tags, sort_column],
-                    outputs=[available_extensions_table, install_result]
-                )
-
-                sort_column.change(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
-                    inputs=[hide_tags, sort_column],
-                    outputs=[available_extensions_table, install_result]
-                )
-
-            with gr.TabItem("Install from URL"):
-                install_url = gr.Text(label="URL for extension's git repository")
-                install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
-                install_button = gr.Button(value="Install", variant="primary")
-                install_result = gr.HTML(elem_id="extension_install_result")
-
-                install_button.click(
-                    fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
-                    inputs=[install_dirname, install_url],
-                    outputs=[extensions_table, install_result],
-                )
-
-    return ui
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/inference_tests/test_synthesizer.py b/spaces/artificialguybr/video-dubbing/TTS/tests/inference_tests/test_synthesizer.py
deleted file mode 100644
index 40e830178c20493f5b1a6c670a9aeb1d2ce8992f..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/inference_tests/test_synthesizer.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-import unittest
-
-from tests import get_tests_input_path
-from TTS.config import load_config
-from TTS.tts.models import setup_model
-from TTS.utils.io import save_checkpoint
-from TTS.utils.synthesizer import Synthesizer
-
-
-class SynthesizerTest(unittest.TestCase):
-    # pylint: disable=R0201
-    def _create_random_model(self):
-        # pylint: disable=global-statement
-        config = load_config(os.path.join(get_tests_input_path(), "dummy_model_config.json"))
-        model = setup_model(config)
-        output_path = os.path.join(get_tests_input_path())
-        save_checkpoint(config, model, None, None, 10, 1, output_path)
-
-    def test_in_out(self):
-        self._create_random_model()
-        tts_root_path = get_tests_input_path()
-        tts_checkpoint = os.path.join(tts_root_path, "checkpoint_10.pth")
-        tts_config = os.path.join(tts_root_path, "dummy_model_config.json")
-        synthesizer = Synthesizer(tts_checkpoint, tts_config, None, None)
-        synthesizer.tts("Better this test works!!")
-
-    def test_split_into_sentences(self):
-        """Check demo server sentences split as expected"""
-        print("\n > Testing demo server sentence splitting")
-        # pylint: disable=attribute-defined-outside-init, protected-access
-        self.seg = Synthesizer._get_segmenter("en")
-        sis = Synthesizer.split_into_sentences
-        assert sis(self, "Hello. Two sentences") == ["Hello.", "Two sentences"]
-        assert sis(self, "He went to meet the adviser from Scott, Waltman & Co. next morning.") == [
-            "He went to meet the adviser from Scott, Waltman & Co. next morning."
-        ]
-        assert sis(self, "Let's run it past Sarah and co. They'll want to see this.") == [
-            "Let's run it past Sarah and co.",
-            "They'll want to see this.",
-        ]
-        assert sis(self, "Where is Bobby Jr.'s rabbit?") == ["Where is Bobby Jr.'s rabbit?"]
-        assert sis(self, "Please inform the U.K. authorities right away.") == [
-            "Please inform the U.K. authorities right away."
-        ]
-        assert sis(self, "Were David and co. at the event?") == ["Were David and co. at the event?"]
-        assert sis(self, "paging dr. green, please come to theatre four immediately.") == [
-            "paging dr. green, please come to theatre four immediately."
-        ]
-        assert sis(self, "The email format is Firstname.Lastname@example.com. I think you reversed them.") == [
-            "The email format is Firstname.Lastname@example.com.",
-            "I think you reversed them.",
-        ]
-        assert sis(
-            self,
-            "The demo site is: https://top100.example.com/subsection/latestnews.html. Please send us your feedback.",
-        ) == [
-            "The demo site is: https://top100.example.com/subsection/latestnews.html.",
-            "Please send us your feedback.",
-        ]
-        assert sis(self, "Scowling at him, 'You are not done yet!' she yelled.") == [
-            "Scowling at him, 'You are not done yet!' she yelled."
-        ]  # with the  final lowercase "she" we see it's all one sentence
-        assert sis(self, "Hey!! So good to see you.") == ["Hey!!", "So good to see you."]
-        assert sis(self, "He went to Yahoo! but I don't know the division.") == [
-            "He went to Yahoo! but I don't know the division."
-        ]
-        assert sis(self, "If you can't remember a quote, “at least make up a memorable one that's plausible...\"") == [
-            "If you can't remember a quote, “at least make up a memorable one that's plausible...\""
-        ]
-        assert sis(self, "The address is not google.com.") == ["The address is not google.com."]
-        assert sis(self, "1.) The first item 2.) The second item") == ["1.) The first item", "2.) The second item"]
-        assert sis(self, "1) The first item 2) The second item") == ["1) The first item", "2) The second item"]
-        assert sis(self, "a. The first item b. The second item c. The third list item") == [
-            "a. The first item",
-            "b. The second item",
-            "c. The third list item",
-        ]
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Protocol/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Protocol/__init__.py
deleted file mode 100644
index efdf034f76d91b9e3c79415c54212cb9a9e7835f..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Protocol/__init__.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin <helderijs@gmail.com>
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-__all__ = ['KDF', 'SecretSharing']
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_DES3.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_DES3.py
deleted file mode 100644
index 8d6a648510606c70116a5e88099db4939a6542c6..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_DES3.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  SelfTest/Cipher/DES3.py: Self-test for the Triple-DES cipher
-#
-# Written in 2008 by Dwayne C. Litzenberger <dlitz@dlitz.net>
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-
-"""Self-test suite for Crypto.Cipher.DES3"""
-
-import unittest
-from binascii import hexlify, unhexlify
-
-from Crypto.Cipher import DES3
-
-from Crypto.Util.strxor import strxor_c
-from Crypto.Util.py3compat import bchr, tostr
-from Crypto.SelfTest.loader import load_test_vectors
-from Crypto.SelfTest.st_common import list_test_cases
-
-# This is a list of (plaintext, ciphertext, key, description) tuples.
-test_data = [
-    # Test vector from Appendix B of NIST SP 800-67
-    # "Recommendation for the Triple Data Encryption Algorithm (TDEA) Block
-    # Cipher"
-    # http://csrc.nist.gov/publications/nistpubs/800-67/SP800-67.pdf
-    ('54686520717566636b2062726f776e20666f78206a756d70',
-        'a826fd8ce53b855fcce21c8112256fe668d5c05dd9b6b900',
-        '0123456789abcdef23456789abcdef01456789abcdef0123',
-        'NIST SP800-67 B.1'),
-
-    # This test is designed to test the DES3 API, not the correctness of the
-    # output.
-    ('21e81b7ade88a259', '5c577d4d9b20c0f8',
-        '9b397ebf81b1181e282f4bb8adbadc6b', 'Two-key 3DES'),
-]
-
-# NIST CAVP test vectors
-
-nist_tdes_mmt_files = ("TECBMMT2.rsp", "TECBMMT3.rsp")
-
-for tdes_file in nist_tdes_mmt_files:
-
-    test_vectors = load_test_vectors(
-                        ("Cipher", "TDES"),
-                        tdes_file,
-                        "TDES ECB (%s)" % tdes_file,
-                        {"count": lambda x: int(x)}) or []
-
-    for index, tv in enumerate(test_vectors):
-
-        # The test vector file contains some directive lines
-        if isinstance(tv, str):
-            continue
-
-        key = tv.key1 + tv.key2 + tv.key3
-        test_data_item = (tostr(hexlify(tv.plaintext)),
-                          tostr(hexlify(tv.ciphertext)),
-                          tostr(hexlify(key)),
-                          "%s (%s)" % (tdes_file, index))
-        test_data.append(test_data_item)
-
-
-class CheckParity(unittest.TestCase):
-
-    def test_parity_option2(self):
-        before_2k = unhexlify("CABF326FA56734324FFCCABCDEFACABF")
-        after_2k = DES3.adjust_key_parity(before_2k)
-        self.assertEqual(after_2k,
-                         unhexlify("CBBF326EA46734324FFDCBBCDFFBCBBF"))
-
-    def test_parity_option3(self):
-        before_3k = unhexlify("AAAAAAAAAAAAAAAABBBBBBBBBBBBBBBBCCCCCCCCCCCCCCCC")
-        after_3k = DES3.adjust_key_parity(before_3k)
-        self.assertEqual(after_3k,
-                         unhexlify("ABABABABABABABABBABABABABABABABACDCDCDCDCDCDCDCD"))
-
-    def test_degradation(self):
-        sub_key1 = bchr(1) * 8
-        sub_key2 = bchr(255) * 8
-
-        # K1 == K2
-        self.assertRaises(ValueError, DES3.adjust_key_parity,
-                          sub_key1 * 2 + sub_key2)
-
-        # K2 == K3
-        self.assertRaises(ValueError, DES3.adjust_key_parity,
-                          sub_key1 + sub_key2 * 2)
-
-        # K1 == K2 == K3
-        self.assertRaises(ValueError, DES3.adjust_key_parity,
-                          sub_key1 * 3)
-
-        # K1 == K2 (with different parity)
-        self.assertRaises(ValueError, DES3.adjust_key_parity,
-                          sub_key1 + strxor_c(sub_key1, 1) + sub_key2)
-
-
-class DegenerateToDESTest(unittest.TestCase):
-
-    def runTest(self):
-        sub_key1 = bchr(1) * 8
-        sub_key2 = bchr(255) * 8
-
-        # K1 == K2
-        self.assertRaises(ValueError, DES3.new,
-                          sub_key1 * 2 + sub_key2,
-                          DES3.MODE_ECB)
-
-        # K2 == K3
-        self.assertRaises(ValueError, DES3.new,
-                          sub_key1 + sub_key2 * 2,
-                          DES3.MODE_ECB)
-
-        # K1 == K2 == K3
-        self.assertRaises(ValueError, DES3.new,
-                          sub_key1 * 3,
-                          DES3.MODE_ECB)
-
-        # K2 == K3 (parity is ignored)
-        self.assertRaises(ValueError, DES3.new,
-                          sub_key1 + sub_key2 + strxor_c(sub_key2, 0x1),
-                          DES3.MODE_ECB)
-
-
-class TestOutput(unittest.TestCase):
-
-    def runTest(self):
-        # Encrypt/Decrypt data and test output parameter
-
-        cipher = DES3.new(b'4'*8 + b'G'*8 + b'T'*8, DES3.MODE_ECB)
-
-        pt = b'5' * 16
-        ct = cipher.encrypt(pt)
-
-        output = bytearray(16)
-        res = cipher.encrypt(pt, output=output)
-        self.assertEqual(ct, output)
-        self.assertEqual(res, None)
-
-        res = cipher.decrypt(ct, output=output)
-        self.assertEqual(pt, output)
-        self.assertEqual(res, None)
-
-        output = memoryview(bytearray(16))
-        cipher.encrypt(pt, output=output)
-        self.assertEqual(ct, output)
-
-        cipher.decrypt(ct, output=output)
-        self.assertEqual(pt, output)
-
-        self.assertRaises(TypeError, cipher.encrypt, pt, output=b'0'*16)
-        self.assertRaises(TypeError, cipher.decrypt, ct, output=b'0'*16)
-
-        shorter_output = bytearray(7)
-        self.assertRaises(ValueError, cipher.encrypt, pt, output=shorter_output)
-        self.assertRaises(ValueError, cipher.decrypt, ct, output=shorter_output)
-
-
-def get_tests(config={}):
-    from .common import make_block_tests
-
-    tests = []
-    tests = make_block_tests(DES3, "DES3", test_data)
-    tests.append(DegenerateToDESTest())
-    tests += list_test_cases(CheckParity)
-    tests += [TestOutput()]
-    return tests
-
-
-if __name__ == '__main__':
-    import unittest
-
-    def suite():
-        unittest.TestSuite(get_tests())
-
-    unittest.main(defaultTest='suite')
-
-# vim:set ts=4 sw=4 sts=4 expandtab:
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/__init__.py
deleted file mode 100644
index fa81adaff68e06d8e915a6afa375f62f7e5a8fad..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# empty file
diff --git a/spaces/avans06/whisper-webui-translate/src/segments.py b/spaces/avans06/whisper-webui-translate/src/segments.py
deleted file mode 100644
index ec2650dceade5d0b2022264f6419115eab085aea..0000000000000000000000000000000000000000
--- a/spaces/avans06/whisper-webui-translate/src/segments.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import Any, Dict, List
-
-import copy
-
-def merge_timestamps(timestamps: List[Dict[str, Any]], merge_window: float = 5, max_merge_size: float = 30, padding_left: float = 1, padding_right: float = 1):
-    result = []
-
-    if len(timestamps) == 0:
-        return result
-    if max_merge_size is None:
-        return timestamps
-
-    if padding_left is None:
-        padding_left = 0
-    if padding_right is None:
-        padding_right = 0
-
-    processed_time = 0
-    current_segment = None
-
-    for i in range(len(timestamps)):
-        next_segment = timestamps[i]
-
-        delta = next_segment['start'] - processed_time
-
-        # Note that segments can still be longer than the max merge size, they just won't be merged in that case
-        if current_segment is None or (merge_window is not None and delta > merge_window) \
-                 or next_segment['end'] - current_segment['start'] > max_merge_size:
-            # Finish the current segment
-            if current_segment is not None:
-                # Add right padding
-                finish_padding = min(padding_right, delta / 2) if delta < padding_left + padding_right else padding_right
-                current_segment['end'] += finish_padding
-                delta -= finish_padding
-
-                result.append(current_segment)
-
-            # Start a new segment
-            current_segment = copy.deepcopy(next_segment)
-
-            # Pad the segment
-            current_segment['start'] = current_segment['start'] - min(padding_left, delta)
-            processed_time = current_segment['end']
-
-        else:
-            # Merge the segment
-            current_segment['end'] = next_segment['end']
-            processed_time = current_segment['end']
-        
-    # Add the last segment
-    if current_segment is not None:
-        current_segment['end'] += padding_right
-        result.append(current_segment)
-    
-    return result
\ No newline at end of file
diff --git a/spaces/awacke1/HTML5-BabylonJS-Javascript-3DAnimation/backup2.html b/spaces/awacke1/HTML5-BabylonJS-Javascript-3DAnimation/backup2.html
deleted file mode 100644
index 5125daa465b07cf2a29ab3a70befe8769c2d4011..0000000000000000000000000000000000000000
--- a/spaces/awacke1/HTML5-BabylonJS-Javascript-3DAnimation/backup2.html
+++ /dev/null
@@ -1,75 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-    <meta charset="utf-8" />
-    <title>Babylon.js Game Example</title>
-    <script src="https://cdn.babylonjs.com/babylon.js"></script>
-    <script src="https://cdn.babylonjs.com/loaders/babylonjs.loaders.min.js"></script>
-    <style>
-        canvas {
-            width: 100%;
-            height: 100%;
-            touch-action: none;
-        }
-    </style>
-</head>
-<body>
-    <canvas id="renderCanvas"></canvas>
-    <script>
-        window.addEventListener('DOMContentLoaded', function () {
-            var canvas = document.getElementById('renderCanvas');
-            var engine = new BABYLON.Engine(canvas, true);
-
-            var createScene = function () {
-                var scene = new BABYLON.Scene(engine);
-
-                // Create a camera
-                var camera = new BABYLON.ArcRotateCamera("Camera", 0, 0, 10, BABYLON.Vector3.Zero(), scene);
-
-                // Create a light
-                var light = new BABYLON.HemisphericLight("light1", new BABYLON.Vector3(0, 1, 0), scene);
-
-                // Create an array to hold the spheres
-                var spheres = [];
-
-                // Create a fountain of spheres that follow the mouse
-                canvas.addEventListener('mousemove', function (event) {
-                    // Create a new sphere
-                    var sphere = BABYLON.MeshBuilder.CreateSphere("sphere", {diameter: Math.random() * 2 + 0.5}, scene);
-                    sphere.position = new BABYLON.Vector3(Math.random() * 4 - 2, Math.random() * 4 - 2, Math.random() * 4 - 2);
-                    spheres.push(sphere);
-
-                    // Follow the mouse with the spheres
-                    var ray = scene.createPickingRay(event.clientX, event.clientY, BABYLON.Matrix.Identity(), camera);
-                    var pickInfo = scene.pickWithRay(ray, function (mesh) { return mesh == sphere; });
-                    if (pickInfo.hit) {
-                        sphere.position.copyFrom(pickInfo.pickedPoint);
-                    }
-
-                    // Animate the spheres
-                    var animation = new BABYLON.Animation("myAnimation", "position.y", 30, BABYLON.Animation.ANIMATIONTYPE_FLOAT, BABYLON.Animation.ANIMATIONLOOPMODE_CYCLE);
-                    var keys = [];
-                    keys.push({frame: 0, value: sphere.position.y});
-                    keys.push({frame: 50, value: sphere.position.y + 2});
-                    keys.push({frame: 100, value: sphere.position.y});
-                    animation.setKeys(keys);
-                    sphere.animations.push(animation);
-                    scene.beginAnimation(sphere, 0, 100, true);
-                });
-
-                return scene;
-            }
-
-            var scene = createScene();
-
-            engine.runRenderLoop(function () {
-                scene.render();
-            });
-
-            window.addEventListener('resize', function () {
-                engine.resize();
-            });
-        });
-    </script>
-</body>
-</html>
diff --git a/spaces/badayvedat/LLaVA/llava/model/language_model/mpt/modeling_mpt.py b/spaces/badayvedat/LLaVA/llava/model/language_model/mpt/modeling_mpt.py
deleted file mode 100644
index 13313441b13fc7a66cb65fd21b482a5de982e2c8..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/LLaVA/llava/model/language_model/mpt/modeling_mpt.py
+++ /dev/null
@@ -1,331 +0,0 @@
-"""A simple, flexible implementation of a GPT model.
-
-Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
-"""
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from .attention import attn_bias_shape, build_attn_bias
-from .blocks import MPTBlock
-from .custom_embedding import SharedEmbedding
-from .norm import NORM_CLASS_REGISTRY
-from .configuration_mpt import MPTConfig
-from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
-from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
-from .meta_init_context import init_empty_weights
-from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_
-try:
-    from .flash_attn_triton import flash_attn_func
-except:
-    pass
-Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
-
-class MPTPreTrainedModel(PreTrainedModel):
-    config_class = MPTConfig
-    base_model_prefix = 'model'
-    _no_split_modules = ['MPTBlock']
-
-class MPTModel(MPTPreTrainedModel):
-
-    def __init__(self, config: MPTConfig):
-        config._validate_config()
-        super().__init__(config)
-        self.attn_impl = config.attn_config['attn_impl']
-        self.prefix_lm = config.attn_config['prefix_lm']
-        self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
-        self.alibi = config.attn_config['alibi']
-        self.alibi_bias_max = config.attn_config['alibi_bias_max']
-        if config.init_device == 'mixed':
-            if dist.get_local_rank() == 0:
-                config.init_device = 'cpu'
-            else:
-                config.init_device = 'meta'
-        if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
-            norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
-            raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).')
-        norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]
-        self.embedding_fraction = config.embedding_fraction
-        self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device)
-        if not self.alibi:
-            self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)
-        self.emb_drop = nn.Dropout(config.emb_pdrop)
-        self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
-        self.norm_f = norm_class(config.d_model, device=config.init_device)
-        if config.init_device != 'meta':
-            print(f'You are using config.init_device={config.init_device!r}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.')
-            self.apply(self.param_init_fn)
-        self.is_causal = not self.prefix_lm
-        self._attn_bias_initialized = False
-        self.attn_bias = None
-        self.attn_bias_shape = attn_bias_shape(self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id)
-        if config.no_bias:
-            for module in self.modules():
-                if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
-                    if config.verbose:
-                        warnings.warn(f'Removing bias ({module.bias}) from {module}.')
-                    module.register_parameter('bias', None)
-        if config.verbose and config.verbose > 2:
-            print(self)
-        if 'verbose' not in self.config.init_config:
-            self.config.init_config['verbose'] = self.config.verbose
-        if self.config.init_config['verbose'] > 1:
-            init_fn_name = self.config.init_config['name']
-            warnings.warn(f'Using {init_fn_name} initialization.')
-        self.gradient_checkpointing = False
-
-    def get_input_embeddings(self):
-        return self.wte
-
-    def set_input_embeddings(self, value):
-        self.wte = value
-
-    @torch.no_grad()
-    def _attn_bias(self, device, dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None):
-        if not self._attn_bias_initialized:
-            if self.attn_bias_shape:
-                self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype)
-                self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max)
-            self._attn_bias_initialized = True
-        if self.attn_impl == 'flash':
-            return (self.attn_bias, attention_mask)
-        if self.attn_bias is not None:
-            self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
-        attn_bias = self.attn_bias
-        if self.prefix_lm:
-            assert isinstance(attn_bias, torch.Tensor)
-            assert isinstance(prefix_mask, torch.Tensor)
-            attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
-        if self.attn_uses_sequence_id and sequence_id is not None:
-            assert isinstance(attn_bias, torch.Tensor)
-            attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
-        if attention_mask is not None:
-            s_k = attention_mask.shape[-1]
-            if attn_bias is None:
-                attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
-            else:
-                _s_k = max(0, attn_bias.size(-1) - s_k)
-                attn_bias = attn_bias[:, :, :, _s_k:]
-            if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:
-                raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
-            min_val = torch.finfo(attn_bias.dtype).min
-            attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
-        return (attn_bias, None)
-
-    def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor):
-        (s_k, s_q) = attn_bias.shape[-2:]
-        if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:
-            raise ValueError('attn_bias does not match the expected shape. ' + f'The last two dimensions should both be {self.config.max_length} ' + f'but are {s_k} and {s_q}.')
-        seq_len = prefix_mask.shape[-1]
-        if seq_len > self.config.max_seq_len:
-            raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
-        attn_bias = attn_bias[..., :seq_len, :seq_len]
-        causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
-        prefix = prefix_mask.view(-1, 1, 1, seq_len)
-        cannot_attend = ~torch.logical_or(causal, prefix.bool())
-        min_val = torch.finfo(attn_bias.dtype).min
-        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
-        return attn_bias
-
-    def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor):
-        seq_len = sequence_id.shape[-1]
-        if seq_len > self.config.max_seq_len:
-            raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
-        attn_bias = attn_bias[..., :seq_len, :seq_len]
-        cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
-        min_val = torch.finfo(attn_bias.dtype).min
-        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
-        return attn_bias
-
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None):
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        if attention_mask is not None:
-            attention_mask = attention_mask.bool()
-        if prefix_mask is not None:
-            prefix_mask = prefix_mask.bool()
-        if not return_dict:
-            raise NotImplementedError('return_dict False is not implemented yet for MPT')
-        if output_attentions:
-            if self.attn_impl != 'torch':
-                raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.')
-        if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0] and self.training:
-            raise NotImplementedError('MPT does not support training with left padding.')
-        if self.prefix_lm and prefix_mask is None:
-            raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
-        if self.training:
-            if self.attn_uses_sequence_id and sequence_id is None:
-                raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.')
-            elif self.attn_uses_sequence_id is False and sequence_id is not None:
-                warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.')
-        if input_ids is not None:
-            S = input_ids.size(1)
-            assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
-            tok_emb = self.wte(input_ids)
-        else:
-            assert inputs_embeds is not None
-            assert self.alibi, 'inputs_embeds is not implemented for MPT unless for alibi.'
-            S = inputs_embeds.size(1)
-            tok_emb = inputs_embeds
-        if self.alibi:
-            x = tok_emb
-        else:
-            past_position = 0
-            if past_key_values is not None:
-                if len(past_key_values) != self.config.n_layers:
-                    raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')
-                past_position = past_key_values[0][0].size(1)
-                if self.attn_impl == 'torch':
-                    past_position = past_key_values[0][0].size(3)
-            if S + past_position > self.config.max_seq_len:
-                raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
-            pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
-            if attention_mask is not None:
-                pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
-            pos_emb = self.wpe(pos)
-            x = tok_emb + pos_emb
-        if self.embedding_fraction == 1:
-            x = self.emb_drop(x)
-        else:
-            x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction)
-            assert isinstance(self.emb_drop, nn.Module)
-            x = self.emb_drop(x_shrunk)
-        (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
-        if use_cache and past_key_values is None:
-            past_key_values = [() for _ in range(self.config.n_layers)]
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        for (b_idx, block) in enumerate(self.blocks):
-            if output_hidden_states:
-                assert all_hidden_states is not None
-                all_hidden_states = all_hidden_states + (x,)
-            past_key_value = past_key_values[b_idx] if past_key_values is not None else None
-            if self.gradient_checkpointing and self.training:
-                (x, attn_weights, past_key_value) = torch.utils.checkpoint.checkpoint(block, x, past_key_value, attn_bias, attention_mask, self.is_causal)
-            else:
-                (x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal)
-            if past_key_values is not None:
-                past_key_values[b_idx] = past_key_value
-            if output_attentions:
-                assert all_self_attns is not None
-                all_self_attns = all_self_attns + (attn_weights,)
-        x = self.norm_f(x)
-        if output_hidden_states:
-            assert all_hidden_states is not None
-            all_hidden_states = all_hidden_states + (x,)
-        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns)
-
-    def param_init_fn(self, module):
-        init_fn_name = self.config.init_config['name']
-        MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
-
-    def fsdp_wrap_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-    def activation_checkpointing_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-class MPTForCausalLM(MPTPreTrainedModel):
-
-    def __init__(self, config: MPTConfig):
-        super().__init__(config)
-        if not config.tie_word_embeddings:
-            raise ValueError('MPTForCausalLM only supports tied word embeddings')
-        print(f'Instantiating an MPTForCausalLM model from {__file__}')
-        self.transformer = MPTModel(config)
-        for child in self.transformer.children():
-            if isinstance(child, torch.nn.ModuleList):
-                continue
-            if isinstance(child, torch.nn.Module):
-                child._fsdp_wrap = True
-        self.logit_scale = None
-        if config.logit_scale is not None:
-            logit_scale = config.logit_scale
-            if isinstance(logit_scale, str):
-                if logit_scale == 'inv_sqrt_d_model':
-                    logit_scale = 1 / math.sqrt(config.d_model)
-                else:
-                    raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
-            self.logit_scale = logit_scale
-
-    def get_input_embeddings(self):
-        return self.transformer.wte
-
-    def set_input_embeddings(self, value):
-        self.transformer.wte = value
-
-    def get_output_embeddings(self):
-        return self.transformer.wte
-
-    def set_output_embeddings(self, new_embeddings):
-        self.transformer.wte = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.transformer = decoder
-
-    def get_decoder(self):
-        return self.transformer
-
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None):
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).')
-        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
-        logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True)
-        if self.logit_scale is not None:
-            if self.logit_scale == 0:
-                warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
-            logits *= self.logit_scale
-        loss = None
-        if labels is not None:
-            labels = torch.roll(labels, shifts=-1)
-            labels[:, -1] = -100
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1))
-        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
-
-    def param_init_fn(self, module):
-        init_fn_name = self.config.init_config['name']
-        MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
-
-    def fsdp_wrap_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-    def activation_checkpointing_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
-        attention_mask = kwargs['attention_mask'].bool()
-        if attention_mask[:, -1].sum() != attention_mask.shape[0]:
-            raise NotImplementedError('MPT does not support generation with right padding.')
-        if self.transformer.attn_uses_sequence_id and self.training:
-            sequence_id = torch.zeros_like(input_ids[:1])
-        else:
-            sequence_id = None
-        if past_key_values is not None:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-        if self.transformer.prefix_lm:
-            prefix_mask = torch.ones_like(attention_mask)
-            if kwargs.get('use_cache') == False:
-                raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
-        else:
-            prefix_mask = None
-        return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)}
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        """Used by HuggingFace generate when using beam search with kv-caching.
-
-        See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
-        for an example in transformers.
-        """
-        reordered_past = []
-        for layer_past in past_key_values:
-            reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))]
-        return reordered_past
\ No newline at end of file
diff --git a/spaces/badmonk/model/README.md b/spaces/badmonk/model/README.md
deleted file mode 100644
index f50d7ed38081f141b5c0c2f67486ba917044e67f..0000000000000000000000000000000000000000
--- a/spaces/badmonk/model/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MODEL
-emoji: 💀
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/SSAARenderPass.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/SSAARenderPass.js
deleted file mode 100644
index 76c1848ff5abda06a3fec8cc6eaa8d6ebac05675..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/SSAARenderPass.js
+++ /dev/null
@@ -1,181 +0,0 @@
-/**
-*
-* Supersample Anti-Aliasing Render Pass
-*
-* @author bhouston / http://clara.io/
-*
-* This manual approach to SSAA re-renders the scene ones for each sample with camera jitter and accumulates the results.
-*
-* References: https://en.wikipedia.org/wiki/Supersampling
-*
-*/
-
-THREE.SSAARenderPass = function ( scene, camera, clearColor, clearAlpha ) {
-
-	THREE.Pass.call( this );
-
-	this.scene = scene;
-	this.camera = camera;
-
-	this.sampleLevel = 4; // specified as n, where the number of samples is 2^n, so sampleLevel = 4, is 2^4 samples, 16.
-	this.unbiased = true;
-
-	// as we need to clear the buffer in this pass, clearColor must be set to something, defaults to black.
-	this.clearColor = ( clearColor !== undefined ) ? clearColor : 0x000000;
-	this.clearAlpha = ( clearAlpha !== undefined ) ? clearAlpha : 0;
-
-	if ( THREE.CopyShader === undefined ) console.error( "THREE.SSAARenderPass relies on THREE.CopyShader" );
-
-	var copyShader = THREE.CopyShader;
-	this.copyUniforms = THREE.UniformsUtils.clone( copyShader.uniforms );
-
-	this.copyMaterial = new THREE.ShaderMaterial(	{
-		uniforms: this.copyUniforms,
-		vertexShader: copyShader.vertexShader,
-		fragmentShader: copyShader.fragmentShader,
-		premultipliedAlpha: true,
-		transparent: true,
-		blending: THREE.AdditiveBlending,
-		depthTest: false,
-		depthWrite: false
-	} );
-
-	this.fsQuad = new THREE.Pass.FullScreenQuad( this.copyMaterial );
-
-};
-
-THREE.SSAARenderPass.prototype = Object.assign( Object.create( THREE.Pass.prototype ), {
-
-	constructor: THREE.SSAARenderPass,
-
-	dispose: function () {
-
-		if ( this.sampleRenderTarget ) {
-
-			this.sampleRenderTarget.dispose();
-			this.sampleRenderTarget = null;
-
-		}
-
-	},
-
-	setSize: function ( width, height ) {
-
-		if ( this.sampleRenderTarget )	this.sampleRenderTarget.setSize( width, height );
-
-	},
-
-	render: function ( renderer, writeBuffer, readBuffer ) {
-
-		if ( ! this.sampleRenderTarget ) {
-
-			this.sampleRenderTarget = new THREE.WebGLRenderTarget( readBuffer.width, readBuffer.height, { minFilter: THREE.LinearFilter, magFilter: THREE.LinearFilter, format: THREE.RGBAFormat } );
-			this.sampleRenderTarget.texture.name = "SSAARenderPass.sample";
-
-		}
-
-		var jitterOffsets = THREE.SSAARenderPass.JitterVectors[ Math.max( 0, Math.min( this.sampleLevel, 5 ) ) ];
-
-		var autoClear = renderer.autoClear;
-		renderer.autoClear = false;
-
-		var oldClearColor = renderer.getClearColor().getHex();
-		var oldClearAlpha = renderer.getClearAlpha();
-
-		var baseSampleWeight = 1.0 / jitterOffsets.length;
-		var roundingRange = 1 / 32;
-		this.copyUniforms[ "tDiffuse" ].value = this.sampleRenderTarget.texture;
-
-		var width = readBuffer.width, height = readBuffer.height;
-
-		// render the scene multiple times, each slightly jitter offset from the last and accumulate the results.
-		for ( var i = 0; i < jitterOffsets.length; i ++ ) {
-
-			var jitterOffset = jitterOffsets[ i ];
-
-			if ( this.camera.setViewOffset ) {
-
-				this.camera.setViewOffset( width, height,
-					jitterOffset[ 0 ] * 0.0625, jitterOffset[ 1 ] * 0.0625, // 0.0625 = 1 / 16
-					width, height );
-
-			}
-
-			var sampleWeight = baseSampleWeight;
-
-			if ( this.unbiased ) {
-
-				// the theory is that equal weights for each sample lead to an accumulation of rounding errors.
-				// The following equation varies the sampleWeight per sample so that it is uniformly distributed
-				// across a range of values whose rounding errors cancel each other out.
-
-				var uniformCenteredDistribution = ( - 0.5 + ( i + 0.5 ) / jitterOffsets.length );
-				sampleWeight += roundingRange * uniformCenteredDistribution;
-
-			}
-
-			this.copyUniforms[ "opacity" ].value = sampleWeight;
-			renderer.setClearColor( this.clearColor, this.clearAlpha );
-			renderer.setRenderTarget( this.sampleRenderTarget );
-			renderer.clear();
-			renderer.render( this.scene, this.camera );
-
-			renderer.setRenderTarget( this.renderToScreen ? null : writeBuffer );
-
-			if ( i === 0 ) {
-
-				renderer.setClearColor( 0x000000, 0.0 );
-				renderer.clear();
-
-			}
-
-			this.fsQuad.render( renderer );
-
-		}
-
-		if ( this.camera.clearViewOffset ) this.camera.clearViewOffset();
-
-		renderer.autoClear = autoClear;
-		renderer.setClearColor( oldClearColor, oldClearAlpha );
-
-	}
-
-} );
-
-
-// These jitter vectors are specified in integers because it is easier.
-// I am assuming a [-8,8) integer grid, but it needs to be mapped onto [-0.5,0.5)
-// before being used, thus these integers need to be scaled by 1/16.
-//
-// Sample patterns reference: https://msdn.microsoft.com/en-us/library/windows/desktop/ff476218%28v=vs.85%29.aspx?f=255&MSPPError=-2147217396
-THREE.SSAARenderPass.JitterVectors = [
-	[
-		[ 0, 0 ]
-	],
-	[
-		[ 4, 4 ], [ - 4, - 4 ]
-	],
-	[
-		[ - 2, - 6 ], [ 6, - 2 ], [ - 6, 2 ], [ 2, 6 ]
-	],
-	[
-		[ 1, - 3 ], [ - 1, 3 ], [ 5, 1 ], [ - 3, - 5 ],
-		[ - 5, 5 ], [ - 7, - 1 ], [ 3, 7 ], [ 7, - 7 ]
-	],
-	[
-		[ 1, 1 ], [ - 1, - 3 ], [ - 3, 2 ], [ 4, - 1 ],
-		[ - 5, - 2 ], [ 2, 5 ], [ 5, 3 ], [ 3, - 5 ],
-		[ - 2, 6 ], [ 0, - 7 ], [ - 4, - 6 ], [ - 6, 4 ],
-		[ - 8, 0 ], [ 7, - 4 ], [ 6, 7 ], [ - 7, - 8 ]
-	],
-	[
-		[ - 4, - 7 ], [ - 7, - 5 ], [ - 3, - 5 ], [ - 5, - 4 ],
-		[ - 1, - 4 ], [ - 2, - 2 ], [ - 6, - 1 ], [ - 4, 0 ],
-		[ - 7, 1 ], [ - 1, 2 ], [ - 6, 3 ], [ - 3, 3 ],
-		[ - 7, 6 ], [ - 3, 6 ], [ - 5, 7 ], [ - 1, 7 ],
-		[ 5, - 7 ], [ 1, - 6 ], [ 6, - 5 ], [ 4, - 4 ],
-		[ 2, - 3 ], [ 7, - 2 ], [ 1, - 1 ], [ 4, - 1 ],
-		[ 2, 1 ], [ 6, 2 ], [ 0, 4 ], [ 4, 4 ],
-		[ 2, 5 ], [ 7, 5 ], [ 5, 6 ], [ 3, 7 ]
-	]
-];
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/cameras/StereoCamera.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/cameras/StereoCamera.d.ts
deleted file mode 100644
index cf27ebddd26891cd83d49378e3d38b6d8f68a60f..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/cameras/StereoCamera.d.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-import { PerspectiveCamera } from './PerspectiveCamera';
-import { Camera } from './Camera';
-
-export class StereoCamera extends Camera {
-  constructor();
-
-  type: 'StereoCamera';
-
-  aspect: number;
-  eyeSep: number;
-  cameraL: PerspectiveCamera;
-  cameraR: PerspectiveCamera;
-
-  update(camera: PerspectiveCamera): void;
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/uv2_vertex.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/uv2_vertex.glsl.js
deleted file mode 100644
index 0c9fb86482df046aeeaf6bb407935dfe70d6b667..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/uv2_vertex.glsl.js
+++ /dev/null
@@ -1,7 +0,0 @@
-export default /* glsl */`
-#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )
-
-	vUv2 = uv2;
-
-#endif
-`;
diff --git a/spaces/bioriAsaeru/text-to-voice/El Guerrero Tolteca A Book that Will Change Your Life Forever (PDF Download).md b/spaces/bioriAsaeru/text-to-voice/El Guerrero Tolteca A Book that Will Change Your Life Forever (PDF Download).md
deleted file mode 100644
index 139d68509bf09853aba61335e578542abd3fe36d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/El Guerrero Tolteca A Book that Will Change Your Life Forever (PDF Download).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>el guerrero tolteca pdf download</h2><br /><p><b><b>Download</b> &#11088; <a href="https://urloso.com/2uyObi">https://urloso.com/2uyObi</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Laadla Hai Full Movie Free Download In Mp4l Anil Kapoor and Sridevis Romantic Chemistry in this Bollywood Classic.md b/spaces/bioriAsaeru/text-to-voice/Laadla Hai Full Movie Free Download In Mp4l Anil Kapoor and Sridevis Romantic Chemistry in this Bollywood Classic.md
deleted file mode 100644
index e9c33e6f9ad6b497ad91a13af12e910fc0b8f172..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Laadla Hai Full Movie Free Download In Mp4l Anil Kapoor and Sridevis Romantic Chemistry in this Bollywood Classic.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-
-<p><strong>Mp3 Juice</strong> is the most popular free mp3 search engine tool and music downloader, is very popular. MP3 Juice is a great tool to convert and download youtube videos and music. The Mp3 Juice website is the best way to quickly and easily download mp3 music. Its simplicity makes Mp3juice easy to use, so anyone can search for and download high-quality audio files</p>
-<p>This website offers unlimited downloading of youtube music and Mp3 juice song free download in HD quality. You can also click "PLAY" to play the audio file before you download it. Mp3juices take only 2-5 seconds to convert and download audio files.</p>
-<h2>Laadla Hai Full Movie Free Download In Mp4l</h2><br /><p><b><b>Download</b> &gt;&gt;&gt;&gt;&gt; <a href="https://urloso.com/2uyP0c">https://urloso.com/2uyP0c</a></b></p><br /><br />
-<p>You can access this free mp3 download website online via an internet connection or WiFi. Bookmark this website to make it easy to access on a regular basis. Once you have downloaded the audio file, open it in any audio player to listen offline in high-quality.</p>
-<p>MP3 juice music is easy to navigate through and provides a simple interface for downloading the audio. You might be wondering why people prefer mp3juices to get mp3 juice for free. This tool provides high-speed audio downloads, and users don't need to give any personal information.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/metrics/inception_score.py b/spaces/birsardar/stable-diffusion-mat-outpainting-primer/metrics/inception_score.py
deleted file mode 100644
index 3822c1435901a47e8c192b52cd3ed1ce5de67acd..0000000000000000000000000000000000000000
--- a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/metrics/inception_score.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Inception Score (IS) from the paper "Improved techniques for training
-GANs". Matches the original implementation by Salimans et al. at
-https://github.com/openai/improved-gan/blob/master/inception_score/model.py"""
-
-import numpy as np
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_is(opts, num_gen, num_splits):
-    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
-    detector_kwargs = dict(no_output_bias=True) # Match the original implementation by not applying bias in the softmax layer.
-
-    gen_probs = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        capture_all=True, max_items=num_gen).get_all()
-
-    if opts.rank != 0:
-        return float('nan'), float('nan')
-
-    scores = []
-    for i in range(num_splits):
-        part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits]
-        kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True)))
-        kl = np.mean(np.sum(kl, axis=1))
-        scores.append(np.exp(kl))
-    return float(np.mean(scores)), float(np.std(scores))
-
-#----------------------------------------------------------------------------
diff --git a/spaces/bla/tranny/App/Embedding/utils/Summarizer.py b/spaces/bla/tranny/App/Embedding/utils/Summarizer.py
deleted file mode 100644
index b4533dbd595d5f3dd6a0ddc10b762cd2847be100..0000000000000000000000000000000000000000
--- a/spaces/bla/tranny/App/Embedding/utils/Summarizer.py
+++ /dev/null
@@ -1,118 +0,0 @@
-from langchain import OpenAI
-from langchain.chains.summarize import load_summarize_chain
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain import PromptTemplate
-import openai,os
-
-openai.api_base = os.environ.get("api_base")
-openai.api_key = os.environ.get("api_key")
-openai_api_key = openai.api_key
-
-
-import json
-def read_transcriptions_from_file(file_path):
-    transcriptions = []
-    with open(file_path, 'r') as file:
-        lines = file.readlines()
-        for line in lines:
-            part = json.loads(line)
-
-            transcriptions.append(part)
-    return transcriptions
-def combine_n_segments(transcriptions, n=2000):
-    combined_segments = []
-    current_segment = None
-
-    for i, transcription in enumerate(transcriptions):
-        if current_segment is None:
-            current_segment = transcription
-        else:
-            # Calculate the new end time based on the current segment and the next transcription
-            current_segment['end'] = transcription['end']
-            # Combine the texts with a space between
-            current_segment['text'] += transcription['text']
-
-        # If the next transcription is not continuous or n segments are combined, start a new segment
-        if (transcription['end'] != current_segment['end']) or (i % n == n - 1):
-            combined_segments.append(current_segment)
-            current_segment = None
-
-    # Append the last segment if needed
-    if current_segment is not None:
-        combined_segments.append(current_segment)
-
-    return combined_segments
-
-
-
-
-llm = OpenAI(
-    temperature=0 ,openai_api_key=openai.api_key, openai_api_base=openai.api_base
-)
-
-
-paul_graham_essay = "./sample.txt"
-
-with open(paul_graham_essay, "r") as file:
-    essay = file.read()
-
-essay = read_transcriptions_from_file("./sample.txt")
-essay = combine_n_segments(essay)
-
-def generate_summary(data):
-    template = """ {data} """.format(data=data)
-    chunk_size = 2500
-    inc = 100
-    max_tokens = 2000
-    min_tokens = 1500
-    max_token_doc = 0
-
-    # choose the appropriate chunk size
-    while True:
-        # initialize text splitter
-        text_splitter = RecursiveCharacterTextSplitter(
-            separators=["\n\n", "\n"],
-            chunk_size=chunk_size,
-            # chunk_overlap=int(chunk_size * 0.1),
-        )
-        docs = text_splitter.create_documents([template])
-        temp =[]
-        for doc in docs:
-            temp.append(llm.get_num_tokens(doc.page_content))
-        max_token_doc = max(temp)
-        if max_tokens < max_token_doc or max_token_doc < min_tokens:
-            if max_tokens < max_token_doc:
-                chunk_size -= inc
-            else:
-                chunk_size += inc
-            print(max_token_doc,chunk_size)
-            continue
-            
-        else:
-            break
-    map_prompt = """
-    ### Write a summary of the following, video transcript segment, return a detailed summary  citing the time stamps which are in seconds
-    ### cite using seconds ONLY
-    "{text}"
-    Detailed SUMMARY:
-"""
-
-
-    map_prompt_template = PromptTemplate(template=map_prompt, input_variables=["text"])
-    combine_prompt = """
-    ### Write a summary of the following, video transcript segment summaries, return a detailed summary.
-    Return your response citing the time stamps which are in seconds.
-    #cite using seconds ONLY
-    ```{text}```
-    SUMMARY:
-    """
-    combine_prompt_template = PromptTemplate(template=combine_prompt, input_variables=["text"])
-    summary_chain = load_summarize_chain(llm=llm,
-                                     chain_type='map_reduce',
-                                     map_prompt=map_prompt_template,
-                                     combine_prompt=combine_prompt_template,
-                                     verbose=True
-                                    )
-    summary=summary_chain.run(docs)
-    print(summary)
-generate_summary(essay)
diff --git a/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/patch_match.py b/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/patch_match.py
deleted file mode 100644
index 14febe43c78f49120c8be9f02941c3c1f8fdc3b1..0000000000000000000000000000000000000000
--- a/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/patch_match.py
+++ /dev/null
@@ -1,263 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-# File   : patch_match.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 01/09/2020
-#
-# Distributed under terms of the MIT license.
-
-import ctypes
-import os.path as osp
-from typing import Optional, Union
-
-import numpy as np
-from PIL import Image
-
-
-import os
-if os.name!="nt":
-    # Otherwise, fall back to the subprocess.
-    import subprocess
-    print('Compiling and loading c extensions from "{}".'.format(osp.realpath(osp.dirname(__file__))))
-    # subprocess.check_call(['./travis.sh'], cwd=osp.dirname(__file__))
-    subprocess.check_call("make clean && make", cwd=osp.dirname(__file__), shell=True)
-
-
-__all__ = ['set_random_seed', 'set_verbose', 'inpaint', 'inpaint_regularity']
-
-
-class CShapeT(ctypes.Structure):
-    _fields_ = [
-        ('width', ctypes.c_int),
-        ('height', ctypes.c_int),
-        ('channels', ctypes.c_int),
-    ]
-
-
-class CMatT(ctypes.Structure):
-    _fields_ = [
-        ('data_ptr', ctypes.c_void_p),
-        ('shape', CShapeT),
-        ('dtype', ctypes.c_int)
-    ]
-
-import tempfile
-from urllib.request import urlopen, Request
-import shutil
-from pathlib import Path
-from tqdm import tqdm
-
-def download_url_to_file(url, dst, hash_prefix=None, progress=True):
-    r"""Download object at the given URL to a local path.
-
-    Args:
-        url (string): URL of the object to download
-        dst (string): Full path where object will be saved, e.g. ``/tmp/temporary_file``
-        hash_prefix (string, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
-            Default: None
-        progress (bool, optional): whether or not to display a progress bar to stderr
-            Default: True
-    https://pytorch.org/docs/stable/_modules/torch/hub.html#load_state_dict_from_url
-    """
-    file_size = None
-    req = Request(url)
-    u = urlopen(req)
-    meta = u.info()
-    if hasattr(meta, 'getheaders'):
-        content_length = meta.getheaders("Content-Length")
-    else:
-        content_length = meta.get_all("Content-Length")
-    if content_length is not None and len(content_length) > 0:
-        file_size = int(content_length[0])
-
-    # We deliberately save it in a temp file and move it after
-    # download is complete. This prevents a local working checkpoint
-    # being overridden by a broken download.
-    dst = os.path.expanduser(dst)
-    dst_dir = os.path.dirname(dst)
-    f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir)
-
-    try:
-        with tqdm(total=file_size, disable=not progress,
-                  unit='B', unit_scale=True, unit_divisor=1024) as pbar:
-            while True:
-                buffer = u.read(8192)
-                if len(buffer) == 0:
-                    break
-                f.write(buffer)
-                pbar.update(len(buffer))
-
-        f.close()
-        shutil.move(f.name, dst)
-    finally:
-        f.close()
-        if os.path.exists(f.name):
-            os.remove(f.name)
-
-if os.name!="nt":
-    PMLIB = ctypes.CDLL(osp.join(osp.dirname(__file__), 'libpatchmatch.so'))
-else:
-    if not os.path.exists(osp.join(osp.dirname(__file__), 'libpatchmatch.dll')):
-        download_url_to_file(url="https://github.com/lkwq007/PyPatchMatch/releases/download/v0.1/libpatchmatch.dll",dst=osp.join(osp.dirname(__file__), 'libpatchmatch.dll'))
-    if not os.path.exists(osp.join(osp.dirname(__file__), 'opencv_world460.dll')):
-        download_url_to_file(url="https://github.com/lkwq007/PyPatchMatch/releases/download/v0.1/opencv_world460.dll",dst=osp.join(osp.dirname(__file__), 'opencv_world460.dll'))
-    if not os.path.exists(osp.join(osp.dirname(__file__), 'libpatchmatch.dll')):
-        print("[Dependency Missing] Please download https://github.com/lkwq007/PyPatchMatch/releases/download/v0.1/libpatchmatch.dll and put it into the PyPatchMatch folder")
-    if not os.path.exists(osp.join(osp.dirname(__file__), 'opencv_world460.dll')):
-        print("[Dependency Missing] Please download https://github.com/lkwq007/PyPatchMatch/releases/download/v0.1/opencv_world460.dll and put it into the PyPatchMatch folder")
-    PMLIB = ctypes.CDLL(osp.join(osp.dirname(__file__), 'libpatchmatch.dll'))
-
-PMLIB.PM_set_random_seed.argtypes = [ctypes.c_uint]
-PMLIB.PM_set_verbose.argtypes = [ctypes.c_int]
-PMLIB.PM_free_pymat.argtypes = [CMatT]
-PMLIB.PM_inpaint.argtypes = [CMatT, CMatT, ctypes.c_int]
-PMLIB.PM_inpaint.restype = CMatT
-PMLIB.PM_inpaint_regularity.argtypes = [CMatT, CMatT, CMatT, ctypes.c_int, ctypes.c_float]
-PMLIB.PM_inpaint_regularity.restype = CMatT
-PMLIB.PM_inpaint2.argtypes = [CMatT, CMatT, CMatT, ctypes.c_int]
-PMLIB.PM_inpaint2.restype = CMatT
-PMLIB.PM_inpaint2_regularity.argtypes = [CMatT, CMatT, CMatT, CMatT, ctypes.c_int, ctypes.c_float]
-PMLIB.PM_inpaint2_regularity.restype = CMatT
-
-
-def set_random_seed(seed: int):
-    PMLIB.PM_set_random_seed(ctypes.c_uint(seed))
-
-
-def set_verbose(verbose: bool):
-    PMLIB.PM_set_verbose(ctypes.c_int(verbose))
-
-
-def inpaint(
-    image: Union[np.ndarray, Image.Image],
-    mask: Optional[Union[np.ndarray, Image.Image]] = None,
-    *,
-    global_mask: Optional[Union[np.ndarray, Image.Image]] = None,
-    patch_size: int = 15
-) -> np.ndarray:
-    """
-    PatchMatch based inpainting proposed in:
-
-        PatchMatch : A Randomized Correspondence Algorithm for Structural Image Editing
-        C.Barnes, E.Shechtman, A.Finkelstein and Dan B.Goldman
-        SIGGRAPH 2009
-
-    Args:
-        image (Union[np.ndarray, Image.Image]): the input image, should be 3-channel RGB/BGR.
-        mask (Union[np.array, Image.Image], optional): the mask of the hole(s) to be filled, should be 1-channel.
-        If not provided (None), the algorithm will treat all purely white pixels as the holes (255, 255, 255).
-        global_mask (Union[np.array, Image.Image], optional): the target mask of the output image.
-        patch_size (int): the patch size for the inpainting algorithm.
-
-    Return:
-        result (np.ndarray): the repaired image, of the same size as the input image.
-    """
-
-    if isinstance(image, Image.Image):
-        image = np.array(image)
-    image = np.ascontiguousarray(image)
-    assert image.ndim == 3 and image.shape[2] == 3 and image.dtype == 'uint8'
-
-    if mask is None:
-        mask = (image == (255, 255, 255)).all(axis=2, keepdims=True).astype('uint8')
-        mask = np.ascontiguousarray(mask)
-    else:
-        mask = _canonize_mask_array(mask)
-
-    if global_mask is None:
-        ret_pymat = PMLIB.PM_inpaint(np_to_pymat(image), np_to_pymat(mask), ctypes.c_int(patch_size))
-    else:
-        global_mask = _canonize_mask_array(global_mask)
-        ret_pymat = PMLIB.PM_inpaint2(np_to_pymat(image), np_to_pymat(mask), np_to_pymat(global_mask), ctypes.c_int(patch_size))
-
-    ret_npmat = pymat_to_np(ret_pymat)
-    PMLIB.PM_free_pymat(ret_pymat)
-
-    return ret_npmat
-
-
-def inpaint_regularity(
-    image: Union[np.ndarray, Image.Image],
-    mask: Optional[Union[np.ndarray, Image.Image]],
-    ijmap: np.ndarray,
-    *,
-    global_mask: Optional[Union[np.ndarray, Image.Image]] = None,
-    patch_size: int = 15, guide_weight: float = 0.25
-) -> np.ndarray:
-    if isinstance(image, Image.Image):
-        image = np.array(image)
-    image = np.ascontiguousarray(image)
-
-    assert isinstance(ijmap, np.ndarray) and ijmap.ndim == 3 and ijmap.shape[2] == 3 and ijmap.dtype == 'float32'
-    ijmap = np.ascontiguousarray(ijmap)
-
-    assert image.ndim == 3 and image.shape[2] == 3 and image.dtype == 'uint8'
-    if mask is None:
-        mask = (image == (255, 255, 255)).all(axis=2, keepdims=True).astype('uint8')
-        mask = np.ascontiguousarray(mask)
-    else:
-        mask = _canonize_mask_array(mask)
-
-
-    if global_mask is None:
-        ret_pymat = PMLIB.PM_inpaint_regularity(np_to_pymat(image), np_to_pymat(mask), np_to_pymat(ijmap), ctypes.c_int(patch_size), ctypes.c_float(guide_weight))
-    else:
-        global_mask = _canonize_mask_array(global_mask)
-        ret_pymat = PMLIB.PM_inpaint2_regularity(np_to_pymat(image), np_to_pymat(mask), np_to_pymat(global_mask), np_to_pymat(ijmap), ctypes.c_int(patch_size), ctypes.c_float(guide_weight))
-
-    ret_npmat = pymat_to_np(ret_pymat)
-    PMLIB.PM_free_pymat(ret_pymat)
-
-    return ret_npmat
-
-
-def _canonize_mask_array(mask):
-    if isinstance(mask, Image.Image):
-        mask = np.array(mask)
-    if mask.ndim == 2 and mask.dtype == 'uint8':
-        mask = mask[..., np.newaxis]
-    assert mask.ndim == 3 and mask.shape[2] == 1 and mask.dtype == 'uint8'
-    return np.ascontiguousarray(mask)
-
-
-dtype_pymat_to_ctypes = [
-    ctypes.c_uint8,
-    ctypes.c_int8,
-    ctypes.c_uint16,
-    ctypes.c_int16,
-    ctypes.c_int32,
-    ctypes.c_float,
-    ctypes.c_double,
-]
-
-
-dtype_np_to_pymat = {
-    'uint8': 0,
-    'int8': 1,
-    'uint16': 2,
-    'int16': 3,
-    'int32': 4,
-    'float32': 5,
-    'float64': 6,
-}
-
-
-def np_to_pymat(npmat):
-    assert npmat.ndim == 3
-    return CMatT(
-        ctypes.cast(npmat.ctypes.data, ctypes.c_void_p),
-        CShapeT(npmat.shape[1], npmat.shape[0], npmat.shape[2]),
-        dtype_np_to_pymat[str(npmat.dtype)]
-    )
-
-
-def pymat_to_np(pymat):
-    npmat = np.ctypeslib.as_array(
-        ctypes.cast(pymat.data_ptr, ctypes.POINTER(dtype_pymat_to_ctypes[pymat.dtype])),
-        (pymat.shape.height, pymat.shape.width, pymat.shape.channels)
-    )
-    ret = np.empty(npmat.shape, npmat.dtype)
-    ret[:] = npmat
-    return ret
-
diff --git a/spaces/brjathu/HMR2.0/hmr2/models/losses.py b/spaces/brjathu/HMR2.0/hmr2/models/losses.py
deleted file mode 100644
index 0949d0630b7308aaa05be79a759931115b002bb9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/hmr2/models/losses.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import torch.nn as nn
-
-class Keypoint2DLoss(nn.Module):
-
-    def __init__(self, loss_type: str = 'l1'):
-        """
-        2D keypoint loss module.
-        Args:
-            loss_type (str): Choose between l1 and l2 losses.
-        """
-        super(Keypoint2DLoss, self).__init__()
-        if loss_type == 'l1':
-            self.loss_fn = nn.L1Loss(reduction='none')
-        elif loss_type == 'l2':
-            self.loss_fn = nn.MSELoss(reduction='none')
-        else:
-            raise NotImplementedError('Unsupported loss function')
-
-    def forward(self, pred_keypoints_2d: torch.Tensor, gt_keypoints_2d: torch.Tensor) -> torch.Tensor:
-        """
-        Compute 2D reprojection loss on the keypoints.
-        Args:
-            pred_keypoints_2d (torch.Tensor): Tensor of shape [B, S, N, 2] containing projected 2D keypoints (B: batch_size, S: num_samples, N: num_keypoints)
-            gt_keypoints_2d (torch.Tensor): Tensor of shape [B, S, N, 3] containing the ground truth 2D keypoints and confidence.
-        Returns:
-            torch.Tensor: 2D keypoint loss.
-        """
-        conf = gt_keypoints_2d[:, :, -1].unsqueeze(-1).clone()
-        batch_size = conf.shape[0]
-        loss = (conf * self.loss_fn(pred_keypoints_2d, gt_keypoints_2d[:, :, :-1])).sum(dim=(1,2))
-        return loss.sum()
-
-
-class Keypoint3DLoss(nn.Module):
-
-    def __init__(self, loss_type: str = 'l1'):
-        """
-        3D keypoint loss module.
-        Args:
-            loss_type (str): Choose between l1 and l2 losses.
-        """
-        super(Keypoint3DLoss, self).__init__()
-        if loss_type == 'l1':
-            self.loss_fn = nn.L1Loss(reduction='none')
-        elif loss_type == 'l2':
-            self.loss_fn = nn.MSELoss(reduction='none')
-        else:
-            raise NotImplementedError('Unsupported loss function')
-
-    def forward(self, pred_keypoints_3d: torch.Tensor, gt_keypoints_3d: torch.Tensor, pelvis_id: int = 39):
-        """
-        Compute 3D keypoint loss.
-        Args:
-            pred_keypoints_3d (torch.Tensor): Tensor of shape [B, S, N, 3] containing the predicted 3D keypoints (B: batch_size, S: num_samples, N: num_keypoints)
-            gt_keypoints_3d (torch.Tensor): Tensor of shape [B, S, N, 4] containing the ground truth 3D keypoints and confidence.
-        Returns:
-            torch.Tensor: 3D keypoint loss.
-        """
-        batch_size = pred_keypoints_3d.shape[0]
-        gt_keypoints_3d = gt_keypoints_3d.clone()
-        pred_keypoints_3d = pred_keypoints_3d - pred_keypoints_3d[:, pelvis_id, :].unsqueeze(dim=1)
-        gt_keypoints_3d[:, :, :-1] = gt_keypoints_3d[:, :, :-1] - gt_keypoints_3d[:, pelvis_id, :-1].unsqueeze(dim=1)
-        conf = gt_keypoints_3d[:, :, -1].unsqueeze(-1).clone()
-        gt_keypoints_3d = gt_keypoints_3d[:, :, :-1]
-        loss = (conf * self.loss_fn(pred_keypoints_3d, gt_keypoints_3d)).sum(dim=(1,2))
-        return loss.sum()
-
-class ParameterLoss(nn.Module):
-
-    def __init__(self):
-        """
-        SMPL parameter loss module.
-        """
-        super(ParameterLoss, self).__init__()
-        self.loss_fn = nn.MSELoss(reduction='none')
-
-    def forward(self, pred_param: torch.Tensor, gt_param: torch.Tensor, has_param: torch.Tensor):
-        """
-        Compute SMPL parameter loss.
-        Args:
-            pred_param (torch.Tensor): Tensor of shape [B, S, ...] containing the predicted parameters (body pose / global orientation / betas)
-            gt_param (torch.Tensor): Tensor of shape [B, S, ...] containing the ground truth SMPL parameters.
-        Returns:
-            torch.Tensor: L2 parameter loss loss.
-        """
-        batch_size = pred_param.shape[0]
-        num_dims = len(pred_param.shape)
-        mask_dimension = [batch_size] + [1] * (num_dims-1)
-        has_param = has_param.type(pred_param.type()).view(*mask_dimension)
-        loss_param = (has_param * self.loss_fn(pred_param, gt_param))
-        return loss_param.sum()
diff --git "a/spaces/bugbounted/Whisper-Auto-Subtitled-Video-Generator/pages/03_\360\237\223\235_Upload_Video_File_and_Transcript.py" "b/spaces/bugbounted/Whisper-Auto-Subtitled-Video-Generator/pages/03_\360\237\223\235_Upload_Video_File_and_Transcript.py"
deleted file mode 100644
index 270df7f9ca1164ac24ae98bd56d3b7dffce11e93..0000000000000000000000000000000000000000
--- "a/spaces/bugbounted/Whisper-Auto-Subtitled-Video-Generator/pages/03_\360\237\223\235_Upload_Video_File_and_Transcript.py"
+++ /dev/null
@@ -1,130 +0,0 @@
-import streamlit as st
-from streamlit_lottie import st_lottie
-from utils import write_vtt, write_srt
-import ffmpeg
-import requests
-from typing import Iterator
-from io import StringIO
-import numpy as np
-import pathlib
-import os
-
-
-st.set_page_config(page_title="تولید کننده ویدیو زیرنویس خودکار", page_icon=":movie_camera:", layout="wide")
-
-# Define a function that we can use to load lottie files from a link.
-@st.cache(allow_output_mutation=True)
-def load_lottieurl(url: str):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-
-APP_DIR = pathlib.Path(__file__).parent.absolute()
-
-LOCAL_DIR = APP_DIR / "local_transcript"
-LOCAL_DIR.mkdir(exist_ok=True)
-save_dir = LOCAL_DIR / "output"
-save_dir.mkdir(exist_ok=True)
-
-
-col1, col2 = st.columns([1, 3])
-with col1:
-    lottie = load_lottieurl("https://assets6.lottiefiles.com/packages/lf20_cjnxwrkt.json")
-    st_lottie(lottie)
-
-with col2:
-    st.write("""
-    ## تولید کننده ویدئو زیرنویس خودکار
-    ##### ➠ یک فایل ویدیویی و یک رونوشت را به صورت فایل .srt یا .vtt آپلود کنید و یک ویدیو با زیرنویس دریافت کنید.
-    ##### ➠ با افزایش طول ویدئو، زمان پردازش افزایش خواهد یافت. """)
-
-
-def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
-    segmentStream = StringIO()
-
-    if format == 'vtt':
-        write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    elif format == 'srt':
-        write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    else:
-        raise Exception("Unknown format " + format)
-
-    segmentStream.seek(0)
-    return segmentStream.read()
-
-
-def split_video_audio(uploaded_file):
-    with open(f"{save_dir}/input.mp4", "wb") as f:
-            f.write(uploaded_file.read())
-    audio = ffmpeg.input(f"{save_dir}/input.mp4")
-    audio = ffmpeg.output(audio, f"{save_dir}/output.wav", acodec="pcm_s16le", ac=1, ar="16k")
-    ffmpeg.run(audio, overwrite_output=True)
-
-
-def main():
-    uploaded_video = st.file_uploader("Upload Video File", type=["mp4", "avi", "mov", "mkv"])
-    # get the name of the input_file
-    if uploaded_video is not None:
-        filename = uploaded_video.name[:-4]
-    else:
-        filename = None
-    transcript_file = st.file_uploader("Upload Transcript File", type=["srt", "vtt"])
-    if transcript_file is not None:
-        transcript_name = transcript_file.name
-    else:
-        transcript_name = None
-    if uploaded_video is not None and transcript_file is not None:
-        if transcript_name[-3:] == "vtt":
-            with open("uploaded_transcript.vtt", "wb") as f:
-                f.writelines(transcript_file)
-                f.close()
-            with open(os.path.join(os.getcwd(), "uploaded_transcript.vtt"), "rb") as f:
-                vtt_file = f.read()
-            if st.button("Generate Video with Subtitles"):
-                with st.spinner("Generating Subtitled Video"):
-                    split_video_audio(uploaded_video)
-                    video_file = ffmpeg.input(f"{save_dir}/input.mp4")
-                    audio_file = ffmpeg.input(f"{save_dir}/output.wav")
-                    ffmpeg.concat(video_file.filter("subtitles", "uploaded_transcript.vtt"), audio_file, v=1, a=1).output("final.mp4").global_args('-report').run(quiet=True, overwrite_output=True)
-                    video_with_subs = open("final.mp4", "rb")
-                col3, col4 = st.columns(2)
-                with col3:
-                    st.video(uploaded_video)
-                with col4:
-                    st.video(video_with_subs)
-                st.download_button(label="Download Video with Subtitles",
-                                    data=video_with_subs,
-                                    file_name=f"{filename}_with_subs.mp4")
-
-        elif transcript_name[-3:] == "srt":
-            with open("uploaded_transcript.srt", "wb") as f:
-                f.writelines(transcript_file)
-                f.close()
-            with open(os.path.join(os.getcwd(), "uploaded_transcript.srt"), "rb") as f:
-                srt_file = f.read()
-            if st.button("Generate Video with Subtitles"):
-                with st.spinner("Generating Subtitled Video"):
-                    split_video_audio(uploaded_video)
-                    video_file = ffmpeg.input(f"{save_dir}/input.mp4")
-                    audio_file = ffmpeg.input(f"{save_dir}/output.wav")
-                    ffmpeg.concat(video_file.filter("subtitles",  "uploaded_transcript.srt"), audio_file, v=1, a=1).output("final.mp4").run(quiet=True, overwrite_output=True)
-                    video_with_subs = open("final.mp4", "rb")
-                col3, col4 = st.columns(2)
-                with col3:
-                    st.video(uploaded_video)
-                with col4:
-                    st.video(video_with_subs)
-                st.download_button(label="Download Video with Subtitles",
-                                    data=video_with_subs,
-                                    file_name=f"{filename}_with_subs.mp4")
-        else:
-            st.error("Please upload a .srt or .vtt file")
-    else:
-        st.info("Please upload a video file and a transcript file")
-
-
-if __name__ == "__main__":
-    main()
-    st.markdown("###### Made with :heart: by [@bugbounted](https://github.com/bugbounted)")    
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
-            'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
-            'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
-            'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
-            'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
-            'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
-    try:
-        converters[dialect] = opencc.OpenCC(dialect)
-    except:
-        pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
-    dialect = dialects[dialect]
-    text = converters[dialect].convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/codeparrot/scripts/codeparrot_training.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/codeparrot/scripts/codeparrot_training.py
deleted file mode 100644
index 2510e02c94700dd8c2e7156e8c4e6cc46e7d9515..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/codeparrot/scripts/codeparrot_training.py
+++ /dev/null
@@ -1,326 +0,0 @@
-import logging
-import os
-import time
-from argparse import Namespace
-from pathlib import Path
-
-import datasets
-import torch
-from accelerate import Accelerator, DistributedType
-from arguments import TrainingArguments
-from datasets import load_dataset
-from huggingface_hub import Repository
-from torch.optim import AdamW
-from torch.utils.data import IterableDataset
-from torch.utils.data.dataloader import DataLoader
-from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
-
-import transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
-
-
-class ConstantLengthDataset(IterableDataset):
-    """
-    Iterable dataset that returns constant length chunks of tokens from stream of text files.
-        Args:
-            tokenizer (Tokenizer): The processor used for proccessing the data.
-            dataset (dataset.Dataset): Dataset with text files.
-            infinite (bool): If True the iterator is reset after dataset reaches end else stops.
-            seq_length (int): Length of token sequences to return.
-            num_of_sequences (int): Number of token sequences to keep in buffer.
-            chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
-            tokenized (bool): If true we use a pretokenized dataset.
-    """
-
-    def __init__(
-        self,
-        tokenizer,
-        dataset,
-        infinite=False,
-        seq_length=1024,
-        num_of_sequences=1024,
-        chars_per_token=3.6,
-        tokenized=False,
-    ):
-        self.tokenizer = tokenizer
-        self.concat_token_id = tokenizer.bos_token_id
-        self.dataset = dataset
-        self.seq_length = seq_length
-        self.epoch = 0
-        self.infinite = infinite
-        self.current_size = 0
-        self.tokenized = tokenized
-
-        if self.tokenized:
-            self.max_buffer_size = seq_length * num_of_sequences
-            self.content_field = "input_ids"
-        else:
-            self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
-            self.content_field = "content"
-
-    def __iter__(self):
-        iterator = iter(self.dataset)
-        more_examples = True
-        while more_examples:
-            buffer, buffer_len = [], 0
-            while True:
-                if buffer_len >= self.max_buffer_size:
-                    break
-                try:
-                    buffer.append(next(iterator)[self.content_field])
-                    buffer_len += len(buffer[-1])
-                except StopIteration:
-                    if self.infinite:
-                        iterator = iter(self.dataset)
-                        self.epoch += 1
-                        logger.info(f"Dataset epoch: {self.epoch}")
-                    else:
-                        more_examples = False
-                        break
-            if self.tokenized:
-                tokenized_inputs = buffer
-            else:
-                tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
-            all_token_ids = []
-            for tokenized_input in tokenized_inputs:
-                all_token_ids.extend(tokenized_input + [self.concat_token_id])
-            for i in range(0, len(all_token_ids), self.seq_length):
-                input_ids = all_token_ids[i : i + self.seq_length]
-                if len(input_ids) == self.seq_length:
-                    self.current_size += 1
-                    yield torch.tensor(input_ids)
-
-    def shuffle(self, buffer_size=1000):
-        return ShufflerIterDataPipe(self, buffer_size=buffer_size)
-
-
-def setup_logging(args):
-    project_name = args.model_ckpt.split("/")[-1]
-    logger = logging.getLogger(__name__)
-    log_dir = Path(args.save_dir) / "log/"
-    log_dir.mkdir(exist_ok=True)
-    filename = f"debug_{accelerator.process_index}.log"
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-        handlers=[logging.FileHandler(log_dir / filename), logging.StreamHandler()],
-    )
-    if accelerator.is_main_process:  # we only want to setup logging once
-        accelerator.init_trackers(project_name, vars(args))
-        run_name = accelerator.trackers[0].run.name
-        logger.setLevel(logging.INFO)
-        datasets.utils.logging.set_verbosity_info()
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        run_name = ""
-        logger.setLevel(logging.ERROR)
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-    return logger, run_name
-
-
-def create_dataloaders(args):
-    ds_kwargs = {"streaming": True}
-    train_data = load_dataset(args.dataset_name_train, split="train", **ds_kwargs)
-    train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
-    valid_data = load_dataset(args.dataset_name_valid, split="train", **ds_kwargs)
-    train_dataset = ConstantLengthDataset(
-        tokenizer, train_data, infinite=True, seq_length=args.seq_length, tokenized=args.tokenized
-    )
-    valid_dataset = ConstantLengthDataset(
-        tokenizer, valid_data, infinite=False, seq_length=args.seq_length, tokenized=args.tokenized
-    )
-    train_dataset = train_dataset.shuffle(buffer_size=args.shuffle_buffer)
-    train_dataloader = DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
-    eval_dataloader = DataLoader(valid_dataset, batch_size=args.valid_batch_size)
-    return train_dataloader, eval_dataloader
-
-
-def get_grouped_params(model, args, no_decay=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]):
-    params_with_wd, params_without_wd = [], []
-    for n, p in model.named_parameters():
-        if any(nd in n for nd in no_decay):
-            params_without_wd.append(p)
-        else:
-            params_with_wd.append(p)
-    return [
-        {"params": params_with_wd, "weight_decay": args.weight_decay},
-        {"params": params_without_wd, "weight_decay": 0.0},
-    ]
-
-
-def log_metrics(step, metrics):
-    logger.info(f"Step {step}: {metrics}")
-    if accelerator.is_main_process:
-        accelerator.log(metrics, step)
-
-
-def compute_tflops(elapsed_time, accelerator, args):
-    # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
-    config_model = accelerator.unwrap_model(model).config
-    checkpoint_factor = 4 if args.gradient_checkpointing else 3
-    batch_size = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
-    factor = 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
-    flops_per_iteration = factor * (
-        1.0
-        + (args.seq_length / (6.0 * config_model.n_embd))
-        + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
-    )
-    tflops = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
-    return tflops
-
-
-def evaluate(args):
-    model.eval()
-    losses = []
-    for step, batch in enumerate(eval_dataloader):
-        with torch.no_grad():
-            outputs = model(batch, labels=batch)
-        loss = outputs.loss.repeat(args.valid_batch_size)
-        losses.append(accelerator.gather(loss))
-        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
-            break
-    losses = torch.cat(losses)
-    loss = losses[: eval_dataloader.dataset.current_size].mean()
-    try:
-        perplexity = torch.exp(loss)
-    except OverflowError:
-        perplexity = float("inf")
-    return loss.item(), perplexity.item()
-
-
-# Settings
-parser = HfArgumentParser(TrainingArguments)
-args = parser.parse_args()
-
-# Accelerator
-accelerator = Accelerator(log_with=["wandb", "tensorboard"], logging_dir=f"{args.save_dir}/log")
-acc_state = {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
-
-args = Namespace(**vars(args), **acc_state)
-samples_per_step = accelerator.state.num_processes * args.train_batch_size
-set_seed(args.seed)
-
-# Clone model repository
-if accelerator.is_main_process:
-    hf_repo = Repository(args.save_dir, clone_from=args.model_ckpt)
-
-# Logging
-logger, run_name = setup_logging(args)
-logger.info(accelerator.state)
-
-# Checkout new branch on repo
-if accelerator.is_main_process:
-    hf_repo.git_checkout(run_name, create_branch_ok=True)
-
-# Load model and tokenizer
-model = AutoModelForCausalLM.from_pretrained(args.save_dir)
-if args.gradient_checkpointing:
-    model.gradient_checkpointing_enable()
-tokenizer = AutoTokenizer.from_pretrained(args.save_dir)
-
-# Load dataset and dataloader
-train_dataloader, eval_dataloader = create_dataloaders(args)
-
-# Prepare the optimizer and learning rate scheduler
-optimizer = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
-lr_scheduler = get_scheduler(
-    name=args.lr_scheduler_type,
-    optimizer=optimizer,
-    num_warmup_steps=args.num_warmup_steps,
-    num_training_steps=args.max_train_steps,
-)
-accelerator.register_for_checkpointing(lr_scheduler)
-
-
-def get_lr():
-    return optimizer.param_groups[0]["lr"]
-
-
-# Prepare everything with our `accelerator`.
-model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
-    model, optimizer, train_dataloader, eval_dataloader
-)
-
-# load in the weights and states from a previous save
-if args.resume_from_checkpoint:
-    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
-        accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
-        accelerator.load_state(args.resume_from_checkpoint)
-        path = os.path.basename(args.resume_from_checkpoint)
-    else:
-        # Get the most recent checkpoint
-        dirs = [f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
-        dirs.sort(key=os.path.getctime)
-        path = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
-    # Extract the step of the checkpoint to continue from there
-    training_difference = os.path.splitext(path)[0]
-    resume_step = int(training_difference.replace("step_", ""))
-
-# Train model
-model.train()
-completed_steps = 0
-t_start = time.time()
-loss_tracking = 0
-for step, batch in enumerate(train_dataloader, start=1):
-    if args.resume_from_checkpoint and step < resume_step:
-        continue  # we need to skip steps until we reach the resumed step
-    loss = model(batch, labels=batch, use_cache=False).loss
-    avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
-    loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
-    log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
-    loss = loss / args.gradient_accumulation_steps
-    if step % args.gradient_accumulation_steps != 0:
-        # Prevent backward from doing gradient all_reduce in every step
-        if accelerator.distributed_type == DistributedType.MULTI_GPU:
-            with model.no_sync():
-                accelerator.backward(loss)
-        else:
-            accelerator.backward(loss)
-    else:
-        lr = get_lr()
-        accelerator.backward(loss)
-        accelerator.clip_grad_norm_(model.parameters(), 1.0)
-        optimizer.step()
-        lr_scheduler.step()
-        optimizer.zero_grad()
-        elapsed_time = time.time() - t_start
-        tflops = compute_tflops(elapsed_time, accelerator, args)
-        log_metrics(
-            step,
-            {
-                "steps": completed_steps,
-                "loss/train": loss_tracking,
-                "lr": lr,
-                "tflops": tflops,
-                "time_per_iteration": elapsed_time,
-            },
-        )
-        t_start = time.time()
-        loss_tracking = 0
-        completed_steps += 1
-    if step % args.save_checkpoint_steps == 0:
-        logger.info("Evaluating and saving model checkpoint")
-        eval_loss, perplexity = evaluate(args)
-        log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
-        accelerator.wait_for_everyone()
-        save_dir = os.path.join(args.save_dir, f"step_{step}")
-        accelerator.save_state(save_dir)
-        if accelerator.is_main_process:
-            hf_repo.push_to_hub(commit_message=f"step {step}")
-        model.train()
-    if completed_steps >= args.max_train_steps:
-        break
-
-# Evaluate and save the last checkpoint
-logger.info("Evaluating and saving model after training")
-eval_loss, perplexity = evaluate(args)
-log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
-accelerator.wait_for_everyone()
-unwrapped_model = accelerator.unwrap_model(model)
-unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
-save_dir = os.path.join(args.save_dir, f"step_{step}")
-accelerator.save_state(save_dir)
-if accelerator.is_main_process:
-    hf_repo.push_to_hub(commit_message="final model")
diff --git a/spaces/chrisjay/masakhane-benchmarks/app.py b/spaces/chrisjay/masakhane-benchmarks/app.py
deleted file mode 100644
index 8e8440f27e1d15200c3e54b227748cb3058144b8..0000000000000000000000000000000000000000
--- a/spaces/chrisjay/masakhane-benchmarks/app.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import gradio as gr
-import yaml
-from joeynmt.prediction import load_params_for_prediction,translate_for_hf_space
-from huggingface_hub import hf_hub_download
-
-language_map = {'English':'en','Swahili':'sw','Fon':'fon','Igbo':'ig',
-                'Arabic':'ar','Shona':'sn','Ẹ̀dó':'bin','Hausa':'ha',
-                'Efik':'efi','Twi':'twi','Afrikaans':'af','Yoruba':'yo','Urhobo':'urh','Dendi':'ddn','̀Ẹ̀sán':'ish','Isoko':'iso',
-                'Kamba':'kam','Luo':'luo','Southern Ndebele':'nr','Tshivenda':'ve'}  
-  
- 
-#List of available languages I worked on.
-#...
-available_language_pairs =['en-sw','en-af','en-ar','en-ddn','en-ish','en-iso','en-kam','en-luo','en-nr','en-ve','efi-en','en-bin','en-ha','en-ig','en-fon','en-twi','sn-en','sw-en','yo-en','en-urh']
-available_languages = list(language_map.keys())
-
-def load_config(path="configs/default.yaml") -> dict:
-    """
-    CODE ADAPTED FROM: https://github.com/joeynmt/joeynmt
-    Loads and parses a YAML configuration file.
-
-    :param path: path to YAML configuration file
-    :return: configuration dictionary
-    """
-    with open(path, 'r', encoding="utf-8") as ymlfile:
-      
-        cfg = yaml.safe_load(ymlfile)
-    return cfg
-  
-def load_model(source_language,target_language):  
-    #source_language = language_map[source_language_]
-    #target_language = language_map[target_language_]
-    
-  
-    translation_dir = 'main'
-
-    try:
-      file_yaml = hf_hub_download("chrisjay/masakhane_benchmarks", filename=f"{source_language}-{target_language}/{translation_dir}/config.yaml",force_filename='config.yaml')
-      src_vocab = hf_hub_download("chrisjay/masakhane_benchmarks", filename=f"{source_language}-{target_language}/{translation_dir}/src_vocab.txt")
-      trg_vocab  = hf_hub_download("chrisjay/masakhane_benchmarks", filename=f"{source_language}-{target_language}/{translation_dir}/trg_vocab.txt")
-      best_ckpt = hf_hub_download("chrisjay/masakhane_benchmarks", filename=f"{source_language}-{target_language}/{translation_dir}/best.ckpt")
-    except Exception:
-      raise Exception(f'It seems we do not have a working configuration repo yet for {source_language} -> {target_language}. \n You could help us by creating it here: https://huggingface.co/chrisjay/masakhane_benchmarks/tree/main')
-
-
-    parsed_yaml_file = load_config(file_yaml)
-    parsed_yaml_file['data']['src_vocab']=src_vocab
-    parsed_yaml_file['data']['trg_vocab']=trg_vocab
-
-    params = load_params_for_prediction(parsed_yaml_file,best_ckpt)
-    return params
-
-
-#Load models of all available language pairs
-model_mapping = {} 
-examples_available_models=[] # Keep track of models that loaded successfully and display only them in the Examples.
-for availabe_lang in available_language_pairs:
-    try:
-        model_mapping.update({availabe_lang:load_model(availabe_lang.split('-')[0],availabe_lang.split('-')[1])})
-        examples_available_models.append([f"{list(language_map.keys())[list(language_map.values()).index(availabe_lang.split('-')[0])]}",f"{list(language_map.keys())[list(language_map.values()).index(availabe_lang.split('-')[1])]}"]) #idea to extract key from value got from https://stackoverflow.com/questions/8023306/get-key-by-value-in-dictionary
-    except Exception:
-        continue
-
-if examples_available_models==[]:
-    raise Exception(f'Available models for Space cannot be empty!')
-
-
-def get_translation(source_language,target_language,source_sentence=None,source_file=None):
-    '''
-    This takes a sentence and gets the translation.
-    '''
-    
-    source_language_ = language_map[source_language]
-    target_language_ = language_map[target_language]
-   
-    
-    source = source_sentence
-    translation_type='sentence'
-    if source_file!=None:
-        translation_type='file'
-        source = source_file.name
-    try:     
-        params = model_mapping[f'{source_language_}-{target_language_}']
-        pred = translate_for_hf_space(params,source,translation_type)
-    except Exception:
-        return f'There was an issue loading the translation model for {source_language} -> {target_language}. Try another pair please'
-    
-    return pred[0] if source_file==None else pred
- 
-
-
-
-title = "Interact with Masakhane Benchmark Models"
-description = "This enables you to interact with some of the Masakhane Benchmark Models and keep up with their improvement. Some of these models undergo finetuning on a regular basis. This way, you can easily use the best model with no hassles."
-
-iface = gr.Interface(fn=get_translation, 
-  inputs=[gr.inputs.Dropdown(choices = available_languages,default='English'),
-  gr.inputs.Dropdown(choices = available_languages,default='Swahili'),
-  gr.inputs.Textbox(label="Input"),
-  gr.inputs.File(file_count="single", type="file", label='Or upload txt file containing sentences', optional=True)],
-  outputs=gr.outputs.Textbox(type="auto", label='Translation'),
-  title=title,
-  description=description,
-  examples=examples_available_models,
-  enable_queue=True,
-  theme='huggingface')
-iface.launch()
-
-
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/JpegImagePlugin.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/JpegImagePlugin.py
deleted file mode 100644
index dfc7e6e9f569e05e3a1f9e3fd1407b5f202a6d56..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/JpegImagePlugin.py
+++ /dev/null
@@ -1,849 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# JPEG (JFIF) file handling
-#
-# See "Digital Compression and Coding of Continuous-Tone Still Images,
-# Part 1, Requirements and Guidelines" (CCITT T.81 / ISO 10918-1)
-#
-# History:
-# 1995-09-09 fl   Created
-# 1995-09-13 fl   Added full parser
-# 1996-03-25 fl   Added hack to use the IJG command line utilities
-# 1996-05-05 fl   Workaround Photoshop 2.5 CMYK polarity bug
-# 1996-05-28 fl   Added draft support, JFIF version (0.1)
-# 1996-12-30 fl   Added encoder options, added progression property (0.2)
-# 1997-08-27 fl   Save mode 1 images as BW (0.3)
-# 1998-07-12 fl   Added YCbCr to draft and save methods (0.4)
-# 1998-10-19 fl   Don't hang on files using 16-bit DQT's (0.4.1)
-# 2001-04-16 fl   Extract DPI settings from JFIF files (0.4.2)
-# 2002-07-01 fl   Skip pad bytes before markers; identify Exif files (0.4.3)
-# 2003-04-25 fl   Added experimental EXIF decoder (0.5)
-# 2003-06-06 fl   Added experimental EXIF GPSinfo decoder
-# 2003-09-13 fl   Extract COM markers
-# 2009-09-06 fl   Added icc_profile support (from Florian Hoech)
-# 2009-03-06 fl   Changed CMYK handling; always use Adobe polarity (0.6)
-# 2009-03-08 fl   Added subsampling support (from Justin Huff).
-#
-# Copyright (c) 1997-2003 by Secret Labs AB.
-# Copyright (c) 1995-1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-import array
-import io
-import math
-import os
-import struct
-import subprocess
-import sys
-import tempfile
-import warnings
-
-from . import Image, ImageFile
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-from ._binary import o8
-from ._binary import o16be as o16
-from .JpegPresets import presets
-
-#
-# Parser
-
-
-def Skip(self, marker):
-    n = i16(self.fp.read(2)) - 2
-    ImageFile._safe_read(self.fp, n)
-
-
-def APP(self, marker):
-    #
-    # Application marker.  Store these in the APP dictionary.
-    # Also look for well-known application markers.
-
-    n = i16(self.fp.read(2)) - 2
-    s = ImageFile._safe_read(self.fp, n)
-
-    app = "APP%d" % (marker & 15)
-
-    self.app[app] = s  # compatibility
-    self.applist.append((app, s))
-
-    if marker == 0xFFE0 and s[:4] == b"JFIF":
-        # extract JFIF information
-        self.info["jfif"] = version = i16(s, 5)  # version
-        self.info["jfif_version"] = divmod(version, 256)
-        # extract JFIF properties
-        try:
-            jfif_unit = s[7]
-            jfif_density = i16(s, 8), i16(s, 10)
-        except Exception:
-            pass
-        else:
-            if jfif_unit == 1:
-                self.info["dpi"] = jfif_density
-            self.info["jfif_unit"] = jfif_unit
-            self.info["jfif_density"] = jfif_density
-    elif marker == 0xFFE1 and s[:5] == b"Exif\0":
-        if "exif" not in self.info:
-            # extract EXIF information (incomplete)
-            self.info["exif"] = s  # FIXME: value will change
-            self._exif_offset = self.fp.tell() - n + 6
-    elif marker == 0xFFE2 and s[:5] == b"FPXR\0":
-        # extract FlashPix information (incomplete)
-        self.info["flashpix"] = s  # FIXME: value will change
-    elif marker == 0xFFE2 and s[:12] == b"ICC_PROFILE\0":
-        # Since an ICC profile can be larger than the maximum size of
-        # a JPEG marker (64K), we need provisions to split it into
-        # multiple markers. The format defined by the ICC specifies
-        # one or more APP2 markers containing the following data:
-        #   Identifying string      ASCII "ICC_PROFILE\0"  (12 bytes)
-        #   Marker sequence number  1, 2, etc (1 byte)
-        #   Number of markers       Total of APP2's used (1 byte)
-        #   Profile data            (remainder of APP2 data)
-        # Decoders should use the marker sequence numbers to
-        # reassemble the profile, rather than assuming that the APP2
-        # markers appear in the correct sequence.
-        self.icclist.append(s)
-    elif marker == 0xFFED and s[:14] == b"Photoshop 3.0\x00":
-        # parse the image resource block
-        offset = 14
-        photoshop = self.info.setdefault("photoshop", {})
-        while s[offset : offset + 4] == b"8BIM":
-            try:
-                offset += 4
-                # resource code
-                code = i16(s, offset)
-                offset += 2
-                # resource name (usually empty)
-                name_len = s[offset]
-                # name = s[offset+1:offset+1+name_len]
-                offset += 1 + name_len
-                offset += offset & 1  # align
-                # resource data block
-                size = i32(s, offset)
-                offset += 4
-                data = s[offset : offset + size]
-                if code == 0x03ED:  # ResolutionInfo
-                    data = {
-                        "XResolution": i32(data, 0) / 65536,
-                        "DisplayedUnitsX": i16(data, 4),
-                        "YResolution": i32(data, 8) / 65536,
-                        "DisplayedUnitsY": i16(data, 12),
-                    }
-                photoshop[code] = data
-                offset += size
-                offset += offset & 1  # align
-            except struct.error:
-                break  # insufficient data
-
-    elif marker == 0xFFEE and s[:5] == b"Adobe":
-        self.info["adobe"] = i16(s, 5)
-        # extract Adobe custom properties
-        try:
-            adobe_transform = s[11]
-        except IndexError:
-            pass
-        else:
-            self.info["adobe_transform"] = adobe_transform
-    elif marker == 0xFFE2 and s[:4] == b"MPF\0":
-        # extract MPO information
-        self.info["mp"] = s[4:]
-        # offset is current location minus buffer size
-        # plus constant header size
-        self.info["mpoffset"] = self.fp.tell() - n + 4
-
-    # If DPI isn't in JPEG header, fetch from EXIF
-    if "dpi" not in self.info and "exif" in self.info:
-        try:
-            exif = self.getexif()
-            resolution_unit = exif[0x0128]
-            x_resolution = exif[0x011A]
-            try:
-                dpi = float(x_resolution[0]) / x_resolution[1]
-            except TypeError:
-                dpi = x_resolution
-            if math.isnan(dpi):
-                raise ValueError
-            if resolution_unit == 3:  # cm
-                # 1 dpcm = 2.54 dpi
-                dpi *= 2.54
-            self.info["dpi"] = dpi, dpi
-        except (TypeError, KeyError, SyntaxError, ValueError, ZeroDivisionError):
-            # SyntaxError for invalid/unreadable EXIF
-            # KeyError for dpi not included
-            # ZeroDivisionError for invalid dpi rational value
-            # ValueError or TypeError for dpi being an invalid float
-            self.info["dpi"] = 72, 72
-
-
-def COM(self, marker):
-    #
-    # Comment marker.  Store these in the APP dictionary.
-    n = i16(self.fp.read(2)) - 2
-    s = ImageFile._safe_read(self.fp, n)
-
-    self.info["comment"] = s
-    self.app["COM"] = s  # compatibility
-    self.applist.append(("COM", s))
-
-
-def SOF(self, marker):
-    #
-    # Start of frame marker.  Defines the size and mode of the
-    # image.  JPEG is colour blind, so we use some simple
-    # heuristics to map the number of layers to an appropriate
-    # mode.  Note that this could be made a bit brighter, by
-    # looking for JFIF and Adobe APP markers.
-
-    n = i16(self.fp.read(2)) - 2
-    s = ImageFile._safe_read(self.fp, n)
-    self._size = i16(s, 3), i16(s, 1)
-
-    self.bits = s[0]
-    if self.bits != 8:
-        msg = f"cannot handle {self.bits}-bit layers"
-        raise SyntaxError(msg)
-
-    self.layers = s[5]
-    if self.layers == 1:
-        self.mode = "L"
-    elif self.layers == 3:
-        self.mode = "RGB"
-    elif self.layers == 4:
-        self.mode = "CMYK"
-    else:
-        msg = f"cannot handle {self.layers}-layer images"
-        raise SyntaxError(msg)
-
-    if marker in [0xFFC2, 0xFFC6, 0xFFCA, 0xFFCE]:
-        self.info["progressive"] = self.info["progression"] = 1
-
-    if self.icclist:
-        # fixup icc profile
-        self.icclist.sort()  # sort by sequence number
-        if self.icclist[0][13] == len(self.icclist):
-            profile = []
-            for p in self.icclist:
-                profile.append(p[14:])
-            icc_profile = b"".join(profile)
-        else:
-            icc_profile = None  # wrong number of fragments
-        self.info["icc_profile"] = icc_profile
-        self.icclist = []
-
-    for i in range(6, len(s), 3):
-        t = s[i : i + 3]
-        # 4-tuples: id, vsamp, hsamp, qtable
-        self.layer.append((t[0], t[1] // 16, t[1] & 15, t[2]))
-
-
-def DQT(self, marker):
-    #
-    # Define quantization table.  Note that there might be more
-    # than one table in each marker.
-
-    # FIXME: The quantization tables can be used to estimate the
-    # compression quality.
-
-    n = i16(self.fp.read(2)) - 2
-    s = ImageFile._safe_read(self.fp, n)
-    while len(s):
-        v = s[0]
-        precision = 1 if (v // 16 == 0) else 2  # in bytes
-        qt_length = 1 + precision * 64
-        if len(s) < qt_length:
-            msg = "bad quantization table marker"
-            raise SyntaxError(msg)
-        data = array.array("B" if precision == 1 else "H", s[1:qt_length])
-        if sys.byteorder == "little" and precision > 1:
-            data.byteswap()  # the values are always big-endian
-        self.quantization[v & 15] = [data[i] for i in zigzag_index]
-        s = s[qt_length:]
-
-
-#
-# JPEG marker table
-
-MARKER = {
-    0xFFC0: ("SOF0", "Baseline DCT", SOF),
-    0xFFC1: ("SOF1", "Extended Sequential DCT", SOF),
-    0xFFC2: ("SOF2", "Progressive DCT", SOF),
-    0xFFC3: ("SOF3", "Spatial lossless", SOF),
-    0xFFC4: ("DHT", "Define Huffman table", Skip),
-    0xFFC5: ("SOF5", "Differential sequential DCT", SOF),
-    0xFFC6: ("SOF6", "Differential progressive DCT", SOF),
-    0xFFC7: ("SOF7", "Differential spatial", SOF),
-    0xFFC8: ("JPG", "Extension", None),
-    0xFFC9: ("SOF9", "Extended sequential DCT (AC)", SOF),
-    0xFFCA: ("SOF10", "Progressive DCT (AC)", SOF),
-    0xFFCB: ("SOF11", "Spatial lossless DCT (AC)", SOF),
-    0xFFCC: ("DAC", "Define arithmetic coding conditioning", Skip),
-    0xFFCD: ("SOF13", "Differential sequential DCT (AC)", SOF),
-    0xFFCE: ("SOF14", "Differential progressive DCT (AC)", SOF),
-    0xFFCF: ("SOF15", "Differential spatial (AC)", SOF),
-    0xFFD0: ("RST0", "Restart 0", None),
-    0xFFD1: ("RST1", "Restart 1", None),
-    0xFFD2: ("RST2", "Restart 2", None),
-    0xFFD3: ("RST3", "Restart 3", None),
-    0xFFD4: ("RST4", "Restart 4", None),
-    0xFFD5: ("RST5", "Restart 5", None),
-    0xFFD6: ("RST6", "Restart 6", None),
-    0xFFD7: ("RST7", "Restart 7", None),
-    0xFFD8: ("SOI", "Start of image", None),
-    0xFFD9: ("EOI", "End of image", None),
-    0xFFDA: ("SOS", "Start of scan", Skip),
-    0xFFDB: ("DQT", "Define quantization table", DQT),
-    0xFFDC: ("DNL", "Define number of lines", Skip),
-    0xFFDD: ("DRI", "Define restart interval", Skip),
-    0xFFDE: ("DHP", "Define hierarchical progression", SOF),
-    0xFFDF: ("EXP", "Expand reference component", Skip),
-    0xFFE0: ("APP0", "Application segment 0", APP),
-    0xFFE1: ("APP1", "Application segment 1", APP),
-    0xFFE2: ("APP2", "Application segment 2", APP),
-    0xFFE3: ("APP3", "Application segment 3", APP),
-    0xFFE4: ("APP4", "Application segment 4", APP),
-    0xFFE5: ("APP5", "Application segment 5", APP),
-    0xFFE6: ("APP6", "Application segment 6", APP),
-    0xFFE7: ("APP7", "Application segment 7", APP),
-    0xFFE8: ("APP8", "Application segment 8", APP),
-    0xFFE9: ("APP9", "Application segment 9", APP),
-    0xFFEA: ("APP10", "Application segment 10", APP),
-    0xFFEB: ("APP11", "Application segment 11", APP),
-    0xFFEC: ("APP12", "Application segment 12", APP),
-    0xFFED: ("APP13", "Application segment 13", APP),
-    0xFFEE: ("APP14", "Application segment 14", APP),
-    0xFFEF: ("APP15", "Application segment 15", APP),
-    0xFFF0: ("JPG0", "Extension 0", None),
-    0xFFF1: ("JPG1", "Extension 1", None),
-    0xFFF2: ("JPG2", "Extension 2", None),
-    0xFFF3: ("JPG3", "Extension 3", None),
-    0xFFF4: ("JPG4", "Extension 4", None),
-    0xFFF5: ("JPG5", "Extension 5", None),
-    0xFFF6: ("JPG6", "Extension 6", None),
-    0xFFF7: ("JPG7", "Extension 7", None),
-    0xFFF8: ("JPG8", "Extension 8", None),
-    0xFFF9: ("JPG9", "Extension 9", None),
-    0xFFFA: ("JPG10", "Extension 10", None),
-    0xFFFB: ("JPG11", "Extension 11", None),
-    0xFFFC: ("JPG12", "Extension 12", None),
-    0xFFFD: ("JPG13", "Extension 13", None),
-    0xFFFE: ("COM", "Comment", COM),
-}
-
-
-def _accept(prefix):
-    # Magic number was taken from https://en.wikipedia.org/wiki/JPEG
-    return prefix[:3] == b"\xFF\xD8\xFF"
-
-
-##
-# Image plugin for JPEG and JFIF images.
-
-
-class JpegImageFile(ImageFile.ImageFile):
-    format = "JPEG"
-    format_description = "JPEG (ISO 10918)"
-
-    def _open(self):
-        s = self.fp.read(3)
-
-        if not _accept(s):
-            msg = "not a JPEG file"
-            raise SyntaxError(msg)
-        s = b"\xFF"
-
-        # Create attributes
-        self.bits = self.layers = 0
-
-        # JPEG specifics (internal)
-        self.layer = []
-        self.huffman_dc = {}
-        self.huffman_ac = {}
-        self.quantization = {}
-        self.app = {}  # compatibility
-        self.applist = []
-        self.icclist = []
-
-        while True:
-            i = s[0]
-            if i == 0xFF:
-                s = s + self.fp.read(1)
-                i = i16(s)
-            else:
-                # Skip non-0xFF junk
-                s = self.fp.read(1)
-                continue
-
-            if i in MARKER:
-                name, description, handler = MARKER[i]
-                if handler is not None:
-                    handler(self, i)
-                if i == 0xFFDA:  # start of scan
-                    rawmode = self.mode
-                    if self.mode == "CMYK":
-                        rawmode = "CMYK;I"  # assume adobe conventions
-                    self.tile = [("jpeg", (0, 0) + self.size, 0, (rawmode, ""))]
-                    # self.__offset = self.fp.tell()
-                    break
-                s = self.fp.read(1)
-            elif i == 0 or i == 0xFFFF:
-                # padded marker or junk; move on
-                s = b"\xff"
-            elif i == 0xFF00:  # Skip extraneous data (escaped 0xFF)
-                s = self.fp.read(1)
-            else:
-                msg = "no marker found"
-                raise SyntaxError(msg)
-
-    def load_read(self, read_bytes):
-        """
-        internal: read more image data
-        For premature EOF and LOAD_TRUNCATED_IMAGES adds EOI marker
-        so libjpeg can finish decoding
-        """
-        s = self.fp.read(read_bytes)
-
-        if not s and ImageFile.LOAD_TRUNCATED_IMAGES and not hasattr(self, "_ended"):
-            # Premature EOF.
-            # Pretend file is finished adding EOI marker
-            self._ended = True
-            return b"\xFF\xD9"
-
-        return s
-
-    def draft(self, mode, size):
-        if len(self.tile) != 1:
-            return
-
-        # Protect from second call
-        if self.decoderconfig:
-            return
-
-        d, e, o, a = self.tile[0]
-        scale = 1
-        original_size = self.size
-
-        if a[0] == "RGB" and mode in ["L", "YCbCr"]:
-            self.mode = mode
-            a = mode, ""
-
-        if size:
-            scale = min(self.size[0] // size[0], self.size[1] // size[1])
-            for s in [8, 4, 2, 1]:
-                if scale >= s:
-                    break
-            e = (
-                e[0],
-                e[1],
-                (e[2] - e[0] + s - 1) // s + e[0],
-                (e[3] - e[1] + s - 1) // s + e[1],
-            )
-            self._size = ((self.size[0] + s - 1) // s, (self.size[1] + s - 1) // s)
-            scale = s
-
-        self.tile = [(d, e, o, a)]
-        self.decoderconfig = (scale, 0)
-
-        box = (0, 0, original_size[0] / scale, original_size[1] / scale)
-        return self.mode, box
-
-    def load_djpeg(self):
-        # ALTERNATIVE: handle JPEGs via the IJG command line utilities
-
-        f, path = tempfile.mkstemp()
-        os.close(f)
-        if os.path.exists(self.filename):
-            subprocess.check_call(["djpeg", "-outfile", path, self.filename])
-        else:
-            try:
-                os.unlink(path)
-            except OSError:
-                pass
-
-            msg = "Invalid Filename"
-            raise ValueError(msg)
-
-        try:
-            with Image.open(path) as _im:
-                _im.load()
-                self.im = _im.im
-        finally:
-            try:
-                os.unlink(path)
-            except OSError:
-                pass
-
-        self.mode = self.im.mode
-        self._size = self.im.size
-
-        self.tile = []
-
-    def _getexif(self):
-        return _getexif(self)
-
-    def _getmp(self):
-        return _getmp(self)
-
-    def getxmp(self):
-        """
-        Returns a dictionary containing the XMP tags.
-        Requires defusedxml to be installed.
-
-        :returns: XMP tags in a dictionary.
-        """
-
-        for segment, content in self.applist:
-            if segment == "APP1":
-                marker, xmp_tags = content.rsplit(b"\x00", 1)
-                if marker == b"http://ns.adobe.com/xap/1.0/":
-                    return self._getxmp(xmp_tags)
-        return {}
-
-
-def _getexif(self):
-    if "exif" not in self.info:
-        return None
-    return self.getexif()._get_merged_dict()
-
-
-def _getmp(self):
-    # Extract MP information.  This method was inspired by the "highly
-    # experimental" _getexif version that's been in use for years now,
-    # itself based on the ImageFileDirectory class in the TIFF plugin.
-
-    # The MP record essentially consists of a TIFF file embedded in a JPEG
-    # application marker.
-    try:
-        data = self.info["mp"]
-    except KeyError:
-        return None
-    file_contents = io.BytesIO(data)
-    head = file_contents.read(8)
-    endianness = ">" if head[:4] == b"\x4d\x4d\x00\x2a" else "<"
-    # process dictionary
-    from . import TiffImagePlugin
-
-    try:
-        info = TiffImagePlugin.ImageFileDirectory_v2(head)
-        file_contents.seek(info.next)
-        info.load(file_contents)
-        mp = dict(info)
-    except Exception as e:
-        msg = "malformed MP Index (unreadable directory)"
-        raise SyntaxError(msg) from e
-    # it's an error not to have a number of images
-    try:
-        quant = mp[0xB001]
-    except KeyError as e:
-        msg = "malformed MP Index (no number of images)"
-        raise SyntaxError(msg) from e
-    # get MP entries
-    mpentries = []
-    try:
-        rawmpentries = mp[0xB002]
-        for entrynum in range(0, quant):
-            unpackedentry = struct.unpack_from(
-                f"{endianness}LLLHH", rawmpentries, entrynum * 16
-            )
-            labels = ("Attribute", "Size", "DataOffset", "EntryNo1", "EntryNo2")
-            mpentry = dict(zip(labels, unpackedentry))
-            mpentryattr = {
-                "DependentParentImageFlag": bool(mpentry["Attribute"] & (1 << 31)),
-                "DependentChildImageFlag": bool(mpentry["Attribute"] & (1 << 30)),
-                "RepresentativeImageFlag": bool(mpentry["Attribute"] & (1 << 29)),
-                "Reserved": (mpentry["Attribute"] & (3 << 27)) >> 27,
-                "ImageDataFormat": (mpentry["Attribute"] & (7 << 24)) >> 24,
-                "MPType": mpentry["Attribute"] & 0x00FFFFFF,
-            }
-            if mpentryattr["ImageDataFormat"] == 0:
-                mpentryattr["ImageDataFormat"] = "JPEG"
-            else:
-                msg = "unsupported picture format in MPO"
-                raise SyntaxError(msg)
-            mptypemap = {
-                0x000000: "Undefined",
-                0x010001: "Large Thumbnail (VGA Equivalent)",
-                0x010002: "Large Thumbnail (Full HD Equivalent)",
-                0x020001: "Multi-Frame Image (Panorama)",
-                0x020002: "Multi-Frame Image: (Disparity)",
-                0x020003: "Multi-Frame Image: (Multi-Angle)",
-                0x030000: "Baseline MP Primary Image",
-            }
-            mpentryattr["MPType"] = mptypemap.get(mpentryattr["MPType"], "Unknown")
-            mpentry["Attribute"] = mpentryattr
-            mpentries.append(mpentry)
-        mp[0xB002] = mpentries
-    except KeyError as e:
-        msg = "malformed MP Index (bad MP Entry)"
-        raise SyntaxError(msg) from e
-    # Next we should try and parse the individual image unique ID list;
-    # we don't because I've never seen this actually used in a real MPO
-    # file and so can't test it.
-    return mp
-
-
-# --------------------------------------------------------------------
-# stuff to save JPEG files
-
-RAWMODE = {
-    "1": "L",
-    "L": "L",
-    "RGB": "RGB",
-    "RGBX": "RGB",
-    "CMYK": "CMYK;I",  # assume adobe conventions
-    "YCbCr": "YCbCr",
-}
-
-# fmt: off
-zigzag_index = (
-    0,  1,  5,  6, 14, 15, 27, 28,
-    2,  4,  7, 13, 16, 26, 29, 42,
-    3,  8, 12, 17, 25, 30, 41, 43,
-    9, 11, 18, 24, 31, 40, 44, 53,
-    10, 19, 23, 32, 39, 45, 52, 54,
-    20, 22, 33, 38, 46, 51, 55, 60,
-    21, 34, 37, 47, 50, 56, 59, 61,
-    35, 36, 48, 49, 57, 58, 62, 63,
-)
-
-samplings = {
-    (1, 1, 1, 1, 1, 1): 0,
-    (2, 1, 1, 1, 1, 1): 1,
-    (2, 2, 1, 1, 1, 1): 2,
-}
-# fmt: on
-
-
-def get_sampling(im):
-    # There's no subsampling when images have only 1 layer
-    # (grayscale images) or when they are CMYK (4 layers),
-    # so set subsampling to the default value.
-    #
-    # NOTE: currently Pillow can't encode JPEG to YCCK format.
-    # If YCCK support is added in the future, subsampling code will have
-    # to be updated (here and in JpegEncode.c) to deal with 4 layers.
-    if not hasattr(im, "layers") or im.layers in (1, 4):
-        return -1
-    sampling = im.layer[0][1:3] + im.layer[1][1:3] + im.layer[2][1:3]
-    return samplings.get(sampling, -1)
-
-
-def _save(im, fp, filename):
-    if im.width == 0 or im.height == 0:
-        msg = "cannot write empty image as JPEG"
-        raise ValueError(msg)
-
-    try:
-        rawmode = RAWMODE[im.mode]
-    except KeyError as e:
-        msg = f"cannot write mode {im.mode} as JPEG"
-        raise OSError(msg) from e
-
-    info = im.encoderinfo
-
-    dpi = [round(x) for x in info.get("dpi", (0, 0))]
-
-    quality = info.get("quality", -1)
-    subsampling = info.get("subsampling", -1)
-    qtables = info.get("qtables")
-
-    if quality == "keep":
-        quality = -1
-        subsampling = "keep"
-        qtables = "keep"
-    elif quality in presets:
-        preset = presets[quality]
-        quality = -1
-        subsampling = preset.get("subsampling", -1)
-        qtables = preset.get("quantization")
-    elif not isinstance(quality, int):
-        msg = "Invalid quality setting"
-        raise ValueError(msg)
-    else:
-        if subsampling in presets:
-            subsampling = presets[subsampling].get("subsampling", -1)
-        if isinstance(qtables, str) and qtables in presets:
-            qtables = presets[qtables].get("quantization")
-
-    if subsampling == "4:4:4":
-        subsampling = 0
-    elif subsampling == "4:2:2":
-        subsampling = 1
-    elif subsampling == "4:2:0":
-        subsampling = 2
-    elif subsampling == "4:1:1":
-        # For compatibility. Before Pillow 4.3, 4:1:1 actually meant 4:2:0.
-        # Set 4:2:0 if someone is still using that value.
-        subsampling = 2
-    elif subsampling == "keep":
-        if im.format != "JPEG":
-            msg = "Cannot use 'keep' when original image is not a JPEG"
-            raise ValueError(msg)
-        subsampling = get_sampling(im)
-
-    def validate_qtables(qtables):
-        if qtables is None:
-            return qtables
-        if isinstance(qtables, str):
-            try:
-                lines = [
-                    int(num)
-                    for line in qtables.splitlines()
-                    for num in line.split("#", 1)[0].split()
-                ]
-            except ValueError as e:
-                msg = "Invalid quantization table"
-                raise ValueError(msg) from e
-            else:
-                qtables = [lines[s : s + 64] for s in range(0, len(lines), 64)]
-        if isinstance(qtables, (tuple, list, dict)):
-            if isinstance(qtables, dict):
-                qtables = [
-                    qtables[key] for key in range(len(qtables)) if key in qtables
-                ]
-            elif isinstance(qtables, tuple):
-                qtables = list(qtables)
-            if not (0 < len(qtables) < 5):
-                msg = "None or too many quantization tables"
-                raise ValueError(msg)
-            for idx, table in enumerate(qtables):
-                try:
-                    if len(table) != 64:
-                        raise TypeError
-                    table = array.array("H", table)
-                except TypeError as e:
-                    msg = "Invalid quantization table"
-                    raise ValueError(msg) from e
-                else:
-                    qtables[idx] = list(table)
-            return qtables
-
-    if qtables == "keep":
-        if im.format != "JPEG":
-            msg = "Cannot use 'keep' when original image is not a JPEG"
-            raise ValueError(msg)
-        qtables = getattr(im, "quantization", None)
-    qtables = validate_qtables(qtables)
-
-    extra = info.get("extra", b"")
-
-    MAX_BYTES_IN_MARKER = 65533
-    icc_profile = info.get("icc_profile")
-    if icc_profile:
-        ICC_OVERHEAD_LEN = 14
-        MAX_DATA_BYTES_IN_MARKER = MAX_BYTES_IN_MARKER - ICC_OVERHEAD_LEN
-        markers = []
-        while icc_profile:
-            markers.append(icc_profile[:MAX_DATA_BYTES_IN_MARKER])
-            icc_profile = icc_profile[MAX_DATA_BYTES_IN_MARKER:]
-        i = 1
-        for marker in markers:
-            size = o16(2 + ICC_OVERHEAD_LEN + len(marker))
-            extra += (
-                b"\xFF\xE2"
-                + size
-                + b"ICC_PROFILE\0"
-                + o8(i)
-                + o8(len(markers))
-                + marker
-            )
-            i += 1
-
-    comment = info.get("comment", im.info.get("comment"))
-
-    # "progressive" is the official name, but older documentation
-    # says "progression"
-    # FIXME: issue a warning if the wrong form is used (post-1.1.7)
-    progressive = info.get("progressive", False) or info.get("progression", False)
-
-    optimize = info.get("optimize", False)
-
-    exif = info.get("exif", b"")
-    if isinstance(exif, Image.Exif):
-        exif = exif.tobytes()
-    if len(exif) > MAX_BYTES_IN_MARKER:
-        msg = "EXIF data is too long"
-        raise ValueError(msg)
-
-    # get keyword arguments
-    im.encoderconfig = (
-        quality,
-        progressive,
-        info.get("smooth", 0),
-        optimize,
-        info.get("streamtype", 0),
-        dpi[0],
-        dpi[1],
-        subsampling,
-        qtables,
-        comment,
-        extra,
-        exif,
-    )
-
-    # if we optimize, libjpeg needs a buffer big enough to hold the whole image
-    # in a shot. Guessing on the size, at im.size bytes. (raw pixel size is
-    # channels*size, this is a value that's been used in a django patch.
-    # https://github.com/matthewwithanm/django-imagekit/issues/50
-    bufsize = 0
-    if optimize or progressive:
-        # CMYK can be bigger
-        if im.mode == "CMYK":
-            bufsize = 4 * im.size[0] * im.size[1]
-        # keep sets quality to -1, but the actual value may be high.
-        elif quality >= 95 or quality == -1:
-            bufsize = 2 * im.size[0] * im.size[1]
-        else:
-            bufsize = im.size[0] * im.size[1]
-
-    # The EXIF info needs to be written as one block, + APP1, + one spare byte.
-    # Ensure that our buffer is big enough. Same with the icc_profile block.
-    bufsize = max(ImageFile.MAXBLOCK, bufsize, len(exif) + 5, len(extra) + 1)
-
-    ImageFile._save(im, fp, [("jpeg", (0, 0) + im.size, 0, rawmode)], bufsize)
-
-
-def _save_cjpeg(im, fp, filename):
-    # ALTERNATIVE: handle JPEGs via the IJG command line utilities.
-    tempfile = im._dump()
-    subprocess.check_call(["cjpeg", "-outfile", filename, tempfile])
-    try:
-        os.unlink(tempfile)
-    except OSError:
-        pass
-
-
-##
-# Factory for making JPEG and MPO instances
-def jpeg_factory(fp=None, filename=None):
-    im = JpegImageFile(fp, filename)
-    try:
-        mpheader = im._getmp()
-        if mpheader[45057] > 1:
-            # It's actually an MPO
-            from .MpoImagePlugin import MpoImageFile
-
-            # Don't reload everything, just convert it.
-            im = MpoImageFile.adopt(im, mpheader)
-    except (TypeError, IndexError):
-        # It is really a JPEG
-        pass
-    except SyntaxError:
-        warnings.warn(
-            "Image appears to be a malformed MPO file, it will be "
-            "interpreted as a base JPEG file"
-        )
-    return im
-
-
-# ---------------------------------------------------------------------
-# Registry stuff
-
-Image.register_open(JpegImageFile.format, jpeg_factory, _accept)
-Image.register_save(JpegImageFile.format, _save)
-
-Image.register_extensions(JpegImageFile.format, [".jfif", ".jpe", ".jpg", ".jpeg"])
-
-Image.register_mime(JpegImageFile.format, "image/jpeg")
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/catalogue/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/catalogue/__init__.py
deleted file mode 100644
index f13587f940af23f8e2cf24488a3f6d0bb44c520e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/catalogue/__init__.py
+++ /dev/null
@@ -1,244 +0,0 @@
-from typing import Sequence, Any, Dict, Tuple, Callable, Optional, TypeVar, Union
-from typing import List
-import inspect
-
-try:  # Python 3.8
-    import importlib.metadata as importlib_metadata
-except ImportError:
-    from . import _importlib_metadata as importlib_metadata  # type: ignore
-
-# Only ever call this once for performance reasons
-AVAILABLE_ENTRY_POINTS = importlib_metadata.entry_points()  # type: ignore
-
-# This is where functions will be registered
-REGISTRY: Dict[Tuple[str, ...], Any] = {}
-
-
-InFunc = TypeVar("InFunc")
-
-
-def create(*namespace: str, entry_points: bool = False) -> "Registry":
-    """Create a new registry.
-
-    *namespace (str): The namespace, e.g. "spacy" or "spacy", "architectures".
-    entry_points (bool): Accept registered functions from entry points.
-    RETURNS (Registry): The Registry object.
-    """
-    if check_exists(*namespace):
-        raise RegistryError(f"Namespace already exists: {namespace}")
-    return Registry(namespace, entry_points=entry_points)
-
-
-class Registry(object):
-    def __init__(self, namespace: Sequence[str], entry_points: bool = False) -> None:
-        """Initialize a new registry.
-
-        namespace (Sequence[str]): The namespace.
-        entry_points (bool): Whether to also check for entry points.
-        """
-        self.namespace = namespace
-        self.entry_point_namespace = "_".join(namespace)
-        self.entry_points = entry_points
-
-    def __contains__(self, name: str) -> bool:
-        """Check whether a name is in the registry.
-
-        name (str): The name to check.
-        RETURNS (bool): Whether the name is in the registry.
-        """
-        namespace = tuple(list(self.namespace) + [name])
-        has_entry_point = self.entry_points and self.get_entry_point(name)
-        return has_entry_point or namespace in REGISTRY
-
-    def __call__(
-        self, name: str, func: Optional[Any] = None
-    ) -> Callable[[InFunc], InFunc]:
-        """Register a function for a given namespace. Same as Registry.register.
-
-        name (str): The name to register under the namespace.
-        func (Any): Optional function to register (if not used as decorator).
-        RETURNS (Callable): The decorator.
-        """
-        return self.register(name, func=func)
-
-    def register(
-        self, name: str, *, func: Optional[Any] = None
-    ) -> Callable[[InFunc], InFunc]:
-        """Register a function for a given namespace.
-
-        name (str): The name to register under the namespace.
-        func (Any): Optional function to register (if not used as decorator).
-        RETURNS (Callable): The decorator.
-        """
-
-        def do_registration(func):
-            _set(list(self.namespace) + [name], func)
-            return func
-
-        if func is not None:
-            return do_registration(func)
-        return do_registration
-
-    def get(self, name: str) -> Any:
-        """Get the registered function for a given name.
-
-        name (str): The name.
-        RETURNS (Any): The registered function.
-        """
-        if self.entry_points:
-            from_entry_point = self.get_entry_point(name)
-            if from_entry_point:
-                return from_entry_point
-        namespace = list(self.namespace) + [name]
-        if not check_exists(*namespace):
-            current_namespace = " -> ".join(self.namespace)
-            available = ", ".join(sorted(self.get_all().keys())) or "none"
-            raise RegistryError(
-                f"Cant't find '{name}' in registry {current_namespace}. Available names: {available}"
-            )
-        return _get(namespace)
-
-    def get_all(self) -> Dict[str, Any]:
-        """Get a all functions for a given namespace.
-
-        namespace (Tuple[str]): The namespace to get.
-        RETURNS (Dict[str, Any]): The functions, keyed by name.
-        """
-        global REGISTRY
-        result = {}
-        if self.entry_points:
-            result.update(self.get_entry_points())
-        for keys, value in REGISTRY.copy().items():
-            if len(self.namespace) == len(keys) - 1 and all(
-                self.namespace[i] == keys[i] for i in range(len(self.namespace))
-            ):
-                result[keys[-1]] = value
-        return result
-
-    def get_entry_points(self) -> Dict[str, Any]:
-        """Get registered entry points from other packages for this namespace.
-
-        RETURNS (Dict[str, Any]): Entry points, keyed by name.
-        """
-        result = {}
-        for entry_point in self._get_entry_points():
-            result[entry_point.name] = entry_point.load()
-        return result
-
-    def get_entry_point(self, name: str, default: Optional[Any] = None) -> Any:
-        """Check if registered entry point is available for a given name in the
-        namespace and load it. Otherwise, return the default value.
-
-        name (str): Name of entry point to load.
-        default (Any): The default value to return.
-        RETURNS (Any): The loaded entry point or the default value.
-        """
-        for entry_point in self._get_entry_points():
-            if entry_point.name == name:
-                return entry_point.load()
-        return default
-
-    def _get_entry_points(self) -> List[importlib_metadata.EntryPoint]:
-        if hasattr(AVAILABLE_ENTRY_POINTS, "select"):
-            return AVAILABLE_ENTRY_POINTS.select(group=self.entry_point_namespace)
-        else:  # dict
-            return AVAILABLE_ENTRY_POINTS.get(self.entry_point_namespace, [])
-
-    def find(self, name: str) -> Dict[str, Optional[Union[str, int]]]:
-        """Find the information about a registered function, including the
-        module and path to the file it's defined in, the line number and the
-        docstring, if available.
-
-        name (str): Name of the registered function.
-        RETURNS (Dict[str, Optional[Union[str, int]]]): The function info.
-        """
-        func = self.get(name)
-        module = inspect.getmodule(func)
-        # These calls will fail for Cython modules so we need to work around them
-        line_no: Optional[int] = None
-        file_name: Optional[str] = None
-        try:
-            _, line_no = inspect.getsourcelines(func)
-            file_name = inspect.getfile(func)
-        except (TypeError, ValueError):
-            pass
-        docstring = inspect.getdoc(func)
-        return {
-            "module": module.__name__ if module else None,
-            "file": file_name,
-            "line_no": line_no,
-            "docstring": inspect.cleandoc(docstring) if docstring else None,
-        }
-
-
-def check_exists(*namespace: str) -> bool:
-    """Check if a namespace exists.
-
-    *namespace (str): The namespace.
-    RETURNS (bool): Whether the namespace exists.
-    """
-    return namespace in REGISTRY
-
-
-def _get(namespace: Sequence[str]) -> Any:
-    """Get the value for a given namespace.
-
-    namespace (Sequence[str]): The namespace.
-    RETURNS (Any): The value for the namespace.
-    """
-    global REGISTRY
-    if not all(isinstance(name, str) for name in namespace):
-        raise ValueError(
-            f"Invalid namespace. Expected tuple of strings, but got: {namespace}"
-        )
-    namespace = tuple(namespace)
-    if namespace not in REGISTRY:
-        raise RegistryError(f"Can't get namespace {namespace} (not in registry)")
-    return REGISTRY[namespace]
-
-
-def _get_all(namespace: Sequence[str]) -> Dict[Tuple[str, ...], Any]:
-    """Get all matches for a given namespace, e.g. ("a", "b", "c") and
-    ("a", "b") for namespace ("a", "b").
-
-    namespace (Sequence[str]): The namespace.
-    RETURNS (Dict[Tuple[str], Any]): All entries for the namespace, keyed
-        by their full namespaces.
-    """
-    global REGISTRY
-    result = {}
-    for keys, value in REGISTRY.copy().items():
-        if len(namespace) <= len(keys) and all(
-            namespace[i] == keys[i] for i in range(len(namespace))
-        ):
-            result[keys] = value
-    return result
-
-
-def _set(namespace: Sequence[str], func: Any) -> None:
-    """Set a value for a given namespace.
-
-    namespace (Sequence[str]): The namespace.
-    func (Callable): The value to set.
-    """
-    global REGISTRY
-    REGISTRY[tuple(namespace)] = func
-
-
-def _remove(namespace: Sequence[str]) -> Any:
-    """Remove a value for a given namespace.
-
-    namespace (Sequence[str]): The namespace.
-    RETURNS (Any): The removed value.
-    """
-    global REGISTRY
-    namespace = tuple(namespace)
-    if namespace not in REGISTRY:
-        raise RegistryError(f"Can't get namespace {namespace} (not in registry)")
-    removed = REGISTRY[namespace]
-    del REGISTRY[namespace]
-    return removed
-
-
-class RegistryError(ValueError):
-    pass
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/core.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/core.py
deleted file mode 100644
index cc65e896bf2d754d74b54a84ac501b80127f83ca..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/core.py
+++ /dev/null
@@ -1,3042 +0,0 @@
-import enum
-import errno
-import inspect
-import os
-import sys
-import typing as t
-from collections import abc
-from contextlib import contextmanager
-from contextlib import ExitStack
-from functools import update_wrapper
-from gettext import gettext as _
-from gettext import ngettext
-from itertools import repeat
-from types import TracebackType
-
-from . import types
-from .exceptions import Abort
-from .exceptions import BadParameter
-from .exceptions import ClickException
-from .exceptions import Exit
-from .exceptions import MissingParameter
-from .exceptions import UsageError
-from .formatting import HelpFormatter
-from .formatting import join_options
-from .globals import pop_context
-from .globals import push_context
-from .parser import _flag_needs_value
-from .parser import OptionParser
-from .parser import split_opt
-from .termui import confirm
-from .termui import prompt
-from .termui import style
-from .utils import _detect_program_name
-from .utils import _expand_args
-from .utils import echo
-from .utils import make_default_short_help
-from .utils import make_str
-from .utils import PacifyFlushWrapper
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-    from .shell_completion import CompletionItem
-
-F = t.TypeVar("F", bound=t.Callable[..., t.Any])
-V = t.TypeVar("V")
-
-
-def _complete_visible_commands(
-    ctx: "Context", incomplete: str
-) -> t.Iterator[t.Tuple[str, "Command"]]:
-    """List all the subcommands of a group that start with the
-    incomplete value and aren't hidden.
-
-    :param ctx: Invocation context for the group.
-    :param incomplete: Value being completed. May be empty.
-    """
-    multi = t.cast(MultiCommand, ctx.command)
-
-    for name in multi.list_commands(ctx):
-        if name.startswith(incomplete):
-            command = multi.get_command(ctx, name)
-
-            if command is not None and not command.hidden:
-                yield name, command
-
-
-def _check_multicommand(
-    base_command: "MultiCommand", cmd_name: str, cmd: "Command", register: bool = False
-) -> None:
-    if not base_command.chain or not isinstance(cmd, MultiCommand):
-        return
-    if register:
-        hint = (
-            "It is not possible to add multi commands as children to"
-            " another multi command that is in chain mode."
-        )
-    else:
-        hint = (
-            "Found a multi command as subcommand to a multi command"
-            " that is in chain mode. This is not supported."
-        )
-    raise RuntimeError(
-        f"{hint}. Command {base_command.name!r} is set to chain and"
-        f" {cmd_name!r} was added as a subcommand but it in itself is a"
-        f" multi command. ({cmd_name!r} is a {type(cmd).__name__}"
-        f" within a chained {type(base_command).__name__} named"
-        f" {base_command.name!r})."
-    )
-
-
-def batch(iterable: t.Iterable[V], batch_size: int) -> t.List[t.Tuple[V, ...]]:
-    return list(zip(*repeat(iter(iterable), batch_size)))
-
-
-@contextmanager
-def augment_usage_errors(
-    ctx: "Context", param: t.Optional["Parameter"] = None
-) -> t.Iterator[None]:
-    """Context manager that attaches extra information to exceptions."""
-    try:
-        yield
-    except BadParameter as e:
-        if e.ctx is None:
-            e.ctx = ctx
-        if param is not None and e.param is None:
-            e.param = param
-        raise
-    except UsageError as e:
-        if e.ctx is None:
-            e.ctx = ctx
-        raise
-
-
-def iter_params_for_processing(
-    invocation_order: t.Sequence["Parameter"],
-    declaration_order: t.Sequence["Parameter"],
-) -> t.List["Parameter"]:
-    """Given a sequence of parameters in the order as should be considered
-    for processing and an iterable of parameters that exist, this returns
-    a list in the correct order as they should be processed.
-    """
-
-    def sort_key(item: "Parameter") -> t.Tuple[bool, float]:
-        try:
-            idx: float = invocation_order.index(item)
-        except ValueError:
-            idx = float("inf")
-
-        return not item.is_eager, idx
-
-    return sorted(declaration_order, key=sort_key)
-
-
-class ParameterSource(enum.Enum):
-    """This is an :class:`~enum.Enum` that indicates the source of a
-    parameter's value.
-
-    Use :meth:`click.Context.get_parameter_source` to get the
-    source for a parameter by name.
-
-    .. versionchanged:: 8.0
-        Use :class:`~enum.Enum` and drop the ``validate`` method.
-
-    .. versionchanged:: 8.0
-        Added the ``PROMPT`` value.
-    """
-
-    COMMANDLINE = enum.auto()
-    """The value was provided by the command line args."""
-    ENVIRONMENT = enum.auto()
-    """The value was provided with an environment variable."""
-    DEFAULT = enum.auto()
-    """Used the default specified by the parameter."""
-    DEFAULT_MAP = enum.auto()
-    """Used a default provided by :attr:`Context.default_map`."""
-    PROMPT = enum.auto()
-    """Used a prompt to confirm a default or provide a value."""
-
-
-class Context:
-    """The context is a special internal object that holds state relevant
-    for the script execution at every single level.  It's normally invisible
-    to commands unless they opt-in to getting access to it.
-
-    The context is useful as it can pass internal objects around and can
-    control special execution features such as reading data from
-    environment variables.
-
-    A context can be used as context manager in which case it will call
-    :meth:`close` on teardown.
-
-    :param command: the command class for this context.
-    :param parent: the parent context.
-    :param info_name: the info name for this invocation.  Generally this
-                      is the most descriptive name for the script or
-                      command.  For the toplevel script it is usually
-                      the name of the script, for commands below it it's
-                      the name of the script.
-    :param obj: an arbitrary object of user data.
-    :param auto_envvar_prefix: the prefix to use for automatic environment
-                               variables.  If this is `None` then reading
-                               from environment variables is disabled.  This
-                               does not affect manually set environment
-                               variables which are always read.
-    :param default_map: a dictionary (like object) with default values
-                        for parameters.
-    :param terminal_width: the width of the terminal.  The default is
-                           inherit from parent context.  If no context
-                           defines the terminal width then auto
-                           detection will be applied.
-    :param max_content_width: the maximum width for content rendered by
-                              Click (this currently only affects help
-                              pages).  This defaults to 80 characters if
-                              not overridden.  In other words: even if the
-                              terminal is larger than that, Click will not
-                              format things wider than 80 characters by
-                              default.  In addition to that, formatters might
-                              add some safety mapping on the right.
-    :param resilient_parsing: if this flag is enabled then Click will
-                              parse without any interactivity or callback
-                              invocation.  Default values will also be
-                              ignored.  This is useful for implementing
-                              things such as completion support.
-    :param allow_extra_args: if this is set to `True` then extra arguments
-                             at the end will not raise an error and will be
-                             kept on the context.  The default is to inherit
-                             from the command.
-    :param allow_interspersed_args: if this is set to `False` then options
-                                    and arguments cannot be mixed.  The
-                                    default is to inherit from the command.
-    :param ignore_unknown_options: instructs click to ignore options it does
-                                   not know and keeps them for later
-                                   processing.
-    :param help_option_names: optionally a list of strings that define how
-                              the default help parameter is named.  The
-                              default is ``['--help']``.
-    :param token_normalize_func: an optional function that is used to
-                                 normalize tokens (options, choices,
-                                 etc.).  This for instance can be used to
-                                 implement case insensitive behavior.
-    :param color: controls if the terminal supports ANSI colors or not.  The
-                  default is autodetection.  This is only needed if ANSI
-                  codes are used in texts that Click prints which is by
-                  default not the case.  This for instance would affect
-                  help output.
-    :param show_default: Show the default value for commands. If this
-        value is not set, it defaults to the value from the parent
-        context. ``Command.show_default`` overrides this default for the
-        specific command.
-
-    .. versionchanged:: 8.1
-        The ``show_default`` parameter is overridden by
-        ``Command.show_default``, instead of the other way around.
-
-    .. versionchanged:: 8.0
-        The ``show_default`` parameter defaults to the value from the
-        parent context.
-
-    .. versionchanged:: 7.1
-       Added the ``show_default`` parameter.
-
-    .. versionchanged:: 4.0
-        Added the ``color``, ``ignore_unknown_options``, and
-        ``max_content_width`` parameters.
-
-    .. versionchanged:: 3.0
-        Added the ``allow_extra_args`` and ``allow_interspersed_args``
-        parameters.
-
-    .. versionchanged:: 2.0
-        Added the ``resilient_parsing``, ``help_option_names``, and
-        ``token_normalize_func`` parameters.
-    """
-
-    #: The formatter class to create with :meth:`make_formatter`.
-    #:
-    #: .. versionadded:: 8.0
-    formatter_class: t.Type["HelpFormatter"] = HelpFormatter
-
-    def __init__(
-        self,
-        command: "Command",
-        parent: t.Optional["Context"] = None,
-        info_name: t.Optional[str] = None,
-        obj: t.Optional[t.Any] = None,
-        auto_envvar_prefix: t.Optional[str] = None,
-        default_map: t.Optional[t.MutableMapping[str, t.Any]] = None,
-        terminal_width: t.Optional[int] = None,
-        max_content_width: t.Optional[int] = None,
-        resilient_parsing: bool = False,
-        allow_extra_args: t.Optional[bool] = None,
-        allow_interspersed_args: t.Optional[bool] = None,
-        ignore_unknown_options: t.Optional[bool] = None,
-        help_option_names: t.Optional[t.List[str]] = None,
-        token_normalize_func: t.Optional[t.Callable[[str], str]] = None,
-        color: t.Optional[bool] = None,
-        show_default: t.Optional[bool] = None,
-    ) -> None:
-        #: the parent context or `None` if none exists.
-        self.parent = parent
-        #: the :class:`Command` for this context.
-        self.command = command
-        #: the descriptive information name
-        self.info_name = info_name
-        #: Map of parameter names to their parsed values. Parameters
-        #: with ``expose_value=False`` are not stored.
-        self.params: t.Dict[str, t.Any] = {}
-        #: the leftover arguments.
-        self.args: t.List[str] = []
-        #: protected arguments.  These are arguments that are prepended
-        #: to `args` when certain parsing scenarios are encountered but
-        #: must be never propagated to another arguments.  This is used
-        #: to implement nested parsing.
-        self.protected_args: t.List[str] = []
-        #: the collected prefixes of the command's options.
-        self._opt_prefixes: t.Set[str] = set(parent._opt_prefixes) if parent else set()
-
-        if obj is None and parent is not None:
-            obj = parent.obj
-
-        #: the user object stored.
-        self.obj: t.Any = obj
-        self._meta: t.Dict[str, t.Any] = getattr(parent, "meta", {})
-
-        #: A dictionary (-like object) with defaults for parameters.
-        if (
-            default_map is None
-            and info_name is not None
-            and parent is not None
-            and parent.default_map is not None
-        ):
-            default_map = parent.default_map.get(info_name)
-
-        self.default_map: t.Optional[t.MutableMapping[str, t.Any]] = default_map
-
-        #: This flag indicates if a subcommand is going to be executed. A
-        #: group callback can use this information to figure out if it's
-        #: being executed directly or because the execution flow passes
-        #: onwards to a subcommand. By default it's None, but it can be
-        #: the name of the subcommand to execute.
-        #:
-        #: If chaining is enabled this will be set to ``'*'`` in case
-        #: any commands are executed.  It is however not possible to
-        #: figure out which ones.  If you require this knowledge you
-        #: should use a :func:`result_callback`.
-        self.invoked_subcommand: t.Optional[str] = None
-
-        if terminal_width is None and parent is not None:
-            terminal_width = parent.terminal_width
-
-        #: The width of the terminal (None is autodetection).
-        self.terminal_width: t.Optional[int] = terminal_width
-
-        if max_content_width is None and parent is not None:
-            max_content_width = parent.max_content_width
-
-        #: The maximum width of formatted content (None implies a sensible
-        #: default which is 80 for most things).
-        self.max_content_width: t.Optional[int] = max_content_width
-
-        if allow_extra_args is None:
-            allow_extra_args = command.allow_extra_args
-
-        #: Indicates if the context allows extra args or if it should
-        #: fail on parsing.
-        #:
-        #: .. versionadded:: 3.0
-        self.allow_extra_args = allow_extra_args
-
-        if allow_interspersed_args is None:
-            allow_interspersed_args = command.allow_interspersed_args
-
-        #: Indicates if the context allows mixing of arguments and
-        #: options or not.
-        #:
-        #: .. versionadded:: 3.0
-        self.allow_interspersed_args: bool = allow_interspersed_args
-
-        if ignore_unknown_options is None:
-            ignore_unknown_options = command.ignore_unknown_options
-
-        #: Instructs click to ignore options that a command does not
-        #: understand and will store it on the context for later
-        #: processing.  This is primarily useful for situations where you
-        #: want to call into external programs.  Generally this pattern is
-        #: strongly discouraged because it's not possibly to losslessly
-        #: forward all arguments.
-        #:
-        #: .. versionadded:: 4.0
-        self.ignore_unknown_options: bool = ignore_unknown_options
-
-        if help_option_names is None:
-            if parent is not None:
-                help_option_names = parent.help_option_names
-            else:
-                help_option_names = ["--help"]
-
-        #: The names for the help options.
-        self.help_option_names: t.List[str] = help_option_names
-
-        if token_normalize_func is None and parent is not None:
-            token_normalize_func = parent.token_normalize_func
-
-        #: An optional normalization function for tokens.  This is
-        #: options, choices, commands etc.
-        self.token_normalize_func: t.Optional[
-            t.Callable[[str], str]
-        ] = token_normalize_func
-
-        #: Indicates if resilient parsing is enabled.  In that case Click
-        #: will do its best to not cause any failures and default values
-        #: will be ignored. Useful for completion.
-        self.resilient_parsing: bool = resilient_parsing
-
-        # If there is no envvar prefix yet, but the parent has one and
-        # the command on this level has a name, we can expand the envvar
-        # prefix automatically.
-        if auto_envvar_prefix is None:
-            if (
-                parent is not None
-                and parent.auto_envvar_prefix is not None
-                and self.info_name is not None
-            ):
-                auto_envvar_prefix = (
-                    f"{parent.auto_envvar_prefix}_{self.info_name.upper()}"
-                )
-        else:
-            auto_envvar_prefix = auto_envvar_prefix.upper()
-
-        if auto_envvar_prefix is not None:
-            auto_envvar_prefix = auto_envvar_prefix.replace("-", "_")
-
-        self.auto_envvar_prefix: t.Optional[str] = auto_envvar_prefix
-
-        if color is None and parent is not None:
-            color = parent.color
-
-        #: Controls if styling output is wanted or not.
-        self.color: t.Optional[bool] = color
-
-        if show_default is None and parent is not None:
-            show_default = parent.show_default
-
-        #: Show option default values when formatting help text.
-        self.show_default: t.Optional[bool] = show_default
-
-        self._close_callbacks: t.List[t.Callable[[], t.Any]] = []
-        self._depth = 0
-        self._parameter_source: t.Dict[str, ParameterSource] = {}
-        self._exit_stack = ExitStack()
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        """Gather information that could be useful for a tool generating
-        user-facing documentation. This traverses the entire CLI
-        structure.
-
-        .. code-block:: python
-
-            with Context(cli) as ctx:
-                info = ctx.to_info_dict()
-
-        .. versionadded:: 8.0
-        """
-        return {
-            "command": self.command.to_info_dict(self),
-            "info_name": self.info_name,
-            "allow_extra_args": self.allow_extra_args,
-            "allow_interspersed_args": self.allow_interspersed_args,
-            "ignore_unknown_options": self.ignore_unknown_options,
-            "auto_envvar_prefix": self.auto_envvar_prefix,
-        }
-
-    def __enter__(self) -> "Context":
-        self._depth += 1
-        push_context(self)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: t.Optional[t.Type[BaseException]],
-        exc_value: t.Optional[BaseException],
-        tb: t.Optional[TracebackType],
-    ) -> None:
-        self._depth -= 1
-        if self._depth == 0:
-            self.close()
-        pop_context()
-
-    @contextmanager
-    def scope(self, cleanup: bool = True) -> t.Iterator["Context"]:
-        """This helper method can be used with the context object to promote
-        it to the current thread local (see :func:`get_current_context`).
-        The default behavior of this is to invoke the cleanup functions which
-        can be disabled by setting `cleanup` to `False`.  The cleanup
-        functions are typically used for things such as closing file handles.
-
-        If the cleanup is intended the context object can also be directly
-        used as a context manager.
-
-        Example usage::
-
-            with ctx.scope():
-                assert get_current_context() is ctx
-
-        This is equivalent::
-
-            with ctx:
-                assert get_current_context() is ctx
-
-        .. versionadded:: 5.0
-
-        :param cleanup: controls if the cleanup functions should be run or
-                        not.  The default is to run these functions.  In
-                        some situations the context only wants to be
-                        temporarily pushed in which case this can be disabled.
-                        Nested pushes automatically defer the cleanup.
-        """
-        if not cleanup:
-            self._depth += 1
-        try:
-            with self as rv:
-                yield rv
-        finally:
-            if not cleanup:
-                self._depth -= 1
-
-    @property
-    def meta(self) -> t.Dict[str, t.Any]:
-        """This is a dictionary which is shared with all the contexts
-        that are nested.  It exists so that click utilities can store some
-        state here if they need to.  It is however the responsibility of
-        that code to manage this dictionary well.
-
-        The keys are supposed to be unique dotted strings.  For instance
-        module paths are a good choice for it.  What is stored in there is
-        irrelevant for the operation of click.  However what is important is
-        that code that places data here adheres to the general semantics of
-        the system.
-
-        Example usage::
-
-            LANG_KEY = f'{__name__}.lang'
-
-            def set_language(value):
-                ctx = get_current_context()
-                ctx.meta[LANG_KEY] = value
-
-            def get_language():
-                return get_current_context().meta.get(LANG_KEY, 'en_US')
-
-        .. versionadded:: 5.0
-        """
-        return self._meta
-
-    def make_formatter(self) -> HelpFormatter:
-        """Creates the :class:`~click.HelpFormatter` for the help and
-        usage output.
-
-        To quickly customize the formatter class used without overriding
-        this method, set the :attr:`formatter_class` attribute.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`formatter_class` attribute.
-        """
-        return self.formatter_class(
-            width=self.terminal_width, max_width=self.max_content_width
-        )
-
-    def with_resource(self, context_manager: t.ContextManager[V]) -> V:
-        """Register a resource as if it were used in a ``with``
-        statement. The resource will be cleaned up when the context is
-        popped.
-
-        Uses :meth:`contextlib.ExitStack.enter_context`. It calls the
-        resource's ``__enter__()`` method and returns the result. When
-        the context is popped, it closes the stack, which calls the
-        resource's ``__exit__()`` method.
-
-        To register a cleanup function for something that isn't a
-        context manager, use :meth:`call_on_close`. Or use something
-        from :mod:`contextlib` to turn it into a context manager first.
-
-        .. code-block:: python
-
-            @click.group()
-            @click.option("--name")
-            @click.pass_context
-            def cli(ctx):
-                ctx.obj = ctx.with_resource(connect_db(name))
-
-        :param context_manager: The context manager to enter.
-        :return: Whatever ``context_manager.__enter__()`` returns.
-
-        .. versionadded:: 8.0
-        """
-        return self._exit_stack.enter_context(context_manager)
-
-    def call_on_close(self, f: t.Callable[..., t.Any]) -> t.Callable[..., t.Any]:
-        """Register a function to be called when the context tears down.
-
-        This can be used to close resources opened during the script
-        execution. Resources that support Python's context manager
-        protocol which would be used in a ``with`` statement should be
-        registered with :meth:`with_resource` instead.
-
-        :param f: The function to execute on teardown.
-        """
-        return self._exit_stack.callback(f)
-
-    def close(self) -> None:
-        """Invoke all close callbacks registered with
-        :meth:`call_on_close`, and exit all context managers entered
-        with :meth:`with_resource`.
-        """
-        self._exit_stack.close()
-        # In case the context is reused, create a new exit stack.
-        self._exit_stack = ExitStack()
-
-    @property
-    def command_path(self) -> str:
-        """The computed command path.  This is used for the ``usage``
-        information on the help page.  It's automatically created by
-        combining the info names of the chain of contexts to the root.
-        """
-        rv = ""
-        if self.info_name is not None:
-            rv = self.info_name
-        if self.parent is not None:
-            parent_command_path = [self.parent.command_path]
-
-            if isinstance(self.parent.command, Command):
-                for param in self.parent.command.get_params(self):
-                    parent_command_path.extend(param.get_usage_pieces(self))
-
-            rv = f"{' '.join(parent_command_path)} {rv}"
-        return rv.lstrip()
-
-    def find_root(self) -> "Context":
-        """Finds the outermost context."""
-        node = self
-        while node.parent is not None:
-            node = node.parent
-        return node
-
-    def find_object(self, object_type: t.Type[V]) -> t.Optional[V]:
-        """Finds the closest object of a given type."""
-        node: t.Optional["Context"] = self
-
-        while node is not None:
-            if isinstance(node.obj, object_type):
-                return node.obj
-
-            node = node.parent
-
-        return None
-
-    def ensure_object(self, object_type: t.Type[V]) -> V:
-        """Like :meth:`find_object` but sets the innermost object to a
-        new instance of `object_type` if it does not exist.
-        """
-        rv = self.find_object(object_type)
-        if rv is None:
-            self.obj = rv = object_type()
-        return rv
-
-    @t.overload
-    def lookup_default(
-        self, name: str, call: "te.Literal[True]" = True
-    ) -> t.Optional[t.Any]:
-        ...
-
-    @t.overload
-    def lookup_default(
-        self, name: str, call: "te.Literal[False]" = ...
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        ...
-
-    def lookup_default(self, name: str, call: bool = True) -> t.Optional[t.Any]:
-        """Get the default for a parameter from :attr:`default_map`.
-
-        :param name: Name of the parameter.
-        :param call: If the default is a callable, call it. Disable to
-            return the callable instead.
-
-        .. versionchanged:: 8.0
-            Added the ``call`` parameter.
-        """
-        if self.default_map is not None:
-            value = self.default_map.get(name)
-
-            if call and callable(value):
-                return value()
-
-            return value
-
-        return None
-
-    def fail(self, message: str) -> "te.NoReturn":
-        """Aborts the execution of the program with a specific error
-        message.
-
-        :param message: the error message to fail with.
-        """
-        raise UsageError(message, self)
-
-    def abort(self) -> "te.NoReturn":
-        """Aborts the script."""
-        raise Abort()
-
-    def exit(self, code: int = 0) -> "te.NoReturn":
-        """Exits the application with a given exit code."""
-        raise Exit(code)
-
-    def get_usage(self) -> str:
-        """Helper method to get formatted usage string for the current
-        context and command.
-        """
-        return self.command.get_usage(self)
-
-    def get_help(self) -> str:
-        """Helper method to get formatted help page for the current
-        context and command.
-        """
-        return self.command.get_help(self)
-
-    def _make_sub_context(self, command: "Command") -> "Context":
-        """Create a new context of the same type as this context, but
-        for a new command.
-
-        :meta private:
-        """
-        return type(self)(command, info_name=command.name, parent=self)
-
-    @t.overload
-    def invoke(
-        __self,  # noqa: B902
-        __callback: "t.Callable[..., V]",
-        *args: t.Any,
-        **kwargs: t.Any,
-    ) -> V:
-        ...
-
-    @t.overload
-    def invoke(
-        __self,  # noqa: B902
-        __callback: "Command",
-        *args: t.Any,
-        **kwargs: t.Any,
-    ) -> t.Any:
-        ...
-
-    def invoke(
-        __self,  # noqa: B902
-        __callback: t.Union["Command", "t.Callable[..., V]"],
-        *args: t.Any,
-        **kwargs: t.Any,
-    ) -> t.Union[t.Any, V]:
-        """Invokes a command callback in exactly the way it expects.  There
-        are two ways to invoke this method:
-
-        1.  the first argument can be a callback and all other arguments and
-            keyword arguments are forwarded directly to the function.
-        2.  the first argument is a click command object.  In that case all
-            arguments are forwarded as well but proper click parameters
-            (options and click arguments) must be keyword arguments and Click
-            will fill in defaults.
-
-        Note that before Click 3.2 keyword arguments were not properly filled
-        in against the intention of this code and no context was created.  For
-        more information about this change and why it was done in a bugfix
-        release see :ref:`upgrade-to-3.2`.
-
-        .. versionchanged:: 8.0
-            All ``kwargs`` are tracked in :attr:`params` so they will be
-            passed if :meth:`forward` is called at multiple levels.
-        """
-        if isinstance(__callback, Command):
-            other_cmd = __callback
-
-            if other_cmd.callback is None:
-                raise TypeError(
-                    "The given command does not have a callback that can be invoked."
-                )
-            else:
-                __callback = t.cast("t.Callable[..., V]", other_cmd.callback)
-
-            ctx = __self._make_sub_context(other_cmd)
-
-            for param in other_cmd.params:
-                if param.name not in kwargs and param.expose_value:
-                    kwargs[param.name] = param.type_cast_value(  # type: ignore
-                        ctx, param.get_default(ctx)
-                    )
-
-            # Track all kwargs as params, so that forward() will pass
-            # them on in subsequent calls.
-            ctx.params.update(kwargs)
-        else:
-            ctx = __self
-
-        with augment_usage_errors(__self):
-            with ctx:
-                return __callback(*args, **kwargs)
-
-    def forward(
-        __self, __cmd: "Command", *args: t.Any, **kwargs: t.Any  # noqa: B902
-    ) -> t.Any:
-        """Similar to :meth:`invoke` but fills in default keyword
-        arguments from the current context if the other command expects
-        it.  This cannot invoke callbacks directly, only other commands.
-
-        .. versionchanged:: 8.0
-            All ``kwargs`` are tracked in :attr:`params` so they will be
-            passed if ``forward`` is called at multiple levels.
-        """
-        # Can only forward to other commands, not direct callbacks.
-        if not isinstance(__cmd, Command):
-            raise TypeError("Callback is not a command.")
-
-        for param in __self.params:
-            if param not in kwargs:
-                kwargs[param] = __self.params[param]
-
-        return __self.invoke(__cmd, *args, **kwargs)
-
-    def set_parameter_source(self, name: str, source: ParameterSource) -> None:
-        """Set the source of a parameter. This indicates the location
-        from which the value of the parameter was obtained.
-
-        :param name: The name of the parameter.
-        :param source: A member of :class:`~click.core.ParameterSource`.
-        """
-        self._parameter_source[name] = source
-
-    def get_parameter_source(self, name: str) -> t.Optional[ParameterSource]:
-        """Get the source of a parameter. This indicates the location
-        from which the value of the parameter was obtained.
-
-        This can be useful for determining when a user specified a value
-        on the command line that is the same as the default value. It
-        will be :attr:`~click.core.ParameterSource.DEFAULT` only if the
-        value was actually taken from the default.
-
-        :param name: The name of the parameter.
-        :rtype: ParameterSource
-
-        .. versionchanged:: 8.0
-            Returns ``None`` if the parameter was not provided from any
-            source.
-        """
-        return self._parameter_source.get(name)
-
-
-class BaseCommand:
-    """The base command implements the minimal API contract of commands.
-    Most code will never use this as it does not implement a lot of useful
-    functionality but it can act as the direct subclass of alternative
-    parsing methods that do not depend on the Click parser.
-
-    For instance, this can be used to bridge Click and other systems like
-    argparse or docopt.
-
-    Because base commands do not implement a lot of the API that other
-    parts of Click take for granted, they are not supported for all
-    operations.  For instance, they cannot be used with the decorators
-    usually and they have no built-in callback system.
-
-    .. versionchanged:: 2.0
-       Added the `context_settings` parameter.
-
-    :param name: the name of the command to use unless a group overrides it.
-    :param context_settings: an optional dictionary with defaults that are
-                             passed to the context object.
-    """
-
-    #: The context class to create with :meth:`make_context`.
-    #:
-    #: .. versionadded:: 8.0
-    context_class: t.Type[Context] = Context
-    #: the default for the :attr:`Context.allow_extra_args` flag.
-    allow_extra_args = False
-    #: the default for the :attr:`Context.allow_interspersed_args` flag.
-    allow_interspersed_args = True
-    #: the default for the :attr:`Context.ignore_unknown_options` flag.
-    ignore_unknown_options = False
-
-    def __init__(
-        self,
-        name: t.Optional[str],
-        context_settings: t.Optional[t.MutableMapping[str, t.Any]] = None,
-    ) -> None:
-        #: the name the command thinks it has.  Upon registering a command
-        #: on a :class:`Group` the group will default the command name
-        #: with this information.  You should instead use the
-        #: :class:`Context`\'s :attr:`~Context.info_name` attribute.
-        self.name = name
-
-        if context_settings is None:
-            context_settings = {}
-
-        #: an optional dictionary with defaults passed to the context.
-        self.context_settings: t.MutableMapping[str, t.Any] = context_settings
-
-    def to_info_dict(self, ctx: Context) -> t.Dict[str, t.Any]:
-        """Gather information that could be useful for a tool generating
-        user-facing documentation. This traverses the entire structure
-        below this command.
-
-        Use :meth:`click.Context.to_info_dict` to traverse the entire
-        CLI structure.
-
-        :param ctx: A :class:`Context` representing this command.
-
-        .. versionadded:: 8.0
-        """
-        return {"name": self.name}
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} {self.name}>"
-
-    def get_usage(self, ctx: Context) -> str:
-        raise NotImplementedError("Base commands cannot get usage")
-
-    def get_help(self, ctx: Context) -> str:
-        raise NotImplementedError("Base commands cannot get help")
-
-    def make_context(
-        self,
-        info_name: t.Optional[str],
-        args: t.List[str],
-        parent: t.Optional[Context] = None,
-        **extra: t.Any,
-    ) -> Context:
-        """This function when given an info name and arguments will kick
-        off the parsing and create a new :class:`Context`.  It does not
-        invoke the actual command callback though.
-
-        To quickly customize the context class used without overriding
-        this method, set the :attr:`context_class` attribute.
-
-        :param info_name: the info name for this invocation.  Generally this
-                          is the most descriptive name for the script or
-                          command.  For the toplevel script it's usually
-                          the name of the script, for commands below it's
-                          the name of the command.
-        :param args: the arguments to parse as list of strings.
-        :param parent: the parent context if available.
-        :param extra: extra keyword arguments forwarded to the context
-                      constructor.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`context_class` attribute.
-        """
-        for key, value in self.context_settings.items():
-            if key not in extra:
-                extra[key] = value
-
-        ctx = self.context_class(
-            self, info_name=info_name, parent=parent, **extra  # type: ignore
-        )
-
-        with ctx.scope(cleanup=False):
-            self.parse_args(ctx, args)
-        return ctx
-
-    def parse_args(self, ctx: Context, args: t.List[str]) -> t.List[str]:
-        """Given a context and a list of arguments this creates the parser
-        and parses the arguments, then modifies the context as necessary.
-        This is automatically invoked by :meth:`make_context`.
-        """
-        raise NotImplementedError("Base commands do not know how to parse arguments.")
-
-    def invoke(self, ctx: Context) -> t.Any:
-        """Given a context, this invokes the command.  The default
-        implementation is raising a not implemented error.
-        """
-        raise NotImplementedError("Base commands are not invocable by default")
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. Looks
-        at the names of chained multi-commands.
-
-        Any command could be part of a chained multi-command, so sibling
-        commands are valid at any point during command completion. Other
-        command classes will return more completions.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        results: t.List["CompletionItem"] = []
-
-        while ctx.parent is not None:
-            ctx = ctx.parent
-
-            if isinstance(ctx.command, MultiCommand) and ctx.command.chain:
-                results.extend(
-                    CompletionItem(name, help=command.get_short_help_str())
-                    for name, command in _complete_visible_commands(ctx, incomplete)
-                    if name not in ctx.protected_args
-                )
-
-        return results
-
-    @t.overload
-    def main(
-        self,
-        args: t.Optional[t.Sequence[str]] = None,
-        prog_name: t.Optional[str] = None,
-        complete_var: t.Optional[str] = None,
-        standalone_mode: "te.Literal[True]" = True,
-        **extra: t.Any,
-    ) -> "te.NoReturn":
-        ...
-
-    @t.overload
-    def main(
-        self,
-        args: t.Optional[t.Sequence[str]] = None,
-        prog_name: t.Optional[str] = None,
-        complete_var: t.Optional[str] = None,
-        standalone_mode: bool = ...,
-        **extra: t.Any,
-    ) -> t.Any:
-        ...
-
-    def main(
-        self,
-        args: t.Optional[t.Sequence[str]] = None,
-        prog_name: t.Optional[str] = None,
-        complete_var: t.Optional[str] = None,
-        standalone_mode: bool = True,
-        windows_expand_args: bool = True,
-        **extra: t.Any,
-    ) -> t.Any:
-        """This is the way to invoke a script with all the bells and
-        whistles as a command line application.  This will always terminate
-        the application after a call.  If this is not wanted, ``SystemExit``
-        needs to be caught.
-
-        This method is also available by directly calling the instance of
-        a :class:`Command`.
-
-        :param args: the arguments that should be used for parsing.  If not
-                     provided, ``sys.argv[1:]`` is used.
-        :param prog_name: the program name that should be used.  By default
-                          the program name is constructed by taking the file
-                          name from ``sys.argv[0]``.
-        :param complete_var: the environment variable that controls the
-                             bash completion support.  The default is
-                             ``"_<prog_name>_COMPLETE"`` with prog_name in
-                             uppercase.
-        :param standalone_mode: the default behavior is to invoke the script
-                                in standalone mode.  Click will then
-                                handle exceptions and convert them into
-                                error messages and the function will never
-                                return but shut down the interpreter.  If
-                                this is set to `False` they will be
-                                propagated to the caller and the return
-                                value of this function is the return value
-                                of :meth:`invoke`.
-        :param windows_expand_args: Expand glob patterns, user dir, and
-            env vars in command line args on Windows.
-        :param extra: extra keyword arguments are forwarded to the context
-                      constructor.  See :class:`Context` for more information.
-
-        .. versionchanged:: 8.0.1
-            Added the ``windows_expand_args`` parameter to allow
-            disabling command line arg expansion on Windows.
-
-        .. versionchanged:: 8.0
-            When taking arguments from ``sys.argv`` on Windows, glob
-            patterns, user dir, and env vars are expanded.
-
-        .. versionchanged:: 3.0
-           Added the ``standalone_mode`` parameter.
-        """
-        if args is None:
-            args = sys.argv[1:]
-
-            if os.name == "nt" and windows_expand_args:
-                args = _expand_args(args)
-        else:
-            args = list(args)
-
-        if prog_name is None:
-            prog_name = _detect_program_name()
-
-        # Process shell completion requests and exit early.
-        self._main_shell_completion(extra, prog_name, complete_var)
-
-        try:
-            try:
-                with self.make_context(prog_name, args, **extra) as ctx:
-                    rv = self.invoke(ctx)
-                    if not standalone_mode:
-                        return rv
-                    # it's not safe to `ctx.exit(rv)` here!
-                    # note that `rv` may actually contain data like "1" which
-                    # has obvious effects
-                    # more subtle case: `rv=[None, None]` can come out of
-                    # chained commands which all returned `None` -- so it's not
-                    # even always obvious that `rv` indicates success/failure
-                    # by its truthiness/falsiness
-                    ctx.exit()
-            except (EOFError, KeyboardInterrupt) as e:
-                echo(file=sys.stderr)
-                raise Abort() from e
-            except ClickException as e:
-                if not standalone_mode:
-                    raise
-                e.show()
-                sys.exit(e.exit_code)
-            except OSError as e:
-                if e.errno == errno.EPIPE:
-                    sys.stdout = t.cast(t.TextIO, PacifyFlushWrapper(sys.stdout))
-                    sys.stderr = t.cast(t.TextIO, PacifyFlushWrapper(sys.stderr))
-                    sys.exit(1)
-                else:
-                    raise
-        except Exit as e:
-            if standalone_mode:
-                sys.exit(e.exit_code)
-            else:
-                # in non-standalone mode, return the exit code
-                # note that this is only reached if `self.invoke` above raises
-                # an Exit explicitly -- thus bypassing the check there which
-                # would return its result
-                # the results of non-standalone execution may therefore be
-                # somewhat ambiguous: if there are codepaths which lead to
-                # `ctx.exit(1)` and to `return 1`, the caller won't be able to
-                # tell the difference between the two
-                return e.exit_code
-        except Abort:
-            if not standalone_mode:
-                raise
-            echo(_("Aborted!"), file=sys.stderr)
-            sys.exit(1)
-
-    def _main_shell_completion(
-        self,
-        ctx_args: t.MutableMapping[str, t.Any],
-        prog_name: str,
-        complete_var: t.Optional[str] = None,
-    ) -> None:
-        """Check if the shell is asking for tab completion, process
-        that, then exit early. Called from :meth:`main` before the
-        program is invoked.
-
-        :param prog_name: Name of the executable in the shell.
-        :param complete_var: Name of the environment variable that holds
-            the completion instruction. Defaults to
-            ``_{PROG_NAME}_COMPLETE``.
-
-        .. versionchanged:: 8.2.0
-            Dots (``.``) in ``prog_name`` are replaced with underscores (``_``).
-        """
-        if complete_var is None:
-            complete_name = prog_name.replace("-", "_").replace(".", "_")
-            complete_var = f"_{complete_name}_COMPLETE".upper()
-
-        instruction = os.environ.get(complete_var)
-
-        if not instruction:
-            return
-
-        from .shell_completion import shell_complete
-
-        rv = shell_complete(self, ctx_args, prog_name, complete_var, instruction)
-        sys.exit(rv)
-
-    def __call__(self, *args: t.Any, **kwargs: t.Any) -> t.Any:
-        """Alias for :meth:`main`."""
-        return self.main(*args, **kwargs)
-
-
-class Command(BaseCommand):
-    """Commands are the basic building block of command line interfaces in
-    Click.  A basic command handles command line parsing and might dispatch
-    more parsing to commands nested below it.
-
-    :param name: the name of the command to use unless a group overrides it.
-    :param context_settings: an optional dictionary with defaults that are
-                             passed to the context object.
-    :param callback: the callback to invoke.  This is optional.
-    :param params: the parameters to register with this command.  This can
-                   be either :class:`Option` or :class:`Argument` objects.
-    :param help: the help string to use for this command.
-    :param epilog: like the help string but it's printed at the end of the
-                   help page after everything else.
-    :param short_help: the short help to use for this command.  This is
-                       shown on the command listing of the parent command.
-    :param add_help_option: by default each command registers a ``--help``
-                            option.  This can be disabled by this parameter.
-    :param no_args_is_help: this controls what happens if no arguments are
-                            provided.  This option is disabled by default.
-                            If enabled this will add ``--help`` as argument
-                            if no arguments are passed
-    :param hidden: hide this command from help outputs.
-
-    :param deprecated: issues a message indicating that
-                             the command is deprecated.
-
-    .. versionchanged:: 8.1
-        ``help``, ``epilog``, and ``short_help`` are stored unprocessed,
-        all formatting is done when outputting help text, not at init,
-        and is done even if not using the ``@command`` decorator.
-
-    .. versionchanged:: 8.0
-        Added a ``repr`` showing the command name.
-
-    .. versionchanged:: 7.1
-        Added the ``no_args_is_help`` parameter.
-
-    .. versionchanged:: 2.0
-        Added the ``context_settings`` parameter.
-    """
-
-    def __init__(
-        self,
-        name: t.Optional[str],
-        context_settings: t.Optional[t.MutableMapping[str, t.Any]] = None,
-        callback: t.Optional[t.Callable[..., t.Any]] = None,
-        params: t.Optional[t.List["Parameter"]] = None,
-        help: t.Optional[str] = None,
-        epilog: t.Optional[str] = None,
-        short_help: t.Optional[str] = None,
-        options_metavar: t.Optional[str] = "[OPTIONS]",
-        add_help_option: bool = True,
-        no_args_is_help: bool = False,
-        hidden: bool = False,
-        deprecated: bool = False,
-    ) -> None:
-        super().__init__(name, context_settings)
-        #: the callback to execute when the command fires.  This might be
-        #: `None` in which case nothing happens.
-        self.callback = callback
-        #: the list of parameters for this command in the order they
-        #: should show up in the help page and execute.  Eager parameters
-        #: will automatically be handled before non eager ones.
-        self.params: t.List["Parameter"] = params or []
-        self.help = help
-        self.epilog = epilog
-        self.options_metavar = options_metavar
-        self.short_help = short_help
-        self.add_help_option = add_help_option
-        self.no_args_is_help = no_args_is_help
-        self.hidden = hidden
-        self.deprecated = deprecated
-
-    def to_info_dict(self, ctx: Context) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict(ctx)
-        info_dict.update(
-            params=[param.to_info_dict() for param in self.get_params(ctx)],
-            help=self.help,
-            epilog=self.epilog,
-            short_help=self.short_help,
-            hidden=self.hidden,
-            deprecated=self.deprecated,
-        )
-        return info_dict
-
-    def get_usage(self, ctx: Context) -> str:
-        """Formats the usage line into a string and returns it.
-
-        Calls :meth:`format_usage` internally.
-        """
-        formatter = ctx.make_formatter()
-        self.format_usage(ctx, formatter)
-        return formatter.getvalue().rstrip("\n")
-
-    def get_params(self, ctx: Context) -> t.List["Parameter"]:
-        rv = self.params
-        help_option = self.get_help_option(ctx)
-
-        if help_option is not None:
-            rv = [*rv, help_option]
-
-        return rv
-
-    def format_usage(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the usage line into the formatter.
-
-        This is a low-level method called by :meth:`get_usage`.
-        """
-        pieces = self.collect_usage_pieces(ctx)
-        formatter.write_usage(ctx.command_path, " ".join(pieces))
-
-    def collect_usage_pieces(self, ctx: Context) -> t.List[str]:
-        """Returns all the pieces that go into the usage line and returns
-        it as a list of strings.
-        """
-        rv = [self.options_metavar] if self.options_metavar else []
-
-        for param in self.get_params(ctx):
-            rv.extend(param.get_usage_pieces(ctx))
-
-        return rv
-
-    def get_help_option_names(self, ctx: Context) -> t.List[str]:
-        """Returns the names for the help option."""
-        all_names = set(ctx.help_option_names)
-        for param in self.params:
-            all_names.difference_update(param.opts)
-            all_names.difference_update(param.secondary_opts)
-        return list(all_names)
-
-    def get_help_option(self, ctx: Context) -> t.Optional["Option"]:
-        """Returns the help option object."""
-        help_options = self.get_help_option_names(ctx)
-
-        if not help_options or not self.add_help_option:
-            return None
-
-        def show_help(ctx: Context, param: "Parameter", value: str) -> None:
-            if value and not ctx.resilient_parsing:
-                echo(ctx.get_help(), color=ctx.color)
-                ctx.exit()
-
-        return Option(
-            help_options,
-            is_flag=True,
-            is_eager=True,
-            expose_value=False,
-            callback=show_help,
-            help=_("Show this message and exit."),
-        )
-
-    def make_parser(self, ctx: Context) -> OptionParser:
-        """Creates the underlying option parser for this command."""
-        parser = OptionParser(ctx)
-        for param in self.get_params(ctx):
-            param.add_to_parser(parser, ctx)
-        return parser
-
-    def get_help(self, ctx: Context) -> str:
-        """Formats the help into a string and returns it.
-
-        Calls :meth:`format_help` internally.
-        """
-        formatter = ctx.make_formatter()
-        self.format_help(ctx, formatter)
-        return formatter.getvalue().rstrip("\n")
-
-    def get_short_help_str(self, limit: int = 45) -> str:
-        """Gets short help for the command or makes it by shortening the
-        long help string.
-        """
-        if self.short_help:
-            text = inspect.cleandoc(self.short_help)
-        elif self.help:
-            text = make_default_short_help(self.help, limit)
-        else:
-            text = ""
-
-        if self.deprecated:
-            text = _("(Deprecated) {text}").format(text=text)
-
-        return text.strip()
-
-    def format_help(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the help into the formatter if it exists.
-
-        This is a low-level method called by :meth:`get_help`.
-
-        This calls the following methods:
-
-        -   :meth:`format_usage`
-        -   :meth:`format_help_text`
-        -   :meth:`format_options`
-        -   :meth:`format_epilog`
-        """
-        self.format_usage(ctx, formatter)
-        self.format_help_text(ctx, formatter)
-        self.format_options(ctx, formatter)
-        self.format_epilog(ctx, formatter)
-
-    def format_help_text(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the help text to the formatter if it exists."""
-        if self.help is not None:
-            # truncate the help text to the first form feed
-            text = inspect.cleandoc(self.help).partition("\f")[0]
-        else:
-            text = ""
-
-        if self.deprecated:
-            text = _("(Deprecated) {text}").format(text=text)
-
-        if text:
-            formatter.write_paragraph()
-
-            with formatter.indentation():
-                formatter.write_text(text)
-
-    def format_options(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes all the options into the formatter if they exist."""
-        opts = []
-        for param in self.get_params(ctx):
-            rv = param.get_help_record(ctx)
-            if rv is not None:
-                opts.append(rv)
-
-        if opts:
-            with formatter.section(_("Options")):
-                formatter.write_dl(opts)
-
-    def format_epilog(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the epilog into the formatter if it exists."""
-        if self.epilog:
-            epilog = inspect.cleandoc(self.epilog)
-            formatter.write_paragraph()
-
-            with formatter.indentation():
-                formatter.write_text(epilog)
-
-    def parse_args(self, ctx: Context, args: t.List[str]) -> t.List[str]:
-        if not args and self.no_args_is_help and not ctx.resilient_parsing:
-            echo(ctx.get_help(), color=ctx.color)
-            ctx.exit()
-
-        parser = self.make_parser(ctx)
-        opts, args, param_order = parser.parse_args(args=args)
-
-        for param in iter_params_for_processing(param_order, self.get_params(ctx)):
-            value, args = param.handle_parse_result(ctx, opts, args)
-
-        if args and not ctx.allow_extra_args and not ctx.resilient_parsing:
-            ctx.fail(
-                ngettext(
-                    "Got unexpected extra argument ({args})",
-                    "Got unexpected extra arguments ({args})",
-                    len(args),
-                ).format(args=" ".join(map(str, args)))
-            )
-
-        ctx.args = args
-        ctx._opt_prefixes.update(parser._opt_prefixes)
-        return args
-
-    def invoke(self, ctx: Context) -> t.Any:
-        """Given a context, this invokes the attached callback (if it exists)
-        in the right way.
-        """
-        if self.deprecated:
-            message = _(
-                "DeprecationWarning: The command {name!r} is deprecated."
-            ).format(name=self.name)
-            echo(style(message, fg="red"), err=True)
-
-        if self.callback is not None:
-            return ctx.invoke(self.callback, **ctx.params)
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. Looks
-        at the names of options and chained multi-commands.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        results: t.List["CompletionItem"] = []
-
-        if incomplete and not incomplete[0].isalnum():
-            for param in self.get_params(ctx):
-                if (
-                    not isinstance(param, Option)
-                    or param.hidden
-                    or (
-                        not param.multiple
-                        and ctx.get_parameter_source(param.name)  # type: ignore
-                        is ParameterSource.COMMANDLINE
-                    )
-                ):
-                    continue
-
-                results.extend(
-                    CompletionItem(name, help=param.help)
-                    for name in [*param.opts, *param.secondary_opts]
-                    if name.startswith(incomplete)
-                )
-
-        results.extend(super().shell_complete(ctx, incomplete))
-        return results
-
-
-class MultiCommand(Command):
-    """A multi command is the basic implementation of a command that
-    dispatches to subcommands.  The most common version is the
-    :class:`Group`.
-
-    :param invoke_without_command: this controls how the multi command itself
-                                   is invoked.  By default it's only invoked
-                                   if a subcommand is provided.
-    :param no_args_is_help: this controls what happens if no arguments are
-                            provided.  This option is enabled by default if
-                            `invoke_without_command` is disabled or disabled
-                            if it's enabled.  If enabled this will add
-                            ``--help`` as argument if no arguments are
-                            passed.
-    :param subcommand_metavar: the string that is used in the documentation
-                               to indicate the subcommand place.
-    :param chain: if this is set to `True` chaining of multiple subcommands
-                  is enabled.  This restricts the form of commands in that
-                  they cannot have optional arguments but it allows
-                  multiple commands to be chained together.
-    :param result_callback: The result callback to attach to this multi
-        command. This can be set or changed later with the
-        :meth:`result_callback` decorator.
-    :param attrs: Other command arguments described in :class:`Command`.
-    """
-
-    allow_extra_args = True
-    allow_interspersed_args = False
-
-    def __init__(
-        self,
-        name: t.Optional[str] = None,
-        invoke_without_command: bool = False,
-        no_args_is_help: t.Optional[bool] = None,
-        subcommand_metavar: t.Optional[str] = None,
-        chain: bool = False,
-        result_callback: t.Optional[t.Callable[..., t.Any]] = None,
-        **attrs: t.Any,
-    ) -> None:
-        super().__init__(name, **attrs)
-
-        if no_args_is_help is None:
-            no_args_is_help = not invoke_without_command
-
-        self.no_args_is_help = no_args_is_help
-        self.invoke_without_command = invoke_without_command
-
-        if subcommand_metavar is None:
-            if chain:
-                subcommand_metavar = "COMMAND1 [ARGS]... [COMMAND2 [ARGS]...]..."
-            else:
-                subcommand_metavar = "COMMAND [ARGS]..."
-
-        self.subcommand_metavar = subcommand_metavar
-        self.chain = chain
-        # The result callback that is stored. This can be set or
-        # overridden with the :func:`result_callback` decorator.
-        self._result_callback = result_callback
-
-        if self.chain:
-            for param in self.params:
-                if isinstance(param, Argument) and not param.required:
-                    raise RuntimeError(
-                        "Multi commands in chain mode cannot have"
-                        " optional arguments."
-                    )
-
-    def to_info_dict(self, ctx: Context) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict(ctx)
-        commands = {}
-
-        for name in self.list_commands(ctx):
-            command = self.get_command(ctx, name)
-
-            if command is None:
-                continue
-
-            sub_ctx = ctx._make_sub_context(command)
-
-            with sub_ctx.scope(cleanup=False):
-                commands[name] = command.to_info_dict(sub_ctx)
-
-        info_dict.update(commands=commands, chain=self.chain)
-        return info_dict
-
-    def collect_usage_pieces(self, ctx: Context) -> t.List[str]:
-        rv = super().collect_usage_pieces(ctx)
-        rv.append(self.subcommand_metavar)
-        return rv
-
-    def format_options(self, ctx: Context, formatter: HelpFormatter) -> None:
-        super().format_options(ctx, formatter)
-        self.format_commands(ctx, formatter)
-
-    def result_callback(self, replace: bool = False) -> t.Callable[[F], F]:
-        """Adds a result callback to the command.  By default if a
-        result callback is already registered this will chain them but
-        this can be disabled with the `replace` parameter.  The result
-        callback is invoked with the return value of the subcommand
-        (or the list of return values from all subcommands if chaining
-        is enabled) as well as the parameters as they would be passed
-        to the main callback.
-
-        Example::
-
-            @click.group()
-            @click.option('-i', '--input', default=23)
-            def cli(input):
-                return 42
-
-            @cli.result_callback()
-            def process_result(result, input):
-                return result + input
-
-        :param replace: if set to `True` an already existing result
-                        callback will be removed.
-
-        .. versionchanged:: 8.0
-            Renamed from ``resultcallback``.
-
-        .. versionadded:: 3.0
-        """
-
-        def decorator(f: F) -> F:
-            old_callback = self._result_callback
-
-            if old_callback is None or replace:
-                self._result_callback = f
-                return f
-
-            def function(__value, *args, **kwargs):  # type: ignore
-                inner = old_callback(__value, *args, **kwargs)
-                return f(inner, *args, **kwargs)
-
-            self._result_callback = rv = update_wrapper(t.cast(F, function), f)
-            return rv
-
-        return decorator
-
-    def format_commands(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Extra format methods for multi methods that adds all the commands
-        after the options.
-        """
-        commands = []
-        for subcommand in self.list_commands(ctx):
-            cmd = self.get_command(ctx, subcommand)
-            # What is this, the tool lied about a command.  Ignore it
-            if cmd is None:
-                continue
-            if cmd.hidden:
-                continue
-
-            commands.append((subcommand, cmd))
-
-        # allow for 3 times the default spacing
-        if len(commands):
-            limit = formatter.width - 6 - max(len(cmd[0]) for cmd in commands)
-
-            rows = []
-            for subcommand, cmd in commands:
-                help = cmd.get_short_help_str(limit)
-                rows.append((subcommand, help))
-
-            if rows:
-                with formatter.section(_("Commands")):
-                    formatter.write_dl(rows)
-
-    def parse_args(self, ctx: Context, args: t.List[str]) -> t.List[str]:
-        if not args and self.no_args_is_help and not ctx.resilient_parsing:
-            echo(ctx.get_help(), color=ctx.color)
-            ctx.exit()
-
-        rest = super().parse_args(ctx, args)
-
-        if self.chain:
-            ctx.protected_args = rest
-            ctx.args = []
-        elif rest:
-            ctx.protected_args, ctx.args = rest[:1], rest[1:]
-
-        return ctx.args
-
-    def invoke(self, ctx: Context) -> t.Any:
-        def _process_result(value: t.Any) -> t.Any:
-            if self._result_callback is not None:
-                value = ctx.invoke(self._result_callback, value, **ctx.params)
-            return value
-
-        if not ctx.protected_args:
-            if self.invoke_without_command:
-                # No subcommand was invoked, so the result callback is
-                # invoked with the group return value for regular
-                # groups, or an empty list for chained groups.
-                with ctx:
-                    rv = super().invoke(ctx)
-                    return _process_result([] if self.chain else rv)
-            ctx.fail(_("Missing command."))
-
-        # Fetch args back out
-        args = [*ctx.protected_args, *ctx.args]
-        ctx.args = []
-        ctx.protected_args = []
-
-        # If we're not in chain mode, we only allow the invocation of a
-        # single command but we also inform the current context about the
-        # name of the command to invoke.
-        if not self.chain:
-            # Make sure the context is entered so we do not clean up
-            # resources until the result processor has worked.
-            with ctx:
-                cmd_name, cmd, args = self.resolve_command(ctx, args)
-                assert cmd is not None
-                ctx.invoked_subcommand = cmd_name
-                super().invoke(ctx)
-                sub_ctx = cmd.make_context(cmd_name, args, parent=ctx)
-                with sub_ctx:
-                    return _process_result(sub_ctx.command.invoke(sub_ctx))
-
-        # In chain mode we create the contexts step by step, but after the
-        # base command has been invoked.  Because at that point we do not
-        # know the subcommands yet, the invoked subcommand attribute is
-        # set to ``*`` to inform the command that subcommands are executed
-        # but nothing else.
-        with ctx:
-            ctx.invoked_subcommand = "*" if args else None
-            super().invoke(ctx)
-
-            # Otherwise we make every single context and invoke them in a
-            # chain.  In that case the return value to the result processor
-            # is the list of all invoked subcommand's results.
-            contexts = []
-            while args:
-                cmd_name, cmd, args = self.resolve_command(ctx, args)
-                assert cmd is not None
-                sub_ctx = cmd.make_context(
-                    cmd_name,
-                    args,
-                    parent=ctx,
-                    allow_extra_args=True,
-                    allow_interspersed_args=False,
-                )
-                contexts.append(sub_ctx)
-                args, sub_ctx.args = sub_ctx.args, []
-
-            rv = []
-            for sub_ctx in contexts:
-                with sub_ctx:
-                    rv.append(sub_ctx.command.invoke(sub_ctx))
-            return _process_result(rv)
-
-    def resolve_command(
-        self, ctx: Context, args: t.List[str]
-    ) -> t.Tuple[t.Optional[str], t.Optional[Command], t.List[str]]:
-        cmd_name = make_str(args[0])
-        original_cmd_name = cmd_name
-
-        # Get the command
-        cmd = self.get_command(ctx, cmd_name)
-
-        # If we can't find the command but there is a normalization
-        # function available, we try with that one.
-        if cmd is None and ctx.token_normalize_func is not None:
-            cmd_name = ctx.token_normalize_func(cmd_name)
-            cmd = self.get_command(ctx, cmd_name)
-
-        # If we don't find the command we want to show an error message
-        # to the user that it was not provided.  However, there is
-        # something else we should do: if the first argument looks like
-        # an option we want to kick off parsing again for arguments to
-        # resolve things like --help which now should go to the main
-        # place.
-        if cmd is None and not ctx.resilient_parsing:
-            if split_opt(cmd_name)[0]:
-                self.parse_args(ctx, ctx.args)
-            ctx.fail(_("No such command {name!r}.").format(name=original_cmd_name))
-        return cmd_name if cmd else None, cmd, args[1:]
-
-    def get_command(self, ctx: Context, cmd_name: str) -> t.Optional[Command]:
-        """Given a context and a command name, this returns a
-        :class:`Command` object if it exists or returns `None`.
-        """
-        raise NotImplementedError
-
-    def list_commands(self, ctx: Context) -> t.List[str]:
-        """Returns a list of subcommand names in the order they should
-        appear.
-        """
-        return []
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. Looks
-        at the names of options, subcommands, and chained
-        multi-commands.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        results = [
-            CompletionItem(name, help=command.get_short_help_str())
-            for name, command in _complete_visible_commands(ctx, incomplete)
-        ]
-        results.extend(super().shell_complete(ctx, incomplete))
-        return results
-
-
-class Group(MultiCommand):
-    """A group allows a command to have subcommands attached. This is
-    the most common way to implement nesting in Click.
-
-    :param name: The name of the group command.
-    :param commands: A dict mapping names to :class:`Command` objects.
-        Can also be a list of :class:`Command`, which will use
-        :attr:`Command.name` to create the dict.
-    :param attrs: Other command arguments described in
-        :class:`MultiCommand`, :class:`Command`, and
-        :class:`BaseCommand`.
-
-    .. versionchanged:: 8.0
-        The ``commands`` argument can be a list of command objects.
-    """
-
-    #: If set, this is used by the group's :meth:`command` decorator
-    #: as the default :class:`Command` class. This is useful to make all
-    #: subcommands use a custom command class.
-    #:
-    #: .. versionadded:: 8.0
-    command_class: t.Optional[t.Type[Command]] = None
-
-    #: If set, this is used by the group's :meth:`group` decorator
-    #: as the default :class:`Group` class. This is useful to make all
-    #: subgroups use a custom group class.
-    #:
-    #: If set to the special value :class:`type` (literally
-    #: ``group_class = type``), this group's class will be used as the
-    #: default class. This makes a custom group class continue to make
-    #: custom groups.
-    #:
-    #: .. versionadded:: 8.0
-    group_class: t.Optional[t.Union[t.Type["Group"], t.Type[type]]] = None
-    # Literal[type] isn't valid, so use Type[type]
-
-    def __init__(
-        self,
-        name: t.Optional[str] = None,
-        commands: t.Optional[
-            t.Union[t.MutableMapping[str, Command], t.Sequence[Command]]
-        ] = None,
-        **attrs: t.Any,
-    ) -> None:
-        super().__init__(name, **attrs)
-
-        if commands is None:
-            commands = {}
-        elif isinstance(commands, abc.Sequence):
-            commands = {c.name: c for c in commands if c.name is not None}
-
-        #: The registered subcommands by their exported names.
-        self.commands: t.MutableMapping[str, Command] = commands
-
-    def add_command(self, cmd: Command, name: t.Optional[str] = None) -> None:
-        """Registers another :class:`Command` with this group.  If the name
-        is not provided, the name of the command is used.
-        """
-        name = name or cmd.name
-        if name is None:
-            raise TypeError("Command has no name.")
-        _check_multicommand(self, name, cmd, register=True)
-        self.commands[name] = cmd
-
-    @t.overload
-    def command(self, __func: t.Callable[..., t.Any]) -> Command:
-        ...
-
-    @t.overload
-    def command(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Callable[[t.Callable[..., t.Any]], Command]:
-        ...
-
-    def command(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Union[t.Callable[[t.Callable[..., t.Any]], Command], Command]:
-        """A shortcut decorator for declaring and attaching a command to
-        the group. This takes the same arguments as :func:`command` and
-        immediately registers the created command with this group by
-        calling :meth:`add_command`.
-
-        To customize the command class used, set the
-        :attr:`command_class` attribute.
-
-        .. versionchanged:: 8.1
-            This decorator can be applied without parentheses.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`command_class` attribute.
-        """
-        from .decorators import command
-
-        func: t.Optional[t.Callable[..., t.Any]] = None
-
-        if args and callable(args[0]):
-            assert (
-                len(args) == 1 and not kwargs
-            ), "Use 'command(**kwargs)(callable)' to provide arguments."
-            (func,) = args
-            args = ()
-
-        if self.command_class and kwargs.get("cls") is None:
-            kwargs["cls"] = self.command_class
-
-        def decorator(f: t.Callable[..., t.Any]) -> Command:
-            cmd: Command = command(*args, **kwargs)(f)
-            self.add_command(cmd)
-            return cmd
-
-        if func is not None:
-            return decorator(func)
-
-        return decorator
-
-    @t.overload
-    def group(self, __func: t.Callable[..., t.Any]) -> "Group":
-        ...
-
-    @t.overload
-    def group(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Callable[[t.Callable[..., t.Any]], "Group"]:
-        ...
-
-    def group(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Union[t.Callable[[t.Callable[..., t.Any]], "Group"], "Group"]:
-        """A shortcut decorator for declaring and attaching a group to
-        the group. This takes the same arguments as :func:`group` and
-        immediately registers the created group with this group by
-        calling :meth:`add_command`.
-
-        To customize the group class used, set the :attr:`group_class`
-        attribute.
-
-        .. versionchanged:: 8.1
-            This decorator can be applied without parentheses.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`group_class` attribute.
-        """
-        from .decorators import group
-
-        func: t.Optional[t.Callable[..., t.Any]] = None
-
-        if args and callable(args[0]):
-            assert (
-                len(args) == 1 and not kwargs
-            ), "Use 'group(**kwargs)(callable)' to provide arguments."
-            (func,) = args
-            args = ()
-
-        if self.group_class is not None and kwargs.get("cls") is None:
-            if self.group_class is type:
-                kwargs["cls"] = type(self)
-            else:
-                kwargs["cls"] = self.group_class
-
-        def decorator(f: t.Callable[..., t.Any]) -> "Group":
-            cmd: Group = group(*args, **kwargs)(f)
-            self.add_command(cmd)
-            return cmd
-
-        if func is not None:
-            return decorator(func)
-
-        return decorator
-
-    def get_command(self, ctx: Context, cmd_name: str) -> t.Optional[Command]:
-        return self.commands.get(cmd_name)
-
-    def list_commands(self, ctx: Context) -> t.List[str]:
-        return sorted(self.commands)
-
-
-class CommandCollection(MultiCommand):
-    """A command collection is a multi command that merges multiple multi
-    commands together into one.  This is a straightforward implementation
-    that accepts a list of different multi commands as sources and
-    provides all the commands for each of them.
-
-    See :class:`MultiCommand` and :class:`Command` for the description of
-    ``name`` and ``attrs``.
-    """
-
-    def __init__(
-        self,
-        name: t.Optional[str] = None,
-        sources: t.Optional[t.List[MultiCommand]] = None,
-        **attrs: t.Any,
-    ) -> None:
-        super().__init__(name, **attrs)
-        #: The list of registered multi commands.
-        self.sources: t.List[MultiCommand] = sources or []
-
-    def add_source(self, multi_cmd: MultiCommand) -> None:
-        """Adds a new multi command to the chain dispatcher."""
-        self.sources.append(multi_cmd)
-
-    def get_command(self, ctx: Context, cmd_name: str) -> t.Optional[Command]:
-        for source in self.sources:
-            rv = source.get_command(ctx, cmd_name)
-
-            if rv is not None:
-                if self.chain:
-                    _check_multicommand(self, cmd_name, rv)
-
-                return rv
-
-        return None
-
-    def list_commands(self, ctx: Context) -> t.List[str]:
-        rv: t.Set[str] = set()
-
-        for source in self.sources:
-            rv.update(source.list_commands(ctx))
-
-        return sorted(rv)
-
-
-def _check_iter(value: t.Any) -> t.Iterator[t.Any]:
-    """Check if the value is iterable but not a string. Raises a type
-    error, or return an iterator over the value.
-    """
-    if isinstance(value, str):
-        raise TypeError
-
-    return iter(value)
-
-
-class Parameter:
-    r"""A parameter to a command comes in two versions: they are either
-    :class:`Option`\s or :class:`Argument`\s.  Other subclasses are currently
-    not supported by design as some of the internals for parsing are
-    intentionally not finalized.
-
-    Some settings are supported by both options and arguments.
-
-    :param param_decls: the parameter declarations for this option or
-                        argument.  This is a list of flags or argument
-                        names.
-    :param type: the type that should be used.  Either a :class:`ParamType`
-                 or a Python type.  The latter is converted into the former
-                 automatically if supported.
-    :param required: controls if this is optional or not.
-    :param default: the default value if omitted.  This can also be a callable,
-                    in which case it's invoked when the default is needed
-                    without any arguments.
-    :param callback: A function to further process or validate the value
-        after type conversion. It is called as ``f(ctx, param, value)``
-        and must return the value. It is called for all sources,
-        including prompts.
-    :param nargs: the number of arguments to match.  If not ``1`` the return
-                  value is a tuple instead of single value.  The default for
-                  nargs is ``1`` (except if the type is a tuple, then it's
-                  the arity of the tuple). If ``nargs=-1``, all remaining
-                  parameters are collected.
-    :param metavar: how the value is represented in the help page.
-    :param expose_value: if this is `True` then the value is passed onwards
-                         to the command callback and stored on the context,
-                         otherwise it's skipped.
-    :param is_eager: eager values are processed before non eager ones.  This
-                     should not be set for arguments or it will inverse the
-                     order of processing.
-    :param envvar: a string or list of strings that are environment variables
-                   that should be checked.
-    :param shell_complete: A function that returns custom shell
-        completions. Used instead of the param's type completion if
-        given. Takes ``ctx, param, incomplete`` and must return a list
-        of :class:`~click.shell_completion.CompletionItem` or a list of
-        strings.
-
-    .. versionchanged:: 8.0
-        ``process_value`` validates required parameters and bounded
-        ``nargs``, and invokes the parameter callback before returning
-        the value. This allows the callback to validate prompts.
-        ``full_process_value`` is removed.
-
-    .. versionchanged:: 8.0
-        ``autocompletion`` is renamed to ``shell_complete`` and has new
-        semantics described above. The old name is deprecated and will
-        be removed in 8.1, until then it will be wrapped to match the
-        new requirements.
-
-    .. versionchanged:: 8.0
-        For ``multiple=True, nargs>1``, the default must be a list of
-        tuples.
-
-    .. versionchanged:: 8.0
-        Setting a default is no longer required for ``nargs>1``, it will
-        default to ``None``. ``multiple=True`` or ``nargs=-1`` will
-        default to ``()``.
-
-    .. versionchanged:: 7.1
-        Empty environment variables are ignored rather than taking the
-        empty string value. This makes it possible for scripts to clear
-        variables if they can't unset them.
-
-    .. versionchanged:: 2.0
-        Changed signature for parameter callback to also be passed the
-        parameter. The old callback format will still work, but it will
-        raise a warning to give you a chance to migrate the code easier.
-    """
-
-    param_type_name = "parameter"
-
-    def __init__(
-        self,
-        param_decls: t.Optional[t.Sequence[str]] = None,
-        type: t.Optional[t.Union[types.ParamType, t.Any]] = None,
-        required: bool = False,
-        default: t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]] = None,
-        callback: t.Optional[t.Callable[[Context, "Parameter", t.Any], t.Any]] = None,
-        nargs: t.Optional[int] = None,
-        multiple: bool = False,
-        metavar: t.Optional[str] = None,
-        expose_value: bool = True,
-        is_eager: bool = False,
-        envvar: t.Optional[t.Union[str, t.Sequence[str]]] = None,
-        shell_complete: t.Optional[
-            t.Callable[
-                [Context, "Parameter", str],
-                t.Union[t.List["CompletionItem"], t.List[str]],
-            ]
-        ] = None,
-    ) -> None:
-        self.name: t.Optional[str]
-        self.opts: t.List[str]
-        self.secondary_opts: t.List[str]
-        self.name, self.opts, self.secondary_opts = self._parse_decls(
-            param_decls or (), expose_value
-        )
-        self.type: types.ParamType = types.convert_type(type, default)
-
-        # Default nargs to what the type tells us if we have that
-        # information available.
-        if nargs is None:
-            if self.type.is_composite:
-                nargs = self.type.arity
-            else:
-                nargs = 1
-
-        self.required = required
-        self.callback = callback
-        self.nargs = nargs
-        self.multiple = multiple
-        self.expose_value = expose_value
-        self.default = default
-        self.is_eager = is_eager
-        self.metavar = metavar
-        self.envvar = envvar
-        self._custom_shell_complete = shell_complete
-
-        if __debug__:
-            if self.type.is_composite and nargs != self.type.arity:
-                raise ValueError(
-                    f"'nargs' must be {self.type.arity} (or None) for"
-                    f" type {self.type!r}, but it was {nargs}."
-                )
-
-            # Skip no default or callable default.
-            check_default = default if not callable(default) else None
-
-            if check_default is not None:
-                if multiple:
-                    try:
-                        # Only check the first value against nargs.
-                        check_default = next(_check_iter(check_default), None)
-                    except TypeError:
-                        raise ValueError(
-                            "'default' must be a list when 'multiple' is true."
-                        ) from None
-
-                # Can be None for multiple with empty default.
-                if nargs != 1 and check_default is not None:
-                    try:
-                        _check_iter(check_default)
-                    except TypeError:
-                        if multiple:
-                            message = (
-                                "'default' must be a list of lists when 'multiple' is"
-                                " true and 'nargs' != 1."
-                            )
-                        else:
-                            message = "'default' must be a list when 'nargs' != 1."
-
-                        raise ValueError(message) from None
-
-                    if nargs > 1 and len(check_default) != nargs:
-                        subject = "item length" if multiple else "length"
-                        raise ValueError(
-                            f"'default' {subject} must match nargs={nargs}."
-                        )
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        """Gather information that could be useful for a tool generating
-        user-facing documentation.
-
-        Use :meth:`click.Context.to_info_dict` to traverse the entire
-        CLI structure.
-
-        .. versionadded:: 8.0
-        """
-        return {
-            "name": self.name,
-            "param_type_name": self.param_type_name,
-            "opts": self.opts,
-            "secondary_opts": self.secondary_opts,
-            "type": self.type.to_info_dict(),
-            "required": self.required,
-            "nargs": self.nargs,
-            "multiple": self.multiple,
-            "default": self.default,
-            "envvar": self.envvar,
-        }
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} {self.name}>"
-
-    def _parse_decls(
-        self, decls: t.Sequence[str], expose_value: bool
-    ) -> t.Tuple[t.Optional[str], t.List[str], t.List[str]]:
-        raise NotImplementedError()
-
-    @property
-    def human_readable_name(self) -> str:
-        """Returns the human readable name of this parameter.  This is the
-        same as the name for options, but the metavar for arguments.
-        """
-        return self.name  # type: ignore
-
-    def make_metavar(self) -> str:
-        if self.metavar is not None:
-            return self.metavar
-
-        metavar = self.type.get_metavar(self)
-
-        if metavar is None:
-            metavar = self.type.name.upper()
-
-        if self.nargs != 1:
-            metavar += "..."
-
-        return metavar
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: "te.Literal[True]" = True
-    ) -> t.Optional[t.Any]:
-        ...
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: bool = ...
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        ...
-
-    def get_default(
-        self, ctx: Context, call: bool = True
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        """Get the default for the parameter. Tries
-        :meth:`Context.lookup_default` first, then the local default.
-
-        :param ctx: Current context.
-        :param call: If the default is a callable, call it. Disable to
-            return the callable instead.
-
-        .. versionchanged:: 8.0.2
-            Type casting is no longer performed when getting a default.
-
-        .. versionchanged:: 8.0.1
-            Type casting can fail in resilient parsing mode. Invalid
-            defaults will not prevent showing help text.
-
-        .. versionchanged:: 8.0
-            Looks at ``ctx.default_map`` first.
-
-        .. versionchanged:: 8.0
-            Added the ``call`` parameter.
-        """
-        value = ctx.lookup_default(self.name, call=False)  # type: ignore
-
-        if value is None:
-            value = self.default
-
-        if call and callable(value):
-            value = value()
-
-        return value
-
-    def add_to_parser(self, parser: OptionParser, ctx: Context) -> None:
-        raise NotImplementedError()
-
-    def consume_value(
-        self, ctx: Context, opts: t.Mapping[str, t.Any]
-    ) -> t.Tuple[t.Any, ParameterSource]:
-        value = opts.get(self.name)  # type: ignore
-        source = ParameterSource.COMMANDLINE
-
-        if value is None:
-            value = self.value_from_envvar(ctx)
-            source = ParameterSource.ENVIRONMENT
-
-        if value is None:
-            value = ctx.lookup_default(self.name)  # type: ignore
-            source = ParameterSource.DEFAULT_MAP
-
-        if value is None:
-            value = self.get_default(ctx)
-            source = ParameterSource.DEFAULT
-
-        return value, source
-
-    def type_cast_value(self, ctx: Context, value: t.Any) -> t.Any:
-        """Convert and validate a value against the option's
-        :attr:`type`, :attr:`multiple`, and :attr:`nargs`.
-        """
-        if value is None:
-            return () if self.multiple or self.nargs == -1 else None
-
-        def check_iter(value: t.Any) -> t.Iterator[t.Any]:
-            try:
-                return _check_iter(value)
-            except TypeError:
-                # This should only happen when passing in args manually,
-                # the parser should construct an iterable when parsing
-                # the command line.
-                raise BadParameter(
-                    _("Value must be an iterable."), ctx=ctx, param=self
-                ) from None
-
-        if self.nargs == 1 or self.type.is_composite:
-
-            def convert(value: t.Any) -> t.Any:
-                return self.type(value, param=self, ctx=ctx)
-
-        elif self.nargs == -1:
-
-            def convert(value: t.Any) -> t.Any:  # t.Tuple[t.Any, ...]
-                return tuple(self.type(x, self, ctx) for x in check_iter(value))
-
-        else:  # nargs > 1
-
-            def convert(value: t.Any) -> t.Any:  # t.Tuple[t.Any, ...]
-                value = tuple(check_iter(value))
-
-                if len(value) != self.nargs:
-                    raise BadParameter(
-                        ngettext(
-                            "Takes {nargs} values but 1 was given.",
-                            "Takes {nargs} values but {len} were given.",
-                            len(value),
-                        ).format(nargs=self.nargs, len=len(value)),
-                        ctx=ctx,
-                        param=self,
-                    )
-
-                return tuple(self.type(x, self, ctx) for x in value)
-
-        if self.multiple:
-            return tuple(convert(x) for x in check_iter(value))
-
-        return convert(value)
-
-    def value_is_missing(self, value: t.Any) -> bool:
-        if value is None:
-            return True
-
-        if (self.nargs != 1 or self.multiple) and value == ():
-            return True
-
-        return False
-
-    def process_value(self, ctx: Context, value: t.Any) -> t.Any:
-        value = self.type_cast_value(ctx, value)
-
-        if self.required and self.value_is_missing(value):
-            raise MissingParameter(ctx=ctx, param=self)
-
-        if self.callback is not None:
-            value = self.callback(ctx, self, value)
-
-        return value
-
-    def resolve_envvar_value(self, ctx: Context) -> t.Optional[str]:
-        if self.envvar is None:
-            return None
-
-        if isinstance(self.envvar, str):
-            rv = os.environ.get(self.envvar)
-
-            if rv:
-                return rv
-        else:
-            for envvar in self.envvar:
-                rv = os.environ.get(envvar)
-
-                if rv:
-                    return rv
-
-        return None
-
-    def value_from_envvar(self, ctx: Context) -> t.Optional[t.Any]:
-        rv: t.Optional[t.Any] = self.resolve_envvar_value(ctx)
-
-        if rv is not None and self.nargs != 1:
-            rv = self.type.split_envvar_value(rv)
-
-        return rv
-
-    def handle_parse_result(
-        self, ctx: Context, opts: t.Mapping[str, t.Any], args: t.List[str]
-    ) -> t.Tuple[t.Any, t.List[str]]:
-        with augment_usage_errors(ctx, param=self):
-            value, source = self.consume_value(ctx, opts)
-            ctx.set_parameter_source(self.name, source)  # type: ignore
-
-            try:
-                value = self.process_value(ctx, value)
-            except Exception:
-                if not ctx.resilient_parsing:
-                    raise
-
-                value = None
-
-        if self.expose_value:
-            ctx.params[self.name] = value  # type: ignore
-
-        return value, args
-
-    def get_help_record(self, ctx: Context) -> t.Optional[t.Tuple[str, str]]:
-        pass
-
-    def get_usage_pieces(self, ctx: Context) -> t.List[str]:
-        return []
-
-    def get_error_hint(self, ctx: Context) -> str:
-        """Get a stringified version of the param for use in error messages to
-        indicate which param caused the error.
-        """
-        hint_list = self.opts or [self.human_readable_name]
-        return " / ".join(f"'{x}'" for x in hint_list)
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. If a
-        ``shell_complete`` function was given during init, it is used.
-        Otherwise, the :attr:`type`
-        :meth:`~click.types.ParamType.shell_complete` function is used.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        if self._custom_shell_complete is not None:
-            results = self._custom_shell_complete(ctx, self, incomplete)
-
-            if results and isinstance(results[0], str):
-                from click.shell_completion import CompletionItem
-
-                results = [CompletionItem(c) for c in results]
-
-            return t.cast(t.List["CompletionItem"], results)
-
-        return self.type.shell_complete(ctx, self, incomplete)
-
-
-class Option(Parameter):
-    """Options are usually optional values on the command line and
-    have some extra features that arguments don't have.
-
-    All other parameters are passed onwards to the parameter constructor.
-
-    :param show_default: Show the default value for this option in its
-        help text. Values are not shown by default, unless
-        :attr:`Context.show_default` is ``True``. If this value is a
-        string, it shows that string in parentheses instead of the
-        actual value. This is particularly useful for dynamic options.
-        For single option boolean flags, the default remains hidden if
-        its value is ``False``.
-    :param show_envvar: Controls if an environment variable should be
-        shown on the help page. Normally, environment variables are not
-        shown.
-    :param prompt: If set to ``True`` or a non empty string then the
-        user will be prompted for input. If set to ``True`` the prompt
-        will be the option name capitalized.
-    :param confirmation_prompt: Prompt a second time to confirm the
-        value if it was prompted for. Can be set to a string instead of
-        ``True`` to customize the message.
-    :param prompt_required: If set to ``False``, the user will be
-        prompted for input only when the option was specified as a flag
-        without a value.
-    :param hide_input: If this is ``True`` then the input on the prompt
-        will be hidden from the user. This is useful for password input.
-    :param is_flag: forces this option to act as a flag.  The default is
-                    auto detection.
-    :param flag_value: which value should be used for this flag if it's
-                       enabled.  This is set to a boolean automatically if
-                       the option string contains a slash to mark two options.
-    :param multiple: if this is set to `True` then the argument is accepted
-                     multiple times and recorded.  This is similar to ``nargs``
-                     in how it works but supports arbitrary number of
-                     arguments.
-    :param count: this flag makes an option increment an integer.
-    :param allow_from_autoenv: if this is enabled then the value of this
-                               parameter will be pulled from an environment
-                               variable in case a prefix is defined on the
-                               context.
-    :param help: the help string.
-    :param hidden: hide this option from help outputs.
-    :param attrs: Other command arguments described in :class:`Parameter`.
-
-    .. versionchanged:: 8.1.0
-        Help text indentation is cleaned here instead of only in the
-        ``@option`` decorator.
-
-    .. versionchanged:: 8.1.0
-        The ``show_default`` parameter overrides
-        ``Context.show_default``.
-
-    .. versionchanged:: 8.1.0
-        The default of a single option boolean flag is not shown if the
-        default value is ``False``.
-
-    .. versionchanged:: 8.0.1
-        ``type`` is detected from ``flag_value`` if given.
-    """
-
-    param_type_name = "option"
-
-    def __init__(
-        self,
-        param_decls: t.Optional[t.Sequence[str]] = None,
-        show_default: t.Union[bool, str, None] = None,
-        prompt: t.Union[bool, str] = False,
-        confirmation_prompt: t.Union[bool, str] = False,
-        prompt_required: bool = True,
-        hide_input: bool = False,
-        is_flag: t.Optional[bool] = None,
-        flag_value: t.Optional[t.Any] = None,
-        multiple: bool = False,
-        count: bool = False,
-        allow_from_autoenv: bool = True,
-        type: t.Optional[t.Union[types.ParamType, t.Any]] = None,
-        help: t.Optional[str] = None,
-        hidden: bool = False,
-        show_choices: bool = True,
-        show_envvar: bool = False,
-        **attrs: t.Any,
-    ) -> None:
-        if help:
-            help = inspect.cleandoc(help)
-
-        default_is_missing = "default" not in attrs
-        super().__init__(param_decls, type=type, multiple=multiple, **attrs)
-
-        if prompt is True:
-            if self.name is None:
-                raise TypeError("'name' is required with 'prompt=True'.")
-
-            prompt_text: t.Optional[str] = self.name.replace("_", " ").capitalize()
-        elif prompt is False:
-            prompt_text = None
-        else:
-            prompt_text = prompt
-
-        self.prompt = prompt_text
-        self.confirmation_prompt = confirmation_prompt
-        self.prompt_required = prompt_required
-        self.hide_input = hide_input
-        self.hidden = hidden
-
-        # If prompt is enabled but not required, then the option can be
-        # used as a flag to indicate using prompt or flag_value.
-        self._flag_needs_value = self.prompt is not None and not self.prompt_required
-
-        if is_flag is None:
-            if flag_value is not None:
-                # Implicitly a flag because flag_value was set.
-                is_flag = True
-            elif self._flag_needs_value:
-                # Not a flag, but when used as a flag it shows a prompt.
-                is_flag = False
-            else:
-                # Implicitly a flag because flag options were given.
-                is_flag = bool(self.secondary_opts)
-        elif is_flag is False and not self._flag_needs_value:
-            # Not a flag, and prompt is not enabled, can be used as a
-            # flag if flag_value is set.
-            self._flag_needs_value = flag_value is not None
-
-        self.default: t.Union[t.Any, t.Callable[[], t.Any]]
-
-        if is_flag and default_is_missing and not self.required:
-            if multiple:
-                self.default = ()
-            else:
-                self.default = False
-
-        if flag_value is None:
-            flag_value = not self.default
-
-        self.type: types.ParamType
-        if is_flag and type is None:
-            # Re-guess the type from the flag value instead of the
-            # default.
-            self.type = types.convert_type(None, flag_value)
-
-        self.is_flag: bool = is_flag
-        self.is_bool_flag: bool = is_flag and isinstance(self.type, types.BoolParamType)
-        self.flag_value: t.Any = flag_value
-
-        # Counting
-        self.count = count
-        if count:
-            if type is None:
-                self.type = types.IntRange(min=0)
-            if default_is_missing:
-                self.default = 0
-
-        self.allow_from_autoenv = allow_from_autoenv
-        self.help = help
-        self.show_default = show_default
-        self.show_choices = show_choices
-        self.show_envvar = show_envvar
-
-        if __debug__:
-            if self.nargs == -1:
-                raise TypeError("nargs=-1 is not supported for options.")
-
-            if self.prompt and self.is_flag and not self.is_bool_flag:
-                raise TypeError("'prompt' is not valid for non-boolean flag.")
-
-            if not self.is_bool_flag and self.secondary_opts:
-                raise TypeError("Secondary flag is not valid for non-boolean flag.")
-
-            if self.is_bool_flag and self.hide_input and self.prompt is not None:
-                raise TypeError(
-                    "'prompt' with 'hide_input' is not valid for boolean flag."
-                )
-
-            if self.count:
-                if self.multiple:
-                    raise TypeError("'count' is not valid with 'multiple'.")
-
-                if self.is_flag:
-                    raise TypeError("'count' is not valid with 'is_flag'.")
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict.update(
-            help=self.help,
-            prompt=self.prompt,
-            is_flag=self.is_flag,
-            flag_value=self.flag_value,
-            count=self.count,
-            hidden=self.hidden,
-        )
-        return info_dict
-
-    def _parse_decls(
-        self, decls: t.Sequence[str], expose_value: bool
-    ) -> t.Tuple[t.Optional[str], t.List[str], t.List[str]]:
-        opts = []
-        secondary_opts = []
-        name = None
-        possible_names = []
-
-        for decl in decls:
-            if decl.isidentifier():
-                if name is not None:
-                    raise TypeError(f"Name '{name}' defined twice")
-                name = decl
-            else:
-                split_char = ";" if decl[:1] == "/" else "/"
-                if split_char in decl:
-                    first, second = decl.split(split_char, 1)
-                    first = first.rstrip()
-                    if first:
-                        possible_names.append(split_opt(first))
-                        opts.append(first)
-                    second = second.lstrip()
-                    if second:
-                        secondary_opts.append(second.lstrip())
-                    if first == second:
-                        raise ValueError(
-                            f"Boolean option {decl!r} cannot use the"
-                            " same flag for true/false."
-                        )
-                else:
-                    possible_names.append(split_opt(decl))
-                    opts.append(decl)
-
-        if name is None and possible_names:
-            possible_names.sort(key=lambda x: -len(x[0]))  # group long options first
-            name = possible_names[0][1].replace("-", "_").lower()
-            if not name.isidentifier():
-                name = None
-
-        if name is None:
-            if not expose_value:
-                return None, opts, secondary_opts
-            raise TypeError("Could not determine name for option")
-
-        if not opts and not secondary_opts:
-            raise TypeError(
-                f"No options defined but a name was passed ({name})."
-                " Did you mean to declare an argument instead? Did"
-                f" you mean to pass '--{name}'?"
-            )
-
-        return name, opts, secondary_opts
-
-    def add_to_parser(self, parser: OptionParser, ctx: Context) -> None:
-        if self.multiple:
-            action = "append"
-        elif self.count:
-            action = "count"
-        else:
-            action = "store"
-
-        if self.is_flag:
-            action = f"{action}_const"
-
-            if self.is_bool_flag and self.secondary_opts:
-                parser.add_option(
-                    obj=self, opts=self.opts, dest=self.name, action=action, const=True
-                )
-                parser.add_option(
-                    obj=self,
-                    opts=self.secondary_opts,
-                    dest=self.name,
-                    action=action,
-                    const=False,
-                )
-            else:
-                parser.add_option(
-                    obj=self,
-                    opts=self.opts,
-                    dest=self.name,
-                    action=action,
-                    const=self.flag_value,
-                )
-        else:
-            parser.add_option(
-                obj=self,
-                opts=self.opts,
-                dest=self.name,
-                action=action,
-                nargs=self.nargs,
-            )
-
-    def get_help_record(self, ctx: Context) -> t.Optional[t.Tuple[str, str]]:
-        if self.hidden:
-            return None
-
-        any_prefix_is_slash = False
-
-        def _write_opts(opts: t.Sequence[str]) -> str:
-            nonlocal any_prefix_is_slash
-
-            rv, any_slashes = join_options(opts)
-
-            if any_slashes:
-                any_prefix_is_slash = True
-
-            if not self.is_flag and not self.count:
-                rv += f" {self.make_metavar()}"
-
-            return rv
-
-        rv = [_write_opts(self.opts)]
-
-        if self.secondary_opts:
-            rv.append(_write_opts(self.secondary_opts))
-
-        help = self.help or ""
-        extra = []
-
-        if self.show_envvar:
-            envvar = self.envvar
-
-            if envvar is None:
-                if (
-                    self.allow_from_autoenv
-                    and ctx.auto_envvar_prefix is not None
-                    and self.name is not None
-                ):
-                    envvar = f"{ctx.auto_envvar_prefix}_{self.name.upper()}"
-
-            if envvar is not None:
-                var_str = (
-                    envvar
-                    if isinstance(envvar, str)
-                    else ", ".join(str(d) for d in envvar)
-                )
-                extra.append(_("env var: {var}").format(var=var_str))
-
-        # Temporarily enable resilient parsing to avoid type casting
-        # failing for the default. Might be possible to extend this to
-        # help formatting in general.
-        resilient = ctx.resilient_parsing
-        ctx.resilient_parsing = True
-
-        try:
-            default_value = self.get_default(ctx, call=False)
-        finally:
-            ctx.resilient_parsing = resilient
-
-        show_default = False
-        show_default_is_str = False
-
-        if self.show_default is not None:
-            if isinstance(self.show_default, str):
-                show_default_is_str = show_default = True
-            else:
-                show_default = self.show_default
-        elif ctx.show_default is not None:
-            show_default = ctx.show_default
-
-        if show_default_is_str or (show_default and (default_value is not None)):
-            if show_default_is_str:
-                default_string = f"({self.show_default})"
-            elif isinstance(default_value, (list, tuple)):
-                default_string = ", ".join(str(d) for d in default_value)
-            elif inspect.isfunction(default_value):
-                default_string = _("(dynamic)")
-            elif self.is_bool_flag and self.secondary_opts:
-                # For boolean flags that have distinct True/False opts,
-                # use the opt without prefix instead of the value.
-                default_string = split_opt(
-                    (self.opts if self.default else self.secondary_opts)[0]
-                )[1]
-            elif self.is_bool_flag and not self.secondary_opts and not default_value:
-                default_string = ""
-            else:
-                default_string = str(default_value)
-
-            if default_string:
-                extra.append(_("default: {default}").format(default=default_string))
-
-        if (
-            isinstance(self.type, types._NumberRangeBase)
-            # skip count with default range type
-            and not (self.count and self.type.min == 0 and self.type.max is None)
-        ):
-            range_str = self.type._describe_range()
-
-            if range_str:
-                extra.append(range_str)
-
-        if self.required:
-            extra.append(_("required"))
-
-        if extra:
-            extra_str = "; ".join(extra)
-            help = f"{help}  [{extra_str}]" if help else f"[{extra_str}]"
-
-        return ("; " if any_prefix_is_slash else " / ").join(rv), help
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: "te.Literal[True]" = True
-    ) -> t.Optional[t.Any]:
-        ...
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: bool = ...
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        ...
-
-    def get_default(
-        self, ctx: Context, call: bool = True
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        # If we're a non boolean flag our default is more complex because
-        # we need to look at all flags in the same group to figure out
-        # if we're the default one in which case we return the flag
-        # value as default.
-        if self.is_flag and not self.is_bool_flag:
-            for param in ctx.command.params:
-                if param.name == self.name and param.default:
-                    return t.cast(Option, param).flag_value
-
-            return None
-
-        return super().get_default(ctx, call=call)
-
-    def prompt_for_value(self, ctx: Context) -> t.Any:
-        """This is an alternative flow that can be activated in the full
-        value processing if a value does not exist.  It will prompt the
-        user until a valid value exists and then returns the processed
-        value as result.
-        """
-        assert self.prompt is not None
-
-        # Calculate the default before prompting anything to be stable.
-        default = self.get_default(ctx)
-
-        # If this is a prompt for a flag we need to handle this
-        # differently.
-        if self.is_bool_flag:
-            return confirm(self.prompt, default)
-
-        return prompt(
-            self.prompt,
-            default=default,
-            type=self.type,
-            hide_input=self.hide_input,
-            show_choices=self.show_choices,
-            confirmation_prompt=self.confirmation_prompt,
-            value_proc=lambda x: self.process_value(ctx, x),
-        )
-
-    def resolve_envvar_value(self, ctx: Context) -> t.Optional[str]:
-        rv = super().resolve_envvar_value(ctx)
-
-        if rv is not None:
-            return rv
-
-        if (
-            self.allow_from_autoenv
-            and ctx.auto_envvar_prefix is not None
-            and self.name is not None
-        ):
-            envvar = f"{ctx.auto_envvar_prefix}_{self.name.upper()}"
-            rv = os.environ.get(envvar)
-
-            if rv:
-                return rv
-
-        return None
-
-    def value_from_envvar(self, ctx: Context) -> t.Optional[t.Any]:
-        rv: t.Optional[t.Any] = self.resolve_envvar_value(ctx)
-
-        if rv is None:
-            return None
-
-        value_depth = (self.nargs != 1) + bool(self.multiple)
-
-        if value_depth > 0:
-            rv = self.type.split_envvar_value(rv)
-
-            if self.multiple and self.nargs != 1:
-                rv = batch(rv, self.nargs)
-
-        return rv
-
-    def consume_value(
-        self, ctx: Context, opts: t.Mapping[str, "Parameter"]
-    ) -> t.Tuple[t.Any, ParameterSource]:
-        value, source = super().consume_value(ctx, opts)
-
-        # The parser will emit a sentinel value if the option can be
-        # given as a flag without a value. This is different from None
-        # to distinguish from the flag not being given at all.
-        if value is _flag_needs_value:
-            if self.prompt is not None and not ctx.resilient_parsing:
-                value = self.prompt_for_value(ctx)
-                source = ParameterSource.PROMPT
-            else:
-                value = self.flag_value
-                source = ParameterSource.COMMANDLINE
-
-        elif (
-            self.multiple
-            and value is not None
-            and any(v is _flag_needs_value for v in value)
-        ):
-            value = [self.flag_value if v is _flag_needs_value else v for v in value]
-            source = ParameterSource.COMMANDLINE
-
-        # The value wasn't set, or used the param's default, prompt if
-        # prompting is enabled.
-        elif (
-            source in {None, ParameterSource.DEFAULT}
-            and self.prompt is not None
-            and (self.required or self.prompt_required)
-            and not ctx.resilient_parsing
-        ):
-            value = self.prompt_for_value(ctx)
-            source = ParameterSource.PROMPT
-
-        return value, source
-
-
-class Argument(Parameter):
-    """Arguments are positional parameters to a command.  They generally
-    provide fewer features than options but can have infinite ``nargs``
-    and are required by default.
-
-    All parameters are passed onwards to the constructor of :class:`Parameter`.
-    """
-
-    param_type_name = "argument"
-
-    def __init__(
-        self,
-        param_decls: t.Sequence[str],
-        required: t.Optional[bool] = None,
-        **attrs: t.Any,
-    ) -> None:
-        if required is None:
-            if attrs.get("default") is not None:
-                required = False
-            else:
-                required = attrs.get("nargs", 1) > 0
-
-        if "multiple" in attrs:
-            raise TypeError("__init__() got an unexpected keyword argument 'multiple'.")
-
-        super().__init__(param_decls, required=required, **attrs)
-
-        if __debug__:
-            if self.default is not None and self.nargs == -1:
-                raise TypeError("'default' is not supported for nargs=-1.")
-
-    @property
-    def human_readable_name(self) -> str:
-        if self.metavar is not None:
-            return self.metavar
-        return self.name.upper()  # type: ignore
-
-    def make_metavar(self) -> str:
-        if self.metavar is not None:
-            return self.metavar
-        var = self.type.get_metavar(self)
-        if not var:
-            var = self.name.upper()  # type: ignore
-        if not self.required:
-            var = f"[{var}]"
-        if self.nargs != 1:
-            var += "..."
-        return var
-
-    def _parse_decls(
-        self, decls: t.Sequence[str], expose_value: bool
-    ) -> t.Tuple[t.Optional[str], t.List[str], t.List[str]]:
-        if not decls:
-            if not expose_value:
-                return None, [], []
-            raise TypeError("Could not determine name for argument")
-        if len(decls) == 1:
-            name = arg = decls[0]
-            name = name.replace("-", "_").lower()
-        else:
-            raise TypeError(
-                "Arguments take exactly one parameter declaration, got"
-                f" {len(decls)}."
-            )
-        return name, [arg], []
-
-    def get_usage_pieces(self, ctx: Context) -> t.List[str]:
-        return [self.make_metavar()]
-
-    def get_error_hint(self, ctx: Context) -> str:
-        return f"'{self.make_metavar()}'"
-
-    def add_to_parser(self, parser: OptionParser, ctx: Context) -> None:
-        parser.add_argument(dest=self.name, nargs=self.nargs, obj=self)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/button.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/button.py
deleted file mode 100644
index 4d932c75becc1a324630fe6b1ad2442e229500b0..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/button.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""gr.Button() component."""
-
-from __future__ import annotations
-
-from typing import Callable, Literal
-
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import StringSerializable
-
-from gradio.components.base import Component, IOComponent, _Keywords
-from gradio.deprecation import warn_deprecation, warn_style_method_deprecation
-from gradio.events import Clickable
-
-set_documentation_group("component")
-
-
-@document()
-class Button(Clickable, IOComponent, StringSerializable):
-    """
-    Used to create a button, that can be assigned arbitrary click() events. The label (value) of the button can be used as an input or set via the output of a function.
-
-    Preprocessing: passes the button value as a {str} into the function
-    Postprocessing: expects a {str} to be returned from a function, which is set as the label of the button
-    Demos: blocks_inputs, blocks_kinematics
-    """
-
-    def __init__(
-        self,
-        value: str | Callable = "Run",
-        *,
-        variant: Literal["primary", "secondary", "stop"] = "secondary",
-        size: Literal["sm", "lg"] | None = None,
-        visible: bool = True,
-        interactive: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: Default text for the button to display. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            variant: 'primary' for main call-to-action, 'secondary' for a more subdued style, 'stop' for a stop button.
-            size: Size of the button. Can be "sm" or "lg".
-            visible: If False, component will be hidden.
-            interactive: If False, the Button will be in a disabled state.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-        """
-        IOComponent.__init__(
-            self,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            interactive=interactive,
-            scale=scale,
-            min_width=min_width,
-            **kwargs,
-        )
-        if variant == "plain":
-            warn_deprecation("'plain' variant deprecated, using 'secondary' instead.")
-            variant = "secondary"
-        self.variant = variant
-        self.size = size
-
-    def get_config(self):
-        return {
-            "value": self.value,
-            "variant": self.variant,
-            "size": self.size,
-            "interactive": self.interactive,
-            "scale": self.scale,
-            "min_width": self.min_width,
-            **Component.get_config(self),
-        }
-
-    @staticmethod
-    def update(
-        value: str | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
-        variant: Literal["primary", "secondary", "stop"] | None = None,
-        size: Literal["sm", "lg"] | None = None,
-        visible: bool | None = None,
-        interactive: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-    ):
-        return {
-            "variant": variant,
-            "size": size,
-            "visible": visible,
-            "value": value,
-            "interactive": interactive,
-            "scale": scale,
-            "min_width": min_width,
-            "__type__": "update",
-        }
-
-    def style(
-        self,
-        *,
-        full_width: bool | None = None,
-        size: Literal["sm", "lg"] | None = None,
-        **kwargs,
-    ):
-        """
-        This method is deprecated. Please set these arguments in the constructor instead.
-        """
-        warn_style_method_deprecation()
-        if full_width is not None:
-            warn_deprecation(
-                "Use `scale` in place of full_width in the constructor. "
-                "scale=1 will make the button expand, whereas 0 will not."
-            )
-            self.scale = 1 if full_width else None
-        if size is not None:
-            self.size = size
-        return self
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-3ca142e0.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-3ca142e0.css
deleted file mode 100644
index 77ebe6c1fea2e3557f76088bb9f5c30e2cfdb72a..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-3ca142e0.css
+++ /dev/null
@@ -1 +0,0 @@
-.spacer.svelte-1kspdo{display:inline-block;width:0;height:0}.json-node.svelte-1kspdo{display:inline;color:var(--body-text-color);line-height:var(--line-sm);font-family:var(--font-mono)}.expand-array.svelte-1kspdo{border:1px solid var(--border-color-primary);border-radius:var(--radius-sm);background:var(--background-fill-secondary);padding:0 var(--size-1);color:var(--body-text-color)}.expand-array.svelte-1kspdo:hover{background:var(--background-fill-primary)}.children.svelte-1kspdo{padding-left:var(--size-4)}.json-item.svelte-1kspdo{display:inline}.null.svelte-1kspdo{color:var(--body-text-color-subdued)}.string.svelte-1kspdo{color:var(--color-green-500)}.number.svelte-1kspdo{color:var(--color-blue-500)}.bool.svelte-1kspdo{color:var(--color-red-500)}.json-holder.svelte-1trjy9a{padding:var(--size-2)}button.svelte-1trjy9a{display:flex;position:absolute;top:var(--block-label-margin);right:var(--block-label-margin);align-items:center;box-shadow:var(--shadow-drop);border:1px solid var(--border-color-primary);border-top:none;border-right:none;border-radius:var(--block-label-right-radius);background:var(--block-label-background-fill);padding:5px;width:22px;height:22px;overflow:hidden;color:var(--block-label-text-color);font:var(--font);font-size:var(--button-small-text-size)}
diff --git a/spaces/cihyFjudo/fairness-paper-search/ArchiCad 21 3010 (Rus) A Review of the Benefits and Drawbacks.md b/spaces/cihyFjudo/fairness-paper-search/ArchiCad 21 3010 (Rus) A Review of the Benefits and Drawbacks.md
deleted file mode 100644
index f82b0c50f6d3667e9dc4e95a2ffc487a92e0bbb5..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/ArchiCad 21 3010 (Rus) A Review of the Benefits and Drawbacks.md	
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-<h2>ArchiCad 21 3010 (Rus)</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;>>> <a href="https://tinurli.com/2uwhJe">https://tinurli.com/2uwhJe</a></b></p><br /><br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Battlefield 1942 (Pc game Highly Compressed) at Just 216Mb How to Install and Run.md b/spaces/cihyFjudo/fairness-paper-search/Battlefield 1942 (Pc game Highly Compressed) at Just 216Mb How to Install and Run.md
deleted file mode 100644
index 961e59a4b9b7138d6f7d80e2f2ae930d82be8446..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Battlefield 1942 (Pc game Highly Compressed) at Just 216Mb How to Install and Run.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-
-<p>Capturing control points allow the team to reinforce themselves by enabling players and vehicles to spawn in the vicinity of the control point. Consequently, capturing and controlling these points also will reduce enemy reinforcements. <i>Battlefield 1942</i> was one of the first mainstream games to represent a dramatic shift in FPS gameplay mentality by not only favoring individualism, but simultaneously encouraging teamwork and coordination.</p>
-<h2>Battlefield 1942 (Pc game Highly Compressed) | 216Mb only</h2><br /><p><b><b>Download</b> &#187;&#187;&#187; <a href="https://tinurli.com/2uwj1X">https://tinurli.com/2uwj1X</a></b></p><br /><br />
-<p>Battlefield 1942 Game is a shooter game that was developed by DICE and published by Electronic Arts. This game was released on 10 September 2002. We have played many Highly compressed pc games but never ever played Battlefield 1942 game with full features. This game is full of entertainment, High-Quality graphics, a user-friendly interface, and an awesome sound system.</p>
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Modern Warriors Full Movie Kickass Torrent The Best Way to Enjoy the Thrilling Story.md b/spaces/cihyFjudo/fairness-paper-search/Modern Warriors Full Movie Kickass Torrent The Best Way to Enjoy the Thrilling Story.md
deleted file mode 100644
index 4fb06f3cdf098d46273d879927e8cee31807d1b7..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Modern Warriors Full Movie Kickass Torrent The Best Way to Enjoy the Thrilling Story.md	
+++ /dev/null
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-<br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Shadow Of The Tomb Raider Crack [Cpy 3dm Codex] Download Now! - Free and Fast.md b/spaces/cihyFjudo/fairness-paper-search/Shadow Of The Tomb Raider Crack [Cpy 3dm Codex] Download Now! - Free and Fast.md
deleted file mode 100644
index 528a4721515e1a507834fce4e592adad279fe7e0..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Shadow Of The Tomb Raider Crack [Cpy 3dm Codex] Download Now! - Free and Fast.md	
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Volleyball Rules In Marathi Tips And Tricks To Improve Your Skills And Performance.md b/spaces/cihyFjudo/fairness-paper-search/Volleyball Rules In Marathi Tips And Tricks To Improve Your Skills And Performance.md
deleted file mode 100644
index bd64f05e2bc3e00ec3701101975278c2e6b0a91c..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Volleyball Rules In Marathi Tips And Tricks To Improve Your Skills And Performance.md	
+++ /dev/null
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-<p>In volleyball, there are six players on the court for each team. Each player starts in a specific location, but these locations are not to be confused with <strong>player positions</strong><strong></strong>-(setter, middle blocker, outside hitter, opposite or libero). Each player, with the exception of the libero, will rotate to each location in a clockwise manner before each serve.</p>
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-<p>In addition to being a ton of fun, playing volleyball is also one of the best ways to stay in shape. Here are the top five health benefits of playing volleyball at our Smoky Mountain sports complex:</b></p>
-<p>One of the best benefits of playing volleyball is that it helps to burn off calories. As a result, your body can effectively maintain a positive ratio between muscle and fat. It has been estimated that just a half hour game of competitive volleyball can burn anywhere from 120 to 178 calories, while a less competitive game may result in 90 to 133 calories burned off. Volleyball is a great way to maintain a healthy weight, which reduces the risks of heart disease, diabetes, and hypertension.</p>
-<p>The physical motions used while playing volleyball help to build the muscles in your upper and lower body. You squat and use your legs for power when passing the ball, and use your hands, arms, and legs to set the ball. Since playing volleyball requires constant use of the arms and legs, you are effectively gaining the benefits of a full body workout! In addition to building strength and improving your respiratory and cardiovascular systems, you will receive the added benefit of toning your muscles.</p>
-<p>New volleyball players quickly improve their coordination, balance, and speed. The sport involves all of these important abilities, since the game play consists of serving, passing, setting, blocking, and much more. These essential skills are used constantly to be effective in volleyball games, so you will notice yourself developing these skills more with each match!</p>
-<p>While it may seem surprising, studies have shown that someone playing even a slow paced 20 minute game of volleyball can use the identical amount of energy as a person jogging for a mile. By playing a more intense game of volleyball, you use the same amount of energy in just 12 minutes as someone jogging a mile! Longer and more competitive games have even more tremendous health benefits. In addition to enhancing your energy level, your increased aerobic activity will improve your performance in a variety of other sports.</p>
-<p>Volleyball at the YMCA provides youth with low-pressure opportunities to learn the rules and fundamentals of the game in a fun and supportive environment. Micro, Recreational, and Spirit (competitive) leagues are available along with camps, clinics, and tournaments throughout the year.</p>
-<p><b>Kindergarten and 1st Grade</b> | Micro Volleyball is a great introduction to the sport! The six-week, 4 vs. 4 season will include a 25-minute practice followed by a 25-minute game each week. Players enjoy modified rules for different ages to provide maximum success.</p>
-<p><b>Kindergarten - 8th Grade</b><strong> </strong>| YMCA Youth Sports offers some great ways to refine your child's volleyball skills with programs that focus on fundamentals, positive coaching and personal attention.</p>
-<p>Like basketball, volleyball was invented at a YMCA to give members and participants an active game to play indoors. And did it ever catch on. Now volleyball is one of the most popular games at outdoor barbecues, in high school and collegiate athletics, and even at the Olympics.</p>
-<p>4. The server must get the ball over the net from behind the back line using a legal serve technique to start play. Teammates cannot help the ball over the net on the serve. If the ball hits the net on a serve but still goes over that is a <strong>legal</strong> serve. If the ball hits anything else other than the net on the serve including the ceiling it is considered <strong>out of bounds</strong> and is a point for the other team. There are no 'faults' in volleyball such as in tennis.</p>
-<p>Raleigh Parks offers open play times for participants to play sports in our indoor facilities. Sports vary by location and may include basketball, volleyball, futsal, table tennis, or cricket. Participants may reach out to the individual centers to learn more about the opportunities at that location.</p>
-<p>The above are general rules for suspension. These rules are not all-inclusive. Violation of these rules can lead to suspension from one day to an indefinite time period from all locations. Please see the community center staff for a more detailed explanation.</p>
-<p>We are especially excited that these are SAFE and FUN esports competitions designed for players of all ages. GGLeagues will have tournament admins and referees who will be present to enforce the rules, ensure the safety of all players, and most importantly make sure everyone is having a great time.</p>
-<p>Montgomery County Recreation offers a variety of youth volleyball leagues for ages 10-17 for both girls and boys. Our leagues on average run 7-8 weeks and include and evaluation day (not a try out), 5-6 weeks of practices/matches, and a playoff day to end the season. Please see our upcoming seasons, dates, and locations below.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageEnhance.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageEnhance.py
deleted file mode 100644
index 3b79d5c46a16ce89dfff1694f0121a743d8fa0c7..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageEnhance.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# image enhancement classes
-#
-# For a background, see "Image Processing By Interpolation and
-# Extrapolation", Paul Haeberli and Douglas Voorhies.  Available
-# at http://www.graficaobscura.com/interp/index.html
-#
-# History:
-# 1996-03-23 fl  Created
-# 2009-06-16 fl  Fixed mean calculation
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image, ImageFilter, ImageStat
-
-
-class _Enhance:
-    def enhance(self, factor):
-        """
-        Returns an enhanced image.
-
-        :param factor: A floating point value controlling the enhancement.
-                       Factor 1.0 always returns a copy of the original image,
-                       lower factors mean less color (brightness, contrast,
-                       etc), and higher values more. There are no restrictions
-                       on this value.
-        :rtype: :py:class:`~PIL.Image.Image`
-        """
-        return Image.blend(self.degenerate, self.image, factor)
-
-
-class Color(_Enhance):
-    """Adjust image color balance.
-
-    This class can be used to adjust the colour balance of an image, in
-    a manner similar to the controls on a colour TV set. An enhancement
-    factor of 0.0 gives a black and white image. A factor of 1.0 gives
-    the original image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        self.intermediate_mode = "L"
-        if "A" in image.getbands():
-            self.intermediate_mode = "LA"
-
-        self.degenerate = image.convert(self.intermediate_mode).convert(image.mode)
-
-
-class Contrast(_Enhance):
-    """Adjust image contrast.
-
-    This class can be used to control the contrast of an image, similar
-    to the contrast control on a TV set. An enhancement factor of 0.0
-    gives a solid grey image. A factor of 1.0 gives the original image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        mean = int(ImageStat.Stat(image.convert("L")).mean[0] + 0.5)
-        self.degenerate = Image.new("L", image.size, mean).convert(image.mode)
-
-        if "A" in image.getbands():
-            self.degenerate.putalpha(image.getchannel("A"))
-
-
-class Brightness(_Enhance):
-    """Adjust image brightness.
-
-    This class can be used to control the brightness of an image.  An
-    enhancement factor of 0.0 gives a black image. A factor of 1.0 gives the
-    original image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        self.degenerate = Image.new(image.mode, image.size, 0)
-
-        if "A" in image.getbands():
-            self.degenerate.putalpha(image.getchannel("A"))
-
-
-class Sharpness(_Enhance):
-    """Adjust image sharpness.
-
-    This class can be used to adjust the sharpness of an image. An
-    enhancement factor of 0.0 gives a blurred image, a factor of 1.0 gives the
-    original image, and a factor of 2.0 gives a sharpened image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        self.degenerate = image.filter(ImageFilter.SMOOTH)
-
-        if "A" in image.getbands():
-            self.degenerate.putalpha(image.getchannel("A"))
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/server.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/server.py
deleted file mode 100644
index f2dfc29ec4b5d1cbf37a87fe7ce70fff27b022a5..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/server.py
+++ /dev/null
@@ -1,148 +0,0 @@
-"""
-A Simple server used to show altair graphics from a prompt or script.
-
-This is adapted from the mpld3 package; see
-https://github.com/mpld3/mpld3/blob/master/mpld3/_server.py
-"""
-import sys
-import threading
-import webbrowser
-import socket
-from http import server
-from io import BytesIO as IO
-import itertools
-import random
-
-JUPYTER_WARNING = """
-Note: if you're in the Jupyter notebook, Chart.serve() is not the best
-      way to view plots. Consider using Chart.display().
-You must interrupt the kernel to cancel this command.
-"""
-
-
-# Mock server used for testing
-
-
-class MockRequest:
-    def makefile(self, *args, **kwargs):
-        return IO(b"GET /")
-
-    def sendall(self, response):
-        pass
-
-
-class MockServer:
-    def __init__(self, ip_port, Handler):
-        Handler(MockRequest(), ip_port[0], self)
-
-    def serve_forever(self):
-        pass
-
-    def server_close(self):
-        pass
-
-
-def generate_handler(html, files=None):
-    if files is None:
-        files = {}
-
-    class MyHandler(server.BaseHTTPRequestHandler):
-        def do_GET(self):
-            """Respond to a GET request."""
-            if self.path == "/":
-                self.send_response(200)
-                self.send_header("Content-type", "text/html")
-                self.end_headers()
-                self.wfile.write(html.encode())
-            elif self.path in files:
-                content_type, content = files[self.path]
-                self.send_response(200)
-                self.send_header("Content-type", content_type)
-                self.end_headers()
-                self.wfile.write(content.encode())
-            else:
-                self.send_error(404)
-
-    return MyHandler
-
-
-def find_open_port(ip, port, n=50):
-    """Find an open port near the specified port"""
-    ports = itertools.chain(
-        (port + i for i in range(n)), (port + random.randint(-2 * n, 2 * n))
-    )
-
-    for port in ports:
-        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        result = s.connect_ex((ip, port))
-        s.close()
-        if result != 0:
-            return port
-    raise ValueError("no open ports found")
-
-
-def serve(
-    html,
-    ip="127.0.0.1",
-    port=8888,
-    n_retries=50,
-    files=None,
-    jupyter_warning=True,
-    open_browser=True,
-    http_server=None,
-):
-    """Start a server serving the given HTML, and (optionally) open a browser
-
-    Parameters
-    ----------
-    html : string
-        HTML to serve
-    ip : string (default = '127.0.0.1')
-        ip address at which the HTML will be served.
-    port : int (default = 8888)
-        the port at which to serve the HTML
-    n_retries : int (default = 50)
-        the number of nearby ports to search if the specified port is in use.
-    files : dictionary (optional)
-        dictionary of extra content to serve
-    jupyter_warning : bool (optional)
-        if True (default), then print a warning if this is used within Jupyter
-    open_browser : bool (optional)
-        if True (default), then open a web browser to the given HTML
-    http_server : class (optional)
-        optionally specify an HTTPServer class to use for showing the
-        figure. The default is Python's basic HTTPServer.
-    """
-    port = find_open_port(ip, port, n_retries)
-    Handler = generate_handler(html, files)
-
-    if http_server is None:
-        srvr = server.HTTPServer((ip, port), Handler)
-    else:
-        srvr = http_server((ip, port), Handler)
-
-    if jupyter_warning:
-        try:
-            __IPYTHON__  # noqa
-        except NameError:
-            pass
-        else:
-            print(JUPYTER_WARNING)
-
-    # Start the server
-    print("Serving to http://{}:{}/    [Ctrl-C to exit]".format(ip, port))
-    sys.stdout.flush()
-
-    if open_browser:
-        # Use a thread to open a web browser pointing to the server
-        def b():
-            return webbrowser.open("http://{}:{}".format(ip, port))
-
-        threading.Thread(target=b).start()
-
-    try:
-        srvr.serve_forever()
-    except (KeyboardInterrupt, SystemExit):
-        print("\nstopping Server...")
-
-    srvr.server_close()
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/utils.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/utils.py
deleted file mode 100644
index 267d64ce8aee9eaf5462d9cbd47deca44cfdef28..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/utils.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import re
-import warnings
-from dataclasses import is_dataclass
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    MutableMapping,
-    Optional,
-    Set,
-    Type,
-    Union,
-    cast,
-)
-from weakref import WeakKeyDictionary
-
-import fastapi
-from fastapi._compat import (
-    PYDANTIC_V2,
-    BaseConfig,
-    ModelField,
-    PydanticSchemaGenerationError,
-    Undefined,
-    UndefinedType,
-    Validator,
-    lenient_issubclass,
-)
-from fastapi.datastructures import DefaultPlaceholder, DefaultType
-from pydantic import BaseModel, create_model
-from pydantic.fields import FieldInfo
-from typing_extensions import Literal
-
-if TYPE_CHECKING:  # pragma: nocover
-    from .routing import APIRoute
-
-# Cache for `create_cloned_field`
-_CLONED_TYPES_CACHE: MutableMapping[
-    Type[BaseModel], Type[BaseModel]
-] = WeakKeyDictionary()
-
-
-def is_body_allowed_for_status_code(status_code: Union[int, str, None]) -> bool:
-    if status_code is None:
-        return True
-    # Ref: https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.1.0.md#patterned-fields-1
-    if status_code in {
-        "default",
-        "1XX",
-        "2XX",
-        "3XX",
-        "4XX",
-        "5XX",
-    }:
-        return True
-    current_status_code = int(status_code)
-    return not (current_status_code < 200 or current_status_code in {204, 304})
-
-
-def get_path_param_names(path: str) -> Set[str]:
-    return set(re.findall("{(.*?)}", path))
-
-
-def create_response_field(
-    name: str,
-    type_: Type[Any],
-    class_validators: Optional[Dict[str, Validator]] = None,
-    default: Optional[Any] = Undefined,
-    required: Union[bool, UndefinedType] = Undefined,
-    model_config: Type[BaseConfig] = BaseConfig,
-    field_info: Optional[FieldInfo] = None,
-    alias: Optional[str] = None,
-    mode: Literal["validation", "serialization"] = "validation",
-) -> ModelField:
-    """
-    Create a new response field. Raises if type_ is invalid.
-    """
-    class_validators = class_validators or {}
-    if PYDANTIC_V2:
-        field_info = field_info or FieldInfo(
-            annotation=type_, default=default, alias=alias
-        )
-    else:
-        field_info = field_info or FieldInfo()
-    kwargs = {"name": name, "field_info": field_info}
-    if PYDANTIC_V2:
-        kwargs.update({"mode": mode})
-    else:
-        kwargs.update(
-            {
-                "type_": type_,
-                "class_validators": class_validators,
-                "default": default,
-                "required": required,
-                "model_config": model_config,
-                "alias": alias,
-            }
-        )
-    try:
-        return ModelField(**kwargs)  # type: ignore[arg-type]
-    except (RuntimeError, PydanticSchemaGenerationError):
-        raise fastapi.exceptions.FastAPIError(
-            "Invalid args for response field! Hint: "
-            f"check that {type_} is a valid Pydantic field type. "
-            "If you are using a return type annotation that is not a valid Pydantic "
-            "field (e.g. Union[Response, dict, None]) you can disable generating the "
-            "response model from the type annotation with the path operation decorator "
-            "parameter response_model=None. Read more: "
-            "https://fastapi.tiangolo.com/tutorial/response-model/"
-        ) from None
-
-
-def create_cloned_field(
-    field: ModelField,
-    *,
-    cloned_types: Optional[MutableMapping[Type[BaseModel], Type[BaseModel]]] = None,
-) -> ModelField:
-    if PYDANTIC_V2:
-        return field
-    # cloned_types caches already cloned types to support recursive models and improve
-    # performance by avoiding unecessary cloning
-    if cloned_types is None:
-        cloned_types = _CLONED_TYPES_CACHE
-
-    original_type = field.type_
-    if is_dataclass(original_type) and hasattr(original_type, "__pydantic_model__"):
-        original_type = original_type.__pydantic_model__
-    use_type = original_type
-    if lenient_issubclass(original_type, BaseModel):
-        original_type = cast(Type[BaseModel], original_type)
-        use_type = cloned_types.get(original_type)
-        if use_type is None:
-            use_type = create_model(original_type.__name__, __base__=original_type)
-            cloned_types[original_type] = use_type
-            for f in original_type.__fields__.values():
-                use_type.__fields__[f.name] = create_cloned_field(
-                    f, cloned_types=cloned_types
-                )
-    new_field = create_response_field(name=field.name, type_=use_type)
-    new_field.has_alias = field.has_alias  # type: ignore[attr-defined]
-    new_field.alias = field.alias  # type: ignore[misc]
-    new_field.class_validators = field.class_validators  # type: ignore[attr-defined]
-    new_field.default = field.default  # type: ignore[misc]
-    new_field.required = field.required  # type: ignore[misc]
-    new_field.model_config = field.model_config  # type: ignore[attr-defined]
-    new_field.field_info = field.field_info
-    new_field.allow_none = field.allow_none  # type: ignore[attr-defined]
-    new_field.validate_always = field.validate_always  # type: ignore[attr-defined]
-    if field.sub_fields:  # type: ignore[attr-defined]
-        new_field.sub_fields = [  # type: ignore[attr-defined]
-            create_cloned_field(sub_field, cloned_types=cloned_types)
-            for sub_field in field.sub_fields  # type: ignore[attr-defined]
-        ]
-    if field.key_field:  # type: ignore[attr-defined]
-        new_field.key_field = create_cloned_field(  # type: ignore[attr-defined]
-            field.key_field, cloned_types=cloned_types  # type: ignore[attr-defined]
-        )
-    new_field.validators = field.validators  # type: ignore[attr-defined]
-    new_field.pre_validators = field.pre_validators  # type: ignore[attr-defined]
-    new_field.post_validators = field.post_validators  # type: ignore[attr-defined]
-    new_field.parse_json = field.parse_json  # type: ignore[attr-defined]
-    new_field.shape = field.shape  # type: ignore[attr-defined]
-    new_field.populate_validators()  # type: ignore[attr-defined]
-    return new_field
-
-
-def generate_operation_id_for_path(
-    *, name: str, path: str, method: str
-) -> str:  # pragma: nocover
-    warnings.warn(
-        "fastapi.utils.generate_operation_id_for_path() was deprecated, "
-        "it is not used internally, and will be removed soon",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    operation_id = name + path
-    operation_id = re.sub(r"\W", "_", operation_id)
-    operation_id = operation_id + "_" + method.lower()
-    return operation_id
-
-
-def generate_unique_id(route: "APIRoute") -> str:
-    operation_id = route.name + route.path_format
-    operation_id = re.sub(r"\W", "_", operation_id)
-    assert route.methods
-    operation_id = operation_id + "_" + list(route.methods)[0].lower()
-    return operation_id
-
-
-def deep_dict_update(main_dict: Dict[Any, Any], update_dict: Dict[Any, Any]) -> None:
-    for key, value in update_dict.items():
-        if (
-            key in main_dict
-            and isinstance(main_dict[key], dict)
-            and isinstance(value, dict)
-        ):
-            deep_dict_update(main_dict[key], value)
-        elif (
-            key in main_dict
-            and isinstance(main_dict[key], list)
-            and isinstance(update_dict[key], list)
-        ):
-            main_dict[key] = main_dict[key] + update_dict[key]
-        else:
-            main_dict[key] = value
-
-
-def get_value_or_default(
-    first_item: Union[DefaultPlaceholder, DefaultType],
-    *extra_items: Union[DefaultPlaceholder, DefaultType],
-) -> Union[DefaultPlaceholder, DefaultType]:
-    """
-    Pass items or `DefaultPlaceholder`s by descending priority.
-
-    The first one to _not_ be a `DefaultPlaceholder` will be returned.
-
-    Otherwise, the first item (a `DefaultPlaceholder`) will be returned.
-    """
-    items = (first_item,) + extra_items
-    for item in items:
-        if not isinstance(item, DefaultPlaceholder):
-            return item
-    return first_item
-
-
-def match_pydantic_error_url(error_type: str) -> Any:
-    from dirty_equals import IsStr
-
-    return IsStr(regex=rf"^https://errors\.pydantic\.dev/.*/v/{error_type}")
diff --git a/spaces/colakin/video-generater/public/ffmpeg/doc/examples/qsv_decode.c b/spaces/colakin/video-generater/public/ffmpeg/doc/examples/qsv_decode.c
deleted file mode 100644
index cc2662d5bdd4bcc1c9bc379c58a4f50d0a2caeb1..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/doc/examples/qsv_decode.c
+++ /dev/null
@@ -1,243 +0,0 @@
-/*
- * Copyright (c) 2015 Anton Khirnov
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
- * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-
-/**
- * @file Intel QSV-accelerated H.264 decoding API usage example
- * @example qsv_decode.c
- *
- * Perform QSV-accelerated H.264 decoding with output frames in the
- * GPU video surfaces, write the decoded frames to an output file.
- */
-
-#include "config.h"
-
-#include <stdio.h>
-
-#include "libavformat/avformat.h"
-#include "libavformat/avio.h"
-
-#include "libavcodec/avcodec.h"
-
-#include "libavutil/buffer.h"
-#include "libavutil/error.h"
-#include "libavutil/hwcontext.h"
-#include "libavutil/hwcontext_qsv.h"
-#include "libavutil/mem.h"
-
-static int get_format(AVCodecContext *avctx, const enum AVPixelFormat *pix_fmts)
-{
-    while (*pix_fmts != AV_PIX_FMT_NONE) {
-        if (*pix_fmts == AV_PIX_FMT_QSV) {
-            return AV_PIX_FMT_QSV;
-        }
-
-        pix_fmts++;
-    }
-
-    fprintf(stderr, "The QSV pixel format not offered in get_format()\n");
-
-    return AV_PIX_FMT_NONE;
-}
-
-static int decode_packet(AVCodecContext *decoder_ctx,
-                         AVFrame *frame, AVFrame *sw_frame,
-                         AVPacket *pkt, AVIOContext *output_ctx)
-{
-    int ret = 0;
-
-    ret = avcodec_send_packet(decoder_ctx, pkt);
-    if (ret < 0) {
-        fprintf(stderr, "Error during decoding\n");
-        return ret;
-    }
-
-    while (ret >= 0) {
-        int i, j;
-
-        ret = avcodec_receive_frame(decoder_ctx, frame);
-        if (ret == AVERROR(EAGAIN) || ret == AVERROR_EOF)
-            break;
-        else if (ret < 0) {
-            fprintf(stderr, "Error during decoding\n");
-            return ret;
-        }
-
-        /* A real program would do something useful with the decoded frame here.
-         * We just retrieve the raw data and write it to a file, which is rather
-         * useless but pedagogic. */
-        ret = av_hwframe_transfer_data(sw_frame, frame, 0);
-        if (ret < 0) {
-            fprintf(stderr, "Error transferring the data to system memory\n");
-            goto fail;
-        }
-
-        for (i = 0; i < FF_ARRAY_ELEMS(sw_frame->data) && sw_frame->data[i]; i++)
-            for (j = 0; j < (sw_frame->height >> (i > 0)); j++)
-                avio_write(output_ctx, sw_frame->data[i] + j * sw_frame->linesize[i], sw_frame->width);
-
-fail:
-        av_frame_unref(sw_frame);
-        av_frame_unref(frame);
-
-        if (ret < 0)
-            return ret;
-    }
-
-    return 0;
-}
-
-int main(int argc, char **argv)
-{
-    AVFormatContext *input_ctx = NULL;
-    AVStream *video_st = NULL;
-    AVCodecContext *decoder_ctx = NULL;
-    const AVCodec *decoder;
-
-    AVPacket *pkt = NULL;
-    AVFrame *frame = NULL, *sw_frame = NULL;
-
-    AVIOContext *output_ctx = NULL;
-
-    int ret, i;
-
-    AVBufferRef *device_ref = NULL;
-
-    if (argc < 3) {
-        fprintf(stderr, "Usage: %s <input file> <output file>\n", argv[0]);
-        return 1;
-    }
-
-    /* open the input file */
-    ret = avformat_open_input(&input_ctx, argv[1], NULL, NULL);
-    if (ret < 0) {
-        fprintf(stderr, "Cannot open input file '%s': ", argv[1]);
-        goto finish;
-    }
-
-    /* find the first H.264 video stream */
-    for (i = 0; i < input_ctx->nb_streams; i++) {
-        AVStream *st = input_ctx->streams[i];
-
-        if (st->codecpar->codec_id == AV_CODEC_ID_H264 && !video_st)
-            video_st = st;
-        else
-            st->discard = AVDISCARD_ALL;
-    }
-    if (!video_st) {
-        fprintf(stderr, "No H.264 video stream in the input file\n");
-        goto finish;
-    }
-
-    /* open the hardware device */
-    ret = av_hwdevice_ctx_create(&device_ref, AV_HWDEVICE_TYPE_QSV,
-                                 "auto", NULL, 0);
-    if (ret < 0) {
-        fprintf(stderr, "Cannot open the hardware device\n");
-        goto finish;
-    }
-
-    /* initialize the decoder */
-    decoder = avcodec_find_decoder_by_name("h264_qsv");
-    if (!decoder) {
-        fprintf(stderr, "The QSV decoder is not present in libavcodec\n");
-        goto finish;
-    }
-
-    decoder_ctx = avcodec_alloc_context3(decoder);
-    if (!decoder_ctx) {
-        ret = AVERROR(ENOMEM);
-        goto finish;
-    }
-    decoder_ctx->codec_id = AV_CODEC_ID_H264;
-    if (video_st->codecpar->extradata_size) {
-        decoder_ctx->extradata = av_mallocz(video_st->codecpar->extradata_size +
-                                            AV_INPUT_BUFFER_PADDING_SIZE);
-        if (!decoder_ctx->extradata) {
-            ret = AVERROR(ENOMEM);
-            goto finish;
-        }
-        memcpy(decoder_ctx->extradata, video_st->codecpar->extradata,
-               video_st->codecpar->extradata_size);
-        decoder_ctx->extradata_size = video_st->codecpar->extradata_size;
-    }
-
-
-    decoder_ctx->hw_device_ctx = av_buffer_ref(device_ref);
-    decoder_ctx->get_format  = get_format;
-
-    ret = avcodec_open2(decoder_ctx, NULL, NULL);
-    if (ret < 0) {
-        fprintf(stderr, "Error opening the decoder: ");
-        goto finish;
-    }
-
-    /* open the output stream */
-    ret = avio_open(&output_ctx, argv[2], AVIO_FLAG_WRITE);
-    if (ret < 0) {
-        fprintf(stderr, "Error opening the output context: ");
-        goto finish;
-    }
-
-    frame    = av_frame_alloc();
-    sw_frame = av_frame_alloc();
-    pkt      = av_packet_alloc();
-    if (!frame || !sw_frame || !pkt) {
-        ret = AVERROR(ENOMEM);
-        goto finish;
-    }
-
-    /* actual decoding */
-    while (ret >= 0) {
-        ret = av_read_frame(input_ctx, pkt);
-        if (ret < 0)
-            break;
-
-        if (pkt->stream_index == video_st->index)
-            ret = decode_packet(decoder_ctx, frame, sw_frame, pkt, output_ctx);
-
-        av_packet_unref(pkt);
-    }
-
-    /* flush the decoder */
-    ret = decode_packet(decoder_ctx, frame, sw_frame, NULL, output_ctx);
-
-finish:
-    if (ret < 0) {
-        char buf[1024];
-        av_strerror(ret, buf, sizeof(buf));
-        fprintf(stderr, "%s\n", buf);
-    }
-
-    avformat_close_input(&input_ctx);
-
-    av_frame_free(&frame);
-    av_frame_free(&sw_frame);
-    av_packet_free(&pkt);
-
-    avcodec_free_context(&decoder_ctx);
-
-    av_buffer_unref(&device_ref);
-
-    avio_close(output_ctx);
-
-    return ret;
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/vp9dsp_init_16bpp_aarch64_template.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/vp9dsp_init_16bpp_aarch64_template.c
deleted file mode 100644
index d2a4e90b3ad89823d4fd63400f88b1106ce28092..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/vp9dsp_init_16bpp_aarch64_template.c
+++ /dev/null
@@ -1,274 +0,0 @@
-/*
- * Copyright (c) 2017 Google Inc.
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include <stdint.h>
-
-#include "libavutil/attributes.h"
-#include "libavutil/internal.h"
-#include "libavutil/mem_internal.h"
-#include "libavutil/aarch64/cpu.h"
-#include "vp9dsp_init.h"
-
-#define declare_fpel(type, sz, suffix)                                          \
-void ff_vp9_##type##sz##suffix##_neon(uint8_t *dst, ptrdiff_t dst_stride,       \
-                                      const uint8_t *src, ptrdiff_t src_stride, \
-                                      int h, int mx, int my)
-
-#define decl_mc_func(op, filter, dir, sz, bpp)                                                   \
-void ff_vp9_##op##_##filter##sz##_##dir##_##bpp##_neon(uint8_t *dst, ptrdiff_t dst_stride,       \
-                                                       const uint8_t *src, ptrdiff_t src_stride, \
-                                                       int h, int mx, int my)
-
-#define define_8tap_2d_fn(op, filter, sz, bpp)                                      \
-static void op##_##filter##sz##_hv_##bpp##_neon(uint8_t *dst, ptrdiff_t dst_stride, \
-                                                const uint8_t *src,                 \
-                                                ptrdiff_t src_stride,               \
-                                                int h, int mx, int my)              \
-{                                                                                   \
-    LOCAL_ALIGNED_16(uint8_t, temp, [((1 + (sz < 64)) * sz + 8) * sz * 2]);         \
-    /* We only need h + 7 lines, but the horizontal filter assumes an               \
-     * even number of rows, so filter h + 8 lines here. */                          \
-    ff_vp9_put_##filter##sz##_h_##bpp##_neon(temp, 2 * sz,                          \
-                                             src - 3 * src_stride, src_stride,      \
-                                             h + 8, mx, 0);                         \
-    ff_vp9_##op##_##filter##sz##_v_##bpp##_neon(dst, dst_stride,                    \
-                                                temp + 3 * 2 * sz, 2 * sz,          \
-                                                h, 0, my);                          \
-}
-
-#define decl_filter_funcs(op, dir, sz, bpp)  \
-    decl_mc_func(op, regular, dir, sz, bpp); \
-    decl_mc_func(op, sharp,   dir, sz, bpp); \
-    decl_mc_func(op, smooth,  dir, sz, bpp)
-
-#define decl_mc_funcs(sz, bpp)           \
-    decl_filter_funcs(put, h,  sz, bpp); \
-    decl_filter_funcs(avg, h,  sz, bpp); \
-    decl_filter_funcs(put, v,  sz, bpp); \
-    decl_filter_funcs(avg, v,  sz, bpp); \
-    decl_filter_funcs(put, hv, sz, bpp); \
-    decl_filter_funcs(avg, hv, sz, bpp)
-
-#define ff_vp9_copy32_neon  ff_vp9_copy32_aarch64
-#define ff_vp9_copy64_neon  ff_vp9_copy64_aarch64
-#define ff_vp9_copy128_neon ff_vp9_copy128_aarch64
-
-declare_fpel(copy, 128, );
-declare_fpel(copy, 64,  );
-declare_fpel(copy, 32,  );
-declare_fpel(copy, 16,  );
-declare_fpel(copy, 8,   );
-declare_fpel(avg, 64, _16);
-declare_fpel(avg, 32, _16);
-declare_fpel(avg, 16, _16);
-declare_fpel(avg, 8,  _16);
-declare_fpel(avg, 4,  _16);
-
-decl_mc_funcs(64, BPP);
-decl_mc_funcs(32, BPP);
-decl_mc_funcs(16, BPP);
-decl_mc_funcs(8, BPP);
-decl_mc_funcs(4, BPP);
-
-#define define_8tap_2d_funcs(sz, bpp)        \
-    define_8tap_2d_fn(put, regular, sz, bpp) \
-    define_8tap_2d_fn(put, sharp,   sz, bpp) \
-    define_8tap_2d_fn(put, smooth,  sz, bpp) \
-    define_8tap_2d_fn(avg, regular, sz, bpp) \
-    define_8tap_2d_fn(avg, sharp,   sz, bpp) \
-    define_8tap_2d_fn(avg, smooth,  sz, bpp)
-
-define_8tap_2d_funcs(64, BPP)
-define_8tap_2d_funcs(32, BPP)
-define_8tap_2d_funcs(16, BPP)
-define_8tap_2d_funcs(8,  BPP)
-define_8tap_2d_funcs(4,  BPP)
-
-static av_cold void vp9dsp_mc_init_aarch64(VP9DSPContext *dsp)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-#define init_fpel(idx1, idx2, sz, type, suffix)      \
-    dsp->mc[idx1][FILTER_8TAP_SMOOTH ][idx2][0][0] = \
-    dsp->mc[idx1][FILTER_8TAP_REGULAR][idx2][0][0] = \
-    dsp->mc[idx1][FILTER_8TAP_SHARP  ][idx2][0][0] = \
-    dsp->mc[idx1][FILTER_BILINEAR    ][idx2][0][0] = ff_vp9_##type##sz##suffix
-
-#define init_copy(idx, sz, suffix) \
-    init_fpel(idx, 0, sz, copy, suffix)
-
-#define init_avg(idx, sz, suffix) \
-    init_fpel(idx, 1, sz, avg,  suffix)
-
-#define init_copy_avg(idx, sz1, sz2) \
-    init_copy(idx, sz2, _neon);      \
-    init_avg (idx, sz1, _16_neon)
-
-    if (have_armv8(cpu_flags)) {
-        init_copy(0, 128, _aarch64);
-        init_copy(1, 64,  _aarch64);
-        init_copy(2, 32,  _aarch64);
-    }
-
-    if (have_neon(cpu_flags)) {
-#define init_mc_func(idx1, idx2, op, filter, fname, dir, mx, my, sz, pfx, bpp) \
-    dsp->mc[idx1][filter][idx2][mx][my] = pfx##op##_##fname##sz##_##dir##_##bpp##_neon
-
-#define init_mc_funcs(idx, dir, mx, my, sz, pfx, bpp)                                   \
-    init_mc_func(idx, 0, put, FILTER_8TAP_REGULAR, regular, dir, mx, my, sz, pfx, bpp); \
-    init_mc_func(idx, 0, put, FILTER_8TAP_SHARP,   sharp,   dir, mx, my, sz, pfx, bpp); \
-    init_mc_func(idx, 0, put, FILTER_8TAP_SMOOTH,  smooth,  dir, mx, my, sz, pfx, bpp); \
-    init_mc_func(idx, 1, avg, FILTER_8TAP_REGULAR, regular, dir, mx, my, sz, pfx, bpp); \
-    init_mc_func(idx, 1, avg, FILTER_8TAP_SHARP,   sharp,   dir, mx, my, sz, pfx, bpp); \
-    init_mc_func(idx, 1, avg, FILTER_8TAP_SMOOTH,  smooth,  dir, mx, my, sz, pfx, bpp)
-
-#define init_mc_funcs_dirs(idx, sz, bpp)            \
-    init_mc_funcs(idx, v,  0, 1, sz, ff_vp9_, bpp); \
-    init_mc_funcs(idx, h,  1, 0, sz, ff_vp9_, bpp); \
-    init_mc_funcs(idx, hv, 1, 1, sz,        , bpp)
-
-
-        init_avg(0, 64, _16_neon);
-        init_avg(1, 32, _16_neon);
-        init_avg(2, 16, _16_neon);
-        init_copy_avg(3, 8, 16);
-        init_copy_avg(4, 4, 8);
-
-        init_mc_funcs_dirs(0, 64, BPP);
-        init_mc_funcs_dirs(1, 32, BPP);
-        init_mc_funcs_dirs(2, 16, BPP);
-        init_mc_funcs_dirs(3, 8,  BPP);
-        init_mc_funcs_dirs(4, 4,  BPP);
-    }
-}
-
-#define define_itxfm2(type_a, type_b, sz, bpp)                                     \
-void ff_vp9_##type_a##_##type_b##_##sz##x##sz##_add_##bpp##_neon(uint8_t *_dst,    \
-                                                                 ptrdiff_t stride, \
-                                                                 int16_t *_block, int eob)
-#define define_itxfm(type_a, type_b, sz, bpp) define_itxfm2(type_a, type_b, sz, bpp)
-
-#define define_itxfm_funcs(sz, bpp)      \
-    define_itxfm(idct,  idct,  sz, bpp); \
-    define_itxfm(iadst, idct,  sz, bpp); \
-    define_itxfm(idct,  iadst, sz, bpp); \
-    define_itxfm(iadst, iadst, sz, bpp)
-
-define_itxfm_funcs(4,  BPP);
-define_itxfm_funcs(8,  BPP);
-define_itxfm_funcs(16, BPP);
-define_itxfm(idct, idct, 32, BPP);
-define_itxfm(iwht, iwht, 4,  BPP);
-
-
-static av_cold void vp9dsp_itxfm_init_aarch64(VP9DSPContext *dsp)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-    if (have_neon(cpu_flags)) {
-#define init_itxfm2(tx, sz, bpp)                                               \
-    dsp->itxfm_add[tx][DCT_DCT]   = ff_vp9_idct_idct_##sz##_add_##bpp##_neon;  \
-    dsp->itxfm_add[tx][DCT_ADST]  = ff_vp9_iadst_idct_##sz##_add_##bpp##_neon; \
-    dsp->itxfm_add[tx][ADST_DCT]  = ff_vp9_idct_iadst_##sz##_add_##bpp##_neon; \
-    dsp->itxfm_add[tx][ADST_ADST] = ff_vp9_iadst_iadst_##sz##_add_##bpp##_neon
-#define init_itxfm(tx, sz, bpp) init_itxfm2(tx, sz, bpp)
-
-#define init_idct2(tx, nm, bpp)     \
-    dsp->itxfm_add[tx][DCT_DCT]   = \
-    dsp->itxfm_add[tx][ADST_DCT]  = \
-    dsp->itxfm_add[tx][DCT_ADST]  = \
-    dsp->itxfm_add[tx][ADST_ADST] = ff_vp9_##nm##_add_##bpp##_neon
-#define init_idct(tx, nm, bpp) init_idct2(tx, nm, bpp)
-
-        init_itxfm(TX_4X4,   4x4,   BPP);
-        init_itxfm(TX_8X8,   8x8,   BPP);
-        init_itxfm(TX_16X16, 16x16, BPP);
-        init_idct(TX_32X32, idct_idct_32x32, BPP);
-        init_idct(4,        iwht_iwht_4x4,   BPP);
-    }
-}
-
-#define define_loop_filter(dir, wd, size, bpp) \
-void ff_vp9_loop_filter_##dir##_##wd##_##size##_##bpp##_neon(uint8_t *dst, ptrdiff_t stride, int E, int I, int H)
-
-#define define_loop_filters(wd, size, bpp) \
-    define_loop_filter(h, wd, size, bpp);  \
-    define_loop_filter(v, wd, size, bpp)
-
-define_loop_filters(4,  8,  BPP);
-define_loop_filters(8,  8,  BPP);
-define_loop_filters(16, 8,  BPP);
-
-define_loop_filters(16, 16, BPP);
-
-define_loop_filters(44, 16, BPP);
-define_loop_filters(48, 16, BPP);
-define_loop_filters(84, 16, BPP);
-define_loop_filters(88, 16, BPP);
-
-static av_cold void vp9dsp_loopfilter_init_aarch64(VP9DSPContext *dsp)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-    if (have_neon(cpu_flags)) {
-#define init_lpf_func_8(idx1, idx2, dir, wd, bpp) \
-    dsp->loop_filter_8[idx1][idx2] = ff_vp9_loop_filter_##dir##_##wd##_8_##bpp##_neon
-
-#define init_lpf_func_16(idx, dir, bpp) \
-    dsp->loop_filter_16[idx] = ff_vp9_loop_filter_##dir##_16_16_##bpp##_neon
-
-#define init_lpf_func_mix2(idx1, idx2, idx3, dir, wd, bpp) \
-    dsp->loop_filter_mix2[idx1][idx2][idx3] = ff_vp9_loop_filter_##dir##_##wd##_16_##bpp##_neon
-
-#define init_lpf_funcs_8_wd(idx, wd, bpp) \
-    init_lpf_func_8(idx, 0, h, wd, bpp);  \
-    init_lpf_func_8(idx, 1, v, wd, bpp)
-
-#define init_lpf_funcs_16(bpp)   \
-    init_lpf_func_16(0, h, bpp); \
-    init_lpf_func_16(1, v, bpp)
-
-#define init_lpf_funcs_mix2_wd(idx1, idx2, wd, bpp) \
-    init_lpf_func_mix2(idx1, idx2, 0, h, wd, bpp);  \
-    init_lpf_func_mix2(idx1, idx2, 1, v, wd, bpp)
-
-#define init_lpf_funcs_8(bpp)        \
-    init_lpf_funcs_8_wd(0, 4,  bpp); \
-    init_lpf_funcs_8_wd(1, 8,  bpp); \
-    init_lpf_funcs_8_wd(2, 16, bpp)
-
-#define init_lpf_funcs_mix2(bpp)           \
-    init_lpf_funcs_mix2_wd(0, 0, 44, bpp); \
-    init_lpf_funcs_mix2_wd(0, 1, 48, bpp); \
-    init_lpf_funcs_mix2_wd(1, 0, 84, bpp); \
-    init_lpf_funcs_mix2_wd(1, 1, 88, bpp)
-
-        init_lpf_funcs_8(BPP);
-        init_lpf_funcs_16(BPP);
-        init_lpf_funcs_mix2(BPP);
-    }
-}
-
-av_cold void INIT_FUNC(VP9DSPContext *dsp)
-{
-    vp9dsp_mc_init_aarch64(dsp);
-    vp9dsp_loopfilter_init_aarch64(dsp);
-    vp9dsp_itxfm_init_aarch64(dsp);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/idctdsp_alpha.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/idctdsp_alpha.h
deleted file mode 100644
index 8cc969d7dee0f773402ef676dbb4b8072bde3311..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/idctdsp_alpha.h
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_ALPHA_IDCTDSP_ALPHA_H
-#define AVCODEC_ALPHA_IDCTDSP_ALPHA_H
-
-#include <stddef.h>
-#include <stdint.h>
-
-extern void (*put_pixels_clamped_axp_p)(const int16_t *block, uint8_t *pixels,
-                                        ptrdiff_t line_size);
-extern void (*add_pixels_clamped_axp_p)(const int16_t *block, uint8_t *pixels,
-                                        ptrdiff_t line_size);
-
-void ff_simple_idct_axp(int16_t *block);
-void ff_simple_idct_put_axp(uint8_t *dest, ptrdiff_t line_size, int16_t *block);
-void ff_simple_idct_add_axp(uint8_t *dest, ptrdiff_t line_size, int16_t *block);
-
-#endif /* AVCODEC_ALPHA_IDCTDSP_ALPHA_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Enjoy the New Patch of Genshin Impact APK with These Tips and Tricks.md b/spaces/congsaPfin/Manga-OCR/logs/Enjoy the New Patch of Genshin Impact APK with These Tips and Tricks.md
deleted file mode 100644
index 9508e488e57e39f48dfaf3a025039f85fab01e2f..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Enjoy the New Patch of Genshin Impact APK with These Tips and Tricks.md	
+++ /dev/null
@@ -1,132 +0,0 @@
-
-<h1>Genshin Impact New Patch APK: Everything You Need to Know</h1>
-<p>Genshin Impact is one of the most popular and immersive open-world action RPGs of recent times. It has captivated millions of players with its stunning graphics, engaging gameplay, and rich story. If you are a fan of this game, you might be wondering what the latest update has in store for you. In this article, we will tell you everything you need to know about the genshin impact new patch apk, including what is new, how to download and install it, and some tips and tricks for playing the game.</p>
- <h2>What is Genshin Impact?</h2>
-<p>Genshin Impact is a free-to-play game developed by miHoYo that was released in September 2020. In the game, you set forth on a journey across a fantasy world called Teyvat. In this vast world, you can explore seven nations, meet a diverse cast of characters with unique personalities and abilities, and fight powerful enemies together with them, all on the way during your quest to find your lost sibling.</p>
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-<p>The game features a gacha system that allows you to obtain new characters and weapons by spending in-game currency or real money. You can also customize your own character with different outfits, accessories, and hairstyles. The game also supports cross-play between different platforms, such as PC, mobile devices, PlayStation 4, PlayStation 5, and Nintendo Switch.</p>
- <h2>What is new in the version 3.6 update?</h2>
-<p>The version 3.6 update for Genshin Impact was released on April 28th, 2023. It adds a lot of new content to the game, such as new areas, characters, domains, main story, and more . Here are some of the highlights:</p>
- <h3>New areas: Inazuma and Chasm</h3>
-<p>Inazuma is the third nation in Teyvat that you can visit after Mondstadt and Liyue. It is an archipelago of islands that are ruled by the Electro Archon, Baal. Inazuma is known for its strict isolationist policy and its reverence for eternity. You will encounter many new challenges and mysteries in this land of thunder.</p>
-<p>Chasm is a massive cavern that lies between Liyue and Mondstadt. It is filled with ancient ruins, mysterious ores, and dangerous monsters. You will need to explore this underground world with caution and curiosity.</p>
- <h3>New characters: Ayaka, Yoimiya, and Sayu</h3>
-<p>Ayaka is a five-star Cryo sword user who is the daughter of the Kamisato Clan in Inazuma. She is elegant, graceful, and loyal to her family. She can use her elemental skill to dash through enemies and deal Cryo damage. She can also summon a blizzard with her elemental burst that continuously deals Cryo damage to enemies within its range.</p>
-<p>Yoimiya is a five-star Pyro bow user who is the owner of Naganohara Fireworks in Inazuma. She is cheerful, energetic, and passionate about fireworks. She can use her elemental skill to imbue her arrows with Pyro damage and cause explosions on hit. She can also unleash a barrage of flaming arrows with her elemental burst that can mark enemies and cause them to explode after a delay.</p>
-<p>Sayu is a four-star Anemo claymore user who is a ninja from the Shiyuumatsu-Ban in Inazuma. She is small, cute, and sleepy. She can use her elemental skill to roll around in a giant wind wheel and deal Anemo damage to enemies along the way. She can also summon a healing spirit with her elemental burst that can heal allies and deal Anemo damage to enemies.</p>
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- <h3>New domains: Momiji-Dyed Court and Empty Boat of a Thousand Gates</h3>
-<p>Momiji-Dyed Court is a new domain that is located in Inazuma. It is a beautiful garden that is decorated with maple leaves and lanterns. You can challenge this domain to obtain new artifacts sets, such as the Emblem of Severed Fate and the Shimenawa's Reminiscence.</p>
-<p>Empty Boat of a Thousand Gates is another new domain that is located in Inazuma. It is a mysterious ship that floats on the sea. You can challenge this domain to obtain new weapons, such as the Mistsplitter Reforged, the Thundering Pulse, and the Amenoma Kageuchi.</p>
- <h3>New main story: Chapter II - Act I: The Immovable God and the Eternal Euthymia</h3>
-<p>The new main story continues your adventure in Teyvat as you travel to Inazuma and meet the Electro Archon, Baal. You will learn more about her vision hunt decree, her pursuit of eternity, and her conflict with the resistance. You will also encounter new allies and enemies, such as Kazuha, Thoma, Sara, and Scaramouche.</p>
- <h3>New events: Thunder Sojourn, Phantom Flow, and more</h3>
-<p>The new update also brings a lot of new events that you can participate in to earn rewards, such as primogems, mora, talent books, and more. Some of the events are:</p>
-<ul>
-<li>Thunder Sojourn: A series of challenges that test your speed and combat skills in Inazuma. You can use special devices called Thunder Spheres and Thunder Dwellings to enhance your abilities.</li>
-<li>Phantom Flow: A martial arts tournament that pits you against different opponents with different rules and conditions. You can choose your difficulty level and earn different rewards accordingly.</li>
-<li>Ley Line Overflow: A limited-time event that allows you to get double rewards from completing ley line outcrops. You can do this up to three times per day.</li>
-<li>Lost Riches: A treasure hunting event that lets you find special coins called Iron Coins by following clues from a Seelie. You can exchange these coins for various items, such as primogems, mora, and a mini Seelie pet.</li>
-</ul>
- <h2>How to download and install the new patch APK?</h2>
-<p>If you want to play the new update of Genshin Impact on your Android or PC device, you will need to download and install the new patch APK. Here are the steps for doing so:</p>
- <h3>Steps for Android devices</h3>
-<ol>
-<li>Go to the official website of Genshin Impact and tap on the "Download Now" button.</li>
-<li>Select the "Android" option and tap on the "Download APK" button.</li>
-<li>Wait for the APK file to download on your device. It should be around 1.5 GB in size.</li>
-<li>Once the download is complete, locate the APK file on your device and tap on it to install it. You may need to enable the "Unknown Sources" option in your settings to allow the installation.</li>
-<li>After the installation is done, launch the game and wait for it to download additional data. This may take some time depending on your internet speed.</li>
-<li>Enjoy playing the new update of Genshin Impact!</li>
-</ol>
- <h3>Steps for PC devices</h3>
-<ol>
-<li>Go to the official website of Genshin Impact and click on the "Download Now" button.</li>
-<li>Select the "Windows" option and click on the "Download Game" button.</li>
-<li>Wait for the ZIP file to download on your PC. It should be around 1.5 GB in size.</li>
-<li>Once the download is complete, extract the ZIP file to a folder of your choice.</li>
-<li>Open the folder and double-click on the "GenshinImpact.exe" file to install it. You may need to allow it to run as an administrator.</li>
-<li>After the installation is done, launch the game and wait for it to download additional data. This may take some time depending on your internet speed.</li>
-<li>Enjoy playing the new update of Genshin Impact!</li>
-</ol>
- <h2>Tips and tricks for playing Genshin Impact</h2>
-<p>Genshin Impact is a game that offers a lot of freedom and fun for its players. However, it can also be challenging and complex at times. If you want to make the most out of your gaming experience, here are some tips and tricks that you can use:</p>
- <h3>How to level up your characters and weapons</h3>
-<p>Leveling up your characters and weapons is essential for increasing your combat power and unlocking new skills and abilities. You can level up your characters by using character EXP materials that you can obtain from various sources, such as quests, events, domains, and ley line outcrops. You can also use other characters as materials to level up your main characters, but this is not recommended as it will consume a lot of resources and potential.</p>
-<p>Leveling up your weapons is similar to leveling up your characters. You can use weapon EXP materials that you can obtain from various sources, such as quests, events, domains, and forging. You can also use other weapons as materials to level up your main weapons, but this is also not recommended for the same reasons as above.</p>
-<p>Besides leveling up, you can also ascend your characters and weapons to increase their level cap and unlock new talents and stats. You will need specific materials for each ascension, such as elemental stones, boss drops, local specialties, and common items. You can check the required materials in the character or weapon menu.</p>
- <h3>How to optimize your team composition and elemental reactions</h3>
-<p>Genshin Impact is a game that relies heavily on elemental interactions and synergies. You can have up to four characters in your active party, each with their own elemental affinity. By switching between different characters and using their elemental skills and bursts, you can trigger various elemental reactions that can deal extra damage, inflict status effects, or provide buffs or debuffs to yourself or your enemies.</p>
-<p>Some of the elemental reactions are:</p>
-<table>
-<tr><th>Reaction</th><th>Effect</th></tr>
-<tr><td>Melt</td><td>Increases damage when Pyro meets Cryo or vice versa</td></tr>
-<tr><td>Vaporize</td><td>Increases damage when Pyro meets Hydro or vice versa</td></tr>
-<tr><td>Overloaded</td><td>Causes an explosion that deals AoE Pyro damage when Pyro meets Electro</td></tr>
-<tr><td>Superconduct</td><td>Causes an explosion that deals AoE Cryo damage and reduces physical resistance when Cryo meets Electro</td></tr>
-<tr><td>Electro-Charged</td><td>Causes continuous Electro damage to enemies affected by Hydro</td></tr>
-<tr><td>Frozen</td><td>Freezes enemies when Hydro meets Cryo</td></tr>
-<tr><td>Shattered</td><td>Deals extra physical damage to frozen enemies when hit by claymore, geo, or plunge attack</td></tr>
-<tr><td>Swirl</td><td>Spreads the element that Anemo meets to nearby enemies and deals extra elemental damage</td></tr>
-<tr><td>Crystallize</td><td>Creates a shield that absorbs damage of the same element when Geo meets any other element except Anemo or Dendro</td></tr>
-<tr><td>Burning</td><td>Causes continuous Pyro damage to enemies affected by Dendro</td></tr>
-<tr><td>Burnt</td><td>Causes continuous Pyro damage to grass or wooden objects</td></tr>
-<tr><td>Searing Onslaught</td><td>A special reaction that occurs when Diluc uses his elemental skill on enemies affected by Diona's elemental burst. It deals massive Pyro damage and reduces enemy Cryo resistance.</td></tr>
-<tr><td>Frostbite</td><td>A special reaction that occurs when Rosaria uses her elemental skill on enemies affected by Xingqiu's elemental burst. It deals massive Cryo damage and reduces enemy Hydro resistance.</td></tr>
-<tr><td>Rimechaser Blade</td><td>A special reaction that occurs when Eula uses her elemental burst on enemies affected by Fischl's elemental skill. It deals massive Physical damage and reduces enemy Electro resistance.</td></tr>
-<tr><td>Nature's Wrath</td><td>A special reaction that occurs when Kazuha uses his elemental burst on enemies affected by any element except Anemo. It deals massive Anemo damage and increases the damage of the swirled element.</td></tr>
-<tr><td>Musou Shinsetsu</td><td>A special reaction that occurs when Ayaka uses her elemental burst on enemies affected by Cryo. It deals massive Cryo damage and increases her critical rate and damage.</td></tr>
-</table>
-<p>As you can see, there are many possible combinations and effects of elemental reactions. You should experiment with different characters and elements to find the best team composition for your playstyle and situation. You should also pay attention to the elemental resonance, which is a passive bonus that you get when you have two or more characters of the same element in your party. For example, having two Pyro characters will give you a 25% increase in attack power, while having two Anemo characters will reduce your stamina consumption by 15%.</p>
- <h3>How to explore the open world and find hidden treasures</h3>
-<p>Genshin Impact is a game that rewards exploration and curiosity. The open world of Teyvat is full of secrets, puzzles, quests, and treasures that you can discover and collect. You can use your map, your elemental sight, and your glider to navigate the terrain and find hidden locations. You can also interact with various NPCs, animals, plants, and objects to trigger events or obtain items.</p>
-<p>Some of the things that you can find in the open world are:</p>
-<ul>
-<li>Chests: These are containers that hold various rewards, such as primogems, mora, weapons, artifacts, and more. There are different types of chests, such as common, exquisite, precious, and luxurious. Some chests are easy to find, while others require you to solve puzzles, defeat enemies, or complete quests to unlock them.</li>
-<li>Oculi: These are glowing orbs that represent the seven elements. You can collect them and offer them to the Statues of The Seven to increase your stamina, adventure rank, and primogems. There are different types of oculi for each region, such as Anemoculi in Mondstadt, Geoculi in Liyue, and Electroculi in Inazuma.</li>
-<li>Seelies: These are cute spirits that guide you to hidden chests or locations. You can follow them and activate their pedestals to unlock their rewards. There are different types of seelies for each region, such as blue seelies in Mondstadt, yellow seelies in Liyue, and purple seelies in Inazuma.</li>
-<li>Shrines: These are ancient structures that require keys to open. You can obtain keys by completing certain quests or domains. Inside the shrines, you can find luxurious chests that contain valuable items.</li>
-<li>Domains: These are special dungeons that offer different challenges and rewards. You can enter them by spending resin or original resin, which are resources that regenerate over time. You can obtain various items from domains, such as artifacts, weapons, talent books, ascension materials, and more.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Genshin Impact is a game that offers a lot of fun and excitement for its players. The new patch APK adds a lot of new content to the game, such as new areas, characters, domains, main story, and more. You can download and install the new patch APK on your Android or PC device by following the steps we provided. You can also use our tips and tricks to optimize your team composition, level up your characters and weapons, and explore the open world. We hope you enjoy playing the new update of Genshin Impact!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about the genshin impact new patch apk:</p>
-<ol>
-<li>Q: Is the new patch APK safe to download and install?</li>
-<li>A: Yes, the new patch APK is safe to download and install as long as you get it from the official website of Genshin Impact. You should avoid downloading it from any third-party sources or websites that may contain viruses or malware.</li>
-<li>Q: Do I need to uninstall the previous version of Genshin Impact before installing the new patch APK?</li>
-<li>A: No, you do not need to uninstall the previous version of Genshin Impact before installing the new patch APK. The new patch APK will overwrite the previous version and update your game data automatically.</li>
-<li>Q: How much storage space do I need to download and install the new patch APK?</li>
-<li>A: You will need at least 1.5 GB of free storage space on your device to download and install the new patch APK. You may also need additional space for downloading additional data after launching the game.</li>
-<li>Q: How long does it take to download and install the new patch APK?</li>
-<li>A: The time it takes to download and install the new patch APK depends on your internet speed and device performance. It may take anywhere from a few minutes to a few hours. You can check the progress of your download and installation in the game launcher.</li>
-<li>Q: Can I play the new update of Genshin Impact offline?</li>
-<li>A: No, you cannot play the new update of Genshin Impact offline. You will need a stable internet connection to play the game and access its features.</li>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Mod apk warriors io aksi royal tempur customize your character and weapons.md b/spaces/congsaPfin/Manga-OCR/logs/Mod apk warriors io aksi royal tempur customize your character and weapons.md
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-<h1>Mod Apk Warriors Io Aksi Royal Tempur: A Fun and Exciting Battle Royale Game for Android</h1>
- <p>If you are looking for a fun and exciting battle royale game for your Android device, you should try Mod Apk Warriors Io Aksi Royal Tempur. This is a modded version of the original game that gives you unlimited money, unlock all characters and weapons, and remove ads. In this article, we will tell you what Mod Apk Warriors Io Aksi Royal Tempur is, how to download and install it, and some tips and tricks for playing it.</p>
- <h2>What is Mod Apk Warriors Io Aksi Royal Tempur?</h2>
- <p>Mod Apk Warriors Io Aksi Royal Tempur is a modded version of the original game Warriors.io - Battle Royale Action. This is a multiplayer online game where you can choose from over 50 different characters and fight against other players in various modes. You can also customize your character with different weapons, skins, hats, and backpacks. The game has simple controls, colorful graphics, and addictive gameplay.</p>
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- <h3>Features of Mod Apk Warriors Io Aksi Royal Tempur</h3>
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- <p>The modded version of the game unlocks all the characters and weapons that are available in the game. You can choose from over 50 different characters, each with their own unique abilities and stats. You can also use any weapon you want, from swords and axes to guns and grenades. You can mix and match different combinations of characters and weapons to suit your play style.</p>
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- <p>If you want to download and install Mod Apk Warriors Io Aksi Royal Tempur on your Android device, you need to follow these simple steps:</p>
- <h4>Step 1: Download the mod apk file from a trusted source</h4>
- <p>You can find many websites that offer mod apk files for various games, but not all of them are safe or reliable. You need to be careful when downloading mod apk files from unknown sources, as they may contain viruses or malware that can harm your device or steal your personal information. We recommend that you download the mod apk file from [this link](^1^), which is a trusted source that has been tested by many users.</p>
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- <p>Now that you have installed Mod Apk Warriors Io Aksi Royal Tempur on your device, you can start playing the game and have fun. Here are some tips and tricks that can help you improve your skills and win more battles:</p>
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- <p>As you play the game, you will earn coins and gems that you can use to upgrade your weapons and skills. Upgrading your weapons will increase their damage, range, accuracy, and fire rate. Upgrading your skills will reduce their cooldown time, increase their duration, and enhance their effects. You should upgrade your weapons and skills regularly to keep up with the increasing difficulty of the game.</p>
- <h4>Use the map and the radar to locate enemies and items</h4>
- <p>The game has a map and a radar that show you the location of enemies and items on the battlefield. You should use them to plan your movements and avoid being ambushed or surrounded by enemies. You should also use them to find items such as health packs, ammo boxes, chests, and crates that can help you survive longer and gain an advantage over your opponents.</p>
- <h4>Collect coins and gems to buy more items and characters</h4>
- <p>The game has a shop where you can buy more items and characters using coins and gems. Items include consumables such as grenades, mines, rockets, shields, medkits, etc. Characters include new ones that are not available in the original game or require real money. You should collect as many coins and gems as possible by playing the game modes, completing missions, watching ads, etc. You should also spend them wisely on items and characters that can benefit you in the game.</p>
- <h2>Conclusion</h2>
- <p>Mod Apk Warriors Io Aksi Royal Tempur is a fun and exciting battle royale game for Android devices that offers unlimited money, unlock all characters and weapons, and no ads. You can download and install it easily by following the steps above. You can also improve your skills and win more battles by following the tips and tricks above. If you are looking for a new game to play with your friends or online players, you should give Mod Apk Warriors Io Aksi Royal Tempur a try.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Mod Apk Warriors Io Aksi Royal Tempur:</p>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Mod Apk Warriors Io Aksi Royal Tempur safe to download and install?</td>
-<td>Yes, as long as you download it from a trusted source like [this link]. The mod apk file has been tested by many users and does not contain any viruses or malware.</td>
-</tr>
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-<td>Will Mod Apk Warriors Io Aksi Royal Tempur affect my original game data?</td>
-<td>No, Mod Apk Warriors Io Aksi Royal Tempur will not affect your original game data. You can play both versions of the game separately without any problems.</td>
-</tr>
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-<td>Can I play Mod Apk Warriors Io Aksi Royal Tempur online with other players?</td>
-<td>Yes, you can play Mod Apk Warriors Io Aksi Royal Tempur online with other players who have the same version of the game. You can also play with your friends by creating a private room and inviting them.</td>
-</tr>
-<tr>
-<td>What are the best characters and weapons to use in Mod Apk Warriors Io Aksi Royal Tempur?</td>
-<td>There is no definitive answer to this question, as different characters and weapons have different advantages and disadvantages. You should experiment with different combinations and find out what works best for you. However, some of the popular characters and weapons are: Ninja, Samurai, Sniper, Rocket Launcher, Machine Gun, and Shotgun.</td>
-</tr>
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-<td>How can I contact the developer of Mod Apk Warriors Io Aksi Royal Tempur?</td>
-<td>You can contact the developer of Mod Apk Warriors Io Aksi Royal Tempur by visiting their official website [here] or by sending them an email at [this address].</td>
-</tr>
-</table>
- <p>I hope this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Play 123 Free Solitaire Online or Offline - No Registration Required.md b/spaces/congsaPfin/Manga-OCR/logs/Play 123 Free Solitaire Online or Offline - No Registration Required.md
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-<h1>Download Solitaire 123 Free: How to Play the Classic Card Game on Your PC</h1>
- <p>Solitaire is one of the most popular and timeless card games in the world. It is a game of patience, strategy, and skill that can keep you entertained for hours. But did you know that you can play solitaire on your PC for free? In this article, we will show you how to download and play Solitaire 123 Free, a Microsoft Store version of the award-winning solitaire card game with millions of downloads since 1998. We will also share with you some features, tips, and benefits of playing this game. So, if you are ready to have some fun and challenge yourself, read on!</p>
- <h2>What is Solitaire 123 Free?</h2>
- <p>Solitaire 123 Free is a solitaire card game that you can download and play on your PC for free. It is developed by TreeCardGames, a company that specializes in creating high-quality card games for Windows devices. Solitaire 123 Free is based on the classic solitaire game, also known as Klondike, where you have to move all the cards from the tableau to the foundations in ascending order by suit. However, Solitaire 123 Free also offers other exciting game modes, such as Spider solitaire, FreeCell, Pyramid, TriPeaks, and more. You can choose from different levels of difficulty, card sets, backgrounds, and animations to customize your gaming experience.</p>
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- <h3>Features of Solitaire 123 Free</h3>
- <p>Some of the features that make Solitaire 123 Free stand out from other solitaire games are:</p>
-<ul>
-<li>Smooth fluid and advanced animations</li>
-<li>Many beautiful card sets, card backs and backgrounds to choose from</li>
-<li>Automatic card flipping</li>
-<li>Deal animation and winning animation</li>
-<li>Single click or single touch for touch screens to auto move cards</li>
-<li>"Automatically move cards to the foundations" option on/off</li>
-<li>Unlimited undo and redo options</li>
-<li>Statistics and Achievements tracking</li>
-<li>Scores, Time and Moves counters</li>
-<li>Save game progress</li>
-<li>Hints will show you possible moves</li>
-</ul>
- <h3>How to download and install Solitaire 123 Free from the Microsoft Store</h3>
- <p>To download and install Solitaire 123 Free on your PC, you need to follow these simple steps:</p>
-<ol>
-<li>Go to the Microsoft Store app on your PC or visit this link: <a href="(^1^)">Get 123 Free Solitaire - Microsoft Store</a></li>
-<li>Click on the "Get" button and sign in with your Microsoft account.</li>
-<li>Wait for the download and installation process to complete.</li>
-<li>Launch the game from your Start menu or desktop shortcut.</li>
-<li>Enjoy playing Solitaire 123 Free!</li>
-</ol>
- <h2>How to play Solitaire 123 Free</h2>
- <p>Playing Solitaire 123 Free is easy and fun. You just need to know the basic rules and controls of the game.</p>
- <h3>The rules of Solitaire 123 Free</h3>
- <p>The rules of Solitaire 123 Free are similar to the classic solitaire game. Here are the main points:</p>
-<ul>
-<li>The game consists of a tableau (the main area where you arrange the cards), four foundations (the top right area where you place the cards in ascending order by suit), and a stock and a waste (the top left area where you draw and discard cards).</li>
-<li>The goal of the game is to move all the cards from the tableau and the waste to the foundations.</li>
-<li>You can move one card at a time, or a group of cards that are in sequence and of the same suit.</li>
-<li>You can only move a card or a group of cards to another column if the top card of that column is of the opposite color and one rank higher than the card you are moving.</li>
-<li>You can also move a card or a group of cards to an empty column.</li>
-<li>You can move an ace or a group of cards starting with an ace to the foundations.</li>
-<li>You can draw one card or three cards at a time from the stock to the waste, depending on your preference.</li>
-<li>You can move a card from the waste to the tableau or the foundations, following the same rules as above.</li>
-<li>You can restart the stock by clicking on its empty space, but you can only do this once per game.</li>
-<li>The game is over when you have moved all the cards to the foundations, or when you have no more moves left.</li>
-</ul>
- <h3>The different game modes of Solitaire 123 Free</h3>
- <p>Solitaire 123 Free offers 12 different game modes for you to choose from. Each game mode has its own rules and challenges. Here are some examples:</p>
-<ul>
-<li>Spider solitaire: You have to arrange the cards in descending order by suit, from king to ace. You can move any group of cards that are in sequence and of the same suit. You can deal 10 new cards from the stock when you have no more moves. You win when you have created eight complete sequences from king to ace.</li>
-<li>FreeCell: You have four free cells where you can temporarily store one card each. You can move any card or group of cards that are in sequence and of alternating colors. You win when you have moved all the cards to the foundations.</li>
-<li>Pyramid: You have to remove all the cards from the pyramid by pairing them up with another card that adds up to 13. You can only pair up cards that are not covered by other cards. You can also use the waste card or the stock card for pairing. You win when you have removed all the cards from the pyramid and the stock.</li>
-<li>TriPeaks: You have to remove all the cards from the three peaks by selecting a card that is one rank higher or lower than the waste card. You can also use the stock card for selecting. You win when you have removed all the cards from the peaks and the stock.</li>
-</ul>
- <h3>Tips and tricks for Solitaire 123 Free</h3>
- <p>If you want to improve your skills and score in Solitaire 123 Free, here are some tips and tricks that you can use:</p>
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-<ul>
-<li>Plan ahead: Before making a move, think about how it will affect your future moves. Try to avoid blocking cards that you need later, or creating empty columns that you cannot fill.</li>
-<li>Use the undo button: If you make a mistake or change your mind, you can use the undo button to go back to your previous move. You can undo as many times as you want, but it will affect your score and time.</li>
-<li>Use the hints button: If you are stuck or need some guidance, you can use the hints button to show you possible moves. However, using hints will also reduce your score and time.</li>
-<li>Play regularly: The more you play, the more familiar you will become with the game modes, rules, and strategies. You will also earn more achievements and improve your statistics.</li>
-</ul>
- <h2>Why you should play Solitaire 123 Free</h2>
- <p>Solitaire 123 Free is not only a fun and relaxing game, but also a beneficial one. Here are some reasons why you should play Solitaire 123 Free:</p>
- <h3>Benefits of playing Solitaire 123 Free</h3>
- <p>Playing Solitaire 123 Free can help you:</p>
-<ul>
-<li>Improve your concentration, memory, and problem-solving skills</li>
-<li>Reduce stress and anxiety</li>
-<li>Increase your self-esteem and confidence</li>
-<li>Enhance your mood and creativity</li>
-<li>Kill time and boredom</li>
-</ul>
- <h3>Challenges and achievements of Solitaire 123 Free</h3>
- <p>If you are looking for some extra motivation and excitement, Solitaire 123 Free offers various challenges and achievements for you to complete. Some examples are:</p>
-<ul>
-<li>Winning streak: Win 5 games in a row without using undo or hints</li>
-<li>Perfect game : Win a game without using undo or hints, and without moving any card to the free cells (in FreeCell mode)</li>
-<li>Speed demon: Win a game in less than 3 minutes</li>
-<li>Master of solitaire: Win 100 games in total</li>
-<li>And many more!</li>
-</ul>
- <h3>Alternatives to Solitaire 123 Free</h3>
- <p>If you want to try other solitaire games, there are plenty of alternatives to Solitaire 123 Free. Some of them are:</p>
-<ul>
-<li>Solitaire Collection Free: A collection of 50 solitaire games, including Klondike, Spider, FreeCell, Pyramid, TriPeaks, Golf, and more.</li>
-<li>Solitaire by MobilityWare: A classic solitaire game with daily challenges, themes, and leaderboards.</li>
-<li>Solitaire Grand Harvest: A solitaire game with a farming twist. You can grow crops, harvest rewards, and play with cute animals.</li>
-<li>Solitaire by Zynga: A solitaire game with social features. You can chat with other players, send gifts, and compete in tournaments.</li>
-<li>Solitaire by Brainium: A solitaire game with elegant graphics, intuitive controls, and smart hints.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Solitaire 123 Free is a great way to enjoy the classic card game on your PC for free. You can download and install it from the Microsoft Store in a few minutes. You can play different game modes, customize your settings, and track your progress. You can also improve your skills, challenge yourself, and have fun. Solitaire 123 Free is a game that can benefit your mind, mood, and time. So what are you waiting for? Download Solitaire 123 Free today and start playing!</p>
- <h3>Summary of the main points</h3>
- <p>In this article, we have covered the following points:</p>
-<ul>
-<li>What is Solitaire 123 Free and what are its features</li>
-<li>How to download and install Solitaire 123 Free from the Microsoft Store</li>
-<li>How to play Solitaire 123 Free and what are the rules and tips</li>
-<li>Why you should play Solitaire 123 Free and what are the benefits and challenges</li>
-<li>What are some alternatives to Solitaire 123 Free</li>
-</ul>
- <h3>Call to action</h3>
- <p>If you liked this article, please share it with your friends and family who might be interested in playing solitaire on their PC. You can also leave us a comment below and let us know what you think about Solitaire 123 Free. We would love to hear from you!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Solitaire 123 Free:</p>
- <ol>
-<li><b>Is Solitaire 123 Free safe to download?</b></li>
-<p>Yes, Solitaire 123 Free is safe to download from the Microsoft Store. It does not contain any viruses, malware, or spyware. It also does not require any personal information or permissions from your device.</p>
- <li><b>Is Solitaire 123 Free compatible with Windows 10?</b></li>
-<p>Yes, Solitaire 123 Free is compatible with Windows 10. It works well on both desktop and laptop computers. It also supports touch screen devices.</p>
- <li><b>How much space does Solitaire 123 Free take on my PC?</b></li>
-<p>Solitaire 123 Free takes about 50 MB of space on your PC. It is a lightweight and fast game that does not affect your PC's performance or battery life.</p>
- <li><b>Can I play Solitaire 123 Free offline?</b></li>
-<p>Yes, you can play Solitaire 123 Free offline. You do not need an internet connection to play the game. However, you will need an internet connection to download and update the game from the Microsoft Store.</p>
- <li><b>Can I play Solitaire 123 Free with other people?</b></li>
-<p>No, Solitaire 123 Free is a single-player game. You cannot play it with other people online or locally. However, you can compare your scores and achievements with other players on the leaderboards.</p>
-</ol></p> 197e85843d<br />
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-
-<h1>Idle Prison Manager Mod APK: How to Download and Install</h1>
-<p>If you are a fan of management and idle games, you might want to try Idle Prison Manager Mod APK, a casual and easy-to-play game where you can build and run your own prison business. In this article, we will show you what Idle Prison Manager Mod APK is, how to download and install it on your Android device, how to play it, and how it compares with other prison tycoon games.</p>
- <h2>What is Idle Prison Manager Mod APK?</h2>
-<p>Idle Prison Manager Mod APK is a modified version of the original Idle Prison Manager game developed by Dotjoy. It is a simulation game where you can create your own prison from scratch, hire staff, manage resources, rehabilitate prisoners, and earn money. The modded version of the game gives you unlimited money to spend on upgrading your prison facilities, hiring more staff, and unlocking new features. You can also enjoy the game without any ads or in-app purchases.</p>
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- <h3>Features of Idle Prison Manager Mod APK</h3>
-<p>Some of the features of Idle Prison Manager Mod APK are:</p>
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-<li>You can build various types of buildings, such as cells, workshops, kitchens, clinics, libraries, gyms, and more.</li>
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-<li>You can earn money from prisoner factories, contracts, donations, and more.</li>
-<li>You can use unlimited money to upgrade your prison and unlock new features.</li>
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- <h3>Benefits of Idle Prison Manager Mod APK</h3>
-<p>Some of the benefits of Idle Prison Manager Mod APK are:</p>
-<ul>
-<li>You can enjoy the game without any ads or interruptions.</li>
-<li>You can play the game without any internet connection or registration.</li>
-<li>You can experiment with different strategies and scenarios without worrying about money or resources.</li>
-<li>You can have fun with the humorous and colorful graphics and animations.</li>
-<li>You can learn about prison management and rehabilitation in a casual and entertaining way.</li>
-</ul>
- <h2>How to Download Idle Prison Manager Mod APK</h2>
-<p>To download and install Idle Prison Manager Mod APK on your Android device, you need to follow these steps:</p>
- <h3>Step 1: Allow Unknown Apps on Android</h3>
-<p>Before you can download APK files using Chrome or any other browser, you need to allow unknown apps on your device. To do this:</p>
-<ol>
-<li>Go to your device settings and tap Apps & Notifications (or Apps in older versions of Android).</li>
-<li>Tap the three dots in the upper-right corner.</li>
-<li>Tap Special access.</li>
-<li>Tap Install unknown apps.</li>
-<li>Tap Chrome (or whichever web browser you use).</li>
-<li>Move Allow from this source to the On position.</li>
-</ol>
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-<p>To access the APK file that you downloaded, you need to install a file manager app on your device. A file manager app allows you to browse and manage the files and folders on your device. There are many file manager apps available on the Google Play Store, such as ES File Explorer, File Manager, and Files by Google. You can choose any file manager app that you like and install it on your device.</p>
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-<p>Now that you have allowed unknown apps and installed a file manager app, you can download the APK file of Idle Prison Manager Mod APK from a trusted source. There are many websites that offer APK files for free, but some of them may contain malware or viruses that can harm your device. Therefore, you should always download APK files from reputable and reliable sources. One of the sources that we recommend is APKPure.com, which is a popular and safe website that provides APK files for various Android apps and games. To download the APK file from APKPure.com, follow these steps:</p>
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-<li>Open Chrome (or any other web browser) on your device and go to https://apkpure.com/.</li>
-<li>In the search box, type Idle Prison Manager Mod APK and tap the magnifying glass icon.</li>
-<li>From the search results, tap the Idle Prison Manager Mod APK icon.</li>
-<li>On the app page, tap Download APK.</li>
-<li>Wait for the download to complete. You can check the progress in the notification bar.</li>
-</ol>
- <h3>Step 4: Install the APK File on Your Device</h3>
-<p>Once the download is finished, you can install the APK file on your device using the file manager app that you installed earlier. To do this:</p>
-<ol>
-<li>Open the file manager app on your device and locate the APK file that you downloaded. It should be in the Downloads folder by default.</li>
-<li>Tap the APK file to open it.</li>
-<li>A pop-up window will appear asking you to confirm the installation. Tap Install.</li>
-<li>Wait for the installation to complete. You can check the progress in the notification bar.</li>
-<li>Once the installation is done, tap Open to launch the game or Done to exit.</li>
-</ol>
- <h2>How to Play Idle Prison Manager Mod APK</h2>
-<p>Now that you have downloaded and installed Idle Prison Manager Mod APK on your device, you can start playing it and enjoy its features. Here are some tips on how to play Idle Prison Manager Mod APK:</p>
- <h3>Build and Manage Your Own Prison</h3>
-<p>Your main goal in Idle Prison Manager Mod APK is to build and manage your own prison business. You can start by building basic facilities, such as cells, offices, power plants, and water tanks. Then, you can expand your prison by adding more buildings, such as workshops, kitchens, clinics, libraries, gyms, and more. You can also customize your prison layout, design, and decoration according to your preference. You can use unlimited money to buy and upgrade anything you want in the game.</p>
- <h3>Hire and Train Staff</h3>
-<p>To run your prison smoothly, you need to hire staff to perform various tasks and duties. You can hire different kinds of staff, such as guards, doctors, teachers, psychologists, and more. Each staff member has different skills and abilities that affect their performance and efficiency. You can assign staff to different buildings and areas of your prison and monitor their work status. You can also train staff to improve their skills and productivity. You can use unlimited money to hire and train as many staff as you want in the game.</p>
- <h3>Rehabilitate and Release Prisoners</h3>
-<p>The most important aspect of Idle Prison Manager Mod APK is to rehabilitate prisoners and prepare them for release. You can admit prisoners from different backgrounds and crimes into your prison and assign them to different cells. Each prisoner has different needs and behaviors that affect their mood and satisfaction. You can rehabilitate prisoners by sending them to various therapies, such as VR therapy, zero gravity room, dance therapy, and more. These therapies can reduce their stress level and increase their happiness level. You can also educate prisoners by sending them to classes, such as math, language, art, and more. These classes can increase their intelligence level and skills level. You can release prisoners on parole or keep them locked up for longer depending on their behavior and progress. You can earn money from releasing prisoners or keeping them in your prison.</p>
- <h2>Comparison with Other Prison Tycoon Games</h2>
-<p>If you like Idle Prison Manager Mod APK, you might also want to check out other prison tycoon games that are available on various platforms. Here are some of the best prison tycoon games that you can play:</p>
- <h3>Prison Tycoon®: Under New Management</h3>
-<p>This is a new and improved version of the classic Prison Tycoon series, developed by Abylight Studios and published by Ziggurat. It is a simulation game where you can build and manage your own prison from the ground up, hiring staff, rehabilitating prisoners, and dealing with challenges. You can choose from five different climates, each with different difficulties and opportunities. You can also customize your prison layout, design, and decoration. You can use various therapies, such as VR therapy, zero gravity room, dance therapy, and more, to reduce the stress and increase the happiness of your prisoners. You can also educate them by sending them to classes, such as math, language, art, and more. You can release them on parole or keep them locked up for longer depending on their behavior and progress. You can earn money from prisoner factories, contracts, donations, and more. You can also play the game in sandbox mode, where you have unlimited resources to create the perfect prison. The game is available on Steam for $19.99.</p>
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-<p>This is an idle game developed by Codigames where you can become a prison tycoon by managing your own prison business. You can start with a small low-security prison and expand it to a high-security prison with hundreds of inmates. You can build different facilities for your prisoners, such as cells, bathrooms, cafeterias, infirmaries, libraries, workshops, recreational areas, and more. You can hire staff to take care of your prisoners' needs and security. You can improve your prison's conditions by upgrading your facilities and equipment. You can rehabilitate your prisoners by offering them activities and programs that will improve their skills and behavior. You can also deal with events that will challenge your management skills, such as riots, fires, fights, escapes, inspections, lawsuits, and more. You can earn money from your prison's production and contracts. You can also invest in other businesses that will boost your income. The game is free to play on Google Play Store.</p>
- <h2>Conclusion</h2>
-<p>Idle Prison Manager Mod APK is a fun and easy-to-play game where you can build and manage your own prison business with unlimited money and no ads. You can download and install it on your Android device by following the steps in this article. You can also check out other prison tycoon games that are available on various platforms if you want to try different scenarios and challenges.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Idle Prison Manager Mod APK:</p>
-<ul>
-<li><b>Q: Is Idle Prison Manager Mod APK safe to download?</b></li>
-<li>A: Yes, Idle Prison Manager Mod APK is safe to download as long as you download it from a trusted source like APKPure.com.</li>
-<li><b>Q: Do I need to root my device to install Idle Prison Manager Mod APK?</b></li>
-<li>A: No, you do not need to root your device to install Idle Prison Manager Mod APK.</li>
-<li><b>Q: Can I play Idle Prison Manager Mod APK offline?</b></li>
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-<h1>Kendrick Lamar Section 80 Album Download: A Guide for Hip Hop Fans</h1>
- <p>If you are a fan of hip hop music, you have probably heard of Kendrick Lamar, one of the most influential and acclaimed rappers of his generation. Known for his progressive musical styles and socially conscious songwriting, he has won numerous awards and accolades, including a Pulitzer Prize for his album DAMN. in 2018.</p>
-<h2>kendrick lamar section 80 album download</h2><br /><p><b><b>Download</b> ---> <a href="https://urlca.com/2uOaAm">https://urlca.com/2uOaAm</a></b></p><br /><br />
- <p>But before he became a global superstar, Kendrick Lamar was a young rapper from Compton, California, who released his debut studio album Section.80 in 2011. The album was a breakthrough for Lamar, as it showcased his lyrical skills, storytelling abilities, and artistic vision.</p>
- <p>Section.80 is a concept album that explores the lives and struggles of the children born in the 1980s under the influence of the crack epidemic, the Reagan administration, and the AIDS crisis. The album tackles topics such as racism, self-hate, drug abuse, poverty, violence, and spirituality.</p>
- <p>In this article, we will take a closer look at Section.80 by Kendrick Lamar, and explain why it is a must-listen for hip hop fans. We will also provide you with some options and platforms where you can download Section.80 and enjoy its music.</p>
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- <h2>A Track-by-Track Analysis of Section.80</h2>
- <p>Section.80 consists of 15 tracks that are divided into chapters that follow a narrative structure. The album features guest appearances from other rappers such as Ab-Soul, ScHoolboy Q, RZA, GLC, BJ the Chicago Kid, as well as singers such as Colin Munroe and Alori Joh.</p>
- <p>The album also features production from various producers such as Sounwave, J. Cole, THC, Wyldfyer, Dave Free, Terrace Martin, Willie B., Tommy Black, Tae Beast, and Kendrick Lamar himself.</p>
- <p>Here is a brief summary of each track on Section.80:</p>
- <ul>
-<li><strong>F*ck Your Ethnicity</strong>: The opening track sets the tone for the album, as Lamar addresses his listeners regardless of their race or background. He urges them to unite under a common cause and to embrace their individuality.</li>
-<li><strong>Hol' Up</strong>: The second track is a more upbeat and playful song that showcases Lamar's charisma and confidence. He raps about his ambitions, his skills, his lifestyle, and his hometown.</li>
-<li><strong>A.D.H.D</strong>: The third track is one of the most popular songs on the album, as it deals with the effects of attention deficit hyperactivity disorder (ADHD) on Lamar's generation. He describes how they cope with drugs, alcohol, sex, and parties to escape their reality.</li>
-<li><strong>No Make-Up (Her Vice)</strong>: The fourth track is a song that focuses on the insecurities and pressures that women face in society. Lamar raps from the perspective of a woman who feels like she needs to wear make-up to be accepted and loved.</li>
-<h2>A Track-by-Track Analysis of Section.80 (Continued)</h2>
- <ul>
-<li><strong>Tammy's Song (Her Evils)</strong>: The fifth track is a song that tells the story of two women, Tammy and Keisha, who are betrayed by their boyfriends and decide to turn to each other for comfort and revenge. Lamar raps from the perspective of Tammy, who expresses her anger and frustration.</li>
-<li><strong>Chapter Six</strong>: The sixth track is a song that references the sixth chapter of the Book of John in the Bible, where Jesus feeds a multitude of people with five loaves of bread and two fish. Lamar compares himself to Jesus, as he claims to feed his fans with his music and his words.</li>
-<li><strong>Ronald Reagan Era (His Evils)</strong>: The seventh track is a song that explores the impact of the Reagan administration on Lamar's generation, especially in terms of the crack epidemic, the war on drugs, and the gang violence. Lamar raps about his experiences growing up in Compton during that era, and how he survived and thrived despite the odds.</li>
-<li><strong>Poe Mans Dreams (His Vice)</strong>: The eighth track is a song that features a guest verse from GLC, a rapper from Chicago who was affiliated with Kanye West. The song is about the dreams and aspirations of poor men who resort to vices such as drugs, money, and women to cope with their hardships.</li>
-<li><strong>The Spiteful Chant</strong>: The ninth track is a song that features a guest verse from ScHoolboy Q, another rapper from Compton who is part of Lamar's group Black Hippy. The song is about the resentment and jealousy that Lamar and Q face from their haters and rivals, who try to bring them down.</li>
-<li><strong>Chapter Ten</strong>: The tenth track is a short interlude that references the tenth chapter of the Book of John in the Bible, where Jesus says that he is the good shepherd who lays down his life for his sheep. Lamar compares himself to Jesus again, as he says that he is willing to sacrifice himself for his fans and his people.</li>
-<li><strong>Keisha's Song (Her Pain)</strong>: The eleventh track is a song that tells the story of Keisha, a 17-year-old girl who becomes a prostitute to support her family. Lamar raps from the perspective of Keisha, who reveals her pain and trauma. The song is based on a true story of one of Lamar's friends who died in a similar situation.</li>
-<li><strong>Rigamortus</strong>: The twelfth track is a song that showcases Lamar's technical skills and rapid-fire flow. He raps over a jazzy beat that samples "The Thorn" by Willie Jones III. He claims to kill his competition with his rhymes, leaving them in a state of rigamortus.</li>
-<li><strong>Kush & Corinthians (His Pain)</strong>: The thirteenth track is a song that features a guest verse from BJ the Chicago Kid, a singer from Chicago who has worked with artists such as Kanye West, Chance the Rapper, and Dr. Dre. The song is about the contradictions and dilemmas that Lamar faces as a rapper and as a human being. He questions his morality, his faith, his purpose, and his destiny.</li>
-<li><strong>Blow My High (Members Only)</strong>: The fourteenth track is a song that pays tribute to Aaliyah, the late R&B singer who died in a plane crash in 2001. The song samples her song "4 Page Letter" and includes references to her lyrics and songs. Lamar raps about his admiration for Aaliyah and how her music influenced him.</li>
-<li><strong>Ab-Soul's Outro</strong>: The fifteenth track is a song that features a guest verse from Ab-Soul, another rapper from Compton who is part of Lamar's group Black Hippy. The song also features vocals from Alori Joh, a singer who was Ab-Soul's girlfriend and collaborator before she died in 2012. The song is an outro that summarizes the themes and messages of Section.80, as Ab-Soul raps about politics, religion, philosophy, history, and culture.</li>
-<h2>A Track-by-Track Analysis of Section.80 (Continued)</h2>
- <ul>
-<li><strong>HiiiPoWeR</strong>: The sixteenth and final track is a bonus track that was produced by J. Cole, a rapper and producer from North Carolina who has collaborated with Lamar on several occasions. The song is about HiiiPoWeR, a movement and philosophy that Lamar created with Ab-Soul and J. Cole. HiiiPoWeR stands for Heart, Honor, and Respect, and it represents the three "i"s in their names. The song is a call to action for Lamar's fans and peers to elevate themselves and their communities, and to challenge the status quo and the system. The song also references influential figures such as Martin Luther King Jr., Malcolm X, Nelson Mandela, Tupac Shakur, and Nipsey Hussle.</li>
-</ul>
- <h2>A Comparison of Section.80 with Other Albums by Kendrick Lamar and Other Contemporary Hip Hop Artists</h2>
- <p>Section.80 is widely regarded as one of the best albums by Kendrick Lamar, and one of the best hip hop albums of the 2010s. It is also considered as a precursor to Lamar's later albums, such as Good Kid, M.A.A.D City (2012), To Pimp a Butterfly (2015), and DAMN. (2017), which are also concept albums that explore different aspects of Lamar's identity, culture, history, and society.</p>
- <p>Section.80 is different from Lamar's other albums in terms of its sound, style, and scope. It is more experimental and eclectic, as it incorporates elements from various genres such as jazz, soul, funk, rock, and electronic music. It is also more personal and intimate, as it reflects Lamar's experiences and thoughts as a young man growing up in Compton.</p>
- <p>Section.80 is also different from other hip hop albums that were released around the same time, such as Watch the Throne by Jay-Z and Kanye West (2011), Take Care by Drake (2011), Tha Carter IV by Lil Wayne (2011), My Beautiful Dark Twisted Fantasy by Kanye West (2010), and Recovery by Eminem (2010). These albums were more mainstream and commercial, as they focused on themes such as fame, wealth, power, love, and ego.</p>
- <p>Section.80 was more underground and independent, as it focused on themes such as social justice, self-awareness, spirituality, and empowerment. It was also more innovative and creative, as it challenged the conventions and expectations of hip hop music.</p>
- <h2>A Summary of the Critical Reception and Commercial Performance of Section.80</h2>
- <p>Section.80 received critical acclaim from music critics and fans alike, who praised Lamar's lyricism, storytelling, production, and originality. The album was ranked among the best albums of 2011 by various publications such as Complex, Pitchfork, Rolling Stone, Spin, The Source, XXL, and others.</p>
- <p>Section.80 also performed well commercially, despite being released independently through Top Dawg Entertainment without any major label support or promotion. The album debuted at number 113 on the Billboard 200 chart, selling 5,300 copies in its first week. It later peaked at number 100 on the chart, selling over 130,000 copies as of 2013.</p>
- <h2>A Summary of the Critical Reception and Commercial Performance of Section.80 (Continued)</h2>
- <p>Section.80 also spawned several singles that gained popularity and recognition in the hip hop scene, such as "HiiiPoWeR", "A.D.H.D", "Ronald Reagan Era", "The Spiteful Chant", and "Rigamortus". The album also earned Lamar several nominations and awards, such as the BET Hip Hop Award for Lyricist of the Year in 2012, and the MTV Video Music Award for Best New Artist in 2013.</p>
- <p>Section.80 also influenced and inspired many other artists and listeners, who appreciated Lamar's artistic vision and social commentary. The album is considered as a classic and a landmark in hip hop history, as it marked the rise of Kendrick Lamar as one of the most important and influential rappers of his era.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Section.80 by Kendrick Lamar is a masterpiece of hip hop music that deserves to be heard and appreciated by anyone who loves music and culture. The album is a powerful and profound expression of Lamar's experiences, thoughts, and emotions as a young man growing up in Compton in the 1980s and 1990s.</p>
- <p>The album is also a reflection and a critique of the society and the system that shaped and affected Lamar's generation, who faced many challenges and obstacles such as racism, poverty, violence, drugs, and disease. The album is also a celebration and a tribute to the people and the music that inspired and influenced Lamar, such as his family, his friends, his mentors, his idols, and his peers.</p>
- <p>If you want to download Section.80 by Kendrick Lamar and enjoy its music, you have several options and platforms to choose from. You can buy the album from online stores such as iTunes, Amazon, or Google Play. You can also stream the album from online services such as Spotify, Apple Music, Tidal, or YouTube. You can also download the album from torrent sites or file-sharing sites, but be careful of viruses and malware.</p>
- <p>Whatever option you choose, we hope that you will enjoy Section.80 by Kendrick Lamar as much as we did. We also hope that you will learn something from the album, and that you will be inspired by its messages and its music.</p>
- <p>Thank you for reading this article, and remember: HiiiPoWeR!</p>
- <h2>FAQs</h2>
- <ul>
-<li><strong>Where can I download Section.80 by Kendrick Lamar?</strong>: You can buy the album from online stores such as iTunes, Amazon, or Google Play. You can also stream the album from online services such as Spotify, Apple Music, Tidal, or YouTube. You can also download the album from torrent sites or file-sharing sites, but be careful of viruses and malware.</li>
-<li><strong>When was Section.80 released and how long is it?</strong>: Section.80 was released on July 2, 2011 by Top Dawg Entertainment. The album is 59 minutes and 55 seconds long.</li>
-<h2>FAQs (Continued)</h2>
- <ul>
-<li><strong>Who are the guest artists and producers on Section.80?</strong>: The album features guest appearances from other rappers such as Ab-Soul, ScHoolboy Q, RZA, GLC, BJ the Chicago Kid, as well as singers such as Colin Munroe and Alori Joh. The album also features production from various producers such as Sounwave, J. Cole, THC, Wyldfyer, Dave Free, Terrace Martin, Willie B., Tommy Black, Tae Beast, and Kendrick Lamar himself.</li>
-<li><strong>What is the meaning of HiiiPoWeR and how does it relate to Section.80?</strong>: HiiiPoWeR is a movement and philosophy that Lamar created with Ab-Soul and J. Cole. HiiiPoWeR stands for Heart, Honor, and Respect, and it represents the three "i"s in their names. The song "HiiiPoWeR" is the bonus track on Section.80, and it is a call to action for Lamar's fans and peers to elevate themselves and their communities, and to challenge the status quo and the system.</li>
-<li><strong>What is the difference between Section.80 and Good Kid, M.A.A.D City?</strong>: Section.80 and Good Kid, M.A.A.D City are both concept albums by Kendrick Lamar that explore different aspects of his life and identity. Section.80 is more focused on the generational and societal issues that Lamar faced as a child of the 1980s in Compton. Good Kid, M.A.A.D City is more focused on the personal and familial issues that Lamar faced as a teenager in Compton.</li>
-</ul></p> 197e85843d<br />
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-<h1>Stockfish Chess APK Download: How to Get the Strongest Chess Engine on Your Device</h1>
- <p>If you are a chess enthusiast, you probably have heard of Stockfish, the strongest chess engine in the world. But do you know how to get it on your device? In this article, we will show you how to download and install Stockfish chess APK, a free and open-source program that can help you improve your chess skills and enjoy the game more. We will also cover some of the features, history, and comparison of Stockfish chess APK with other chess engines.</p>
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- <p>Stockfish chess APK is not just a powerful chess engine, but also a versatile and user-friendly tool that offers many features for chess players of all levels. Here are some of the features that make Stockfish chess APK stand out:</p>
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-<li><strong>Strength and accuracy of analysis:</strong> Stockfish chess APK can evaluate any position with incredible depth and speed, using advanced algorithms and techniques. It can also suggest the best moves and variations for both sides, giving you a clear understanding of the position. Its estimated Elo rating is over 3500, making it stronger than any human player in history.</li>
-<li><strong>Support for Chess960 and Syzygy tablebases:</strong> Stockfish chess APK can play not only standard chess, but also Chess960, a variant that randomizes the initial position of the pieces. This adds more variety and challenge to the game, as you have to adapt to different setups. Stockfish chess APK also supports Syzygy tablebases, which are databases that contain perfect information for all endgames with up to seven pieces. This allows Stockfish chess APK to play these endgames flawlessly.</li>
-<li><strong>Compatibility with various platforms and GUIs:</strong> Stockfish chess APK can run on various devices, such as Windows, Mac OS X, Linux, iOS, and Android. It can also work with different graphical user interfaces (GUIs), such as Chess.com, Lichess.org, SmallFish, DroidFish, Scid vs PC, Arena, Fritz, ChessBase, etc. This means you can use Stockfish chess APK in your favorite chess app or software.  </li>
-<li><strong>Open-source and free license:</strong> Stockfish chess APK is an open-source project that is developed and maintained by a community of volunteers. It is distributed under the GNU General Public License version 3 (GPL v3), which means you can use it for free, modify it, share it, or contribute to it. You can also access the source code and learn how it works. </li>
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-<li><strong>Use Stockfish in your preferred chess app or software:</strong> After installing Stockfish chess APK, you can use it in your preferred chess app or software. To do this, you need to select Stockfish as the engine or analysis tool in your app or software settings. This may also vary depending on your app or software, but generally, you can find this option under Engine or Analysis settings. Once you have selected Stockfish, you can start using it to play or analyze chess games.</li>
-</ol>
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- <p>Stockfish chess APK is not only a powerful chess engine, but also a great tool for improving your chess skills. Here are some ways you can use Stockfish chess APK for learning and training:</p>
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- <p>Stockfish chess APK is not the only chess engine available, but it is widely considered to be the strongest one. How does it compare with other chess engines? Here are some comparisons of Stockfish chess APK with some of the most popular and powerful chess engines:</p>
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- <table>
-<tr>
-<th>Engine</th>
-<th>Elo Rating</th>
-<th>Type</th>
-<th>Features</th>
-<th>Advantages</th>
-<th>Disadvantages</th>
-</tr>
-<tr>
-<td><strong>Stockfish</strong></td>
-<td>~3500</td>
-<td>Traditional (AB)</td>
-<td>- Free and open-source<br>- Supports Chess960 and Syzygy<br>- Compatible with various platforms and GUIs<br>- Constantly updated by community</td>
-<td>- Strongest engine in terms of raw power<br>- Fastest engine in terms of speed<br>- Most versatile engine in terms of features and compatibility</td>
-<td>- None</td>
-</tr>
-<tr>
-<td><strong>AlphaZero</strong></td>
-<td>~3400</td>
-<td>Neural network (NN)</td>
-<td>- Self-taught by playing against itself<br>- Uses deep reinforcement learning and Monte Carlo tree search<br>- Has a unique and dynamic style of play<br>- Only available for research purposes</td>
-<td>- Strongest engine in terms of creativity and intuition<br>- Most human-like engine in terms of style and understanding<br>- Most innovative engine in terms of learning and development</td>
-<td>- Not free or open-source<br>- Not compatible with most platforms and GUIs<br>- Not updated or improved by community</td>
-</tr>
-<tr>
-<td><strong>Leela Chess Zero</strong></td>
-<td>~3300</td>
-<td>Neural network (NN)</td>
-<td>- Inspired by AlphaZero and based on the same principles<br>- Uses distributed computing and crowdsourcing to train itself<br>- Has a similar and dynamic style of play<br>- Free and open-source</td>
-<td>- Strongest engine in terms of creativity and intuition after AlphaZero<br>- Most human-like engine in terms of style and understanding after AlphaZero<br>- Most innovative engine in terms of learning and development after AlphaZero</td>
-<td>- Not as strong or fast as Stockfish or AlphaZero<br>- Not compatible with some platforms and GUIs<br>- Requires a lot of computing power and resources to run optimally</td>
-</tr>
-<tr>
-<td><strong>Komodo</strong></td>
-<td>~3200</td>
-<td>Traditional (AB)</td>
-<td>- Commercial and proprietary<br>- Supports Chess960 and Syzygy<br>- Compatible with various platforms and GUIs<br>- Has a balanced and solid style of play</td>
-<td>- Strongest engine in terms of positional play and endgames<br>- Fastest engine in terms of search speed after Stockfish<br>- Most versatile engine in terms of features and compatibility after Stockfish</td>
-<td>- Not free or open-source<br>- Not as strong or creative as Stockfish, AlphaZero, or Leela Chess Zero<br>- Not updated or improved by community as frequently as Stockfish or Leela Chess Zero</td>
-</tr>
-<tr>
-<td><strong>Houdini</strong></td>
-<td>~3100</td>
-<td>Traditional (AB)</td>
-<td>- Commercial and proprietary<br>- Supports Chess960 and Syzygy<br>- Compatible with various platforms and GUIs<br>- Has a tactical and aggressive style of play</td>
-<td>- Strongest engine in terms of tactical play and attacks<br>- Fastest engine in terms of search speed after Stockfish and Komodo<br>- Most versatile engine in terms of features and compatibility after Stockfish and Komodo</td>
-<td>- Not free or open-source<br>- Not as strong or creative as Stockfish, AlphaZero, Leela Chess Zero, or Komodo<br>- Not updated or improved by community as frequently as Stockfish or Leela Chess Zero</td>
-</tr>
- <tr> <td><strong>Rybka</strong></td>
-<td>~3000</td>
-<td>Traditional (AB)</td>
-<td>- Commercial and proprietary<br>- Supports Chess960 and Syzygy<br>- Compatible with various platforms and GUIs<br>- Has a dynamic and flexible style of play</td>
-<td>- Strongest engine in terms of dynamic play and sacrifices<br>- Fastest engine in terms of search speed after Stockfish, Komodo, and Houdini<br>- Most versatile engine in terms of features and compatibility after Stockfish, Komodo, and Houdini</td>
-<td>- Not free or open-source<br>- Not as strong or creative as Stockfish, AlphaZero, Leela Chess Zero, Komodo, or Houdini<br>- Controversial and disputed due to allegations of plagiarism and cheating  </td>
-</tr>
-</table>
- <h2>History and Development of Stockfish Chess APK</h2>
- <p>Stockfish chess APK has a long and rich history and development that spans over a decade. Here are some of the milestones and highlights of Stockfish chess APK's evolution:</p>
- <ul>
-<li><strong>Origin from Glaurung and evolution over the years:</strong> Stockfish chess APK originated from Glaurung, a chess engine created by Tord Romstad in 2004. Glaurung was one of the first engines to support Chess960 and to use bitboards, a data structure that represents the chess board as a series of bits. In 2008, Marco Costalba forked Glaurung and renamed it Stockfish, which means codfish in Norwegian. Since then, Stockfish has been improved by many developers, such as Joona Kiiski, Gary Linscott, Stéphane Nicolet, Daniel José Queraltó, etc. Stockfish has also incorporated ideas and code from other projects, such as Ippolit, RobboLito, Crafty, Fruit, etc. </li>
-<li><strong>Contribution from the open-source community and collaboration with other projects:</strong> Stockfish chess APK is an open-source project that is developed and maintained by a community of volunteers. Anyone can contribute to Stockfish by submitting patches, testing new versions, reporting bugs, suggesting features, etc. Stockfish also collaborates with other projects, such as Fishtest, a distributed testing framework that allows thousands of users to run Stockfish games on their computers; Syzygy, a project that provides endgame tablebases for Stockfish; OpenBench, a project that provides a platform for testing and comparing different chess engines; etc.  </li>
-<li><strong>Achievements and records in computer chess competitions and rating lists:</strong> Stockfish chess APK has achieved many impressive results and records in computer chess competitions and rating lists. Some of them are: winning the Top Chess Engine Championship (TCEC) 10 times; winning the Computer Chess Rating Lists (CCRL) 40/40 rating list 18 times; winning the Chess.com Computer Chess Championship (CCCC) 6 times; setting the world record for the highest Elo rating ever achieved by a chess engine (over 3600); defeating several human grandmasters and world champions in exhibition matches; etc.  </li>
-</ul>
- <h2>Conclusion and FAQs</h2>
- <p>In conclusion, Stockfish chess APK is the strongest chess engine in the world that offers many features and benefits for chess players of all levels. It is free and open-source, supports Chess960 and Syzygy tablebases, compatible with various platforms and GUIs, constantly updated by the community, and can help you improve your chess skills and enjoy the game more. If you want to get Stockfish chess APK on your device, you just need to follow the simple steps we have shown you in this article.</p>
- <p>Here are some FAQs about Stockfish chess APK that you may find useful:</p>
- <ol>
-<li><strong>Q: Is Stockfish chess APK safe to download?</strong><br>A: Yes, Stockfish chess APK is safe to download as long as you download it from a trusted source. You can either download it directly from the official website of Stockfish or from a reputable third-party site.</li>
-<li><strong>Q: How can I update Stockfish chess APK?</strong><br>A: You can update Stockfish chess APK by downloading the latest version from the same source you downloaded it from. You can also check for updates in your app or software settings.</li>
-<li><strong>Q: How can I contact the developers of Stockfish chess APK?</strong><br>A: You can contact the developers of Stockfish chess APK by visiting their official website or their GitHub page. You can also join their online forum or mailing list. You can also follow them on social media platforms, such as Facebook, Twitter, or YouTube.</li>
-<li><strong>Q: How can I support the development of Stockfish chess APK?</strong><br>A: You can support the development of Stockfish chess APK by donating to the project, contributing to the code, testing new versions, reporting bugs, suggesting features, etc. You can also spread the word about Stockfish chess APK and share it with your friends and family.</li>
-<li><strong>Q: How can I learn more about Stockfish chess APK?</strong><br>A: You can learn more about Stockfish chess APK by visiting their official website or their GitHub page. You can also read their documentation, wiki, blog, or articles. You can also watch their videos, podcasts, or webinars.</li>
-<li><strong>Q: What are some alternatives to Stockfish chess APK?</strong><br>A: Some alternatives to Stockfish chess APK are AlphaZero, Leela Chess Zero, Komodo, Houdini, Rybka, etc. You can compare them with Stockfish chess APK and see which one suits your needs and preferences better.</li>
-</ol></p> 401be4b1e0<br />
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index 0f13ed11c71052546aec743d8f4ec185563f618e..0000000000000000000000000000000000000000
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diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/hub.py b/spaces/cooelf/Multimodal-CoT/timm/models/hub.py
deleted file mode 100644
index 9a9b553031fb9d1846338990cd3b6f77228174c6..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/hub.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import json
-import logging
-import os
-from functools import partial
-from typing import Union, Optional
-
-import torch
-from torch.hub import load_state_dict_from_url, download_url_to_file, urlparse, HASH_REGEX
-try:
-    from torch.hub import get_dir
-except ImportError:
-    from torch.hub import _get_torch_home as get_dir
-
-from timm import __version__
-try:
-    from huggingface_hub import hf_hub_url
-    from huggingface_hub import cached_download
-    cached_download = partial(cached_download, library_name="timm", library_version=__version__)
-except ImportError:
-    hf_hub_url = None
-    cached_download = None
-
-_logger = logging.getLogger(__name__)
-
-
-def get_cache_dir(child_dir=''):
-    """
-    Returns the location of the directory where models are cached (and creates it if necessary).
-    """
-    # Issue warning to move data if old env is set
-    if os.getenv('TORCH_MODEL_ZOO'):
-        _logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
-
-    hub_dir = get_dir()
-    child_dir = () if not child_dir else (child_dir,)
-    model_dir = os.path.join(hub_dir, 'checkpoints', *child_dir)
-    os.makedirs(model_dir, exist_ok=True)
-    return model_dir
-
-
-def download_cached_file(url, check_hash=True, progress=False):
-    parts = urlparse(url)
-    filename = os.path.basename(parts.path)
-    cached_file = os.path.join(get_cache_dir(), filename)
-    if not os.path.exists(cached_file):
-        _logger.info('Downloading: "{}" to {}\n'.format(url, cached_file))
-        hash_prefix = None
-        if check_hash:
-            r = HASH_REGEX.search(filename)  # r is Optional[Match[str]]
-            hash_prefix = r.group(1) if r else None
-        download_url_to_file(url, cached_file, hash_prefix, progress=progress)
-    return cached_file
-
-
-def has_hf_hub(necessary=False):
-    if hf_hub_url is None and necessary:
-        # if no HF Hub module installed and it is necessary to continue, raise error
-        raise RuntimeError(
-            'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
-    return hf_hub_url is not None
-
-
-def hf_split(hf_id):
-    rev_split = hf_id.split('@')
-    assert 0 < len(rev_split) <= 2, 'hf_hub id should only contain one @ character to identify revision.'
-    hf_model_id = rev_split[0]
-    hf_revision = rev_split[-1] if len(rev_split) > 1 else None
-    return hf_model_id, hf_revision
-
-
-def load_cfg_from_json(json_file: Union[str, os.PathLike]):
-    with open(json_file, "r", encoding="utf-8") as reader:
-        text = reader.read()
-    return json.loads(text)
-
-
-def _download_from_hf(model_id: str, filename: str):
-    hf_model_id, hf_revision = hf_split(model_id)
-    url = hf_hub_url(hf_model_id, filename, revision=hf_revision)
-    return cached_download(url, cache_dir=get_cache_dir('hf'))
-
-
-def load_model_config_from_hf(model_id: str):
-    assert has_hf_hub(True)
-    cached_file = _download_from_hf(model_id, 'config.json')
-    default_cfg = load_cfg_from_json(cached_file)
-    default_cfg['hf_hub'] = model_id  # insert hf_hub id for pretrained weight load during model creation
-    model_name = default_cfg.get('architecture')
-    return default_cfg, model_name
-
-
-def load_state_dict_from_hf(model_id: str):
-    assert has_hf_hub(True)
-    cached_file = _download_from_hf(model_id, 'pytorch_model.bin')
-    state_dict = torch.load(cached_file, map_location='cpu')
-    return state_dict
diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/selecsls.py b/spaces/cooelf/Multimodal-CoT/timm/models/selecsls.py
deleted file mode 100644
index 1f3379db3da5e5303d9af2c78098b9a5424a5fde..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/selecsls.py
+++ /dev/null
@@ -1,362 +0,0 @@
-"""PyTorch SelecSLS Net example for ImageNet Classification
-License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode)
-Author: Dushyant Mehta (@mehtadushy)
-
-SelecSLS (core) Network Architecture as proposed in "XNect: Real-time Multi-person 3D
-Human Pose Estimation with a Single RGB Camera, Mehta et al."
-https://arxiv.org/abs/1907.00837
-
-Based on ResNet implementation in https://github.com/rwightman/pytorch-image-models
-and SelecSLS Net implementation in https://github.com/mehtadushy/SelecSLS-Pytorch
-"""
-from typing import List
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import create_classifier
-from .registry import register_model
-
-__all__ = ['SelecSLS']  # model_registry will add each entrypoint fn to this
-
-
-def _cfg(url='', **kwargs):
-    return {
-        'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (4, 4),
-        'crop_pct': 0.875, 'interpolation': 'bilinear',
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'stem.0', 'classifier': 'fc',
-        **kwargs
-    }
-
-
-default_cfgs = {
-    'selecsls42': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'selecsls42b': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls42b-8af30141.pth',
-        interpolation='bicubic'),
-    'selecsls60': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60-bbf87526.pth',
-        interpolation='bicubic'),
-    'selecsls60b': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60b-94e619b5.pth',
-        interpolation='bicubic'),
-    'selecsls84': _cfg(
-        url='',
-        interpolation='bicubic'),
-}
-
-
-class SequentialList(nn.Sequential):
-
-    def __init__(self, *args):
-        super(SequentialList, self).__init__(*args)
-
-    @torch.jit._overload_method  # noqa: F811
-    def forward(self, x):
-        # type: (List[torch.Tensor]) -> (List[torch.Tensor])
-        pass
-
-    @torch.jit._overload_method  # noqa: F811
-    def forward(self, x):
-        # type: (torch.Tensor) -> (List[torch.Tensor])
-        pass
-
-    def forward(self, x) -> List[torch.Tensor]:
-        for module in self:
-            x = module(x)
-        return x
-
-
-class SelectSeq(nn.Module):
-    def __init__(self, mode='index', index=0):
-        super(SelectSeq, self).__init__()
-        self.mode = mode
-        self.index = index
-
-    @torch.jit._overload_method  # noqa: F811
-    def forward(self, x):
-        # type: (List[torch.Tensor]) -> (torch.Tensor)
-        pass
-
-    @torch.jit._overload_method  # noqa: F811
-    def forward(self, x):
-        # type: (Tuple[torch.Tensor]) -> (torch.Tensor)
-        pass
-
-    def forward(self, x) -> torch.Tensor:
-        if self.mode == 'index':
-            return x[self.index]
-        else:
-            return torch.cat(x, dim=1)
-
-
-def conv_bn(in_chs, out_chs, k=3, stride=1, padding=None, dilation=1):
-    if padding is None:
-        padding = ((stride - 1) + dilation * (k - 1)) // 2
-    return nn.Sequential(
-        nn.Conv2d(in_chs, out_chs, k, stride, padding=padding, dilation=dilation, bias=False),
-        nn.BatchNorm2d(out_chs),
-        nn.ReLU(inplace=True)
-    )
-
-
-class SelecSLSBlock(nn.Module):
-    def __init__(self, in_chs, skip_chs, mid_chs, out_chs, is_first, stride, dilation=1):
-        super(SelecSLSBlock, self).__init__()
-        self.stride = stride
-        self.is_first = is_first
-        assert stride in [1, 2]
-
-        # Process input with 4 conv blocks with the same number of input and output channels
-        self.conv1 = conv_bn(in_chs, mid_chs, 3, stride, dilation=dilation)
-        self.conv2 = conv_bn(mid_chs, mid_chs, 1)
-        self.conv3 = conv_bn(mid_chs, mid_chs // 2, 3)
-        self.conv4 = conv_bn(mid_chs // 2, mid_chs, 1)
-        self.conv5 = conv_bn(mid_chs, mid_chs // 2, 3)
-        self.conv6 = conv_bn(2 * mid_chs + (0 if is_first else skip_chs), out_chs, 1)
-
-    def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
-        if not isinstance(x, list):
-            x = [x]
-        assert len(x) in [1, 2]
-
-        d1 = self.conv1(x[0])
-        d2 = self.conv3(self.conv2(d1))
-        d3 = self.conv5(self.conv4(d2))
-        if self.is_first:
-            out = self.conv6(torch.cat([d1, d2, d3], 1))
-            return [out, out]
-        else:
-            return [self.conv6(torch.cat([d1, d2, d3, x[1]], 1)), x[1]]
-
-
-class SelecSLS(nn.Module):
-    """SelecSLS42 / SelecSLS60 / SelecSLS84
-
-    Parameters
-    ----------
-    cfg : network config dictionary specifying block type, feature, and head args
-    num_classes : int, default 1000
-        Number of classification classes.
-    in_chans : int, default 3
-        Number of input (color) channels.
-    drop_rate : float, default 0.
-        Dropout probability before classifier, for training
-    global_pool : str, default 'avg'
-        Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
-    """
-
-    def __init__(self, cfg, num_classes=1000, in_chans=3, drop_rate=0.0, global_pool='avg'):
-        self.num_classes = num_classes
-        self.drop_rate = drop_rate
-        super(SelecSLS, self).__init__()
-
-        self.stem = conv_bn(in_chans, 32, stride=2)
-        self.features = SequentialList(*[cfg['block'](*block_args) for block_args in cfg['features']])
-        self.from_seq = SelectSeq()  # from List[tensor] -> Tensor in module compatible way
-        self.head = nn.Sequential(*[conv_bn(*conv_args) for conv_args in cfg['head']])
-        self.num_features = cfg['num_features']
-        self.feature_info = cfg['feature_info']
-
-        self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
-
-        for n, m in self.named_modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1.)
-                nn.init.constant_(m.bias, 0.)
-
-    def get_classifier(self):
-        return self.fc
-
-    def reset_classifier(self, num_classes, global_pool='avg'):
-        self.num_classes = num_classes
-        self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
-
-    def forward_features(self, x):
-        x = self.stem(x)
-        x = self.features(x)
-        x = self.head(self.from_seq(x))
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.global_pool(x)
-        if self.drop_rate > 0.:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
-        x = self.fc(x)
-        return x
-
-
-def _create_selecsls(variant, pretrained, **kwargs):
-    cfg = {}
-    feature_info = [dict(num_chs=32, reduction=2, module='stem.2')]
-    if variant.startswith('selecsls42'):
-        cfg['block'] = SelecSLSBlock
-        # Define configuration of the network after the initial neck
-        cfg['features'] = [
-            # in_chs, skip_chs, mid_chs, out_chs, is_first, stride
-            (32, 0, 64, 64, True, 2),
-            (64, 64, 64, 128, False, 1),
-            (128, 0, 144, 144, True, 2),
-            (144, 144, 144, 288, False, 1),
-            (288, 0, 304, 304, True, 2),
-            (304, 304, 304, 480, False, 1),
-        ]
-        feature_info.extend([
-            dict(num_chs=128, reduction=4, module='features.1'),
-            dict(num_chs=288, reduction=8, module='features.3'),
-            dict(num_chs=480, reduction=16, module='features.5'),
-        ])
-        # Head can be replaced with alternative configurations depending on the problem
-        feature_info.append(dict(num_chs=1024, reduction=32, module='head.1'))
-        if variant == 'selecsls42b':
-            cfg['head'] = [
-                (480, 960, 3, 2),
-                (960, 1024, 3, 1),
-                (1024, 1280, 3, 2),
-                (1280, 1024, 1, 1),
-            ]
-            feature_info.append(dict(num_chs=1024, reduction=64, module='head.3'))
-            cfg['num_features'] = 1024
-        else:
-            cfg['head'] = [
-                (480, 960, 3, 2),
-                (960, 1024, 3, 1),
-                (1024, 1024, 3, 2),
-                (1024, 1280, 1, 1),
-            ]
-            feature_info.append(dict(num_chs=1280, reduction=64, module='head.3'))
-            cfg['num_features'] = 1280
-
-    elif variant.startswith('selecsls60'):
-        cfg['block'] = SelecSLSBlock
-        # Define configuration of the network after the initial neck
-        cfg['features'] = [
-            # in_chs, skip_chs, mid_chs, out_chs, is_first, stride
-            (32, 0, 64, 64, True, 2),
-            (64, 64, 64, 128, False, 1),
-            (128, 0, 128, 128, True, 2),
-            (128, 128, 128, 128, False, 1),
-            (128, 128, 128, 288, False, 1),
-            (288, 0, 288, 288, True, 2),
-            (288, 288, 288, 288, False, 1),
-            (288, 288, 288, 288, False, 1),
-            (288, 288, 288, 416, False, 1),
-        ]
-        feature_info.extend([
-            dict(num_chs=128, reduction=4, module='features.1'),
-            dict(num_chs=288, reduction=8, module='features.4'),
-            dict(num_chs=416, reduction=16, module='features.8'),
-        ])
-        # Head can be replaced with alternative configurations depending on the problem
-        feature_info.append(dict(num_chs=1024, reduction=32, module='head.1'))
-        if variant == 'selecsls60b':
-            cfg['head'] = [
-                (416, 756, 3, 2),
-                (756, 1024, 3, 1),
-                (1024, 1280, 3, 2),
-                (1280, 1024, 1, 1),
-            ]
-            feature_info.append(dict(num_chs=1024, reduction=64, module='head.3'))
-            cfg['num_features'] = 1024
-        else:
-            cfg['head'] = [
-                (416, 756, 3, 2),
-                (756, 1024, 3, 1),
-                (1024, 1024, 3, 2),
-                (1024, 1280, 1, 1),
-            ]
-            feature_info.append(dict(num_chs=1280, reduction=64, module='head.3'))
-            cfg['num_features'] = 1280
-
-    elif variant == 'selecsls84':
-        cfg['block'] = SelecSLSBlock
-        # Define configuration of the network after the initial neck
-        cfg['features'] = [
-            # in_chs, skip_chs, mid_chs, out_chs, is_first, stride
-            (32, 0, 64, 64, True, 2),
-            (64, 64, 64, 144, False, 1),
-            (144, 0, 144, 144, True, 2),
-            (144, 144, 144, 144, False, 1),
-            (144, 144, 144, 144, False, 1),
-            (144, 144, 144, 144, False, 1),
-            (144, 144, 144, 304, False, 1),
-            (304, 0, 304, 304, True, 2),
-            (304, 304, 304, 304, False, 1),
-            (304, 304, 304, 304, False, 1),
-            (304, 304, 304, 304, False, 1),
-            (304, 304, 304, 304, False, 1),
-            (304, 304, 304, 512, False, 1),
-        ]
-        feature_info.extend([
-            dict(num_chs=144, reduction=4, module='features.1'),
-            dict(num_chs=304, reduction=8, module='features.6'),
-            dict(num_chs=512, reduction=16, module='features.12'),
-        ])
-        # Head can be replaced with alternative configurations depending on the problem
-        cfg['head'] = [
-            (512, 960, 3, 2),
-            (960, 1024, 3, 1),
-            (1024, 1024, 3, 2),
-            (1024, 1280, 3, 1),
-        ]
-        cfg['num_features'] = 1280
-        feature_info.extend([
-            dict(num_chs=1024, reduction=32, module='head.1'),
-            dict(num_chs=1280, reduction=64, module='head.3')
-        ])
-    else:
-        raise ValueError('Invalid net configuration ' + variant + ' !!!')
-    cfg['feature_info'] = feature_info
-
-    # this model can do 6 feature levels by default, unlike most others, leave as 0-4 to avoid surprises?
-    return build_model_with_cfg(
-        SelecSLS, variant, pretrained,
-        default_cfg=default_cfgs[variant],
-        model_cfg=cfg,
-        feature_cfg=dict(out_indices=(0, 1, 2, 3, 4), flatten_sequential=True),
-        **kwargs)
-
-
-@register_model
-def selecsls42(pretrained=False, **kwargs):
-    """Constructs a SelecSLS42 model.
-    """
-    return _create_selecsls('selecsls42', pretrained, **kwargs)
-
-
-@register_model
-def selecsls42b(pretrained=False, **kwargs):
-    """Constructs a SelecSLS42_B model.
-    """
-    return _create_selecsls('selecsls42b', pretrained, **kwargs)
-
-
-@register_model
-def selecsls60(pretrained=False, **kwargs):
-    """Constructs a SelecSLS60 model.
-    """
-    return _create_selecsls('selecsls60', pretrained, **kwargs)
-
-
-@register_model
-def selecsls60b(pretrained=False, **kwargs):
-    """Constructs a SelecSLS60_B model.
-    """
-    return _create_selecsls('selecsls60b', pretrained, **kwargs)
-
-
-@register_model
-def selecsls84(pretrained=False, **kwargs):
-    """Constructs a SelecSLS84 model.
-    """
-    return _create_selecsls('selecsls84', pretrained, **kwargs)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/carafe.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/carafe.py
deleted file mode 100644
index 5154cb3abfccfbbe0a1b2daa67018dbf80aaf6d2..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/carafe.py
+++ /dev/null
@@ -1,287 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.nn.modules.module import Module
-
-from ..cnn import UPSAMPLE_LAYERS, normal_init, xavier_init
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'carafe_naive_forward', 'carafe_naive_backward', 'carafe_forward',
-    'carafe_backward'
-])
-
-
-class CARAFENaiveFunction(Function):
-
-    @staticmethod
-    def symbolic(g, features, masks, kernel_size, group_size, scale_factor):
-        return g.op(
-            'mmcv::MMCVCARAFENaive',
-            features,
-            masks,
-            kernel_size_i=kernel_size,
-            group_size_i=group_size,
-            scale_factor_f=scale_factor)
-
-    @staticmethod
-    def forward(ctx, features, masks, kernel_size, group_size, scale_factor):
-        assert scale_factor >= 1
-        assert masks.size(1) == kernel_size * kernel_size * group_size
-        assert masks.size(-1) == features.size(-1) * scale_factor
-        assert masks.size(-2) == features.size(-2) * scale_factor
-        assert features.size(1) % group_size == 0
-        assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1
-        ctx.kernel_size = kernel_size
-        ctx.group_size = group_size
-        ctx.scale_factor = scale_factor
-        ctx.feature_size = features.size()
-        ctx.mask_size = masks.size()
-
-        n, c, h, w = features.size()
-        output = features.new_zeros((n, c, h * scale_factor, w * scale_factor))
-        ext_module.carafe_naive_forward(
-            features,
-            masks,
-            output,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        if features.requires_grad or masks.requires_grad:
-            ctx.save_for_backward(features, masks)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        assert grad_output.is_cuda
-
-        features, masks = ctx.saved_tensors
-        kernel_size = ctx.kernel_size
-        group_size = ctx.group_size
-        scale_factor = ctx.scale_factor
-
-        grad_input = torch.zeros_like(features)
-        grad_masks = torch.zeros_like(masks)
-        ext_module.carafe_naive_backward(
-            grad_output.contiguous(),
-            features,
-            masks,
-            grad_input,
-            grad_masks,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        return grad_input, grad_masks, None, None, None
-
-
-carafe_naive = CARAFENaiveFunction.apply
-
-
-class CARAFENaive(Module):
-
-    def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFENaive, self).__init__()
-
-        assert isinstance(kernel_size, int) and isinstance(
-            group_size, int) and isinstance(scale_factor, int)
-        self.kernel_size = kernel_size
-        self.group_size = group_size
-        self.scale_factor = scale_factor
-
-    def forward(self, features, masks):
-        return carafe_naive(features, masks, self.kernel_size, self.group_size,
-                            self.scale_factor)
-
-
-class CARAFEFunction(Function):
-
-    @staticmethod
-    def symbolic(g, features, masks, kernel_size, group_size, scale_factor):
-        return g.op(
-            'mmcv::MMCVCARAFE',
-            features,
-            masks,
-            kernel_size_i=kernel_size,
-            group_size_i=group_size,
-            scale_factor_f=scale_factor)
-
-    @staticmethod
-    def forward(ctx, features, masks, kernel_size, group_size, scale_factor):
-        assert scale_factor >= 1
-        assert masks.size(1) == kernel_size * kernel_size * group_size
-        assert masks.size(-1) == features.size(-1) * scale_factor
-        assert masks.size(-2) == features.size(-2) * scale_factor
-        assert features.size(1) % group_size == 0
-        assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1
-        ctx.kernel_size = kernel_size
-        ctx.group_size = group_size
-        ctx.scale_factor = scale_factor
-        ctx.feature_size = features.size()
-        ctx.mask_size = masks.size()
-
-        n, c, h, w = features.size()
-        output = features.new_zeros((n, c, h * scale_factor, w * scale_factor))
-        routput = features.new_zeros(output.size(), requires_grad=False)
-        rfeatures = features.new_zeros(features.size(), requires_grad=False)
-        rmasks = masks.new_zeros(masks.size(), requires_grad=False)
-        ext_module.carafe_forward(
-            features,
-            masks,
-            rfeatures,
-            routput,
-            rmasks,
-            output,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        if features.requires_grad or masks.requires_grad:
-            ctx.save_for_backward(features, masks, rfeatures)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        assert grad_output.is_cuda
-
-        features, masks, rfeatures = ctx.saved_tensors
-        kernel_size = ctx.kernel_size
-        group_size = ctx.group_size
-        scale_factor = ctx.scale_factor
-
-        rgrad_output = torch.zeros_like(grad_output, requires_grad=False)
-        rgrad_input_hs = torch.zeros_like(grad_output, requires_grad=False)
-        rgrad_input = torch.zeros_like(features, requires_grad=False)
-        rgrad_masks = torch.zeros_like(masks, requires_grad=False)
-        grad_input = torch.zeros_like(features, requires_grad=False)
-        grad_masks = torch.zeros_like(masks, requires_grad=False)
-        ext_module.carafe_backward(
-            grad_output.contiguous(),
-            rfeatures,
-            masks,
-            rgrad_output,
-            rgrad_input_hs,
-            rgrad_input,
-            rgrad_masks,
-            grad_input,
-            grad_masks,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-        return grad_input, grad_masks, None, None, None
-
-
-carafe = CARAFEFunction.apply
-
-
-class CARAFE(Module):
-    """ CARAFE: Content-Aware ReAssembly of FEatures
-
-    Please refer to https://arxiv.org/abs/1905.02188 for more details.
-
-    Args:
-        kernel_size (int): reassemble kernel size
-        group_size (int): reassemble group size
-        scale_factor (int): upsample ratio
-
-    Returns:
-        upsampled feature map
-    """
-
-    def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFE, self).__init__()
-
-        assert isinstance(kernel_size, int) and isinstance(
-            group_size, int) and isinstance(scale_factor, int)
-        self.kernel_size = kernel_size
-        self.group_size = group_size
-        self.scale_factor = scale_factor
-
-    def forward(self, features, masks):
-        return carafe(features, masks, self.kernel_size, self.group_size,
-                      self.scale_factor)
-
-
-@UPSAMPLE_LAYERS.register_module(name='carafe')
-class CARAFEPack(nn.Module):
-    """A unified package of CARAFE upsampler that contains: 1) channel
-    compressor 2) content encoder 3) CARAFE op.
-
-    Official implementation of ICCV 2019 paper
-    CARAFE: Content-Aware ReAssembly of FEatures
-    Please refer to https://arxiv.org/abs/1905.02188 for more details.
-
-    Args:
-        channels (int): input feature channels
-        scale_factor (int): upsample ratio
-        up_kernel (int): kernel size of CARAFE op
-        up_group (int): group size of CARAFE op
-        encoder_kernel (int): kernel size of content encoder
-        encoder_dilation (int): dilation of content encoder
-        compressed_channels (int): output channels of channels compressor
-
-    Returns:
-        upsampled feature map
-    """
-
-    def __init__(self,
-                 channels,
-                 scale_factor,
-                 up_kernel=5,
-                 up_group=1,
-                 encoder_kernel=3,
-                 encoder_dilation=1,
-                 compressed_channels=64):
-        super(CARAFEPack, self).__init__()
-        self.channels = channels
-        self.scale_factor = scale_factor
-        self.up_kernel = up_kernel
-        self.up_group = up_group
-        self.encoder_kernel = encoder_kernel
-        self.encoder_dilation = encoder_dilation
-        self.compressed_channels = compressed_channels
-        self.channel_compressor = nn.Conv2d(channels, self.compressed_channels,
-                                            1)
-        self.content_encoder = nn.Conv2d(
-            self.compressed_channels,
-            self.up_kernel * self.up_kernel * self.up_group *
-            self.scale_factor * self.scale_factor,
-            self.encoder_kernel,
-            padding=int((self.encoder_kernel - 1) * self.encoder_dilation / 2),
-            dilation=self.encoder_dilation,
-            groups=1)
-        self.init_weights()
-
-    def init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-        normal_init(self.content_encoder, std=0.001)
-
-    def kernel_normalizer(self, mask):
-        mask = F.pixel_shuffle(mask, self.scale_factor)
-        n, mask_c, h, w = mask.size()
-        # use float division explicitly,
-        # to void inconsistency while exporting to onnx
-        mask_channel = int(mask_c / float(self.up_kernel**2))
-        mask = mask.view(n, mask_channel, -1, h, w)
-
-        mask = F.softmax(mask, dim=2, dtype=mask.dtype)
-        mask = mask.view(n, mask_c, h, w).contiguous()
-
-        return mask
-
-    def feature_reassemble(self, x, mask):
-        x = carafe(x, mask, self.up_kernel, self.up_group, self.scale_factor)
-        return x
-
-    def forward(self, x):
-        compressed_x = self.channel_compressor(x)
-        mask = self.content_encoder(compressed_x)
-        mask = self.kernel_normalizer(mask)
-
-        x = self.feature_reassemble(x, mask)
-        return x
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/data_mono.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/data_mono.py
deleted file mode 100644
index 80a8486f239a35331df553f490e213f9bf71e735..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/data/data_mono.py
+++ /dev/null
@@ -1,573 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# File author: Shariq Farooq Bhat
-
-# This file is partly inspired from BTS (https://github.com/cleinc/bts/blob/master/pytorch/bts_dataloader.py); author: Jin Han Lee
-
-import itertools
-import os
-import random
-
-import numpy as np
-import cv2
-import torch
-import torch.nn as nn
-import torch.utils.data.distributed
-from zoedepth.utils.easydict import EasyDict as edict
-from PIL import Image, ImageOps
-from torch.utils.data import DataLoader, Dataset
-from torchvision import transforms
-
-from zoedepth.utils.config import change_dataset
-
-from .ddad import get_ddad_loader
-from .diml_indoor_test import get_diml_indoor_loader
-from .diml_outdoor_test import get_diml_outdoor_loader
-from .diode import get_diode_loader
-from .hypersim import get_hypersim_loader
-from .ibims import get_ibims_loader
-from .sun_rgbd_loader import get_sunrgbd_loader
-from .vkitti import get_vkitti_loader
-from .vkitti2 import get_vkitti2_loader
-
-from .preprocess import CropParams, get_white_border, get_black_border
-
-
-def _is_pil_image(img):
-    return isinstance(img, Image.Image)
-
-
-def _is_numpy_image(img):
-    return isinstance(img, np.ndarray) and (img.ndim in {2, 3})
-
-
-def preprocessing_transforms(mode, **kwargs):
-    return transforms.Compose([
-        ToTensor(mode=mode, **kwargs)
-    ])
-
-
-class DepthDataLoader(object):
-    def __init__(self, config, mode, device='cpu', transform=None, **kwargs):
-        """
-        Data loader for depth datasets
-
-        Args:
-            config (dict): Config dictionary. Refer to utils/config.py
-            mode (str): "train" or "online_eval"
-            device (str, optional): Device to load the data on. Defaults to 'cpu'.
-            transform (torchvision.transforms, optional): Transform to apply to the data. Defaults to None.
-        """
-
-        self.config = config
-
-        if config.dataset == 'ibims':
-            self.data = get_ibims_loader(config, batch_size=1, num_workers=1)
-            return
-
-        if config.dataset == 'sunrgbd':
-            self.data = get_sunrgbd_loader(
-                data_dir_root=config.sunrgbd_root, batch_size=1, num_workers=1)
-            return
-
-        if config.dataset == 'diml_indoor':
-            self.data = get_diml_indoor_loader(
-                data_dir_root=config.diml_indoor_root, batch_size=1, num_workers=1)
-            return
-
-        if config.dataset == 'diml_outdoor':
-            self.data = get_diml_outdoor_loader(
-                data_dir_root=config.diml_outdoor_root, batch_size=1, num_workers=1)
-            return
-
-        if "diode" in config.dataset:
-            self.data = get_diode_loader(
-                config[config.dataset+"_root"], batch_size=1, num_workers=1)
-            return
-
-        if config.dataset == 'hypersim_test':
-            self.data = get_hypersim_loader(
-                config.hypersim_test_root, batch_size=1, num_workers=1)
-            return
-
-        if config.dataset == 'vkitti':
-            self.data = get_vkitti_loader(
-                config.vkitti_root, batch_size=1, num_workers=1)
-            return
-
-        if config.dataset == 'vkitti2':
-            self.data = get_vkitti2_loader(
-                config.vkitti2_root, batch_size=1, num_workers=1)
-            return
-
-        if config.dataset == 'ddad':
-            self.data = get_ddad_loader(config.ddad_root, resize_shape=(
-                352, 1216), batch_size=1, num_workers=1)
-            return
-
-        img_size = self.config.get("img_size", None)
-        img_size = img_size if self.config.get(
-            "do_input_resize", False) else None
-
-        if transform is None:
-            transform = preprocessing_transforms(mode, size=img_size)
-
-        if mode == 'train':
-
-            Dataset = DataLoadPreprocess
-            self.training_samples = Dataset(
-                config, mode, transform=transform, device=device)
-
-            if config.distributed:
-                self.train_sampler = torch.utils.data.distributed.DistributedSampler(
-                    self.training_samples)
-            else:
-                self.train_sampler = None
-
-            self.data = DataLoader(self.training_samples,
-                                   batch_size=config.batch_size,
-                                   shuffle=(self.train_sampler is None),
-                                   num_workers=config.workers,
-                                   pin_memory=True,
-                                   persistent_workers=True,
-                                #    prefetch_factor=2,
-                                   sampler=self.train_sampler)
-
-        elif mode == 'online_eval':
-            self.testing_samples = DataLoadPreprocess(
-                config, mode, transform=transform)
-            if config.distributed:  # redundant. here only for readability and to be more explicit
-                # Give whole test set to all processes (and report evaluation only on one) regardless
-                self.eval_sampler = None
-            else:
-                self.eval_sampler = None
-            self.data = DataLoader(self.testing_samples, 1,
-                                   shuffle=kwargs.get("shuffle_test", False),
-                                   num_workers=1,
-                                   pin_memory=False,
-                                   sampler=self.eval_sampler)
-
-        elif mode == 'test':
-            self.testing_samples = DataLoadPreprocess(
-                config, mode, transform=transform)
-            self.data = DataLoader(self.testing_samples,
-                                   1, shuffle=False, num_workers=1)
-
-        else:
-            print(
-                'mode should be one of \'train, test, online_eval\'. Got {}'.format(mode))
-
-
-def repetitive_roundrobin(*iterables):
-    """
-    cycles through iterables but sample wise
-    first yield first sample from first iterable then first sample from second iterable and so on
-    then second sample from first iterable then second sample from second iterable and so on
-
-    If one iterable is shorter than the others, it is repeated until all iterables are exhausted
-    repetitive_roundrobin('ABC', 'D', 'EF') --> A D E B D F C D E
-    """
-    # Repetitive roundrobin
-    iterables_ = [iter(it) for it in iterables]
-    exhausted = [False] * len(iterables)
-    while not all(exhausted):
-        for i, it in enumerate(iterables_):
-            try:
-                yield next(it)
-            except StopIteration:
-                exhausted[i] = True
-                iterables_[i] = itertools.cycle(iterables[i])
-                # First elements may get repeated if one iterable is shorter than the others
-                yield next(iterables_[i])
-
-
-class RepetitiveRoundRobinDataLoader(object):
-    def __init__(self, *dataloaders):
-        self.dataloaders = dataloaders
-
-    def __iter__(self):
-        return repetitive_roundrobin(*self.dataloaders)
-
-    def __len__(self):
-        # First samples get repeated, thats why the plus one
-        return len(self.dataloaders) * (max(len(dl) for dl in self.dataloaders) + 1)
-
-
-class MixedNYUKITTI(object):
-    def __init__(self, config, mode, device='cpu', **kwargs):
-        config = edict(config)
-        config.workers = config.workers // 2
-        self.config = config
-        nyu_conf = change_dataset(edict(config), 'nyu')
-        kitti_conf = change_dataset(edict(config), 'kitti')
-
-        # make nyu default for testing
-        self.config = config = nyu_conf
-        img_size = self.config.get("img_size", None)
-        img_size = img_size if self.config.get(
-            "do_input_resize", False) else None
-        if mode == 'train':
-            nyu_loader = DepthDataLoader(
-                nyu_conf, mode, device=device, transform=preprocessing_transforms(mode, size=img_size)).data
-            kitti_loader = DepthDataLoader(
-                kitti_conf, mode, device=device, transform=preprocessing_transforms(mode, size=img_size)).data
-            # It has been changed to repetitive roundrobin
-            self.data = RepetitiveRoundRobinDataLoader(
-                nyu_loader, kitti_loader)
-        else:
-            self.data = DepthDataLoader(nyu_conf, mode, device=device).data
-
-
-def remove_leading_slash(s):
-    if s[0] == '/' or s[0] == '\\':
-        return s[1:]
-    return s
-
-
-class CachedReader:
-    def __init__(self, shared_dict=None):
-        if shared_dict:
-            self._cache = shared_dict
-        else:
-            self._cache = {}
-
-    def open(self, fpath):
-        im = self._cache.get(fpath, None)
-        if im is None:
-            im = self._cache[fpath] = Image.open(fpath)
-        return im
-
-
-class ImReader:
-    def __init__(self):
-        pass
-
-    # @cache
-    def open(self, fpath):
-        return Image.open(fpath)
-
-
-class DataLoadPreprocess(Dataset):
-    def __init__(self, config, mode, transform=None, is_for_online_eval=False, **kwargs):
-        self.config = config
-        if mode == 'online_eval':
-            with open(config.filenames_file_eval, 'r') as f:
-                self.filenames = f.readlines()
-        else:
-            with open(config.filenames_file, 'r') as f:
-                self.filenames = f.readlines()
-
-        self.mode = mode
-        self.transform = transform
-        self.to_tensor = ToTensor(mode)
-        self.is_for_online_eval = is_for_online_eval
-        if config.use_shared_dict:
-            self.reader = CachedReader(config.shared_dict)
-        else:
-            self.reader = ImReader()
-
-    def postprocess(self, sample):
-        return sample
-
-    def __getitem__(self, idx):
-        sample_path = self.filenames[idx]
-        focal = float(sample_path.split()[2])
-        sample = {}
-
-        if self.mode == 'train':
-            if self.config.dataset == 'kitti' and self.config.use_right and random.random() > 0.5:
-                image_path = os.path.join(
-                    self.config.data_path, remove_leading_slash(sample_path.split()[3]))
-                depth_path = os.path.join(
-                    self.config.gt_path, remove_leading_slash(sample_path.split()[4]))
-            else:
-                image_path = os.path.join(
-                    self.config.data_path, remove_leading_slash(sample_path.split()[0]))
-                depth_path = os.path.join(
-                    self.config.gt_path, remove_leading_slash(sample_path.split()[1]))
-
-            image = self.reader.open(image_path)
-            depth_gt = self.reader.open(depth_path)
-            w, h = image.size
-
-            if self.config.do_kb_crop:
-                height = image.height
-                width = image.width
-                top_margin = int(height - 352)
-                left_margin = int((width - 1216) / 2)
-                depth_gt = depth_gt.crop(
-                    (left_margin, top_margin, left_margin + 1216, top_margin + 352))
-                image = image.crop(
-                    (left_margin, top_margin, left_margin + 1216, top_margin + 352))
-
-            # Avoid blank boundaries due to pixel registration?
-            # Train images have white border. Test images have black border.
-            if self.config.dataset == 'nyu' and self.config.avoid_boundary:
-                # print("Avoiding Blank Boundaries!")
-                # We just crop and pad again with reflect padding to original size
-                # original_size = image.size
-                crop_params = get_white_border(np.array(image, dtype=np.uint8))
-                image = image.crop((crop_params.left, crop_params.top, crop_params.right, crop_params.bottom))
-                depth_gt = depth_gt.crop((crop_params.left, crop_params.top, crop_params.right, crop_params.bottom))
-
-                # Use reflect padding to fill the blank
-                image = np.array(image)
-                image = np.pad(image, ((crop_params.top, h - crop_params.bottom), (crop_params.left, w - crop_params.right), (0, 0)), mode='reflect')
-                image = Image.fromarray(image)
-
-                depth_gt = np.array(depth_gt)
-                depth_gt = np.pad(depth_gt, ((crop_params.top, h - crop_params.bottom), (crop_params.left, w - crop_params.right)), 'constant', constant_values=0)
-                depth_gt = Image.fromarray(depth_gt)
-
-
-            if self.config.do_random_rotate and (self.config.aug):
-                random_angle = (random.random() - 0.5) * 2 * self.config.degree
-                image = self.rotate_image(image, random_angle)
-                depth_gt = self.rotate_image(
-                    depth_gt, random_angle, flag=Image.NEAREST)
-
-            image = np.asarray(image, dtype=np.float32) / 255.0
-            depth_gt = np.asarray(depth_gt, dtype=np.float32)
-            depth_gt = np.expand_dims(depth_gt, axis=2)
-
-            if self.config.dataset == 'nyu':
-                depth_gt = depth_gt / 1000.0
-            else:
-                depth_gt = depth_gt / 256.0
-
-            if self.config.aug and (self.config.random_crop):
-                image, depth_gt = self.random_crop(
-                    image, depth_gt, self.config.input_height, self.config.input_width)
-            
-            if self.config.aug and self.config.random_translate:
-                # print("Random Translation!")
-                image, depth_gt = self.random_translate(image, depth_gt, self.config.max_translation)
-
-            image, depth_gt = self.train_preprocess(image, depth_gt)
-            mask = np.logical_and(depth_gt > self.config.min_depth,
-                                  depth_gt < self.config.max_depth).squeeze()[None, ...]
-            sample = {'image': image, 'depth': depth_gt, 'focal': focal,
-                      'mask': mask, **sample}
-
-        else:
-            if self.mode == 'online_eval':
-                data_path = self.config.data_path_eval
-            else:
-                data_path = self.config.data_path
-
-            image_path = os.path.join(
-                data_path, remove_leading_slash(sample_path.split()[0]))
-            image = np.asarray(self.reader.open(image_path),
-                               dtype=np.float32) / 255.0
-
-            if self.mode == 'online_eval':
-                gt_path = self.config.gt_path_eval
-                depth_path = os.path.join(
-                    gt_path, remove_leading_slash(sample_path.split()[1]))
-                has_valid_depth = False
-                try:
-                    depth_gt = self.reader.open(depth_path)
-                    has_valid_depth = True
-                except IOError:
-                    depth_gt = False
-                    # print('Missing gt for {}'.format(image_path))
-
-                if has_valid_depth:
-                    depth_gt = np.asarray(depth_gt, dtype=np.float32)
-                    depth_gt = np.expand_dims(depth_gt, axis=2)
-                    if self.config.dataset == 'nyu':
-                        depth_gt = depth_gt / 1000.0
-                    else:
-                        depth_gt = depth_gt / 256.0
-
-                    mask = np.logical_and(
-                        depth_gt >= self.config.min_depth, depth_gt <= self.config.max_depth).squeeze()[None, ...]
-                else:
-                    mask = False
-
-            if self.config.do_kb_crop:
-                height = image.shape[0]
-                width = image.shape[1]
-                top_margin = int(height - 352)
-                left_margin = int((width - 1216) / 2)
-                image = image[top_margin:top_margin + 352,
-                              left_margin:left_margin + 1216, :]
-                if self.mode == 'online_eval' and has_valid_depth:
-                    depth_gt = depth_gt[top_margin:top_margin +
-                                        352, left_margin:left_margin + 1216, :]
-
-            if self.mode == 'online_eval':
-                sample = {'image': image, 'depth': depth_gt, 'focal': focal, 'has_valid_depth': has_valid_depth,
-                          'image_path': sample_path.split()[0], 'depth_path': sample_path.split()[1],
-                          'mask': mask}
-            else:
-                sample = {'image': image, 'focal': focal}
-
-        if (self.mode == 'train') or ('has_valid_depth' in sample and sample['has_valid_depth']):
-            mask = np.logical_and(depth_gt > self.config.min_depth,
-                                  depth_gt < self.config.max_depth).squeeze()[None, ...]
-            sample['mask'] = mask
-
-        if self.transform:
-            sample = self.transform(sample)
-
-        sample = self.postprocess(sample)
-        sample['dataset'] = self.config.dataset
-        sample = {**sample, 'image_path': sample_path.split()[0], 'depth_path': sample_path.split()[1]}
-
-        return sample
-
-    def rotate_image(self, image, angle, flag=Image.BILINEAR):
-        result = image.rotate(angle, resample=flag)
-        return result
-
-    def random_crop(self, img, depth, height, width):
-        assert img.shape[0] >= height
-        assert img.shape[1] >= width
-        assert img.shape[0] == depth.shape[0]
-        assert img.shape[1] == depth.shape[1]
-        x = random.randint(0, img.shape[1] - width)
-        y = random.randint(0, img.shape[0] - height)
-        img = img[y:y + height, x:x + width, :]
-        depth = depth[y:y + height, x:x + width, :]
-
-        return img, depth
-    
-    def random_translate(self, img, depth, max_t=20):
-        assert img.shape[0] == depth.shape[0]
-        assert img.shape[1] == depth.shape[1]
-        p = self.config.translate_prob
-        do_translate = random.random()
-        if do_translate > p:
-            return img, depth
-        x = random.randint(-max_t, max_t)
-        y = random.randint(-max_t, max_t)
-        M = np.float32([[1, 0, x], [0, 1, y]])
-        # print(img.shape, depth.shape)
-        img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))
-        depth = cv2.warpAffine(depth, M, (depth.shape[1], depth.shape[0]))
-        depth = depth.squeeze()[..., None]  # add channel dim back. Affine warp removes it
-        # print("after", img.shape, depth.shape)
-        return img, depth
-
-    def train_preprocess(self, image, depth_gt):
-        if self.config.aug:
-            # Random flipping
-            do_flip = random.random()
-            if do_flip > 0.5:
-                image = (image[:, ::-1, :]).copy()
-                depth_gt = (depth_gt[:, ::-1, :]).copy()
-
-            # Random gamma, brightness, color augmentation
-            do_augment = random.random()
-            if do_augment > 0.5:
-                image = self.augment_image(image)
-
-        return image, depth_gt
-
-    def augment_image(self, image):
-        # gamma augmentation
-        gamma = random.uniform(0.9, 1.1)
-        image_aug = image ** gamma
-
-        # brightness augmentation
-        if self.config.dataset == 'nyu':
-            brightness = random.uniform(0.75, 1.25)
-        else:
-            brightness = random.uniform(0.9, 1.1)
-        image_aug = image_aug * brightness
-
-        # color augmentation
-        colors = np.random.uniform(0.9, 1.1, size=3)
-        white = np.ones((image.shape[0], image.shape[1]))
-        color_image = np.stack([white * colors[i] for i in range(3)], axis=2)
-        image_aug *= color_image
-        image_aug = np.clip(image_aug, 0, 1)
-
-        return image_aug
-
-    def __len__(self):
-        return len(self.filenames)
-
-
-class ToTensor(object):
-    def __init__(self, mode, do_normalize=False, size=None):
-        self.mode = mode
-        self.normalize = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) if do_normalize else nn.Identity()
-        self.size = size
-        if size is not None:
-            self.resize = transforms.Resize(size=size)
-        else:
-            self.resize = nn.Identity()
-
-    def __call__(self, sample):
-        image, focal = sample['image'], sample['focal']
-        image = self.to_tensor(image)
-        image = self.normalize(image)
-        image = self.resize(image)
-
-        if self.mode == 'test':
-            return {'image': image, 'focal': focal}
-
-        depth = sample['depth']
-        if self.mode == 'train':
-            depth = self.to_tensor(depth)
-            return {**sample, 'image': image, 'depth': depth, 'focal': focal}
-        else:
-            has_valid_depth = sample['has_valid_depth']
-            image = self.resize(image)
-            return {**sample, 'image': image, 'depth': depth, 'focal': focal, 'has_valid_depth': has_valid_depth,
-                    'image_path': sample['image_path'], 'depth_path': sample['depth_path']}
-
-    def to_tensor(self, pic):
-        if not (_is_pil_image(pic) or _is_numpy_image(pic)):
-            raise TypeError(
-                'pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
-
-        if isinstance(pic, np.ndarray):
-            img = torch.from_numpy(pic.transpose((2, 0, 1)))
-            return img
-
-        # handle PIL Image
-        if pic.mode == 'I':
-            img = torch.from_numpy(np.array(pic, np.int32, copy=False))
-        elif pic.mode == 'I;16':
-            img = torch.from_numpy(np.array(pic, np.int16, copy=False))
-        else:
-            img = torch.ByteTensor(
-                torch.ByteStorage.from_buffer(pic.tobytes()))
-        # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
-        if pic.mode == 'YCbCr':
-            nchannel = 3
-        elif pic.mode == 'I;16':
-            nchannel = 1
-        else:
-            nchannel = len(pic.mode)
-        img = img.view(pic.size[1], pic.size[0], nchannel)
-
-        img = img.transpose(0, 1).transpose(0, 2).contiguous()
-        if isinstance(img, torch.ByteTensor):
-            return img.float()
-        else:
-            return img
diff --git a/spaces/cosmicdream/Image_Variations/README.md b/spaces/cosmicdream/Image_Variations/README.md
deleted file mode 100644
index ad1301b7ac10c74726fb4b5258fc27aba95a5367..0000000000000000000000000000000000000000
--- a/spaces/cosmicdream/Image_Variations/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image Variations
-emoji: 😻
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/daddyjin/TalkingFaceGeneration/FONT/train.py b/spaces/daddyjin/TalkingFaceGeneration/FONT/train.py
deleted file mode 100644
index c9a2dfd5feae7b2a48c689df94797db7acbc3680..0000000000000000000000000000000000000000
--- a/spaces/daddyjin/TalkingFaceGeneration/FONT/train.py
+++ /dev/null
@@ -1,430 +0,0 @@
-from tqdm import trange
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-
-from logger import Logger
-from modules.model import DiscriminatorFullModel, TrainPart1Model, TrainPart2Model
-import itertools
-
-from torch.optim.lr_scheduler import MultiStepLR
-
-from sync_batchnorm import DataParallelWithCallback
-
-from frames_dataset import DatasetRepeater,TestsetRepeater
-import time
-from tensorboardX import SummaryWriter
-
-def train_part1(config, generator, discriminator, kp_detector, kp_detector_a,audio_feature, checkpoint, audio_checkpoint, log_dir, dataset, test_dataset, device_ids, name):
-    train_params = config['train_params']
-
-    optimizer_audio_feature = torch.optim.Adam(itertools.chain(audio_feature.parameters(),kp_detector_a.parameters()), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999))
-    optimizer_generator = None
-    optimizer_discriminator = None
-    optimizer_kp_detector = None
-
-    if checkpoint is not None:
-        start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, audio_feature,
-                                      optimizer_generator, optimizer_discriminator,
-                                      None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector,
-                                      None if train_params['lr_audio_feature'] == 0 else optimizer_audio_feature)
-
-
-    # audio_feature load wav2lip
-    wav2lip_ckpt_path = "/data/liujin/Wav2Lip-master/checkpoints/wav2lip.pth"
-    checkpoint = torch.load(wav2lip_ckpt_path)
-    s = checkpoint["state_dict"]
-    new_s = {}
-    for k, v in s.items():
-        new_s[k.replace('module.', '')] = v
-    audio_feature.load_state_dict(new_s, strict=False)
-
-
-    if audio_checkpoint is not None:
-        pretrain = torch.load(audio_checkpoint)
-        kp_detector_a.load_state_dict(pretrain['kp_detector_a'])
-        audio_feature.load_state_dict(pretrain['audio_feature'])
-        optimizer_audio_feature.load_state_dict(pretrain['optimizer_audio_feature'])
-        start_epoch = pretrain['epoch']
-
-    else:
-        start_epoch = 0
-
- 
-    scheduler_audio_feature = MultiStepLR(optimizer_audio_feature, train_params['epoch_milestones'], gamma=0.1,
-                                        last_epoch=-1 + start_epoch * (train_params['lr_audio_feature'] != 0))
-
-    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
-        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
-        test_dataset = TestsetRepeater(test_dataset, train_params['num_repeats'])
-    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6
-    test_dataloader = DataLoader(test_dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6
-    num_steps_per_epoch = len(dataloader)
-    num_steps_test_epoch = len(test_dataloader)
-    generator_full = TrainPart1Model(kp_detector, kp_detector_a, audio_feature, generator, discriminator, train_params,device_ids)
-    discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params)
-   
-    if len(device_ids)>1:
-        generator_full=torch.nn.DataParallel(generator_full)
-        discriminator_full=torch.nn.DataParallel(discriminator_full)
-
-    if torch.cuda.is_available():
-        if len(device_ids) == 1:
-            generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids)
-            discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids)
-        elif len(device_ids)>1:
-            generator_full = generator_full.to(device_ids[0])
-            discriminator_full = discriminator_full.to(device_ids[0])
-
-    step = 0
-    t0 = time.time()
-
-    writer=SummaryWriter(comment=name)
-    train_itr=0
-    test_itr=0
-    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], checkpoint_freq=train_params['checkpoint_freq']) as logger:
-        for epoch in trange(start_epoch, train_params['num_epochs']):
-
-            for x in dataloader:
-
-                losses_generator, generated = generator_full(x)
-
-        
-                loss_values = [val.mean() for val in losses_generator.values()]
-                loss = sum(loss_values)
-
-                writer.add_scalar('Train',loss,train_itr)
-
-                writer.add_scalar('Train_value',loss_values[0],train_itr)
-                writer.add_scalar('Train_heatmap',loss_values[1],train_itr)
-                writer.add_scalar('Train_jacobian',loss_values[2],train_itr)
-
-                train_itr+=1
-                loss.backward()
-
-              
-                optimizer_audio_feature.step()
-                optimizer_audio_feature.zero_grad()
-                d = time.time()
-         
-                # if train_params['loss_weights']['generator_gan'] != 0:
-                #     optimizer_discriminator.zero_grad()
-                # else:
-                #     losses_discriminator = {}
-
-                # losses_generator.update(losses_discriminator)
-                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()}
-                logger.log_iter(losses=losses)
-                e = time.time()
-          
-                step += 1
-             
-                if(step % 2500 == 0):
-                    print('Save ckpt and training visualization!')
-                    logger.log_epoch(epoch,step, {'audio_feature': audio_feature,
-                                     'kp_detector_a':kp_detector_a,
-                                     'optimizer_audio_feature': optimizer_audio_feature}, inp=x, out=generated)
-
-
-            scheduler_audio_feature.step()
-
-
-            for x in test_dataloader:
-                with torch.no_grad():
-                    losses_generator, generated = generator_full(x)
-
-                    loss_values = [val.mean() for val in losses_generator.values()]
-                    loss = sum(loss_values)
-
-                    writer.add_scalar('Test',loss,test_itr)
-
-                    writer.add_scalar('Test_value',loss_values[0],test_itr)
-                    writer.add_scalar('Test_heatmap',loss_values[1],test_itr)
-                    writer.add_scalar('Test_jacobian',loss_values[2],test_itr)
-
-                    test_itr+=1
-
-
-              
-def train_part1_fine_tune(config, generator, discriminator, kp_detector, kp_detector_a,audio_feature, checkpoint, audio_checkpoint, log_dir, dataset, test_dataset, device_ids, name):
-    train_params = config['train_params']
-
-    optimizer_generator = torch.optim.Adam(generator.parameters(), lr=train_params['lr_generator'], betas=(0.5, 0.999))
-    optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=train_params['lr_discriminator'], betas=(0.5, 0.999))
-    optimizer_audio_feature = torch.optim.Adam(itertools.chain(audio_feature.parameters(),kp_detector_a.parameters()), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999))
-  #  optimizer_kp_detector_a = torch.optim.Adam(kp_detector_a.parameters(), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999))
-    optimizer_kp_detector = None
-
-
-    if checkpoint is not None:
-        start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, audio_feature,
-                                      optimizer_generator, optimizer_discriminator,
-                                      None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector,
-                                      None if train_params['lr_audio_feature'] == 0 else optimizer_audio_feature)
-    if audio_checkpoint is not None:
-        pretrain = torch.load(audio_checkpoint)
-        kp_detector_a.load_state_dict(pretrain['kp_detector_a'])
-        audio_feature.load_state_dict(pretrain['audio_feature'])
-   #     optimizer_kp_detector_a.load_state_dict(pretrain['optimizer_kp_detector_a'])
-        optimizer_audio_feature.load_state_dict(pretrain['optimizer_audio_feature'])
-        start_epoch = pretrain['epoch']
-
-
-    else:
-        start_epoch = 0
-
-    scheduler_generator = MultiStepLR(optimizer_generator, train_params['epoch_milestones'], gamma=0.1,
-                                      last_epoch=start_epoch - 1)
-    scheduler_discriminator = MultiStepLR(optimizer_discriminator, train_params['epoch_milestones'], gamma=0.1,
-                                          last_epoch=start_epoch - 1)
-    scheduler_audio_feature = MultiStepLR(optimizer_audio_feature, train_params['epoch_milestones'], gamma=0.1,
-                                        last_epoch=-1 + start_epoch * (train_params['lr_audio_feature'] != 0))
-
-    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
-        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
-        test_dataset = TestsetRepeater(test_dataset, train_params['num_repeats'])
-    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6
-    test_dataloader = DataLoader(test_dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6
-    num_steps_per_epoch = len(dataloader)
-    num_steps_test_epoch = len(test_dataloader)
-    # generator_full = TrainFullModel(kp_detector, kp_detector_a, audio_feature, generator, discriminator, train_params,device_ids)
-    generator_full = TrainPart1Model(kp_detector, kp_detector_a, audio_feature, generator, discriminator, train_params, device_ids)
-
-
-    discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params)
-    print('End dataload ', file=open('log/MEAD_LRW_test_a.txt', 'a'))
-    if len(device_ids)>1:
-        generator_full=torch.nn.DataParallel(generator_full)
-        discriminator_full=torch.nn.DataParallel(discriminator_full)
-
-    if torch.cuda.is_available():
-        if len(device_ids) == 1:
-            generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids)
-            discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids)
-        elif len(device_ids)>1:
-            generator_full = generator_full.to(device_ids[0])
-            discriminator_full = discriminator_full.to(device_ids[0])
-
-    step = 0
-    t0 = time.time()
-
-    writer=SummaryWriter(comment=name)
-    train_itr=0
-    test_itr=0
-    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], checkpoint_freq=train_params['checkpoint_freq']) as logger:
-        for epoch in trange(start_epoch, train_params['num_epochs']):
-          
-            for x in dataloader:
-      
-         
-                losses_generator, generated = generator_full(x)
-             
-                loss_values = [val.mean() for val in losses_generator.values()]
-                loss = sum(loss_values)
-
-                writer.add_scalar('Train',loss,train_itr)
-
-                writer.add_scalar('Train_value',loss_values[0],train_itr)
-                writer.add_scalar('Train_heatmap',loss_values[1],train_itr)
-                writer.add_scalar('Train_jacobian',loss_values[2],train_itr)
-                writer.add_scalar('Train_perceptual',loss_values[3],train_itr)
-
-
-                train_itr+=1
-                loss.backward()
-
-          
-
-                optimizer_audio_feature.step()
-                optimizer_audio_feature.zero_grad()
-        
-                optimizer_generator.step()
-                optimizer_generator.zero_grad()
-            #    optimizer_kp_detector_a.step()
-            #    optimizer_kp_detector_a.zero_grad()
-            
-                if train_params['loss_weights']['discriminator_gan'] != 0:
-                    optimizer_discriminator.zero_grad()
-           #         losses_discriminator = discriminator_full(x, generated)
-           #         loss_values = [val.mean() for val in losses_discriminator.values()]
-           #         loss = sum(loss_values)
-
-           #         loss.backward()
-           #         optimizer_discriminator.step()
-           #         optimizer_discriminator.zero_grad()
-                else:
-                    losses_discriminator = {}
-
-                losses_generator.update(losses_discriminator)
-                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()}
-                logger.log_iter(losses=losses)
-         
-                step += 1
-             
-                if(step % 5000 == 0):
-       
-                    logger.log_epoch(epoch,step, {'audio_feature': audio_feature,
-                                     'kp_detector_a':kp_detector_a,
-                                     'generator': generator,
-                                     'optimizer_generator':optimizer_generator,
-                                     'optimizer_audio_feature': optimizer_audio_feature}, inp=x, out=generated)
-               
-            scheduler_generator.step()
-            scheduler_discriminator.step()
-            scheduler_audio_feature.step()
-
-           
-            for x in test_dataloader:
-                with torch.no_grad():
-                    losses_generator, generated = generator_full(x)
-
-                    loss_values = [val.mean() for val in losses_generator.values()]
-                    loss = sum(loss_values)
-
-                    writer.add_scalar('Test',loss,test_itr)
-
-                    writer.add_scalar('Test_value',loss_values[0],test_itr)
-                    writer.add_scalar('Test_heatmap',loss_values[1],test_itr)
-                    writer.add_scalar('Test_jacobian',loss_values[2],test_itr)
-                    writer.add_scalar('Test_perceptual',loss_values[3],test_itr)
-
-                    test_itr+=1
-
-
-def train_part2(config, generator, discriminator, kp_detector, emo_detector, kp_detector_a,audio_feature, checkpoint, audio_checkpoint, emo_checkpoint, log_dir, dataset, test_dataset, device_ids, exp_name):
-    train_params = config['train_params']
-  
-    optimizer_emo_detector = torch.optim.Adam(emo_detector.parameters(), lr=train_params['lr_audio_feature'], betas=(0.5, 0.999))
- 
-    if checkpoint is not None:
-        start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, audio_feature,
-                                      optimizer_generator, optimizer_discriminator,
-                                      None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector,
-                                      None if train_params['lr_audio_feature'] == 0 else optimizer_audio_feature)
-    if emo_checkpoint is not None:
-        pretrain = torch.load(emo_checkpoint)
-        tgt_state = emo_detector.state_dict()
-        strip = 'module.'
-        if 'emo_detector' in pretrain:
-            emo_detector.load_state_dict(pretrain['emo_detector'])
-            optimizer_emo_detector.load_state_dict(pretrain['optimizer_emo_detector'])
-            print('emo_detector in pretrain + load', file=open('log/'+exp_name+'.txt', 'a'))
-        for name, param in pretrain.items():
-            if isinstance(param, nn.Parameter):
-                param = param.data
-            if strip is not None and name.startswith(strip):
-                name = name[len(strip):]
-            if name not in tgt_state:
-                continue
-            tgt_state[name].copy_(param)
-            print(name)
-    if audio_checkpoint is not None:
-        pretrain = torch.load(audio_checkpoint)
-        kp_detector_a.load_state_dict(pretrain['kp_detector_a'])
-        audio_feature.load_state_dict(pretrain['audio_feature'])
-        optimizer_audio_feature.load_state_dict(pretrain['optimizer_audio_feature'])
-        if 'emo_detector' in pretrain:
-            emo_detector.load_state_dict(pretrain['emo_detector'])
-            optimizer_emo_detector.load_state_dict(pretrain['optimizer_emo_detector'])
-        start_epoch = pretrain['epoch']
-   
-    else:
-        start_epoch = 0
-
-
-    scheduler_emo_detector = MultiStepLR(optimizer_emo_detector, train_params['epoch_milestones'], gamma=0.1,
-                                        last_epoch=-1 + start_epoch * (train_params['lr_audio_feature'] != 0))
-
-    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
-        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
-        test_dataset = TestsetRepeater(test_dataset, train_params['num_repeats'])
-    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6
-    test_dataloader = DataLoader(test_dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=0, drop_last=True)#6
-    num_steps_per_epoch = len(dataloader)
-    num_steps_test_epoch = len(test_dataloader)
-    generator_full = TrainPart2Model(kp_detector, emo_detector,kp_detector_a, audio_feature,generator, discriminator, train_params,device_ids)
-    discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params)
-
-    if len(device_ids)>1:
-        generator_full=torch.nn.DataParallel(generator_full)
-        discriminator_full=torch.nn.DataParallel(discriminator_full)
-        
-    if torch.cuda.is_available():
-        if len(device_ids) == 1:
-            generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids)
-            discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids)
-        elif len(device_ids)>1:
-            generator_full = generator_full.to(device_ids[0])
-            discriminator_full = discriminator_full.to(device_ids[0])
-    
-    step = 0
-    t0 = time.time()
-    
-    writer=SummaryWriter(comment=exp_name)
-    train_itr=0
-    test_itr=0
-    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], checkpoint_freq=train_params['checkpoint_freq']) as logger:
-        for epoch in trange(start_epoch, train_params['num_epochs']):
-         
-            for x in dataloader:
-      
-                losses_generator, generated = generator_full(x)
-               
-                loss_values = [val.mean() for val in losses_generator.values()]
-                loss = sum(loss_values)
-                
-                writer.add_scalar('Train',loss,train_itr)
-                
-                writer.add_scalar('Train_value',loss_values[0],train_itr)
-            #    writer.add_scalar('Train_heatmap',loss_values[1],train_itr)
-                writer.add_scalar('Train_jacobian',loss_values[1],train_itr)
-                writer.add_scalar('Train_classify',loss_values[2],train_itr)
-               
-                
-                
-                train_itr+=1
-                loss.backward()
-                
-  
-                optimizer_emo_detector.step()
-                optimizer_emo_detector.zero_grad()
-           
-           
-                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()}
-                logger.log_iter(losses=losses)
-             
-                step += 1
-               
-                if(step % 1000 == 0):
-                    
-                    logger.log_epoch(epoch,step, {'audio_feature': audio_feature,
-                                     'kp_detector_a':kp_detector_a,
-                                     'emo_detector':emo_detector,
-                                     'optimizer_emo_detector': optimizer_emo_detector,
-             #                        'optimizer_kp_detector_a':optimizer_kp_detector_a,
-                                     'optimizer_audio_feature': optimizer_audio_feature}, inp=x, out=generated)
-                
-            scheduler_emo_detector.step()
-            
-        
-            for x in test_dataloader:
-                with torch.no_grad():
-                    losses_generator, generated = generator_full(x)
-
-                    loss_values = [val.mean() for val in losses_generator.values()]
-                    loss = sum(loss_values)
-                
-                    writer.add_scalar('Test',loss,test_itr)
-                   
-                    writer.add_scalar('Test_value',loss_values[0],test_itr)
-                #    writer.add_scalar('Test_heatmap',loss_values[1],test_itr)
-                    writer.add_scalar('Test_jacobian',loss_values[1],test_itr)
-                    writer.add_scalar('Test_classify',loss_values[2],test_itr)
-                    
-                
-                    test_itr+=1
-                
-                   
-            
-
-
diff --git a/spaces/datasciencedojo/Finger-Counting-Right-Hand/README.md b/spaces/datasciencedojo/Finger-Counting-Right-Hand/README.md
deleted file mode 100644
index 0675040eac9debe9c9feda4c9ef7860f83c18abb..0000000000000000000000000000000000000000
--- a/spaces/datasciencedojo/Finger-Counting-Right-Hand/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Finger Counting Right Hand
-emoji: 🌖
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/subset/cff.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/subset/cff.py
deleted file mode 100644
index dd79f6db37a482891b6f151159ef4c9b89475b8e..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/subset/cff.py
+++ /dev/null
@@ -1,536 +0,0 @@
-from fontTools.misc import psCharStrings
-from fontTools import ttLib
-from fontTools.pens.basePen import NullPen
-from fontTools.misc.roundTools import otRound
-from fontTools.misc.loggingTools import deprecateFunction
-from fontTools.subset.util import _add_method, _uniq_sort
-
-
-class _ClosureGlyphsT2Decompiler(psCharStrings.SimpleT2Decompiler):
-    def __init__(self, components, localSubrs, globalSubrs):
-        psCharStrings.SimpleT2Decompiler.__init__(self, localSubrs, globalSubrs)
-        self.components = components
-
-    def op_endchar(self, index):
-        args = self.popall()
-        if len(args) >= 4:
-            from fontTools.encodings.StandardEncoding import StandardEncoding
-
-            # endchar can do seac accent bulding; The T2 spec says it's deprecated,
-            # but recent software that shall remain nameless does output it.
-            adx, ady, bchar, achar = args[-4:]
-            baseGlyph = StandardEncoding[bchar]
-            accentGlyph = StandardEncoding[achar]
-            self.components.add(baseGlyph)
-            self.components.add(accentGlyph)
-
-
-@_add_method(ttLib.getTableClass("CFF "))
-def closure_glyphs(self, s):
-    cff = self.cff
-    assert len(cff) == 1
-    font = cff[cff.keys()[0]]
-    glyphSet = font.CharStrings
-
-    decompose = s.glyphs
-    while decompose:
-        components = set()
-        for g in decompose:
-            if g not in glyphSet:
-                continue
-            gl = glyphSet[g]
-
-            subrs = getattr(gl.private, "Subrs", [])
-            decompiler = _ClosureGlyphsT2Decompiler(components, subrs, gl.globalSubrs)
-            decompiler.execute(gl)
-        components -= s.glyphs
-        s.glyphs.update(components)
-        decompose = components
-
-
-def _empty_charstring(font, glyphName, isCFF2, ignoreWidth=False):
-    c, fdSelectIndex = font.CharStrings.getItemAndSelector(glyphName)
-    if isCFF2 or ignoreWidth:
-        # CFF2 charstrings have no widths nor 'endchar' operators
-        c.setProgram([] if isCFF2 else ["endchar"])
-    else:
-        if hasattr(font, "FDArray") and font.FDArray is not None:
-            private = font.FDArray[fdSelectIndex].Private
-        else:
-            private = font.Private
-        dfltWdX = private.defaultWidthX
-        nmnlWdX = private.nominalWidthX
-        pen = NullPen()
-        c.draw(pen)  # this will set the charstring's width
-        if c.width != dfltWdX:
-            c.program = [c.width - nmnlWdX, "endchar"]
-        else:
-            c.program = ["endchar"]
-
-
-@_add_method(ttLib.getTableClass("CFF "))
-def prune_pre_subset(self, font, options):
-    cff = self.cff
-    # CFF table must have one font only
-    cff.fontNames = cff.fontNames[:1]
-
-    if options.notdef_glyph and not options.notdef_outline:
-        isCFF2 = cff.major > 1
-        for fontname in cff.keys():
-            font = cff[fontname]
-            _empty_charstring(font, ".notdef", isCFF2=isCFF2)
-
-    # Clear useless Encoding
-    for fontname in cff.keys():
-        font = cff[fontname]
-        # https://github.com/fonttools/fonttools/issues/620
-        font.Encoding = "StandardEncoding"
-
-    return True  # bool(cff.fontNames)
-
-
-@_add_method(ttLib.getTableClass("CFF "))
-def subset_glyphs(self, s):
-    cff = self.cff
-    for fontname in cff.keys():
-        font = cff[fontname]
-        cs = font.CharStrings
-
-        glyphs = s.glyphs.union(s.glyphs_emptied)
-
-        # Load all glyphs
-        for g in font.charset:
-            if g not in glyphs:
-                continue
-            c, _ = cs.getItemAndSelector(g)
-
-        if cs.charStringsAreIndexed:
-            indices = [i for i, g in enumerate(font.charset) if g in glyphs]
-            csi = cs.charStringsIndex
-            csi.items = [csi.items[i] for i in indices]
-            del csi.file, csi.offsets
-            if hasattr(font, "FDSelect"):
-                sel = font.FDSelect
-                sel.format = None
-                sel.gidArray = [sel.gidArray[i] for i in indices]
-            newCharStrings = {}
-            for indicesIdx, charsetIdx in enumerate(indices):
-                g = font.charset[charsetIdx]
-                if g in cs.charStrings:
-                    newCharStrings[g] = indicesIdx
-            cs.charStrings = newCharStrings
-        else:
-            cs.charStrings = {g: v for g, v in cs.charStrings.items() if g in glyphs}
-        font.charset = [g for g in font.charset if g in glyphs]
-        font.numGlyphs = len(font.charset)
-
-        if s.options.retain_gids:
-            isCFF2 = cff.major > 1
-            for g in s.glyphs_emptied:
-                _empty_charstring(font, g, isCFF2=isCFF2, ignoreWidth=True)
-
-    return True  # any(cff[fontname].numGlyphs for fontname in cff.keys())
-
-
-@_add_method(psCharStrings.T2CharString)
-def subset_subroutines(self, subrs, gsubrs):
-    p = self.program
-    for i in range(1, len(p)):
-        if p[i] == "callsubr":
-            assert isinstance(p[i - 1], int)
-            p[i - 1] = subrs._used.index(p[i - 1] + subrs._old_bias) - subrs._new_bias
-        elif p[i] == "callgsubr":
-            assert isinstance(p[i - 1], int)
-            p[i - 1] = (
-                gsubrs._used.index(p[i - 1] + gsubrs._old_bias) - gsubrs._new_bias
-            )
-
-
-@_add_method(psCharStrings.T2CharString)
-def drop_hints(self):
-    hints = self._hints
-
-    if hints.deletions:
-        p = self.program
-        for idx in reversed(hints.deletions):
-            del p[idx - 2 : idx]
-
-    if hints.has_hint:
-        assert not hints.deletions or hints.last_hint <= hints.deletions[0]
-        self.program = self.program[hints.last_hint :]
-        if not self.program:
-            # TODO CFF2 no need for endchar.
-            self.program.append("endchar")
-        if hasattr(self, "width"):
-            # Insert width back if needed
-            if self.width != self.private.defaultWidthX:
-                # For CFF2 charstrings, this should never happen
-                assert (
-                    self.private.defaultWidthX is not None
-                ), "CFF2 CharStrings must not have an initial width value"
-                self.program.insert(0, self.width - self.private.nominalWidthX)
-
-    if hints.has_hintmask:
-        i = 0
-        p = self.program
-        while i < len(p):
-            if p[i] in ["hintmask", "cntrmask"]:
-                assert i + 1 <= len(p)
-                del p[i : i + 2]
-                continue
-            i += 1
-
-    assert len(self.program)
-
-    del self._hints
-
-
-class _MarkingT2Decompiler(psCharStrings.SimpleT2Decompiler):
-    def __init__(self, localSubrs, globalSubrs, private):
-        psCharStrings.SimpleT2Decompiler.__init__(
-            self, localSubrs, globalSubrs, private
-        )
-        for subrs in [localSubrs, globalSubrs]:
-            if subrs and not hasattr(subrs, "_used"):
-                subrs._used = set()
-
-    def op_callsubr(self, index):
-        self.localSubrs._used.add(self.operandStack[-1] + self.localBias)
-        psCharStrings.SimpleT2Decompiler.op_callsubr(self, index)
-
-    def op_callgsubr(self, index):
-        self.globalSubrs._used.add(self.operandStack[-1] + self.globalBias)
-        psCharStrings.SimpleT2Decompiler.op_callgsubr(self, index)
-
-
-class _DehintingT2Decompiler(psCharStrings.T2WidthExtractor):
-    class Hints(object):
-        def __init__(self):
-            # Whether calling this charstring produces any hint stems
-            # Note that if a charstring starts with hintmask, it will
-            # have has_hint set to True, because it *might* produce an
-            # implicit vstem if called under certain conditions.
-            self.has_hint = False
-            # Index to start at to drop all hints
-            self.last_hint = 0
-            # Index up to which we know more hints are possible.
-            # Only relevant if status is 0 or 1.
-            self.last_checked = 0
-            # The status means:
-            # 0: after dropping hints, this charstring is empty
-            # 1: after dropping hints, there may be more hints
-            # 	continuing after this, or there might be
-            # 	other things.  Not clear yet.
-            # 2: no more hints possible after this charstring
-            self.status = 0
-            # Has hintmask instructions; not recursive
-            self.has_hintmask = False
-            # List of indices of calls to empty subroutines to remove.
-            self.deletions = []
-
-        pass
-
-    def __init__(
-        self, css, localSubrs, globalSubrs, nominalWidthX, defaultWidthX, private=None
-    ):
-        self._css = css
-        psCharStrings.T2WidthExtractor.__init__(
-            self, localSubrs, globalSubrs, nominalWidthX, defaultWidthX
-        )
-        self.private = private
-
-    def execute(self, charString):
-        old_hints = charString._hints if hasattr(charString, "_hints") else None
-        charString._hints = self.Hints()
-
-        psCharStrings.T2WidthExtractor.execute(self, charString)
-
-        hints = charString._hints
-
-        if hints.has_hint or hints.has_hintmask:
-            self._css.add(charString)
-
-        if hints.status != 2:
-            # Check from last_check, make sure we didn't have any operators.
-            for i in range(hints.last_checked, len(charString.program) - 1):
-                if isinstance(charString.program[i], str):
-                    hints.status = 2
-                    break
-                else:
-                    hints.status = 1  # There's *something* here
-            hints.last_checked = len(charString.program)
-
-        if old_hints:
-            assert hints.__dict__ == old_hints.__dict__
-
-    def op_callsubr(self, index):
-        subr = self.localSubrs[self.operandStack[-1] + self.localBias]
-        psCharStrings.T2WidthExtractor.op_callsubr(self, index)
-        self.processSubr(index, subr)
-
-    def op_callgsubr(self, index):
-        subr = self.globalSubrs[self.operandStack[-1] + self.globalBias]
-        psCharStrings.T2WidthExtractor.op_callgsubr(self, index)
-        self.processSubr(index, subr)
-
-    def op_hstem(self, index):
-        psCharStrings.T2WidthExtractor.op_hstem(self, index)
-        self.processHint(index)
-
-    def op_vstem(self, index):
-        psCharStrings.T2WidthExtractor.op_vstem(self, index)
-        self.processHint(index)
-
-    def op_hstemhm(self, index):
-        psCharStrings.T2WidthExtractor.op_hstemhm(self, index)
-        self.processHint(index)
-
-    def op_vstemhm(self, index):
-        psCharStrings.T2WidthExtractor.op_vstemhm(self, index)
-        self.processHint(index)
-
-    def op_hintmask(self, index):
-        rv = psCharStrings.T2WidthExtractor.op_hintmask(self, index)
-        self.processHintmask(index)
-        return rv
-
-    def op_cntrmask(self, index):
-        rv = psCharStrings.T2WidthExtractor.op_cntrmask(self, index)
-        self.processHintmask(index)
-        return rv
-
-    def processHintmask(self, index):
-        cs = self.callingStack[-1]
-        hints = cs._hints
-        hints.has_hintmask = True
-        if hints.status != 2:
-            # Check from last_check, see if we may be an implicit vstem
-            for i in range(hints.last_checked, index - 1):
-                if isinstance(cs.program[i], str):
-                    hints.status = 2
-                    break
-            else:
-                # We are an implicit vstem
-                hints.has_hint = True
-                hints.last_hint = index + 1
-                hints.status = 0
-        hints.last_checked = index + 1
-
-    def processHint(self, index):
-        cs = self.callingStack[-1]
-        hints = cs._hints
-        hints.has_hint = True
-        hints.last_hint = index
-        hints.last_checked = index
-
-    def processSubr(self, index, subr):
-        cs = self.callingStack[-1]
-        hints = cs._hints
-        subr_hints = subr._hints
-
-        # Check from last_check, make sure we didn't have
-        # any operators.
-        if hints.status != 2:
-            for i in range(hints.last_checked, index - 1):
-                if isinstance(cs.program[i], str):
-                    hints.status = 2
-                    break
-            hints.last_checked = index
-
-        if hints.status != 2:
-            if subr_hints.has_hint:
-                hints.has_hint = True
-
-                # Decide where to chop off from
-                if subr_hints.status == 0:
-                    hints.last_hint = index
-                else:
-                    hints.last_hint = index - 2  # Leave the subr call in
-
-        elif subr_hints.status == 0:
-            hints.deletions.append(index)
-
-        hints.status = max(hints.status, subr_hints.status)
-
-
-@_add_method(ttLib.getTableClass("CFF "))
-def prune_post_subset(self, ttfFont, options):
-    cff = self.cff
-    for fontname in cff.keys():
-        font = cff[fontname]
-        cs = font.CharStrings
-
-        # Drop unused FontDictionaries
-        if hasattr(font, "FDSelect"):
-            sel = font.FDSelect
-            indices = _uniq_sort(sel.gidArray)
-            sel.gidArray = [indices.index(ss) for ss in sel.gidArray]
-            arr = font.FDArray
-            arr.items = [arr[i] for i in indices]
-            del arr.file, arr.offsets
-
-    # Desubroutinize if asked for
-    if options.desubroutinize:
-        cff.desubroutinize()
-
-    # Drop hints if not needed
-    if not options.hinting:
-        self.remove_hints()
-    elif not options.desubroutinize:
-        self.remove_unused_subroutines()
-    return True
-
-
-def _delete_empty_subrs(private_dict):
-    if hasattr(private_dict, "Subrs") and not private_dict.Subrs:
-        if "Subrs" in private_dict.rawDict:
-            del private_dict.rawDict["Subrs"]
-        del private_dict.Subrs
-
-
-@deprecateFunction(
-    "use 'CFFFontSet.desubroutinize()' instead", category=DeprecationWarning
-)
-@_add_method(ttLib.getTableClass("CFF "))
-def desubroutinize(self):
-    self.cff.desubroutinize()
-
-
-@_add_method(ttLib.getTableClass("CFF "))
-def remove_hints(self):
-    cff = self.cff
-    for fontname in cff.keys():
-        font = cff[fontname]
-        cs = font.CharStrings
-        # This can be tricky, but doesn't have to. What we do is:
-        #
-        # - Run all used glyph charstrings and recurse into subroutines,
-        # - For each charstring (including subroutines), if it has any
-        #   of the hint stem operators, we mark it as such.
-        #   Upon returning, for each charstring we note all the
-        #   subroutine calls it makes that (recursively) contain a stem,
-        # - Dropping hinting then consists of the following two ops:
-        #   * Drop the piece of the program in each charstring before the
-        #     last call to a stem op or a stem-calling subroutine,
-        #   * Drop all hintmask operations.
-        # - It's trickier... A hintmask right after hints and a few numbers
-        #    will act as an implicit vstemhm. As such, we track whether
-        #    we have seen any non-hint operators so far and do the right
-        #    thing, recursively... Good luck understanding that :(
-        css = set()
-        for g in font.charset:
-            c, _ = cs.getItemAndSelector(g)
-            c.decompile()
-            subrs = getattr(c.private, "Subrs", [])
-            decompiler = _DehintingT2Decompiler(
-                css,
-                subrs,
-                c.globalSubrs,
-                c.private.nominalWidthX,
-                c.private.defaultWidthX,
-                c.private,
-            )
-            decompiler.execute(c)
-            c.width = decompiler.width
-        for charstring in css:
-            charstring.drop_hints()
-        del css
-
-        # Drop font-wide hinting values
-        all_privs = []
-        if hasattr(font, "FDArray"):
-            all_privs.extend(fd.Private for fd in font.FDArray)
-        else:
-            all_privs.append(font.Private)
-        for priv in all_privs:
-            for k in [
-                "BlueValues",
-                "OtherBlues",
-                "FamilyBlues",
-                "FamilyOtherBlues",
-                "BlueScale",
-                "BlueShift",
-                "BlueFuzz",
-                "StemSnapH",
-                "StemSnapV",
-                "StdHW",
-                "StdVW",
-                "ForceBold",
-                "LanguageGroup",
-                "ExpansionFactor",
-            ]:
-                if hasattr(priv, k):
-                    setattr(priv, k, None)
-    self.remove_unused_subroutines()
-
-
-@_add_method(ttLib.getTableClass("CFF "))
-def remove_unused_subroutines(self):
-    cff = self.cff
-    for fontname in cff.keys():
-        font = cff[fontname]
-        cs = font.CharStrings
-        # Renumber subroutines to remove unused ones
-
-        # Mark all used subroutines
-        for g in font.charset:
-            c, _ = cs.getItemAndSelector(g)
-            subrs = getattr(c.private, "Subrs", [])
-            decompiler = _MarkingT2Decompiler(subrs, c.globalSubrs, c.private)
-            decompiler.execute(c)
-
-        all_subrs = [font.GlobalSubrs]
-        if hasattr(font, "FDArray"):
-            all_subrs.extend(
-                fd.Private.Subrs
-                for fd in font.FDArray
-                if hasattr(fd.Private, "Subrs") and fd.Private.Subrs
-            )
-        elif hasattr(font.Private, "Subrs") and font.Private.Subrs:
-            all_subrs.append(font.Private.Subrs)
-
-        subrs = set(subrs)  # Remove duplicates
-
-        # Prepare
-        for subrs in all_subrs:
-            if not hasattr(subrs, "_used"):
-                subrs._used = set()
-            subrs._used = _uniq_sort(subrs._used)
-            subrs._old_bias = psCharStrings.calcSubrBias(subrs)
-            subrs._new_bias = psCharStrings.calcSubrBias(subrs._used)
-
-        # Renumber glyph charstrings
-        for g in font.charset:
-            c, _ = cs.getItemAndSelector(g)
-            subrs = getattr(c.private, "Subrs", [])
-            c.subset_subroutines(subrs, font.GlobalSubrs)
-
-        # Renumber subroutines themselves
-        for subrs in all_subrs:
-            if subrs == font.GlobalSubrs:
-                if not hasattr(font, "FDArray") and hasattr(font.Private, "Subrs"):
-                    local_subrs = font.Private.Subrs
-                else:
-                    local_subrs = []
-            else:
-                local_subrs = subrs
-
-            subrs.items = [subrs.items[i] for i in subrs._used]
-            if hasattr(subrs, "file"):
-                del subrs.file
-            if hasattr(subrs, "offsets"):
-                del subrs.offsets
-
-            for subr in subrs.items:
-                subr.subset_subroutines(local_subrs, font.GlobalSubrs)
-
-        # Delete local SubrsIndex if empty
-        if hasattr(font, "FDArray"):
-            for fd in font.FDArray:
-                _delete_empty_subrs(fd.Private)
-        else:
-            _delete_empty_subrs(font.Private)
-
-        # Cleanup
-        for subrs in all_subrs:
-            del subrs._used, subrs._old_bias, subrs._new_bias
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ufoLib/plistlib.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ufoLib/plistlib.py
deleted file mode 100644
index 1f52f20a2b4836e39d3e292496928185dfe08534..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ufoLib/plistlib.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""DEPRECATED - This module is kept here only as a backward compatibility shim
-for the old ufoLib.plistlib module, which was moved to fontTools.misc.plistlib.
-Please use the latter instead.
-"""
-from fontTools.misc.plistlib import dump, dumps, load, loads
-from fontTools.misc.textTools import tobytes
-
-# The following functions were part of the old py2-like ufoLib.plistlib API.
-# They are kept only for backward compatiblity.
-from fontTools.ufoLib.utils import deprecated
-
-
-@deprecated("Use 'fontTools.misc.plistlib.load' instead")
-def readPlist(path_or_file):
-    did_open = False
-    if isinstance(path_or_file, str):
-        path_or_file = open(path_or_file, "rb")
-        did_open = True
-    try:
-        return load(path_or_file, use_builtin_types=False)
-    finally:
-        if did_open:
-            path_or_file.close()
-
-
-@deprecated("Use 'fontTools.misc.plistlib.dump' instead")
-def writePlist(value, path_or_file):
-    did_open = False
-    if isinstance(path_or_file, str):
-        path_or_file = open(path_or_file, "wb")
-        did_open = True
-    try:
-        dump(value, path_or_file, use_builtin_types=False)
-    finally:
-        if did_open:
-            path_or_file.close()
-
-
-@deprecated("Use 'fontTools.misc.plistlib.loads' instead")
-def readPlistFromString(data):
-    return loads(tobytes(data, encoding="utf-8"), use_builtin_types=False)
-
-
-@deprecated("Use 'fontTools.misc.plistlib.dumps' instead")
-def writePlistToString(value):
-    return dumps(value, use_builtin_types=False)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-49c152ed.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-49c152ed.css
deleted file mode 100644
index 0af4e02cc7c84a94d8719b02e4d6d8d67e582557..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-49c152ed.css
+++ /dev/null
@@ -1 +0,0 @@
-.wrap.svelte-1cl284s{display:flex;flex-direction:column;width:100%}.head.svelte-1cl284s{display:flex;justify-content:space-between}input[type=number].svelte-1cl284s{display:block;position:relative;outline:none!important;box-shadow:var(--input-shadow);border:var(--input-border-width) solid var(--input-border-color);border-radius:var(--input-radius);background:var(--input-background-fill);padding:var(--size-2) var(--size-2);height:var(--size-6);color:var(--body-text-color);font-size:var(--input-text-size);line-height:var(--line-sm);text-align:center}input.svelte-1cl284s:disabled{-webkit-text-fill-color:var(--body-text-color);-webkit-opacity:1;opacity:1}input[type=number].svelte-1cl284s:focus{box-shadow:var(--input-shadow-focus);border-color:var(--input-border-color-focus)}input.svelte-1cl284s::placeholder{color:var(--input-placeholder-color)}input[type=range].svelte-1cl284s{width:100%;accent-color:var(--slider-color)}input[disabled].svelte-1cl284s{cursor:not-allowed}
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/h11/tests/test_helpers.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/h11/tests/test_helpers.py
deleted file mode 100644
index c329c767834f73b1dd8991a4ac12d4972a41e98a..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/h11/tests/test_helpers.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from .._events import (
-    ConnectionClosed,
-    Data,
-    EndOfMessage,
-    Event,
-    InformationalResponse,
-    Request,
-    Response,
-)
-from .helpers import normalize_data_events
-
-
-def test_normalize_data_events() -> None:
-    assert normalize_data_events(
-        [
-            Data(data=bytearray(b"1")),
-            Data(data=b"2"),
-            Response(status_code=200, headers=[]),  # type: ignore[arg-type]
-            Data(data=b"3"),
-            Data(data=b"4"),
-            EndOfMessage(),
-            Data(data=b"5"),
-            Data(data=b"6"),
-            Data(data=b"7"),
-        ]
-    ) == [
-        Data(data=b"12"),
-        Response(status_code=200, headers=[]),  # type: ignore[arg-type]
-        Data(data=b"34"),
-        EndOfMessage(),
-        Data(data=b"567"),
-    ]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/__init__.py
deleted file mode 100644
index 79924cf7e51665d03295ecdb7d0930bdeb806d4f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/__init__.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""
-An implementation of JSON Schema for Python.
-
-The main functionality is provided by the validator classes for each of the
-supported JSON Schema versions.
-
-Most commonly, `jsonschema.validators.validate` is the quickest way to simply
-validate a given instance under a schema, and will create a validator
-for you.
-"""
-import warnings
-
-from jsonschema._format import FormatChecker
-from jsonschema._types import TypeChecker
-from jsonschema.exceptions import SchemaError, ValidationError
-from jsonschema.validators import (
-    Draft3Validator,
-    Draft4Validator,
-    Draft6Validator,
-    Draft7Validator,
-    Draft201909Validator,
-    Draft202012Validator,
-    validate,
-)
-
-
-def __getattr__(name):
-    if name == "__version__":
-        warnings.warn(
-            "Accessing jsonschema.__version__ is deprecated and will be "
-            "removed in a future release. Use importlib.metadata directly "
-            "to query for jsonschema's version.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        from importlib import metadata
-        return metadata.version("jsonschema")
-    elif name == "RefResolver":
-        from jsonschema.validators import _RefResolver
-        warnings.warn(
-            _RefResolver._DEPRECATION_MESSAGE,
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return _RefResolver
-    elif name == "ErrorTree":
-        warnings.warn(
-            "Importing ErrorTree directly from the jsonschema package "
-            "is deprecated and will become an ImportError. Import it from "
-            "jsonschema.exceptions instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        from jsonschema.exceptions import ErrorTree
-        return ErrorTree
-    elif name == "FormatError":
-        warnings.warn(
-            "Importing FormatError directly from the jsonschema package "
-            "is deprecated and will become an ImportError. Import it from "
-            "jsonschema.exceptions instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        from jsonschema.exceptions import FormatError
-        return FormatError
-    elif name == "Validator":
-        warnings.warn(
-            "Importing Validator directly from the jsonschema package "
-            "is deprecated and will become an ImportError. Import it from "
-            "jsonschema.protocols instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        from jsonschema.protocols import Validator
-        return Validator
-    elif name == "RefResolutionError":
-        from jsonschema.exceptions import _RefResolutionError
-        warnings.warn(
-            _RefResolutionError._DEPRECATION_MESSAGE,
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return _RefResolutionError
-
-    format_checkers = {
-        "draft3_format_checker": Draft3Validator,
-        "draft4_format_checker": Draft4Validator,
-        "draft6_format_checker": Draft6Validator,
-        "draft7_format_checker": Draft7Validator,
-        "draft201909_format_checker": Draft201909Validator,
-        "draft202012_format_checker": Draft202012Validator,
-    }
-    ValidatorForFormat = format_checkers.get(name)
-    if ValidatorForFormat is not None:
-        warnings.warn(
-            f"Accessing jsonschema.{name} is deprecated and will be "
-            "removed in a future release. Instead, use the FORMAT_CHECKER "
-            "attribute on the corresponding Validator.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return ValidatorForFormat.FORMAT_CHECKER
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")
-
-
-__all__ = [
-    "Draft201909Validator",
-    "Draft202012Validator",
-    "Draft3Validator",
-    "Draft4Validator",
-    "Draft6Validator",
-    "Draft7Validator",
-    "FormatChecker",
-    "SchemaError",
-    "TypeChecker",
-    "ValidationError",
-    "validate",
-]
diff --git a/spaces/deepghs/auto_image_censor/nudenet.py b/spaces/deepghs/auto_image_censor/nudenet.py
deleted file mode 100644
index 5b04f657c2c6d2ca75b38d4fc77c7e20705dd94d..0000000000000000000000000000000000000000
--- a/spaces/deepghs/auto_image_censor/nudenet.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from functools import lru_cache
-from pathlib import Path
-from typing import Tuple, List
-
-import numpy as np
-import onnxruntime
-from PIL import Image
-from huggingface_hub import hf_hub_download
-
-
-def _read_bgr_data(image) -> np.ndarray:
-    """ Read an image in BGR format.
-    Args
-        path: Path to the image.
-    """
-    data = np.ascontiguousarray(image.convert('RGB'))
-    return data[:, :, ::-1]
-
-
-CAFFE_MASK = np.asarray([103.939, 116.779, 123.68]).astype(np.float32)
-
-
-def _preprocess_image(x) -> np.ndarray:
-    return x.astype(np.float32) - CAFFE_MASK
-
-
-def _get_resize_scale(size, min_side=800, max_side=1333):
-    width, height = size
-    smallest_side = min(width, height)
-    largest_side = max(width, height)
-
-    scale = min_side / smallest_side
-    if largest_side * scale > max_side:
-        scale = max_side / largest_side
-
-    return scale
-
-
-def preprocess_image(
-        image: Image.Image, min_side=800, max_side=1333,
-):
-    width, height = image.size
-    scale = _get_resize_scale((width, height), min_side=min_side, max_side=max_side)
-    new_width, new_height = map(lambda x: int(x * scale), (width, height))
-    data = _read_bgr_data(image.resize((new_width, new_height)))
-    data = _preprocess_image(data)
-    return data, scale
-
-
-FILE_URLS = {
-    "default": {
-        "checkpoint": ('narugo/gchar_models', 'nudenet/baseline/detector_v2_default_checkpoint.onnx'),
-        "classes": ('narugo/gchar_models', 'nudenet/baseline/detector_v2_default_classes'),
-    },
-    "base": {
-        "checkpoint": ('narugo/gchar_models', 'nudenet/baseline/detector_v2_base_checkpoint.onnx'),
-        "classes": ('narugo/gchar_models', 'nudenet/baseline/detector_v2_base_classes'),
-    },
-}
-
-
-@lru_cache()
-def open_model_session(model: str = 'default') -> Tuple[onnxruntime.InferenceSession, List[str]]:
-    ckpt_repo_id, ckpt_repo_file = FILE_URLS[model]['checkpoint']
-    ckpt_file = hf_hub_download(ckpt_repo_id, ckpt_repo_file)
-    classes_repo_id, classes_repo_file = FILE_URLS[model]['classes']
-    classes_file = hf_hub_download(classes_repo_id, classes_repo_file)
-
-    onnx_model = onnxruntime.InferenceSession(ckpt_file)
-    classes = [line.strip() for line in Path(classes_file).read_text().splitlines(keepends=False) if line]
-    return onnx_model, classes
diff --git a/spaces/deepozzzie/chatgpt/app.py b/spaces/deepozzzie/chatgpt/app.py
deleted file mode 100644
index 0239be9a490df6aa09e8951a3561eecaf71662bd..0000000000000000000000000000000000000000
--- a/spaces/deepozzzie/chatgpt/app.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import gradio as gr
-import os
-import time
-
-from langchain.document_loaders import OnlinePDFLoader
-
-from langchain.text_splitter import CharacterTextSplitter
-
-
-from langchain.llms import OpenAI
-
-from langchain.embeddings import OpenAIEmbeddings
-
-
-from langchain.vectorstores import Chroma
-
-from langchain.chains import ConversationalRetrievalChain
-
-def loading_pdf():
-    print("loading_pdf")
-    return "Loading..."
-
-def pdf_changes(pdf_doc, open_ai_key):
-    print("pdf_change")
-    if openai_key is not None:
-        os.environ['OPENAI_API_KEY'] = open_ai_key
-        loader = OnlinePDFLoader(pdf_doc.name)
-        print(loader)
-        documents = loader.load()
-        print(documents)
-        text_splitter = CharacterTextSplitter(chunk_size=2048, chunk_overlap=0)
-        print(text_splitter)
-        texts = text_splitter.split_documents(documents)
-        print(texts)
-        embeddings = OpenAIEmbeddings()
-        print(embeddings)
-        db = Chroma.from_documents(texts, embeddings)
-        print(db)
-        retriever = db.as_retriever()
-        print(retriever)
-        global qa
-        qa = ConversationalRetrievalChain.from_llm(
-            llm=OpenAI(temperature=0.3),
-            retriever=retriever,
-            return_source_documents=False)
-        return "Ready"
-    else:
-        return "You forgot OpenAI API key"
-
-def add_text(history, text):
-    history = history + [(text, None)]
-    print(history)
-    return history, ""
-
-def bot(history):
-    response = infer(history[-1][0], history)
-    history[-1][1] = ""
-
-    for character in response:
-        history[-1][1] += character
-        time.sleep(0.05)
-        yield history
-
-
-def infer(question, history):
-
-    res = []
-    for human, ai in history[:-1]:
-        pair = (human, ai)
-        res.append(pair)
-
-    chat_history = res
-    #print(chat_history)
-    query = question
-    result = qa({"question": query, "chat_history": chat_history})
-    #print(result)
-    print(result["answer"])
-    return result["answer"]
-
-css="""
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-"""
-
-title = """
-<div style="text-align: center;max-width: 700px;">
-    <h1>Chat with PDF • OpenAI</h1>
-    <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF" button, <br />
-    when everything is ready, you can start asking questions about the pdf <br />
-    This version is set to store chat history, and uses OpenAI as LLM, don't forget to copy/paste your OpenAI API key</p>
-</div>
-"""
-
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML(title)
-
-        with gr.Column():
-            openai_key = gr.Textbox(label="You OpenAI API key", type="password")
-            pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
-            with gr.Row():
-                langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
-                load_pdf = gr.Button("Load pdf")
-
-        chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
-        question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
-        submit_btn = gr.Button("Send Message")
-    load_pdf.click(loading_pdf, None, langchain_status, queue=False)
-    load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False)
-    question.submit(add_text, [chatbot, question], [chatbot, question]).then(
-        bot, chatbot, chatbot
-    )
-    submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(
-        bot, chatbot, chatbot)
-
-
-
-demo.launch()
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/actions/write_prd.py b/spaces/deepwisdom/MetaGPT/metagpt/actions/write_prd.py
deleted file mode 100644
index 97f9138fd3fd2a94d8213eed848fd3d23d84ea23..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/actions/write_prd.py
+++ /dev/null
@@ -1,174 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 17:45
-@Author  : alexanderwu
-@File    : write_prd.py
-"""
-from typing import List, Tuple
-
-import aiofiles
-
-from metagpt.actions import Action, ActionOutput
-from metagpt.actions.search_and_summarize import SearchAndSummarize
-from metagpt.config import CONFIG
-from metagpt.logs import logger
-from metagpt.utils.common import CodeParser
-from metagpt.utils.mermaid import mermaid_to_file
-
-PROMPT_TEMPLATE = """
-# Context
-## Original Requirements
-{requirements}
-
-## Search Information
-{search_information}
-
-## mermaid quadrantChart code syntax example. DONT USE QUOTO IN CODE DUE TO INVALID SYNTAX. Replace the <Campain X> with REAL COMPETITOR NAME
-```mermaid
-quadrantChart
-    title Reach and engagement of campaigns
-    x-axis Low Reach --> High Reach
-    y-axis Low Engagement --> High Engagement
-    quadrant-1 We should expand
-    quadrant-2 Need to promote
-    quadrant-3 Re-evaluate
-    quadrant-4 May be improved
-    "Campaign: A": [0.3, 0.6]
-    "Campaign B": [0.45, 0.23]
-    "Campaign C": [0.57, 0.69]
-    "Campaign D": [0.78, 0.34]
-    "Campaign E": [0.40, 0.34]
-    "Campaign F": [0.35, 0.78]
-    "Our Target Product": [0.5, 0.6]
-```
-
-## Format example
-{format_example}
------
-Role: You are a professional product manager; the goal is to design a concise, usable, efficient product
-Requirements: According to the context, fill in the following missing information, note that each sections are returned in Python code triple quote form seperatedly. If the requirements are unclear, ensure minimum viability and avoid excessive design
-ATTENTION: Use '##' to SPLIT SECTIONS, not '#'. AND '## <SECTION_NAME>' SHOULD WRITE BEFORE the code and triple quote. Output carefully referenced "Format example" in format.
-
-## Original Requirements: Provide as Plain text, place the polished complete original requirements here
-
-## Product Goals: Provided as Python list[str], up to 3 clear, orthogonal product goals. If the requirement itself is simple, the goal should also be simple
-
-## User Stories: Provided as Python list[str], up to 5 scenario-based user stories, If the requirement itself is simple, the user stories should also be less
-
-## Competitive Analysis: Provided as Python list[str], up to 7 competitive product analyses, consider as similar competitors as possible
-
-## Competitive Quadrant Chart: Use mermaid quadrantChart code syntax. up to 14 competitive products. Translation: Distribute these competitor scores evenly between 0 and 1, trying to conform to a normal distribution centered around 0.5 as much as possible.
-
-## Requirement Analysis: Provide as Plain text. Be simple. LESS IS MORE. Make your requirements less dumb. Delete the parts unnessasery.
-
-## Requirement Pool: Provided as Python list[str, str], the parameters are requirement description, priority(P0/P1/P2), respectively, comply with PEP standards; no more than 5 requirements and consider to make its difficulty lower
-
-## UI Design draft: Provide as Plain text. Be simple. Describe the elements and functions, also provide a simple style description and layout description.
-## Anything UNCLEAR: Provide as Plain text. Make clear here.
-"""
-FORMAT_EXAMPLE = """
----
-## Original Requirements
-The boss ... 
-
-## Product Goals
-```python
-[
-    "Create a ...",
-]
-```
-
-## User Stories
-```python
-[
-    "As a user, ...",
-]
-```
-
-## Competitive Analysis
-```python
-[
-    "Python Snake Game: ...",
-]
-```
-
-## Competitive Quadrant Chart
-```mermaid
-quadrantChart
-    title Reach and engagement of campaigns
-    ...
-    "Our Target Product": [0.6, 0.7]
-```
-
-## Requirement Analysis
-The product should be a ...
-
-## Requirement Pool
-```python
-[
-    ("End game ...", "P0")
-]
-```
-
-## UI Design draft
-Give a basic function description, and a draft
-
-## Anything UNCLEAR
-There are no unclear points.
----
-"""
-OUTPUT_MAPPING = {
-    "Original Requirements": (str, ...),
-    "Product Goals": (List[str], ...),
-    "User Stories": (List[str], ...),
-    "Competitive Analysis": (List[str], ...),
-    "Competitive Quadrant Chart": (str, ...),
-    "Requirement Analysis": (str, ...),
-    "Requirement Pool": (List[Tuple[str, str]], ...),
-    "UI Design draft": (str, ...),
-    "Anything UNCLEAR": (str, ...),
-}
-
-
-class WritePRD(Action):
-    def __init__(self, name="", context=None, llm=None):
-        super().__init__(name, context, llm)
-
-    async def run(self, requirements, *args, **kwargs) -> ActionOutput:
-        sas = SearchAndSummarize()
-        # rsp = await sas.run(context=requirements, system_text=SEARCH_AND_SUMMARIZE_SYSTEM_EN_US)
-        rsp = ""
-        info = f"### Search Results\n{sas.result}\n\n### Search Summary\n{rsp}"
-        if sas.result:
-            logger.info(sas.result)
-            logger.info(rsp)
-
-        prompt = PROMPT_TEMPLATE.format(
-            requirements=requirements, search_information=info, format_example=FORMAT_EXAMPLE
-        )
-        logger.debug(prompt)
-        prd = await self._aask_v1(prompt, "prd", OUTPUT_MAPPING)
-
-        await self._save(prd.content)
-        return prd
-
-    async def _save_prd(self, docs_path, resources_path, prd):
-        prd_file = docs_path / "prd.md"
-        quadrant_chart = CodeParser.parse_code(block="Competitive Quadrant Chart", text=prd)
-        await mermaid_to_file(
-            mermaid_code=quadrant_chart, output_file_without_suffix=resources_path / "competitive_analysis"
-        )
-        async with aiofiles.open(prd_file, "w") as f:
-            await f.write(prd)
-        logger.info(f"Saving PRD to {prd_file}")
-
-    async def _save(self, prd):
-        workspace = CONFIG.workspace
-        workspace.mkdir(parents=True, exist_ok=True)
-
-        docs_path = workspace / "docs"
-        resources_path = workspace / "resources"
-        docs_path.mkdir(parents=True, exist_ok=True)
-        resources_path.mkdir(parents=True, exist_ok=True)
-        await self._save_prd(docs_path, resources_path, prd)
diff --git a/spaces/derek-thomas/disc-golf-simulator/utilities/visualize.py b/spaces/derek-thomas/disc-golf-simulator/utilities/visualize.py
deleted file mode 100644
index 7c46e28aee8388c40437638aeb629319cae8d023..0000000000000000000000000000000000000000
--- a/spaces/derek-thomas/disc-golf-simulator/utilities/visualize.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import math
-import numpy as np
-import plotly.graph_objects as go
-from pathlib import Path
-from plotly.colors import sequential
-from stl.mesh import Mesh
-
-from .extrema import find_extrema
-
-proj_dir = Path(__file__).parents[1]
-
-# Taken from https://community.plotly.com/t/view-3d-cad-data/16920/9
-stl_meshes = {disc.stem: Mesh.from_file(disc) for disc in (proj_dir / 'shotshaper' / 'discs').glob('*.stl')}
-
-
-def visualize_disc(stl_mesh, nose, roll):
-    """
-    Taken from https://community.plotly.com/t/view-3d-cad-data/16920/9
-    """
-    stl_mesh.rotate([1, 0, 0], math.radians(-1 * nose))
-    stl_mesh.rotate([0, 1, 0], math.radians(roll))
-    # stl_mesh.rotate([0, 0, 1], math.radians(z_angle))
-
-    p, q, r = stl_mesh.vectors.shape  # (p, 3, 3)
-    # the array stl_mesh.vectors.reshape(p*q, r) can contain multiple copies of the same vertex;
-    # extract unique vertices from all mesh triangles
-    vertices, ixr = np.unique(stl_mesh.vectors.reshape(p * q, r), return_inverse=True, axis=0)
-    I = np.take(ixr, [3 * k for k in range(p)])
-    J = np.take(ixr, [3 * k + 1 for k in range(p)])
-    K = np.take(ixr, [3 * k + 2 for k in range(p)])
-
-    x, y, z = vertices.T
-    trace = go.Mesh3d(x=x, y=y, z=z, i=I, j=J, k=K)
-    # optional parameters to make it look nicer
-    trace.update(flatshading=True, lighting_facenormalsepsilon=0, lighting_ambient=0.7)
-
-    fig = go.Figure(trace)
-
-    # Add camera controls to the plot
-    camera = dict(
-            eye=dict(x=0, y=-3, z=0)
-            )
-    fig.update_layout(scene_camera=camera)
-
-    fig.update_layout(
-            scene=dict(
-                    dragmode=False,
-                    xaxis=dict(nticks=4, range=[-0.11, 0.11], ),
-                    yaxis=dict(nticks=4, range=[-0.11, 0.11], ),
-                    zaxis=dict(nticks=4, range=[-0.11, 0.11], ), )
-            )
-    return fig
-
-
-import plotly.subplots as sp
-
-
-def get_plot(x, y, z):
-    xm = np.min(x) - 1.5
-    xM = np.max(x) + 1.5
-    ym = -5
-    yM = np.max(y) + 1.5
-    zm = np.min(z)
-    zM = np.max(z)
-    N = len(x)
-    category = 'Height'
-    x_extrema, y_extrema, extrema_type = find_extrema(x, y)
-
-    xM_abs = max(abs(xm), abs(xM))
-    xm_abs = -1 * xM_abs
-
-    carats_v = go.Scatter(
-            x=[category, category],
-            y=[min(z), max(z)],
-            mode='markers',
-            showlegend=False,
-            marker=dict(symbol=['arrow-up', 'arrow-down'], size=20, color=['red', 'red']),
-            name='Carets',
-            )
-    carats_h = go.Scatter(
-            x=[min(x), max(x)],
-            y=['', ''],
-            mode='markers',
-            showlegend=False,
-            marker=dict(symbol=['arrow-right', 'arrow-left'], size=20, color=['red', 'red']),
-            name='Carets',
-            )
-
-    extrema = go.Scatter(
-            x=x_extrema,
-            y=y_extrema,
-            mode='markers',
-            showlegend=False,
-            marker=dict(symbol=extrema_type, size=20, color='blue'),
-            name='Extrema',
-            )
-
-    # Create figure with subplots
-    fig = sp.make_subplots(rows=2, cols=2, subplot_titles=("Flight Path", "Height (over time)", "Lateral Deviance (left and right motion)"),
-                           specs=[[{"rowspan": 2}, {}], [None, {}]], row_heights=[0.5, 0.5])
-
-    # Add traces to the main plot
-    fig.add_trace(
-            go.Scatter(x=x, y=y,
-                       mode="lines",
-                       showlegend=False,
-                       line=dict(width=1, color='black')),
-            row=1, col=1
-            )
-
-    fig.add_trace(
-            go.Scatter(x=x, y=y,
-                       showlegend=False,
-                       mode="markers", marker_colorscale=sequential.Peach,
-                       marker=dict(color=z, size=3, showscale=True)),
-            row=1, col=1
-            )
-
-    fig.add_trace(
-            extrema,
-            row=1, col=1
-            )
-
-    # Add trace for the subplot
-    fig.add_trace(carats_v,
-                  row=1, col=2
-                  )
-    fig.add_trace(
-            go.Bar(
-                    x=[],
-                    y=[],
-                    showlegend=False,
-                    ),
-            row=1, col=2
-            )
-    # Add trace for the subplot
-    fig.add_trace(carats_h,
-                  row=2, col=2
-                  )
-    fig.add_trace(
-            go.Bar(
-                    x=[],
-                    y=[],
-                    showlegend=False,
-                    orientation='h',
-                    ),
-            row=2, col=2
-            )
-
-    # Update layout
-    fig.update_layout(
-            xaxis=dict(range=[xm, xM], autorange=False, zeroline=False),
-            yaxis=dict(range=[ym, yM], autorange=False, zeroline=False),
-            title_text="Flight Path",
-            hovermode="closest",
-            updatemenus=[
-                dict(
-                        type="buttons",
-                        buttons=[
-                            dict(
-                                    label="Play",
-                                    method="animate",
-                                    args=[None, {"frame": {"duration": 30, "redraw": True}, "fromcurrent": True}]
-                                    ),
-                            dict(
-                                    label="Pause",
-                                    method="animate",
-                                    args=[[None], {"frame": {"duration": 0, "redraw": False}, "mode": "immediate",
-                                                   "transition": {"duration": 0}}]
-                                    )
-                            ],
-                        showactive=False,
-                        x=0.05,
-                        y=0.05
-                        )
-                ],
-            )
-
-    # Create frames for the main plot and subplot
-    all_frames = [
-        go.Frame(data=[
-            go.Scatter(
-                    x=[x[k]],
-                    y=[y[k]],
-                    mode="markers",
-                    showlegend=False,
-                    marker=dict(color="red", size=10)),
-            go.Scatter(x=x, y=y,
-                       mode="markers", marker_colorscale=sequential.Peach,
-                       marker=dict(color=z, size=3, showscale=True)),
-            extrema,
-            carats_v,
-            go.Bar(
-                    x=['Height'],
-                    y=[z[k]],
-                    showlegend=False,
-                    name='Value',
-                    marker=dict(color='orange', line=dict(width=1))
-                    # Set color of value bar to orange and line width to 1
-                    ),
-            carats_h,
-            go.Bar(
-                    x=[x[k]],
-                    y=[''],
-                    showlegend=False,
-                    name='Value',
-                    orientation='h',
-                    marker=dict(color='orange', line=dict(width=1))
-                    # Set color of value bar to orange and line width to 1
-                    )
-            ])
-        for k in range(N)
-        ]
-
-    # Combine frames for the main plot and subplot
-    fig.frames = all_frames
-    fig.update_yaxes(scaleanchor="x", scaleratio=1, row=1, col=1)
-    fig.update_yaxes(range=[zm - 2, zM + 2], fixedrange=True, row=1, col=2)
-    fig.update_xaxes(range=[xm_abs, xM_abs], fixedrange=True, row=2, col=2)
-
-    # # Add green rectangle at the bottom of the plot
-    fig.update_layout(
-            shapes=[dict(type="rect", xref="x", yref="y",
-                         x0=-1, y0=0, x1=1, y1=-4, fillcolor="gray",
-                         opacity=1, layer="below")],
-            plot_bgcolor="green",
-            )
-
-    return fig
-
-
-import plotly.graph_objs as go
-from plotly.subplots import make_subplots
-
-
-def get_subplots(arc, alphas, lifts, drags, moms, rolls, velocity):
-    fig = make_subplots(rows=2, cols=3, specs=[[{}, {}, {}], [{}, {}, {}]],
-                        subplot_titles=("Lift force (N)", "Drag force (N)", "Moment (Nm)",
-                                        "Angle of attack (deg)", "Velocities (m/s)", "Roll rate (rad/s)"),
-                        shared_xaxes=True)
-
-    fig.add_trace(go.Scatter(x=arc, y=lifts, name="Lift force (N)"), row=1, col=1)
-    fig.update_xaxes(title_text="Distance (m)", row=1, col=1)
-    fig.update_yaxes(title_text="Lift force (N)", row=1, col=1)
-
-    fig.add_trace(go.Scatter(x=arc, y=drags, name="Drag force (N)"), row=1, col=2)
-    fig.update_xaxes(title_text="Distance (m)", row=1, col=2)
-    fig.update_yaxes(title_text="Drag force (N)", row=1, col=2)
-
-    fig.add_trace(go.Scatter(x=arc, y=moms, name="Moment (Nm)"), row=1, col=3)
-    fig.update_xaxes(title_text="Distance (m)", row=1, col=3)
-    fig.update_yaxes(title_text="Moment (Nm)", row=1, col=3)
-
-    fig.add_trace(go.Scatter(x=arc, y=alphas, name="Angle of attack (deg)"), row=2, col=1)
-    fig.update_xaxes(title_text="Distance (m)", row=2, col=1)
-    fig.update_yaxes(title_text="Angle of attack (deg)", row=2, col=1)
-
-    fig.add_trace(go.Scatter(x=arc, y=velocity[0, :], name="u"), row=2, col=2)
-    fig.add_trace(go.Scatter(x=arc, y=velocity[1, :], name="v"), row=2, col=2)
-    fig.add_trace(go.Scatter(x=arc, y=velocity[2, :], name="w"), row=2, col=2)
-    fig.update_xaxes(title_text="Distance (m)", row=2, col=2)
-    fig.update_yaxes(title_text="Velocities (m/s)", row=2, col=2)
-    fig.update_traces(mode='lines', row=2, col=2)
-
-    fig.add_trace(go.Scatter(x=arc, y=rolls, name="Roll rate (rad/s)"), row=2, col=3)
-    fig.update_xaxes(title_text="Distance (m)", row=2, col=3)
-    fig.update_yaxes(title_text="Roll rate (rad/s)", row=2, col=3)
-
-    fig.update_layout(height=600, width=1000, title_text="Analysis Subplots", hovermode='x')
-    return fig
diff --git a/spaces/diacanFperku/AutoGPT/Change Language In Paragon Partition Manager 12 [VERIFIED].md b/spaces/diacanFperku/AutoGPT/Change Language In Paragon Partition Manager 12 [VERIFIED].md
deleted file mode 100644
index 29ec8ffd76be116b3d341f0833d61500fe1e3932..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Change Language In Paragon Partition Manager 12 [VERIFIED].md	
+++ /dev/null
@@ -1,52 +0,0 @@
-<h2>change language in paragon partition manager 12</h2><br /><p><b><b>Download</b> &#9745; <a href="https://gohhs.com/2uFTxM">https://gohhs.com/2uFTxM</a></b></p><br /><br />
-
- . . including managing the disk space.
-
-Learn how to easily move partitions from one hard drive to another.Ferrari 488
-
-The Ferrari 488 is a sports car which is produced by the Italian automobile manufacturer Ferrari. The 488 is the replacement for the 488 Spider, which in turn replaced the 458 Spider. It is the successor to the 488 GTB and the Ferrari 488 GTE.
-
-Development 
-
-The car was first unveiled at the 2017 Frankfurt Motor Show as a replacement to the 458 Spider, the open top version of the 458 Italia. It has a  carbon fibre chassis, rear-wheel-drive layout with a revised version of the 675 hp (512 kW) twin-turbocharged V8 from the 458 and the carbon fibre body which weighs  less than the 488 GTB. It is fitted with a new-generation Ferrari Dynamic Activation system (FDAC). The 488 is also the first Ferrari sports car to be fitted with the   G-Class Transmission.
-
-Launch
-
-The 488 was unveiled at the 2017 Frankfurt Motor Show on 7 September 2017 as a replacement for the 458 Spider. It will be available to customers in late 2017 with prices from €222,900 (UK) and €237,900 (US).
-
-The first customer delivery took place in Monaco on 27 November 2017. The first public launch was in St. Petersburg, Russia on 3 December 2017.
-
-Performance
-
-The 488 produces  of torque and accelerates from  to  in 2.8 seconds. Top speed is.
-
-The 488 GTE race car was entered in the 2018 24 Hours of Le Mans with three drivers: Dries Vanthoor, Maxime Martin and Alessandro Pier Guidi. It finished the race in 4th place.
-
-Gallery
-
-Notes
-
-References
-
-External links
-
- 
-
-488
-
-Category:Sports cars
-
-Category:Rear mid-engine, rear-wheel-drive vehicles
-
-Category:Cars introduced in 2017
-
-Category:Coupés
-
-Category:Grand tourersQ:
-
-How do I declare an array of ints and manipulate the values in C#?
-
-The basic task is to get a user input integer and then have a while loop that asks for more integers until user enters -1. I think my code is fairly close, but I'm not really sure about how to declare int[] number = new int 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Ecologia Evolutiva Pianka 73.pdf.md b/spaces/diacanFperku/AutoGPT/Ecologia Evolutiva Pianka 73.pdf.md
deleted file mode 100644
index ed0420acf545e8971eb4bc7e67fac7f85868ebcb..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Ecologia Evolutiva Pianka 73.pdf.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Ecologia Evolutiva Pianka 73.pdf</h2><br /><p><b><b>DOWNLOAD</b> &#127379; <a href="https://gohhs.com/2uFUnH">https://gohhs.com/2uFUnH</a></b></p><br /><br />
-<br />
-all efforts from my part to help in the computer lab of the school, and this for our school dont forget to give a piece of chocolate :) give free chocolate to my friend, and don't take one person here a lot is necessary. I tried downloading this for both windows and mac as well as for vista and windows 7, downloaded it to my desktop, started it up, but couldn't do anything with it. There is no concrete definition of "Mac" software. How to Create a Navigation Menu in Microsoft Word. This is a duplicate of the page:  html or  To manage the certificates, you will need to use your computer's browser to navigate to the website of the company which is issuing the certificate and download the private key of the certificate and the public key. This is a duplicate of the page:  * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diagaiwei/ir_chinese_medqa/utility/evaluate/annotate_EM_helpers.py b/spaces/diagaiwei/ir_chinese_medqa/utility/evaluate/annotate_EM_helpers.py
deleted file mode 100644
index b0d357ac4e8b47629604af9b4e6db0a088a3bed1..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/utility/evaluate/annotate_EM_helpers.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from colbert.utils.utils import print_message
-from utility.utils.dpr import DPR_normalize, has_answer
-
-
-def tokenize_all_answers(args):
-    qid, question, answers = args
-    return qid, question, [DPR_normalize(ans) for ans in answers]
-
-
-def assign_label_to_passage(args):
-    idx, (qid, pid, rank, passage, tokenized_answers) = args
-
-    if idx % (1*1000*1000) == 0:
-        print(idx)
-
-    return qid, pid, rank, has_answer(tokenized_answers, passage)
-
-
-def check_sizes(qid2answers, qid2rankings):
-    num_judged_queries = len(qid2answers)
-    num_ranked_queries = len(qid2rankings)
-
-    print_message('num_judged_queries =', num_judged_queries)
-    print_message('num_ranked_queries =', num_ranked_queries)
-
-    if num_judged_queries != num_ranked_queries:
-        assert num_ranked_queries <= num_judged_queries
-
-        print('\n\n')
-        print_message('[WARNING] num_judged_queries != num_ranked_queries')
-        print('\n\n')
-    
-    return num_judged_queries, num_ranked_queries
-
-
-def compute_and_write_labels(output_path, qid2answers, qid2rankings):
-    cutoffs = [1, 5, 10, 20, 30, 50, 100, 1000, 'all']
-    success = {cutoff: 0.0 for cutoff in cutoffs}
-    counts = {cutoff: 0.0 for cutoff in cutoffs}
-
-    with open(output_path, 'w') as f:
-        for qid in qid2answers:
-            if qid not in qid2rankings:
-                continue
-
-            prev_rank = 0  # ranks should start at one (i.e., and not zero)
-            labels = []
-
-            for pid, rank, label in qid2rankings[qid]:
-                assert rank == prev_rank+1, (qid, pid, (prev_rank, rank))
-                prev_rank = rank
-
-                labels.append(label)
-                line = '\t'.join(map(str, [qid, pid, rank, int(label)])) + '\n'
-                f.write(line)
-
-            for cutoff in cutoffs:
-                if cutoff != 'all':
-                    success[cutoff] += sum(labels[:cutoff]) > 0
-                    counts[cutoff] += sum(labels[:cutoff])
-                else:
-                    success[cutoff] += sum(labels) > 0
-                    counts[cutoff] += sum(labels)
-
-    return success, counts
-
-
-# def dump_metrics(f, nqueries, cutoffs, success, counts):
-#     for cutoff in cutoffs:
-#         success_log = "#> P@{} = {}".format(cutoff, success[cutoff] / nqueries)
-#         counts_log = "#> D@{} = {}".format(cutoff, counts[cutoff] / nqueries)
-#         print('\n'.join([success_log, counts_log]) + '\n')
-
-#         f.write('\n'.join([success_log, counts_log]) + '\n\n')
diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/README.md b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/README.md
deleted file mode 100644
index 87e2ad5f80a408f869d609e8b8a43de8442caf46..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 步非烟 Ver.a
-emoji: 🌟
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/short_audio_transcribe.py b/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/short_audio_transcribe.py
deleted file mode 100644
index f1e8b30671f2c2f2fa3c93feb1f4edd3fbe2f545..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/short_audio_transcribe.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import whisper
-import os
-import json
-import torchaudio
-import argparse
-import torch
-
-lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-def transcribe_one(audio_path):
-    # load audio and pad/trim it to fit 30 seconds
-    audio = whisper.load_audio(audio_path)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
-    # detect the spoken language
-    _, probs = model.detect_language(mel)
-    print(f"Detected language: {max(probs, key=probs.get)}")
-    lang = max(probs, key=probs.get)
-    # decode the audio
-    options = whisper.DecodingOptions(beam_size=5)
-    result = whisper.decode(model, mel, options)
-
-    # print the recognized text
-    print(result.text)
-    return lang, result.text
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--languages", default="CJE")
-    parser.add_argument("--whisper_size", default="medium")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-    elif args.languages == "CJ":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-        }
-    elif args.languages == "C":
-        lang2token = {
-            'zh': "[ZH]",
-        }
-    assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
-    model = whisper.load_model(args.whisper_size)
-    parent_dir = "./custom_character_voice/"
-    speaker_names = list(os.walk(parent_dir))[0][1]
-    speaker_annos = []
-    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
-    # resample audios
-    # 2023/4/21: Get the target sampling rate
-    with open("./configs/config.json", 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-    target_sr = hps['data']['sampling_rate']
-    processed_files = 0
-    for speaker in speaker_names:
-        for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
-            # try to load file as audio
-            if wavfile.startswith("processed_"):
-                continue
-            try:
-                wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
-                                          channels_first=True)
-                wav = wav.mean(dim=0).unsqueeze(0)
-                if sr != target_sr:
-                    wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
-                if wav.shape[1] / sr > 20:
-                    print(f"{wavfile} too long, ignoring\n")
-                save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
-                torchaudio.save(save_path, wav, target_sr, channels_first=True)
-                # transcribe text
-                lang, text = transcribe_one(save_path)
-                if lang not in list(lang2token.keys()):
-                    print(f"{lang} not supported, ignoring\n")
-                    continue
-                text = "ZH|" + text + "\n"#
-                #text = lang2token[lang] + text + lang2token[lang] + "\n"
-                speaker_annos.append(save_path + "|" + speaker + "|" + text)
-                
-                processed_files += 1
-                print(f"Processed: {processed_files}/{total_files}")
-            except:
-                continue
-
-    # # clean annotation
-    # import argparse
-    # import text
-    # from utils import load_filepaths_and_text
-    # for i, line in enumerate(speaker_annos):
-    #     path, sid, txt = line.split("|")
-    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
-    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
-    # write into annotation
-    if len(speaker_annos) == 0:
-        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
-        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
-    with open("./filelists/short_character_anno.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-
-    # import json
-    # # generate new config
-    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-    #     hps = json.load(f)
-    # # modify n_speakers
-    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
-    # # add speaker names
-    # for speaker in speaker_names:
-    #     hps['speakers'][speaker] = speaker2id[speaker]
-    # # save modified config
-    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-    #     json.dump(hps, f, indent=2)
-    # print("finished")
diff --git a/spaces/digitalxingtong/Lixiang-Bert-Vits2/text/english_bert_mock.py b/spaces/digitalxingtong/Lixiang-Bert-Vits2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Lixiang-Bert-Vits2/text/english_bert_mock.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-
-
-def get_bert_feature(norm_text, word2ph):
-    return torch.zeros(1024, sum(word2ph))
diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/monotonic_align/core.c b/spaces/digitalxingtong/Nailv-Bert-Vits2/monotonic_align/core.c
deleted file mode 100644
index 5f8af54d32474f821e9d1f4d2679d78128722596..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/monotonic_align/core.c
+++ /dev/null
@@ -1,26530 +0,0 @@
-/* Generated by Cython 3.0.0 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "name": "monotonic_align.core",
-        "sources": [
-            "core.pyx"
-        ]
-    },
-    "module_name": "monotonic_align.core"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#if defined(CYTHON_LIMITED_API) && 0
-  #ifndef Py_LIMITED_API
-    #if CYTHON_LIMITED_API+0 > 0x03030000
-      #define Py_LIMITED_API CYTHON_LIMITED_API
-    #else
-      #define Py_LIMITED_API 0x03030000
-    #endif
-  #endif
-#endif
-
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
-    #error Cython requires Python 2.7+ or Python 3.3+.
-#else
-#define CYTHON_ABI "3_0_0"
-#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI
-#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "."
-#define CYTHON_HEX_VERSION 0x030000F0
-#define CYTHON_FUTURE_DIVISION 1
-#include <stddef.h>
-#ifndef offsetof
-  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
-  #define HAVE_LONG_LONG
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#if defined(GRAALVM_PYTHON)
-  /* For very preliminary testing purposes. Most variables are set the same as PyPy.
-     The existence of this section does not imply that anything works or is even tested */
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 1
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PYPY_VERSION)
-  #define CYTHON_COMPILING_IN_PYPY 1
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #if PY_VERSION_HEX < 0x03090000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00)
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(CYTHON_LIMITED_API)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 1
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_CLINE_IN_TRACEBACK
-  #define CYTHON_CLINE_IN_TRACEBACK 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 1
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #endif
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 1
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PY_NOGIL)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 1
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #ifndef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_TYPE_SPECS
-    #define CYTHON_USE_TYPE_SPECS 0
-  #endif
-  #ifndef CYTHON_USE_PYTYPE_LOOKUP
-    #define CYTHON_USE_PYTYPE_LOOKUP 1
-  #endif
-  #if PY_MAJOR_VERSION < 3
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_PYLONG_INTERNALS
-    #define CYTHON_USE_PYLONG_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
-    #undef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #elif !defined(CYTHON_USE_UNICODE_WRITER)
-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #ifndef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_GIL
-    #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6)
-  #endif
-  #ifndef CYTHON_METH_FASTCALL
-    #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1)
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL 1
-  #endif
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_MODULE_STATE
-    #define CYTHON_USE_MODULE_STATE 0
-  #endif
-  #if PY_VERSION_HEX < 0x030400a1
-    #undef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 0
-  #elif !defined(CYTHON_USE_TP_FINALIZE)
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #if PY_VERSION_HEX < 0x030600B1
-    #undef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS 0
-  #elif !defined(CYTHON_USE_DICT_VERSIONS)
-    #define CYTHON_USE_DICT_VERSIONS  (PY_VERSION_HEX < 0x030C00A5)
-  #endif
-  #if PY_VERSION_HEX < 0x030700A3
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK 1
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if !defined(CYTHON_VECTORCALL)
-#define CYTHON_VECTORCALL  (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1)
-#endif
-#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1)
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(maybe_unused)
-        #define CYTHON_UNUSED [[maybe_unused]]
-      #endif
-    #endif
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-  #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x)
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-            typedef unsigned char     uint8_t;
-            typedef unsigned short    uint16_t;
-            typedef unsigned int      uint32_t;
-        #else
-            typedef unsigned __int8   uint8_t;
-            typedef unsigned __int16  uint16_t;
-            typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-    #if _MSC_VER < 1300
-        #ifdef _WIN64
-            typedef unsigned long long  __pyx_uintptr_t;
-        #else
-            typedef unsigned int        __pyx_uintptr_t;
-        #endif
-    #else
-        #ifdef _WIN64
-            typedef unsigned __int64    __pyx_uintptr_t;
-        #else
-            typedef unsigned __int32    __pyx_uintptr_t;
-        #endif
-    #endif
-#else
-    #include <stdint.h>
-    typedef uintptr_t  __pyx_uintptr_t;
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(fallthrough) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(fallthrough)
-        #define CYTHON_FALLTHROUGH [[fallthrough]]
-      #endif
-    #endif
-    #ifndef CYTHON_FALLTHROUGH
-      #if __has_cpp_attribute(clang::fallthrough)
-        #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-      #elif __has_cpp_attribute(gnu::fallthrough)
-        #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-      #endif
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-#ifdef __cplusplus
-  template <typename T>
-  struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);};
-  #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL<type>::value)
-#else
-  #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0)
-#endif
-#if CYTHON_COMPILING_IN_PYPY == 1
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000)
-#else
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000)
-#endif
-#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer))
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_DefaultClassType PyClass_Type
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject *co=NULL, *result=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(p))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end;
-        if (!(empty = PyTuple_New(0))) goto end;
-        result = (PyCodeObject*) PyObject_Call(replace, empty, kwds);
-    end:
-        Py_XDECREF((PyObject*) co);
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return result;
-    }
-#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-#endif
-#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE)
-  #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type)
-#else
-  #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is)
-  #define __Pyx_Py_Is(x, y)  Py_Is(x, y)
-#else
-  #define __Pyx_Py_Is(x, y) ((x) == (y))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone)
-  #define __Pyx_Py_IsNone(ob) Py_IsNone(ob)
-#else
-  #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue)
-  #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob)
-#else
-  #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse)
-  #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob)
-#else
-  #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False)
-#endif
-#define __Pyx_NoneAsNull(obj)  (__Pyx_Py_IsNone(obj) ? NULL : (obj))
-#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o)
-#else
-  #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o)
-#endif
-#ifndef CO_COROUTINE
-  #define CO_COROUTINE 0x80
-#endif
-#ifndef CO_ASYNC_GENERATOR
-  #define CO_ASYNC_GENERATOR 0x200
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef Py_TPFLAGS_SEQUENCE
-  #define Py_TPFLAGS_SEQUENCE 0
-#endif
-#ifndef Py_TPFLAGS_MAPPING
-  #define Py_TPFLAGS_MAPPING 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_METH_FASTCALL
-  #define __Pyx_METH_FASTCALL METH_FASTCALL
-  #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast
-  #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords
-#else
-  #define __Pyx_METH_FASTCALL METH_VARARGS
-  #define __Pyx_PyCFunction_FastCall PyCFunction
-  #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords
-#endif
-#if CYTHON_VECTORCALL
-  #define __pyx_vectorcallfunc vectorcallfunc
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  PY_VECTORCALL_ARGUMENTS_OFFSET
-  #define __Pyx_PyVectorcall_NARGS(n)  PyVectorcall_NARGS((size_t)(n))
-#elif CYTHON_BACKPORT_VECTORCALL
-  typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args,
-                                            size_t nargsf, PyObject *kwnames);
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  ((size_t)1 << (8 * sizeof(size_t) - 1))
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET))
-#else
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  0
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(n))
-#endif
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  ((void)m, PyType_FromSpecWithBases(s, b))
-  typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *);
-#else
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  PyType_FromModuleAndSpec(m, s, b)
-  #define __Pyx_PyCMethod  PyCMethod
-#endif
-#ifndef METH_METHOD
-  #define METH_METHOD 0x200
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
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-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u)
-{
-    const wchar_t *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#else
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
-{
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#endif
-#define __Pyx_PyUnicode_FromOrdinal(o)       PyUnicode_FromOrdinal((int)o)
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_VERSION_HEX >= 0x030C00A7
-  #ifndef _PyLong_SIGN_MASK
-    #define _PyLong_SIGN_MASK 3
-  #endif
-  #ifndef _PyLong_NON_SIZE_BITS
-    #define _PyLong_NON_SIZE_BITS 3
-  #endif
-  #define __Pyx_PyLong_Sign(x)  (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK)
-  #define __Pyx_PyLong_IsNeg(x)  ((__Pyx_PyLong_Sign(x) & 2) != 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (!__Pyx_PyLong_IsNeg(x))
-  #define __Pyx_PyLong_IsZero(x)  (__Pyx_PyLong_Sign(x) & 1)
-  #define __Pyx_PyLong_IsPos(x)  (__Pyx_PyLong_Sign(x) == 0)
-  #define __Pyx_PyLong_CompactValueUnsigned(x)  (__Pyx_PyLong_Digits(x)[0])
-  #define __Pyx_PyLong_DigitCount(x)  ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS))
-  #define __Pyx_PyLong_SignedDigitCount(x)\
-        ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x))
-  #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue)
-    #define __Pyx_PyLong_IsCompact(x)     PyUnstable_Long_IsCompact((PyLongObject*) x)
-    #define __Pyx_PyLong_CompactValue(x)  PyUnstable_Long_CompactValue((PyLongObject*) x)
-  #else
-    #define __Pyx_PyLong_IsCompact(x)     (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS))
-    #define __Pyx_PyLong_CompactValue(x)  ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0])
-  #endif
-  typedef Py_ssize_t  __Pyx_compact_pylong;
-  typedef size_t  __Pyx_compact_upylong;
-  #else  // Py < 3.12
-  #define __Pyx_PyLong_IsNeg(x)  (Py_SIZE(x) < 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (Py_SIZE(x) >= 0)
-  #define __Pyx_PyLong_IsZero(x)  (Py_SIZE(x) == 0)
-  #define __Pyx_PyLong_IsPos(x)  (Py_SIZE(x) > 0)
-  #define __Pyx_PyLong_CompactValueUnsigned(x)  ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0])
-  #define __Pyx_PyLong_DigitCount(x)  __Pyx_sst_abs(Py_SIZE(x))
-  #define __Pyx_PyLong_SignedDigitCount(x)  Py_SIZE(x)
-  #define __Pyx_PyLong_IsCompact(x)  (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1)
-  #define __Pyx_PyLong_CompactValue(x)\
-        ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0]))
-  typedef sdigit  __Pyx_compact_pylong;
-  typedef digit  __Pyx_compact_upylong;
-  #endif
-  #if PY_VERSION_HEX >= 0x030C00A5
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->long_value.ob_digit)
-  #else
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->ob_digit)
-  #endif
-#endif
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = (char) c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-#if !CYTHON_USE_MODULE_STATE
-static PyObject *__pyx_m = NULL;
-#endif
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm = __FILE__;
-static const char *__pyx_filename;
-
-/* #### Code section: filename_table ### */
-
-static const char *__pyx_f[] = {
-  "core.pyx",
-  "<stringsource>",
-};
-/* #### Code section: utility_code_proto_before_types ### */
-/* ForceInitThreads.proto */
-#ifndef __PYX_FORCE_INIT_THREADS
-  #define __PYX_FORCE_INIT_THREADS 0
-#endif
-
-/* NoFastGil.proto */
-#define __Pyx_PyGILState_Ensure PyGILState_Ensure
-#define __Pyx_PyGILState_Release PyGILState_Release
-#define __Pyx_FastGIL_Remember()
-#define __Pyx_FastGIL_Forget()
-#define __Pyx_FastGilFuncInit()
-
-/* BufferFormatStructs.proto */
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
-  const char* name;
-  struct __Pyx_StructField_* fields;
-  size_t size;
-  size_t arraysize[8];
-  int ndim;
-  char typegroup;
-  char is_unsigned;
-  int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
-  size_t offset;
-} __Pyx_StructField;
-typedef struct {
-  __Pyx_StructField* field;
-  size_t parent_offset;
-} __Pyx_BufFmt_StackElem;
-typedef struct {
-  __Pyx_StructField root;
-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
-  size_t new_count, enc_count;
-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-/* Atomics.proto */
-#include <pythread.h>
-#ifndef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 1
-#endif
-#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS
-#define __pyx_atomic_int_type int
-#define __pyx_nonatomic_int_type int
-#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\
-                        (__STDC_VERSION__ >= 201112L) &&\
-                        !defined(__STDC_NO_ATOMICS__))
-    #include <stdatomic.h>
-#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\
-                    (__cplusplus >= 201103L) ||\
-                    (defined(_MSC_VER) && _MSC_VER >= 1700)))
-    #include <atomic>
-#endif
-#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\
-                        (__STDC_VERSION__ >= 201112L) &&\
-                        !defined(__STDC_NO_ATOMICS__) &&\
-                       ATOMIC_INT_LOCK_FREE == 2)
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type atomic_int
-    #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed)
-    #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel)
-    #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER)
-        #pragma message ("Using standard C atomics")
-    #elif defined(__PYX_DEBUG_ATOMICS)
-        #warning "Using standard C atomics"
-    #endif
-#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\
-                    (__cplusplus >= 201103L) ||\
-\
-                    (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\
-                    ATOMIC_INT_LOCK_FREE == 2)
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type std::atomic_int
-    #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed)
-    #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel)
-    #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER)
-        #pragma message ("Using standard C++ atomics")
-    #elif defined(__PYX_DEBUG_ATOMICS)
-        #warning "Using standard C++ atomics"
-    #endif
-#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\
-                    (__GNUC_MINOR__ > 1 ||\
-                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2))))
-    #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1)
-    #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using GNU atomics"
-    #endif
-#elif CYTHON_ATOMICS && defined(_MSC_VER)
-    #include <intrin.h>
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type long
-    #define __pyx_nonatomic_int_type long
-    #pragma intrinsic (_InterlockedExchangeAdd)
-    #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1)
-    #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #pragma message ("Using MSVC atomics")
-    #endif
-#else
-    #undef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 0
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Not using atomics"
-    #endif
-#endif
-#if CYTHON_ATOMICS
-    #define __pyx_add_acquisition_count(memview)\
-             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview))
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview))
-#else
-    #define __pyx_add_acquisition_count(memview)\
-            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-#endif
-
-/* MemviewSliceStruct.proto */
-struct __pyx_memoryview_obj;
-typedef struct {
-  struct __pyx_memoryview_obj *memview;
-  char *data;
-  Py_ssize_t shape[8];
-  Py_ssize_t strides[8];
-  Py_ssize_t suboffsets[8];
-} __Pyx_memviewslice;
-#define __Pyx_MemoryView_Len(m)  (m.shape[0])
-
-/* #### Code section: numeric_typedefs ### */
-/* #### Code section: complex_type_declarations ### */
-/* #### Code section: type_declarations ### */
-
-/*--- Type declarations ---*/
-struct __pyx_array_obj;
-struct __pyx_MemviewEnum_obj;
-struct __pyx_memoryview_obj;
-struct __pyx_memoryviewslice_obj;
-struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each;
-
-/* "monotonic_align/core.pyx":7
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
- *   cdef int x
- *   cdef int y
- */
-struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each {
-  int __pyx_n;
-  float max_neg_val;
-};
-
-/* "View.MemoryView":114
- * @cython.collection_type("sequence")
- * @cname("__pyx_array")
- * cdef class array:             # <<<<<<<<<<<<<<
- * 
- *     cdef:
- */
-struct __pyx_array_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_array *__pyx_vtab;
-  char *data;
-  Py_ssize_t len;
-  char *format;
-  int ndim;
-  Py_ssize_t *_shape;
-  Py_ssize_t *_strides;
-  Py_ssize_t itemsize;
-  PyObject *mode;
-  PyObject *_format;
-  void (*callback_free_data)(void *);
-  int free_data;
-  int dtype_is_object;
-};
-
-
-/* "View.MemoryView":302
- * 
- * @cname('__pyx_MemviewEnum')
- * cdef class Enum(object):             # <<<<<<<<<<<<<<
- *     cdef object name
- *     def __init__(self, name):
- */
-struct __pyx_MemviewEnum_obj {
-  PyObject_HEAD
-  PyObject *name;
-};
-
-
-/* "View.MemoryView":337
- * 
- * @cname('__pyx_memoryview')
- * cdef class memoryview:             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-struct __pyx_memoryview_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
-  PyObject *obj;
-  PyObject *_size;
-  PyObject *_array_interface;
-  PyThread_type_lock lock;
-  __pyx_atomic_int_type acquisition_count;
-  Py_buffer view;
-  int flags;
-  int dtype_is_object;
-  __Pyx_TypeInfo *typeinfo;
-};
-
-
-/* "View.MemoryView":952
- * @cython.collection_type("sequence")
- * @cname('__pyx_memoryviewslice')
- * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
- *     "Internal class for passing memoryview slices to Python"
- * 
- */
-struct __pyx_memoryviewslice_obj {
-  struct __pyx_memoryview_obj __pyx_base;
-  __Pyx_memviewslice from_slice;
-  PyObject *from_object;
-  PyObject *(*to_object_func)(char *);
-  int (*to_dtype_func)(char *, PyObject *);
-};
-
-
-
-/* "View.MemoryView":114
- * @cython.collection_type("sequence")
- * @cname("__pyx_array")
- * cdef class array:             # <<<<<<<<<<<<<<
- * 
- *     cdef:
- */
-
-struct __pyx_vtabstruct_array {
-  PyObject *(*get_memview)(struct __pyx_array_obj *);
-};
-static struct __pyx_vtabstruct_array *__pyx_vtabptr_array;
-
-
-/* "View.MemoryView":337
- * 
- * @cname('__pyx_memoryview')
- * cdef class memoryview:             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-
-struct __pyx_vtabstruct_memoryview {
-  char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
-  PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
-  PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);
-  PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);
-  PyObject *(*_get_base)(struct __pyx_memoryview_obj *);
-};
-static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview;
-
-
-/* "View.MemoryView":952
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-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
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-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
-        (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
-        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
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-#define __Pyx_GetModuleGlobalNameUncached(var, name)  do {\
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-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
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-/* AssertionsEnabled.proto */
-#define __Pyx_init_assertions_enabled()
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
-  #define __pyx_assertions_enabled() (1)
-#elif PY_VERSION_HEX < 0x03080000  ||  CYTHON_COMPILING_IN_PYPY  ||  defined(Py_LIMITED_API)
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6
-  static int __pyx_assertions_enabled_flag;
-  #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag)
-  #undef __Pyx_init_assertions_enabled
-  static void __Pyx_init_assertions_enabled(void) {
-    __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level;
-  }
-#else
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#endif
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* RaiseNoneIterError.proto */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
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-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
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-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
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-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
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-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* ImportDottedModule.proto */
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple);
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple);
-#endif
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-/* ssize_strlen.proto */
-static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2))
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyErr_ExceptionMatches2(err1, err2)  __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2)
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-/* ListCompAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
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-    return PyList_Append(list, x);
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-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
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-/* PySequenceMultiply.proto */
-#define __Pyx_PySequence_Multiply_Left(mul, seq)  __Pyx_PySequence_Multiply(seq, mul)
-static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul);
-
-/* SetItemInt.proto */
-#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\
-               __Pyx_SetItemInt_Generic(o, to_py_func(i), v)))
-static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
-static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
-                                               int is_list, int wraparound, int boundscheck);
-
-/* RaiseUnboundLocalError.proto */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-/* DivInt[long].proto */
-static CYTHON_INLINE long __Pyx_div_long(long, long);
-
-/* PySequenceContains.proto */
-static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
-    int result = PySequence_Contains(seq, item);
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* HasAttr.proto */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
-
-/* ErrOccurredWithGIL.proto */
-static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void);
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* PyObject_GenericGetAttr.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
-#endif
-
-/* IncludeStructmemberH.proto */
-#include <structmember.h>
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-/* FixUpExtensionType.proto */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type);
-#endif
-
-/* PyObjectCallNoArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-
-/* PyObjectGetMethod.proto */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method);
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-/* PyObjectCallMethod0.proto */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name);
-
-/* ValidateBasesTuple.proto */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases);
-#endif
-
-/* PyType_Ready.proto */
-CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t);
-
-/* SetVTable.proto */
-static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable);
-
-/* GetVTable.proto */
-static void* __Pyx_GetVtable(PyTypeObject *type);
-
-/* MergeVTables.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_MergeVtables(PyTypeObject *type);
-#endif
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-/* SetupReduce.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_setup_reduce(PyObject* type_obj);
-#endif
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-/* FetchSharedCythonModule.proto */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void);
-
-/* FetchCommonType.proto */
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-#else
-static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases);
-#endif
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-/* PyMethodNew.proto */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
-    CYTHON_UNUSED_VAR(typ);
-    if (!self)
-        return __Pyx_NewRef(func);
-    return PyMethod_New(func, self);
-}
-#else
-    #define __Pyx_PyMethod_New PyMethod_New
-#endif
-
-/* PyVectorcallFastCallDict.proto */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw);
-#endif
-
-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CYFUNCTION_COROUTINE     0x08
-#define __Pyx_CyFunction_GetClosure(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_closure)
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#else
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      ((PyObject*) ((PyCMethodObject *) (f))->mm_class)
-#endif
-#define __Pyx_CyFunction_SetClassObj(f, classobj)\
-    __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj))
-#define __Pyx_CyFunction_Defaults(type, f)\
-    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
-    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
-typedef struct {
-#if PY_VERSION_HEX < 0x030900B1
-    PyCFunctionObject func;
-#else
-    PyCMethodObject func;
-#endif
-#if CYTHON_BACKPORT_VECTORCALL
-    __pyx_vectorcallfunc func_vectorcall;
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    PyObject *func_weakreflist;
-#endif
-    PyObject *func_dict;
-    PyObject *func_name;
-    PyObject *func_qualname;
-    PyObject *func_doc;
-    PyObject *func_globals;
-    PyObject *func_code;
-    PyObject *func_closure;
-#if PY_VERSION_HEX < 0x030900B1
-    PyObject *func_classobj;
-#endif
-    void *defaults;
-    int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
-    int flags;
-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-    PyObject *func_is_coroutine;
-} __pyx_CyFunctionObject;
-#define __Pyx_CyFunction_Check(obj)  __Pyx_TypeCheck(obj, __pyx_CyFunctionType)
-#define __Pyx_IsCyOrPyCFunction(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
-#define __Pyx_CyFunction_CheckExact(obj)  __Pyx_IS_TYPE(obj, __pyx_CyFunctionType)
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
-                                                            PyObject *tuple);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
-                                                             PyObject *dict);
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
-                                                              PyObject *dict);
-static int __pyx_CyFunction_init(PyObject *module);
-#if CYTHON_METH_FASTCALL
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-#if CYTHON_BACKPORT_VECTORCALL
-#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall)
-#else
-#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall)
-#endif
-#endif
-
-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-#endif
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
-
-
-/* BufferStructDeclare.proto */
-typedef struct {
-  Py_ssize_t shape, strides, suboffsets;
-} __Pyx_Buf_DimInfo;
-typedef struct {
-  size_t refcount;
-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
-  __Pyx_Buffer *rcbuffer;
-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
-
-/* MemviewSliceIsContig.proto */
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
-
-/* OverlappingSlices.proto */
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                                __Pyx_memviewslice *slice2,
-                                int ndim, size_t itemsize);
-
-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
-
-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type);
-
-/* TypeInfoCompare.proto */
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-/* MemviewSliceValidateAndInit.proto */
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag);
-
-/* MemviewSliceCopyTemplate.proto */
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object);
-
-/* MemviewSliceInit.proto */
-#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d
-#define __Pyx_MEMVIEW_DIRECT   1
-#define __Pyx_MEMVIEW_PTR      2
-#define __Pyx_MEMVIEW_FULL     4
-#define __Pyx_MEMVIEW_CONTIG   8
-#define __Pyx_MEMVIEW_STRIDED  16
-#define __Pyx_MEMVIEW_FOLLOW   32
-#define __Pyx_IS_C_CONTIG 1
-#define __Pyx_IS_F_CONTIG 2
-static int __Pyx_init_memviewslice(
-                struct __pyx_memoryview_obj *memview,
-                int ndim,
-                __Pyx_memviewslice *memviewslice,
-                int memview_is_new_reference);
-static CYTHON_INLINE int __pyx_add_acquisition_count_locked(
-    __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock);
-static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(
-    __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock);
-#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count)
-#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__)
-#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__)
-static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int);
-static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-/* FormatTypeName.proto */
-#if CYTHON_COMPILING_IN_LIMITED_API
-typedef PyObject *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%U"
-static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp);
-#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj)
-#else
-typedef const char *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%.200s"
-#define __Pyx_PyType_GetName(tp) ((tp)->tp_name)
-#define __Pyx_DECREF_TypeName(obj)
-#endif
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-/* #### Code section: module_declarations ### */
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/
-
-/* Module declarations from "cython.view" */
-
-/* Module declarations from "cython.dataclasses" */
-
-/* Module declarations from "cython" */
-
-/* Module declarations from "monotonic_align.core" */
-static PyObject *__pyx_collections_abc_Sequence = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static int __pyx_memoryview_thread_locks_used;
-static PyThread_type_lock __pyx_memoryview_thread_locks[8];
-static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/
-static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/
-static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/
-static int __pyx_memoryview_err_no_memory(void); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
-/* #### Code section: typeinfo ### */
-static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 };
-static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };
-/* #### Code section: before_global_var ### */
-#define __Pyx_MODULE_NAME "monotonic_align.core"
-extern int __pyx_module_is_main_monotonic_align__core;
-int __pyx_module_is_main_monotonic_align__core = 0;
-
-/* Implementation of "monotonic_align.core" */
-/* #### Code section: global_var ### */
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin___import__;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_AssertionError;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-/* #### Code section: string_decls ### */
-static const char __pyx_k_[] = ": ";
-static const char __pyx_k_O[] = "O";
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k__2[] = ".";
-static const char __pyx_k__3[] = "*";
-static const char __pyx_k__6[] = "'";
-static const char __pyx_k__7[] = ")";
-static const char __pyx_k_gc[] = "gc";
-static const char __pyx_k_id[] = "id";
-static const char __pyx_k__23[] = "?";
-static const char __pyx_k_abc[] = "abc";
-static const char __pyx_k_and[] = " and ";
-static const char __pyx_k_got[] = " (got ";
-static const char __pyx_k_new[] = "__new__";
-static const char __pyx_k_obj[] = "obj";
-static const char __pyx_k_sys[] = "sys";
-static const char __pyx_k_base[] = "base";
-static const char __pyx_k_dict[] = "__dict__";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_mode[] = "mode";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ndim[] = "ndim";
-static const char __pyx_k_pack[] = "pack";
-static const char __pyx_k_size[] = "size";
-static const char __pyx_k_spec[] = "__spec__";
-static const char __pyx_k_step[] = "step";
-static const char __pyx_k_stop[] = "stop";
-static const char __pyx_k_t_xs[] = "t_xs";
-static const char __pyx_k_t_ys[] = "t_ys";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_ASCII[] = "ASCII";
-static const char __pyx_k_class[] = "__class__";
-static const char __pyx_k_count[] = "count";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_flags[] = "flags";
-static const char __pyx_k_index[] = "index";
-static const char __pyx_k_paths[] = "paths";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_enable[] = "enable";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_pickle[] = "pickle";
-static const char __pyx_k_reduce[] = "__reduce__";
-static const char __pyx_k_struct[] = "struct";
-static const char __pyx_k_unpack[] = "unpack";
-static const char __pyx_k_update[] = "update";
-static const char __pyx_k_values[] = "values";
-static const char __pyx_k_disable[] = "disable";
-static const char __pyx_k_fortran[] = "fortran";
-static const char __pyx_k_memview[] = "memview";
-static const char __pyx_k_Ellipsis[] = "Ellipsis";
-static const char __pyx_k_Sequence[] = "Sequence";
-static const char __pyx_k_core_pyx[] = "core.pyx";
-static const char __pyx_k_getstate[] = "__getstate__";
-static const char __pyx_k_itemsize[] = "itemsize";
-static const char __pyx_k_pyx_type[] = "__pyx_type";
-static const char __pyx_k_register[] = "register";
-static const char __pyx_k_setstate[] = "__setstate__";
-static const char __pyx_k_TypeError[] = "TypeError";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_isenabled[] = "isenabled";
-static const char __pyx_k_pyx_state[] = "__pyx_state";
-static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
-static const char __pyx_k_IndexError[] = "IndexError";
-static const char __pyx_k_ValueError[] = "ValueError";
-static const char __pyx_k_pyx_result[] = "__pyx_result";
-static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static const char __pyx_k_MemoryError[] = "MemoryError";
-static const char __pyx_k_PickleError[] = "PickleError";
-static const char __pyx_k_collections[] = "collections";
-static const char __pyx_k_initializing[] = "_initializing";
-static const char __pyx_k_is_coroutine[] = "_is_coroutine";
-static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
-static const char __pyx_k_stringsource[] = "<stringsource>";
-static const char __pyx_k_version_info[] = "version_info";
-static const char __pyx_k_class_getitem[] = "__class_getitem__";
-static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
-static const char __pyx_k_AssertionError[] = "AssertionError";
-static const char __pyx_k_maximum_path_c[] = "maximum_path_c";
-static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
-static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static const char __pyx_k_collections_abc[] = "collections.abc";
-static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
-static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
-static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
-static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
-static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core";
-static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
-static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis ";
-static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
-static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '";
-static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
-static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
-static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
-static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct";
-static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)";
-static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)";
-static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
-static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced";
-static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
-static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
-static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions";
-static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))";
-static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got ";
-static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis ";
-static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension ";
-static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
-static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-/* #### Code section: decls ### */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
-static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
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-  Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_array_data);
-  Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str);
-  Py_CLEAR(clear_module_state->__pyx_n_s_unpack);
-  Py_CLEAR(clear_module_state->__pyx_n_s_update);
-  Py_CLEAR(clear_module_state->__pyx_n_s_values);
-  Py_CLEAR(clear_module_state->__pyx_n_s_version_info);
-  Py_CLEAR(clear_module_state->__pyx_int_0);
-  Py_CLEAR(clear_module_state->__pyx_int_1);
-  Py_CLEAR(clear_module_state->__pyx_int_3);
-  Py_CLEAR(clear_module_state->__pyx_int_112105877);
-  Py_CLEAR(clear_module_state->__pyx_int_136983863);
-  Py_CLEAR(clear_module_state->__pyx_int_184977713);
-  Py_CLEAR(clear_module_state->__pyx_int_neg_1);
-  Py_CLEAR(clear_module_state->__pyx_slice__5);
-  Py_CLEAR(clear_module_state->__pyx_tuple__4);
-  Py_CLEAR(clear_module_state->__pyx_tuple__8);
-  Py_CLEAR(clear_module_state->__pyx_tuple__10);
-  Py_CLEAR(clear_module_state->__pyx_tuple__11);
-  Py_CLEAR(clear_module_state->__pyx_tuple__12);
-  Py_CLEAR(clear_module_state->__pyx_tuple__13);
-  Py_CLEAR(clear_module_state->__pyx_tuple__14);
-  Py_CLEAR(clear_module_state->__pyx_tuple__15);
-  Py_CLEAR(clear_module_state->__pyx_tuple__16);
-  Py_CLEAR(clear_module_state->__pyx_tuple__17);
-  Py_CLEAR(clear_module_state->__pyx_tuple__18);
-  Py_CLEAR(clear_module_state->__pyx_tuple__19);
-  Py_CLEAR(clear_module_state->__pyx_tuple__21);
-  Py_CLEAR(clear_module_state->__pyx_codeobj__20);
-  Py_CLEAR(clear_module_state->__pyx_codeobj__22);
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-}
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-/* #### Code section: module_state_traverse ### */
-#if CYTHON_USE_MODULE_STATE
-static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) {
-  __pyx_mstate *traverse_module_state = __pyx_mstate(m);
-  if (!traverse_module_state) return 0;
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-  Py_VISIT(traverse_module_state->__pyx_b);
-  Py_VISIT(traverse_module_state->__pyx_cython_runtime);
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-  Py_VISIT(traverse_module_state->__pyx_empty_bytes);
-  Py_VISIT(traverse_module_state->__pyx_empty_unicode);
-  #ifdef __Pyx_CyFunction_USED
-  Py_VISIT(traverse_module_state->__pyx_CyFunctionType);
-  #endif
-  #ifdef __Pyx_FusedFunction_USED
-  Py_VISIT(traverse_module_state->__pyx_FusedFunctionType);
-  #endif
-  Py_VISIT(traverse_module_state->__pyx_array_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_array);
-  Py_VISIT(traverse_module_state->__pyx_MemviewEnum_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_MemviewEnum);
-  Py_VISIT(traverse_module_state->__pyx_memoryview_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryview);
-  Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice);
-  Py_VISIT(traverse_module_state->__pyx_kp_u_);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_AssertionError);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi);
-  Py_VISIT(traverse_module_state->__pyx_kp_u_Cannot_index_with_type);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_Ellipsis);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_asyncio_coroutines);
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-  Py_VISIT(traverse_module_state->__pyx_n_u_c);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_class_getitem);
-  Py_VISIT(traverse_module_state->__pyx_n_s_cline_in_traceback);
-  Py_VISIT(traverse_module_state->__pyx_n_s_collections);
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-  Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_direct);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_indirect);
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-  Py_VISIT(traverse_module_state->__pyx_kp_u_enable);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_enumerate);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_values);
-  Py_VISIT(traverse_module_state->__pyx_n_s_version_info);
-  Py_VISIT(traverse_module_state->__pyx_int_0);
-  Py_VISIT(traverse_module_state->__pyx_int_1);
-  Py_VISIT(traverse_module_state->__pyx_int_3);
-  Py_VISIT(traverse_module_state->__pyx_int_112105877);
-  Py_VISIT(traverse_module_state->__pyx_int_136983863);
-  Py_VISIT(traverse_module_state->__pyx_int_184977713);
-  Py_VISIT(traverse_module_state->__pyx_int_neg_1);
-  Py_VISIT(traverse_module_state->__pyx_slice__5);
-  Py_VISIT(traverse_module_state->__pyx_tuple__4);
-  Py_VISIT(traverse_module_state->__pyx_tuple__8);
-  Py_VISIT(traverse_module_state->__pyx_tuple__10);
-  Py_VISIT(traverse_module_state->__pyx_tuple__11);
-  Py_VISIT(traverse_module_state->__pyx_tuple__12);
-  Py_VISIT(traverse_module_state->__pyx_tuple__13);
-  Py_VISIT(traverse_module_state->__pyx_tuple__14);
-  Py_VISIT(traverse_module_state->__pyx_tuple__15);
-  Py_VISIT(traverse_module_state->__pyx_tuple__16);
-  Py_VISIT(traverse_module_state->__pyx_tuple__17);
-  Py_VISIT(traverse_module_state->__pyx_tuple__18);
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-  Py_VISIT(traverse_module_state->__pyx_tuple__21);
-  Py_VISIT(traverse_module_state->__pyx_codeobj__20);
-  Py_VISIT(traverse_module_state->__pyx_codeobj__22);
-  return 0;
-}
-#endif
-/* #### Code section: module_state_defines ### */
-#define __pyx_d __pyx_mstate_global->__pyx_d
-#define __pyx_b __pyx_mstate_global->__pyx_b
-#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime
-#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple
-#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes
-#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode
-#ifdef __Pyx_CyFunction_USED
-#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType
-#endif
-#ifdef __Pyx_FusedFunction_USED
-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType
-#endif
-#ifdef __Pyx_Generator_USED
-#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType
-#endif
-#ifdef __Pyx_IterableCoroutine_USED
-#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#define __pyx_type___pyx_array __pyx_mstate_global->__pyx_type___pyx_array
-#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum
-#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview
-#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice
-#endif
-#define __pyx_array_type __pyx_mstate_global->__pyx_array_type
-#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type
-#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type
-#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type
-#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_
-#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII
-#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi
-#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError
-#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri
-#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is
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-#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct
-#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis
-#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr
-#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0
-#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError
-#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d
-#define __pyx_kp_s_Indirect_dimensions_not_supporte __pyx_mstate_global->__pyx_kp_s_Indirect_dimensions_not_supporte
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-#define __pyx_kp_u_Invalid_shape_in_axis __pyx_mstate_global->__pyx_kp_u_Invalid_shape_in_axis
-#define __pyx_n_s_MemoryError __pyx_mstate_global->__pyx_n_s_MemoryError
-#define __pyx_kp_s_MemoryView_of_r_at_0x_x __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_at_0x_x
-#define __pyx_kp_s_MemoryView_of_r_object __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_object
-#define __pyx_n_b_O __pyx_mstate_global->__pyx_n_b_O
-#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_mstate_global->__pyx_kp_u_Out_of_bounds_on_buffer_access_a
-#define __pyx_n_s_PickleError __pyx_mstate_global->__pyx_n_s_PickleError
-#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence
-#define __pyx_kp_s_Step_may_not_be_zero_axis_d __pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d
-#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError
-#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object
-#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError
-#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView
-#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2
-#define __pyx_n_s__23 __pyx_mstate_global->__pyx_n_s__23
-#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3
-#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6
-#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7
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-#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer
-#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and
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-#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base
-#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c
-#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c
-#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class
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-#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect
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-#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython
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- *         return memoryview_fromslice(dst, new_ndim,
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-    /* "View.MemoryView":779
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
-    __Pyx_XDECREF((PyObject *)__pyx_r);
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-    /* "View.MemoryView":780
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_dtype_func,
- *                                     memview.dtype_is_object)
- */
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- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
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- */
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-    /* "View.MemoryView":779
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
-    __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 779, __pyx_L1_error)
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-
-    /* "View.MemoryView":778
- *             new_ndim += 1
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,
- */
-  }
-
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- *                                     memview.dtype_is_object)
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-    /* "View.MemoryView":785
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- *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 784, __pyx_L1_error)
-    __Pyx_GOTREF(__pyx_t_2);
-
-    /* "View.MemoryView":784
- *                                     memview.dtype_is_object)
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- * 
- */
-    if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_memoryview_type))))) __PYX_ERR(1, 784, __pyx_L1_error)
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-  /* "View.MemoryView":711
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- * @cname('__pyx_memview_slice')
- * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
- *     cdef int new_ndim = 0, suboffset_dim = -1, dim
- *     cdef bint negative_step
- */
-
-  /* function exit code */
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-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_8);
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-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);
-  __Pyx_XDECREF(__pyx_v_index);
-  __Pyx_XGIVEREF((PyObject *)__pyx_r);
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-/* "View.MemoryView":793
- * 
- * @cname('__pyx_memoryview_slice_memviewslice')
- * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
- *         __Pyx_memviewslice *dst,
- *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
- */
-
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {
-  Py_ssize_t __pyx_v_new_shape;
-  int __pyx_v_negative_step;
-  int __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
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-  PyGILState_STATE __pyx_gilstate_save;
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-
-  /* "View.MemoryView":813
- *     cdef bint negative_step
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- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-  __pyx_t_1 = (!__pyx_v_is_slice);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":815
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    __pyx_t_1 = (__pyx_v_start < 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":816
- * 
- *         if start < 0:
- *             start += shape             # <<<<<<<<<<<<<<
- *         if not 0 <= start < shape:
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- */
-      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-      /* "View.MemoryView":815
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    }
-
-    /* "View.MemoryView":817
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    __pyx_t_1 = (0 <= __pyx_v_start);
-    if (__pyx_t_1) {
-      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
-    }
-    __pyx_t_2 = (!__pyx_t_1);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":818
- *             start += shape
- *         if not 0 <= start < shape:
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
- *     else:
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 818, __pyx_L1_error)
-
-      /* "View.MemoryView":817
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    }
-
-    /* "View.MemoryView":813
- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":821
- *     else:
- * 
- *         if have_step:             # <<<<<<<<<<<<<<
- *             negative_step = step < 0
- *             if step == 0:
- */
-  /*else*/ {
-    __pyx_t_2 = (__pyx_v_have_step != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":822
- * 
- *         if have_step:
- *             negative_step = step < 0             # <<<<<<<<<<<<<<
- *             if step == 0:
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- */
-      __pyx_v_negative_step = (__pyx_v_step < 0);
-
-      /* "View.MemoryView":823
- *         if have_step:
- *             negative_step = step < 0
- *             if step == 0:             # <<<<<<<<<<<<<<
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- */
-      __pyx_t_2 = (__pyx_v_step == 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":824
- *             negative_step = step < 0
- *             if step == 0:
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
- *         else:
- *             negative_step = False
- */
-        __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 824, __pyx_L1_error)
-
-        /* "View.MemoryView":823
- *         if have_step:
- *             negative_step = step < 0
- *             if step == 0:             # <<<<<<<<<<<<<<
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- */
-      }
-
-      /* "View.MemoryView":821
- *     else:
- * 
- *         if have_step:             # <<<<<<<<<<<<<<
- *             negative_step = step < 0
- *             if step == 0:
- */
-      goto __pyx_L6;
-    }
-
-    /* "View.MemoryView":826
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- *             negative_step = False             # <<<<<<<<<<<<<<
- *             step = 1
- * 
- */
-    /*else*/ {
-      __pyx_v_negative_step = 0;
-
-      /* "View.MemoryView":827
- *         else:
- *             negative_step = False
- *             step = 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_step = 1;
-    }
-    __pyx_L6:;
-
-    /* "View.MemoryView":830
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-    __pyx_t_2 = (__pyx_v_have_start != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":831
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-      __pyx_t_2 = (__pyx_v_start < 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":832
- *         if have_start:
- *             if start < 0:
- *                 start += shape             # <<<<<<<<<<<<<<
- *                 if start < 0:
- *                     start = 0
- */
-        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-        /* "View.MemoryView":833
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        __pyx_t_2 = (__pyx_v_start < 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":834
- *                 start += shape
- *                 if start < 0:
- *                     start = 0             # <<<<<<<<<<<<<<
- *             elif start >= shape:
- *                 if negative_step:
- */
-          __pyx_v_start = 0;
-
-          /* "View.MemoryView":833
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        }
-
-        /* "View.MemoryView":831
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-        goto __pyx_L9;
-      }
-
-      /* "View.MemoryView":835
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":836
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-        if (__pyx_v_negative_step) {
-
-          /* "View.MemoryView":837
- *             elif start >= shape:
- *                 if negative_step:
- *                     start = shape - 1             # <<<<<<<<<<<<<<
- *                 else:
- *                     start = shape
- */
-          __pyx_v_start = (__pyx_v_shape - 1);
-
-          /* "View.MemoryView":836
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-          goto __pyx_L11;
-        }
-
-        /* "View.MemoryView":839
- *                     start = shape - 1
- *                 else:
- *                     start = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        /*else*/ {
-          __pyx_v_start = __pyx_v_shape;
-        }
-        __pyx_L11:;
-
-        /* "View.MemoryView":835
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      }
-      __pyx_L9:;
-
-      /* "View.MemoryView":830
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-      goto __pyx_L8;
-    }
-
-    /* "View.MemoryView":841
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-    /*else*/ {
-      if (__pyx_v_negative_step) {
-
-        /* "View.MemoryView":842
- *         else:
- *             if negative_step:
- *                 start = shape - 1             # <<<<<<<<<<<<<<
- *             else:
- *                 start = 0
- */
-        __pyx_v_start = (__pyx_v_shape - 1);
-
-        /* "View.MemoryView":841
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-        goto __pyx_L12;
-      }
-
-      /* "View.MemoryView":844
- *                 start = shape - 1
- *             else:
- *                 start = 0             # <<<<<<<<<<<<<<
- * 
- *         if have_stop:
- */
-      /*else*/ {
-        __pyx_v_start = 0;
-      }
-      __pyx_L12:;
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-    __pyx_L8:;
-
-    /* "View.MemoryView":846
- *                 start = 0
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- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-    __pyx_t_2 = (__pyx_v_have_stop != 0);
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- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-      __pyx_t_2 = (__pyx_v_stop < 0);
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-
-        /* "View.MemoryView":848
- *         if have_stop:
- *             if stop < 0:
- *                 stop += shape             # <<<<<<<<<<<<<<
- *                 if stop < 0:
- *                     stop = 0
- */
-        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
-
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- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        __pyx_t_2 = (__pyx_v_stop < 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":850
- *                 stop += shape
- *                 if stop < 0:
- *                     stop = 0             # <<<<<<<<<<<<<<
- *             elif stop > shape:
- *                 stop = shape
- */
-          __pyx_v_stop = 0;
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- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        }
-
-        /* "View.MemoryView":847
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-        goto __pyx_L14;
-      }
-
-      /* "View.MemoryView":851
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      __pyx_t_2 = (__pyx_v_stop > __pyx_v_shape);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":852
- *                     stop = 0
- *             elif stop > shape:
- *                 stop = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        __pyx_v_stop = __pyx_v_shape;
-
-        /* "View.MemoryView":851
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      }
-      __pyx_L14:;
-
-      /* "View.MemoryView":846
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-      goto __pyx_L13;
-    }
-
-    /* "View.MemoryView":854
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-    /*else*/ {
-      if (__pyx_v_negative_step) {
-
-        /* "View.MemoryView":855
- *         else:
- *             if negative_step:
- *                 stop = -1             # <<<<<<<<<<<<<<
- *             else:
- *                 stop = shape
- */
-        __pyx_v_stop = -1L;
-
-        /* "View.MemoryView":854
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-        goto __pyx_L16;
-      }
-
-      /* "View.MemoryView":857
- *                 stop = -1
- *             else:
- *                 stop = shape             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      /*else*/ {
-        __pyx_v_stop = __pyx_v_shape;
-      }
-      __pyx_L16:;
-    }
-    __pyx_L13:;
-
-    /* "View.MemoryView":861
- * 
- *         with cython.cdivision(True):
- *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
- * 
- *             if (stop - start) - step * new_shape:
- */
-    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);
-
-    /* "View.MemoryView":863
- *             new_shape = (stop - start) // step
- * 
- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
- *                 new_shape += 1
- * 
- */
-    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":864
- * 
- *             if (stop - start) - step * new_shape:
- *                 new_shape += 1             # <<<<<<<<<<<<<<
- * 
- *         if new_shape < 0:
- */
-      __pyx_v_new_shape = (__pyx_v_new_shape + 1);
-
-      /* "View.MemoryView":863
- *             new_shape = (stop - start) // step
- * 
- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
- *                 new_shape += 1
- * 
- */
-    }
-
-    /* "View.MemoryView":866
- *                 new_shape += 1
- * 
- *         if new_shape < 0:             # <<<<<<<<<<<<<<
- *             new_shape = 0
- * 
- */
-    __pyx_t_2 = (__pyx_v_new_shape < 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":867
- * 
- *         if new_shape < 0:
- *             new_shape = 0             # <<<<<<<<<<<<<<
- * 
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- *             break             # <<<<<<<<<<<<<<
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- */
-      goto __pyx_L7_break;
-
-      /* "View.MemoryView":1127
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L7_break:;
-
-  /* "View.MemoryView":1131
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  __pyx_t_2 = (abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride));
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1132
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- *         return 'C'             # <<<<<<<<<<<<<<
- *     else:
- *         return 'F'
- */
-    __pyx_r = 'C';
-    goto __pyx_L0;
-
-    /* "View.MemoryView":1131
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  }
-
-  /* "View.MemoryView":1134
- *         return 'C'
- *     else:
- *         return 'F'             # <<<<<<<<<<<<<<
- * 
- * @cython.cdivision(True)
- */
-  /*else*/ {
-    __pyx_r = 'F';
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1113
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1137
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
-  Py_ssize_t __pyx_v_dst_extent;
-  Py_ssize_t __pyx_v_src_stride;
-  Py_ssize_t __pyx_v_dst_stride;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  Py_ssize_t __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-
-  /* "View.MemoryView":1144
- * 
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- */
-  __pyx_v_src_extent = (__pyx_v_src_shape[0]);
-
-  /* "View.MemoryView":1145
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- */
-  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
-
-  /* "View.MemoryView":1146
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- */
-  __pyx_v_src_stride = (__pyx_v_src_strides[0]);
-
-  /* "View.MemoryView":1147
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
- * 
- *     if ndim == 1:
- */
-  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
-
-  /* "View.MemoryView":1149
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-  __pyx_t_1 = (__pyx_v_ndim == 1);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    __pyx_t_2 = (__pyx_v_src_stride > 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-    __pyx_t_2 = (__pyx_v_dst_stride > 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-
-    /* "View.MemoryView":1151
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- *         else:
- */
-    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
-    if (__pyx_t_2) {
-      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
-    }
-    __pyx_t_1 = __pyx_t_2;
-    __pyx_L5_bool_binop_done:;
-
-    /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":1152
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
- *         else:
- *             for i in range(dst_extent):
- */
-      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
-
-      /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-      goto __pyx_L4;
-    }
-
-    /* "View.MemoryView":1154
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- *         else:
- *             for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride
- */
-    /*else*/ {
-      __pyx_t_3 = __pyx_v_dst_extent;
-      __pyx_t_4 = __pyx_t_3;
-      for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-        __pyx_v_i = __pyx_t_5;
-
-        /* "View.MemoryView":1155
- *         else:
- *             for i in range(dst_extent):
- *                 memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
- *                 src_data += src_stride
- *                 dst_data += dst_stride
- */
-        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
-
-        /* "View.MemoryView":1156
- *             for i in range(dst_extent):
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride             # <<<<<<<<<<<<<<
- *                 dst_data += dst_stride
- *     else:
- */
-        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-        /* "View.MemoryView":1157
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride
- *                 dst_data += dst_stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(dst_extent):
- */
-        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-      }
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":1149
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1159
- *                 dst_data += dst_stride
- *     else:
- *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *             _copy_strided_to_strided(src_data, src_strides + 1,
- *                                      dst_data, dst_strides + 1,
- */
-  /*else*/ {
-    __pyx_t_3 = __pyx_v_dst_extent;
-    __pyx_t_4 = __pyx_t_3;
-    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-      __pyx_v_i = __pyx_t_5;
-
-      /* "View.MemoryView":1160
- *     else:
- *         for i in range(dst_extent):
- *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
- *                                      dst_data, dst_strides + 1,
- *                                      src_shape + 1, dst_shape + 1,
- */
-      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
-      /* "View.MemoryView":1164
- *                                      src_shape + 1, dst_shape + 1,
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride             # <<<<<<<<<<<<<<
- *             dst_data += dst_stride
- * 
- */
-      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-      /* "View.MemoryView":1165
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride
- *             dst_data += dst_stride             # <<<<<<<<<<<<<<
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
-      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1137
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1167
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
-  /* "View.MemoryView":1170
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
- *                              src.shape, dst.shape, ndim, itemsize)
- * 
- */
-  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1167
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1174
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
-  Py_ssize_t __pyx_v_shape;
-  Py_ssize_t __pyx_v_size;
-  Py_ssize_t __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  Py_ssize_t *__pyx_t_3;
-  Py_ssize_t *__pyx_t_4;
-
-  /* "View.MemoryView":1176
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     for shape in src.shape[:ndim]:
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_size = __pyx_t_1;
-
-  /* "View.MemoryView":1178
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- * 
- *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
- *         size *= shape
- * 
- */
-  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
-  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
-    __pyx_t_2 = __pyx_t_4;
-    __pyx_v_shape = (__pyx_t_2[0]);
-
-    /* "View.MemoryView":1179
- * 
- *     for shape in src.shape[:ndim]:
- *         size *= shape             # <<<<<<<<<<<<<<
- * 
- *     return size
- */
-    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
-  }
-
-  /* "View.MemoryView":1181
- *         size *= shape
- * 
- *     return size             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_fill_contig_strides_array')
- */
-  __pyx_r = __pyx_v_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1174
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1184
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) noexcept nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
-  /* "View.MemoryView":1193
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-  __pyx_t_1 = (__pyx_v_order == 'F');
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1194
- * 
- *     if order == 'F':
- *         for idx in range(ndim):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-    __pyx_t_2 = __pyx_v_ndim;
-    __pyx_t_3 = __pyx_t_2;
-    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-      __pyx_v_idx = __pyx_t_4;
-
-      /* "View.MemoryView":1195
- *     if order == 'F':
- *         for idx in range(ndim):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- *     else:
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1196
- *         for idx in range(ndim):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-
-    /* "View.MemoryView":1193
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1198
- *             stride *= shape[idx]
- *     else:
- *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-  /*else*/ {
-    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
-      __pyx_v_idx = __pyx_t_2;
-
-      /* "View.MemoryView":1199
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- * 
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1200
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- * 
- *     return stride
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1202
- *             stride *= shape[idx]
- * 
- *     return stride             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- */
-  __pyx_r = __pyx_v_stride;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1184
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) noexcept nogil:
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1205
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                              __Pyx_memviewslice *tmpslice,
- *                              char order,
- */
-
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  void *__pyx_v_result;
-  size_t __pyx_v_itemsize;
-  size_t __pyx_v_size;
-  void *__pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  struct __pyx_memoryview_obj *__pyx_t_4;
-  int __pyx_t_5;
-  int __pyx_t_6;
-  int __pyx_lineno = 0;
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-  int __pyx_t_5;
-  int __pyx_t_6;
-  void *__pyx_t_7;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save;
-  #endif
-
-  /* "View.MemoryView":1273
- *     Check for overlapping memory and verify the shapes.
- *     """
- *     cdef void *tmpdata = NULL             # <<<<<<<<<<<<<<
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- */
-  __pyx_v_tmpdata = NULL;
-
-  /* "View.MemoryView":1274
- *     """
- *     cdef void *tmpdata = NULL
- *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- */
-  __pyx_t_1 = __pyx_v_src.memview->view.itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
-
-  /* "View.MemoryView":1276
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)             # <<<<<<<<<<<<<<
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False
- */
-  __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim);
-
-  /* "View.MemoryView":1277
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False             # <<<<<<<<<<<<<<
- *     cdef bint direct_copy = False
- *     cdef __Pyx_memviewslice tmp
- */
-  __pyx_v_broadcasting = 0;
-
-  /* "View.MemoryView":1278
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False             # <<<<<<<<<<<<<<
- *     cdef __Pyx_memviewslice tmp
- * 
- */
-  __pyx_v_direct_copy = 0;
-
-  /* "View.MemoryView":1281
- *     cdef __Pyx_memviewslice tmp
- * 
- *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- */
-  __pyx_t_2 = (__pyx_v_src_ndim < __pyx_v_dst_ndim);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1282
- * 
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim);
-
-    /* "View.MemoryView":1281
- *     cdef __Pyx_memviewslice tmp
- * 
- *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1283
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- */
-  __pyx_t_2 = (__pyx_v_dst_ndim < __pyx_v_src_ndim);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1284
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)             # <<<<<<<<<<<<<<
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);
-
-    /* "View.MemoryView":1283
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- */
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1286
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-  __pyx_t_3 = __pyx_v_dst_ndim;
-  __pyx_t_4 = __pyx_v_src_ndim;
-  if ((__pyx_t_3 > __pyx_t_4)) {
-    __pyx_t_5 = __pyx_t_3;
-  } else {
-    __pyx_t_5 = __pyx_t_4;
-  }
-  __pyx_v_ndim = __pyx_t_5;
-
-  /* "View.MemoryView":1288
- *     cdef int ndim = max(src_ndim, dst_ndim)
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- */
-  __pyx_t_5 = __pyx_v_ndim;
-  __pyx_t_3 = __pyx_t_5;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-
-    /* "View.MemoryView":1289
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i]));
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1290
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-      __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) == 1);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":1291
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- *                 broadcasting = True             # <<<<<<<<<<<<<<
- *                 src.strides[i] = 0
- *             else:
- */
-        __pyx_v_broadcasting = 1;
-
-        /* "View.MemoryView":1292
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- */
-        (__pyx_v_src.strides[__pyx_v_i]) = 0;
-
-        /* "View.MemoryView":1290
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-        goto __pyx_L7;
-      }
-
-      /* "View.MemoryView":1294
- *                 src.strides[i] = 0
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
- * 
- *         if src.suboffsets[i] >= 0:
- */
-      /*else*/ {
-        __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1294, __pyx_L1_error)
-      }
-      __pyx_L7:;
-
-      /* "View.MemoryView":1289
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    }
-
-    /* "View.MemoryView":1296
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- */
-    __pyx_t_2 = ((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1297
- * 
- *         if src.suboffsets[i] >= 0:
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- */
-      __pyx_t_6 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error)
-
-      /* "View.MemoryView":1296
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- */
-    }
-  }
-
-  /* "View.MemoryView":1299
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  __pyx_t_2 = __pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1301
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    __pyx_t_2 = (!__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim));
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1302
- * 
- *         if not slice_is_contig(src, order, ndim):
- *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- */
-      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
-
-      /* "View.MemoryView":1301
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    }
-
-    /* "View.MemoryView":1304
- *             order = get_best_order(&dst, ndim)
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
- *         src = tmp
- * 
- */
-    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1304, __pyx_L1_error)
-    __pyx_v_tmpdata = __pyx_t_7;
-
-    /* "View.MemoryView":1305
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- *         src = tmp             # <<<<<<<<<<<<<<
- * 
- *     if not broadcasting:
- */
-    __pyx_v_src = __pyx_v_tmp;
-
-    /* "View.MemoryView":1299
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  }
-
-  /* "View.MemoryView":1307
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (!__pyx_v_broadcasting);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1310
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-    __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1311
- * 
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
-
-      /* "View.MemoryView":1310
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-      goto __pyx_L12;
-    }
-
-    /* "View.MemoryView":1312
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1313
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
- * 
- *         if direct_copy:
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
-
-      /* "View.MemoryView":1312
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    }
-    __pyx_L12:;
-
-    /* "View.MemoryView":1315
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    if (__pyx_v_direct_copy) {
-
-      /* "View.MemoryView":1317
- *         if direct_copy:
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)             # <<<<<<<<<<<<<<
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-      /* "View.MemoryView":1318
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)
- */
-      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
-
-      /* "View.MemoryView":1319
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)             # <<<<<<<<<<<<<<
- *             free(tmpdata)
- *             return 0
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-      /* "View.MemoryView":1320
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)             # <<<<<<<<<<<<<<
- *             return 0
- * 
- */
-      free(__pyx_v_tmpdata);
-
-      /* "View.MemoryView":1321
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)
- *             return 0             # <<<<<<<<<<<<<<
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):
- */
-      __pyx_r = 0;
-      goto __pyx_L0;
-
-      /* "View.MemoryView":1315
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    }
-
-    /* "View.MemoryView":1307
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1323
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (__pyx_v_order == 'F');
-  if (__pyx_t_2) {
-    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
-  }
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1326
- * 
- * 
- *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
- *         transpose_memslice(&dst)
- * 
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error)
-
-    /* "View.MemoryView":1327
- * 
- *         transpose_memslice(&src)
- *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error)
-
-    /* "View.MemoryView":1323
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1329
- *         transpose_memslice(&dst)
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)             # <<<<<<<<<<<<<<
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-  /* "View.MemoryView":1330
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- * 
- */
-  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1331
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)             # <<<<<<<<<<<<<<
- * 
- *     free(tmpdata)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-  /* "View.MemoryView":1333
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- * 
- *     free(tmpdata)             # <<<<<<<<<<<<<<
- *     return 0
- * 
- */
-  free(__pyx_v_tmpdata);
-
-  /* "View.MemoryView":1334
- * 
- *     free(tmpdata)
- *     return 0             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- */
-  __pyx_r = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1265
- * 
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice dst,
- *                                   int src_ndim, int dst_ndim,
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  #ifdef WITH_THREAD
-  __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-  #endif
-  __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  #ifdef WITH_THREAD
-  __Pyx_PyGILState_Release(__pyx_gilstate_save);
-  #endif
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1337
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
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- *                                     Py_ssize_t *strides, int ndim, bint inc) noexcept:
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-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
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- *         v_cur = value[y-1, x]             # <<<<<<<<<<<<<<
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-
-          /* "monotonic_align/core.pyx":23
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-
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- *       else:
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- *       value[y, x] += max(v_prev, v_cur)
- * 
- */
-      /*else*/ {
-        __pyx_t_10 = (__pyx_v_y - 1);
-        __pyx_t_9 = (__pyx_v_x - 1);
-        __pyx_v_v_prev = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_10 * __pyx_v_value.strides[0]) )) + __pyx_t_9)) )));
-      }
-      __pyx_L8:;
-
-      /* "monotonic_align/core.pyx":28
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- *       value[y, x] += max(v_prev, v_cur)             # <<<<<<<<<<<<<<
- * 
- *   for y in range(t_y - 1, -1, -1):
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-      __pyx_t_11 = __pyx_v_v_cur;
-      __pyx_t_12 = __pyx_v_v_prev;
-      if ((__pyx_t_11 > __pyx_t_12)) {
-        __pyx_t_13 = __pyx_t_11;
-      } else {
-        __pyx_t_13 = __pyx_t_12;
-      }
-      __pyx_t_9 = __pyx_v_y;
-      __pyx_t_10 = __pyx_v_x;
-      *((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) )) += __pyx_t_13;
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-  }
-
-  /* "monotonic_align/core.pyx":30
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- * 
- *   for y in range(t_y - 1, -1, -1):             # <<<<<<<<<<<<<<
- *     path[y, index] = 1
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-  for (__pyx_t_1 = (__pyx_v_t_y - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_y = __pyx_t_1;
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- *   for y in range(t_y - 1, -1, -1):
- *     path[y, index] = 1             # <<<<<<<<<<<<<<
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
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-    __pyx_t_10 = __pyx_v_y;
-    __pyx_t_9 = __pyx_v_index;
-    *((int *) ( /* dim=1 */ ((char *) (((int *) ( /* dim=0 */ (__pyx_v_path.data + __pyx_t_10 * __pyx_v_path.strides[0]) )) + __pyx_t_9)) )) = 1;
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- *       index = index - 1
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- */
-    __pyx_t_14 = (__pyx_v_index != 0);
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-    } else {
-      __pyx_t_8 = __pyx_t_14;
-      goto __pyx_L13_bool_binop_done;
-    }
-    __pyx_t_14 = (__pyx_v_index == __pyx_v_y);
-    if (!__pyx_t_14) {
-    } else {
-      __pyx_t_8 = __pyx_t_14;
-      goto __pyx_L13_bool_binop_done;
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-    __pyx_t_10 = __pyx_v_index;
-    __pyx_t_15 = (__pyx_v_y - 1);
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-    __pyx_t_14 = ((*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) ))) < (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_15 * __pyx_v_value.strides[0]) )) + __pyx_t_16)) ))));
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- *   cdef int x
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-  /* function exit code */
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- *   cdef int b = paths.shape[0]
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-static PyObject *__pyx_pw_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, 
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-PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds
-#else
-PyObject *__pyx_args, PyObject *__pyx_kwds
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-static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs, CYTHON_UNUSED int __pyx_skip_dispatch) {
-  CYTHON_UNUSED int __pyx_v_b;
-  int __pyx_v_i;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  __Pyx_memviewslice __pyx_t_4 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  Py_ssize_t __pyx_t_6;
-  Py_ssize_t __pyx_t_7;
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-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save;
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-  /* "monotonic_align/core.pyx":39
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
- *   cdef int b = paths.shape[0]             # <<<<<<<<<<<<<<
- *   cdef int i
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- */
-  __pyx_v_b = (__pyx_v_paths.shape[0]);
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-  /* "monotonic_align/core.pyx":41
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- *   cdef int i
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-      if (PyGILState_Check()) {
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-      __Pyx_FastGIL_Remember();
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-            PyObject *__pyx_parallel_exc_type = NULL, *__pyx_parallel_exc_value = NULL, *__pyx_parallel_exc_tb = NULL;
-            int __pyx_parallel_why;
-            __pyx_parallel_why = 0;
-            #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
-                #undef likely
-                #undef unlikely
-                #define likely(x)   (x)
-                #define unlikely(x) (x)
-            #endif
-            __pyx_t_3 = (__pyx_t_1 - 0 + 1 - 1/abs(1)) / 1;
-            if (__pyx_t_3 > 0)
-            {
-                #ifdef _OPENMP
-                #pragma omp parallel private(__pyx_t_6, __pyx_t_7) firstprivate(__pyx_t_4, __pyx_t_5) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb)
-                #endif /* _OPENMP */
-                {
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-                    #endif /* _OPENMP */
-                    #ifdef _OPENMP
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-
-                            /* "monotonic_align/core.pyx":42
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- *     maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])             # <<<<<<<<<<<<<<
- */
-                            __pyx_t_4.data = __pyx_v_paths.data;
-                            __pyx_t_4.memview = __pyx_v_paths.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_4, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_paths.strides[0];
-        __pyx_t_4.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_4.shape[0] = __pyx_v_paths.shape[1];
-__pyx_t_4.strides[0] = __pyx_v_paths.strides[1];
-    __pyx_t_4.suboffsets[0] = -1;
-
-__pyx_t_4.shape[1] = __pyx_v_paths.shape[2];
-__pyx_t_4.strides[1] = __pyx_v_paths.strides[2];
-    __pyx_t_4.suboffsets[1] = -1;
-
-__pyx_t_5.data = __pyx_v_values.data;
-                            __pyx_t_5.memview = __pyx_v_values.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_5, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_values.strides[0];
-        __pyx_t_5.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_5.shape[0] = __pyx_v_values.shape[1];
-__pyx_t_5.strides[0] = __pyx_v_values.strides[1];
-    __pyx_t_5.suboffsets[0] = -1;
-
-__pyx_t_5.shape[1] = __pyx_v_values.shape[2];
-__pyx_t_5.strides[1] = __pyx_v_values.strides[2];
-    __pyx_t_5.suboffsets[1] = -1;
-
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-static PyType_Spec __pyx_type___pyx_array_spec = {
-  "monotonic_align.core.array",
-  sizeof(struct __pyx_array_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE,
-  __pyx_type___pyx_array_slots,
-};
-#else
-
-static PySequenceMethods __pyx_tp_as_sequence_array = {
-  __pyx_array___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_array, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_array = {
-  __pyx_array___len__, /*mp_length*/
-  __pyx_array___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_array = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_array_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_array = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""array", /*tp_name*/
-  sizeof(struct __pyx_array_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_array, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  0, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  __pyx_tp_getattro_array, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, /*tp_flags*/
-  0, /*tp_doc*/
-  0, /*tp_traverse*/
-  0, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_array, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_array, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_array, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
-  struct __pyx_MemviewEnum_obj *p;
-  PyObject *o;
-  #if CYTHON_COMPILING_IN_LIMITED_API
-  allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc);
-  o = alloc_func(t, 0);
-  #else
-  if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  #endif
-  p = ((struct __pyx_MemviewEnum_obj *)o);
-  p->name = Py_None; Py_INCREF(Py_None);
-  return o;
-}
-
-static void __pyx_tp_dealloc_Enum(PyObject *o) {
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) {
-    if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_Enum) {
-      if (PyObject_CallFinalizerFromDealloc(o)) return;
-    }
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  Py_CLEAR(p->name);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  if (p->name) {
-    e = (*v)(p->name, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_Enum(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  tmp = ((PyObject*)p->name);
-  p->name = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  return 0;
-}
-
-static PyObject *__pyx_specialmethod___pyx_MemviewEnum___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) {
-  return __pyx_MemviewEnum___repr__(self);
-}
-
-static PyMethodDef __pyx_methods_Enum[] = {
-  {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_MemviewEnum___repr__, METH_NOARGS|METH_COEXIST, 0},
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum},
-  {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__},
-  {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum},
-  {Py_tp_clear, (void *)__pyx_tp_clear_Enum},
-  {Py_tp_methods, (void *)__pyx_methods_Enum},
-  {Py_tp_init, (void *)__pyx_MemviewEnum___init__},
-  {Py_tp_new, (void *)__pyx_tp_new_Enum},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = {
-  "monotonic_align.core.Enum",
-  sizeof(struct __pyx_MemviewEnum_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC,
-  __pyx_type___pyx_MemviewEnum_slots,
-};
-#else
-
-static PyTypeObject __pyx_type___pyx_MemviewEnum = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""Enum", /*tp_name*/
-  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_MemviewEnum___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_Enum, /*tp_traverse*/
-  __pyx_tp_clear_Enum, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_Enum, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  __pyx_MemviewEnum___init__, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_Enum, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
-
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryview_obj *p;
-  PyObject *o;
-  #if CYTHON_COMPILING_IN_LIMITED_API
-  allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc);
-  o = alloc_func(t, 0);
-  #else
-  if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  #endif
-  p = ((struct __pyx_memoryview_obj *)o);
-  p->__pyx_vtab = __pyx_vtabptr_memoryview;
-  p->obj = Py_None; Py_INCREF(Py_None);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  p->view.obj = NULL;
-  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
-  return o;
-  bad:
-  Py_DECREF(o); o = 0;
-  return NULL;
-}
-
-static void __pyx_tp_dealloc_memoryview(PyObject *o) {
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) {
-    if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) {
-      if (PyObject_CallFinalizerFromDealloc(o)) return;
-    }
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryview___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->obj);
-  Py_CLEAR(p->_size);
-  Py_CLEAR(p->_array_interface);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  if (p->obj) {
-    e = (*v)(p->obj, a); if (e) return e;
-  }
-  if (p->_size) {
-    e = (*v)(p->_size, a); if (e) return e;
-  }
-  if (p->_array_interface) {
-    e = (*v)(p->_array_interface, a); if (e) return e;
-  }
-  if (p->view.obj) {
-    e = (*v)(p->view.obj, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  tmp = ((PyObject*)p->obj);
-  p->obj = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_size);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_array_interface);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  Py_CLEAR(p->view.obj);
-  return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_memoryview___setitem__(o, i, v);
-  }
-  else {
-    __Pyx_TypeName o_type_name;
-    o_type_name = __Pyx_PyType_GetName(Py_TYPE(o));
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name);
-    __Pyx_DECREF_TypeName(o_type_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
-}
-
-static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) {
-  return __pyx_memoryview___repr__(self);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
-  {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0},
-  {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
-  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
-  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
-  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
-  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
-  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
-  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
-  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
-  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
-  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-#if !CYTHON_COMPILING_IN_LIMITED_API
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-#endif
-static PyType_Slot __pyx_type___pyx_memoryview_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview},
-  {Py_tp_repr, (void *)__pyx_memoryview___repr__},
-  {Py_sq_length, (void *)__pyx_memoryview___len__},
-  {Py_sq_item, (void *)__pyx_sq_item_memoryview},
-  {Py_mp_length, (void *)__pyx_memoryview___len__},
-  {Py_mp_subscript, (void *)__pyx_memoryview___getitem__},
-  {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview},
-  {Py_tp_str, (void *)__pyx_memoryview___str__},
-  #if defined(Py_bf_getbuffer)
-  {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer},
-  #endif
-  {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview},
-  {Py_tp_clear, (void *)__pyx_tp_clear_memoryview},
-  {Py_tp_methods, (void *)__pyx_methods_memoryview},
-  {Py_tp_getset, (void *)__pyx_getsets_memoryview},
-  {Py_tp_new, (void *)__pyx_tp_new_memoryview},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_memoryview_spec = {
-  "monotonic_align.core.memoryview",
-  sizeof(struct __pyx_memoryview_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC,
-  __pyx_type___pyx_memoryview_slots,
-};
-#else
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
-  __pyx_memoryview___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_memoryview, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
-  __pyx_memoryview___len__, /*mp_length*/
-  __pyx_memoryview___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""memoryview", /*tp_name*/
-  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_memoryview___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  __pyx_memoryview___str__, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_memoryview, /*tp_traverse*/
-  __pyx_tp_clear_memoryview, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_memoryview, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_memoryview, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_memoryview, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryviewslice_obj *p;
-  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryviewslice_obj *)o);
-  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  p->from_slice.memview = NULL;
-  return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) {
-    if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) {
-      if (PyObject_CallFinalizerFromDealloc(o)) return;
-    }
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryviewslice___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->from_object);
-  PyObject_GC_Track(o);
-  __pyx_tp_dealloc_memoryview(o);
-}
-
-static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
-  if (p->from_object) {
-    e = (*v)(p->from_object, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  __pyx_tp_clear_memoryview(o);
-  tmp = ((PyObject*)p->from_object);
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1);
-  return 0;
-}
-
-static PyMethodDef __pyx_methods__memoryviewslice[] = {
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc__memoryviewslice},
-  {Py_tp_doc, (void *)PyDoc_STR("Internal class for passing memoryview slices to Python")},
-  {Py_tp_traverse, (void *)__pyx_tp_traverse__memoryviewslice},
-  {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice},
-  {Py_tp_methods, (void *)__pyx_methods__memoryviewslice},
-  {Py_tp_new, (void *)__pyx_tp_new__memoryviewslice},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_memoryviewslice_spec = {
-  "monotonic_align.core._memoryviewslice",
-  sizeof(struct __pyx_memoryviewslice_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE,
-  __pyx_type___pyx_memoryviewslice_slots,
-};
-#else
-
-static PyTypeObject __pyx_type___pyx_memoryviewslice = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""_memoryviewslice", /*tp_name*/
-  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY || 0
-  __pyx_memoryview___repr__, /*tp_repr*/
-  #else
-  0, /*tp_repr*/
-  #endif
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  #if CYTHON_COMPILING_IN_PYPY || 0
-  __pyx_memoryview___str__, /*tp_str*/
-  #else
-  0, /*tp_str*/
-  #endif
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, /*tp_flags*/
-  PyDoc_STR("Internal class for passing memoryview slices to Python"), /*tp_doc*/
-  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
-  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods__memoryviewslice, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new__memoryviewslice, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-
-static PyMethodDef __pyx_methods[] = {
-  {0, 0, 0, 0}
-};
-#ifndef CYTHON_SMALL_CODE
-#if defined(__clang__)
-    #define CYTHON_SMALL_CODE
-#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3))
-    #define CYTHON_SMALL_CODE __attribute__((cold))
-#else
-    #define CYTHON_SMALL_CODE
-#endif
-#endif
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-static int __Pyx_CreateStringTabAndInitStrings(void) {
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-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* fastcall */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s)
-{
-    Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames);
-    for (i = 0; i < n; i++)
-    {
-        if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i];
-    }
-    for (i = 0; i < n; i++)
-    {
-        int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ);
-        if (unlikely(eq != 0)) {
-            if (unlikely(eq < 0)) return NULL;  // error
-            return kwvalues[i];
-        }
-    }
-    return NULL;  // not found (no exception set)
-}
-#endif
-
-/* RaiseArgTupleInvalid */
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
-}
-
-/* RaiseDoubleKeywords */
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-/* ParseKeywords */
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds));
-    while (1) {
-        if (kwds_is_tuple) {
-            if (pos >= PyTuple_GET_SIZE(kwds)) break;
-            key = PyTuple_GET_ITEM(kwds, pos);
-            value = kwvalues[pos];
-            pos++;
-        }
-        else
-        {
-            if (!PyDict_Next(kwds, &pos, &key, &value)) break;
-        }
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key)
-                );
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    __Pyx_TypeName type_name;
-    __Pyx_TypeName obj_type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    type_name = __Pyx_PyType_GetName(type);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME
-        ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    CYTHON_UNUSED_VAR(cause);
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-      #if PY_VERSION_HEX >= 0x030C00A6
-        PyException_SetTraceback(value, tb);
-      #elif CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectFastCall */
-static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) {
-    PyObject *argstuple;
-    PyObject *result;
-    size_t i;
-    argstuple = PyTuple_New((Py_ssize_t)nargs);
-    if (unlikely(!argstuple)) return NULL;
-    for (i = 0; i < nargs; i++) {
-        Py_INCREF(args[i]);
-        PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]);
-    }
-    result = __Pyx_PyObject_Call(func, argstuple, kwargs);
-    Py_DECREF(argstuple);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) {
-    Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (nargs == 0 && kwargs == NULL) {
-#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG)
-        if (__Pyx_IsCyOrPyCFunction(func))
-#else
-        if (PyCFunction_Check(func))
-#endif
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-                return __Pyx_PyObject_CallMethO(func, NULL);
-            }
-        }
-    }
-    else if (nargs == 1 && kwargs == NULL) {
-        if (PyCFunction_Check(func))
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-                return __Pyx_PyObject_CallMethO(func, args[0]);
-            }
-        }
-    }
-#endif
-    #if PY_VERSION_HEX < 0x030800B1
-    #if CYTHON_FAST_PYCCALL
-    if (PyCFunction_Check(func)) {
-        if (kwargs) {
-            return _PyCFunction_FastCallDict(func, args, nargs, kwargs);
-        } else {
-            return _PyCFunction_FastCallKeywords(func, args, nargs, NULL);
-        }
-    }
-    #if PY_VERSION_HEX >= 0x030700A1
-    if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) {
-        return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL);
-    }
-    #endif
-    #endif
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs);
-    }
-    #endif
-    #endif
-    #if CYTHON_VECTORCALL
-    vectorcallfunc f = _PyVectorcall_Function(func);
-    if (f) {
-        return f(func, args, (size_t)nargs, kwargs);
-    }
-    #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL
-    if (__Pyx_CyFunction_CheckExact(func)) {
-        __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func);
-        if (f) return f(func, args, (size_t)nargs, kwargs);
-    }
-    #endif
-    if (nargs == 0) {
-        return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs);
-    }
-    return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs);
-}
-
-/* RaiseUnexpectedTypeError */
-static int
-__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj)
-{
-    __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME,
-                 expected, obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* CIntToDigits */
-static const char DIGIT_PAIRS_10[2*10*10+1] = {
-    "00010203040506070809"
-    "10111213141516171819"
-    "20212223242526272829"
-    "30313233343536373839"
-    "40414243444546474849"
-    "50515253545556575859"
-    "60616263646566676869"
-    "70717273747576777879"
-    "80818283848586878889"
-    "90919293949596979899"
-};
-static const char DIGIT_PAIRS_8[2*8*8+1] = {
-    "0001020304050607"
-    "1011121314151617"
-    "2021222324252627"
-    "3031323334353637"
-    "4041424344454647"
-    "5051525354555657"
-    "6061626364656667"
-    "7071727374757677"
-};
-static const char DIGITS_HEX[2*16+1] = {
-    "0123456789abcdef"
-    "0123456789ABCDEF"
-};
-
-/* BuildPyUnicode */
-static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength,
-                                                int prepend_sign, char padding_char) {
-    PyObject *uval;
-    Py_ssize_t uoffset = ulength - clength;
-#if CYTHON_USE_UNICODE_INTERNALS
-    Py_ssize_t i;
-#if CYTHON_PEP393_ENABLED
-    void *udata;
-    uval = PyUnicode_New(ulength, 127);
-    if (unlikely(!uval)) return NULL;
-    udata = PyUnicode_DATA(uval);
-#else
-    Py_UNICODE *udata;
-    uval = PyUnicode_FromUnicode(NULL, ulength);
-    if (unlikely(!uval)) return NULL;
-    udata = PyUnicode_AS_UNICODE(uval);
-#endif
-    if (uoffset > 0) {
-        i = 0;
-        if (prepend_sign) {
-            __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-');
-            i++;
-        }
-        for (; i < uoffset; i++) {
-            __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char);
-        }
-    }
-    for (i=0; i < clength; i++) {
-        __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]);
-    }
-#else
-    {
-        PyObject *sign = NULL, *padding = NULL;
-        uval = NULL;
-        if (uoffset > 0) {
-            prepend_sign = !!prepend_sign;
-            if (uoffset > prepend_sign) {
-                padding = PyUnicode_FromOrdinal(padding_char);
-                if (likely(padding) && uoffset > prepend_sign + 1) {
-                    PyObject *tmp;
-                    PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign);
-                    if (unlikely(!repeat)) goto done_or_error;
-                    tmp = PyNumber_Multiply(padding, repeat);
-                    Py_DECREF(repeat);
-                    Py_DECREF(padding);
-                    padding = tmp;
-                }
-                if (unlikely(!padding)) goto done_or_error;
-            }
-            if (prepend_sign) {
-                sign = PyUnicode_FromOrdinal('-');
-                if (unlikely(!sign)) goto done_or_error;
-            }
-        }
-        uval = PyUnicode_DecodeASCII(chars, clength, NULL);
-        if (likely(uval) && padding) {
-            PyObject *tmp = PyNumber_Add(padding, uval);
-            Py_DECREF(uval);
-            uval = tmp;
-        }
-        if (likely(uval) && sign) {
-            PyObject *tmp = PyNumber_Add(sign, uval);
-            Py_DECREF(uval);
-            uval = tmp;
-        }
-done_or_error:
-        Py_XDECREF(padding);
-        Py_XDECREF(sign);
-    }
-#endif
-    return uval;
-}
-
-/* CIntToPyUnicode */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) {
-    char digits[sizeof(int)*3+2];
-    char *dpos, *end = digits + sizeof(int)*3+2;
-    const char *hex_digits = DIGITS_HEX;
-    Py_ssize_t length, ulength;
-    int prepend_sign, last_one_off;
-    int remaining;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (format_char == 'X') {
-        hex_digits += 16;
-        format_char = 'x';
-    }
-    remaining = value;
-    last_one_off = 0;
-    dpos = end;
-    do {
-        int digit_pos;
-        switch (format_char) {
-        case 'o':
-            digit_pos = abs((int)(remaining % (8*8)));
-            remaining = (int) (remaining / (8*8));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 8);
-            break;
-        case 'd':
-            digit_pos = abs((int)(remaining % (10*10)));
-            remaining = (int) (remaining / (10*10));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 10);
-            break;
-        case 'x':
-            *(--dpos) = hex_digits[abs((int)(remaining % 16))];
-            remaining = (int) (remaining / 16);
-            break;
-        default:
-            assert(0);
-            break;
-        }
-    } while (unlikely(remaining != 0));
-    assert(!last_one_off || *dpos == '0');
-    dpos += last_one_off;
-    length = end - dpos;
-    ulength = length;
-    prepend_sign = 0;
-    if (!is_unsigned && value <= neg_one) {
-        if (padding_char == ' ' || width <= length + 1) {
-            *(--dpos) = '-';
-            ++length;
-        } else {
-            prepend_sign = 1;
-        }
-        ++ulength;
-    }
-    if (width > ulength) {
-        ulength = width;
-    }
-    if (ulength == 1) {
-        return PyUnicode_FromOrdinal(*dpos);
-    }
-    return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char);
-}
-
-/* CIntToPyUnicode */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) {
-    char digits[sizeof(Py_ssize_t)*3+2];
-    char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2;
-    const char *hex_digits = DIGITS_HEX;
-    Py_ssize_t length, ulength;
-    int prepend_sign, last_one_off;
-    Py_ssize_t remaining;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (format_char == 'X') {
-        hex_digits += 16;
-        format_char = 'x';
-    }
-    remaining = value;
-    last_one_off = 0;
-    dpos = end;
-    do {
-        int digit_pos;
-        switch (format_char) {
-        case 'o':
-            digit_pos = abs((int)(remaining % (8*8)));
-            remaining = (Py_ssize_t) (remaining / (8*8));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 8);
-            break;
-        case 'd':
-            digit_pos = abs((int)(remaining % (10*10)));
-            remaining = (Py_ssize_t) (remaining / (10*10));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 10);
-            break;
-        case 'x':
-            *(--dpos) = hex_digits[abs((int)(remaining % 16))];
-            remaining = (Py_ssize_t) (remaining / 16);
-            break;
-        default:
-            assert(0);
-            break;
-        }
-    } while (unlikely(remaining != 0));
-    assert(!last_one_off || *dpos == '0');
-    dpos += last_one_off;
-    length = end - dpos;
-    ulength = length;
-    prepend_sign = 0;
-    if (!is_unsigned && value <= neg_one) {
-        if (padding_char == ' ' || width <= length + 1) {
-            *(--dpos) = '-';
-            ++length;
-        } else {
-            prepend_sign = 1;
-        }
-        ++ulength;
-    }
-    if (width > ulength) {
-        ulength = width;
-    }
-    if (ulength == 1) {
-        return PyUnicode_FromOrdinal(*dpos);
-    }
-    return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char);
-}
-
-/* JoinPyUnicode */
-static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength,
-                                      Py_UCS4 max_char) {
-#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    PyObject *result_uval;
-    int result_ukind, kind_shift;
-    Py_ssize_t i, char_pos;
-    void *result_udata;
-    CYTHON_MAYBE_UNUSED_VAR(max_char);
-#if CYTHON_PEP393_ENABLED
-    result_uval = PyUnicode_New(result_ulength, max_char);
-    if (unlikely(!result_uval)) return NULL;
-    result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND;
-    kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1;
-    result_udata = PyUnicode_DATA(result_uval);
-#else
-    result_uval = PyUnicode_FromUnicode(NULL, result_ulength);
-    if (unlikely(!result_uval)) return NULL;
-    result_ukind = sizeof(Py_UNICODE);
-    kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1;
-    result_udata = PyUnicode_AS_UNICODE(result_uval);
-#endif
-    assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0);
-    char_pos = 0;
-    for (i=0; i < value_count; i++) {
-        int ukind;
-        Py_ssize_t ulength;
-        void *udata;
-        PyObject *uval = PyTuple_GET_ITEM(value_tuple, i);
-        if (unlikely(__Pyx_PyUnicode_READY(uval)))
-            goto bad;
-        ulength = __Pyx_PyUnicode_GET_LENGTH(uval);
-        if (unlikely(!ulength))
-            continue;
-        if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos))
-            goto overflow;
-        ukind = __Pyx_PyUnicode_KIND(uval);
-        udata = __Pyx_PyUnicode_DATA(uval);
-        if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) {
-            memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift));
-        } else {
-            #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters)
-            _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength);
-            #else
-            Py_ssize_t j;
-            for (j=0; j < ulength; j++) {
-                Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j);
-                __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar);
-            }
-            #endif
-        }
-        char_pos += ulength;
-    }
-    return result_uval;
-overflow:
-    PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string");
-bad:
-    Py_DECREF(result_uval);
-    return NULL;
-#else
-    CYTHON_UNUSED_VAR(max_char);
-    CYTHON_UNUSED_VAR(result_ulength);
-    CYTHON_UNUSED_VAR(value_count);
-    return PyUnicode_Join(__pyx_empty_unicode, value_tuple);
-#endif
-}
-
-/* GetAttr */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_USE_TYPE_SLOTS
-#if PY_MAJOR_VERSION >= 3
-    if (likely(PyUnicode_Check(n)))
-#else
-    if (likely(PyString_Check(n)))
-#endif
-        return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
-    return PyObject_GetAttr(o, n);
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (unlikely(!j)) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_subscript) {
-            PyObject *r, *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return NULL;
-            r = mm->mp_subscript(o, key);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* PyObjectCallOneArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *args[2] = {NULL, arg};
-    return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index));
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError,
-            "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name);
-        __Pyx_DECREF_TypeName(index_type_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) {
-    __Pyx_TypeName obj_type_name;
-    if (likely(PyType_Check(obj))) {
-        PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem);
-        if (meth) {
-            PyObject *result = __Pyx_PyObject_CallOneArg(meth, key);
-            Py_DECREF(meth);
-            return result;
-        }
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) {
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyMappingMethods *mm = tp->tp_as_mapping;
-    PySequenceMethods *sm = tp->tp_as_sequence;
-    if (likely(mm && mm->mp_subscript)) {
-        return mm->mp_subscript(obj, key);
-    }
-    if (likely(sm && sm->sq_item)) {
-        return __Pyx_PyObject_GetIndex(obj, key);
-    }
-    return __Pyx_PyObject_GetItem_Slow(obj, key);
-}
-#endif
-
-/* KeywordStringCheck */
-static int __Pyx_CheckKeywordStrings(
-    PyObject *kw,
-    const char* function_name,
-    int kw_allowed)
-{
-    PyObject* key = 0;
-    Py_ssize_t pos = 0;
-#if CYTHON_COMPILING_IN_PYPY
-    if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0))
-        goto invalid_keyword;
-    return 1;
-#else
-    if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) {
-        if (unlikely(PyTuple_GET_SIZE(kw) == 0))
-            return 1;
-        if (!kw_allowed) {
-            key = PyTuple_GET_ITEM(kw, 0);
-            goto invalid_keyword;
-        }
-#if PY_VERSION_HEX < 0x03090000
-        for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) {
-            key = PyTuple_GET_ITEM(kw, pos);
-            if (unlikely(!PyUnicode_Check(key)))
-                goto invalid_keyword_type;
-        }
-#endif
-        return 1;
-    }
-    while (PyDict_Next(kw, &pos, &key, 0)) {
-        #if PY_MAJOR_VERSION < 3
-        if (unlikely(!PyString_Check(key)))
-        #endif
-            if (unlikely(!PyUnicode_Check(key)))
-                goto invalid_keyword_type;
-    }
-    if (!kw_allowed && unlikely(key))
-        goto invalid_keyword;
-    return 1;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    return 0;
-#endif
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-    return 0;
-}
-
-/* DivInt[Py_ssize_t] */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
-    Py_ssize_t q = a / b;
-    Py_ssize_t r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* GetAttr3 */
-static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        return NULL;
-    __Pyx_PyErr_Clear();
-    Py_INCREF(d);
-    return d;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
-    PyObject *r;
-#if CYTHON_USE_TYPE_SLOTS
-    if (likely(PyString_Check(n))) {
-        r = __Pyx_PyObject_GetAttrStrNoError(o, n);
-        if (unlikely(!r) && likely(!PyErr_Occurred())) {
-            r = __Pyx_NewRef(d);
-        }
-        return r;
-    }
-#endif
-    r = PyObject_GetAttr(o, n);
-    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
-}
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#elif CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(!__pyx_m)) {
-        return NULL;
-    }
-    result = PyObject_GetAttr(__pyx_m, name);
-    if (likely(result)) {
-        return result;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* ExtTypeTest */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    __Pyx_TypeName obj_type_name;
-    __Pyx_TypeName type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(__Pyx_TypeCheck(obj, type)))
-        return 1;
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    type_name = __Pyx_PyType_GetName(type);
-    PyErr_Format(PyExc_TypeError,
-                 "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME,
-                 obj_type_name, type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-}
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    PyObject *exc_value = exc_info->exc_value;
-    if (exc_value == NULL || exc_value == Py_None) {
-        *value = NULL;
-        *type = NULL;
-        *tb = NULL;
-    } else {
-        *value = exc_value;
-        Py_INCREF(*value);
-        *type = (PyObject*) Py_TYPE(exc_value);
-        Py_INCREF(*type);
-        *tb = PyException_GetTraceback(exc_value);
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #endif
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    PyObject *tmp_value = exc_info->exc_value;
-    exc_info->exc_value = value;
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-  #else
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-  #endif
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type = NULL, *local_value, *local_tb = NULL;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if PY_VERSION_HEX >= 0x030C00A6
-    local_value = tstate->current_exception;
-    tstate->current_exception = 0;
-    if (likely(local_value)) {
-        local_type = (PyObject*) Py_TYPE(local_value);
-        Py_INCREF(local_type);
-        local_tb = PyException_GetTraceback(local_value);
-    }
-  #else
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-  #endif
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6
-    if (unlikely(tstate->current_exception))
-#elif CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-      #if PY_VERSION_HEX >= 0x030B00a4
-        tmp_value = exc_info->exc_value;
-        exc_info->exc_value = local_value;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-        Py_XDECREF(local_type);
-        Py_XDECREF(local_tb);
-      #else
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-      #endif
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_value = exc_info->exc_value;
-    exc_info->exc_value = *value;
-    if (tmp_value == NULL || tmp_value == Py_None) {
-        Py_XDECREF(tmp_value);
-        tmp_value = NULL;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-    } else {
-        tmp_type = (PyObject*) Py_TYPE(tmp_value);
-        Py_INCREF(tmp_type);
-        #if CYTHON_COMPILING_IN_CPYTHON
-        tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback;
-        Py_XINCREF(tmp_tb);
-        #else
-        tmp_tb = PyException_GetTraceback(tmp_value);
-        #endif
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-  #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-  #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *module = 0;
-    PyObject *empty_dict = 0;
-    PyObject *empty_list = 0;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (unlikely(!py_import))
-        goto bad;
-    if (!from_list) {
-        empty_list = PyList_New(0);
-        if (unlikely(!empty_list))
-            goto bad;
-        from_list = empty_list;
-    }
-    #endif
-    empty_dict = PyDict_New();
-    if (unlikely(!empty_dict))
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                #if CYTHON_COMPILING_IN_LIMITED_API
-                module = PyImport_ImportModuleLevelObject(
-                    name, empty_dict, empty_dict, from_list, 1);
-                #else
-                module = PyImport_ImportModuleLevelObject(
-                    name, __pyx_d, empty_dict, from_list, 1);
-                #endif
-                if (unlikely(!module)) {
-                    if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError)))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (unlikely(!py_level))
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            #if CYTHON_COMPILING_IN_LIMITED_API
-            module = PyImport_ImportModuleLevelObject(
-                name, empty_dict, empty_dict, from_list, level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, __pyx_d, empty_dict, from_list, level);
-            #endif
-            #endif
-        }
-    }
-bad:
-    Py_XDECREF(empty_dict);
-    Py_XDECREF(empty_list);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    return module;
-}
-
-/* ImportDottedModule */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) {
-    PyObject *partial_name = NULL, *slice = NULL, *sep = NULL;
-    if (unlikely(PyErr_Occurred())) {
-        PyErr_Clear();
-    }
-    if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) {
-        partial_name = name;
-    } else {
-        slice = PySequence_GetSlice(parts_tuple, 0, count);
-        if (unlikely(!slice))
-            goto bad;
-        sep = PyUnicode_FromStringAndSize(".", 1);
-        if (unlikely(!sep))
-            goto bad;
-        partial_name = PyUnicode_Join(sep, slice);
-    }
-    PyErr_Format(
-#if PY_MAJOR_VERSION < 3
-        PyExc_ImportError,
-        "No module named '%s'", PyString_AS_STRING(partial_name));
-#else
-#if PY_VERSION_HEX >= 0x030600B1
-        PyExc_ModuleNotFoundError,
-#else
-        PyExc_ImportError,
-#endif
-        "No module named '%U'", partial_name);
-#endif
-bad:
-    Py_XDECREF(sep);
-    Py_XDECREF(slice);
-    Py_XDECREF(partial_name);
-    return NULL;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) {
-    PyObject *imported_module;
-#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-    PyObject *modules = PyImport_GetModuleDict();
-    if (unlikely(!modules))
-        return NULL;
-    imported_module = __Pyx_PyDict_GetItemStr(modules, name);
-    Py_XINCREF(imported_module);
-#else
-    imported_module = PyImport_GetModule(name);
-#endif
-    return imported_module;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) {
-    Py_ssize_t i, nparts;
-    nparts = PyTuple_GET_SIZE(parts_tuple);
-    for (i=1; i < nparts && module; i++) {
-        PyObject *part, *submodule;
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        part = PyTuple_GET_ITEM(parts_tuple, i);
-#else
-        part = PySequence_ITEM(parts_tuple, i);
-#endif
-        submodule = __Pyx_PyObject_GetAttrStrNoError(module, part);
-#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-        Py_DECREF(part);
-#endif
-        Py_DECREF(module);
-        module = submodule;
-    }
-    if (unlikely(!module)) {
-        return __Pyx__ImportDottedModule_Error(name, parts_tuple, i);
-    }
-    return module;
-}
-#endif
-static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if PY_MAJOR_VERSION < 3
-    PyObject *module, *from_list, *star = __pyx_n_s__3;
-    CYTHON_UNUSED_VAR(parts_tuple);
-    from_list = PyList_New(1);
-    if (unlikely(!from_list))
-        return NULL;
-    Py_INCREF(star);
-    PyList_SET_ITEM(from_list, 0, star);
-    module = __Pyx_Import(name, from_list, 0);
-    Py_DECREF(from_list);
-    return module;
-#else
-    PyObject *imported_module;
-    PyObject *module = __Pyx_Import(name, NULL, 0);
-    if (!parts_tuple || unlikely(!module))
-        return module;
-    imported_module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(imported_module)) {
-        Py_DECREF(module);
-        return imported_module;
-    }
-    PyErr_Clear();
-    return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple);
-#endif
-}
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1
-    PyObject *module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(module)) {
-        PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec);
-        if (likely(spec)) {
-            PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing);
-            if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) {
-                Py_DECREF(spec);
-                spec = NULL;
-            }
-            Py_XDECREF(unsafe);
-        }
-        if (likely(!spec)) {
-            PyErr_Clear();
-            return module;
-        }
-        Py_DECREF(spec);
-        Py_DECREF(module);
-    } else if (PyErr_Occurred()) {
-        PyErr_Clear();
-    }
-#endif
-    return __Pyx__ImportDottedModule(name, parts_tuple);
-}
-
-/* ssize_strlen */
-static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) {
-    size_t len = strlen(s);
-    if (unlikely(len > PY_SSIZE_T_MAX)) {
-        PyErr_SetString(PyExc_OverflowError, "byte string is too long");
-        return -1;
-    }
-    return (Py_ssize_t) len;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*);
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (cls == a || cls == b) return 1;
-    mro = cls->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            PyObject *base = PyTuple_GET_ITEM(mro, i);
-            if (base == (PyObject *)a || base == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    if (exc_type1) {
-        return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2);
-    } else {
-        return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* PySequenceMultiply */
-static PyObject* __Pyx_PySequence_Multiply_Generic(PyObject *seq, Py_ssize_t mul) {
-    PyObject *result, *pymul = PyInt_FromSsize_t(mul);
-    if (unlikely(!pymul))
-        return NULL;
-    result = PyNumber_Multiply(seq, pymul);
-    Py_DECREF(pymul);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul) {
-#if CYTHON_USE_TYPE_SLOTS
-    PyTypeObject *type = Py_TYPE(seq);
-    if (likely(type->tp_as_sequence && type->tp_as_sequence->sq_repeat)) {
-        return type->tp_as_sequence->sq_repeat(seq, mul);
-    } else
-#endif
-    {
-        return __Pyx_PySequence_Multiply_Generic(seq, mul);
-    }
-}
-
-/* SetItemInt */
-static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
-    int r;
-    if (unlikely(!j)) return -1;
-    r = PyObject_SetItem(o, j, v);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list,
-                                               CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o));
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) {
-            PyObject* old = PyList_GET_ITEM(o, n);
-            Py_INCREF(v);
-            PyList_SET_ITEM(o, n, v);
-            Py_DECREF(old);
-            return 1;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_ass_subscript) {
-            int r;
-            PyObject *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return -1;
-            r = mm->mp_ass_subscript(o, key, v);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_ass_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return -1;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_ass_item(o, i, v);
-        }
-    }
-#else
-#if CYTHON_COMPILING_IN_PYPY
-    if (is_list || (PySequence_Check(o) && !PyDict_Check(o)))
-#else
-    if (is_list || PySequence_Check(o))
-#endif
-    {
-        return PySequence_SetItem(o, i, v);
-    }
-#endif
-    return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
-}
-
-/* RaiseUnboundLocalError */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* DivInt[long] */
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        const char* module_name_str = 0;
-        PyObject* module_name = 0;
-        PyObject* module_dot = 0;
-        PyObject* full_name = 0;
-        PyErr_Clear();
-        module_name_str = PyModule_GetName(module);
-        if (unlikely(!module_name_str)) { goto modbad; }
-        module_name = PyUnicode_FromString(module_name_str);
-        if (unlikely(!module_name)) { goto modbad; }
-        module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2);
-        if (unlikely(!module_dot)) { goto modbad; }
-        full_name = PyUnicode_Concat(module_dot, name);
-        if (unlikely(!full_name)) { goto modbad; }
-        #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-        {
-            PyObject *modules = PyImport_GetModuleDict();
-            if (unlikely(!modules))
-                goto modbad;
-            value = PyObject_GetItem(modules, full_name);
-        }
-        #else
-        value = PyImport_GetModule(full_name);
-        #endif
-      modbad:
-        Py_XDECREF(full_name);
-        Py_XDECREF(module_dot);
-        Py_XDECREF(module_name);
-    }
-    if (unlikely(!value)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* HasAttr */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
-    PyObject *r;
-    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "hasattr(): attribute name must be string");
-        return -1;
-    }
-    r = __Pyx_GetAttr(o, n);
-    if (!r) {
-        PyErr_Clear();
-        return 0;
-    } else {
-        Py_DECREF(r);
-        return 1;
-    }
-}
-
-/* ErrOccurredWithGIL */
-static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) {
-  int err;
-  #ifdef WITH_THREAD
-  PyGILState_STATE _save = PyGILState_Ensure();
-  #endif
-  err = !!PyErr_Occurred();
-  #ifdef WITH_THREAD
-  PyGILState_Release(_save);
-  #endif
-  return err;
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, attr_name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(attr_name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* PyObject_GenericGetAttr */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
-    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
-}
-#endif
-
-/* FixUpExtensionType */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) {
-#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    CYTHON_UNUSED_VAR(spec);
-    CYTHON_UNUSED_VAR(type);
-#else
-    const PyType_Slot *slot = spec->slots;
-    while (slot && slot->slot && slot->slot != Py_tp_members)
-        slot++;
-    if (slot && slot->slot == Py_tp_members) {
-        int changed = 0;
-#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON)
-        const
-#endif
-            PyMemberDef *memb = (PyMemberDef*) slot->pfunc;
-        while (memb && memb->name) {
-            if (memb->name[0] == '_' && memb->name[1] == '_') {
-#if PY_VERSION_HEX < 0x030900b1
-                if (strcmp(memb->name, "__weaklistoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_weaklistoffset = memb->offset;
-                    changed = 1;
-                }
-                else if (strcmp(memb->name, "__dictoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_dictoffset = memb->offset;
-                    changed = 1;
-                }
-#if CYTHON_METH_FASTCALL
-                else if (strcmp(memb->name, "__vectorcalloffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-#if PY_VERSION_HEX >= 0x030800b4
-                    type->tp_vectorcall_offset = memb->offset;
-#else
-                    type->tp_print = (printfunc) memb->offset;
-#endif
-                    changed = 1;
-                }
-#endif
-#else
-                if ((0));
-#endif
-#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON
-                else if (strcmp(memb->name, "__module__") == 0) {
-                    PyObject *descr;
-                    assert(memb->type == T_OBJECT);
-                    assert(memb->flags == 0 || memb->flags == READONLY);
-                    descr = PyDescr_NewMember(type, memb);
-                    if (unlikely(!descr))
-                        return -1;
-                    if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) {
-                        Py_DECREF(descr);
-                        return -1;
-                    }
-                    Py_DECREF(descr);
-                    changed = 1;
-                }
-#endif
-            }
-            memb++;
-        }
-        if (changed)
-            PyType_Modified(type);
-    }
-#endif
-    return 0;
-}
-#endif
-
-/* PyObjectCallNoArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-    PyObject *arg = NULL;
-    return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
-    PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
-    __Pyx_TypeName type_name;
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyObject *descr;
-    descrgetfunc f = NULL;
-    PyObject **dictptr, *dict;
-    int meth_found = 0;
-    assert (*method == NULL);
-    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
-        attr = __Pyx_PyObject_GetAttrStr(obj, name);
-        goto try_unpack;
-    }
-    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
-        return 0;
-    }
-    descr = _PyType_Lookup(tp, name);
-    if (likely(descr != NULL)) {
-        Py_INCREF(descr);
-#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR
-        if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR))
-#elif PY_MAJOR_VERSION >= 3
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type)))
-        #endif
-#else
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr)))
-        #endif
-#endif
-        {
-            meth_found = 1;
-        } else {
-            f = Py_TYPE(descr)->tp_descr_get;
-            if (f != NULL && PyDescr_IsData(descr)) {
-                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-                Py_DECREF(descr);
-                goto try_unpack;
-            }
-        }
-    }
-    dictptr = _PyObject_GetDictPtr(obj);
-    if (dictptr != NULL && (dict = *dictptr) != NULL) {
-        Py_INCREF(dict);
-        attr = __Pyx_PyDict_GetItemStr(dict, name);
-        if (attr != NULL) {
-            Py_INCREF(attr);
-            Py_DECREF(dict);
-            Py_XDECREF(descr);
-            goto try_unpack;
-        }
-        Py_DECREF(dict);
-    }
-    if (meth_found) {
-        *method = descr;
-        return 1;
-    }
-    if (f != NULL) {
-        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-        Py_DECREF(descr);
-        goto try_unpack;
-    }
-    if (likely(descr != NULL)) {
-        *method = descr;
-        return 0;
-    }
-    type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-#else
-    attr = __Pyx_PyObject_GetAttrStr(obj, name);
-    goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
-    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
-        PyObject *function = PyMethod_GET_FUNCTION(attr);
-        Py_INCREF(function);
-        Py_DECREF(attr);
-        *method = function;
-        return 1;
-    }
-#endif
-    *method = attr;
-    return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
-    PyObject *method = NULL, *result = NULL;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_CallOneArg(method, obj);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) goto bad;
-    result = __Pyx_PyObject_CallNoArg(method);
-    Py_DECREF(method);
-bad:
-    return result;
-}
-
-/* ValidateBasesTuple */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) {
-    Py_ssize_t i, n = PyTuple_GET_SIZE(bases);
-    for (i = 1; i < n; i++)
-    {
-        PyObject *b0 = PyTuple_GET_ITEM(bases, i);
-        PyTypeObject *b;
-#if PY_MAJOR_VERSION < 3
-        if (PyClass_Check(b0))
-        {
-            PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class",
-                         PyString_AS_STRING(((PyClassObject*)b0)->cl_name));
-            return -1;
-        }
-#endif
-        b = (PyTypeObject*) b0;
-        if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE))
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-        if (dictoffset == 0 && b->tp_dictoffset)
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "extension type '%.200s' has no __dict__ slot, "
-                "but base type '" __Pyx_FMT_TYPENAME "' has: "
-                "either add 'cdef dict __dict__' to the extension type "
-                "or add '__slots__ = [...]' to the base type",
-                type_name, b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-    }
-    return 0;
-}
-#endif
-
-/* PyType_Ready */
-static int __Pyx_PyType_Ready(PyTypeObject *t) {
-#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION)
-    (void)__Pyx_PyObject_CallMethod0;
-#if CYTHON_USE_TYPE_SPECS
-    (void)__Pyx_validate_bases_tuple;
-#endif
-    return PyType_Ready(t);
-#else
-    int r;
-    PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*);
-    if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1))
-        return -1;
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-    {
-        int gc_was_enabled;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        gc_was_enabled = PyGC_Disable();
-        (void)__Pyx_PyObject_CallMethod0;
-    #else
-        PyObject *ret, *py_status;
-        PyObject *gc = NULL;
-        #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400)
-        gc = PyImport_GetModule(__pyx_kp_u_gc);
-        #endif
-        if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc);
-        if (unlikely(!gc)) return -1;
-        py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled);
-        if (unlikely(!py_status)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-        gc_was_enabled = __Pyx_PyObject_IsTrue(py_status);
-        Py_DECREF(py_status);
-        if (gc_was_enabled > 0) {
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable);
-            if (unlikely(!ret)) {
-                Py_DECREF(gc);
-                return -1;
-            }
-            Py_DECREF(ret);
-        } else if (unlikely(gc_was_enabled == -1)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-    #endif
-        t->tp_flags |= Py_TPFLAGS_HEAPTYPE;
-#if PY_VERSION_HEX >= 0x030A0000
-        t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE;
-#endif
-#else
-        (void)__Pyx_PyObject_CallMethod0;
-#endif
-    r = PyType_Ready(t);
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-        t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        if (gc_was_enabled)
-            PyGC_Enable();
-    #else
-        if (gc_was_enabled) {
-            PyObject *tp, *v, *tb;
-            PyErr_Fetch(&tp, &v, &tb);
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable);
-            if (likely(ret || r == -1)) {
-                Py_XDECREF(ret);
-                PyErr_Restore(tp, v, tb);
-            } else {
-                Py_XDECREF(tp);
-                Py_XDECREF(v);
-                Py_XDECREF(tb);
-                r = -1;
-            }
-        }
-        Py_DECREF(gc);
-    #endif
-    }
-#endif
-    return r;
-#endif
-}
-
-/* SetVTable */
-static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) {
-    PyObject *ob = PyCapsule_New(vtable, 0, 0);
-    if (unlikely(!ob))
-        goto bad;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0))
-#else
-    if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0))
-#endif
-        goto bad;
-    Py_DECREF(ob);
-    return 0;
-bad:
-    Py_XDECREF(ob);
-    return -1;
-}
-
-/* GetVTable */
-static void* __Pyx_GetVtable(PyTypeObject *type) {
-    void* ptr;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable);
-#else
-    PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable);
-#endif
-    if (!ob)
-        goto bad;
-    ptr = PyCapsule_GetPointer(ob, 0);
-    if (!ptr && !PyErr_Occurred())
-        PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type");
-    Py_DECREF(ob);
-    return ptr;
-bad:
-    Py_XDECREF(ob);
-    return NULL;
-}
-
-/* MergeVTables */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_MergeVtables(PyTypeObject *type) {
-    int i;
-    void** base_vtables;
-    __Pyx_TypeName tp_base_name;
-    __Pyx_TypeName base_name;
-    void* unknown = (void*)-1;
-    PyObject* bases = type->tp_bases;
-    int base_depth = 0;
-    {
-        PyTypeObject* base = type->tp_base;
-        while (base) {
-            base_depth += 1;
-            base = base->tp_base;
-        }
-    }
-    base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1));
-    base_vtables[0] = unknown;
-    for (i = 1; i < PyTuple_GET_SIZE(bases); i++) {
-        void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i)));
-        if (base_vtable != NULL) {
-            int j;
-            PyTypeObject* base = type->tp_base;
-            for (j = 0; j < base_depth; j++) {
-                if (base_vtables[j] == unknown) {
-                    base_vtables[j] = __Pyx_GetVtable(base);
-                    base_vtables[j + 1] = unknown;
-                }
-                if (base_vtables[j] == base_vtable) {
-                    break;
-                } else if (base_vtables[j] == NULL) {
-                    goto bad;
-                }
-                base = base->tp_base;
-            }
-        }
-    }
-    PyErr_Clear();
-    free(base_vtables);
-    return 0;
-bad:
-    tp_base_name = __Pyx_PyType_GetName(type->tp_base);
-    base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i));
-    PyErr_Format(PyExc_TypeError,
-        "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name);
-    __Pyx_DECREF_TypeName(tp_base_name);
-    __Pyx_DECREF_TypeName(base_name);
-    free(base_vtables);
-    return -1;
-}
-#endif
-
-/* SetupReduce */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
-  int ret;
-  PyObject *name_attr;
-  name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2);
-  if (likely(name_attr)) {
-      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
-  } else {
-      ret = -1;
-  }
-  if (unlikely(ret < 0)) {
-      PyErr_Clear();
-      ret = 0;
-  }
-  Py_XDECREF(name_attr);
-  return ret;
-}
-static int __Pyx_setup_reduce(PyObject* type_obj) {
-    int ret = 0;
-    PyObject *object_reduce = NULL;
-    PyObject *object_getstate = NULL;
-    PyObject *object_reduce_ex = NULL;
-    PyObject *reduce = NULL;
-    PyObject *reduce_ex = NULL;
-    PyObject *reduce_cython = NULL;
-    PyObject *setstate = NULL;
-    PyObject *setstate_cython = NULL;
-    PyObject *getstate = NULL;
-#if CYTHON_USE_PYTYPE_LOOKUP
-    getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate);
-#else
-    getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate);
-    if (!getstate && PyErr_Occurred()) {
-        goto __PYX_BAD;
-    }
-#endif
-    if (getstate) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate);
-#else
-        object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate);
-        if (!object_getstate && PyErr_Occurred()) {
-            goto __PYX_BAD;
-        }
-#endif
-        if (object_getstate != getstate) {
-            goto __PYX_GOOD;
-        }
-    }
-#if CYTHON_USE_PYTYPE_LOOKUP
-    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#else
-    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#endif
-    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
-    if (reduce_ex == object_reduce_ex) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#else
-        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#endif
-        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
-        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
-            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
-            if (likely(reduce_cython)) {
-                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-            } else if (reduce == object_reduce || PyErr_Occurred()) {
-                goto __PYX_BAD;
-            }
-            setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate);
-            if (!setstate) PyErr_Clear();
-            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
-                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
-                if (likely(setstate_cython)) {
-                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                } else if (!setstate || PyErr_Occurred()) {
-                    goto __PYX_BAD;
-                }
-            }
-            PyType_Modified((PyTypeObject*)type_obj);
-        }
-    }
-    goto __PYX_GOOD;
-__PYX_BAD:
-    if (!PyErr_Occurred()) {
-        __Pyx_TypeName type_obj_name =
-            __Pyx_PyType_GetName((PyTypeObject*)type_obj);
-        PyErr_Format(PyExc_RuntimeError,
-            "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name);
-        __Pyx_DECREF_TypeName(type_obj_name);
-    }
-    ret = -1;
-__PYX_GOOD:
-#if !CYTHON_USE_PYTYPE_LOOKUP
-    Py_XDECREF(object_reduce);
-    Py_XDECREF(object_reduce_ex);
-    Py_XDECREF(object_getstate);
-    Py_XDECREF(getstate);
-#endif
-    Py_XDECREF(reduce);
-    Py_XDECREF(reduce_ex);
-    Py_XDECREF(reduce_cython);
-    Py_XDECREF(setstate);
-    Py_XDECREF(setstate_cython);
-    return ret;
-}
-#endif
-
-/* FetchSharedCythonModule */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
-    PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
-    if (unlikely(!abi_module)) return NULL;
-    Py_INCREF(abi_module);
-    return abi_module;
-}
-
-/* FetchCommonType */
-static int __Pyx_VerifyCachedType(PyObject *cached_type,
-                               const char *name,
-                               Py_ssize_t basicsize,
-                               Py_ssize_t expected_basicsize) {
-    if (!PyType_Check(cached_type)) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s is not a type object", name);
-        return -1;
-    }
-    if (basicsize != expected_basicsize) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s has the wrong size, try recompiling",
-            name);
-        return -1;
-    }
-    return 0;
-}
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* abi_module;
-    const char* object_name;
-    PyTypeObject *cached_type = NULL;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    object_name = strrchr(type->tp_name, '.');
-    object_name = object_name ? object_name+1 : type->tp_name;
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        if (__Pyx_VerifyCachedType(
-              (PyObject *)cached_type,
-              object_name,
-              cached_type->tp_basicsize,
-              type->tp_basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    if (PyType_Ready(type) < 0) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0)
-        goto bad;
-    Py_INCREF(type);
-    cached_type = type;
-done:
-    Py_DECREF(abi_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#else
-static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) {
-    PyObject *abi_module, *cached_type = NULL;
-    const char* object_name = strrchr(spec->name, '.');
-    object_name = object_name ? object_name+1 : spec->name;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    cached_type = PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        Py_ssize_t basicsize;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        PyObject *py_basicsize;
-        py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__");
-        if (unlikely(!py_basicsize)) goto bad;
-        basicsize = PyLong_AsSsize_t(py_basicsize);
-        Py_DECREF(py_basicsize);
-        py_basicsize = 0;
-        if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-#else
-        basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1;
-#endif
-        if (__Pyx_VerifyCachedType(
-              cached_type,
-              object_name,
-              basicsize,
-              spec->basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    CYTHON_UNUSED_VAR(module);
-    cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases);
-    if (unlikely(!cached_type)) goto bad;
-    if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad;
-done:
-    Py_DECREF(abi_module);
-    assert(cached_type == NULL || PyType_Check(cached_type));
-    return (PyTypeObject *) cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#endif
-
-/* PyVectorcallFastCallDict */
-#if CYTHON_METH_FASTCALL
-static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    PyObject *res = NULL;
-    PyObject *kwnames;
-    PyObject **newargs;
-    PyObject **kwvalues;
-    Py_ssize_t i, pos;
-    size_t j;
-    PyObject *key, *value;
-    unsigned long keys_are_strings;
-    Py_ssize_t nkw = PyDict_GET_SIZE(kw);
-    newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0]));
-    if (unlikely(newargs == NULL)) {
-        PyErr_NoMemory();
-        return NULL;
-    }
-    for (j = 0; j < nargs; j++) newargs[j] = args[j];
-    kwnames = PyTuple_New(nkw);
-    if (unlikely(kwnames == NULL)) {
-        PyMem_Free(newargs);
-        return NULL;
-    }
-    kwvalues = newargs + nargs;
-    pos = i = 0;
-    keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS;
-    while (PyDict_Next(kw, &pos, &key, &value)) {
-        keys_are_strings &= Py_TYPE(key)->tp_flags;
-        Py_INCREF(key);
-        Py_INCREF(value);
-        PyTuple_SET_ITEM(kwnames, i, key);
-        kwvalues[i] = value;
-        i++;
-    }
-    if (unlikely(!keys_are_strings)) {
-        PyErr_SetString(PyExc_TypeError, "keywords must be strings");
-        goto cleanup;
-    }
-    res = vc(func, newargs, nargs, kwnames);
-cleanup:
-    Py_DECREF(kwnames);
-    for (i = 0; i < nkw; i++)
-        Py_DECREF(kwvalues[i]);
-    PyMem_Free(newargs);
-    return res;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) {
-        return vc(func, args, nargs, NULL);
-    }
-    return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw);
-}
-#endif
-
-/* CythonFunctionShared */
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1
-    __Pyx_Py_XDECREF_SET(
-        __Pyx_CyFunction_GetClassObj(f),
-            ((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#else
-    __Pyx_Py_XDECREF_SET(
-        ((PyCMethodObject *) (f))->mm_class,
-        (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#endif
-}
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
-{
-    CYTHON_UNUSED_VAR(closure);
-    if (unlikely(op->func_doc == NULL)) {
-        if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_doc, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_dict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(op);
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyTuple_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_tuple, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_tuple;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_kwdict;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_annotations, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->func_annotations;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyObject *
-__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
-    int is_coroutine;
-    CYTHON_UNUSED_VAR(context);
-    if (op->func_is_coroutine) {
-        return __Pyx_NewRef(op->func_is_coroutine);
-    }
-    is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE;
-#if PY_VERSION_HEX >= 0x03050000
-    if (is_coroutine) {
-        PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine;
-        fromlist = PyList_New(1);
-        if (unlikely(!fromlist)) return NULL;
-        Py_INCREF(marker);
-        PyList_SET_ITEM(fromlist, 0, marker);
-        module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
-        Py_DECREF(fromlist);
-        if (unlikely(!module)) goto ignore;
-        op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker);
-        Py_DECREF(module);
-        if (likely(op->func_is_coroutine)) {
-            return __Pyx_NewRef(op->func_is_coroutine);
-        }
-ignore:
-        PyErr_Clear();
-    }
-#endif
-    op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
-    return __Pyx_NewRef(op->func_is_coroutine);
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
-#if CYTHON_METH_FASTCALL
-#if CYTHON_BACKPORT_VECTORCALL
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
-#else
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
-#endif
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
-#else
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
-#endif
-#endif
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args)
-{
-    CYTHON_UNUSED_VAR(args);
-#if PY_MAJOR_VERSION >= 3
-    Py_INCREF(m->func_qualname);
-    return m->func_qualname;
-#else
-    return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyCFunctionObject *cf = (PyCFunctionObject*) op;
-    if (unlikely(op == NULL))
-        return NULL;
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-    cf->m_ml = ml;
-    cf->m_self = (PyObject *) op;
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-    Py_XINCREF(module);
-    cf->m_module = module;
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1
-    op->func_classobj = NULL;
-#else
-    ((PyCMethodObject*)op)->mm_class = NULL;
-#endif
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    op->func_is_coroutine = NULL;
-#if CYTHON_METH_FASTCALL
-    switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) {
-    case METH_NOARGS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS;
-        break;
-    case METH_O:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O;
-        break;
-    case METH_METHOD | METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD;
-        break;
-    case METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS;
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = NULL;
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        Py_DECREF(op);
-        return NULL;
-    }
-#endif
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-    Py_CLEAR(((PyCFunctionObject*)m)->m_module);
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-#if PY_VERSION_HEX < 0x030900B1
-    Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
-#else
-    {
-        PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class;
-        ((PyCMethodObject *) (m))->mm_class = NULL;
-        Py_XDECREF(cls);
-    }
-#endif
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    Py_CLEAR(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    __Pyx_PyHeapTypeObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-    Py_VISIT(((PyCFunctionObject*)m)->m_module);
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-    Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    Py_VISIT(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        return NULL;
-    }
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-#if CYTHON_METH_FASTCALL
-     __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
-    if (vc) {
-#if CYTHON_ASSUME_SAFE_MACROS
-        return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
-#else
-        (void) &__Pyx_PyVectorcall_FastCallDict;
-        return PyVectorcall_Call(func, args, kw);
-#endif
-    }
-#endif
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-        argc = PyTuple_GET_SIZE(args);
-        new_args = PyTuple_GetSlice(args, 1, argc);
-        if (unlikely(!new_args))
-            return NULL;
-        self = PyTuple_GetItem(args, 0);
-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-#if PY_MAJOR_VERSION > 2
-            PyErr_Format(PyExc_TypeError,
-                         "unbound method %.200S() needs an argument",
-                         cyfunc->func_qualname);
-#else
-            PyErr_SetString(PyExc_TypeError,
-                            "unbound method needs an argument");
-#endif
-            return NULL;
-        }
-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
-}
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames)
-{
-    int ret = 0;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        if (unlikely(nargs < 1)) {
-            PyErr_Format(PyExc_TypeError, "%.200s() needs an argument",
-                         ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-            return -1;
-        }
-        ret = 1;
-    }
-    if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) {
-        PyErr_Format(PyExc_TypeError,
-                     "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-        return -1;
-    }
-    return ret;
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 0)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, NULL);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 1)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, args[0]);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-    PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc);
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames);
-}
-#endif
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_CyFunctionType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc},
-    {Py_tp_repr, (void *)__Pyx_CyFunction_repr},
-    {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod},
-    {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse},
-    {Py_tp_clear, (void *)__Pyx_CyFunction_clear},
-    {Py_tp_methods, (void *)__pyx_CyFunction_methods},
-    {Py_tp_members, (void *)__pyx_CyFunction_members},
-    {Py_tp_getset, (void *)__pyx_CyFunction_getsets},
-    {Py_tp_descr_get, (void *)__Pyx_PyMethod_New},
-    {0, 0},
-};
-static PyType_Spec __pyx_CyFunctionType_spec = {
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL)
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    __pyx_CyFunctionType_slots
-};
-#else
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-    (destructor) __Pyx_CyFunction_dealloc,
-#if !CYTHON_METH_FASTCALL
-    0,
-#elif CYTHON_BACKPORT_VECTORCALL
-    (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall),
-#else
-    offsetof(PyCFunctionObject, vectorcall),
-#endif
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#ifdef _Py_TPFLAGS_HAVE_VECTORCALL
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_PyMethod_New,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_CyFunction_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-#endif
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
-    memset(m->defaults, 0, size);
-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
-        ml, flags, qualname, closure, module, globals, code
-    );
-    if (likely(op)) {
-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    CYTHON_MAYBE_UNUSED_VAR(tstate);
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-#endif
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    if (c_line) {
-        (void) __pyx_cfilenm;
-        (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
-    }
-    _PyTraceback_Add(funcname, filename, py_line);
-}
-#else
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-#endif
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-    __Pyx_TypeName obj_type_name;
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-                 "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface",
-                 obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-    if ((0)) {}
-    view->obj = NULL;
-    Py_DECREF(obj);
-}
-#endif
-
-
-/* MemviewSliceIsContig */
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
-{
-    int i, index, step, start;
-    Py_ssize_t itemsize = mvs.memview->view.itemsize;
-    if (order == 'F') {
-        step = 1;
-        start = 0;
-    } else {
-        step = -1;
-        start = ndim - 1;
-    }
-    for (i = 0; i < ndim; i++) {
-        index = start + step * i;
-        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
-            return 0;
-        itemsize *= mvs.shape[index];
-    }
-    return 1;
-}
-
-/* OverlappingSlices */
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
-                               void **out_start, void **out_end,
-                               int ndim, size_t itemsize)
-{
-    char *start, *end;
-    int i;
-    start = end = slice->data;
-    for (i = 0; i < ndim; i++) {
-        Py_ssize_t stride = slice->strides[i];
-        Py_ssize_t extent = slice->shape[i];
-        if (extent == 0) {
-            *out_start = *out_end = start;
-            return;
-        } else {
-            if (stride > 0)
-                end += stride * (extent - 1);
-            else
-                start += stride * (extent - 1);
-        }
-    }
-    *out_start = start;
-    *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                     __Pyx_memviewslice *slice2,
-                     int ndim, size_t itemsize)
-{
-    void *start1, *end1, *start2, *end2;
-    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
-    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
-    return (start1 < end2) && (start2 < end1);
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
-  union {
-    uint32_t u32;
-    uint8_t u8[4];
-  } S;
-  S.u32 = 0x01020304;
-  return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t <= '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case '?': return "'bool'";
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparsable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) {
-  CYTHON_UNUSED_VAR(is_complex);
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) {
-  CYTHON_UNUSED_VAR(is_complex);
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number, ndim;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    ndim = ctx->head->field->type->ndim;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-        CYTHON_FALLTHROUGH;
-      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
-            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-        CYTHON_FALLTHROUGH;
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-
-/* TypeInfoCompare */
-  static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
-    int i;
-    if (!a || !b)
-        return 0;
-    if (a == b)
-        return 1;
-    if (a->size != b->size || a->typegroup != b->typegroup ||
-            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
-        if (a->typegroup == 'H' || b->typegroup == 'H') {
-            return a->size == b->size;
-        } else {
-            return 0;
-        }
-    }
-    if (a->ndim) {
-        for (i = 0; i < a->ndim; i++)
-            if (a->arraysize[i] != b->arraysize[i])
-                return 0;
-    }
-    if (a->typegroup == 'S') {
-        if (a->flags != b->flags)
-            return 0;
-        if (a->fields || b->fields) {
-            if (!(a->fields && b->fields))
-                return 0;
-            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
-                __Pyx_StructField *field_a = a->fields + i;
-                __Pyx_StructField *field_b = b->fields + i;
-                if (field_a->offset != field_b->offset ||
-                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
-                    return 0;
-            }
-            return !a->fields[i].type && !b->fields[i].type;
-        }
-    }
-    return 1;
-}
-
-/* MemviewSliceValidateAndInit */
-  static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    if (buf->shape[dim] <= 1)
-        return 1;
-    if (buf->strides) {
-        if (spec & __Pyx_MEMVIEW_CONTIG) {
-            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
-                if (unlikely(buf->strides[dim] != sizeof(void *))) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "Buffer is not indirectly contiguous "
-                                 "in dimension %d.", dim);
-                    goto fail;
-                }
-            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-        if (spec & __Pyx_MEMVIEW_FOLLOW) {
-            Py_ssize_t stride = buf->strides[dim];
-            if (stride < 0)
-                stride = -stride;
-            if (unlikely(stride < buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-    } else {
-        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not contiguous in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not indirect in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(buf->suboffsets)) {
-            PyErr_SetString(PyExc_ValueError,
-                            "Buffer exposes suboffsets but no strides");
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    CYTHON_UNUSED_VAR(ndim);
-    if (spec & __Pyx_MEMVIEW_DIRECT) {
-        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer not compatible with direct access "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    if (spec & __Pyx_MEMVIEW_PTR) {
-        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer is not indirectly accessible "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
-    int i;
-    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = 0; i < ndim; i++) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not fortran contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = ndim - 1; i >- 1; i--) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not C contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj)
-{
-    struct __pyx_memoryview_obj *memview, *new_memview;
-    __Pyx_RefNannyDeclarations
-    Py_buffer *buf;
-    int i, spec = 0, retval = -1;
-    __Pyx_BufFmt_Context ctx;
-    int from_memoryview = __pyx_memoryview_check(original_obj);
-    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
-    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
-                                                            original_obj)->typeinfo)) {
-        memview = (struct __pyx_memoryview_obj *) original_obj;
-        new_memview = NULL;
-    } else {
-        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                            original_obj, buf_flags, 0, dtype);
-        new_memview = memview;
-        if (unlikely(!memview))
-            goto fail;
-    }
-    buf = &memview->view;
-    if (unlikely(buf->ndim != ndim)) {
-        PyErr_Format(PyExc_ValueError,
-                "Buffer has wrong number of dimensions (expected %d, got %d)",
-                ndim, buf->ndim);
-        goto fail;
-    }
-    if (new_memview) {
-        __Pyx_BufFmt_Init(&ctx, stack, dtype);
-        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
-    }
-    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
-        PyErr_Format(PyExc_ValueError,
-                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
-                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
-                     buf->itemsize,
-                     (buf->itemsize > 1) ? "s" : "",
-                     dtype->name,
-                     dtype->size,
-                     (dtype->size > 1) ? "s" : "");
-        goto fail;
-    }
-    if (buf->len > 0) {
-        for (i = 0; i < ndim; i++) {
-            spec = axes_specs[i];
-            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
-                goto fail;
-            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
-                goto fail;
-        }
-        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
-            goto fail;
-    }
-    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
-                                         new_memview != NULL) == -1)) {
-        goto fail;
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    Py_XDECREF(new_memview);
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_float, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* CIntFromPyVerify */
-  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* MemviewSliceCopyTemplate */
-  static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object)
-{
-    __Pyx_RefNannyDeclarations
-    int i;
-    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
-    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
-    Py_buffer *buf = &from_memview->view;
-    PyObject *shape_tuple = NULL;
-    PyObject *temp_int = NULL;
-    struct __pyx_array_obj *array_obj = NULL;
-    struct __pyx_memoryview_obj *memview_obj = NULL;
-    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
-    for (i = 0; i < ndim; i++) {
-        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
-            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
-                                           "indirect dimensions (axis %d)", i);
-            goto fail;
-        }
-    }
-    shape_tuple = PyTuple_New(ndim);
-    if (unlikely(!shape_tuple)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(shape_tuple);
-    for(i = 0; i < ndim; i++) {
-        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
-        if(unlikely(!temp_int)) {
-            goto fail;
-        } else {
-            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
-            temp_int = NULL;
-        }
-    }
-    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
-    if (unlikely(!array_obj)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(array_obj);
-    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                    (PyObject *) array_obj, contig_flag,
-                                    dtype_is_object,
-                                    from_mvs->memview->typeinfo);
-    if (unlikely(!memview_obj))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
-        goto fail;
-    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
-                                                dtype_is_object) < 0))
-        goto fail;
-    goto no_fail;
-fail:
-    __Pyx_XDECREF(new_mvs.memview);
-    new_mvs.memview = NULL;
-    new_mvs.data = NULL;
-no_fail:
-    __Pyx_XDECREF(shape_tuple);
-    __Pyx_XDECREF(temp_int);
-    __Pyx_XDECREF(array_obj);
-    __Pyx_RefNannyFinishContext();
-    return new_mvs;
-}
-
-/* MemviewSliceInit */
-  static int
-__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
-                        int ndim,
-                        __Pyx_memviewslice *memviewslice,
-                        int memview_is_new_reference)
-{
-    __Pyx_RefNannyDeclarations
-    int i, retval=-1;
-    Py_buffer *buf = &memview->view;
-    __Pyx_RefNannySetupContext("init_memviewslice", 0);
-    if (unlikely(memviewslice->memview || memviewslice->data)) {
-        PyErr_SetString(PyExc_ValueError,
-            "memviewslice is already initialized!");
-        goto fail;
-    }
-    if (buf->strides) {
-        for (i = 0; i < ndim; i++) {
-            memviewslice->strides[i] = buf->strides[i];
-        }
-    } else {
-        Py_ssize_t stride = buf->itemsize;
-        for (i = ndim - 1; i >= 0; i--) {
-            memviewslice->strides[i] = stride;
-            stride *= buf->shape[i];
-        }
-    }
-    for (i = 0; i < ndim; i++) {
-        memviewslice->shape[i]   = buf->shape[i];
-        if (buf->suboffsets) {
-            memviewslice->suboffsets[i] = buf->suboffsets[i];
-        } else {
-            memviewslice->suboffsets[i] = -1;
-        }
-    }
-    memviewslice->memview = memview;
-    memviewslice->data = (char *)buf->buf;
-    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
-        Py_INCREF(memview);
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    memviewslice->memview = 0;
-    memviewslice->data = 0;
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-#ifndef Py_NO_RETURN
-#define Py_NO_RETURN
-#endif
-static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {
-    va_list vargs;
-    char msg[200];
-#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES)
-    va_start(vargs, fmt);
-#else
-    va_start(vargs);
-#endif
-    vsnprintf(msg, 200, fmt, vargs);
-    va_end(vargs);
-    Py_FatalError(msg);
-}
-static CYTHON_INLINE int
-__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)++;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE int
-__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)--;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE void
-__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
-{
-    __pyx_nonatomic_int_type old_acquisition_count;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
-        return;
-    }
-    old_acquisition_count = __pyx_add_acquisition_count(memview);
-    if (unlikely(old_acquisition_count <= 0)) {
-        if (likely(old_acquisition_count == 0)) {
-            if (have_gil) {
-                Py_INCREF((PyObject *) memview);
-            } else {
-                PyGILState_STATE _gilstate = PyGILState_Ensure();
-                Py_INCREF((PyObject *) memview);
-                PyGILState_Release(_gilstate);
-            }
-        } else {
-            __pyx_fatalerror("Acquisition count is %d (line %d)",
-                             old_acquisition_count+1, lineno);
-        }
-    }
-}
-static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice,
-                                             int have_gil, int lineno) {
-    __pyx_nonatomic_int_type old_acquisition_count;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
-        memslice->memview = NULL;
-        return;
-    }
-    old_acquisition_count = __pyx_sub_acquisition_count(memview);
-    memslice->data = NULL;
-    if (likely(old_acquisition_count > 1)) {
-        memslice->memview = NULL;
-    } else if (likely(old_acquisition_count == 1)) {
-        if (have_gil) {
-            Py_CLEAR(memslice->memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_CLEAR(memslice->memview);
-            PyGILState_Release(_gilstate);
-        }
-    } else {
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         old_acquisition_count-1, lineno);
-    }
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(int) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) {
-                                return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) {
-                                return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) {
-                                return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(int) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(int) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (int) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (int) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (int) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (int) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (int) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((int) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((int) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(long) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) {
-                                return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) {
-                                return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) {
-                                return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(long) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(long) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (long) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (long) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (long) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (long) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (long) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((long) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((long) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const char neg_one = (char) -1, const_zero = (char) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(char) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) {
-                                return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) {
-                                return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) {
-                                return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (char) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(char) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                                return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                                return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) {
-                                return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(char) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            char val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (char) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (char) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (char) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (char) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (char) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((char) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((char) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (char) -1;
-        }
-    } else {
-        char val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to char");
-    return (char) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to char");
-    return (char) -1;
-}
-
-/* FormatTypeName */
-  #if CYTHON_COMPILING_IN_LIMITED_API
-static __Pyx_TypeName
-__Pyx_PyType_GetName(PyTypeObject* tp)
-{
-    PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp,
-                                               __pyx_n_s_name_2);
-    if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
-        PyErr_Clear();
-        Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__23));
-    }
-    return name;
-}
-#endif
-
-/* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[5];
-    int same=1, i, found_dot;
-    const char* rt_from_call = Py_GetVersion();
-    PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    found_dot = 0;
-    for (i = 0; i < 4; i++) {
-        if (!ctversion[i]) {
-            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');
-            break;
-        }
-        if (rt_from_call[i] != ctversion[i]) {
-            same = 0;
-            break;
-        }
-    }
-    if (!same) {
-        char rtversion[5] = {'\0'};
-        char message[200];
-        for (i=0; i<4; ++i) {
-            if (rt_from_call[i] == '.') {
-                if (found_dot) break;
-                found_dot = 1;
-            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {
-                break;
-            }
-            rtversion[i] = rt_from_call[i];
-        }
-        PyOS_snprintf(message, sizeof(message),
-                      "compile time version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-  #if PY_MAJOR_VERSION >= 3
-static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) {
-    if (t.is_unicode | t.is_str) {
-        if (t.intern) {
-            *str = PyUnicode_InternFromString(t.s);
-        } else if (t.encoding) {
-            *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL);
-        } else {
-            *str = PyUnicode_FromStringAndSize(t.s, t.n - 1);
-        }
-    } else {
-        *str = PyBytes_FromStringAndSize(t.s, t.n - 1);
-    }
-    if (!*str)
-        return -1;
-    if (PyObject_Hash(*str) == -1)
-        return -1;
-    return 0;
-}
-#endif
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION >= 3
-        __Pyx_InitString(*t, t->p);
-        #else
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        #endif
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-    __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result));
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type " __Pyx_FMT_TYPENAME ").  "
-                "The ability to return an instance of a strict subclass of int is deprecated, "
-                "and may be removed in a future version of Python.",
-                result_type_name)) {
-            __Pyx_DECREF_TypeName(result_type_name);
-            Py_DECREF(result);
-            return NULL;
-        }
-        __Pyx_DECREF_TypeName(result_type_name);
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")",
-                 type_name, type_name, result_type_name);
-    __Pyx_DECREF_TypeName(result_type_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
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diff --git a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/monotonic_align/core.c b/spaces/digitalxingtong/Nailv-read-Bert-Vits2/monotonic_align/core.c
deleted file mode 100644
index 5f8af54d32474f821e9d1f4d2679d78128722596..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/monotonic_align/core.c
+++ /dev/null
@@ -1,26530 +0,0 @@
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-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK 1
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if !defined(CYTHON_VECTORCALL)
-#define CYTHON_VECTORCALL  (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1)
-#endif
-#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1)
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(maybe_unused)
-        #define CYTHON_UNUSED [[maybe_unused]]
-      #endif
-    #endif
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-  #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x)
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-            typedef unsigned char     uint8_t;
-            typedef unsigned short    uint16_t;
-            typedef unsigned int      uint32_t;
-        #else
-            typedef unsigned __int8   uint8_t;
-            typedef unsigned __int16  uint16_t;
-            typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-    #if _MSC_VER < 1300
-        #ifdef _WIN64
-            typedef unsigned long long  __pyx_uintptr_t;
-        #else
-            typedef unsigned int        __pyx_uintptr_t;
-        #endif
-    #else
-        #ifdef _WIN64
-            typedef unsigned __int64    __pyx_uintptr_t;
-        #else
-            typedef unsigned __int32    __pyx_uintptr_t;
-        #endif
-    #endif
-#else
-    #include <stdint.h>
-    typedef uintptr_t  __pyx_uintptr_t;
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(fallthrough) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(fallthrough)
-        #define CYTHON_FALLTHROUGH [[fallthrough]]
-      #endif
-    #endif
-    #ifndef CYTHON_FALLTHROUGH
-      #if __has_cpp_attribute(clang::fallthrough)
-        #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-      #elif __has_cpp_attribute(gnu::fallthrough)
-        #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-      #endif
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-#ifdef __cplusplus
-  template <typename T>
-  struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);};
-  #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL<type>::value)
-#else
-  #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0)
-#endif
-#if CYTHON_COMPILING_IN_PYPY == 1
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000)
-#else
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000)
-#endif
-#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer))
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_DefaultClassType PyClass_Type
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject *co=NULL, *result=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(p))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end;
-        if (!(empty = PyTuple_New(0))) goto end;
-        result = (PyCodeObject*) PyObject_Call(replace, empty, kwds);
-    end:
-        Py_XDECREF((PyObject*) co);
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return result;
-    }
-#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-#endif
-#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE)
-  #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type)
-#else
-  #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is)
-  #define __Pyx_Py_Is(x, y)  Py_Is(x, y)
-#else
-  #define __Pyx_Py_Is(x, y) ((x) == (y))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone)
-  #define __Pyx_Py_IsNone(ob) Py_IsNone(ob)
-#else
-  #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue)
-  #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob)
-#else
-  #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse)
-  #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob)
-#else
-  #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False)
-#endif
-#define __Pyx_NoneAsNull(obj)  (__Pyx_Py_IsNone(obj) ? NULL : (obj))
-#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o)
-#else
-  #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o)
-#endif
-#ifndef CO_COROUTINE
-  #define CO_COROUTINE 0x80
-#endif
-#ifndef CO_ASYNC_GENERATOR
-  #define CO_ASYNC_GENERATOR 0x200
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef Py_TPFLAGS_SEQUENCE
-  #define Py_TPFLAGS_SEQUENCE 0
-#endif
-#ifndef Py_TPFLAGS_MAPPING
-  #define Py_TPFLAGS_MAPPING 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_METH_FASTCALL
-  #define __Pyx_METH_FASTCALL METH_FASTCALL
-  #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast
-  #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords
-#else
-  #define __Pyx_METH_FASTCALL METH_VARARGS
-  #define __Pyx_PyCFunction_FastCall PyCFunction
-  #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords
-#endif
-#if CYTHON_VECTORCALL
-  #define __pyx_vectorcallfunc vectorcallfunc
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  PY_VECTORCALL_ARGUMENTS_OFFSET
-  #define __Pyx_PyVectorcall_NARGS(n)  PyVectorcall_NARGS((size_t)(n))
-#elif CYTHON_BACKPORT_VECTORCALL
-  typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args,
-                                            size_t nargsf, PyObject *kwnames);
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  ((size_t)1 << (8 * sizeof(size_t) - 1))
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET))
-#else
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  0
-  #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(n))
-#endif
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  ((void)m, PyType_FromSpecWithBases(s, b))
-  typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *);
-#else
-  #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  PyType_FromModuleAndSpec(m, s, b)
-  #define __Pyx_PyCMethod  PyCMethod
-#endif
-#ifndef METH_METHOD
-  #define METH_METHOD 0x200
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyThreadState_Current PyThreadState_Get()
-#elif !CYTHON_FAST_THREAD_STATE
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
-  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
-  #define __Pyx_PyThreadState_Current _PyThreadState_Current
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op)
-{
-    void *result;
-    result = PyModule_GetState(op);
-    if (!result)
-        Py_FatalError("Couldn't find the module state");
-    return result;
-}
-#endif
-#define __Pyx_PyObject_GetSlot(obj, name, func_ctype)  __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype)
-#if CYTHON_COMPILING_IN_LIMITED_API
-  #define __Pyx_PyType_GetSlot(type, name, func_ctype)  ((func_ctype) PyType_GetSlot((type), Py_##name))
-#else
-  #define __Pyx_PyType_GetSlot(type, name, func_ctype)  ((type)->name)
-#endif
-#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
-#include "pythread.h"
-#define Py_tss_NEEDS_INIT 0
-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
-  *key = PyThread_create_key();
-  return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
-  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
-  *key = Py_tss_NEEDS_INIT;
-  return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
-  PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
-  return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
-  PyThread_delete_key(*key);
-  *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
-  return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
-  return PyThread_get_key_value(*key);
-}
-#endif
-#if PY_MAJOR_VERSION < 3
-    #if CYTHON_COMPILING_IN_PYPY
-        #if PYPY_VERSION_NUM < 0x07030600
-            #if defined(__cplusplus) && __cplusplus >= 201402L
-                [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]]
-            #elif defined(__GNUC__) || defined(__clang__)
-                __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")))
-            #elif defined(_MSC_VER)
-                __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))
-            #endif
-            static CYTHON_INLINE int PyGILState_Check(void) {
-                return 0;
-            }
-        #else  // PYPY_VERSION_NUM < 0x07030600
-        #endif  // PYPY_VERSION_NUM < 0x07030600
-    #else
-        static CYTHON_INLINE int PyGILState_Check(void) {
-            PyThreadState * tstate = _PyThreadState_Current;
-            return tstate && (tstate == PyGILState_GetThisThreadState());
-        }
-    #endif
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStrWithError(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) {
-    PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name);
-    if (res == NULL) PyErr_Clear();
-    return res;
-}
-#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000)
-#define __Pyx_PyDict_GetItemStrWithError  PyDict_GetItemWithError
-#define __Pyx_PyDict_GetItemStr           PyDict_GetItem
-#else
-static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyDict_GetItem(dict, name);
-#else
-    PyDictEntry *ep;
-    PyDictObject *mp = (PyDictObject*) dict;
-    long hash = ((PyStringObject *) name)->ob_shash;
-    assert(hash != -1);
-    ep = (mp->ma_lookup)(mp, name, hash);
-    if (ep == NULL) {
-        return NULL;
-    }
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-  #define __Pyx_PyUnicode_KIND(u)         ((int)sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
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-  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
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-  #define __Pyx_PySequence_ListKeepNew(obj)\
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-  #define PyBoolObject                 PyLongObject
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-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsHash_t
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-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
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-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
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-#define __PYX_DEFAULT_STRING_ENCODING ""
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-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
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-    #define __Pyx_sst_abs(value) labs(value)
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-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
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-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
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-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
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-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
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-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
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-{
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-{
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-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
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-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
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-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
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-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
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-  #if PY_VERSION_HEX >= 0x030C00A7
-  #ifndef _PyLong_SIGN_MASK
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-  #ifndef _PyLong_NON_SIZE_BITS
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-  #define __Pyx_PyLong_Sign(x)  (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK)
-  #define __Pyx_PyLong_IsNeg(x)  ((__Pyx_PyLong_Sign(x) & 2) != 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (!__Pyx_PyLong_IsNeg(x))
-  #define __Pyx_PyLong_IsZero(x)  (__Pyx_PyLong_Sign(x) & 1)
-  #define __Pyx_PyLong_IsPos(x)  (__Pyx_PyLong_Sign(x) == 0)
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-  #define __Pyx_PyLong_DigitCount(x)  ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS))
-  #define __Pyx_PyLong_SignedDigitCount(x)\
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-  #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue)
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-    #define __Pyx_PyLong_CompactValue(x)  PyUnstable_Long_CompactValue((PyLongObject*) x)
-  #else
-    #define __Pyx_PyLong_IsCompact(x)     (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS))
-    #define __Pyx_PyLong_CompactValue(x)  ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0])
-  #endif
-  typedef Py_ssize_t  __Pyx_compact_pylong;
-  typedef size_t  __Pyx_compact_upylong;
-  #else  // Py < 3.12
-  #define __Pyx_PyLong_IsNeg(x)  (Py_SIZE(x) < 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (Py_SIZE(x) >= 0)
-  #define __Pyx_PyLong_IsZero(x)  (Py_SIZE(x) == 0)
-  #define __Pyx_PyLong_IsPos(x)  (Py_SIZE(x) > 0)
-  #define __Pyx_PyLong_CompactValueUnsigned(x)  ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0])
-  #define __Pyx_PyLong_DigitCount(x)  __Pyx_sst_abs(Py_SIZE(x))
-  #define __Pyx_PyLong_SignedDigitCount(x)  Py_SIZE(x)
-  #define __Pyx_PyLong_IsCompact(x)  (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1)
-  #define __Pyx_PyLong_CompactValue(x)\
-        ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0]))
-  typedef sdigit  __Pyx_compact_pylong;
-  typedef digit  __Pyx_compact_upylong;
-  #endif
-  #if PY_VERSION_HEX >= 0x030C00A5
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->long_value.ob_digit)
-  #else
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->ob_digit)
-  #endif
-#endif
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = (char) c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-#if !CYTHON_USE_MODULE_STATE
-static PyObject *__pyx_m = NULL;
-#endif
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm = __FILE__;
-static const char *__pyx_filename;
-
-/* #### Code section: filename_table ### */
-
-static const char *__pyx_f[] = {
-  "core.pyx",
-  "<stringsource>",
-};
-/* #### Code section: utility_code_proto_before_types ### */
-/* ForceInitThreads.proto */
-#ifndef __PYX_FORCE_INIT_THREADS
-  #define __PYX_FORCE_INIT_THREADS 0
-#endif
-
-/* NoFastGil.proto */
-#define __Pyx_PyGILState_Ensure PyGILState_Ensure
-#define __Pyx_PyGILState_Release PyGILState_Release
-#define __Pyx_FastGIL_Remember()
-#define __Pyx_FastGIL_Forget()
-#define __Pyx_FastGilFuncInit()
-
-/* BufferFormatStructs.proto */
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
-  const char* name;
-  struct __Pyx_StructField_* fields;
-  size_t size;
-  size_t arraysize[8];
-  int ndim;
-  char typegroup;
-  char is_unsigned;
-  int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
-  size_t offset;
-} __Pyx_StructField;
-typedef struct {
-  __Pyx_StructField* field;
-  size_t parent_offset;
-} __Pyx_BufFmt_StackElem;
-typedef struct {
-  __Pyx_StructField root;
-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
-  size_t new_count, enc_count;
-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-/* Atomics.proto */
-#include <pythread.h>
-#ifndef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 1
-#endif
-#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS
-#define __pyx_atomic_int_type int
-#define __pyx_nonatomic_int_type int
-#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\
-                        (__STDC_VERSION__ >= 201112L) &&\
-                        !defined(__STDC_NO_ATOMICS__))
-    #include <stdatomic.h>
-#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\
-                    (__cplusplus >= 201103L) ||\
-                    (defined(_MSC_VER) && _MSC_VER >= 1700)))
-    #include <atomic>
-#endif
-#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\
-                        (__STDC_VERSION__ >= 201112L) &&\
-                        !defined(__STDC_NO_ATOMICS__) &&\
-                       ATOMIC_INT_LOCK_FREE == 2)
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type atomic_int
-    #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed)
-    #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel)
-    #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER)
-        #pragma message ("Using standard C atomics")
-    #elif defined(__PYX_DEBUG_ATOMICS)
-        #warning "Using standard C atomics"
-    #endif
-#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\
-                    (__cplusplus >= 201103L) ||\
-\
-                    (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\
-                    ATOMIC_INT_LOCK_FREE == 2)
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type std::atomic_int
-    #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed)
-    #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel)
-    #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER)
-        #pragma message ("Using standard C++ atomics")
-    #elif defined(__PYX_DEBUG_ATOMICS)
-        #warning "Using standard C++ atomics"
-    #endif
-#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\
-                    (__GNUC_MINOR__ > 1 ||\
-                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2))))
-    #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1)
-    #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using GNU atomics"
-    #endif
-#elif CYTHON_ATOMICS && defined(_MSC_VER)
-    #include <intrin.h>
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type long
-    #define __pyx_nonatomic_int_type long
-    #pragma intrinsic (_InterlockedExchangeAdd)
-    #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1)
-    #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #pragma message ("Using MSVC atomics")
-    #endif
-#else
-    #undef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 0
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Not using atomics"
-    #endif
-#endif
-#if CYTHON_ATOMICS
-    #define __pyx_add_acquisition_count(memview)\
-             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview))
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview))
-#else
-    #define __pyx_add_acquisition_count(memview)\
-            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-#endif
-
-/* MemviewSliceStruct.proto */
-struct __pyx_memoryview_obj;
-typedef struct {
-  struct __pyx_memoryview_obj *memview;
-  char *data;
-  Py_ssize_t shape[8];
-  Py_ssize_t strides[8];
-  Py_ssize_t suboffsets[8];
-} __Pyx_memviewslice;
-#define __Pyx_MemoryView_Len(m)  (m.shape[0])
-
-/* #### Code section: numeric_typedefs ### */
-/* #### Code section: complex_type_declarations ### */
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-
-/*--- Type declarations ---*/
-struct __pyx_array_obj;
-struct __pyx_MemviewEnum_obj;
-struct __pyx_memoryview_obj;
-struct __pyx_memoryviewslice_obj;
-struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each;
-
-/* "monotonic_align/core.pyx":7
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
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- *   cdef int y
- */
-struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each {
-  int __pyx_n;
-  float max_neg_val;
-};
-
-/* "View.MemoryView":114
- * @cython.collection_type("sequence")
- * @cname("__pyx_array")
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-struct __pyx_array_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_array *__pyx_vtab;
-  char *data;
-  Py_ssize_t len;
-  char *format;
-  int ndim;
-  Py_ssize_t *_shape;
-  Py_ssize_t *_strides;
-  Py_ssize_t itemsize;
-  PyObject *mode;
-  PyObject *_format;
-  void (*callback_free_data)(void *);
-  int free_data;
-  int dtype_is_object;
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-
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- * @cname('__pyx_MemviewEnum')
- * cdef class Enum(object):             # <<<<<<<<<<<<<<
- *     cdef object name
- *     def __init__(self, name):
- */
-struct __pyx_MemviewEnum_obj {
-  PyObject_HEAD
-  PyObject *name;
-};
-
-
-/* "View.MemoryView":337
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- * @cname('__pyx_memoryview')
- * cdef class memoryview:             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-struct __pyx_memoryview_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
-  PyObject *obj;
-  PyObject *_size;
-  PyObject *_array_interface;
-  PyThread_type_lock lock;
-  __pyx_atomic_int_type acquisition_count;
-  Py_buffer view;
-  int flags;
-  int dtype_is_object;
-  __Pyx_TypeInfo *typeinfo;
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- * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
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- * 
- */
-struct __pyx_memoryviewslice_obj {
-  struct __pyx_memoryview_obj __pyx_base;
-  __Pyx_memviewslice from_slice;
-  PyObject *from_object;
-  PyObject *(*to_object_func)(char *);
-  int (*to_dtype_func)(char *, PyObject *);
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-
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-/* "View.MemoryView":114
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-struct __pyx_vtabstruct_array {
-  PyObject *(*get_memview)(struct __pyx_array_obj *);
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- * cdef class memoryview:             # <<<<<<<<<<<<<<
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-struct __pyx_vtabstruct_memoryview {
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-  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
-  PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
-  PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);
-  PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);
-  PyObject *(*_get_base)(struct __pyx_memoryview_obj *);
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-
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-struct __pyx_vtabstruct__memoryviewslice {
-  struct __pyx_vtabstruct_memoryview __pyx_base;
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-static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice;
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-#define __Pyx_PyErr_CurrentExceptionType()  (__pyx_tstate->current_exception ? (PyObject*) Py_TYPE(__pyx_tstate->current_exception) : (PyObject*) NULL)
-#else
-#define __Pyx_PyErr_Occurred()  (__pyx_tstate->curexc_type != NULL)
-#define __Pyx_PyErr_CurrentExceptionType()  (__pyx_tstate->curexc_type)
-#endif
-#else
-#define __Pyx_PyThreadState_declare
-#define __Pyx_PyThreadState_assign
-#define __Pyx_PyErr_Occurred()  (PyErr_Occurred() != NULL)
-#define __Pyx_PyErr_CurrentExceptionType()  PyErr_Occurred()
-#endif
-
-/* PyErrFetchRestore.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
-#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6
-#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
-#else
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#endif
-#else
-#define __Pyx_PyErr_Clear() PyErr_Clear()
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestoreInState(tstate, type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchInState(tstate, type, value, tb)  PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
-#endif
-
-/* PyObjectGetAttrStr.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
-#endif
-
-/* PyObjectGetAttrStrNoError.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);
-
-/* GetBuiltinName.proto */
-static PyObject *__Pyx_GetBuiltinName(PyObject *name);
-
-/* TupleAndListFromArray.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n);
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n);
-#endif
-
-/* IncludeStringH.proto */
-#include <string.h>
-
-/* BytesEquals.proto */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* UnicodeEquals.proto */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* fastcall.proto */
-#define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i)
-#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds)
-#define __Pyx_KwValues_VARARGS(args, nargs) NULL
-#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s)
-#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw)
-#if CYTHON_METH_FASTCALL
-    #define __Pyx_Arg_FASTCALL(args, i) args[i]
-    #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds)
-    #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs))
-    static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s);
-    #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw)
-#else
-    #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS
-    #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS
-    #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS
-    #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS
-    #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start)
-#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start)
-#else
-#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop)
-#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop)
-#endif
-
-/* RaiseArgTupleInvalid.proto */
-static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
-    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
-
-/* RaiseDoubleKeywords.proto */
-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
-
-/* ParseKeywords.proto */
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,
-    const char* function_name);
-
-/* ArgTypeTest.proto */
-#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
-    ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\
-        __Pyx__ArgTypeTest(obj, type, name, exact))
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
-
-/* RaiseException.proto */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
-
-/* PyFunctionFastCall.proto */
-#if CYTHON_FAST_PYCALL
-#if !CYTHON_VECTORCALL
-#define __Pyx_PyFunction_FastCall(func, args, nargs)\
-    __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs);
-#endif
-#define __Pyx_BUILD_ASSERT_EXPR(cond)\
-    (sizeof(char [1 - 2*!(cond)]) - 1)
-#ifndef Py_MEMBER_SIZE
-#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member)
-#endif
-#if !CYTHON_VECTORCALL
-#if PY_VERSION_HEX >= 0x03080000
-  #include "frameobject.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-  #define __Pxy_PyFrame_Initialize_Offsets()
-  #define __Pyx_PyFrame_GetLocalsplus(frame)  ((frame)->f_localsplus)
-#else
-  static size_t __pyx_pyframe_localsplus_offset = 0;
-  #include "frameobject.h"
-  #define __Pxy_PyFrame_Initialize_Offsets()\
-    ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\
-     (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus)))
-  #define __Pyx_PyFrame_GetLocalsplus(frame)\
-    (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset))
-#endif
-#endif
-#endif
-
-/* PyObjectCall.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
-#else
-#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
-#endif
-
-/* PyObjectCallMethO.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
-#endif
-
-/* PyObjectFastCall.proto */
-#define __Pyx_PyObject_FastCall(func, args, nargs)  __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL)
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs);
-
-/* RaiseUnexpectedTypeError.proto */
-static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj);
-
-/* GCCDiagnostics.proto */
-#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
-
-/* BuildPyUnicode.proto */
-static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength,
-                                                int prepend_sign, char padding_char);
-
-/* CIntToPyUnicode.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char);
-
-/* CIntToPyUnicode.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char);
-
-/* JoinPyUnicode.proto */
-static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength,
-                                      Py_UCS4 max_char);
-
-/* StrEquals.proto */
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
-#else
-#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
-#endif
-
-/* PyObjectFormatSimple.proto */
-#if CYTHON_COMPILING_IN_PYPY
-    #define __Pyx_PyObject_FormatSimple(s, f) (\
-        likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\
-        PyObject_Format(s, f))
-#elif PY_MAJOR_VERSION < 3
-    #define __Pyx_PyObject_FormatSimple(s, f) (\
-        likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\
-        likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\
-        PyObject_Format(s, f))
-#elif CYTHON_USE_TYPE_SLOTS
-    #define __Pyx_PyObject_FormatSimple(s, f) (\
-        likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\
-        likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\
-        likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\
-        PyObject_Format(s, f))
-#else
-    #define __Pyx_PyObject_FormatSimple(s, f) (\
-        likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\
-        PyObject_Format(s, f))
-#endif
-
-CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/
-/* GetAttr.proto */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
-
-/* GetItemInt.proto */
-#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
-               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
-#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
-/* PyObjectCallOneArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* KeywordStringCheck.proto */
-static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed);
-
-/* DivInt[Py_ssize_t].proto */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t);
-
-/* UnaryNegOverflows.proto */
-#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\
-        (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x)))
-
-/* GetAttr3.proto */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
-
-/* PyDictVersioning.proto */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-#define __PYX_DICT_VERSION_INIT  ((PY_UINT64_T) -1)
-#define __PYX_GET_DICT_VERSION(dict)  (((PyDictObject*)(dict))->ma_version_tag)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\
-    (version_var) = __PYX_GET_DICT_VERSION(dict);\
-    (cache_var) = (value);
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\
-        (VAR) = __pyx_dict_cached_value;\
-    } else {\
-        (VAR) = __pyx_dict_cached_value = (LOOKUP);\
-        __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
-    }\
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
-#else
-#define __PYX_GET_DICT_VERSION(dict)  (0)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
-#endif
-
-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name)  do {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
-        (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
-        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-} while(0)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  do {\
-    PY_UINT64_T __pyx_dict_version;\
-    PyObject *__pyx_dict_cached_value;\
-    (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-} while(0)
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
-#endif
-
-/* AssertionsEnabled.proto */
-#define __Pyx_init_assertions_enabled()
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
-  #define __pyx_assertions_enabled() (1)
-#elif PY_VERSION_HEX < 0x03080000  ||  CYTHON_COMPILING_IN_PYPY  ||  defined(Py_LIMITED_API)
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6
-  static int __pyx_assertions_enabled_flag;
-  #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag)
-  #undef __Pyx_init_assertions_enabled
-  static void __Pyx_init_assertions_enabled(void) {
-    __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level;
-  }
-#else
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#endif
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* RaiseNoneIterError.proto */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
-
-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
-
-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* ImportDottedModule.proto */
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple);
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple);
-#endif
-
-/* ssize_strlen.proto */
-static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2))
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyErr_ExceptionMatches2(err1, err2)  __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2)
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-/* ListCompAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PySequenceMultiply.proto */
-#define __Pyx_PySequence_Multiply_Left(mul, seq)  __Pyx_PySequence_Multiply(seq, mul)
-static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul);
-
-/* SetItemInt.proto */
-#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\
-               __Pyx_SetItemInt_Generic(o, to_py_func(i), v)))
-static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
-static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
-                                               int is_list, int wraparound, int boundscheck);
-
-/* RaiseUnboundLocalError.proto */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-/* DivInt[long].proto */
-static CYTHON_INLINE long __Pyx_div_long(long, long);
-
-/* PySequenceContains.proto */
-static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
-    int result = PySequence_Contains(seq, item);
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* HasAttr.proto */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
-
-/* ErrOccurredWithGIL.proto */
-static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void);
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* PyObject_GenericGetAttr.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
-#endif
-
-/* IncludeStructmemberH.proto */
-#include <structmember.h>
-
-/* FixUpExtensionType.proto */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type);
-#endif
-
-/* PyObjectCallNoArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-
-/* PyObjectGetMethod.proto */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method);
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-/* PyObjectCallMethod0.proto */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name);
-
-/* ValidateBasesTuple.proto */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases);
-#endif
-
-/* PyType_Ready.proto */
-CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t);
-
-/* SetVTable.proto */
-static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable);
-
-/* GetVTable.proto */
-static void* __Pyx_GetVtable(PyTypeObject *type);
-
-/* MergeVTables.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_MergeVtables(PyTypeObject *type);
-#endif
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-/* SetupReduce.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_setup_reduce(PyObject* type_obj);
-#endif
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-/* FetchSharedCythonModule.proto */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void);
-
-/* FetchCommonType.proto */
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-#else
-static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases);
-#endif
-
-/* PyMethodNew.proto */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
-    CYTHON_UNUSED_VAR(typ);
-    if (!self)
-        return __Pyx_NewRef(func);
-    return PyMethod_New(func, self);
-}
-#else
-    #define __Pyx_PyMethod_New PyMethod_New
-#endif
-
-/* PyVectorcallFastCallDict.proto */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw);
-#endif
-
-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CYFUNCTION_COROUTINE     0x08
-#define __Pyx_CyFunction_GetClosure(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_closure)
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#else
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      ((PyObject*) ((PyCMethodObject *) (f))->mm_class)
-#endif
-#define __Pyx_CyFunction_SetClassObj(f, classobj)\
-    __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj))
-#define __Pyx_CyFunction_Defaults(type, f)\
-    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
-    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
-typedef struct {
-#if PY_VERSION_HEX < 0x030900B1
-    PyCFunctionObject func;
-#else
-    PyCMethodObject func;
-#endif
-#if CYTHON_BACKPORT_VECTORCALL
-    __pyx_vectorcallfunc func_vectorcall;
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    PyObject *func_weakreflist;
-#endif
-    PyObject *func_dict;
-    PyObject *func_name;
-    PyObject *func_qualname;
-    PyObject *func_doc;
-    PyObject *func_globals;
-    PyObject *func_code;
-    PyObject *func_closure;
-#if PY_VERSION_HEX < 0x030900B1
-    PyObject *func_classobj;
-#endif
-    void *defaults;
-    int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
-    int flags;
-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-    PyObject *func_is_coroutine;
-} __pyx_CyFunctionObject;
-#define __Pyx_CyFunction_Check(obj)  __Pyx_TypeCheck(obj, __pyx_CyFunctionType)
-#define __Pyx_IsCyOrPyCFunction(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
-#define __Pyx_CyFunction_CheckExact(obj)  __Pyx_IS_TYPE(obj, __pyx_CyFunctionType)
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
-                                                            PyObject *tuple);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
-                                                             PyObject *dict);
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
-                                                              PyObject *dict);
-static int __pyx_CyFunction_init(PyObject *module);
-#if CYTHON_METH_FASTCALL
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-#if CYTHON_BACKPORT_VECTORCALL
-#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall)
-#else
-#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall)
-#endif
-#endif
-
-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-#endif
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
-
-
-/* BufferStructDeclare.proto */
-typedef struct {
-  Py_ssize_t shape, strides, suboffsets;
-} __Pyx_Buf_DimInfo;
-typedef struct {
-  size_t refcount;
-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
-  __Pyx_Buffer *rcbuffer;
-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
-
-/* MemviewSliceIsContig.proto */
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
-
-/* OverlappingSlices.proto */
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                                __Pyx_memviewslice *slice2,
-                                int ndim, size_t itemsize);
-
-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
-
-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type);
-
-/* TypeInfoCompare.proto */
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-/* MemviewSliceValidateAndInit.proto */
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag);
-
-/* MemviewSliceCopyTemplate.proto */
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object);
-
-/* MemviewSliceInit.proto */
-#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d
-#define __Pyx_MEMVIEW_DIRECT   1
-#define __Pyx_MEMVIEW_PTR      2
-#define __Pyx_MEMVIEW_FULL     4
-#define __Pyx_MEMVIEW_CONTIG   8
-#define __Pyx_MEMVIEW_STRIDED  16
-#define __Pyx_MEMVIEW_FOLLOW   32
-#define __Pyx_IS_C_CONTIG 1
-#define __Pyx_IS_F_CONTIG 2
-static int __Pyx_init_memviewslice(
-                struct __pyx_memoryview_obj *memview,
-                int ndim,
-                __Pyx_memviewslice *memviewslice,
-                int memview_is_new_reference);
-static CYTHON_INLINE int __pyx_add_acquisition_count_locked(
-    __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock);
-static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(
-    __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock);
-#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count)
-#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__)
-#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__)
-static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int);
-static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-/* FormatTypeName.proto */
-#if CYTHON_COMPILING_IN_LIMITED_API
-typedef PyObject *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%U"
-static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp);
-#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj)
-#else
-typedef const char *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%.200s"
-#define __Pyx_PyType_GetName(tp) ((tp)->tp_name)
-#define __Pyx_DECREF_TypeName(obj)
-#endif
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-/* #### Code section: module_declarations ### */
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/
-
-/* Module declarations from "cython.view" */
-
-/* Module declarations from "cython.dataclasses" */
-
-/* Module declarations from "cython" */
-
-/* Module declarations from "monotonic_align.core" */
-static PyObject *__pyx_collections_abc_Sequence = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static int __pyx_memoryview_thread_locks_used;
-static PyThread_type_lock __pyx_memoryview_thread_locks[8];
-static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/
-static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/
-static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/
-static int __pyx_memoryview_err_no_memory(void); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
-/* #### Code section: typeinfo ### */
-static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 };
-static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };
-/* #### Code section: before_global_var ### */
-#define __Pyx_MODULE_NAME "monotonic_align.core"
-extern int __pyx_module_is_main_monotonic_align__core;
-int __pyx_module_is_main_monotonic_align__core = 0;
-
-/* Implementation of "monotonic_align.core" */
-/* #### Code section: global_var ### */
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin___import__;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_AssertionError;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-/* #### Code section: string_decls ### */
-static const char __pyx_k_[] = ": ";
-static const char __pyx_k_O[] = "O";
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k__2[] = ".";
-static const char __pyx_k__3[] = "*";
-static const char __pyx_k__6[] = "'";
-static const char __pyx_k__7[] = ")";
-static const char __pyx_k_gc[] = "gc";
-static const char __pyx_k_id[] = "id";
-static const char __pyx_k__23[] = "?";
-static const char __pyx_k_abc[] = "abc";
-static const char __pyx_k_and[] = " and ";
-static const char __pyx_k_got[] = " (got ";
-static const char __pyx_k_new[] = "__new__";
-static const char __pyx_k_obj[] = "obj";
-static const char __pyx_k_sys[] = "sys";
-static const char __pyx_k_base[] = "base";
-static const char __pyx_k_dict[] = "__dict__";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_mode[] = "mode";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ndim[] = "ndim";
-static const char __pyx_k_pack[] = "pack";
-static const char __pyx_k_size[] = "size";
-static const char __pyx_k_spec[] = "__spec__";
-static const char __pyx_k_step[] = "step";
-static const char __pyx_k_stop[] = "stop";
-static const char __pyx_k_t_xs[] = "t_xs";
-static const char __pyx_k_t_ys[] = "t_ys";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_ASCII[] = "ASCII";
-static const char __pyx_k_class[] = "__class__";
-static const char __pyx_k_count[] = "count";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_flags[] = "flags";
-static const char __pyx_k_index[] = "index";
-static const char __pyx_k_paths[] = "paths";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_enable[] = "enable";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_pickle[] = "pickle";
-static const char __pyx_k_reduce[] = "__reduce__";
-static const char __pyx_k_struct[] = "struct";
-static const char __pyx_k_unpack[] = "unpack";
-static const char __pyx_k_update[] = "update";
-static const char __pyx_k_values[] = "values";
-static const char __pyx_k_disable[] = "disable";
-static const char __pyx_k_fortran[] = "fortran";
-static const char __pyx_k_memview[] = "memview";
-static const char __pyx_k_Ellipsis[] = "Ellipsis";
-static const char __pyx_k_Sequence[] = "Sequence";
-static const char __pyx_k_core_pyx[] = "core.pyx";
-static const char __pyx_k_getstate[] = "__getstate__";
-static const char __pyx_k_itemsize[] = "itemsize";
-static const char __pyx_k_pyx_type[] = "__pyx_type";
-static const char __pyx_k_register[] = "register";
-static const char __pyx_k_setstate[] = "__setstate__";
-static const char __pyx_k_TypeError[] = "TypeError";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_isenabled[] = "isenabled";
-static const char __pyx_k_pyx_state[] = "__pyx_state";
-static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
-static const char __pyx_k_IndexError[] = "IndexError";
-static const char __pyx_k_ValueError[] = "ValueError";
-static const char __pyx_k_pyx_result[] = "__pyx_result";
-static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static const char __pyx_k_MemoryError[] = "MemoryError";
-static const char __pyx_k_PickleError[] = "PickleError";
-static const char __pyx_k_collections[] = "collections";
-static const char __pyx_k_initializing[] = "_initializing";
-static const char __pyx_k_is_coroutine[] = "_is_coroutine";
-static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
-static const char __pyx_k_stringsource[] = "<stringsource>";
-static const char __pyx_k_version_info[] = "version_info";
-static const char __pyx_k_class_getitem[] = "__class_getitem__";
-static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
-static const char __pyx_k_AssertionError[] = "AssertionError";
-static const char __pyx_k_maximum_path_c[] = "maximum_path_c";
-static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
-static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static const char __pyx_k_collections_abc[] = "collections.abc";
-static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
-static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
-static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
-static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
-static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core";
-static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
-static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis ";
-static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
-static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '";
-static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
-static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
-static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
-static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct";
-static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)";
-static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)";
-static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
-static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced";
-static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
-static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
-static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions";
-static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))";
-static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got ";
-static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis ";
-static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension ";
-static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
-static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-/* #### Code section: decls ### */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
-static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_pf_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs); /* proto */
-static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-/* #### Code section: late_includes ### */
-/* #### Code section: module_state ### */
-typedef struct {
-  PyObject *__pyx_d;
-  PyObject *__pyx_b;
-  PyObject *__pyx_cython_runtime;
-  PyObject *__pyx_empty_tuple;
-  PyObject *__pyx_empty_bytes;
-  PyObject *__pyx_empty_unicode;
-  #ifdef __Pyx_CyFunction_USED
-  PyTypeObject *__pyx_CyFunctionType;
-  #endif
-  #ifdef __Pyx_FusedFunction_USED
-  PyTypeObject *__pyx_FusedFunctionType;
-  #endif
-  #ifdef __Pyx_Generator_USED
-  PyTypeObject *__pyx_GeneratorType;
-  #endif
-  #ifdef __Pyx_IterableCoroutine_USED
-  PyTypeObject *__pyx_IterableCoroutineType;
-  #endif
-  #ifdef __Pyx_Coroutine_USED
-  PyTypeObject *__pyx_CoroutineAwaitType;
-  #endif
-  #ifdef __Pyx_Coroutine_USED
-  PyTypeObject *__pyx_CoroutineType;
-  #endif
-  #if CYTHON_USE_MODULE_STATE
-  #endif
-  #if CYTHON_USE_MODULE_STATE
-  #endif
-  #if CYTHON_USE_MODULE_STATE
-  #endif
-  #if CYTHON_USE_MODULE_STATE
-  PyObject *__pyx_type___pyx_array;
-  PyObject *__pyx_type___pyx_MemviewEnum;
-  PyObject *__pyx_type___pyx_memoryview;
-  PyObject *__pyx_type___pyx_memoryviewslice;
-  #endif
-  PyTypeObject *__pyx_array_type;
-  PyTypeObject *__pyx_MemviewEnum_type;
-  PyTypeObject *__pyx_memoryview_type;
-  PyTypeObject *__pyx_memoryviewslice_type;
-  PyObject *__pyx_kp_u_;
-  PyObject *__pyx_n_s_ASCII;
-  PyObject *__pyx_kp_s_All_dimensions_preceding_dimensi;
-  PyObject *__pyx_n_s_AssertionError;
-  PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
-  PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
-  PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;
-  PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;
-  PyObject *__pyx_kp_u_Cannot_index_with_type;
-  PyObject *__pyx_kp_s_Cannot_transpose_memoryview_with;
-  PyObject *__pyx_kp_s_Dimension_d_is_not_direct;
-  PyObject *__pyx_n_s_Ellipsis;
-  PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
-  PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0;
-  PyObject *__pyx_n_s_IndexError;
-  PyObject *__pyx_kp_s_Index_out_of_bounds_axis_d;
-  PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
-  PyObject *__pyx_kp_u_Invalid_mode_expected_c_or_fortr;
-  PyObject *__pyx_kp_u_Invalid_shape_in_axis;
-  PyObject *__pyx_n_s_MemoryError;
-  PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
-  PyObject *__pyx_kp_s_MemoryView_of_r_object;
-  PyObject *__pyx_n_b_O;
-  PyObject *__pyx_kp_u_Out_of_bounds_on_buffer_access_a;
-  PyObject *__pyx_n_s_PickleError;
-  PyObject *__pyx_n_s_Sequence;
-  PyObject *__pyx_kp_s_Step_may_not_be_zero_axis_d;
-  PyObject *__pyx_n_s_TypeError;
-  PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
-  PyObject *__pyx_n_s_ValueError;
-  PyObject *__pyx_n_s_View_MemoryView;
-  PyObject *__pyx_kp_u__2;
-  PyObject *__pyx_n_s__23;
-  PyObject *__pyx_n_s__3;
-  PyObject *__pyx_kp_u__6;
-  PyObject *__pyx_kp_u__7;
-  PyObject *__pyx_n_s_abc;
-  PyObject *__pyx_n_s_allocate_buffer;
-  PyObject *__pyx_kp_u_and;
-  PyObject *__pyx_n_s_asyncio_coroutines;
-  PyObject *__pyx_n_s_base;
-  PyObject *__pyx_n_s_c;
-  PyObject *__pyx_n_u_c;
-  PyObject *__pyx_n_s_class;
-  PyObject *__pyx_n_s_class_getitem;
-  PyObject *__pyx_n_s_cline_in_traceback;
-  PyObject *__pyx_n_s_collections;
-  PyObject *__pyx_kp_s_collections_abc;
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-/* #### Code section: module_state_defines ### */
-#define __pyx_d __pyx_mstate_global->__pyx_d
-#define __pyx_b __pyx_mstate_global->__pyx_b
-#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime
-#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple
-#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes
-#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode
-#ifdef __Pyx_CyFunction_USED
-#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType
-#endif
-#ifdef __Pyx_FusedFunction_USED
-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType
-#endif
-#ifdef __Pyx_Generator_USED
-#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType
-#endif
-#ifdef __Pyx_IterableCoroutine_USED
-#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
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-#if CYTHON_USE_MODULE_STATE
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-#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum
-#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview
-#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice
-#endif
-#define __pyx_array_type __pyx_mstate_global->__pyx_array_type
-#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type
-#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type
-#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type
-#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_
-#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII
-#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi
-#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError
-#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri
-#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is
-#define __pyx_kp_s_Cannot_assign_to_read_only_memor __pyx_mstate_global->__pyx_kp_s_Cannot_assign_to_read_only_memor
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-#define __pyx_kp_u_Cannot_index_with_type __pyx_mstate_global->__pyx_kp_u_Cannot_index_with_type
-#define __pyx_kp_s_Cannot_transpose_memoryview_with __pyx_mstate_global->__pyx_kp_s_Cannot_transpose_memoryview_with
-#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct
-#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis
-#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr
-#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0
-#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError
-#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d
-#define __pyx_kp_s_Indirect_dimensions_not_supporte __pyx_mstate_global->__pyx_kp_s_Indirect_dimensions_not_supporte
-#define __pyx_kp_u_Invalid_mode_expected_c_or_fortr __pyx_mstate_global->__pyx_kp_u_Invalid_mode_expected_c_or_fortr
-#define __pyx_kp_u_Invalid_shape_in_axis __pyx_mstate_global->__pyx_kp_u_Invalid_shape_in_axis
-#define __pyx_n_s_MemoryError __pyx_mstate_global->__pyx_n_s_MemoryError
-#define __pyx_kp_s_MemoryView_of_r_at_0x_x __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_at_0x_x
-#define __pyx_kp_s_MemoryView_of_r_object __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_object
-#define __pyx_n_b_O __pyx_mstate_global->__pyx_n_b_O
-#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_mstate_global->__pyx_kp_u_Out_of_bounds_on_buffer_access_a
-#define __pyx_n_s_PickleError __pyx_mstate_global->__pyx_n_s_PickleError
-#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence
-#define __pyx_kp_s_Step_may_not_be_zero_axis_d __pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d
-#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError
-#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object
-#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError
-#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView
-#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2
-#define __pyx_n_s__23 __pyx_mstate_global->__pyx_n_s__23
-#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3
-#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6
-#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7
-#define __pyx_n_s_abc __pyx_mstate_global->__pyx_n_s_abc
-#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer
-#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and
-#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines
-#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base
-#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c
-#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c
-#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class
-#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem
-#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback
-#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections
-#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc
-#define __pyx_kp_s_contiguous_and_direct __pyx_mstate_global->__pyx_kp_s_contiguous_and_direct
-#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect
-#define __pyx_kp_s_core_pyx __pyx_mstate_global->__pyx_kp_s_core_pyx
-#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count
-#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict
-#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable
-#define __pyx_n_s_dtype_is_object __pyx_mstate_global->__pyx_n_s_dtype_is_object
-#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable
-#define __pyx_n_s_encode __pyx_mstate_global->__pyx_n_s_encode
-#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate
-#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error
-#define __pyx_n_s_flags __pyx_mstate_global->__pyx_n_s_flags
-#define __pyx_n_s_format __pyx_mstate_global->__pyx_n_s_format
-#define __pyx_n_s_fortran __pyx_mstate_global->__pyx_n_s_fortran
-#define __pyx_n_u_fortran __pyx_mstate_global->__pyx_n_u_fortran
-#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc
-#define __pyx_n_s_getstate __pyx_mstate_global->__pyx_n_s_getstate
-#define __pyx_kp_u_got __pyx_mstate_global->__pyx_kp_u_got
-#define __pyx_kp_u_got_differing_extents_in_dimensi __pyx_mstate_global->__pyx_kp_u_got_differing_extents_in_dimensi
-#define __pyx_n_s_id __pyx_mstate_global->__pyx_n_s_id
-#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import
-#define __pyx_n_s_index __pyx_mstate_global->__pyx_n_s_index
-#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing
-#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine
-#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled
-#define __pyx_n_s_itemsize __pyx_mstate_global->__pyx_n_s_itemsize
-#define __pyx_kp_s_itemsize_0_for_cython_array __pyx_mstate_global->__pyx_kp_s_itemsize_0_for_cython_array
-#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main
-#define __pyx_n_s_maximum_path_c __pyx_mstate_global->__pyx_n_s_maximum_path_c
-#define __pyx_n_s_memview __pyx_mstate_global->__pyx_n_s_memview
-#define __pyx_n_s_mode __pyx_mstate_global->__pyx_n_s_mode
-#define __pyx_n_s_monotonic_align_core __pyx_mstate_global->__pyx_n_s_monotonic_align_core
-#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name
-#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2
-#define __pyx_n_s_ndim __pyx_mstate_global->__pyx_n_s_ndim
-#define __pyx_n_s_new __pyx_mstate_global->__pyx_n_s_new
-#define __pyx_kp_s_no_default___reduce___due_to_non __pyx_mstate_global->__pyx_kp_s_no_default___reduce___due_to_non
-#define __pyx_n_s_obj __pyx_mstate_global->__pyx_n_s_obj
-#define __pyx_n_s_pack __pyx_mstate_global->__pyx_n_s_pack
-#define __pyx_n_s_paths __pyx_mstate_global->__pyx_n_s_paths
-#define __pyx_n_s_pickle __pyx_mstate_global->__pyx_n_s_pickle
-#define __pyx_n_s_pyx_PickleError __pyx_mstate_global->__pyx_n_s_pyx_PickleError
-#define __pyx_n_s_pyx_checksum __pyx_mstate_global->__pyx_n_s_pyx_checksum
-#define __pyx_n_s_pyx_result __pyx_mstate_global->__pyx_n_s_pyx_result
-#define __pyx_n_s_pyx_state __pyx_mstate_global->__pyx_n_s_pyx_state
-#define __pyx_n_s_pyx_type __pyx_mstate_global->__pyx_n_s_pyx_type
-#define __pyx_n_s_pyx_unpickle_Enum __pyx_mstate_global->__pyx_n_s_pyx_unpickle_Enum
-#define __pyx_n_s_pyx_vtable __pyx_mstate_global->__pyx_n_s_pyx_vtable
-#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range
-#define __pyx_n_s_reduce __pyx_mstate_global->__pyx_n_s_reduce
-#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython
-#define __pyx_n_s_reduce_ex __pyx_mstate_global->__pyx_n_s_reduce_ex
-#define __pyx_n_s_register __pyx_mstate_global->__pyx_n_s_register
-#define __pyx_n_s_setstate __pyx_mstate_global->__pyx_n_s_setstate
-#define __pyx_n_s_setstate_cython __pyx_mstate_global->__pyx_n_s_setstate_cython
-#define __pyx_n_s_shape __pyx_mstate_global->__pyx_n_s_shape
-#define __pyx_n_s_size __pyx_mstate_global->__pyx_n_s_size
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-  int __pyx_t_3;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save;
-  #endif
-
-  /* "View.MemoryView":813
- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-  __pyx_t_1 = (!__pyx_v_is_slice);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":815
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    __pyx_t_1 = (__pyx_v_start < 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":816
- * 
- *         if start < 0:
- *             start += shape             # <<<<<<<<<<<<<<
- *         if not 0 <= start < shape:
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- */
-      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-      /* "View.MemoryView":815
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    }
-
-    /* "View.MemoryView":817
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    __pyx_t_1 = (0 <= __pyx_v_start);
-    if (__pyx_t_1) {
-      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
-    }
-    __pyx_t_2 = (!__pyx_t_1);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":818
- *             start += shape
- *         if not 0 <= start < shape:
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
- *     else:
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 818, __pyx_L1_error)
-
-      /* "View.MemoryView":817
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    }
-
-    /* "View.MemoryView":813
- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":821
- *     else:
- * 
- *         if have_step:             # <<<<<<<<<<<<<<
- *             negative_step = step < 0
- *             if step == 0:
- */
-  /*else*/ {
-    __pyx_t_2 = (__pyx_v_have_step != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":822
- * 
- *         if have_step:
- *             negative_step = step < 0             # <<<<<<<<<<<<<<
- *             if step == 0:
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- */
-      __pyx_v_negative_step = (__pyx_v_step < 0);
-
-      /* "View.MemoryView":823
- *         if have_step:
- *             negative_step = step < 0
- *             if step == 0:             # <<<<<<<<<<<<<<
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- */
-      __pyx_t_2 = (__pyx_v_step == 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":824
- *             negative_step = step < 0
- *             if step == 0:
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
- *         else:
- *             negative_step = False
- */
-        __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 824, __pyx_L1_error)
-
-        /* "View.MemoryView":823
- *         if have_step:
- *             negative_step = step < 0
- *             if step == 0:             # <<<<<<<<<<<<<<
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- */
-      }
-
-      /* "View.MemoryView":821
- *     else:
- * 
- *         if have_step:             # <<<<<<<<<<<<<<
- *             negative_step = step < 0
- *             if step == 0:
- */
-      goto __pyx_L6;
-    }
-
-    /* "View.MemoryView":826
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- *             negative_step = False             # <<<<<<<<<<<<<<
- *             step = 1
- * 
- */
-    /*else*/ {
-      __pyx_v_negative_step = 0;
-
-      /* "View.MemoryView":827
- *         else:
- *             negative_step = False
- *             step = 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_step = 1;
-    }
-    __pyx_L6:;
-
-    /* "View.MemoryView":830
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-    __pyx_t_2 = (__pyx_v_have_start != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":831
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-      __pyx_t_2 = (__pyx_v_start < 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":832
- *         if have_start:
- *             if start < 0:
- *                 start += shape             # <<<<<<<<<<<<<<
- *                 if start < 0:
- *                     start = 0
- */
-        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-        /* "View.MemoryView":833
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        __pyx_t_2 = (__pyx_v_start < 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":834
- *                 start += shape
- *                 if start < 0:
- *                     start = 0             # <<<<<<<<<<<<<<
- *             elif start >= shape:
- *                 if negative_step:
- */
-          __pyx_v_start = 0;
-
-          /* "View.MemoryView":833
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        }
-
-        /* "View.MemoryView":831
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-        goto __pyx_L9;
-      }
-
-      /* "View.MemoryView":835
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":836
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-        if (__pyx_v_negative_step) {
-
-          /* "View.MemoryView":837
- *             elif start >= shape:
- *                 if negative_step:
- *                     start = shape - 1             # <<<<<<<<<<<<<<
- *                 else:
- *                     start = shape
- */
-          __pyx_v_start = (__pyx_v_shape - 1);
-
-          /* "View.MemoryView":836
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-          goto __pyx_L11;
-        }
-
-        /* "View.MemoryView":839
- *                     start = shape - 1
- *                 else:
- *                     start = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        /*else*/ {
-          __pyx_v_start = __pyx_v_shape;
-        }
-        __pyx_L11:;
-
-        /* "View.MemoryView":835
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      }
-      __pyx_L9:;
-
-      /* "View.MemoryView":830
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-      goto __pyx_L8;
-    }
-
-    /* "View.MemoryView":841
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-    /*else*/ {
-      if (__pyx_v_negative_step) {
-
-        /* "View.MemoryView":842
- *         else:
- *             if negative_step:
- *                 start = shape - 1             # <<<<<<<<<<<<<<
- *             else:
- *                 start = 0
- */
-        __pyx_v_start = (__pyx_v_shape - 1);
-
-        /* "View.MemoryView":841
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-        goto __pyx_L12;
-      }
-
-      /* "View.MemoryView":844
- *                 start = shape - 1
- *             else:
- *                 start = 0             # <<<<<<<<<<<<<<
- * 
- *         if have_stop:
- */
-      /*else*/ {
-        __pyx_v_start = 0;
-      }
-      __pyx_L12:;
-    }
-    __pyx_L8:;
-
-    /* "View.MemoryView":846
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-    __pyx_t_2 = (__pyx_v_have_stop != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":847
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-      __pyx_t_2 = (__pyx_v_stop < 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":848
- *         if have_stop:
- *             if stop < 0:
- *                 stop += shape             # <<<<<<<<<<<<<<
- *                 if stop < 0:
- *                     stop = 0
- */
-        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
-
-        /* "View.MemoryView":849
- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        __pyx_t_2 = (__pyx_v_stop < 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":850
- *                 stop += shape
- *                 if stop < 0:
- *                     stop = 0             # <<<<<<<<<<<<<<
- *             elif stop > shape:
- *                 stop = shape
- */
-          __pyx_v_stop = 0;
-
-          /* "View.MemoryView":849
- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        }
-
-        /* "View.MemoryView":847
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-        goto __pyx_L14;
-      }
-
-      /* "View.MemoryView":851
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      __pyx_t_2 = (__pyx_v_stop > __pyx_v_shape);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":852
- *                     stop = 0
- *             elif stop > shape:
- *                 stop = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        __pyx_v_stop = __pyx_v_shape;
-
-        /* "View.MemoryView":851
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      }
-      __pyx_L14:;
-
-      /* "View.MemoryView":846
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-      goto __pyx_L13;
-    }
-
-    /* "View.MemoryView":854
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-    /*else*/ {
-      if (__pyx_v_negative_step) {
-
-        /* "View.MemoryView":855
- *         else:
- *             if negative_step:
- *                 stop = -1             # <<<<<<<<<<<<<<
- *             else:
- *                 stop = shape
- */
-        __pyx_v_stop = -1L;
-
-        /* "View.MemoryView":854
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-        goto __pyx_L16;
-      }
-
-      /* "View.MemoryView":857
- *                 stop = -1
- *             else:
- *                 stop = shape             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      /*else*/ {
-        __pyx_v_stop = __pyx_v_shape;
-      }
-      __pyx_L16:;
-    }
-    __pyx_L13:;
-
-    /* "View.MemoryView":861
- * 
- *         with cython.cdivision(True):
- *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
- * 
- *             if (stop - start) - step * new_shape:
- */
-    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);
-
-    /* "View.MemoryView":863
- *             new_shape = (stop - start) // step
- * 
- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
- *                 new_shape += 1
- * 
- */
-    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":864
- * 
- *             if (stop - start) - step * new_shape:
- *                 new_shape += 1             # <<<<<<<<<<<<<<
- * 
- *         if new_shape < 0:
- */
-      __pyx_v_new_shape = (__pyx_v_new_shape + 1);
-
-      /* "View.MemoryView":863
- *             new_shape = (stop - start) // step
- * 
- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
- *                 new_shape += 1
- * 
- */
-    }
-
-    /* "View.MemoryView":866
- *                 new_shape += 1
- * 
- *         if new_shape < 0:             # <<<<<<<<<<<<<<
- *             new_shape = 0
- * 
- */
-    __pyx_t_2 = (__pyx_v_new_shape < 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":867
- * 
- *         if new_shape < 0:
- *             new_shape = 0             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_new_shape = 0;
-
-      /* "View.MemoryView":866
- *                 new_shape += 1
- * 
- *         if new_shape < 0:             # <<<<<<<<<<<<<<
- *             new_shape = 0
- * 
- */
-    }
-
-    /* "View.MemoryView":870
- * 
- * 
- *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
- *         dst.shape[new_ndim] = new_shape
- *         dst.suboffsets[new_ndim] = suboffset
- */
-    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);
-
-    /* "View.MemoryView":871
- * 
- *         dst.strides[new_ndim] = stride * step
- *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<
- *         dst.suboffsets[new_ndim] = suboffset
- * 
- */
-    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;
-
-    /* "View.MemoryView":872
- *         dst.strides[new_ndim] = stride * step
- *         dst.shape[new_ndim] = new_shape
- *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":875
- * 
- * 
- *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
- *         dst.data += start * stride
- *     else:
- */
-  __pyx_t_2 = ((__pyx_v_suboffset_dim[0]) < 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":876
- * 
- *     if suboffset_dim[0] < 0:
- *         dst.data += start * stride             # <<<<<<<<<<<<<<
- *     else:
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- */
-    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));
-
-    /* "View.MemoryView":875
- * 
- * 
- *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
- *         dst.data += start * stride
- *     else:
- */
-    goto __pyx_L19;
-  }
-
-  /* "View.MemoryView":878
- *         dst.data += start * stride
- *     else:
- *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<
- * 
- *     if suboffset >= 0:
- */
-  /*else*/ {
-    __pyx_t_3 = (__pyx_v_suboffset_dim[0]);
-    (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride));
-  }
-  __pyx_L19:;
-
-  /* "View.MemoryView":880
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- * 
- *     if suboffset >= 0:             # <<<<<<<<<<<<<<
- *         if not is_slice:
- *             if new_ndim == 0:
- */
-  __pyx_t_2 = (__pyx_v_suboffset >= 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":881
- * 
- *     if suboffset >= 0:
- *         if not is_slice:             # <<<<<<<<<<<<<<
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- */
-    __pyx_t_2 = (!__pyx_v_is_slice);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":882
- *     if suboffset >= 0:
- *         if not is_slice:
- *             if new_ndim == 0:             # <<<<<<<<<<<<<<
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- */
-      __pyx_t_2 = (__pyx_v_new_ndim == 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":883
- *         if not is_slice:
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d "
- */
-        __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);
-
-        /* "View.MemoryView":882
- *     if suboffset >= 0:
- *         if not is_slice:
- *             if new_ndim == 0:             # <<<<<<<<<<<<<<
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- */
-        goto __pyx_L22;
-      }
-
-      /* "View.MemoryView":885
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- *                 _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d "             # <<<<<<<<<<<<<<
- *                                      "must be indexed and not sliced", dim)
- *         else:
- */
-      /*else*/ {
-
-        /* "View.MemoryView":886
- *             else:
- *                 _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d "
- *                                      "must be indexed and not sliced", dim)             # <<<<<<<<<<<<<<
- *         else:
- *             suboffset_dim[0] = new_ndim
- */
-        __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_All_dimensions_preceding_dimensi, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 885, __pyx_L1_error)
-      }
-      __pyx_L22:;
-
-      /* "View.MemoryView":881
- * 
- *     if suboffset >= 0:
- *         if not is_slice:             # <<<<<<<<<<<<<<
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- */
-      goto __pyx_L21;
-    }
-
-    /* "View.MemoryView":888
- *                                      "must be indexed and not sliced", dim)
- *         else:
- *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<
- * 
- *     return 0
- */
-    /*else*/ {
-      (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim;
-    }
-    __pyx_L21:;
-
-    /* "View.MemoryView":880
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- * 
- *     if suboffset >= 0:             # <<<<<<<<<<<<<<
- *         if not is_slice:
- *             if new_ndim == 0:
- */
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-    /* "View.MemoryView":1151
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- *         else:
- */
-    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
-    if (__pyx_t_2) {
-      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
-    }
-    __pyx_t_1 = __pyx_t_2;
-    __pyx_L5_bool_binop_done:;
-
-    /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":1152
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
- *         else:
- *             for i in range(dst_extent):
- */
-      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
-
-      /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-      goto __pyx_L4;
-    }
-
-    /* "View.MemoryView":1154
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- *         else:
- *             for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride
- */
-    /*else*/ {
-      __pyx_t_3 = __pyx_v_dst_extent;
-      __pyx_t_4 = __pyx_t_3;
-      for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-        __pyx_v_i = __pyx_t_5;
-
-        /* "View.MemoryView":1155
- *         else:
- *             for i in range(dst_extent):
- *                 memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
- *                 src_data += src_stride
- *                 dst_data += dst_stride
- */
-        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
-
-        /* "View.MemoryView":1156
- *             for i in range(dst_extent):
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride             # <<<<<<<<<<<<<<
- *                 dst_data += dst_stride
- *     else:
- */
-        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-        /* "View.MemoryView":1157
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride
- *                 dst_data += dst_stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(dst_extent):
- */
-        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-      }
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":1149
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1159
- *                 dst_data += dst_stride
- *     else:
- *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *             _copy_strided_to_strided(src_data, src_strides + 1,
- *                                      dst_data, dst_strides + 1,
- */
-  /*else*/ {
-    __pyx_t_3 = __pyx_v_dst_extent;
-    __pyx_t_4 = __pyx_t_3;
-    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-      __pyx_v_i = __pyx_t_5;
-
-      /* "View.MemoryView":1160
- *     else:
- *         for i in range(dst_extent):
- *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
- *                                      dst_data, dst_strides + 1,
- *                                      src_shape + 1, dst_shape + 1,
- */
-      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
-      /* "View.MemoryView":1164
- *                                      src_shape + 1, dst_shape + 1,
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride             # <<<<<<<<<<<<<<
- *             dst_data += dst_stride
- * 
- */
-      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-      /* "View.MemoryView":1165
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride
- *             dst_data += dst_stride             # <<<<<<<<<<<<<<
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
-      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1137
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1167
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
-  /* "View.MemoryView":1170
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
- *                              src.shape, dst.shape, ndim, itemsize)
- * 
- */
-  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1167
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1174
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
-  Py_ssize_t __pyx_v_shape;
-  Py_ssize_t __pyx_v_size;
-  Py_ssize_t __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  Py_ssize_t *__pyx_t_3;
-  Py_ssize_t *__pyx_t_4;
-
-  /* "View.MemoryView":1176
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     for shape in src.shape[:ndim]:
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_size = __pyx_t_1;
-
-  /* "View.MemoryView":1178
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- * 
- *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
- *         size *= shape
- * 
- */
-  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
-  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
-    __pyx_t_2 = __pyx_t_4;
-    __pyx_v_shape = (__pyx_t_2[0]);
-
-    /* "View.MemoryView":1179
- * 
- *     for shape in src.shape[:ndim]:
- *         size *= shape             # <<<<<<<<<<<<<<
- * 
- *     return size
- */
-    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
-  }
-
-  /* "View.MemoryView":1181
- *         size *= shape
- * 
- *     return size             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_fill_contig_strides_array')
- */
-  __pyx_r = __pyx_v_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1174
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1184
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) noexcept nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
-  /* "View.MemoryView":1193
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-  __pyx_t_1 = (__pyx_v_order == 'F');
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1194
- * 
- *     if order == 'F':
- *         for idx in range(ndim):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-    __pyx_t_2 = __pyx_v_ndim;
-    __pyx_t_3 = __pyx_t_2;
-    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-      __pyx_v_idx = __pyx_t_4;
-
-      /* "View.MemoryView":1195
- *     if order == 'F':
- *         for idx in range(ndim):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- *     else:
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1196
- *         for idx in range(ndim):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-
-    /* "View.MemoryView":1193
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1198
- *             stride *= shape[idx]
- *     else:
- *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-  /*else*/ {
-    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
-      __pyx_v_idx = __pyx_t_2;
-
-      /* "View.MemoryView":1199
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- * 
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1200
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- * 
- *     return stride
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1202
- *             stride *= shape[idx]
- * 
- *     return stride             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- */
-  __pyx_r = __pyx_v_stride;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1184
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) noexcept nogil:
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1205
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                              __Pyx_memviewslice *tmpslice,
- *                              char order,
- */
-
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  void *__pyx_v_result;
-  size_t __pyx_v_itemsize;
-  size_t __pyx_v_size;
-  void *__pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  struct __pyx_memoryview_obj *__pyx_t_4;
-  int __pyx_t_5;
-  int __pyx_t_6;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save;
-  #endif
-
-  /* "View.MemoryView":1216
- *     cdef void *result
- * 
- *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- *     cdef size_t size = slice_get_size(src, ndim)
- * 
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
-
-  /* "View.MemoryView":1217
- * 
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<
- * 
- *     result = malloc(size)
- */
-  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);
-
-  /* "View.MemoryView":1219
- *     cdef size_t size = slice_get_size(src, ndim)
- * 
- *     result = malloc(size)             # <<<<<<<<<<<<<<
- *     if not result:
- *         _err_no_memory()
- */
-  __pyx_v_result = malloc(__pyx_v_size);
-
-  /* "View.MemoryView":1220
- * 
- *     result = malloc(size)
- *     if not result:             # <<<<<<<<<<<<<<
- *         _err_no_memory()
- * 
- */
-  __pyx_t_2 = (!(__pyx_v_result != 0));
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1221
- *     result = malloc(size)
- *     if not result:
- *         _err_no_memory()             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_t_3 = __pyx_memoryview_err_no_memory(); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1221, __pyx_L1_error)
-
-    /* "View.MemoryView":1220
- * 
- *     result = malloc(size)
- *     if not result:             # <<<<<<<<<<<<<<
- *         _err_no_memory()
- * 
- */
-  }
-
-  /* "View.MemoryView":1224
- * 
- * 
- *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):
- */
-  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);
-
-  /* "View.MemoryView":1225
- * 
- *     tmpslice.data = <char *> result
- *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]
- */
-  __pyx_t_4 = __pyx_v_src->memview;
-  __pyx_v_tmpslice->memview = __pyx_t_4;
-
-  /* "View.MemoryView":1226
- *     tmpslice.data = <char *> result
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         tmpslice.shape[i] = src.shape[i]
- *         tmpslice.suboffsets[i] = -1
- */
-  __pyx_t_3 = __pyx_v_ndim;
-  __pyx_t_5 = __pyx_t_3;
-  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-    __pyx_v_i = __pyx_t_6;
-
-    /* "View.MemoryView":1227
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<
- *         tmpslice.suboffsets[i] = -1
- * 
- */
-    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);
-
-    /* "View.MemoryView":1228
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]
- *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
- * 
- *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order)
- */
-    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L;
-  }
-
-  /* "View.MemoryView":1230
- *         tmpslice.suboffsets[i] = -1
- * 
- *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order));
-
-  /* "View.MemoryView":1233
- * 
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if tmpslice.shape[i] == 1:
- *             tmpslice.strides[i] = 0
- */
-  __pyx_t_3 = __pyx_v_ndim;
-  __pyx_t_5 = __pyx_t_3;
-  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-    __pyx_v_i = __pyx_t_6;
-
-    /* "View.MemoryView":1234
- * 
- *     for i in range(ndim):
- *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
- *             tmpslice.strides[i] = 0
- * 
- */
-    __pyx_t_2 = ((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1235
- *     for i in range(ndim):
- *         if tmpslice.shape[i] == 1:
- *             tmpslice.strides[i] = 0             # <<<<<<<<<<<<<<
- * 
- *     if slice_is_contig(src[0], order, ndim):
- */
-      (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0;
-
-      /* "View.MemoryView":1234
- * 
- *     for i in range(ndim):
- *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
- *             tmpslice.strides[i] = 0
- * 
- */
-    }
-  }
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-      __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) == 1);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":1291
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- *                 broadcasting = True             # <<<<<<<<<<<<<<
- *                 src.strides[i] = 0
- *             else:
- */
-        __pyx_v_broadcasting = 1;
-
-        /* "View.MemoryView":1292
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- */
-        (__pyx_v_src.strides[__pyx_v_i]) = 0;
-
-        /* "View.MemoryView":1290
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-        goto __pyx_L7;
-      }
-
-      /* "View.MemoryView":1294
- *                 src.strides[i] = 0
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
- * 
- *         if src.suboffsets[i] >= 0:
- */
-      /*else*/ {
-        __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1294, __pyx_L1_error)
-      }
-      __pyx_L7:;
-
-      /* "View.MemoryView":1289
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    }
-
-    /* "View.MemoryView":1296
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- */
-    __pyx_t_2 = ((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1297
- * 
- *         if src.suboffsets[i] >= 0:
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- */
-      __pyx_t_6 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error)
-
-      /* "View.MemoryView":1296
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- */
-    }
-  }
-
-  /* "View.MemoryView":1299
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  __pyx_t_2 = __pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1301
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    __pyx_t_2 = (!__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim));
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1302
- * 
- *         if not slice_is_contig(src, order, ndim):
- *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- */
-      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
-
-      /* "View.MemoryView":1301
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    }
-
-    /* "View.MemoryView":1304
- *             order = get_best_order(&dst, ndim)
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
- *         src = tmp
- * 
- */
-    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1304, __pyx_L1_error)
-    __pyx_v_tmpdata = __pyx_t_7;
-
-    /* "View.MemoryView":1305
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- *         src = tmp             # <<<<<<<<<<<<<<
- * 
- *     if not broadcasting:
- */
-    __pyx_v_src = __pyx_v_tmp;
-
-    /* "View.MemoryView":1299
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  }
-
-  /* "View.MemoryView":1307
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (!__pyx_v_broadcasting);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1310
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-    __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1311
- * 
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
-
-      /* "View.MemoryView":1310
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-      goto __pyx_L12;
-    }
-
-    /* "View.MemoryView":1312
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1313
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
- * 
- *         if direct_copy:
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
-
-      /* "View.MemoryView":1312
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    }
-    __pyx_L12:;
-
-    /* "View.MemoryView":1315
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    if (__pyx_v_direct_copy) {
-
-      /* "View.MemoryView":1317
- *         if direct_copy:
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)             # <<<<<<<<<<<<<<
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-      /* "View.MemoryView":1318
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)
- */
-      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
-
-      /* "View.MemoryView":1319
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)             # <<<<<<<<<<<<<<
- *             free(tmpdata)
- *             return 0
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-      /* "View.MemoryView":1320
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)             # <<<<<<<<<<<<<<
- *             return 0
- * 
- */
-      free(__pyx_v_tmpdata);
-
-      /* "View.MemoryView":1321
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)
- *             return 0             # <<<<<<<<<<<<<<
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):
- */
-      __pyx_r = 0;
-      goto __pyx_L0;
-
-      /* "View.MemoryView":1315
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    }
-
-    /* "View.MemoryView":1307
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1323
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (__pyx_v_order == 'F');
-  if (__pyx_t_2) {
-    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
-  }
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1326
- * 
- * 
- *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
- *         transpose_memslice(&dst)
- * 
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error)
-
-    /* "View.MemoryView":1327
- * 
- *         transpose_memslice(&src)
- *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error)
-
-    /* "View.MemoryView":1323
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1329
- *         transpose_memslice(&dst)
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)             # <<<<<<<<<<<<<<
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-  /* "View.MemoryView":1330
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- * 
- */
-  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1331
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)             # <<<<<<<<<<<<<<
- * 
- *     free(tmpdata)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-  /* "View.MemoryView":1333
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- * 
- *     free(tmpdata)             # <<<<<<<<<<<<<<
- *     return 0
- * 
- */
-  free(__pyx_v_tmpdata);
-
-  /* "View.MemoryView":1334
- * 
- *     free(tmpdata)
- *     return 0             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- */
-  __pyx_r = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1265
- * 
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice dst,
- *                                   int src_ndim, int dst_ndim,
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  #ifdef WITH_THREAD
-  __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-  #endif
-  __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  #ifdef WITH_THREAD
-  __Pyx_PyGILState_Release(__pyx_gilstate_save);
-  #endif
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1337
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
- *                             int ndim,
- *                             int ndim_other) noexcept nogil:
- */
-
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) {
-  int __pyx_v_i;
-  int __pyx_v_offset;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-
-  /* "View.MemoryView":1341
- *                             int ndim_other) noexcept nogil:
- *     cdef int i
- *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim - 1, -1, -1):
- */
-  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);
-
-  /* "View.MemoryView":1343
- *     cdef int offset = ndim_other - ndim
- * 
- *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *         mslice.shape[i + offset] = mslice.shape[i]
- *         mslice.strides[i + offset] = mslice.strides[i]
- */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_i = __pyx_t_1;
-
-    /* "View.MemoryView":1344
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         mslice.shape[i + offset] = mslice.shape[i]             # <<<<<<<<<<<<<<
- *         mslice.strides[i + offset] = mslice.strides[i]
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- */
-    (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);
-
-    /* "View.MemoryView":1345
- *     for i in range(ndim - 1, -1, -1):
- *         mslice.shape[i + offset] = mslice.shape[i]
- *         mslice.strides[i + offset] = mslice.strides[i]             # <<<<<<<<<<<<<<
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- * 
- */
-    (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-    /* "View.MemoryView":1346
- *         mslice.shape[i + offset] = mslice.shape[i]
- *         mslice.strides[i + offset] = mslice.strides[i]
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]             # <<<<<<<<<<<<<<
- * 
- *     for i in range(offset):
- */
-    (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);
-  }
-
-  /* "View.MemoryView":1348
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- * 
- *     for i in range(offset):             # <<<<<<<<<<<<<<
- *         mslice.shape[i] = 1
- *         mslice.strides[i] = mslice.strides[0]
- */
-  __pyx_t_1 = __pyx_v_offset;
-  __pyx_t_2 = __pyx_t_1;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
-
-    /* "View.MemoryView":1349
- * 
- *     for i in range(offset):
- *         mslice.shape[i] = 1             # <<<<<<<<<<<<<<
- *         mslice.strides[i] = mslice.strides[0]
- *         mslice.suboffsets[i] = -1
- */
-    (__pyx_v_mslice->shape[__pyx_v_i]) = 1;
-
-    /* "View.MemoryView":1350
- *     for i in range(offset):
- *         mslice.shape[i] = 1
- *         mslice.strides[i] = mslice.strides[0]             # <<<<<<<<<<<<<<
- *         mslice.suboffsets[i] = -1
- * 
- */
-    (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]);
-
-    /* "View.MemoryView":1351
- *         mslice.shape[i] = 1
- *         mslice.strides[i] = mslice.strides[0]
- *         mslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L;
-  }
-
-  /* "View.MemoryView":1337
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
- *                             int ndim,
- *                             int ndim_other) noexcept nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1359
- * 
- * @cname('__pyx_memoryview_refcount_copying')
- * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil:             # <<<<<<<<<<<<<<
- * 
- *     if dtype_is_object:
- */
-
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {
-
-  /* "View.MemoryView":1361
- * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil:
- * 
- *     if dtype_is_object:             # <<<<<<<<<<<<<<
- *         refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc)
- * 
- */
-  if (__pyx_v_dtype_is_object) {
-
-    /* "View.MemoryView":1362
- * 
- *     if dtype_is_object:
- *         refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc)             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
- */
-    __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);
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-      if (__pyx_t_8) {
-
-        /* "monotonic_align/core.pyx":18
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:
- *         v_cur = max_neg_val             # <<<<<<<<<<<<<<
- *       else:
- *         v_cur = value[y-1, x]
- */
-        __pyx_v_v_cur = __pyx_v_max_neg_val;
-
-        /* "monotonic_align/core.pyx":17
- *   for y in range(t_y):
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:             # <<<<<<<<<<<<<<
- *         v_cur = max_neg_val
- *       else:
- */
-        goto __pyx_L7;
-      }
-
-      /* "monotonic_align/core.pyx":20
- *         v_cur = max_neg_val
- *       else:
- *         v_cur = value[y-1, x]             # <<<<<<<<<<<<<<
- *       if x == 0:
- *         if y == 0:
- */
-      /*else*/ {
-        __pyx_t_9 = (__pyx_v_y - 1);
-        __pyx_t_10 = __pyx_v_x;
-        __pyx_v_v_cur = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) )));
-      }
-      __pyx_L7:;
-
-      /* "monotonic_align/core.pyx":21
- *       else:
- *         v_cur = value[y-1, x]
- *       if x == 0:             # <<<<<<<<<<<<<<
- *         if y == 0:
- *           v_prev = 0.
- */
-      __pyx_t_8 = (__pyx_v_x == 0);
-      if (__pyx_t_8) {
-
-        /* "monotonic_align/core.pyx":22
- *         v_cur = value[y-1, x]
- *       if x == 0:
- *         if y == 0:             # <<<<<<<<<<<<<<
- *           v_prev = 0.
- *         else:
- */
-        __pyx_t_8 = (__pyx_v_y == 0);
-        if (__pyx_t_8) {
-
-          /* "monotonic_align/core.pyx":23
- *       if x == 0:
- *         if y == 0:
- *           v_prev = 0.             # <<<<<<<<<<<<<<
- *         else:
- *           v_prev = max_neg_val
- */
-          __pyx_v_v_prev = 0.;
-
-          /* "monotonic_align/core.pyx":22
- *         v_cur = value[y-1, x]
- *       if x == 0:
- *         if y == 0:             # <<<<<<<<<<<<<<
- *           v_prev = 0.
- *         else:
- */
-          goto __pyx_L9;
-        }
-
-        /* "monotonic_align/core.pyx":25
- *           v_prev = 0.
- *         else:
- *           v_prev = max_neg_val             # <<<<<<<<<<<<<<
- *       else:
- *         v_prev = value[y-1, x-1]
- */
-        /*else*/ {
-          __pyx_v_v_prev = __pyx_v_max_neg_val;
-        }
-        __pyx_L9:;
-
-        /* "monotonic_align/core.pyx":21
- *       else:
- *         v_cur = value[y-1, x]
- *       if x == 0:             # <<<<<<<<<<<<<<
- *         if y == 0:
- *           v_prev = 0.
- */
-        goto __pyx_L8;
-      }
-
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- *           v_prev = max_neg_val
- *       else:
- *         v_prev = value[y-1, x-1]             # <<<<<<<<<<<<<<
- *       value[y, x] += max(v_prev, v_cur)
- * 
- */
-      /*else*/ {
-        __pyx_t_10 = (__pyx_v_y - 1);
-        __pyx_t_9 = (__pyx_v_x - 1);
-        __pyx_v_v_prev = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_10 * __pyx_v_value.strides[0]) )) + __pyx_t_9)) )));
-      }
-      __pyx_L8:;
-
-      /* "monotonic_align/core.pyx":28
- *       else:
- *         v_prev = value[y-1, x-1]
- *       value[y, x] += max(v_prev, v_cur)             # <<<<<<<<<<<<<<
- * 
- *   for y in range(t_y - 1, -1, -1):
- */
-      __pyx_t_11 = __pyx_v_v_cur;
-      __pyx_t_12 = __pyx_v_v_prev;
-      if ((__pyx_t_11 > __pyx_t_12)) {
-        __pyx_t_13 = __pyx_t_11;
-      } else {
-        __pyx_t_13 = __pyx_t_12;
-      }
-      __pyx_t_9 = __pyx_v_y;
-      __pyx_t_10 = __pyx_v_x;
-      *((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) )) += __pyx_t_13;
-    }
-  }
-
-  /* "monotonic_align/core.pyx":30
- *       value[y, x] += max(v_prev, v_cur)
- * 
- *   for y in range(t_y - 1, -1, -1):             # <<<<<<<<<<<<<<
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- */
-  for (__pyx_t_1 = (__pyx_v_t_y - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_y = __pyx_t_1;
-
-    /* "monotonic_align/core.pyx":31
- * 
- *   for y in range(t_y - 1, -1, -1):
- *     path[y, index] = 1             # <<<<<<<<<<<<<<
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- *       index = index - 1
- */
-    __pyx_t_10 = __pyx_v_y;
-    __pyx_t_9 = __pyx_v_index;
-    *((int *) ( /* dim=1 */ ((char *) (((int *) ( /* dim=0 */ (__pyx_v_path.data + __pyx_t_10 * __pyx_v_path.strides[0]) )) + __pyx_t_9)) )) = 1;
-
-    /* "monotonic_align/core.pyx":32
- *   for y in range(t_y - 1, -1, -1):
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):             # <<<<<<<<<<<<<<
- *       index = index - 1
- * 
- */
-    __pyx_t_14 = (__pyx_v_index != 0);
-    if (__pyx_t_14) {
-    } else {
-      __pyx_t_8 = __pyx_t_14;
-      goto __pyx_L13_bool_binop_done;
-    }
-    __pyx_t_14 = (__pyx_v_index == __pyx_v_y);
-    if (!__pyx_t_14) {
-    } else {
-      __pyx_t_8 = __pyx_t_14;
-      goto __pyx_L13_bool_binop_done;
-    }
-    __pyx_t_9 = (__pyx_v_y - 1);
-    __pyx_t_10 = __pyx_v_index;
-    __pyx_t_15 = (__pyx_v_y - 1);
-    __pyx_t_16 = (__pyx_v_index - 1);
-    __pyx_t_14 = ((*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) ))) < (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_15 * __pyx_v_value.strides[0]) )) + __pyx_t_16)) ))));
-    __pyx_t_8 = __pyx_t_14;
-    __pyx_L13_bool_binop_done:;
-    if (__pyx_t_8) {
-
-      /* "monotonic_align/core.pyx":33
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- *       index = index - 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_index = (__pyx_v_index - 1);
-
-      /* "monotonic_align/core.pyx":32
- *   for y in range(t_y - 1, -1, -1):
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):             # <<<<<<<<<<<<<<
- *       index = index - 1
- * 
- */
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-  }
-
-  /* "monotonic_align/core.pyx":7
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
- *   cdef int x
- *   cdef int y
- */
-
-  /* function exit code */
-}
-
-/* "monotonic_align/core.pyx":38
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:             # <<<<<<<<<<<<<<
- *   cdef int b = paths.shape[0]
- *   cdef int i
- */
-
-static PyObject *__pyx_pw_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, 
-#if CYTHON_METH_FASTCALL
-PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds
-#else
-PyObject *__pyx_args, PyObject *__pyx_kwds
-#endif
-); /*proto*/
-static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs, CYTHON_UNUSED int __pyx_skip_dispatch) {
-  CYTHON_UNUSED int __pyx_v_b;
-  int __pyx_v_i;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  __Pyx_memviewslice __pyx_t_4 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  Py_ssize_t __pyx_t_6;
-  Py_ssize_t __pyx_t_7;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save;
-  #endif
-
-  /* "monotonic_align/core.pyx":39
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
- *   cdef int b = paths.shape[0]             # <<<<<<<<<<<<<<
- *   cdef int i
- *   for i in prange(b, nogil=True):
- */
-  __pyx_v_b = (__pyx_v_paths.shape[0]);
-
-  /* "monotonic_align/core.pyx":41
- *   cdef int b = paths.shape[0]
- *   cdef int i
- *   for i in prange(b, nogil=True):             # <<<<<<<<<<<<<<
- *     maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
- */
-  {
-      #ifdef WITH_THREAD
-      PyThreadState *_save;
-      _save = NULL;
-      if (PyGILState_Check()) {
-        Py_UNBLOCK_THREADS
-      }
-      __Pyx_FastGIL_Remember();
-      #endif
-      /*try:*/ {
-        __pyx_t_1 = __pyx_v_b;
-        {
-            int __pyx_parallel_temp0 = ((int)0xbad0bad0);
-            const char *__pyx_parallel_filename = NULL; int __pyx_parallel_lineno = 0, __pyx_parallel_clineno = 0;
-            PyObject *__pyx_parallel_exc_type = NULL, *__pyx_parallel_exc_value = NULL, *__pyx_parallel_exc_tb = NULL;
-            int __pyx_parallel_why;
-            __pyx_parallel_why = 0;
-            #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
-                #undef likely
-                #undef unlikely
-                #define likely(x)   (x)
-                #define unlikely(x) (x)
-            #endif
-            __pyx_t_3 = (__pyx_t_1 - 0 + 1 - 1/abs(1)) / 1;
-            if (__pyx_t_3 > 0)
-            {
-                #ifdef _OPENMP
-                #pragma omp parallel private(__pyx_t_6, __pyx_t_7) firstprivate(__pyx_t_4, __pyx_t_5) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb)
-                #endif /* _OPENMP */
-                {
-                    #ifdef _OPENMP
-                    #ifdef WITH_THREAD
-                    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-                    #endif
-                    Py_BEGIN_ALLOW_THREADS
-                    #endif /* _OPENMP */
-                    #ifdef _OPENMP
-                    #pragma omp for firstprivate(__pyx_v_i) lastprivate(__pyx_v_i)
-                    #endif /* _OPENMP */
-                    for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_3; __pyx_t_2++){
-                        if (__pyx_parallel_why < 2)
-                        {
-                            __pyx_v_i = (int)(0 + 1 * __pyx_t_2);
-
-                            /* "monotonic_align/core.pyx":42
- *   cdef int i
- *   for i in prange(b, nogil=True):
- *     maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])             # <<<<<<<<<<<<<<
- */
-                            __pyx_t_4.data = __pyx_v_paths.data;
-                            __pyx_t_4.memview = __pyx_v_paths.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_4, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_paths.strides[0];
-        __pyx_t_4.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_4.shape[0] = __pyx_v_paths.shape[1];
-__pyx_t_4.strides[0] = __pyx_v_paths.strides[1];
-    __pyx_t_4.suboffsets[0] = -1;
-
-__pyx_t_4.shape[1] = __pyx_v_paths.shape[2];
-__pyx_t_4.strides[1] = __pyx_v_paths.strides[2];
-    __pyx_t_4.suboffsets[1] = -1;
-
-__pyx_t_5.data = __pyx_v_values.data;
-                            __pyx_t_5.memview = __pyx_v_values.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_5, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_values.strides[0];
-        __pyx_t_5.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_5.shape[0] = __pyx_v_values.shape[1];
-__pyx_t_5.strides[0] = __pyx_v_values.strides[1];
-    __pyx_t_5.suboffsets[0] = -1;
-
-__pyx_t_5.shape[1] = __pyx_v_values.shape[2];
-__pyx_t_5.strides[1] = __pyx_v_values.strides[2];
-    __pyx_t_5.suboffsets[1] = -1;
-
-__pyx_t_6 = __pyx_v_i;
-                            __pyx_t_7 = __pyx_v_i;
-                            __pyx_f_15monotonic_align_4core_maximum_path_each(__pyx_t_4, __pyx_t_5, (*((int *) ( /* dim=0 */ ((char *) (((int *) __pyx_v_t_ys.data) + __pyx_t_6)) ))), (*((int *) ( /* dim=0 */ ((char *) (((int *) __pyx_v_t_xs.data) + __pyx_t_7)) ))), NULL); if (unlikely(__Pyx_ErrOccurredWithGIL())) __PYX_ERR(0, 42, __pyx_L8_error)
-                            __PYX_XCLEAR_MEMVIEW(&__pyx_t_4, 0);
-                            __pyx_t_4.memview = NULL; __pyx_t_4.data = NULL;
-                            __PYX_XCLEAR_MEMVIEW(&__pyx_t_5, 0);
-                            __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL;
-                            goto __pyx_L11;
-                            __pyx_L8_error:;
-                            {
-                                #ifdef WITH_THREAD
-                                PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-                                #endif
-                                #ifdef _OPENMP
-                                #pragma omp flush(__pyx_parallel_exc_type)
-                                #endif /* _OPENMP */
-                                if (!__pyx_parallel_exc_type) {
-                                  __Pyx_ErrFetchWithState(&__pyx_parallel_exc_type, &__pyx_parallel_exc_value, &__pyx_parallel_exc_tb);
-                                  __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno;
-                                  __Pyx_GOTREF(__pyx_parallel_exc_type);
-                                }
-                                #ifdef WITH_THREAD
-                                __Pyx_PyGILState_Release(__pyx_gilstate_save);
-                                #endif
-                            }
-                            __pyx_parallel_why = 4;
-                            goto __pyx_L10;
-                            __pyx_L10:;
-                            #ifdef _OPENMP
-                            #pragma omp critical(__pyx_parallel_lastprivates0)
-                            #endif /* _OPENMP */
-                            {
-                                __pyx_parallel_temp0 = __pyx_v_i;
-                            }
-                            __pyx_L11:;
-                            #ifdef _OPENMP
-                            #pragma omp flush(__pyx_parallel_why)
-                            #endif /* _OPENMP */
-                        }
-                    }
-                    #ifdef _OPENMP
-                    Py_END_ALLOW_THREADS
-                    #else
-{
-#ifdef WITH_THREAD
-                    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-                    #endif
-                    #endif /* _OPENMP */
-                    /* Clean up any temporaries */
-                    __PYX_XCLEAR_MEMVIEW(&__pyx_t_4, 0);
-                    __pyx_t_4.memview = NULL; __pyx_t_4.data = NULL;
-                    __PYX_XCLEAR_MEMVIEW(&__pyx_t_5, 0);
-                    __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL;
-                    #ifdef WITH_THREAD
-                    __Pyx_PyGILState_Release(__pyx_gilstate_save);
-                    #endif
-                    #ifndef _OPENMP
-}
-#endif /* _OPENMP */
-                }
-            }
-            if (__pyx_parallel_exc_type) {
-              /* This may have been overridden by a continue, break or return in another thread. Prefer the error. */
-              __pyx_parallel_why = 4;
-            }
-            if (__pyx_parallel_why) {
-              __pyx_v_i = __pyx_parallel_temp0;
-              switch (__pyx_parallel_why) {
-                    case 4:
-                {
-                    #ifdef WITH_THREAD
-                    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-                    #endif
-                    __Pyx_GIVEREF(__pyx_parallel_exc_type);
-                    __Pyx_ErrRestoreWithState(__pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb);
-                    __pyx_filename = __pyx_parallel_filename; __pyx_lineno = __pyx_parallel_lineno; __pyx_clineno = __pyx_parallel_clineno;
-                    #ifdef WITH_THREAD
-                    __Pyx_PyGILState_Release(__pyx_gilstate_save);
-                    #endif
-                }
-                goto __pyx_L4_error;
-              }
-            }
-        }
-        #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
-            #undef likely
-            #undef unlikely
-            #define likely(x)   __builtin_expect(!!(x), 1)
-            #define unlikely(x) __builtin_expect(!!(x), 0)
-        #endif
-      }
-
-      /* "monotonic_align/core.pyx":41
- *   cdef int b = paths.shape[0]
- *   cdef int i
- *   for i in prange(b, nogil=True):             # <<<<<<<<<<<<<<
- *     maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
- */
-      /*finally:*/ {
-        /*normal exit:*/{
-          #ifdef WITH_THREAD
-          __Pyx_FastGIL_Forget();
-          if (_save) {
-            Py_BLOCK_THREADS
-          }
-          #endif
-          goto __pyx_L5;
-        }
-        __pyx_L4_error: {
-          #ifdef WITH_THREAD
-          __Pyx_FastGIL_Forget();
-          if (_save) {
-            Py_BLOCK_THREADS
-          }
-          #endif
-          goto __pyx_L1_error;
-        }
-        __pyx_L5:;
-      }
-  }
-
-  /* "monotonic_align/core.pyx":38
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:             # <<<<<<<<<<<<<<
- *   cdef int b = paths.shape[0]
- *   cdef int i
- */
-
-  /* function exit code */
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  #ifdef WITH_THREAD
-  __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-  #endif
-  __PYX_XCLEAR_MEMVIEW(&__pyx_t_4, 1);
-  __PYX_XCLEAR_MEMVIEW(&__pyx_t_5, 1);
-  __Pyx_AddTraceback("monotonic_align.core.maximum_path_c", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  #ifdef WITH_THREAD
-  __Pyx_PyGILState_Release(__pyx_gilstate_save);
-  #endif
-  __pyx_L0:;
-}
-
-/* Python wrapper */
-static PyObject *__pyx_pw_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, 
-#if CYTHON_METH_FASTCALL
-PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds
-#else
-PyObject *__pyx_args, PyObject *__pyx_kwds
-#endif
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-static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  if (p->name) {
-    e = (*v)(p->name, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_Enum(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  tmp = ((PyObject*)p->name);
-  p->name = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  return 0;
-}
-
-static PyObject *__pyx_specialmethod___pyx_MemviewEnum___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) {
-  return __pyx_MemviewEnum___repr__(self);
-}
-
-static PyMethodDef __pyx_methods_Enum[] = {
-  {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_MemviewEnum___repr__, METH_NOARGS|METH_COEXIST, 0},
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum},
-  {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__},
-  {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum},
-  {Py_tp_clear, (void *)__pyx_tp_clear_Enum},
-  {Py_tp_methods, (void *)__pyx_methods_Enum},
-  {Py_tp_init, (void *)__pyx_MemviewEnum___init__},
-  {Py_tp_new, (void *)__pyx_tp_new_Enum},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = {
-  "monotonic_align.core.Enum",
-  sizeof(struct __pyx_MemviewEnum_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC,
-  __pyx_type___pyx_MemviewEnum_slots,
-};
-#else
-
-static PyTypeObject __pyx_type___pyx_MemviewEnum = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""Enum", /*tp_name*/
-  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_MemviewEnum___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_Enum, /*tp_traverse*/
-  __pyx_tp_clear_Enum, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_Enum, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  __pyx_MemviewEnum___init__, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_Enum, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
-
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryview_obj *p;
-  PyObject *o;
-  #if CYTHON_COMPILING_IN_LIMITED_API
-  allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc);
-  o = alloc_func(t, 0);
-  #else
-  if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  #endif
-  p = ((struct __pyx_memoryview_obj *)o);
-  p->__pyx_vtab = __pyx_vtabptr_memoryview;
-  p->obj = Py_None; Py_INCREF(Py_None);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  p->view.obj = NULL;
-  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
-  return o;
-  bad:
-  Py_DECREF(o); o = 0;
-  return NULL;
-}
-
-static void __pyx_tp_dealloc_memoryview(PyObject *o) {
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) {
-    if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) {
-      if (PyObject_CallFinalizerFromDealloc(o)) return;
-    }
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryview___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->obj);
-  Py_CLEAR(p->_size);
-  Py_CLEAR(p->_array_interface);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  if (p->obj) {
-    e = (*v)(p->obj, a); if (e) return e;
-  }
-  if (p->_size) {
-    e = (*v)(p->_size, a); if (e) return e;
-  }
-  if (p->_array_interface) {
-    e = (*v)(p->_array_interface, a); if (e) return e;
-  }
-  if (p->view.obj) {
-    e = (*v)(p->view.obj, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  tmp = ((PyObject*)p->obj);
-  p->obj = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_size);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_array_interface);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  Py_CLEAR(p->view.obj);
-  return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_memoryview___setitem__(o, i, v);
-  }
-  else {
-    __Pyx_TypeName o_type_name;
-    o_type_name = __Pyx_PyType_GetName(Py_TYPE(o));
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name);
-    __Pyx_DECREF_TypeName(o_type_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
-}
-
-static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) {
-  return __pyx_memoryview___repr__(self);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
-  {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0},
-  {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
-  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
-  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
-  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
-  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
-  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
-  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
-  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
-  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
-  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-#if !CYTHON_COMPILING_IN_LIMITED_API
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-#endif
-static PyType_Slot __pyx_type___pyx_memoryview_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview},
-  {Py_tp_repr, (void *)__pyx_memoryview___repr__},
-  {Py_sq_length, (void *)__pyx_memoryview___len__},
-  {Py_sq_item, (void *)__pyx_sq_item_memoryview},
-  {Py_mp_length, (void *)__pyx_memoryview___len__},
-  {Py_mp_subscript, (void *)__pyx_memoryview___getitem__},
-  {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview},
-  {Py_tp_str, (void *)__pyx_memoryview___str__},
-  #if defined(Py_bf_getbuffer)
-  {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer},
-  #endif
-  {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview},
-  {Py_tp_clear, (void *)__pyx_tp_clear_memoryview},
-  {Py_tp_methods, (void *)__pyx_methods_memoryview},
-  {Py_tp_getset, (void *)__pyx_getsets_memoryview},
-  {Py_tp_new, (void *)__pyx_tp_new_memoryview},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_memoryview_spec = {
-  "monotonic_align.core.memoryview",
-  sizeof(struct __pyx_memoryview_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC,
-  __pyx_type___pyx_memoryview_slots,
-};
-#else
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
-  __pyx_memoryview___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_memoryview, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
-  __pyx_memoryview___len__, /*mp_length*/
-  __pyx_memoryview___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""memoryview", /*tp_name*/
-  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_memoryview___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  __pyx_memoryview___str__, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_memoryview, /*tp_traverse*/
-  __pyx_tp_clear_memoryview, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_memoryview, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_memoryview, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_memoryview, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryviewslice_obj *p;
-  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryviewslice_obj *)o);
-  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  p->from_slice.memview = NULL;
-  return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
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-  {0, 0, 0, 0}
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-  {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice},
-  {Py_tp_methods, (void *)__pyx_methods__memoryviewslice},
-  {Py_tp_new, (void *)__pyx_tp_new__memoryviewslice},
-  {0, 0},
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-}
-
-/* RaiseDoubleKeywords */
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-/* ParseKeywords */
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds));
-    while (1) {
-        if (kwds_is_tuple) {
-            if (pos >= PyTuple_GET_SIZE(kwds)) break;
-            key = PyTuple_GET_ITEM(kwds, pos);
-            value = kwvalues[pos];
-            pos++;
-        }
-        else
-        {
-            if (!PyDict_Next(kwds, &pos, &key, &value)) break;
-        }
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key)
-                );
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    __Pyx_TypeName type_name;
-    __Pyx_TypeName obj_type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    type_name = __Pyx_PyType_GetName(type);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME
-        ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    CYTHON_UNUSED_VAR(cause);
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-      #if PY_VERSION_HEX >= 0x030C00A6
-        PyException_SetTraceback(value, tb);
-      #elif CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectFastCall */
-static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) {
-    PyObject *argstuple;
-    PyObject *result;
-    size_t i;
-    argstuple = PyTuple_New((Py_ssize_t)nargs);
-    if (unlikely(!argstuple)) return NULL;
-    for (i = 0; i < nargs; i++) {
-        Py_INCREF(args[i]);
-        PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]);
-    }
-    result = __Pyx_PyObject_Call(func, argstuple, kwargs);
-    Py_DECREF(argstuple);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) {
-    Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (nargs == 0 && kwargs == NULL) {
-#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG)
-        if (__Pyx_IsCyOrPyCFunction(func))
-#else
-        if (PyCFunction_Check(func))
-#endif
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-                return __Pyx_PyObject_CallMethO(func, NULL);
-            }
-        }
-    }
-    else if (nargs == 1 && kwargs == NULL) {
-        if (PyCFunction_Check(func))
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-                return __Pyx_PyObject_CallMethO(func, args[0]);
-            }
-        }
-    }
-#endif
-    #if PY_VERSION_HEX < 0x030800B1
-    #if CYTHON_FAST_PYCCALL
-    if (PyCFunction_Check(func)) {
-        if (kwargs) {
-            return _PyCFunction_FastCallDict(func, args, nargs, kwargs);
-        } else {
-            return _PyCFunction_FastCallKeywords(func, args, nargs, NULL);
-        }
-    }
-    #if PY_VERSION_HEX >= 0x030700A1
-    if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) {
-        return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL);
-    }
-    #endif
-    #endif
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs);
-    }
-    #endif
-    #endif
-    #if CYTHON_VECTORCALL
-    vectorcallfunc f = _PyVectorcall_Function(func);
-    if (f) {
-        return f(func, args, (size_t)nargs, kwargs);
-    }
-    #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL
-    if (__Pyx_CyFunction_CheckExact(func)) {
-        __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func);
-        if (f) return f(func, args, (size_t)nargs, kwargs);
-    }
-    #endif
-    if (nargs == 0) {
-        return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs);
-    }
-    return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs);
-}
-
-/* RaiseUnexpectedTypeError */
-static int
-__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj)
-{
-    __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME,
-                 expected, obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* CIntToDigits */
-static const char DIGIT_PAIRS_10[2*10*10+1] = {
-    "00010203040506070809"
-    "10111213141516171819"
-    "20212223242526272829"
-    "30313233343536373839"
-    "40414243444546474849"
-    "50515253545556575859"
-    "60616263646566676869"
-    "70717273747576777879"
-    "80818283848586878889"
-    "90919293949596979899"
-};
-static const char DIGIT_PAIRS_8[2*8*8+1] = {
-    "0001020304050607"
-    "1011121314151617"
-    "2021222324252627"
-    "3031323334353637"
-    "4041424344454647"
-    "5051525354555657"
-    "6061626364656667"
-    "7071727374757677"
-};
-static const char DIGITS_HEX[2*16+1] = {
-    "0123456789abcdef"
-    "0123456789ABCDEF"
-};
-
-/* BuildPyUnicode */
-static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength,
-                                                int prepend_sign, char padding_char) {
-    PyObject *uval;
-    Py_ssize_t uoffset = ulength - clength;
-#if CYTHON_USE_UNICODE_INTERNALS
-    Py_ssize_t i;
-#if CYTHON_PEP393_ENABLED
-    void *udata;
-    uval = PyUnicode_New(ulength, 127);
-    if (unlikely(!uval)) return NULL;
-    udata = PyUnicode_DATA(uval);
-#else
-    Py_UNICODE *udata;
-    uval = PyUnicode_FromUnicode(NULL, ulength);
-    if (unlikely(!uval)) return NULL;
-    udata = PyUnicode_AS_UNICODE(uval);
-#endif
-    if (uoffset > 0) {
-        i = 0;
-        if (prepend_sign) {
-            __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-');
-            i++;
-        }
-        for (; i < uoffset; i++) {
-            __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char);
-        }
-    }
-    for (i=0; i < clength; i++) {
-        __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]);
-    }
-#else
-    {
-        PyObject *sign = NULL, *padding = NULL;
-        uval = NULL;
-        if (uoffset > 0) {
-            prepend_sign = !!prepend_sign;
-            if (uoffset > prepend_sign) {
-                padding = PyUnicode_FromOrdinal(padding_char);
-                if (likely(padding) && uoffset > prepend_sign + 1) {
-                    PyObject *tmp;
-                    PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign);
-                    if (unlikely(!repeat)) goto done_or_error;
-                    tmp = PyNumber_Multiply(padding, repeat);
-                    Py_DECREF(repeat);
-                    Py_DECREF(padding);
-                    padding = tmp;
-                }
-                if (unlikely(!padding)) goto done_or_error;
-            }
-            if (prepend_sign) {
-                sign = PyUnicode_FromOrdinal('-');
-                if (unlikely(!sign)) goto done_or_error;
-            }
-        }
-        uval = PyUnicode_DecodeASCII(chars, clength, NULL);
-        if (likely(uval) && padding) {
-            PyObject *tmp = PyNumber_Add(padding, uval);
-            Py_DECREF(uval);
-            uval = tmp;
-        }
-        if (likely(uval) && sign) {
-            PyObject *tmp = PyNumber_Add(sign, uval);
-            Py_DECREF(uval);
-            uval = tmp;
-        }
-done_or_error:
-        Py_XDECREF(padding);
-        Py_XDECREF(sign);
-    }
-#endif
-    return uval;
-}
-
-/* CIntToPyUnicode */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) {
-    char digits[sizeof(int)*3+2];
-    char *dpos, *end = digits + sizeof(int)*3+2;
-    const char *hex_digits = DIGITS_HEX;
-    Py_ssize_t length, ulength;
-    int prepend_sign, last_one_off;
-    int remaining;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (format_char == 'X') {
-        hex_digits += 16;
-        format_char = 'x';
-    }
-    remaining = value;
-    last_one_off = 0;
-    dpos = end;
-    do {
-        int digit_pos;
-        switch (format_char) {
-        case 'o':
-            digit_pos = abs((int)(remaining % (8*8)));
-            remaining = (int) (remaining / (8*8));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 8);
-            break;
-        case 'd':
-            digit_pos = abs((int)(remaining % (10*10)));
-            remaining = (int) (remaining / (10*10));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 10);
-            break;
-        case 'x':
-            *(--dpos) = hex_digits[abs((int)(remaining % 16))];
-            remaining = (int) (remaining / 16);
-            break;
-        default:
-            assert(0);
-            break;
-        }
-    } while (unlikely(remaining != 0));
-    assert(!last_one_off || *dpos == '0');
-    dpos += last_one_off;
-    length = end - dpos;
-    ulength = length;
-    prepend_sign = 0;
-    if (!is_unsigned && value <= neg_one) {
-        if (padding_char == ' ' || width <= length + 1) {
-            *(--dpos) = '-';
-            ++length;
-        } else {
-            prepend_sign = 1;
-        }
-        ++ulength;
-    }
-    if (width > ulength) {
-        ulength = width;
-    }
-    if (ulength == 1) {
-        return PyUnicode_FromOrdinal(*dpos);
-    }
-    return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char);
-}
-
-/* CIntToPyUnicode */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) {
-    char digits[sizeof(Py_ssize_t)*3+2];
-    char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2;
-    const char *hex_digits = DIGITS_HEX;
-    Py_ssize_t length, ulength;
-    int prepend_sign, last_one_off;
-    Py_ssize_t remaining;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (format_char == 'X') {
-        hex_digits += 16;
-        format_char = 'x';
-    }
-    remaining = value;
-    last_one_off = 0;
-    dpos = end;
-    do {
-        int digit_pos;
-        switch (format_char) {
-        case 'o':
-            digit_pos = abs((int)(remaining % (8*8)));
-            remaining = (Py_ssize_t) (remaining / (8*8));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 8);
-            break;
-        case 'd':
-            digit_pos = abs((int)(remaining % (10*10)));
-            remaining = (Py_ssize_t) (remaining / (10*10));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 10);
-            break;
-        case 'x':
-            *(--dpos) = hex_digits[abs((int)(remaining % 16))];
-            remaining = (Py_ssize_t) (remaining / 16);
-            break;
-        default:
-            assert(0);
-            break;
-        }
-    } while (unlikely(remaining != 0));
-    assert(!last_one_off || *dpos == '0');
-    dpos += last_one_off;
-    length = end - dpos;
-    ulength = length;
-    prepend_sign = 0;
-    if (!is_unsigned && value <= neg_one) {
-        if (padding_char == ' ' || width <= length + 1) {
-            *(--dpos) = '-';
-            ++length;
-        } else {
-            prepend_sign = 1;
-        }
-        ++ulength;
-    }
-    if (width > ulength) {
-        ulength = width;
-    }
-    if (ulength == 1) {
-        return PyUnicode_FromOrdinal(*dpos);
-    }
-    return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char);
-}
-
-/* JoinPyUnicode */
-static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength,
-                                      Py_UCS4 max_char) {
-#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    PyObject *result_uval;
-    int result_ukind, kind_shift;
-    Py_ssize_t i, char_pos;
-    void *result_udata;
-    CYTHON_MAYBE_UNUSED_VAR(max_char);
-#if CYTHON_PEP393_ENABLED
-    result_uval = PyUnicode_New(result_ulength, max_char);
-    if (unlikely(!result_uval)) return NULL;
-    result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND;
-    kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1;
-    result_udata = PyUnicode_DATA(result_uval);
-#else
-    result_uval = PyUnicode_FromUnicode(NULL, result_ulength);
-    if (unlikely(!result_uval)) return NULL;
-    result_ukind = sizeof(Py_UNICODE);
-    kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1;
-    result_udata = PyUnicode_AS_UNICODE(result_uval);
-#endif
-    assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0);
-    char_pos = 0;
-    for (i=0; i < value_count; i++) {
-        int ukind;
-        Py_ssize_t ulength;
-        void *udata;
-        PyObject *uval = PyTuple_GET_ITEM(value_tuple, i);
-        if (unlikely(__Pyx_PyUnicode_READY(uval)))
-            goto bad;
-        ulength = __Pyx_PyUnicode_GET_LENGTH(uval);
-        if (unlikely(!ulength))
-            continue;
-        if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos))
-            goto overflow;
-        ukind = __Pyx_PyUnicode_KIND(uval);
-        udata = __Pyx_PyUnicode_DATA(uval);
-        if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) {
-            memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift));
-        } else {
-            #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters)
-            _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength);
-            #else
-            Py_ssize_t j;
-            for (j=0; j < ulength; j++) {
-                Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j);
-                __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar);
-            }
-            #endif
-        }
-        char_pos += ulength;
-    }
-    return result_uval;
-overflow:
-    PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string");
-bad:
-    Py_DECREF(result_uval);
-    return NULL;
-#else
-    CYTHON_UNUSED_VAR(max_char);
-    CYTHON_UNUSED_VAR(result_ulength);
-    CYTHON_UNUSED_VAR(value_count);
-    return PyUnicode_Join(__pyx_empty_unicode, value_tuple);
-#endif
-}
-
-/* GetAttr */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_USE_TYPE_SLOTS
-#if PY_MAJOR_VERSION >= 3
-    if (likely(PyUnicode_Check(n)))
-#else
-    if (likely(PyString_Check(n)))
-#endif
-        return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
-    return PyObject_GetAttr(o, n);
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (unlikely(!j)) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_subscript) {
-            PyObject *r, *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return NULL;
-            r = mm->mp_subscript(o, key);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* PyObjectCallOneArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *args[2] = {NULL, arg};
-    return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index));
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError,
-            "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name);
-        __Pyx_DECREF_TypeName(index_type_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) {
-    __Pyx_TypeName obj_type_name;
-    if (likely(PyType_Check(obj))) {
-        PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem);
-        if (meth) {
-            PyObject *result = __Pyx_PyObject_CallOneArg(meth, key);
-            Py_DECREF(meth);
-            return result;
-        }
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) {
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyMappingMethods *mm = tp->tp_as_mapping;
-    PySequenceMethods *sm = tp->tp_as_sequence;
-    if (likely(mm && mm->mp_subscript)) {
-        return mm->mp_subscript(obj, key);
-    }
-    if (likely(sm && sm->sq_item)) {
-        return __Pyx_PyObject_GetIndex(obj, key);
-    }
-    return __Pyx_PyObject_GetItem_Slow(obj, key);
-}
-#endif
-
-/* KeywordStringCheck */
-static int __Pyx_CheckKeywordStrings(
-    PyObject *kw,
-    const char* function_name,
-    int kw_allowed)
-{
-    PyObject* key = 0;
-    Py_ssize_t pos = 0;
-#if CYTHON_COMPILING_IN_PYPY
-    if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0))
-        goto invalid_keyword;
-    return 1;
-#else
-    if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) {
-        if (unlikely(PyTuple_GET_SIZE(kw) == 0))
-            return 1;
-        if (!kw_allowed) {
-            key = PyTuple_GET_ITEM(kw, 0);
-            goto invalid_keyword;
-        }
-#if PY_VERSION_HEX < 0x03090000
-        for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) {
-            key = PyTuple_GET_ITEM(kw, pos);
-            if (unlikely(!PyUnicode_Check(key)))
-                goto invalid_keyword_type;
-        }
-#endif
-        return 1;
-    }
-    while (PyDict_Next(kw, &pos, &key, 0)) {
-        #if PY_MAJOR_VERSION < 3
-        if (unlikely(!PyString_Check(key)))
-        #endif
-            if (unlikely(!PyUnicode_Check(key)))
-                goto invalid_keyword_type;
-    }
-    if (!kw_allowed && unlikely(key))
-        goto invalid_keyword;
-    return 1;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    return 0;
-#endif
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-    return 0;
-}
-
-/* DivInt[Py_ssize_t] */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
-    Py_ssize_t q = a / b;
-    Py_ssize_t r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* GetAttr3 */
-static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        return NULL;
-    __Pyx_PyErr_Clear();
-    Py_INCREF(d);
-    return d;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
-    PyObject *r;
-#if CYTHON_USE_TYPE_SLOTS
-    if (likely(PyString_Check(n))) {
-        r = __Pyx_PyObject_GetAttrStrNoError(o, n);
-        if (unlikely(!r) && likely(!PyErr_Occurred())) {
-            r = __Pyx_NewRef(d);
-        }
-        return r;
-    }
-#endif
-    r = PyObject_GetAttr(o, n);
-    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
-}
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#elif CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(!__pyx_m)) {
-        return NULL;
-    }
-    result = PyObject_GetAttr(__pyx_m, name);
-    if (likely(result)) {
-        return result;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* ExtTypeTest */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    __Pyx_TypeName obj_type_name;
-    __Pyx_TypeName type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(__Pyx_TypeCheck(obj, type)))
-        return 1;
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    type_name = __Pyx_PyType_GetName(type);
-    PyErr_Format(PyExc_TypeError,
-                 "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME,
-                 obj_type_name, type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-}
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    PyObject *exc_value = exc_info->exc_value;
-    if (exc_value == NULL || exc_value == Py_None) {
-        *value = NULL;
-        *type = NULL;
-        *tb = NULL;
-    } else {
-        *value = exc_value;
-        Py_INCREF(*value);
-        *type = (PyObject*) Py_TYPE(exc_value);
-        Py_INCREF(*type);
-        *tb = PyException_GetTraceback(exc_value);
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #endif
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    PyObject *tmp_value = exc_info->exc_value;
-    exc_info->exc_value = value;
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-  #else
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-  #endif
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type = NULL, *local_value, *local_tb = NULL;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if PY_VERSION_HEX >= 0x030C00A6
-    local_value = tstate->current_exception;
-    tstate->current_exception = 0;
-    if (likely(local_value)) {
-        local_type = (PyObject*) Py_TYPE(local_value);
-        Py_INCREF(local_type);
-        local_tb = PyException_GetTraceback(local_value);
-    }
-  #else
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-  #endif
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6
-    if (unlikely(tstate->current_exception))
-#elif CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-      #if PY_VERSION_HEX >= 0x030B00a4
-        tmp_value = exc_info->exc_value;
-        exc_info->exc_value = local_value;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-        Py_XDECREF(local_type);
-        Py_XDECREF(local_tb);
-      #else
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-      #endif
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_value = exc_info->exc_value;
-    exc_info->exc_value = *value;
-    if (tmp_value == NULL || tmp_value == Py_None) {
-        Py_XDECREF(tmp_value);
-        tmp_value = NULL;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-    } else {
-        tmp_type = (PyObject*) Py_TYPE(tmp_value);
-        Py_INCREF(tmp_type);
-        #if CYTHON_COMPILING_IN_CPYTHON
-        tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback;
-        Py_XINCREF(tmp_tb);
-        #else
-        tmp_tb = PyException_GetTraceback(tmp_value);
-        #endif
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-  #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-  #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *module = 0;
-    PyObject *empty_dict = 0;
-    PyObject *empty_list = 0;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (unlikely(!py_import))
-        goto bad;
-    if (!from_list) {
-        empty_list = PyList_New(0);
-        if (unlikely(!empty_list))
-            goto bad;
-        from_list = empty_list;
-    }
-    #endif
-    empty_dict = PyDict_New();
-    if (unlikely(!empty_dict))
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                #if CYTHON_COMPILING_IN_LIMITED_API
-                module = PyImport_ImportModuleLevelObject(
-                    name, empty_dict, empty_dict, from_list, 1);
-                #else
-                module = PyImport_ImportModuleLevelObject(
-                    name, __pyx_d, empty_dict, from_list, 1);
-                #endif
-                if (unlikely(!module)) {
-                    if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError)))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (unlikely(!py_level))
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            #if CYTHON_COMPILING_IN_LIMITED_API
-            module = PyImport_ImportModuleLevelObject(
-                name, empty_dict, empty_dict, from_list, level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, __pyx_d, empty_dict, from_list, level);
-            #endif
-            #endif
-        }
-    }
-bad:
-    Py_XDECREF(empty_dict);
-    Py_XDECREF(empty_list);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    return module;
-}
-
-/* ImportDottedModule */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) {
-    PyObject *partial_name = NULL, *slice = NULL, *sep = NULL;
-    if (unlikely(PyErr_Occurred())) {
-        PyErr_Clear();
-    }
-    if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) {
-        partial_name = name;
-    } else {
-        slice = PySequence_GetSlice(parts_tuple, 0, count);
-        if (unlikely(!slice))
-            goto bad;
-        sep = PyUnicode_FromStringAndSize(".", 1);
-        if (unlikely(!sep))
-            goto bad;
-        partial_name = PyUnicode_Join(sep, slice);
-    }
-    PyErr_Format(
-#if PY_MAJOR_VERSION < 3
-        PyExc_ImportError,
-        "No module named '%s'", PyString_AS_STRING(partial_name));
-#else
-#if PY_VERSION_HEX >= 0x030600B1
-        PyExc_ModuleNotFoundError,
-#else
-        PyExc_ImportError,
-#endif
-        "No module named '%U'", partial_name);
-#endif
-bad:
-    Py_XDECREF(sep);
-    Py_XDECREF(slice);
-    Py_XDECREF(partial_name);
-    return NULL;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) {
-    PyObject *imported_module;
-#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-    PyObject *modules = PyImport_GetModuleDict();
-    if (unlikely(!modules))
-        return NULL;
-    imported_module = __Pyx_PyDict_GetItemStr(modules, name);
-    Py_XINCREF(imported_module);
-#else
-    imported_module = PyImport_GetModule(name);
-#endif
-    return imported_module;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) {
-    Py_ssize_t i, nparts;
-    nparts = PyTuple_GET_SIZE(parts_tuple);
-    for (i=1; i < nparts && module; i++) {
-        PyObject *part, *submodule;
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        part = PyTuple_GET_ITEM(parts_tuple, i);
-#else
-        part = PySequence_ITEM(parts_tuple, i);
-#endif
-        submodule = __Pyx_PyObject_GetAttrStrNoError(module, part);
-#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-        Py_DECREF(part);
-#endif
-        Py_DECREF(module);
-        module = submodule;
-    }
-    if (unlikely(!module)) {
-        return __Pyx__ImportDottedModule_Error(name, parts_tuple, i);
-    }
-    return module;
-}
-#endif
-static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if PY_MAJOR_VERSION < 3
-    PyObject *module, *from_list, *star = __pyx_n_s__3;
-    CYTHON_UNUSED_VAR(parts_tuple);
-    from_list = PyList_New(1);
-    if (unlikely(!from_list))
-        return NULL;
-    Py_INCREF(star);
-    PyList_SET_ITEM(from_list, 0, star);
-    module = __Pyx_Import(name, from_list, 0);
-    Py_DECREF(from_list);
-    return module;
-#else
-    PyObject *imported_module;
-    PyObject *module = __Pyx_Import(name, NULL, 0);
-    if (!parts_tuple || unlikely(!module))
-        return module;
-    imported_module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(imported_module)) {
-        Py_DECREF(module);
-        return imported_module;
-    }
-    PyErr_Clear();
-    return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple);
-#endif
-}
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1
-    PyObject *module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(module)) {
-        PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec);
-        if (likely(spec)) {
-            PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing);
-            if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) {
-                Py_DECREF(spec);
-                spec = NULL;
-            }
-            Py_XDECREF(unsafe);
-        }
-        if (likely(!spec)) {
-            PyErr_Clear();
-            return module;
-        }
-        Py_DECREF(spec);
-        Py_DECREF(module);
-    } else if (PyErr_Occurred()) {
-        PyErr_Clear();
-    }
-#endif
-    return __Pyx__ImportDottedModule(name, parts_tuple);
-}
-
-/* ssize_strlen */
-static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) {
-    size_t len = strlen(s);
-    if (unlikely(len > PY_SSIZE_T_MAX)) {
-        PyErr_SetString(PyExc_OverflowError, "byte string is too long");
-        return -1;
-    }
-    return (Py_ssize_t) len;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*);
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (cls == a || cls == b) return 1;
-    mro = cls->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            PyObject *base = PyTuple_GET_ITEM(mro, i);
-            if (base == (PyObject *)a || base == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    if (exc_type1) {
-        return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2);
-    } else {
-        return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* PySequenceMultiply */
-static PyObject* __Pyx_PySequence_Multiply_Generic(PyObject *seq, Py_ssize_t mul) {
-    PyObject *result, *pymul = PyInt_FromSsize_t(mul);
-    if (unlikely(!pymul))
-        return NULL;
-    result = PyNumber_Multiply(seq, pymul);
-    Py_DECREF(pymul);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul) {
-#if CYTHON_USE_TYPE_SLOTS
-    PyTypeObject *type = Py_TYPE(seq);
-    if (likely(type->tp_as_sequence && type->tp_as_sequence->sq_repeat)) {
-        return type->tp_as_sequence->sq_repeat(seq, mul);
-    } else
-#endif
-    {
-        return __Pyx_PySequence_Multiply_Generic(seq, mul);
-    }
-}
-
-/* SetItemInt */
-static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
-    int r;
-    if (unlikely(!j)) return -1;
-    r = PyObject_SetItem(o, j, v);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list,
-                                               CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o));
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) {
-            PyObject* old = PyList_GET_ITEM(o, n);
-            Py_INCREF(v);
-            PyList_SET_ITEM(o, n, v);
-            Py_DECREF(old);
-            return 1;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_ass_subscript) {
-            int r;
-            PyObject *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return -1;
-            r = mm->mp_ass_subscript(o, key, v);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_ass_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return -1;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_ass_item(o, i, v);
-        }
-    }
-#else
-#if CYTHON_COMPILING_IN_PYPY
-    if (is_list || (PySequence_Check(o) && !PyDict_Check(o)))
-#else
-    if (is_list || PySequence_Check(o))
-#endif
-    {
-        return PySequence_SetItem(o, i, v);
-    }
-#endif
-    return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
-}
-
-/* RaiseUnboundLocalError */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* DivInt[long] */
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        const char* module_name_str = 0;
-        PyObject* module_name = 0;
-        PyObject* module_dot = 0;
-        PyObject* full_name = 0;
-        PyErr_Clear();
-        module_name_str = PyModule_GetName(module);
-        if (unlikely(!module_name_str)) { goto modbad; }
-        module_name = PyUnicode_FromString(module_name_str);
-        if (unlikely(!module_name)) { goto modbad; }
-        module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2);
-        if (unlikely(!module_dot)) { goto modbad; }
-        full_name = PyUnicode_Concat(module_dot, name);
-        if (unlikely(!full_name)) { goto modbad; }
-        #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-        {
-            PyObject *modules = PyImport_GetModuleDict();
-            if (unlikely(!modules))
-                goto modbad;
-            value = PyObject_GetItem(modules, full_name);
-        }
-        #else
-        value = PyImport_GetModule(full_name);
-        #endif
-      modbad:
-        Py_XDECREF(full_name);
-        Py_XDECREF(module_dot);
-        Py_XDECREF(module_name);
-    }
-    if (unlikely(!value)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* HasAttr */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
-    PyObject *r;
-    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "hasattr(): attribute name must be string");
-        return -1;
-    }
-    r = __Pyx_GetAttr(o, n);
-    if (!r) {
-        PyErr_Clear();
-        return 0;
-    } else {
-        Py_DECREF(r);
-        return 1;
-    }
-}
-
-/* ErrOccurredWithGIL */
-static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) {
-  int err;
-  #ifdef WITH_THREAD
-  PyGILState_STATE _save = PyGILState_Ensure();
-  #endif
-  err = !!PyErr_Occurred();
-  #ifdef WITH_THREAD
-  PyGILState_Release(_save);
-  #endif
-  return err;
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, attr_name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(attr_name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* PyObject_GenericGetAttr */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
-    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
-}
-#endif
-
-/* FixUpExtensionType */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) {
-#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    CYTHON_UNUSED_VAR(spec);
-    CYTHON_UNUSED_VAR(type);
-#else
-    const PyType_Slot *slot = spec->slots;
-    while (slot && slot->slot && slot->slot != Py_tp_members)
-        slot++;
-    if (slot && slot->slot == Py_tp_members) {
-        int changed = 0;
-#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON)
-        const
-#endif
-            PyMemberDef *memb = (PyMemberDef*) slot->pfunc;
-        while (memb && memb->name) {
-            if (memb->name[0] == '_' && memb->name[1] == '_') {
-#if PY_VERSION_HEX < 0x030900b1
-                if (strcmp(memb->name, "__weaklistoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_weaklistoffset = memb->offset;
-                    changed = 1;
-                }
-                else if (strcmp(memb->name, "__dictoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_dictoffset = memb->offset;
-                    changed = 1;
-                }
-#if CYTHON_METH_FASTCALL
-                else if (strcmp(memb->name, "__vectorcalloffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-#if PY_VERSION_HEX >= 0x030800b4
-                    type->tp_vectorcall_offset = memb->offset;
-#else
-                    type->tp_print = (printfunc) memb->offset;
-#endif
-                    changed = 1;
-                }
-#endif
-#else
-                if ((0));
-#endif
-#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON
-                else if (strcmp(memb->name, "__module__") == 0) {
-                    PyObject *descr;
-                    assert(memb->type == T_OBJECT);
-                    assert(memb->flags == 0 || memb->flags == READONLY);
-                    descr = PyDescr_NewMember(type, memb);
-                    if (unlikely(!descr))
-                        return -1;
-                    if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) {
-                        Py_DECREF(descr);
-                        return -1;
-                    }
-                    Py_DECREF(descr);
-                    changed = 1;
-                }
-#endif
-            }
-            memb++;
-        }
-        if (changed)
-            PyType_Modified(type);
-    }
-#endif
-    return 0;
-}
-#endif
-
-/* PyObjectCallNoArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-    PyObject *arg = NULL;
-    return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
-    PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
-    __Pyx_TypeName type_name;
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyObject *descr;
-    descrgetfunc f = NULL;
-    PyObject **dictptr, *dict;
-    int meth_found = 0;
-    assert (*method == NULL);
-    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
-        attr = __Pyx_PyObject_GetAttrStr(obj, name);
-        goto try_unpack;
-    }
-    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
-        return 0;
-    }
-    descr = _PyType_Lookup(tp, name);
-    if (likely(descr != NULL)) {
-        Py_INCREF(descr);
-#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR
-        if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR))
-#elif PY_MAJOR_VERSION >= 3
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type)))
-        #endif
-#else
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr)))
-        #endif
-#endif
-        {
-            meth_found = 1;
-        } else {
-            f = Py_TYPE(descr)->tp_descr_get;
-            if (f != NULL && PyDescr_IsData(descr)) {
-                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-                Py_DECREF(descr);
-                goto try_unpack;
-            }
-        }
-    }
-    dictptr = _PyObject_GetDictPtr(obj);
-    if (dictptr != NULL && (dict = *dictptr) != NULL) {
-        Py_INCREF(dict);
-        attr = __Pyx_PyDict_GetItemStr(dict, name);
-        if (attr != NULL) {
-            Py_INCREF(attr);
-            Py_DECREF(dict);
-            Py_XDECREF(descr);
-            goto try_unpack;
-        }
-        Py_DECREF(dict);
-    }
-    if (meth_found) {
-        *method = descr;
-        return 1;
-    }
-    if (f != NULL) {
-        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-        Py_DECREF(descr);
-        goto try_unpack;
-    }
-    if (likely(descr != NULL)) {
-        *method = descr;
-        return 0;
-    }
-    type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-#else
-    attr = __Pyx_PyObject_GetAttrStr(obj, name);
-    goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
-    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
-        PyObject *function = PyMethod_GET_FUNCTION(attr);
-        Py_INCREF(function);
-        Py_DECREF(attr);
-        *method = function;
-        return 1;
-    }
-#endif
-    *method = attr;
-    return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
-    PyObject *method = NULL, *result = NULL;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_CallOneArg(method, obj);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) goto bad;
-    result = __Pyx_PyObject_CallNoArg(method);
-    Py_DECREF(method);
-bad:
-    return result;
-}
-
-/* ValidateBasesTuple */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) {
-    Py_ssize_t i, n = PyTuple_GET_SIZE(bases);
-    for (i = 1; i < n; i++)
-    {
-        PyObject *b0 = PyTuple_GET_ITEM(bases, i);
-        PyTypeObject *b;
-#if PY_MAJOR_VERSION < 3
-        if (PyClass_Check(b0))
-        {
-            PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class",
-                         PyString_AS_STRING(((PyClassObject*)b0)->cl_name));
-            return -1;
-        }
-#endif
-        b = (PyTypeObject*) b0;
-        if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE))
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-        if (dictoffset == 0 && b->tp_dictoffset)
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "extension type '%.200s' has no __dict__ slot, "
-                "but base type '" __Pyx_FMT_TYPENAME "' has: "
-                "either add 'cdef dict __dict__' to the extension type "
-                "or add '__slots__ = [...]' to the base type",
-                type_name, b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-    }
-    return 0;
-}
-#endif
-
-/* PyType_Ready */
-static int __Pyx_PyType_Ready(PyTypeObject *t) {
-#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION)
-    (void)__Pyx_PyObject_CallMethod0;
-#if CYTHON_USE_TYPE_SPECS
-    (void)__Pyx_validate_bases_tuple;
-#endif
-    return PyType_Ready(t);
-#else
-    int r;
-    PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*);
-    if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1))
-        return -1;
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-    {
-        int gc_was_enabled;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        gc_was_enabled = PyGC_Disable();
-        (void)__Pyx_PyObject_CallMethod0;
-    #else
-        PyObject *ret, *py_status;
-        PyObject *gc = NULL;
-        #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400)
-        gc = PyImport_GetModule(__pyx_kp_u_gc);
-        #endif
-        if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc);
-        if (unlikely(!gc)) return -1;
-        py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled);
-        if (unlikely(!py_status)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-        gc_was_enabled = __Pyx_PyObject_IsTrue(py_status);
-        Py_DECREF(py_status);
-        if (gc_was_enabled > 0) {
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable);
-            if (unlikely(!ret)) {
-                Py_DECREF(gc);
-                return -1;
-            }
-            Py_DECREF(ret);
-        } else if (unlikely(gc_was_enabled == -1)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-    #endif
-        t->tp_flags |= Py_TPFLAGS_HEAPTYPE;
-#if PY_VERSION_HEX >= 0x030A0000
-        t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE;
-#endif
-#else
-        (void)__Pyx_PyObject_CallMethod0;
-#endif
-    r = PyType_Ready(t);
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-        t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        if (gc_was_enabled)
-            PyGC_Enable();
-    #else
-        if (gc_was_enabled) {
-            PyObject *tp, *v, *tb;
-            PyErr_Fetch(&tp, &v, &tb);
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable);
-            if (likely(ret || r == -1)) {
-                Py_XDECREF(ret);
-                PyErr_Restore(tp, v, tb);
-            } else {
-                Py_XDECREF(tp);
-                Py_XDECREF(v);
-                Py_XDECREF(tb);
-                r = -1;
-            }
-        }
-        Py_DECREF(gc);
-    #endif
-    }
-#endif
-    return r;
-#endif
-}
-
-/* SetVTable */
-static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) {
-    PyObject *ob = PyCapsule_New(vtable, 0, 0);
-    if (unlikely(!ob))
-        goto bad;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0))
-#else
-    if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0))
-#endif
-        goto bad;
-    Py_DECREF(ob);
-    return 0;
-bad:
-    Py_XDECREF(ob);
-    return -1;
-}
-
-/* GetVTable */
-static void* __Pyx_GetVtable(PyTypeObject *type) {
-    void* ptr;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable);
-#else
-    PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable);
-#endif
-    if (!ob)
-        goto bad;
-    ptr = PyCapsule_GetPointer(ob, 0);
-    if (!ptr && !PyErr_Occurred())
-        PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type");
-    Py_DECREF(ob);
-    return ptr;
-bad:
-    Py_XDECREF(ob);
-    return NULL;
-}
-
-/* MergeVTables */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_MergeVtables(PyTypeObject *type) {
-    int i;
-    void** base_vtables;
-    __Pyx_TypeName tp_base_name;
-    __Pyx_TypeName base_name;
-    void* unknown = (void*)-1;
-    PyObject* bases = type->tp_bases;
-    int base_depth = 0;
-    {
-        PyTypeObject* base = type->tp_base;
-        while (base) {
-            base_depth += 1;
-            base = base->tp_base;
-        }
-    }
-    base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1));
-    base_vtables[0] = unknown;
-    for (i = 1; i < PyTuple_GET_SIZE(bases); i++) {
-        void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i)));
-        if (base_vtable != NULL) {
-            int j;
-            PyTypeObject* base = type->tp_base;
-            for (j = 0; j < base_depth; j++) {
-                if (base_vtables[j] == unknown) {
-                    base_vtables[j] = __Pyx_GetVtable(base);
-                    base_vtables[j + 1] = unknown;
-                }
-                if (base_vtables[j] == base_vtable) {
-                    break;
-                } else if (base_vtables[j] == NULL) {
-                    goto bad;
-                }
-                base = base->tp_base;
-            }
-        }
-    }
-    PyErr_Clear();
-    free(base_vtables);
-    return 0;
-bad:
-    tp_base_name = __Pyx_PyType_GetName(type->tp_base);
-    base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i));
-    PyErr_Format(PyExc_TypeError,
-        "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name);
-    __Pyx_DECREF_TypeName(tp_base_name);
-    __Pyx_DECREF_TypeName(base_name);
-    free(base_vtables);
-    return -1;
-}
-#endif
-
-/* SetupReduce */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
-  int ret;
-  PyObject *name_attr;
-  name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2);
-  if (likely(name_attr)) {
-      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
-  } else {
-      ret = -1;
-  }
-  if (unlikely(ret < 0)) {
-      PyErr_Clear();
-      ret = 0;
-  }
-  Py_XDECREF(name_attr);
-  return ret;
-}
-static int __Pyx_setup_reduce(PyObject* type_obj) {
-    int ret = 0;
-    PyObject *object_reduce = NULL;
-    PyObject *object_getstate = NULL;
-    PyObject *object_reduce_ex = NULL;
-    PyObject *reduce = NULL;
-    PyObject *reduce_ex = NULL;
-    PyObject *reduce_cython = NULL;
-    PyObject *setstate = NULL;
-    PyObject *setstate_cython = NULL;
-    PyObject *getstate = NULL;
-#if CYTHON_USE_PYTYPE_LOOKUP
-    getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate);
-#else
-    getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate);
-    if (!getstate && PyErr_Occurred()) {
-        goto __PYX_BAD;
-    }
-#endif
-    if (getstate) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate);
-#else
-        object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate);
-        if (!object_getstate && PyErr_Occurred()) {
-            goto __PYX_BAD;
-        }
-#endif
-        if (object_getstate != getstate) {
-            goto __PYX_GOOD;
-        }
-    }
-#if CYTHON_USE_PYTYPE_LOOKUP
-    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#else
-    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#endif
-    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
-    if (reduce_ex == object_reduce_ex) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#else
-        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#endif
-        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
-        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
-            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
-            if (likely(reduce_cython)) {
-                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-            } else if (reduce == object_reduce || PyErr_Occurred()) {
-                goto __PYX_BAD;
-            }
-            setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate);
-            if (!setstate) PyErr_Clear();
-            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
-                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
-                if (likely(setstate_cython)) {
-                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                } else if (!setstate || PyErr_Occurred()) {
-                    goto __PYX_BAD;
-                }
-            }
-            PyType_Modified((PyTypeObject*)type_obj);
-        }
-    }
-    goto __PYX_GOOD;
-__PYX_BAD:
-    if (!PyErr_Occurred()) {
-        __Pyx_TypeName type_obj_name =
-            __Pyx_PyType_GetName((PyTypeObject*)type_obj);
-        PyErr_Format(PyExc_RuntimeError,
-            "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name);
-        __Pyx_DECREF_TypeName(type_obj_name);
-    }
-    ret = -1;
-__PYX_GOOD:
-#if !CYTHON_USE_PYTYPE_LOOKUP
-    Py_XDECREF(object_reduce);
-    Py_XDECREF(object_reduce_ex);
-    Py_XDECREF(object_getstate);
-    Py_XDECREF(getstate);
-#endif
-    Py_XDECREF(reduce);
-    Py_XDECREF(reduce_ex);
-    Py_XDECREF(reduce_cython);
-    Py_XDECREF(setstate);
-    Py_XDECREF(setstate_cython);
-    return ret;
-}
-#endif
-
-/* FetchSharedCythonModule */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
-    PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
-    if (unlikely(!abi_module)) return NULL;
-    Py_INCREF(abi_module);
-    return abi_module;
-}
-
-/* FetchCommonType */
-static int __Pyx_VerifyCachedType(PyObject *cached_type,
-                               const char *name,
-                               Py_ssize_t basicsize,
-                               Py_ssize_t expected_basicsize) {
-    if (!PyType_Check(cached_type)) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s is not a type object", name);
-        return -1;
-    }
-    if (basicsize != expected_basicsize) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s has the wrong size, try recompiling",
-            name);
-        return -1;
-    }
-    return 0;
-}
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* abi_module;
-    const char* object_name;
-    PyTypeObject *cached_type = NULL;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    object_name = strrchr(type->tp_name, '.');
-    object_name = object_name ? object_name+1 : type->tp_name;
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        if (__Pyx_VerifyCachedType(
-              (PyObject *)cached_type,
-              object_name,
-              cached_type->tp_basicsize,
-              type->tp_basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    if (PyType_Ready(type) < 0) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0)
-        goto bad;
-    Py_INCREF(type);
-    cached_type = type;
-done:
-    Py_DECREF(abi_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#else
-static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) {
-    PyObject *abi_module, *cached_type = NULL;
-    const char* object_name = strrchr(spec->name, '.');
-    object_name = object_name ? object_name+1 : spec->name;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    cached_type = PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        Py_ssize_t basicsize;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        PyObject *py_basicsize;
-        py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__");
-        if (unlikely(!py_basicsize)) goto bad;
-        basicsize = PyLong_AsSsize_t(py_basicsize);
-        Py_DECREF(py_basicsize);
-        py_basicsize = 0;
-        if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-#else
-        basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1;
-#endif
-        if (__Pyx_VerifyCachedType(
-              cached_type,
-              object_name,
-              basicsize,
-              spec->basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    CYTHON_UNUSED_VAR(module);
-    cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases);
-    if (unlikely(!cached_type)) goto bad;
-    if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad;
-done:
-    Py_DECREF(abi_module);
-    assert(cached_type == NULL || PyType_Check(cached_type));
-    return (PyTypeObject *) cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#endif
-
-/* PyVectorcallFastCallDict */
-#if CYTHON_METH_FASTCALL
-static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    PyObject *res = NULL;
-    PyObject *kwnames;
-    PyObject **newargs;
-    PyObject **kwvalues;
-    Py_ssize_t i, pos;
-    size_t j;
-    PyObject *key, *value;
-    unsigned long keys_are_strings;
-    Py_ssize_t nkw = PyDict_GET_SIZE(kw);
-    newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0]));
-    if (unlikely(newargs == NULL)) {
-        PyErr_NoMemory();
-        return NULL;
-    }
-    for (j = 0; j < nargs; j++) newargs[j] = args[j];
-    kwnames = PyTuple_New(nkw);
-    if (unlikely(kwnames == NULL)) {
-        PyMem_Free(newargs);
-        return NULL;
-    }
-    kwvalues = newargs + nargs;
-    pos = i = 0;
-    keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS;
-    while (PyDict_Next(kw, &pos, &key, &value)) {
-        keys_are_strings &= Py_TYPE(key)->tp_flags;
-        Py_INCREF(key);
-        Py_INCREF(value);
-        PyTuple_SET_ITEM(kwnames, i, key);
-        kwvalues[i] = value;
-        i++;
-    }
-    if (unlikely(!keys_are_strings)) {
-        PyErr_SetString(PyExc_TypeError, "keywords must be strings");
-        goto cleanup;
-    }
-    res = vc(func, newargs, nargs, kwnames);
-cleanup:
-    Py_DECREF(kwnames);
-    for (i = 0; i < nkw; i++)
-        Py_DECREF(kwvalues[i]);
-    PyMem_Free(newargs);
-    return res;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) {
-        return vc(func, args, nargs, NULL);
-    }
-    return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw);
-}
-#endif
-
-/* CythonFunctionShared */
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1
-    __Pyx_Py_XDECREF_SET(
-        __Pyx_CyFunction_GetClassObj(f),
-            ((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#else
-    __Pyx_Py_XDECREF_SET(
-        ((PyCMethodObject *) (f))->mm_class,
-        (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#endif
-}
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
-{
-    CYTHON_UNUSED_VAR(closure);
-    if (unlikely(op->func_doc == NULL)) {
-        if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_doc, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_dict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(op);
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyTuple_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_tuple, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_tuple;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_kwdict;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_annotations, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->func_annotations;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyObject *
-__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
-    int is_coroutine;
-    CYTHON_UNUSED_VAR(context);
-    if (op->func_is_coroutine) {
-        return __Pyx_NewRef(op->func_is_coroutine);
-    }
-    is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE;
-#if PY_VERSION_HEX >= 0x03050000
-    if (is_coroutine) {
-        PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine;
-        fromlist = PyList_New(1);
-        if (unlikely(!fromlist)) return NULL;
-        Py_INCREF(marker);
-        PyList_SET_ITEM(fromlist, 0, marker);
-        module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
-        Py_DECREF(fromlist);
-        if (unlikely(!module)) goto ignore;
-        op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker);
-        Py_DECREF(module);
-        if (likely(op->func_is_coroutine)) {
-            return __Pyx_NewRef(op->func_is_coroutine);
-        }
-ignore:
-        PyErr_Clear();
-    }
-#endif
-    op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
-    return __Pyx_NewRef(op->func_is_coroutine);
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
-#if CYTHON_METH_FASTCALL
-#if CYTHON_BACKPORT_VECTORCALL
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
-#else
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
-#endif
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
-#else
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
-#endif
-#endif
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args)
-{
-    CYTHON_UNUSED_VAR(args);
-#if PY_MAJOR_VERSION >= 3
-    Py_INCREF(m->func_qualname);
-    return m->func_qualname;
-#else
-    return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyCFunctionObject *cf = (PyCFunctionObject*) op;
-    if (unlikely(op == NULL))
-        return NULL;
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-    cf->m_ml = ml;
-    cf->m_self = (PyObject *) op;
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-    Py_XINCREF(module);
-    cf->m_module = module;
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1
-    op->func_classobj = NULL;
-#else
-    ((PyCMethodObject*)op)->mm_class = NULL;
-#endif
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    op->func_is_coroutine = NULL;
-#if CYTHON_METH_FASTCALL
-    switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) {
-    case METH_NOARGS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS;
-        break;
-    case METH_O:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O;
-        break;
-    case METH_METHOD | METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD;
-        break;
-    case METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS;
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = NULL;
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        Py_DECREF(op);
-        return NULL;
-    }
-#endif
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-    Py_CLEAR(((PyCFunctionObject*)m)->m_module);
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-#if PY_VERSION_HEX < 0x030900B1
-    Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
-#else
-    {
-        PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class;
-        ((PyCMethodObject *) (m))->mm_class = NULL;
-        Py_XDECREF(cls);
-    }
-#endif
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    Py_CLEAR(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    __Pyx_PyHeapTypeObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-    Py_VISIT(((PyCFunctionObject*)m)->m_module);
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-    Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    Py_VISIT(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        return NULL;
-    }
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-#if CYTHON_METH_FASTCALL
-     __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
-    if (vc) {
-#if CYTHON_ASSUME_SAFE_MACROS
-        return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
-#else
-        (void) &__Pyx_PyVectorcall_FastCallDict;
-        return PyVectorcall_Call(func, args, kw);
-#endif
-    }
-#endif
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-        argc = PyTuple_GET_SIZE(args);
-        new_args = PyTuple_GetSlice(args, 1, argc);
-        if (unlikely(!new_args))
-            return NULL;
-        self = PyTuple_GetItem(args, 0);
-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-#if PY_MAJOR_VERSION > 2
-            PyErr_Format(PyExc_TypeError,
-                         "unbound method %.200S() needs an argument",
-                         cyfunc->func_qualname);
-#else
-            PyErr_SetString(PyExc_TypeError,
-                            "unbound method needs an argument");
-#endif
-            return NULL;
-        }
-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
-}
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames)
-{
-    int ret = 0;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        if (unlikely(nargs < 1)) {
-            PyErr_Format(PyExc_TypeError, "%.200s() needs an argument",
-                         ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-            return -1;
-        }
-        ret = 1;
-    }
-    if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) {
-        PyErr_Format(PyExc_TypeError,
-                     "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-        return -1;
-    }
-    return ret;
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 0)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, NULL);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 1)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, args[0]);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-    PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc);
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames);
-}
-#endif
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_CyFunctionType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc},
-    {Py_tp_repr, (void *)__Pyx_CyFunction_repr},
-    {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod},
-    {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse},
-    {Py_tp_clear, (void *)__Pyx_CyFunction_clear},
-    {Py_tp_methods, (void *)__pyx_CyFunction_methods},
-    {Py_tp_members, (void *)__pyx_CyFunction_members},
-    {Py_tp_getset, (void *)__pyx_CyFunction_getsets},
-    {Py_tp_descr_get, (void *)__Pyx_PyMethod_New},
-    {0, 0},
-};
-static PyType_Spec __pyx_CyFunctionType_spec = {
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL)
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    __pyx_CyFunctionType_slots
-};
-#else
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-    (destructor) __Pyx_CyFunction_dealloc,
-#if !CYTHON_METH_FASTCALL
-    0,
-#elif CYTHON_BACKPORT_VECTORCALL
-    (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall),
-#else
-    offsetof(PyCFunctionObject, vectorcall),
-#endif
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#ifdef _Py_TPFLAGS_HAVE_VECTORCALL
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_PyMethod_New,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_CyFunction_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-#endif
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
-    memset(m->defaults, 0, size);
-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
-        ml, flags, qualname, closure, module, globals, code
-    );
-    if (likely(op)) {
-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    CYTHON_MAYBE_UNUSED_VAR(tstate);
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-#endif
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    if (c_line) {
-        (void) __pyx_cfilenm;
-        (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
-    }
-    _PyTraceback_Add(funcname, filename, py_line);
-}
-#else
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-#endif
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-    __Pyx_TypeName obj_type_name;
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-                 "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface",
-                 obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-    if ((0)) {}
-    view->obj = NULL;
-    Py_DECREF(obj);
-}
-#endif
-
-
-/* MemviewSliceIsContig */
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
-{
-    int i, index, step, start;
-    Py_ssize_t itemsize = mvs.memview->view.itemsize;
-    if (order == 'F') {
-        step = 1;
-        start = 0;
-    } else {
-        step = -1;
-        start = ndim - 1;
-    }
-    for (i = 0; i < ndim; i++) {
-        index = start + step * i;
-        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
-            return 0;
-        itemsize *= mvs.shape[index];
-    }
-    return 1;
-}
-
-/* OverlappingSlices */
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
-                               void **out_start, void **out_end,
-                               int ndim, size_t itemsize)
-{
-    char *start, *end;
-    int i;
-    start = end = slice->data;
-    for (i = 0; i < ndim; i++) {
-        Py_ssize_t stride = slice->strides[i];
-        Py_ssize_t extent = slice->shape[i];
-        if (extent == 0) {
-            *out_start = *out_end = start;
-            return;
-        } else {
-            if (stride > 0)
-                end += stride * (extent - 1);
-            else
-                start += stride * (extent - 1);
-        }
-    }
-    *out_start = start;
-    *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                     __Pyx_memviewslice *slice2,
-                     int ndim, size_t itemsize)
-{
-    void *start1, *end1, *start2, *end2;
-    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
-    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
-    return (start1 < end2) && (start2 < end1);
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
-  union {
-    uint32_t u32;
-    uint8_t u8[4];
-  } S;
-  S.u32 = 0x01020304;
-  return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t <= '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case '?': return "'bool'";
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparsable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) {
-  CYTHON_UNUSED_VAR(is_complex);
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) {
-  CYTHON_UNUSED_VAR(is_complex);
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number, ndim;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    ndim = ctx->head->field->type->ndim;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-        CYTHON_FALLTHROUGH;
-      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
-            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-        CYTHON_FALLTHROUGH;
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-
-/* TypeInfoCompare */
-  static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
-    int i;
-    if (!a || !b)
-        return 0;
-    if (a == b)
-        return 1;
-    if (a->size != b->size || a->typegroup != b->typegroup ||
-            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
-        if (a->typegroup == 'H' || b->typegroup == 'H') {
-            return a->size == b->size;
-        } else {
-            return 0;
-        }
-    }
-    if (a->ndim) {
-        for (i = 0; i < a->ndim; i++)
-            if (a->arraysize[i] != b->arraysize[i])
-                return 0;
-    }
-    if (a->typegroup == 'S') {
-        if (a->flags != b->flags)
-            return 0;
-        if (a->fields || b->fields) {
-            if (!(a->fields && b->fields))
-                return 0;
-            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
-                __Pyx_StructField *field_a = a->fields + i;
-                __Pyx_StructField *field_b = b->fields + i;
-                if (field_a->offset != field_b->offset ||
-                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
-                    return 0;
-            }
-            return !a->fields[i].type && !b->fields[i].type;
-        }
-    }
-    return 1;
-}
-
-/* MemviewSliceValidateAndInit */
-  static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    if (buf->shape[dim] <= 1)
-        return 1;
-    if (buf->strides) {
-        if (spec & __Pyx_MEMVIEW_CONTIG) {
-            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
-                if (unlikely(buf->strides[dim] != sizeof(void *))) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "Buffer is not indirectly contiguous "
-                                 "in dimension %d.", dim);
-                    goto fail;
-                }
-            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-        if (spec & __Pyx_MEMVIEW_FOLLOW) {
-            Py_ssize_t stride = buf->strides[dim];
-            if (stride < 0)
-                stride = -stride;
-            if (unlikely(stride < buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-    } else {
-        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not contiguous in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not indirect in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(buf->suboffsets)) {
-            PyErr_SetString(PyExc_ValueError,
-                            "Buffer exposes suboffsets but no strides");
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    CYTHON_UNUSED_VAR(ndim);
-    if (spec & __Pyx_MEMVIEW_DIRECT) {
-        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer not compatible with direct access "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    if (spec & __Pyx_MEMVIEW_PTR) {
-        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer is not indirectly accessible "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
-    int i;
-    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = 0; i < ndim; i++) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not fortran contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = ndim - 1; i >- 1; i--) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not C contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj)
-{
-    struct __pyx_memoryview_obj *memview, *new_memview;
-    __Pyx_RefNannyDeclarations
-    Py_buffer *buf;
-    int i, spec = 0, retval = -1;
-    __Pyx_BufFmt_Context ctx;
-    int from_memoryview = __pyx_memoryview_check(original_obj);
-    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
-    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
-                                                            original_obj)->typeinfo)) {
-        memview = (struct __pyx_memoryview_obj *) original_obj;
-        new_memview = NULL;
-    } else {
-        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                            original_obj, buf_flags, 0, dtype);
-        new_memview = memview;
-        if (unlikely(!memview))
-            goto fail;
-    }
-    buf = &memview->view;
-    if (unlikely(buf->ndim != ndim)) {
-        PyErr_Format(PyExc_ValueError,
-                "Buffer has wrong number of dimensions (expected %d, got %d)",
-                ndim, buf->ndim);
-        goto fail;
-    }
-    if (new_memview) {
-        __Pyx_BufFmt_Init(&ctx, stack, dtype);
-        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
-    }
-    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
-        PyErr_Format(PyExc_ValueError,
-                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
-                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
-                     buf->itemsize,
-                     (buf->itemsize > 1) ? "s" : "",
-                     dtype->name,
-                     dtype->size,
-                     (dtype->size > 1) ? "s" : "");
-        goto fail;
-    }
-    if (buf->len > 0) {
-        for (i = 0; i < ndim; i++) {
-            spec = axes_specs[i];
-            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
-                goto fail;
-            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
-                goto fail;
-        }
-        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
-            goto fail;
-    }
-    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
-                                         new_memview != NULL) == -1)) {
-        goto fail;
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    Py_XDECREF(new_memview);
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_float, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* CIntFromPyVerify */
-  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* MemviewSliceCopyTemplate */
-  static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object)
-{
-    __Pyx_RefNannyDeclarations
-    int i;
-    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
-    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
-    Py_buffer *buf = &from_memview->view;
-    PyObject *shape_tuple = NULL;
-    PyObject *temp_int = NULL;
-    struct __pyx_array_obj *array_obj = NULL;
-    struct __pyx_memoryview_obj *memview_obj = NULL;
-    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
-    for (i = 0; i < ndim; i++) {
-        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
-            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
-                                           "indirect dimensions (axis %d)", i);
-            goto fail;
-        }
-    }
-    shape_tuple = PyTuple_New(ndim);
-    if (unlikely(!shape_tuple)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(shape_tuple);
-    for(i = 0; i < ndim; i++) {
-        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
-        if(unlikely(!temp_int)) {
-            goto fail;
-        } else {
-            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
-            temp_int = NULL;
-        }
-    }
-    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
-    if (unlikely(!array_obj)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(array_obj);
-    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                    (PyObject *) array_obj, contig_flag,
-                                    dtype_is_object,
-                                    from_mvs->memview->typeinfo);
-    if (unlikely(!memview_obj))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
-        goto fail;
-    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
-                                                dtype_is_object) < 0))
-        goto fail;
-    goto no_fail;
-fail:
-    __Pyx_XDECREF(new_mvs.memview);
-    new_mvs.memview = NULL;
-    new_mvs.data = NULL;
-no_fail:
-    __Pyx_XDECREF(shape_tuple);
-    __Pyx_XDECREF(temp_int);
-    __Pyx_XDECREF(array_obj);
-    __Pyx_RefNannyFinishContext();
-    return new_mvs;
-}
-
-/* MemviewSliceInit */
-  static int
-__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
-                        int ndim,
-                        __Pyx_memviewslice *memviewslice,
-                        int memview_is_new_reference)
-{
-    __Pyx_RefNannyDeclarations
-    int i, retval=-1;
-    Py_buffer *buf = &memview->view;
-    __Pyx_RefNannySetupContext("init_memviewslice", 0);
-    if (unlikely(memviewslice->memview || memviewslice->data)) {
-        PyErr_SetString(PyExc_ValueError,
-            "memviewslice is already initialized!");
-        goto fail;
-    }
-    if (buf->strides) {
-        for (i = 0; i < ndim; i++) {
-            memviewslice->strides[i] = buf->strides[i];
-        }
-    } else {
-        Py_ssize_t stride = buf->itemsize;
-        for (i = ndim - 1; i >= 0; i--) {
-            memviewslice->strides[i] = stride;
-            stride *= buf->shape[i];
-        }
-    }
-    for (i = 0; i < ndim; i++) {
-        memviewslice->shape[i]   = buf->shape[i];
-        if (buf->suboffsets) {
-            memviewslice->suboffsets[i] = buf->suboffsets[i];
-        } else {
-            memviewslice->suboffsets[i] = -1;
-        }
-    }
-    memviewslice->memview = memview;
-    memviewslice->data = (char *)buf->buf;
-    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
-        Py_INCREF(memview);
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    memviewslice->memview = 0;
-    memviewslice->data = 0;
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-#ifndef Py_NO_RETURN
-#define Py_NO_RETURN
-#endif
-static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {
-    va_list vargs;
-    char msg[200];
-#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES)
-    va_start(vargs, fmt);
-#else
-    va_start(vargs);
-#endif
-    vsnprintf(msg, 200, fmt, vargs);
-    va_end(vargs);
-    Py_FatalError(msg);
-}
-static CYTHON_INLINE int
-__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)++;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE int
-__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)--;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE void
-__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
-{
-    __pyx_nonatomic_int_type old_acquisition_count;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
-        return;
-    }
-    old_acquisition_count = __pyx_add_acquisition_count(memview);
-    if (unlikely(old_acquisition_count <= 0)) {
-        if (likely(old_acquisition_count == 0)) {
-            if (have_gil) {
-                Py_INCREF((PyObject *) memview);
-            } else {
-                PyGILState_STATE _gilstate = PyGILState_Ensure();
-                Py_INCREF((PyObject *) memview);
-                PyGILState_Release(_gilstate);
-            }
-        } else {
-            __pyx_fatalerror("Acquisition count is %d (line %d)",
-                             old_acquisition_count+1, lineno);
-        }
-    }
-}
-static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice,
-                                             int have_gil, int lineno) {
-    __pyx_nonatomic_int_type old_acquisition_count;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
-        memslice->memview = NULL;
-        return;
-    }
-    old_acquisition_count = __pyx_sub_acquisition_count(memview);
-    memslice->data = NULL;
-    if (likely(old_acquisition_count > 1)) {
-        memslice->memview = NULL;
-    } else if (likely(old_acquisition_count == 1)) {
-        if (have_gil) {
-            Py_CLEAR(memslice->memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_CLEAR(memslice->memview);
-            PyGILState_Release(_gilstate);
-        }
-    } else {
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         old_acquisition_count-1, lineno);
-    }
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(int) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) {
-                                return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) {
-                                return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) {
-                                return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(int) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(int) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (int) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (int) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (int) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (int) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (int) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((int) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((int) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(long) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) {
-                                return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) {
-                                return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) {
-                                return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(long) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(long) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (long) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (long) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (long) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (long) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (long) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((long) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((long) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const char neg_one = (char) -1, const_zero = (char) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(char) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) {
-                                return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) {
-                                return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) {
-                                return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (char) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(char) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                                return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                                return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) {
-                                return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(char) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            char val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (char) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (char) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (char) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (char) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (char) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((char) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((char) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (char) -1;
-        }
-    } else {
-        char val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to char");
-    return (char) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to char");
-    return (char) -1;
-}
-
-/* FormatTypeName */
-  #if CYTHON_COMPILING_IN_LIMITED_API
-static __Pyx_TypeName
-__Pyx_PyType_GetName(PyTypeObject* tp)
-{
-    PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp,
-                                               __pyx_n_s_name_2);
-    if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
-        PyErr_Clear();
-        Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__23));
-    }
-    return name;
-}
-#endif
-
-/* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[5];
-    int same=1, i, found_dot;
-    const char* rt_from_call = Py_GetVersion();
-    PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    found_dot = 0;
-    for (i = 0; i < 4; i++) {
-        if (!ctversion[i]) {
-            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');
-            break;
-        }
-        if (rt_from_call[i] != ctversion[i]) {
-            same = 0;
-            break;
-        }
-    }
-    if (!same) {
-        char rtversion[5] = {'\0'};
-        char message[200];
-        for (i=0; i<4; ++i) {
-            if (rt_from_call[i] == '.') {
-                if (found_dot) break;
-                found_dot = 1;
-            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {
-                break;
-            }
-            rtversion[i] = rt_from_call[i];
-        }
-        PyOS_snprintf(message, sizeof(message),
-                      "compile time version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-  #if PY_MAJOR_VERSION >= 3
-static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) {
-    if (t.is_unicode | t.is_str) {
-        if (t.intern) {
-            *str = PyUnicode_InternFromString(t.s);
-        } else if (t.encoding) {
-            *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL);
-        } else {
-            *str = PyUnicode_FromStringAndSize(t.s, t.n - 1);
-        }
-    } else {
-        *str = PyBytes_FromStringAndSize(t.s, t.n - 1);
-    }
-    if (!*str)
-        return -1;
-    if (PyObject_Hash(*str) == -1)
-        return -1;
-    return 0;
-}
-#endif
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION >= 3
-        __Pyx_InitString(*t, t->p);
-        #else
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        #endif
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-    __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result));
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type " __Pyx_FMT_TYPENAME ").  "
-                "The ability to return an instance of a strict subclass of int is deprecated, "
-                "and may be removed in a future version of Python.",
-                result_type_name)) {
-            __Pyx_DECREF_TypeName(result_type_name);
-            Py_DECREF(result);
-            return NULL;
-        }
-        __Pyx_DECREF_TypeName(result_type_name);
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")",
-                 type_name, type_name, result_type_name);
-    __Pyx_DECREF_TypeName(result_type_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(__Pyx_PyLong_IsCompact(b))) {
-        return __Pyx_PyLong_CompactValue(b);
-    } else {
-      const digit* digits = __Pyx_PyLong_Digits(b);
-      const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b);
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
-  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
-    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
-  } else if (likely(PyInt_CheckExact(o))) {
-    return PyInt_AS_LONG(o);
-#endif
-  } else {
-    Py_ssize_t ival;
-    PyObject *x;
-    x = PyNumber_Index(o);
-    if (!x) return -1;
-    ival = PyInt_AsLong(x);
-    Py_DECREF(x);
-    return ival;
-  }
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-/* #### Code section: utility_code_pragmas_end ### */
-#ifdef _MSC_VER
-#pragma warning( pop )
-#endif
-
-
-
-/* #### Code section: end ### */
-#endif /* Py_PYTHON_H */
diff --git a/spaces/doevent/prompt-generator/README.md b/spaces/doevent/prompt-generator/README.md
deleted file mode 100644
index cf296c22505151c67ad0387e7c072d1e8585897d..0000000000000000000000000000000000000000
--- a/spaces/doevent/prompt-generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Midjourney Prompt Generator
-emoji: 🌍
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.28.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dteam/chatgpt-dteam/bin_public/app/app.py b/spaces/dteam/chatgpt-dteam/bin_public/app/app.py
deleted file mode 100644
index 4542b37a4fd8708378ce259ae369856c47b94152..0000000000000000000000000000000000000000
--- a/spaces/dteam/chatgpt-dteam/bin_public/app/app.py
+++ /dev/null
@@ -1,287 +0,0 @@
-# -*- coding:utf-8 -*-
-import sys
-from overwrites import *
-from chat_func import *
-from bin_public.utils.tools import *
-from bin_public.utils.utils_db import *
-from bin_public.config.presets import *
-
-my_api_key = ""
-
-# if we are running in Docker
-if os.environ.get('dockerrun') == 'yes':
-    dockerflag = True
-else:
-    dockerflag = False
-
-authflag = False
-
-if dockerflag:
-    my_api_key = os.environ.get('my_api_key')
-    if my_api_key == "empty":
-        print("Please give a api key!")
-        sys.exit(1)
-    # auth
-    username = os.environ.get('USERNAME')
-    password = os.environ.get('PASSWORD')
-    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
-        authflag = True
-else:
-    '''if not my_api_key and os.path.exists("api_key.txt") and os.path.getsize("api_key.txt"):  # API key 所在的文件
-        with open("api_key.txt", "r") as f:
-            my_api_key = f.read().strip()'''
-
-
-
-    if os.path.exists("auth.json"):
-        with open("auth.json", "r") as f:
-            auth = json.load(f)
-            username = auth["username"]
-            password = auth["password"]
-            if username != "" and password != "":
-                authflag = True
-
-gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
-
-with gr.Blocks(css=customCSS) as demo:
-    history = gr.State([])
-    token_count = gr.State([])
-    invite_code = gr.State()
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    TRUECOMSTANT = gr.State(True)
-    FALSECONSTANT = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    # gr.HTML("""
-    # <div style="text-align: center; margin-top: 20px;">
-    # """)
-    gr.HTML(title)
-
-    with gr.Row(scale=1).style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row(scale=1):
-                chatbot = gr.Chatbot().style(height=600)  # .style(color_map=("#1D51EE", "#585A5B"))
-            with gr.Row(scale=1):
-                with gr.Column(scale=12):
-                    user_input = gr.Textbox(show_label=False, placeholder="在这里输入").style(
-                        container=False)
-                with gr.Column(min_width=50, scale=1):
-                    submitBtn = gr.Button("🚀", variant="primary")
-            with gr.Row(scale=1):
-                emptyBtn = gr.Button("🧹 新的对话", )
-                retryBtn = gr.Button("🔄 重新生成")
-                delLastBtn = gr.Button("🗑️ 删除一条对话")
-                reduceTokenBtn = gr.Button("♻️ 总结对话")
-
-        with gr.Column():
-            with gr.Column(min_width=50, scale=1):
-                status_display = gr.Markdown("status: ready")
-                with gr.Tab(label="ChatGPT"):
-                    keyTXT = gr.Textbox(show_label=True, placeholder=f"OpenAI API-key...",
-                                        type="password", visible=not HIDE_MY_KEY, label="API-Key/Invite-Code")
-
-                    keyTxt = gr.Textbox(visible=False)
-
-                    key_button = gr.Button("Enter")
-
-                    model_select_dropdown = gr.Dropdown(label="选择模型", choices=MODELS, multiselect=False,
-                                                        value=MODELS[0])
-                    with gr.Accordion("参数", open=False):
-                        temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0,
-                                                step=0.1, interactive=True, label="Temperature", )
-                        top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05,
-                                          interactive=True, label="Top-p (nucleus sampling)", visible=False)
-                    use_streaming_checkbox = gr.Checkbox(label="实时传输回答", value=True, visible=enable_streaming_option)
-                    use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
-                    index_files = gr.Files(label="上传索引文件", type="file", multiple=True)
-
-
-                with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...",
-                                                 label="System prompt", value=initial_prompt).style(container=True)
-                    with gr.Accordion(label="加载Prompt模板", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件",
-                                                                             choices=get_template_names(plain=True),
-                                                                             multiselect=False,
-                                                                             value=get_template_names(plain=True)[0])
-                                with gr.Column(scale=1):
-                                    templateRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(label="从Prompt模板中加载", choices=load_template(
-                                        get_template_names(plain=True)[0], mode=1), multiselect=False, value=
-                                                                         load_template(
-                                                                             get_template_names(plain=True)[0], mode=1)[
-                                                                             0])
-
-                with gr.Tab(label="保存/加载"):
-                    with gr.Accordion(label="保存/加载对话历史记录", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(
-                                        label="从列表中加载对话",
-                                        choices=get_history_names(plain=True),
-                                        multiselect=False,
-                                        value=get_history_names(plain=True)[0],
-                                        visible=False
-                                    )
-
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    saveFileName = gr.Textbox(
-                                        show_label=True,
-                                        placeholder=f"设置文件名: 默认为.json，可选为.md",
-                                        label="设置保存文件名",
-                                        value="对话历史记录",
-                                    ).style(container=True)
-                                with gr.Column(scale=1):
-                                    saveHistoryBtn = gr.Button("💾 保存对话")
-                                    exportMarkdownBtn = gr.Button("📝 导出为Markdown")
-                                    #gr.Markdown("默认保存于history文件夹")
-                            with gr.Row():
-                                with gr.Column():
-                                    downloadFile = gr.File(interactive=True)
-
-                with gr.Tab(label="Davinci-003"):
-                    with gr.Column():
-                        with gr.Row():
-                            with gr.Column():
-                                davinci_user_input = gr.Textbox(show_label=False, placeholder="在这里输入").style(
-                                    container=False)
-                                temperature_davinci = gr.Slider(minimum=-0, maximum=1.0, value=0.7,
-                                                step=0.1, interactive=True, label="Temperature", )
-                                davinci_submitBtn = gr.Button("🚀", variant="primary")
-                            davinci_output = gr.Textbox(show_label=False, placeholder="output").style(
-                                    container=False)
-
-    gr.HTML("""
-    <div style="text-align: center; margin-top: 20px; margin-bottom: 20px;">
-    """)
-    gr.Markdown(description)
-
-    # 输入为api key则保持不变，为邀请码则调用中心的api key
-    key_button.click(key_preprocessing, [keyTXT], [status_display, keyTxt, invite_code])
-
-    user_input.submit(predict, [
-            keyTxt,
-            invite_code,
-            systemPromptTxt,
-            history,
-            user_input,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files],
-                      [chatbot, history, status_display, token_count], show_progress=True)
-    user_input.submit(reset_textbox, [], [user_input])
-
-    submitBtn.click(predict, [
-            keyTxt,
-            invite_code,
-            systemPromptTxt,
-            history,
-            user_input,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files],
-                    [chatbot, history, status_display, token_count], show_progress=True)
-
-    submitBtn.click(reset_textbox, [], [user_input])
-
-    emptyBtn.click(reset_state, outputs=[chatbot, history, token_count, status_display], show_progress=True)
-
-    retryBtn.click(retry,
-                   [keyTxt, invite_code, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox,
-                    model_select_dropdown], [chatbot, history, status_display, token_count], show_progress=True)
-
-    delLastBtn.click(delete_last_conversation, [chatbot, history, token_count], [
-        chatbot, history, token_count, status_display], show_progress=True)
-
-    reduceTokenBtn.click(reduce_token_size, [keyTxt, invite_code, systemPromptTxt, history, chatbot, token_count, top_p,
-                                             temperature, use_streaming_checkbox, model_select_dropdown],
-                         [chatbot, history, status_display, token_count], show_progress=True)
-    # History
-    saveHistoryBtn.click(
-        save_chat_history,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    exportMarkdownBtn.click(
-        export_markdown,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    #historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(
-        load_chat_history,
-        [historyFileSelectDropdown, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-        show_progress=True,
-    )
-    downloadFile.change(
-        load_chat_history,
-        [downloadFile, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-    )
-
-
-    # Template
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-
-    templateFileSelectDropdown.change(load_template, [templateFileSelectDropdown],
-                                      [promptTemplates, templateSelectDropdown], show_progress=True)
-
-    templateSelectDropdown.change(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt],
-                                  [systemPromptTxt], show_progress=True)
-
-    # Davinci
-    davinci_user_input.submit(predict_davinci,
-                      [
-                          keyTxt,
-                          davinci_user_input,
-                          temperature,
-                      ],
-                      [davinci_output], show_progress=True)
-
-    davinci_submitBtn.click(predict_davinci,
-                      [
-                          keyTxt,
-                          davinci_user_input,
-                          temperature_davinci,
-                      ],
-                      [davinci_output], show_progress=True)
-
-logging.info( "\n访问 http://localhost:7860 查看界面")
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "ChatGPT-长江商学院 🚀"
-
-if __name__ == "__main__":
-    #if running in Docker
-    if dockerflag:
-        if authflag:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password))
-        else:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
-    #if not running in Docker
-    else:
-        if authflag:
-            demo.queue().launch(share=False, auth=(username, password))
-        else:
-            demo.queue().launch(share=False) # 改为 share=True 可以创建公开分享链接
diff --git a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/__init__.py b/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/ehristoforu/chat-client/app.py b/spaces/ehristoforu/chat-client/app.py
deleted file mode 100644
index 2a51a3b20b95b189c0271b5a5ceca45997df262e..0000000000000000000000000000000000000000
--- a/spaces/ehristoforu/chat-client/app.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import gradio as gr
-
-with gr.Blocks() as demo:
-  with gr.Tab("🐬 DolphinChat"):
-    gr.load("dolphinchat/dolphinchat-llm-gpt-ui", src="spaces")
-  with gr.Tab("🦅 FalconChat"):
-    gr.load("tiiuae/falcon-chat", src="spaces")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/emre/emre-whisper-medium-turkish-2/README.md b/spaces/emre/emre-whisper-medium-turkish-2/README.md
deleted file mode 100644
index ae7f4a48982dfb952c955d835f13f55e57ecb0bb..0000000000000000000000000000000000000000
--- a/spaces/emre/emre-whisper-medium-turkish-2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Emre Whisper Medium Turkish 2
-emoji: 🔥
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ennet/ChatDev/camel/agents/tool_agents/__init__.py b/spaces/ennet/ChatDev/camel/agents/tool_agents/__init__.py
deleted file mode 100644
index e47fcf82b3b5195696632fc3200ee9e46f4f2554..0000000000000000000000000000000000000000
--- a/spaces/ennet/ChatDev/camel/agents/tool_agents/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-from .base import BaseToolAgent
-from .hugging_face_tool_agent import HuggingFaceToolAgent
-
-__all__ = [
-    'BaseToolAgent',
-    'HuggingFaceToolAgent',
-]
diff --git a/spaces/eson/kplug/kplug/modeling_kplug.py b/spaces/eson/kplug/kplug/modeling_kplug.py
deleted file mode 100644
index fbce0222acc8d21ffd4db6f467eb008a33d6c44a..0000000000000000000000000000000000000000
--- a/spaces/eson/kplug/kplug/modeling_kplug.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# coding=utf-8
-# author: xusong <xusong28@jd.com>
-# time: 2021/9/17 20:02
-
-"""
-1. self.embed_scale
-2.
-"""
-import math
-import torch
-from transformers.models.bert.modeling_bert import BertForMaskedLM, BertEmbeddings, BertModel, BertForMaskedLM, \
-    BertEncoder, BertPooler, BertOnlyMLMHead, BertConfig, logger
-from transformers import MODEL_FOR_MASKED_LM_MAPPING
-
-
-
-class KplugEmbeddings(BertEmbeddings):
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.embed_scale = math.sqrt(config.hidden_size)  # if config.scale_embedding else 1.0
-
-    def forward(
-            self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length: seq_length + past_key_values_length]
-
-        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
-        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
-        # issue #5664
-        if token_type_ids is None:
-            if hasattr(self, "token_type_ids"):
-                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids) * self.embed_scale
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = inputs_embeds + token_type_embeddings
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class KplugModel(BertModel):
-
-    def __init__(self, config, add_pooling_layer=True):
-        super(BertModel, self).__init__(config)
-        self.config = config
-
-        self.embeddings = KplugEmbeddings(config)
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
-
-
-class KplugForMaskedLM(BertForMaskedLM):
-
-    def __init__(self, config):
-        super(BertForMaskedLM, self).__init__(config)
-
-        if config.is_decoder:
-            logger.warning(
-                "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
-                "bi-directional self-attention."
-            )
-
-        self.bert = KplugModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-MODEL_FOR_MASKED_LM_MAPPING[BertConfig] = KplugForMaskedLM
diff --git a/spaces/facebook/ov-seg/open_vocab_seg/data/datasets/register_pascal_context.py b/spaces/facebook/ov-seg/open_vocab_seg/data/datasets/register_pascal_context.py
deleted file mode 100644
index e40f87c945da20e78c0a3ea230bc9f36d1800071..0000000000000000000000000000000000000000
--- a/spaces/facebook/ov-seg/open_vocab_seg/data/datasets/register_pascal_context.py
+++ /dev/null
@@ -1,588 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-
-PASCALCONTEX59_NAMES = (
-    "aeroplane",
-    "bicycle",
-    "bird",
-    "boat",
-    "bottle",
-    "bus",
-    "car",
-    "cat",
-    "chair",
-    "cow",
-    "table",
-    "dog",
-    "horse",
-    "motorbike",
-    "person",
-    "pottedplant",
-    "sheep",
-    "sofa",
-    "train",
-    "tvmonitor",
-    "bag",
-    "bed",
-    "bench",
-    "book",
-    "building",
-    "cabinet",
-    "ceiling",
-    "cloth",
-    "computer",
-    "cup",
-    "door",
-    "fence",
-    "floor",
-    "flower",
-    "food",
-    "grass",
-    "ground",
-    "keyboard",
-    "light",
-    "mountain",
-    "mouse",
-    "curtain",
-    "platform",
-    "sign",
-    "plate",
-    "road",
-    "rock",
-    "shelves",
-    "sidewalk",
-    "sky",
-    "snow",
-    "bedclothes",
-    "track",
-    "tree",
-    "truck",
-    "wall",
-    "water",
-    "window",
-    "wood",
-)
-
-PASCALCONTEX459_NAMES = (
-    "accordion",
-    "aeroplane",
-    "air conditioner",
-    "antenna",
-    "artillery",
-    "ashtray",
-    "atrium",
-    "baby carriage",
-    "bag",
-    "ball",
-    "balloon",
-    "bamboo weaving",
-    "barrel",
-    "baseball bat",
-    "basket",
-    "basketball backboard",
-    "bathtub",
-    "bed",
-    "bedclothes",
-    "beer",
-    "bell",
-    "bench",
-    "bicycle",
-    "binoculars",
-    "bird",
-    "bird cage",
-    "bird feeder",
-    "bird nest",
-    "blackboard",
-    "board",
-    "boat",
-    "bone",
-    "book",
-    "bottle",
-    "bottle opener",
-    "bowl",
-    "box",
-    "bracelet",
-    "brick",
-    "bridge",
-    "broom",
-    "brush",
-    "bucket",
-    "building",
-    "bus",
-    "cabinet",
-    "cabinet door",
-    "cage",
-    "cake",
-    "calculator",
-    "calendar",
-    "camel",
-    "camera",
-    "camera lens",
-    "can",
-    "candle",
-    "candle holder",
-    "cap",
-    "car",
-    "card",
-    "cart",
-    "case",
-    "casette recorder",
-    "cash register",
-    "cat",
-    "cd",
-    "cd player",
-    "ceiling",
-    "cell phone",
-    "cello",
-    "chain",
-    "chair",
-    "chessboard",
-    "chicken",
-    "chopstick",
-    "clip",
-    "clippers",
-    "clock",
-    "closet",
-    "cloth",
-    "clothes tree",
-    "coffee",
-    "coffee machine",
-    "comb",
-    "computer",
-    "concrete",
-    "cone",
-    "container",
-    "control booth",
-    "controller",
-    "cooker",
-    "copying machine",
-    "coral",
-    "cork",
-    "corkscrew",
-    "counter",
-    "court",
-    "cow",
-    "crabstick",
-    "crane",
-    "crate",
-    "cross",
-    "crutch",
-    "cup",
-    "curtain",
-    "cushion",
-    "cutting board",
-    "dais",
-    "disc",
-    "disc case",
-    "dishwasher",
-    "dock",
-    "dog",
-    "dolphin",
-    "door",
-    "drainer",
-    "dray",
-    "drink dispenser",
-    "drinking machine",
-    "drop",
-    "drug",
-    "drum",
-    "drum kit",
-    "duck",
-    "dumbbell",
-    "earphone",
-    "earrings",
-    "egg",
-    "electric fan",
-    "electric iron",
-    "electric pot",
-    "electric saw",
-    "electronic keyboard",
-    "engine",
-    "envelope",
-    "equipment",
-    "escalator",
-    "exhibition booth",
-    "extinguisher",
-    "eyeglass",
-    "fan",
-    "faucet",
-    "fax machine",
-    "fence",
-    "ferris wheel",
-    "fire extinguisher",
-    "fire hydrant",
-    "fire place",
-    "fish",
-    "fish tank",
-    "fishbowl",
-    "fishing net",
-    "fishing pole",
-    "flag",
-    "flagstaff",
-    "flame",
-    "flashlight",
-    "floor",
-    "flower",
-    "fly",
-    "foam",
-    "food",
-    "footbridge",
-    "forceps",
-    "fork",
-    "forklift",
-    "fountain",
-    "fox",
-    "frame",
-    "fridge",
-    "frog",
-    "fruit",
-    "funnel",
-    "furnace",
-    "game controller",
-    "game machine",
-    "gas cylinder",
-    "gas hood",
-    "gas stove",
-    "gift box",
-    "glass",
-    "glass marble",
-    "globe",
-    "glove",
-    "goal",
-    "grandstand",
-    "grass",
-    "gravestone",
-    "ground",
-    "guardrail",
-    "guitar",
-    "gun",
-    "hammer",
-    "hand cart",
-    "handle",
-    "handrail",
-    "hanger",
-    "hard disk drive",
-    "hat",
-    "hay",
-    "headphone",
-    "heater",
-    "helicopter",
-    "helmet",
-    "holder",
-    "hook",
-    "horse",
-    "horse-drawn carriage",
-    "hot-air balloon",
-    "hydrovalve",
-    "ice",
-    "inflator pump",
-    "ipod",
-    "iron",
-    "ironing board",
-    "jar",
-    "kart",
-    "kettle",
-    "key",
-    "keyboard",
-    "kitchen range",
-    "kite",
-    "knife",
-    "knife block",
-    "ladder",
-    "ladder truck",
-    "ladle",
-    "laptop",
-    "leaves",
-    "lid",
-    "life buoy",
-    "light",
-    "light bulb",
-    "lighter",
-    "line",
-    "lion",
-    "lobster",
-    "lock",
-    "machine",
-    "mailbox",
-    "mannequin",
-    "map",
-    "mask",
-    "mat",
-    "match book",
-    "mattress",
-    "menu",
-    "metal",
-    "meter box",
-    "microphone",
-    "microwave",
-    "mirror",
-    "missile",
-    "model",
-    "money",
-    "monkey",
-    "mop",
-    "motorbike",
-    "mountain",
-    "mouse",
-    "mouse pad",
-    "musical instrument",
-    "napkin",
-    "net",
-    "newspaper",
-    "oar",
-    "ornament",
-    "outlet",
-    "oven",
-    "oxygen bottle",
-    "pack",
-    "pan",
-    "paper",
-    "paper box",
-    "paper cutter",
-    "parachute",
-    "parasol",
-    "parterre",
-    "patio",
-    "pelage",
-    "pen",
-    "pen container",
-    "pencil",
-    "person",
-    "photo",
-    "piano",
-    "picture",
-    "pig",
-    "pillar",
-    "pillow",
-    "pipe",
-    "pitcher",
-    "plant",
-    "plastic",
-    "plate",
-    "platform",
-    "player",
-    "playground",
-    "pliers",
-    "plume",
-    "poker",
-    "poker chip",
-    "pole",
-    "pool table",
-    "postcard",
-    "poster",
-    "pot",
-    "pottedplant",
-    "printer",
-    "projector",
-    "pumpkin",
-    "rabbit",
-    "racket",
-    "radiator",
-    "radio",
-    "rail",
-    "rake",
-    "ramp",
-    "range hood",
-    "receiver",
-    "recorder",
-    "recreational machines",
-    "remote control",
-    "road",
-    "robot",
-    "rock",
-    "rocket",
-    "rocking horse",
-    "rope",
-    "rug",
-    "ruler",
-    "runway",
-    "saddle",
-    "sand",
-    "saw",
-    "scale",
-    "scanner",
-    "scissors",
-    "scoop",
-    "screen",
-    "screwdriver",
-    "sculpture",
-    "scythe",
-    "sewer",
-    "sewing machine",
-    "shed",
-    "sheep",
-    "shell",
-    "shelves",
-    "shoe",
-    "shopping cart",
-    "shovel",
-    "sidecar",
-    "sidewalk",
-    "sign",
-    "signal light",
-    "sink",
-    "skateboard",
-    "ski",
-    "sky",
-    "sled",
-    "slippers",
-    "smoke",
-    "snail",
-    "snake",
-    "snow",
-    "snowmobiles",
-    "sofa",
-    "spanner",
-    "spatula",
-    "speaker",
-    "speed bump",
-    "spice container",
-    "spoon",
-    "sprayer",
-    "squirrel",
-    "stage",
-    "stair",
-    "stapler",
-    "stick",
-    "sticky note",
-    "stone",
-    "stool",
-    "stove",
-    "straw",
-    "stretcher",
-    "sun",
-    "sunglass",
-    "sunshade",
-    "surveillance camera",
-    "swan",
-    "sweeper",
-    "swim ring",
-    "swimming pool",
-    "swing",
-    "switch",
-    "table",
-    "tableware",
-    "tank",
-    "tap",
-    "tape",
-    "tarp",
-    "telephone",
-    "telephone booth",
-    "tent",
-    "tire",
-    "toaster",
-    "toilet",
-    "tong",
-    "tool",
-    "toothbrush",
-    "towel",
-    "toy",
-    "toy car",
-    "track",
-    "train",
-    "trampoline",
-    "trash bin",
-    "tray",
-    "tree",
-    "tricycle",
-    "tripod",
-    "trophy",
-    "truck",
-    "tube",
-    "turtle",
-    "tvmonitor",
-    "tweezers",
-    "typewriter",
-    "umbrella",
-    "unknown",
-    "vacuum cleaner",
-    "vending machine",
-    "video camera",
-    "video game console",
-    "video player",
-    "video tape",
-    "violin",
-    "wakeboard",
-    "wall",
-    "wallet",
-    "wardrobe",
-    "washing machine",
-    "watch",
-    "water",
-    "water dispenser",
-    "water pipe",
-    "water skate board",
-    "watermelon",
-    "whale",
-    "wharf",
-    "wheel",
-    "wheelchair",
-    "window",
-    "window blinds",
-    "wineglass",
-    "wire",
-    "wood",
-    "wool",
-
-)
-
-
-def _get_voc_meta(cat_list):
-    ret = {
-        "stuff_classes": cat_list,
-    }
-    return ret
-
-
-def register_pascal_context_59(root):
-    root = os.path.join(root, "VOCdevkit/VOC2010")
-    meta = _get_voc_meta(PASCALCONTEX59_NAMES)
-    for name, image_dirname, sem_seg_dirname in [
-        ("val", "JPEGImages", "annotations_detectron2/pc59_val"),
-    ]:
-        image_dir = os.path.join(root, image_dirname)
-        gt_dir = os.path.join(root, sem_seg_dirname)
-        all_name = f"pascal_context_59_sem_seg_{name}"
-        DatasetCatalog.register(
-            all_name,
-            lambda x=image_dir, y=gt_dir: load_sem_seg(
-                y, x, gt_ext="png", image_ext="jpg"
-            ),
-        )
-        MetadataCatalog.get(all_name).set(
-            image_root=image_dir,
-            sem_seg_root=gt_dir,
-            evaluator_type="sem_seg",
-            ignore_label=255,
-            **meta,
-        )
-
-def register_pascal_context_459(root):
-    root = os.path.join(root, "VOCdevkit/VOC2010")
-    meta = _get_voc_meta(PASCALCONTEX459_NAMES)
-    for name, image_dirname, sem_seg_dirname in [
-        ("val", "JPEGImages", "annotations_detectron2/pc459_val"),
-    ]:
-        image_dir = os.path.join(root, image_dirname)
-        gt_dir = os.path.join(root, sem_seg_dirname)
-        all_name = f"pascal_context_459_sem_seg_{name}"
-        DatasetCatalog.register(
-            all_name,
-            lambda x=image_dir, y=gt_dir: load_sem_seg(
-                y, x, gt_ext="tif", image_ext="jpg"
-            ),
-        )
-        MetadataCatalog.get(all_name).set(
-            image_root=image_dir,
-            sem_seg_root=gt_dir,
-            evaluator_type="sem_seg",
-            ignore_label=65535,  # NOTE: gt is saved in 16-bit TIFF images
-            **meta,
-        )
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_pascal_context_59(_root)
-register_pascal_context_459(_root)
diff --git a/spaces/falcondai/stego-lm/app.py b/spaces/falcondai/stego-lm/app.py
deleted file mode 100644
index 9b35de222a4e20d5282d37d2227be3c42a33b23f..0000000000000000000000000000000000000000
--- a/spaces/falcondai/stego-lm/app.py
+++ /dev/null
@@ -1,359 +0,0 @@
-#!/usr/bin/env python
-# An demo of linguistic steganography with patient-Huffman algorithm.
-# We use symmetric key cryptography to en/decrypt.
-#
-# Reference:
-# Dai FZ, Cai Z. Towards Near-imperceptible Steganographic Text. ACL 2019.
-
-import nacl.secret
-import nacl.utils
-from transformers import GPT2TokenizerFast, GPT2LMHeadModel
-import gradio as gr
-import numpy as np
-import torch as th
-
-from huffman import build_min_heap, huffman_tree, tv_huffman, invert_code_tree
-
-# model_name = 'gpt2-xl'
-# XXX Use GPT-2-small for less compute
-model_name = 'gpt2'
-lm = GPT2LMHeadModel.from_pretrained(model_name)
-tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
-
-def bits_to_recover(max_plaintext_length):
-    return (max_plaintext_length + 40) * 8
-
-def p_next_token(prefix, cache=None, allow_eos=True):
-    t_prefix = th.as_tensor(prefix)
-    with th.no_grad():
-        if cache:
-            # Incremental decoding. Input one token at a time with cache.
-            lm_out = lm.forward(input_ids=t_prefix[-1:], use_cache=True, past_key_values=cache)
-        else:
-            lm_out = lm.forward(input_ids=t_prefix, use_cache=True)
-        if allow_eos:
-            # Assume EOS is the last token in the vocabulary.
-            p_next_token = lm_out.logits[-1].softmax(dim=-1)
-        else:
-            p_next_token = lm_out.logits[-1, :-1].softmax(dim=-1)
-    return p_next_token.numpy(), lm_out.past_key_values
-
-def embed_bits(coin_flips, prefix, tv_threshold=0.1, max_sequence_length=400):
-    '''We use a sequence of coin flips to control the generation of token
-    indices from a language model. This returns _a sequence_ as defined by
-    the language model, e.g. sentence, paragraph.'''
-    # ind = tokenizer.bos_token_id
-    # prefix = [ind]
-
-    hidden_prefix_ind = [tokenizer.bos_token_id] + tokenizer.encode(prefix)
-    n_hidden_prefix_ind = len(hidden_prefix_ind)
-    done_hiding = False
-    p, kv = p_next_token(hidden_prefix_ind, allow_eos=done_hiding)
-    n_skips = 0
-    n_bits_encoded = 0
-    n_tail_fill = 0
-    ind = None
-    prefix_inds = []
-    # Terminate the generation after we generate the EOS token
-    # XXX to save computation, we terminate as soon as all bits are hidden.
-    while not done_hiding and n_hidden_prefix_ind + len(prefix_inds) < max_sequence_length and ind != tokenizer.eos_token_id:
-        # There is still some cipher text to hide
-        if coin_flips:
-            # Build Huffman codes for the conditional distribution
-            heap = build_min_heap(p)
-            hc = huffman_tree(heap)
-            # print(hc)
-            # Check if the total variation is low enough
-            # print(len(prefix_inds) - 1, tv_huffman(hc, p))
-            # print(tv_huffman(hc, p)[0], tv_threshold)
-            if tv_huffman(hc, p)[0] < tv_threshold:
-                # Huffman-decode the cipher text into a token
-                # Consume the cipher text until a token is generated
-                decoder_state = hc
-                while type(decoder_state) is tuple:
-                    left, right = decoder_state
-                    try:
-                        bit = coin_flips.pop(0)
-                        n_bits_encoded += 1
-                    except IndexError:
-                        # No more cipher text. Pad with random bits
-                        bit = np.random.choice(2)
-                        n_tail_fill += 1
-                    # 0 => left, 1 => right
-                    decoder_state = left if bit == '0' else right
-                # Decoder settles in a leaf node
-                ind = decoder_state
-                prefix_inds.append(ind)
-                yield prefix_inds
-                done_hiding = not bool(coin_flips)
-                p, kv = p_next_token(hidden_prefix_ind + prefix_inds, kv, done_hiding)
-                continue
-        # Forward sample according to LM normally
-        n_skips += 1 if coin_flips else 0
-        ind = np.random.choice(tokenizer.vocab_size if done_hiding else tokenizer.vocab_size - 1, p=p)
-        prefix_inds.append(ind)
-        yield prefix_inds
-        p, kv = p_next_token(hidden_prefix_ind + prefix_inds, kv, done_hiding)
-    # Drop the EOS index
-    print(prefix_inds)
-    print(len(prefix_inds), n_skips, n_bits_encoded, n_tail_fill)
-    if prefix_inds[-1] == tokenizer.eos_token_id:
-        prefix_inds = prefix_inds[:-1]
-    yield prefix_inds
-
-def recover_bits(token_inds, tv_threshold, bits_to_recover, prefix):
-    remaining_bits = bits_to_recover
-    hidden_prefix_inds = [tokenizer.bos_token_id] + tokenizer.encode(prefix)
-    p, kv = p_next_token(hidden_prefix_inds, allow_eos=False)
-    cipher_text = []
-    # Terminate the generation after we have consumed all indices or
-    # have extracted all bits
-    while token_inds and 0 < remaining_bits:
-        # Build Huffman codes for the conditional distribution
-        heap = build_min_heap(p)
-        hc = huffman_tree(heap)
-        # Check if the total variation is low enough
-        if tv_huffman(hc, p)[0] < tv_threshold:
-            # We have controlled this step. Some bits are hidden.
-            code = invert_code_tree(hc)
-            # Look up the Huffman code for the token.
-            ind = token_inds.pop(0)
-            # Convert the Huffman code into bits
-            # left => 0, right => 1
-            cipher_text_fragment = code[ind]
-            # Truncate possible trailing paddings
-            cipher_text += cipher_text_fragment[:remaining_bits]
-            remaining_bits -= len(cipher_text_fragment)
-            yield cipher_text
-            # print(remaining_bits)
-            hidden_prefix_inds.append(ind)
-            p, kv = p_next_token(hidden_prefix_inds, cache=kv, allow_eos=False)
-        else:
-            # We did not control this step. Skip.
-            hidden_prefix_inds.append(token_inds.pop(0))
-            p, kv = p_next_token(hidden_prefix_inds, cache=kv, allow_eos=False)
-    print(cipher_text, len(cipher_text), bits_to_recover)
-    yield cipher_text
-
-
-with gr.Blocks() as demo:
-    gr.Markdown('''
-        # Linguistic steganography demo with ``patient-Huffman`` algorithm
-        Instead of sending secrets in plaintext or in ciphertext, we can "hide the hiding" by embedding the encrypted secret in a natural looking message.
-
-        ## Usage for message sender
-        1. Type a short message. Click Encrypt to generate the ciphertext (encrypted text).
-        2. Click Hide to generate the stegotext/covertext.
-
-        ## Usage for message receiver
-        1. Copy-paste the received stegotext/covertext into the stegotext box. Click Recover to extract the hidden ciphertext.
-        2. Click Decrypt to decipher the original message.
-    ''')
-
-    with gr.Accordion(
-        'Secrets shared between sender and receiver',
-        open=False,
-    ):
-        # Shared secrets and parameters.
-        gr.Markdown('''
-        - The proposed stegosystem is agnostic to the choice of cryptosystem. We use the symmetric key encryption implemented in `pyNaCl` library.
-        - An encryption key is randomly generated, you can refresh the page to get a different one.
-        - The _choice_ of language model is a shared secret. Due to computation resource constraints, we use GPT-2 as an example.
-        - The communicating parties can share a prefix to further control the stegotext to appear more appropriate for the channel, e.g., blog posts, social media messages. Take extra care of the whitespaces.
-        - Imperceptibility threshold controls how much the distribution of stegotexts is allowed to deviate from the language model. Lower imperceptibility threshold produces longer stegotext.
-
-
-        Reference: Dai FZ, Cai Z. [Towards Near-imperceptible Steganographic Text](https://arxiv.org/abs/1907.06679). ACL 2019.
-        ''')
-        state = gr.State()
-        with gr.Row():
-            tb_shared_key = gr.Textbox(
-                label='encryption key (hex)',
-                value=lambda : nacl.utils.random(nacl.secret.SecretBox.KEY_SIZE).hex(),
-                interactive=True,
-                scale=1,
-                lines=3,
-            )
-            # dp_shared_lm = gr.Dropdown(
-            #     label='language model',
-            #     choices=[
-            #         'GPT-2',
-            #         # 'GPT-3',
-            #     ],
-            #     value='GPT-2',
-            # )
-            s_shared_imp = gr.Slider(
-                label='imperceptibility threshold',
-                minimum=0,
-                maximum=1,
-                value=0.4,
-                scale=1,
-            )
-            s_shared_max_plaintext_len = gr.Slider(
-                label='max plaintext length',
-                minimum=4,
-                maximum=32,
-                step=1,
-                value=18,
-                scale=1,
-            )
-            with gr.Column(scale=1):
-                tb_shared_prefix = gr.Textbox(
-                    label='prefix',
-                    value='',
-                )
-                gr.Examples(
-                    [
-                        'best dessert recipe: ',
-                        'def solve(x):',
-                        'breaking news ',
-                        '🤗🔒',
-                    ],
-                    tb_shared_prefix,
-                    cache_examples=False,
-                )
-
-    with gr.Row():
-        with gr.Box():
-            with gr.Column():
-                # Sender
-                gr.Markdown('## Sender')
-
-                # Plain text
-                tb_sender_plaintext = gr.Textbox(
-                    label='plaintext',
-                    value='gold in top drawer',
-                )
-                btn_encrypt = gr.Button('🔒 Encrypt')
-
-                # Encrypt
-                # Cipher text
-                tb_sender_ciphertext = gr.Textbox(
-                    label='ciphertext (hex)',
-                )
-                btn_hide = gr.Button('🫣 Hide', interactive=False)
-
-                # Hide
-                # Cover text
-                tb_sender_stegotext = gr.Textbox(
-                    label='stegotext',
-                )
-
-        with gr.Box():
-            with gr.Column():
-                # Receiver
-                gr.Markdown('## Receiver')
-                # Cover text
-                tb_receiver_stegotext = gr.Textbox(
-                    label='stegotext',
-                )
-                btn_recover = gr.Button('🔎 Recover')
-                # Cipher text
-                tb_receiver_ciphertext = gr.Textbox(
-                    label='recovered ciphertext (hex)',
-                )
-                btn_decrypt = gr.Button('🔓 Decrypt', interactive=True)
-                # Plain text
-                tb_receiver_plaintext = gr.Textbox(
-                    label='deciphered plaintext',
-                )
-
-    gr.Markdown('''
-        ## Known issues
-        1. The ciphertext recovered by the receiver might not match the original ciphertext. This is due to LLM tokenization mismatch. This is a fundamental challenge and for now, just Encrypt again (to use a different nonce) and go through the sender's process again.
-        2. The stegotext looks incoherent. GPT-2 small is used for the demo and its fluency is quite limited. A stronger LLM will alleviate this problem. A smaller imperceptibility threshold should also help.
-    ''')
-
-    # Link the UI to handlers
-    def encrypt(saved_state, key_in_hex, plaintext, max_plaintext_length):
-        shared_key = bytes.fromhex(key_in_hex)
-        # print(saved_state)
-        if saved_state is None:
-            # Create the secret boxes if they have not been created.
-            sender_box = nacl.secret.SecretBox(shared_key)
-            receiver_box = nacl.secret.SecretBox(shared_key)
-            saved_state = sender_box, receiver_box
-        else:
-            sender_box, receiver_box = saved_state
-        print('Encode:', bytes(plaintext, 'utf8'), len(bytes(plaintext, 'utf8')))
-        utf8_encoded_plaintext = bytes(plaintext, 'utf8')
-        if len(utf8_encoded_plaintext) > max_plaintext_length:
-            raise gr.Error('Plaintext is too long. Try a shorter one or increase the max plaintext length.')
-        else:
-            # Pad the plaintext to the maximum length.
-            utf8_encoded_plaintext += bytes(' ' * (max_plaintext_length - len(utf8_encoded_plaintext)), encoding='utf8')
-        ciphertext = sender_box.encrypt(utf8_encoded_plaintext)
-        print('Encrypt:', plaintext, len(plaintext), ciphertext, len(ciphertext), len(ciphertext.hex()))
-        return [
-            saved_state,
-            ciphertext.hex(),
-            gr.Button.update(interactive=True),
-        ]
-
-    def decrypt(saved_state, ciphertext, key_in_hex):
-        shared_key = bytes.fromhex(key_in_hex)
-        if saved_state is None:
-            # Create the secret boxes if they have not been created.
-            sender_box = nacl.secret.SecretBox(shared_key)
-            receiver_box = nacl.secret.SecretBox(shared_key)
-            saved_state = sender_box, receiver_box
-        else:
-            sender_box, receiver_box = saved_state
-        try:
-            utf8_encoded_plaintext = receiver_box.decrypt(bytes.fromhex(ciphertext))
-            print('Decrypt:', ciphertext, len(ciphertext), utf8_encoded_plaintext, len(utf8_encoded_plaintext))
-            return [
-                saved_state,
-                utf8_encoded_plaintext.decode('utf8'),
-            ]
-        except:
-            raise gr.Error('Decryption failed. Likely due to tokenization mismatch. Try Encrypting again.')
-
-    def hide(ciphertext, tv_threshold, shared_prefix):
-        # Convert hex to bits
-        ba = bytes.fromhex(ciphertext)
-        bits = [b for h in ba for b in f'{h:08b}']
-        print('Hide:', ciphertext, bits, len(bits))
-        embed_gen = embed_bits(bits, shared_prefix, tv_threshold, lm.config.n_ctx // 2)
-        for inds in embed_gen:
-            yield tokenizer.decode(inds)
-
-    def recover(stegotext, tv_threshold, max_plaintext_length, shared_prefix):
-        inds = tokenizer.encode(stegotext)
-        print('Recover:', stegotext, inds, len(inds))
-        n_bits_to_recover = bits_to_recover(max_plaintext_length)
-        recover_gen = recover_bits(inds, tv_threshold, n_bits_to_recover, shared_prefix)
-        for bits in recover_gen:
-            yield ''.join(bits)
-        ba = bytearray()
-        # Convert bits to bytearray
-        for i in range(0, len(bits), 8):
-            ba.append(int(''.join(bits[i:i+8]), 2))
-        yield ba.hex()
-
-    btn_encrypt.click(
-        encrypt,
-        [state, tb_shared_key, tb_sender_plaintext, s_shared_max_plaintext_len],
-        [state, tb_sender_ciphertext, btn_hide],
-    )
-    btn_hide.click(
-        hide,
-        [tb_sender_ciphertext, s_shared_imp, tb_shared_prefix],
-        [tb_sender_stegotext],
-    )
-    btn_recover.click(
-        recover,
-        [tb_receiver_stegotext, s_shared_imp, s_shared_max_plaintext_len, tb_shared_prefix],
-        [tb_receiver_ciphertext],
-    )
-    btn_decrypt.click(
-        decrypt,
-        [state, tb_receiver_ciphertext, tb_shared_key],
-        [state, tb_receiver_plaintext],
-    )
-
-
-if __name__ == '__main__':
-    demo.queue(concurrency_count=10)
-    demo.launch()
-    # demo.launch(share=True)
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Lingika Rahas Ath Potha Zip !!TOP!!.md b/spaces/falterWliame/Face_Mask_Detection/Lingika Rahas Ath Potha Zip !!TOP!!.md
deleted file mode 100644
index d05604bbfa0053a262cc0877507ca381b237548a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Lingika Rahas Ath Potha Zip !!TOP!!.md	
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deleted file mode 100644
index 3fc43f8ae6ca7e80f0c8b999de2a393085b4772a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Matlab R2009a Portable.torrent.md	
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diff --git a/spaces/fatiXbelha/sd/Apkfolks Among Us The Best Modded Version of the Popular Game.md b/spaces/fatiXbelha/sd/Apkfolks Among Us The Best Modded Version of the Popular Game.md
deleted file mode 100644
index e2cd89837eddacd50368ba2e362d790071c22429..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Apkfolks Among Us The Best Modded Version of the Popular Game.md	
+++ /dev/null
@@ -1,135 +0,0 @@
-<br />
-<h1>Apkfolks Among Us: How to Download and Play the Modded Version of the Popular Game</h1>
- <p>Among Us is one of the most-played online games in the world, with millions of players joining the fun every day. But did you know that there is a modded version of the game that offers more features and customization options? In this article, we will tell you everything you need to know about Apkfolks Among Us, a website that provides modified APK files for Android devices. We will explain what Among Us is, why it is so popular, what Apkfolks is, how it works, how to download and install Apkfolks Among Us, and what are the advantages and disadvantages of using it. By the end of this article, you will be able to decide whether you want to try Apkfolks Among Us or stick to the original game.</p>
- <h2>What is Among Us and Why is it So Popular?</h2>
- <p>Among Us is a multiplayer game that was released in 2018 by Innersloth, a small indie game studio. It is a social deception game, where players have to work together as crewmates on a spaceship, while trying to identify and eliminate one or more impostors who are secretly sabotaging and killing them. The game can be played online or locally, with 4 to 15 players, using voice chat or text chat. The game has four maps to choose from: The Skeld, Mira HQ, Polus, and The Airship.</p>
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- <h3>The Gameplay and Rules of Among Us</h3>
- <p>The gameplay of Among Us is simple but addictive. At the start of each round, players are randomly assigned the role of either crewmate or impostor. Crewmates have to complete tasks around the map, which are mini-games that involve things like wiring, scanning, fueling, etc. Impostors have to kill crewmates without being caught, while also sabotaging the ship's systems, such as lights, oxygen, reactor, etc. Impostors can also use vents to move around the map quickly and discreetly.</p>
- <p>When a dead body is found by a crewmate, they can report it and call an emergency meeting. During the meeting, all players can discuss who they think is the impostor, based on their observations and alibis. Players can also call an emergency meeting by pressing a button in the cafeteria or meeting room. After each meeting, players can vote to eject someone from the ship. If they eject an impostor, they win. If they eject a crewmate, they lose. If there is a tie or no one is ejected, the game continues.</p>
- <p>Crewmates can also win by completing all their tasks before the impostors kill them all. Impostors can also win by killing enough crewmates so that their number equals or exceeds the number of crewmates left alive. Impostors can also win by sabotaging a critical system and preventing crewmates from fixing it in time.</p>
- <h3>The Rise of Among Us in 2020</h3>
- <p>Although Among Us was released in 2018, it did not gain much attention until mid-2020, when several popular Twitch streamers and YouTubers started playing it with their friends and fans. Some of these streamers include PewDiePie, Jacksepticeye, Corpse Husband, Valkyrae, Disguised <h3>The Appeal of Among Us for Streamers and Viewers</h3>
- <p>One of the main reasons why Among Us became so popular in 2020 was because of the streaming community. Many streamers and YouTubers started playing the game with their friends and fans, creating hilarious and suspenseful moments that attracted millions of viewers. Some of the most popular streamers who played Among Us include PewDiePie, Jacksepticeye, Corpse Husband, Valkyrae, Disguised Toast, Pokimane, and more.</p>
- <p>The appeal of Among Us for streamers and viewers is that it is a game that relies heavily on social interaction, deception, and deduction. It is a game that can showcase the personalities, skills, and humor of the players, as well as their reactions to the twists and turns of the game. It is also a game that can create a lot of drama, tension, and excitement, as well as memes and fan art. Moreover, it is a game that anyone can play and enjoy, regardless of their age, gender, or gaming experience.</p>
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- <h2>What is Apkfolks and How Does it Work?</h2>
- <p>Apkfolks is a website that provides modified APK files for Android devices. APK stands for Android Package Kit, which is the file format used by Android to distribute and install applications. A modified APK file is an APK file that has been altered or hacked to change some aspects of the original application. For example, a modified APK file can unlock premium features, remove ads, add cheats, or change the graphics or gameplay of an application.</p>
- <h3>The Definition and Function of Apkfolks</h3>
- <p>Apkfolks is a website that claims to offer "the best modded apps and games for free". It has a large collection of modified APK files for various popular applications and games, such as Spotify, Netflix, WhatsApp, Instagram, PUBG Mobile, Free Fire, and more. Apkfolks also has a section dedicated to Among Us, where it provides different versions of the game with various mods and hacks.</p>
- <p>The function of Apkfolks is to allow users to download and install modified APK files on their Android devices without rooting them. Rooting is the process of gaining full access to the system settings and features of an Android device, which can allow users to install custom ROMs, kernels, mods, and apps. However, rooting can also void the warranty of the device, expose it to security risks, and cause performance issues. Apkfolks claims that its modified APK files do not require rooting and are safe to use.</p>
- <h3>The Benefits and Risks of Using Apkfolks</h3>
- <p>The benefits of using Apkfolks are that it can provide users with more features and options for their applications and games. For example, using Apkfolks Among Us can allow users to access different skins, hats, pets, maps, modes, cheats, hacks, and more. Some of these features are not available in the original game or require real money to purchase. Using Apkfolks can also allow users to customize their applications and games according to their preferences and needs.</p>
- <p>The risks of using Apkfolks are that it can violate the terms of service and policies of the original developers and publishers of the applications and games. For example, using Apkfolks Among Us can be considered as piracy or cheating by Innersloth , the creators of Among Us. This can result in legal actions or bans from the official servers or platforms. Using Apkfolks can also expose users to malware or viruses that can harm their devices or steal their personal information. Moreover, using Apkfolks can ruin the fun and fairness of the applications and games for other users who play legitimately.</p>
- <h3>The Legal and Ethical Issues of Using Apkfolks</h3>
- <p>The legal issues of using Apkfolks are that it can infringe on the intellectual property rights and copyrights of the original developers and publishers of the applications and games. For example, using Apkfolks Among Us can be seen as a violation of the End User License Agreement (EULA) and the Digital Millennium Copyright Act (DMCA) of Among Us. These are legal contracts and laws that protect the rights and interests of the creators and owners of digital content. Using Apkfolks can also be seen as a breach of trust and respect for the hard work and creativity of the developers and publishers of the applications and games.</p>
- <p>The ethical issues of using Apkfolks are that it can undermine the integrity and quality of the applications and games. For example, using Apkfolks Among Us can create an unfair advantage for some players over others, or disrupt the balance and harmony of the game. This can lead to frustration, anger, or boredom for the players who play by the rules. Using Apkfolks can also diminish the value and enjoyment of the applications and games, as they lose their originality, authenticity, and challenge. Moreover, using Apkfolks can harm the reputation and revenue of the developers and publishers of the applications and games, as they lose their loyal customers and potential income.</p>
- <h2>How to Download and Install Apkfolks Among Us</h2>
- <p>If you still want to try Apkfolks Among Us, despite the risks and issues involved, you will need to follow some steps to download and install it on your Android device. Here are the requirements and precautions for installing Apkfolks Among Us:</p>
- <h3>The Requirements and Precautions for Installing Apkfolks Among Us</h3>
- <p>Before you download and install Apkfolks Among Us, you will need to make sure that your Android device meets the following requirements:</p>
- <ul>
-<li>It has Android version 4.4 or higher.</li>
-<li>It has at least 100 MB of free storage space.</li>
-<li>It has a stable internet connection.</li>
-</ul>
- <p>You will also need to take some precautions to avoid any problems or damages to your device or data:</p>
- <ul>
-<li>Backup your device data before installing Apkfolks Among Us, in case something goes wrong.</li>
-<li>Uninstall any previous or official version of Among Us from your device, to avoid any conflicts or errors.</li>
-<li>Enable the option to install apps from unknown sources on your device settings, to allow Apkfolks Among Us to be installed.</li>
-<li>Disable any antivirus or security software on your device, to prevent them from blocking or deleting Apkfolks Among Us.</li>
-<li>Download Apkfolks Among Us only from its official website, to avoid any fake or malicious files.</li>
-</ul>
- <h3>The Steps for Downloading and Installing Apkfolks Among Us</h3>
- <p>After you have met the requirements and taken the precautions for installing Apkfolks Among Us, you can follow these steps to download and install it on your Android device:</p>
- <ol>
-<li>Go to the official website of Apkfolks at <a href="">https://apkfolks.com/</a>.</li>
-<li>Search for Among Us in the search bar or browse through the categories until you find it.</li>
-<li>Select the version of Apkfolks Among Us that you want to download. There are different versions with different mods and hacks, such as Always Impostor, No Kill Cooldown, Unlock All Skins, etc.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Locate the downloaded file on your device's file manager or downloads folder.</li>
-<li>Tap on the file and follow the instructions to install it on your device.</li>
-<li>Launch Apkfolks Among Us from your device's app drawer or home screen.</li>
-<li>Enjoy playing Apkfolks Among Us with its modded features and options.</li>
-</ol>
- <h3>The Features and Differences of Apkfolks Among Us</h3>
- <p>Apkfolks Among Us is different from the original game in many ways. It has various features that can enhance or alter your gaming experience. Some of these features are:</p>
- <ul>
-<li>You can choose to be an impostor or a crewmate at any time, regardless of the game settings or randomization.</li>
-<li>You can kill crewmates without any cooldown time or limit, making it easier to eliminate them all quickly.</li>
-<li>You can unlock all skins, hats, pets, maps, modes, and more without paying any real money or completing any tasks.</li>
-<li>You can see who is an impostor or a crewmate by using a radar or a color indicator.</li>
-<li>You can chat with other players during meetings or while dead, giving you more communication options.</li <li>You can use various cheats and hacks, such as speed hack, wall hack, ghost mode, invisibility, etc.</li>
-</ul>
- <p>However, these features also come with some drawbacks and limitations. Some of these are:</p>
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-<li>You can only play Apkfolks Among Us with other players who have the same version of the game, which can limit your options and compatibility.</li>
-<li>You can face technical issues or errors while playing Apkfolks Among Us, such as crashes, glitches, bugs, etc.</li>
-<li>You can get banned or reported by other players or the official servers for using Apkfolks Among Us, which can affect your account and reputation.</li>
-<li>You can lose the original charm and challenge of the game by using Apkfolks Among Us, which can make it boring or repetitive.</li>
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- <h2>Conclusion</h2>
- <p>Apkfolks Among Us is a modded version of the popular game Among Us that offers more features and customization options for Android users. It is a website that provides modified APK files that can be downloaded and installed on Android devices without rooting them. Apkfolks Among Us has various advantages and disadvantages, as well as legal and ethical issues, that users should be aware of before using it. Apkfolks Among Us can be a fun and exciting way to play Among Us with more variety and freedom, but it can also be a risky and unfair way to play Among Us with less quality and integrity.</p>
- <p>If you want to try Apkfolks Among Us, you can follow the steps we provided in this article to download and install it on your Android device. However, we recommend that you use it at your own risk and discretion, and respect the rights and wishes of the original developers and publishers of Among Us, as well as the other players who play the game. We hope that this article was helpful and informative for you. Thank you for reading!</p>
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- <p>Here are some frequently asked questions about Apkfolks Among Us:</p>
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- <p>Apkfolks claims that its modified APK files are safe to use and do not contain any malware or viruses. However, there is no guarantee that this is true, as Apkfolks is not an official or verified source of APK files. Therefore, users should be careful and cautious when using Apkfolks Among Us, and scan their devices regularly for any potential threats.</p>
- <h3>Is Apkfolks Among Us free to use?</h3>
- <p>Yes, Apkfolks Among Us is free to use and does not require any payment or subscription. However, users should be aware that using Apkfolks Among Us can be considered as piracy or cheating by Innersloth , the creators of Among Us. This can result in legal actions or bans from the official servers or platforms.</p>
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- <p>Yes, you can play Apkfolks Among Us with your friends, but only if they have the same version of the game as you. You cannot play Apkfolks Among Us with players who have the original or different version of the game, as they will not be compatible with each other. You can also create or join private rooms with your friends who have Apkfolks Among Us, or play online with other players who have it.</p>
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- <p>Yes, you can update Apkfolks Among Us whenever there is a new version available on its website. However, you will need to uninstall the previous version of the game from your device before installing the new one. You will also need to check if the new version of Apkfolks Among Us has the same or different features and options as the old one.</p>
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diff --git a/spaces/fatiXbelha/sd/Apprendre les tables de multiplication facilement et rapidement.md b/spaces/fatiXbelha/sd/Apprendre les tables de multiplication facilement et rapidement.md
deleted file mode 100644
index 4f856f92e67489d61a5768505bf155799cef3fce..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Apprendre les tables de multiplication facilement et rapidement.md	
+++ /dev/null
@@ -1,54 +0,0 @@
-<br />
-<h1>Table de multiplication: qu'est-ce que c'est et comment l'apprendre?</h1> | | H2: Introduction | <h2>Introduction</h2><p>La table de multiplication est un outil mathématique qui permet de calculer rapidement le produit de deux nombres entiers. Elle est composée de lignes et de colonnes qui indiquent les facteurs et les produits. Par exemple, dans la table de multiplication de 3, on trouve le produit de 3 par chaque nombre de 1 à 12.</p>
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diff --git a/spaces/fatiXbelha/sd/Car Parking Multiplayer APK GG Tips and Tricks for the Simulation Game.md b/spaces/fatiXbelha/sd/Car Parking Multiplayer APK GG Tips and Tricks for the Simulation Game.md
deleted file mode 100644
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-
-<h1>Car Parking Multiplayer APK GG: A Guide for Beginners</h1>
- <p>If you are looking for a realistic and fun driving simulator game, you might want to check out Car Parking Multiplayer. This game offers more than just parking: you can explore an open world, customize your car, race with other players, and even use a cheat tool called Game Guardian to hack the game. In this article, we will show you what Car Parking Multiplayer is, how to download and install it, what Game Guardian is, how to use it on the game, and some tips and tricks to make the most out of your gaming experience.</p>
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- <h2>What is Car Parking Multiplayer?</h2>
- <p>Car Parking Multiplayer is a simulation game developed by olzhass that lets you drive various cars in different environments and scenarios. You can choose from over 100 cars with realistic interiors and physics, and customize them with different parts and vinyls. You can also interact with other players in the multiplayer mode, where you can chat, exchange cars, race, or even play as a police officer. The game also features a free walking mode, where you can get out of your car and explore the world on foot. You can visit different locations, such as cities, airports, deserts, and more.</p>
- <h3>Features of the game</h3>
- <p>Some of the features that make Car Parking Multiplayer stand out are:</p>
- <ul>
-<li>Free walking: You can walk around freely in the open world, enter buildings, use elevators, and interact with objects.</li>
-<li>Free open world: You can drive anywhere you want, visit real gas stations and car services, and enjoy the realistic graphics and sounds.</li>
-<li>Multiplayer racing: You can compete against real players in various modes, such as drag racing, drifting, off-road racing, and more.</li>
-<li>Car exchange: You can trade your cars with other players, or buy new ones from the online market.</li>
-<li>Voice chat: You can communicate with other players using voice chat, or use text chat if you prefer.</li>
-<li>Police mode: You can play as a police officer and chase criminals, or as a criminal and evade the police.</li>
-<li>Friend list: You can add other players as friends, join their games, or invite them to yours.</li>
-<li>Car customization: You can modify your car's performance, such as engine, turbo, gearbox, exhaust, etc. You can also change its appearance, such as suspension, wheel angle, color, vinyls, body parts, etc.</li>
-</ul>
- <h3>How to download and install the game</h3>
- <p>To download and install Car Parking Multiplayer on your Android device, you can follow these steps:</p>
- <ol>
-<li>Go to the Google Play Store and search for Car Parking Multiplayer.</li>
-<li>Tap on the Install button and wait for the download to finish.</li>
-<li>Once the installation is complete, tap on the Open button to launch the game.</li>
-<li>You can also download the APK file from other sources , but make sure they are safe and trustworthy. To install the APK file, you need to enable unknown sources in your device settings.</li>
-</ol>
- <h2>What is Game Guardian?</h2>
- <p>Game Guardian is an app that can be used to hack a variety of video games on your Android device. It allows you to modify various aspects of the game, such as money, HP, SP, and much more. You can also use it to speed up or slow down the game speed, search for encrypted values, change memory values, dump memory, copy memory, etc. With Game Guardian, you can enjoy the fun part of a game without the hassle of grinding or spending real money.</p>
- <h3>Features of the app</h3>
- <p>Some of the features that make Game Guardian a powerful and versatile tool are:</p>
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- <ul>
-<li>Runs on ARM, x64 and x86 devices, including x86 emulators.</li>
-<li>Supports Android 2.3.3+ (Gingerbread) through Android 11.</li>
-<li>Supports multiple languages, such as English, Chinese, Russian, etc.</li>
-<li>Works on both rooted and non-rooted devices, but requires a virtual environment for the latter.</li>
-<li>Supports multiple search modes, such as exact value, fuzzy value, encrypted value, etc.</li>
-<li>Supports multiple data types, such as DWORD, FLOAT, DOUBLE, QWORD, etc.</li>
-<li>Supports Lua scripting for more advanced hacking.</li>
-<li>Supports stealth mode to hide the app from detection.</li>
-</ul>
- <h3>How to download and install the app</h3>
- <p>To download and install Game Guardian on your Android device, you can follow these steps:</p>
- <ol>
-<li>Go to the official website of Game Guardian and download the latest version of the app.</li>
-<li>Locate the downloaded APK file and tap on it to install it. You may need to enable unknown sources in your device settings.</li>
-<li>Once the installation is complete, launch the app and grant it root or virtual permissions.</li>
-<li>You can also watch this video tutorial for more details on how to install and use Game Guardian.</li>
-</ol>
- <h2>How to use Game Guardian on Car Parking Multiplayer</h2>
- <p>If you want to hack Car Parking Multiplayer with Game Guardian, you need to follow some steps to set up the app and then use some tips and tricks to modify the game values. Here is how you can do it:</p>
- <h3>Steps to set up Game Guardian</h3>
- <ol>
-<li>Launch Game Guardian and select the option to start it with root or virtual mode. If you choose virtual mode, you need to install a virtual space app and clone both Game Guardian and Car Parking Multiplayer in it.</li>
-<li>Open Car Parking Multiplayer from Game Guardian or from the virtual space app.</li>
-<li>Tap on the Game Guardian icon that floats on your screen and select Car Parking Multiplayer as the process to hack.</li>
-<li>You can now use the search function of Game Guardian to find and change the values you want in the game.</li>
-</ol>
- <h3>Tips and tricks to hack the game</h3>
- <p>Here are some examples of what you can hack in Car Parking Multiplayer with Game Guardian:</p>
- <ul>
-<li>Hack money: You can search for your current money value in DWORD type and change it to any amount you want. You can also use the group search function to find multiple values at once, such as money, gold, level, etc.</li>
-<li>Hack speed: You can search for your current speed value in FLOAT type and change it to increase or decrease your speed. You can also freeze the value so that it does not change.</li>
-<li>Hack fuel: You can search for your current fuel value in FLOAT type and change it to make it infinite. You can also freeze the value so that it does not decrease.</li>
-<li>Hack time: You can search for the current time value in DWORD type and change it to manipulate the time in the game. You can also use the speed hack function of Game Guardian to make the game run faster or slower.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you what Car Parking Multiplayer is, how to download and install it, what Game Guardian is, how to use it on the game, and some tips and tricks to hack the game. We hope you have enjoyed reading this article and learned something new. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!</p>
- <h3>Summary of the main points</h3>
- <ul>
-<li>Car Parking Multiplayer is a realistic and fun driving simulator game that offers more than just parking.</li>
-<li>You can download and install Car Parking Multiplayer from the Google Play Store or from other sources.</li>
-<li>Game Guardian is an app that can be used to hack a variety of video games on your Android device.</li>
-<li>You can download and install Game Guardian from its official website or from other sources.</li>
-<li>You can use Game Guardian on Car Parking Multiplayer by following some steps to set up the app and then using some tips and tricks to modify the game values.</li>
-</ul>
- <h3> FAQs</h3>
- <p>Here are some frequently asked questions about Car Parking Multiplayer and Game Guardian:</p>
- <ol>
-<li>Is Car Parking Multiplayer free to play?</li>
-<p>Yes, Car Parking Multiplayer is free to download and play, but it contains ads and in-app purchases. You can disable the ads by turning off your internet connection or by using an ad blocker. You can also use Game Guardian to hack the in-app purchases and get unlimited money and gold.</p>
-<li>Is Game Guardian safe to use?</li>
-<p>Game Guardian is safe to use as long as you download it from its official website or from other trusted sources. However, you should be careful when using it on online games, as you may get banned or detected by the game developers or other players. You should also backup your game data before using Game Guardian, in case something goes wrong.</p>
-<li>How can I update Car Parking Multiplayer and Game Guardian?</li>
-<p>You can update Car Parking Multiplayer and Game Guardian by visiting their respective websites or sources and downloading the latest versions of the apps. You can also enable the auto-update feature in your device settings, so that the apps will update automatically when a new version is available.</p>
-<li>How can I contact the developers of Car Parking Multiplayer and Game Guardian?</li>
-<p>You can contact the developers of Car Parking Multiplayer and Game Guardian by visiting their official websites or social media pages and sending them a message or a feedback. You can also join their online communities and forums and interact with other users and moderators.</p>
-<li>Where can I find more information and tips about Car Parking Multiplayer and Game Guardian?</li>
-<p>You can find more information and tips about Car Parking Multiplayer and Game Guardian by searching online for blogs, articles, videos, guides, tutorials, reviews, etc. You can also visit their official websites or sources and check their FAQs, help sections, news, updates, etc.</p>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/Cookie Run Kingdom - How to Build Your Dream Kingdom with Free Gems.md b/spaces/fatiXbelha/sd/Cookie Run Kingdom - How to Build Your Dream Kingdom with Free Gems.md
deleted file mode 100644
index 499f18a82ee134cc7c6db03a3b2a8f55a1a16b6f..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Cookie Run Kingdom - How to Build Your Dream Kingdom with Free Gems.md	
+++ /dev/null
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-<h1>Cookie Run: Kingdom Free Gems APK - Is It Worth It?</h1>
-<p>If you are a fan of Cookie Run: Kingdom, you might have heard of or searched for a free gems apk that promises to give you unlimited gems in the game. But is it really worth it to download and use such a modded version of the game? In this article, we will explain what Cookie Run: Kingdom is, what a free gems apk is, why you should avoid it, and how to get gems legitimately in the game.</p>
- <h2>What is Cookie Run: Kingdom?</h2>
-<p>Cookie Run: Kingdom is a popular role-playing game (RPG) developed by Devsisters Corporation. It is available for both Android and iOS devices, as well as on PC via an emulator. The game features adorable cookies as the main characters, each with their own voice, personality, skills, and costumes. The game has three main aspects:</p>
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- <h3>A fun and addictive RPG game with cute cookies</h3>
-<p>In Cookie Run: Kingdom, you can create your own team of cookies and fight against enemies in various stages. You can also upgrade your cookies' levels, treasures, toppings, and skills to make them stronger. The game has a turn-based combat system that requires strategy and timing. You can also use special skills and combos to unleash powerful attacks.</p>
- <h3>A rich and immersive story with epic battles</h3>
-<p>The game also has a captivating story that unfolds as you progress through the stages. You will discover the secrets of the ancient cookies and their kingdoms, as well as face the dark forces of the Dark Enchantress Cookie and her minions. The game has stunning graphics, animations, sound effects, and music that enhance the gameplay experience.</p>
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-<p>Another aspect of the game is building your own kingdom with various decors and facilities. You can produce materials, craft items, arrange activities, and decorate your kingdom to your liking. You can also visit other players' kingdoms and interact with them. Building your kingdom will also help you earn more resources and rewards in the game.</p>
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-<p>Gems are the premium currency in Cookie Run: Kingdom. They are used for various purposes, such as summoning new cookies, buying costumes, speeding up production, refreshing shops, and more. Gems can be obtained by spending real money or by playing the game normally. However, some players may want to get more gems without spending money or time. This is where a free gems apk comes in.</p>
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-<p>A free gems apk is a modified version of the original game that claims to give you unlimited gems for free. It usually requires you to download an apk file from an unofficial source and install it on your device. Some free gems apks may also require you to root or jailbreak your device, which can void your warranty or damage your device.</p>
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- <h2>Why You Should Avoid Cookie Run: Kingdom Free Gems APK</h2>
-<p>As you can see, using a free gems apk for Cookie Run: Kingdom is not worth it at all. You could get banned from the game or lose your account, you could harm your device or compromise your personal data, and you could miss out on the fun and satisfaction of playing the game fairly. Here are some more reasons why you should avoid a free gems apk:</p>
- <h3>You could get banned from the game or lose your account</h3>
-<p>The game developers have the right and the ability to detect and ban any players who use a free gems apk or any other cheats in the game. They have stated in their official website that they have a zero-tolerance policy for any form of cheating or hacking in the game. If you are caught using a free gems apk, you could face one or more of the following consequences:</p>
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-<p>These actions are irreversible and non-negotiable. You will not be able to appeal or recover your account or data once they are banned or deleted. You will also lose all your cookies, gems, items, achievements, and rewards that you have earned or bought in the game.</p>
- <h3>You could harm your device or compromise your personal data</h3>
-<p>As mentioned earlier, using a free gems apk could expose your device to malware and viruses that could damage your device or steal your personal data. This could have serious consequences for your privacy and security. Some examples of what could happen if your device is infected by malware or viruses are:</p>
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- <h3>You could miss out on the fun and satisfaction of playing the game fairly</h3>
-<p>The last but not least reason why you should avoid a free gems apk is that it could ruin the fun and satisfaction of playing the game fairly. The game is designed to be challenging and rewarding for players who play by the rules and follow the game mechanics. By using a free gems apk, you are bypassing the intended gameplay and skipping the content that makes the game enjoyable and engaging. Some examples of what you could miss out on by using a free gems apk are:</p>
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-<li>The thrill and excitement of summoning new cookies and discovering their skills and personalities.</li>
-<li>The challenge and strategy of building your team and fighting against enemies in various stages.</li>
-<li>The joy and pride of upgrading your cookies' levels, treasures, toppings, and skills to make them stronger.</li>
-<li>The curiosity and wonder of exploring the story and secrets of the ancient cookies and their kingdoms.</li>
-<li>The creativity and customization of building your own kingdom with various decors and facilities.</li>
-</ul>
-<p>These actions are what make Cookie Run: Kingdom a fun and addictive RPG game with cute cookies. By using a free gems apk, you are cheating yourself and others of the true enjoyment and satisfaction of the game. You are also disrespecting the game developers and the legitimate players who support the game.</p>
- <h2>How to Get Gems Legitimately in Cookie Run: Kingdom</h2>
-<p>Now that you know why you should avoid a free gems apk, you might be wondering how to get gems legitimately in Cookie Run: Kingdom. The good news is that there are many ways to earn gems in the game without spending money or cheating. Here are some of the best ways to get gems legitimately in Cookie Run: Kingdom:</p>
- <h3>Complete quests and achievements</h3>
-<p>One of the easiest and most reliable ways to get gems in Cookie Run: Kingdom is to complete quests and achievements. Quests are tasks that you can do in the game, such as clearing stages, upgrading cookies, building facilities, and more. Achievements are milestones that you can reach in the game, such as collecting cookies, reaching certain levels, winning battles, and more. By completing quests and achievements, you can earn gems as rewards. You can check your quests and achievements in the menu and claim your rewards when you complete them.</p>
- <h3>Participate in events and activities</h3>
-<p>Another way to get gems in Cookie Run: Kingdom is to participate in events and activities. Events are special occasions that happen in the game, such as festivals, holidays, anniversaries, collaborations, and more. Activities are daily or weekly tasks that you can do in the game, such as logging in, playing stages, summoning cookies, and more. By participating in events and activities, you can earn gems as rewards or prizes. You can check the current and upcoming events and activities in the menu and join them when they are available.</p>
- <h3>Join a guild and cooperate with other players</h3>
-<p>The last way to get gems in Cookie Run: Kingdom is to join a guild and cooperate with other players. A guild is a group of players who share a common interest or goal in the game. By joining a guild, you can chat with other players, exchange gifts, request help, and more. You can also cooperate with other players in guild battles, raids, missions, and more. By cooperating with other players, you can earn gems as rewards or bonuses. You can check your guild status and activities in the menu and join or create a guild when you reach level 10.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Cookie Run: Kingdom is a fun and addictive RPG game with cute cookies that you can play on your device or PC. However, you should avoid using a free gems apk that claims to give you unlimited gems in the game. Using a free gems apk is not worth it because it could get you banned from the game or lose your account, it could harm your device or compromise your personal data, and it could miss out on the fun and satisfaction of playing the game fairly. Instead, you should get gems legitimately in Cookie Run: Kingdom by completing quests and achievements, participating in events and activities, and joining a guild and cooperating with other players. By doing so, you can enjoy the game more and support the game developers and the legitimate players.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Cookie Run: Kingdom Free Gems APK:</p>
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-<li><b>Q: Is Cookie Run: Kingdom Free Gems APK safe?</b></li>
-<li>A: No, it is not safe. It could contain malware or viruses that could harm your device or compromise your personal data.</li>
-<li><b>Q: Is Cookie Run: Kingdom Free Gems APK legal?</b></li>
-<li>A: No, it is not legal. It violates the terms of service and the intellectual property rights of the game developers.</li>
-<li><b>Q: Is Cookie Run: Kingdom Free Gems APK worth it?</b></li>
-<li>A: No, it is not worth it. It could get you banned from the game or lose your account, and it could miss out on the fun and satisfaction of playing the game fairly.</li>
-<li><b>Q: How to get gems legitimately in Cookie Run: Kingdom?</b></li>
-<li>A: You can get gems legitimately by completing quests and achievements, participating in events and activities, and joining a guild and cooperating with other players.</li>
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-<p>Growtopia is a popular sandbox MMO game that allows players to create, explore, and interact with different worlds and items. However, some players may want to experience more features, customization, and freedom than the official server can offer. That's where Growtopia GTPS comes in.</p>
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-<p>Growtopia GTPS, or Growtopia Private Server, is a modified version of the game that runs on a separate server from the official one. It gives players access to unlimited resources, exclusive items, custom worlds, and more. In this article, we will show you how to download Growtopia GTPS, what are its benefits and disadvantages, and how to play it.</p>
- <h2>What is Growtopia GTPS?</h2>
-<p>Growtopia GTPS is a fan-made project that aims to provide a different experience for Growtopia players. It is not affiliated with or endorsed by Ubisoft, the developer of the original game. Growtopia GTPS is usually created by programmers who modify the game's code and host it on their own servers. Players can then connect to these servers using their own devices and enjoy the game with new features and settings.</p>
- <h3>Growtopia GTPS vs Growtopia Official Server</h3>
-<p>There are some key differences between Growtopia GTPS and Growtopia Official Server that you should know before downloading and playing it. Here are some of them:</p>
- <h4>Benefits of Growtopia GTPS</h4>
-<ul>
-<li>You can get unlimited gems, world locks, growtokens, and other resources without spending real money or grinding for hours.</li>
-<li>You can access exclusive items, skins, blocks, and worlds that are not available on the official server.</li>
-<li>You can create your own world and customize it with any items, settings, and rules you want.</li>
-<li>You can join different events, challenges, and competitions that are organized by the server owners or other players.</li>
-<li>You can interact with other players who share your interests and preferences.</li>
-</ul>
- <h4>Disadvantages of Growtopia GTPS</h4>
-<ul>
-<li>You may encounter bugs, glitches, errors, and crashes that can affect your gameplay or damage your device.</li>
-<li>You may face security risks such as viruses, malware, phishing, or hacking that can compromise your personal information or account.</li>
-<li>You may lose your progress, items, or worlds if the server shuts down, resets, or gets deleted.</li>
-<li>You may get banned from the official server if you use the same account or device for both servers.</li>
-<li>You may miss out on some features, updates, events, and rewards that are only available on the official server.</li>
-</ul>
- <h2>How to Download Growtopia GTPS?</h2>
-<p>If you want to try out Growtopia GTPS, you will need to download it from a reliable source. There are many websites and forums that offer links to download Growtopia GTPS files, but not all of them are safe or trustworthy. You should always do some research before downloading anything from the internet. Here are some steps you can follow to download Growtopia GTPS:</p>
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- <h3>Step 1: Find a Reliable Source</h3>
-<p>The first step is to find a reliable source that offers Growtopia GTPS files. You can use search engines <p>to find a reliable source that offers Growtopia GTPS files. You can use search engines like Google or Bing to look for websites or forums that have links to download Growtopia GTPS files. You can also use GitHub, a platform where programmers share their projects and codes, to find Growtopia GTPS files. Some examples of reliable sources are:</p>
- <ul>
-<li>[GitHub - TimeGTPS/GTPS-Source-By-TimeTopia](^3^): This is a GitHub repository that contains the source code of a Growtopia Private Server made with ENet. You can download the files and compile them with Visual Studio 2015 or newer versions.</li>
-<li>[gtps · GitHub Topics · GitHub](^1^): This is a GitHub topic page that shows various repositories related to Growtopia GTPS. You can browse through them and find the ones that suit your needs and preferences.</li>
-<li>[growtopia · GitHub Topics · GitHub](^2^): This is another GitHub topic page that shows various repositories related to Growtopia in general. You can also find some Growtopia GTPS files here.</li>
-</ul>
- <p>However, you should always be careful when downloading anything from the internet. You should check the reviews, ratings, comments, and feedback of other users who have downloaded the files before. You should also scan the files with antivirus software before opening them. You should also avoid clicking on suspicious links, pop-ups, or ads that may redirect you to malicious websites or download unwanted programs.</p>
- <h3>Step 2: Download the GTPS Files</h3>
-<p>Once you have found a reliable source, you can download the GTPS files to your device. The files may be in different formats, such as ZIP, RAR, EXE, or APK. You will need to extract the files if they are compressed in ZIP or RAR format. You can use software like WinRAR or 7-Zip to extract the files. You will also need to run the files if they are executable in EXE or APK format. You can use software like BlueStacks or Nox Player to run the APK files on your PC.</p>
- <h3>Step 3: Extract the Files and Run the Server</h3>
-<p>After downloading and extracting the files, you will need to run the server file to start the Growtopia GTPS server. The server file may have different names, such as server.exe, enet.exe, gtps.exe, or growtopiaserver.exe. You will need to double-click on the file to run it. You may also need to configure some settings, such as port number, server name, server description, etc., before running the server. You can edit these settings in a text file, such as config.txt, server_data.php, or settings.json.</p>
- <p>Once you run the server file, you will see a console window that shows the status of the server. It will display messages such as "Server started", "Player connected", "Player disconnected", etc. You will also see some commands that you can use to control the server, such as /stop, /save, /ban, /unban, etc. You can type these commands in the console window and press Enter to execute them.</p>
- <h3>Step 4: Connect to the Server and Enjoy</h3>
-<p>Now that you have successfully run the server file, you can connect to the server and enjoy playing Growtopia GTPS. To connect to the server, you will need to edit your hosts file on your device. The hosts file is a text file that maps hostnames to IP addresses. You will need to add a line that contains the IP address of your server and the hostname of Growtopia's official servers.</p>
- <p>The hosts file is located in different locations depending on your device's operating system. For Windows users, it is located in C:\Windows\System32\drivers\etc\. For Android users, it is located in /system/etc/. For iOS users, it is located in /etc/. You will need to use a text editor or a file manager app to edit the hosts file.</p>
- <p>The line that you need to add to your hosts file may vary depending on your server's IP address and Growtopia's official servers' hostnames. However, a common example is:</p>
- <pre><code>52.229.39.72 growtopia1.com 52.229.39.72 growtopia2.com </code></pre>
- <p>You can replace 52.229.39.72 with your server's IP address if it is different. You can also add more lines if your server has more than one IP address or if Growtopia has more than two official servers' hostnames.</p>
- <p>After editing and saving your hosts file, can also trade, play, or collaborate with other players using the trade function, the party function, or the friend function. You can also join different events that are organized by the server owners or other players, such as giveaways, contests, tournaments, etc. You can also create your own events and invite other players to join them.</p>
- <p>To interact with other players and join events on Growtopia GTPS, you can use the social menu or the event menu. The social menu allows you to access the chat function, the private message function, the trade function, the party function, and the friend function. The event menu allows you to access the event list function, the event create function, and the event join function. You can also use commands to communicate with other players or join events.</p>
- <h2>Conclusion</h2>
-<p>Growtopia GTPS is a modified version of Growtopia that runs on a separate server from the official one. It offers more features, customization, and freedom than the official server. However, it also has some drawbacks and risks that you should be aware of before downloading and playing it. In this article, we have shown you how to download Growtopia GTPS, what are its benefits and disadvantages, and how to play it. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Growtopia GTPS:</p>
- <ol>
-<li>Is Growtopia GTPS legal?</li>
-<p>Growtopia GTPS is not legal according to Ubisoft's terms of service and end-user license agreement. Ubisoft owns the intellectual property rights of Growtopia and does not allow any modification, distribution, or reproduction of the game without their permission. Therefore, downloading and playing Growtopia GTPS may violate Ubisoft's rights and result in legal actions.</p>
-<li>Is Growtopia GTPS safe?</li>
-<p>Growtopia GTPS is not safe according to Ubisoft's privacy policy and security measures. Ubisoft does not guarantee the security or quality of any third-party servers or files that are not authorized by them. Therefore, downloading and playing Growtopia GTPS may expose your device or account to various threats such as viruses, malware, phishing, hacking, etc.</p>
-<li>Can I play Growtopia GTPS on any device?</li>
-<p>Growtopia GTPS can be played on any device that supports Growtopia's official game app. However, you will need to download different files depending on your device's operating system. For example, you will need to download APK files for Android devices and EXE files for Windows devices. You will also need to edit your hosts file on your device to connect to the server.</p>
-<li>Can I play Growtopia GTPS with my friends?</li>
-<p>Growtopia GTPS can be played with your friends if they also download and play Growtopia GTPS on their devices. However, you will not be able to play with your friends who play on the official server or on different servers. You will also need to use the same server IP address and hostname as your friends to connect to the same server.</p>
-<li>Can I switch between Growtopia GTPS and Growtopia Official Server?</li>
-<p>Growtopia GTPS can be switched with Growtopia Official Server if you edit your hosts file on your device accordingly. You will need to add a line that contains the IP address of your server and the hostname of Growtopia's official servers to play Growtopia GTPS. You will need to remove or comment out that line to play Growtopia Official Server. However, you should not use the same account or device for both servers as it may cause conflicts or bans.</p>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Dumb Ways to Die APK Download The Ultimate Guide to Surviving the Craziest Challenges.md b/spaces/fatiXbelha/sd/Dumb Ways to Die APK Download The Ultimate Guide to Surviving the Craziest Challenges.md
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-<li><strong>Is Dumb Ways to Die free?</strong></li>
-<p>Yes, Dumb Ways to Die is free to download and play on both iOS and Android devices. However, the game may contain some optional in-app purchases that can enhance your gaming experience or remove ads.</p>
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-<li><strong>How can I contact the developers of Dumb Ways to Die?</strong></li>
-<p>If you have any questions, feedback, suggestions, or issues regarding Dumb Ways to Die, you can contact the developers by emailing them at <a href="mailto:support@playsidestudios.com">support@playsidestudios.com</a>. You can also visit their <a href="">website</a>, where you can find more information about their other games and projects.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Enjoy the Thrill of Blackjack for Android with No Risk.md b/spaces/fatiXbelha/sd/Enjoy the Thrill of Blackjack for Android with No Risk.md
deleted file mode 100644
index 0f802e4748bc8fe19878f591359cbe5ea3d100db..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Enjoy the Thrill of Blackjack for Android with No Risk.md	
+++ /dev/null
@@ -1,180 +0,0 @@
-
-<h1>Download Blackjack for Android: How to Play and Win at the Classic Casino Game</h1>
- <p>Blackjack is one of the most popular and exciting casino games in the world. It is a game of skill, strategy, and luck, where you have to beat the dealer's hand without going over 21. Whether you are a beginner or a pro, you can enjoy playing blackjack anytime and anywhere with your Android device. In this article, we will show you how to download blackjack for android, how to play and win at the classic casino game, and answer some frequently asked questions.</p>
- <h2>What is Blackjack and Why You Should Play It</h2>
- <p>Blackjack, also known as 21, is a card game where you compete against the dealer. The goal is to get a hand value as close to 21 as possible, without going over. You can do this by hitting (asking for another card), standing (keeping your current hand), doubling down (doubling your bet and getting one more card), splitting (dividing your pair into two separate hands), or surrendering (giving up half your bet and ending the round).</p>
-<h2>download blackjack for android</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733; <a href="https://urllie.com/2uNBpX">https://urllie.com/2uNBpX</a></b></p><br /><br />
- <h3>The Rules and Objectives of Blackjack</h3>
- <p>The rules of blackjack may vary depending on the number of decks, the table rules, and the variations of the game. However, the basic rules are as follows:</p>
- <ul>
-<li>Each card has a value that remains constant throughout the game. Number cards are worth their face value, face cards are worth 10, and aces are worth either 1 or 11.</li>
-<li>You and the dealer are dealt two cards each. One of the dealer's cards is face up, while the other is face down.</li>
-<li>You can see your cards and the dealer's face-up card, and decide what action to take based on your hand value and the dealer's upcard.</li>
-<li>If you get 21 points exactly on the deal, that is called a blackjack, and you win automatically, unless the dealer also has a blackjack, in which case it is a tie.</li>
-<li>If you don't have a blackjack, you can hit or stand as many times as you want, until you either bust (go over 21) or decide to stand.</li>
-<li>Once you stand, the dealer reveals their face-down card and plays their hand according to predetermined rules. Usually, the dealer must hit until they reach 17 or more, and stand on soft 17 (a hand with an ace that can be either 7 or 17).</li>
-<li>If the dealer busts, you win. If the dealer doesn't bust, whoever has a higher hand value wins. If both have the same hand value, it is a tie.</li>
-<li>Blackjack pays 3 to 2, meaning that if you bet $10 and get a blackjack, you win $15. Other winning hands pay even money, meaning that if you bet $10 and win, you get $10.</li>
-</ul>
- <h3>The Benefits and Challenges of Playing Blackjack</h3>
- <p>Playing blackjack can be very fun and rewarding, as it offers many benefits such as:</p>
- <ul>
-<li>It is easy to learn and play. You don't need to memorize complicated rules or strategies. You just need to know the basic math and logic behind the game.</li>
-<li>It is a game of skill as well as luck. You can improve your chances of winning by using optimal strategy and card counting techniques.</li>
-<li>It has a low house edge compared to other casino games. If you play with perfect strategy, you can reduce the house edge to less than 1%, meaning that you can expect to lose less money in the long run.</li>
-<li>It is entertaining and thrilling. You can experience the excitement of beating the dealer, hitting a blackjack, or doubling down on a good hand.</li> <p>However, playing blackjack also has some challenges, such as:</p>
- <ul>
-<li>It can be addictive and risky. You can lose a lot of money if you don't set a budget and stick to it. You can also develop a gambling problem if you play too often or too long.</li>
-<li>It can be stressful and frustrating. You can face bad luck, bad decisions, or bad dealers. You can also encounter hostile or annoying players, or unfair rules and conditions.</li>
-<li>It can be illegal or restricted in some places. You may not be able to play blackjack in some casinos, countries, or states. You may also face legal consequences if you use card counting or other prohibited methods.</li>
-</ul>
- <p>Therefore, you should always play blackjack responsibly and for fun, not for profit or addiction.</p>
- <h2>How to Download Blackjack for Android</h2>
- <p>If you want to play blackjack on your Android device, you have two options: you can either play online or offline. Online blackjack means that you play on a website or an app that connects you to a live dealer or a random number generator. Offline blackjack means that you play on an app that simulates the game without requiring an internet connection.</p>
- <h3>The Best Blackjack Apps and Games for Android</h3>
- <p>There are many blackjack apps and games available for Android, but not all of them are created equal. Some of them may have poor graphics, slow performance, annoying ads, or unfair rules. To help you find the best ones, we have compiled a list of the top 5 blackjack apps and games for Android, based on user reviews, ratings, features, and quality.</p>
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- <table>
-<tr>
-<th>Name</th>
-<th>Description</th>
-<th>Rating</th>
-</tr>
-<tr>
-<td><a href="">Blackjack 21: Blackjackist</a></td>
-<td>A free-to-play online blackjack game that offers realistic graphics, social features, tournaments, and mini-games.</td>
-<td>4.5/5</td>
-</tr>
-<tr>
-<td><a href="">Blackjack by Brainium Studios</a></td>
-<td>A free offline blackjack game that offers simple graphics, smooth gameplay, no ads, and basic strategy hints.</td>
-<td>4.6/5</td>
-</tr>
-<tr>
-<td><a href="">Blackjack 21 by Banana & Co.</a></td>
-<td>A free online and offline blackjack game that offers colorful graphics, customizable tables, daily bonuses, and challenges.</td>
-<td>4.7/5</td>
-</tr>
-<tr>
-<td><a href="">Blackjack Legends: 21 Online Multiplayer Casino</a></td>
-<td>A free-to-play online blackjack game that offers competitive gameplay, leaderboards, tournaments, and chat features.</td>
-<td>4.8/5</td>
-</tr>
-<tr>
-<td><a href="">Blackjack Trainer Pro by HornetApps</a></td>
-<td>A paid offline blackjack game that offers professional graphics, advanced strategy training, statistics, and card counting.</td>
-<td>4.9/5</td>
-</tr>
-</table>
- <h3>How to Install and Use Blackjack Apps on Your Android Device</h3>
- <p>To install and use blackjack apps on your Android device, you need to follow these simple steps:</p>
- <ol>
-<li>Go to the Google Play Store and search for the blackjack app or game that you want to download.</li>
-<li>Tap on the app or game icon and read the description, reviews, and permissions.</li>
-<li>If you are satisfied with the app or game, tap on the Install button and wait for the download to finish.</li>
-<li>Once the app or game is installed, tap on the Open button or find it on your home screen or app drawer.</li>
-<li>Launch the app or game and follow the instructions to create an account, choose a table, place your bets, and start playing.</li>
-</ol>
- <p>Note: Some apps or games may require an internet connection to play online or access certain features. Some apps or games may also offer in-app purchases or ads that may affect your experience. Be careful when spending real money or clicking on ads within the apps or games.</p>
- <h2>How to Play and Win at Blackjack on Android</h2>
- <p>Playing and winning at blackjack on Android is similar to playing and winning at blackjack in a real casino. You need to know the rules of the game, use optimal strategy, manage your bankroll, and avoid common mistakes. However, there are also some differences and challenges that you need to consider when playing on your Android device. Here are some tips and tricks to help you play and win at blackjack on Android:</p>
- <h3>Basic Blackjack Strategy and Tips </h3>
- <p>Basic blackjack strategy is a set of rules that tells you the best action to take for any possible combination of your hand and the dealer's upcard. By following basic strategy, you can reduce the house edge to the minimum and increase your chances of winning. You can find basic strategy charts online or in blackjack books, or you can use a blackjack app that provides you with hints or feedback. Here are some basic blackjack tips to remember:</p>
- <ul>
-<li>Always split aces and eights, and never split tens or fives.</li>
-<li>Always double down on 11, and on 10 or 9 if the dealer has a low card.</li>
-<li>Always stand on hard 17 or higher, and on soft 19 or higher.</li>
-<li>Always hit on hard 11 or lower, and on soft 17 or lower.</li>
-<li>Never take insurance or even money, as they are bad bets that favor the house.</li>
-</ul>
- <h3>Advanced Blackjack Techniques and Tricks</h3>
- <p>If you want to take your blackjack skills to the next level, you can learn some advanced techniques and tricks that can give you an edge over the house. However, these techniques and tricks are not easy to master and may not be allowed or effective in some casinos or apps. You should also be aware of the risks and consequences of using them. Here are some advanced blackjack techniques and tricks to consider:</p>
- <ul>
-<li>Card counting: This is a method of keeping track of the ratio of high cards to low cards in the deck, and adjusting your bets accordingly. The more high cards in the deck, the more favorable it is for the player, and vice versa. Card counting can give you a slight advantage over the house, but it requires a lot of practice, concentration, and memory. It is also illegal in some casinos and may get you banned or arrested.</li>
-<li>Shuffle tracking: This is a method of following certain groups of cards through the shuffling process, and predicting where they will end up in the next deal. Shuffle tracking can help you identify favorable situations and increase your bets accordingly. However, shuffle tracking is very difficult and complex, and it depends on the shuffling method and frequency used by the dealer. It is also frowned upon by casinos and may get you in trouble.</li>
-<li>Hole carding: This is a method of spotting the dealer's face-down card by taking advantage of sloppy dealing or peeking techniques. Hole carding can give you valuable information about the dealer's hand and help you make better decisions. However, hole carding is very rare and hard to pull off, and it is considered cheating by casinos and may get you prosecuted.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Blackjack is a fun and exciting game that you can play on your Android device anytime and anywhere. You can download blackjack for android from various apps and games that offer realistic graphics, smooth gameplay, and different features. You can also play and win at blackjack by following the rules of the game, using optimal strategy, managing your bankroll, and avoiding common mistakes. You can also try some advanced techniques and tricks if you want to challenge yourself and improve your skills. However, you should always play blackjack responsibly and for entertainment purposes only.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about downloading blackjack for android:</p>
- <ol>
-<li><b>Is it safe to download blackjack for android?</b></li>
-<p>It depends on the source and quality of the app or game that you download. You should always download blackjack for android from reputable and trusted sources, such as the Google Play Store or official websites. You should also check the reviews, ratings, permissions, and security features of the app or game before downloading it. You should also avoid downloading any app or game that asks for personal or financial information, or that contains malware or viruses.</p>
-<li><b>Can I play blackjack for real money on android?</b></li>
-<p>Yes, you can play blackjack for real money on android if you live in a country or state where online gambling is legal and regulated. You can find many online casinos that offer blackjack games for real money on android devices. However, you should always do your research and choose a reputable and licensed online casino that offers fair games, secure transactions, and good customer service. You should also be aware of the risks and responsibilities of playing blackjack for real money online.</p>
-<li><b>Can I play blackjack for free on android?</b></li>
-<p>Yes, you can play blackjack for free on android if you just want to have fun or practice your skills. You can find many free-to-play blackjack apps and games that offer unlimited chips, bonuses, rewards, and features. However, you should keep in mind that playing blackjack for free on android does not guarantee that you will win or lose the same as playing blackjack for real money in a real casino. You should also be careful of any app or game that offers you to buy or sell chips or credits for real money, as they may be scams or illegal.</p>
-<li><b>How can I improve my blackjack skills on android?</b></li>
-<p>There are many ways to improve your blackjack skills on android, such as:</p>
- <ul>
-<li>Reading books, articles, blogs, or forums about blackjack theory, strategy, and tips.</li>
-<li>Watching videos, podcasts, or streams of blackjack experts, players, or dealers.</li>
-<li>Using a blackjack trainer app or game that teaches you the rules, the basic strategy, and the advanced techniques of the game.</li>
-<li>Practicing your skills on free or low-stakes blackjack apps or games that simulate different scenarios and situations.</li>
-<li>Testing your skills on real or high-stakes blackjack apps or games that challenge you and reward you for your performance.</li>
-</ul>
- <li><b>What are some common blackjack terms and slang on android?</b></li>
-<p>Here are some common blackjack terms and slang that you may encounter on android:</p>
- <ul>
-<li>Bust: When your hand value goes over 21 and you lose automatically.</li>
-<li>Hit: When you ask for another card from the dealer.</li>
-<li>Stand: When you keep your current hand and end your turn.</li>
-<li>Double down: When you double your bet and get one more card.</li>
-<li>Split: When you divide your pair into two separate hands and play them independently.</li>
-<li>Surrender: When you give up half your bet and end the round.</li>
-<li>Insurance: When you make a side bet that the dealer has a blackjack when their upcard is an ace.</li>
-<li>Even money: When you take a 1 to 1 payout instead of a 3 to 2 payout when you have a blackjack and the dealer has an ace.</li>
-<li>Soft hand: When your hand contains an ace that can be either 1 or 11.</li>
-<li>Hard hand: When your hand does not contain an ace or contains an ace that can only be 1.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/longcode/prod_cons.h b/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/longcode/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/longcode/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/modules/make_animation.py b/spaces/fb700/chatglm-fitness-RLHF/src/facerender/modules/make_animation.py
deleted file mode 100644
index 3360c53501a064f35d7db21a5361f89aa9658b42..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/modules/make_animation.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from scipy.spatial import ConvexHull
-import torch
-import torch.nn.functional as F
-import numpy as np
-from tqdm import tqdm 
-
-def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
-                 use_relative_movement=False, use_relative_jacobian=False):
-    if adapt_movement_scale:
-        source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
-        driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
-        adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
-    else:
-        adapt_movement_scale = 1
-
-    kp_new = {k: v for k, v in kp_driving.items()}
-
-    if use_relative_movement:
-        kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
-        kp_value_diff *= adapt_movement_scale
-        kp_new['value'] = kp_value_diff + kp_source['value']
-
-        if use_relative_jacobian:
-            jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
-            kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
-
-    return kp_new
-
-def headpose_pred_to_degree(pred):
-    device = pred.device
-    idx_tensor = [idx for idx in range(66)]
-    idx_tensor = torch.FloatTensor(idx_tensor).type_as(pred).to(device)
-    pred = F.softmax(pred)
-    degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
-    return degree
-
-def get_rotation_matrix(yaw, pitch, roll):
-    yaw = yaw / 180 * 3.14
-    pitch = pitch / 180 * 3.14
-    roll = roll / 180 * 3.14
-
-    roll = roll.unsqueeze(1)
-    pitch = pitch.unsqueeze(1)
-    yaw = yaw.unsqueeze(1)
-
-    pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch), 
-                          torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch),
-                          torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1)
-    pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
-
-    yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw), 
-                           torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw),
-                           -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1)
-    yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
-
-    roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll),  
-                         torch.sin(roll), torch.cos(roll), torch.zeros_like(roll),
-                         torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1)
-    roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
-
-    rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat)
-
-    return rot_mat
-
-def keypoint_transformation(kp_canonical, he, wo_exp=False):
-    kp = kp_canonical['value']    # (bs, k, 3) 
-    yaw, pitch, roll= he['yaw'], he['pitch'], he['roll']      
-    yaw = headpose_pred_to_degree(yaw) 
-    pitch = headpose_pred_to_degree(pitch)
-    roll = headpose_pred_to_degree(roll)
-
-    if 'yaw_in' in he:
-        yaw = he['yaw_in']
-    if 'pitch_in' in he:
-        pitch = he['pitch_in']
-    if 'roll_in' in he:
-        roll = he['roll_in']
-
-    rot_mat = get_rotation_matrix(yaw, pitch, roll)    # (bs, 3, 3)
-
-    t, exp = he['t'], he['exp']
-    if wo_exp:
-        exp =  exp*0  
-    
-    # keypoint rotation
-    kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
-
-    # keypoint translation
-    t[:, 0] = t[:, 0]*0
-    t[:, 2] = t[:, 2]*0
-    t = t.unsqueeze(1).repeat(1, kp.shape[1], 1)
-    kp_t = kp_rotated + t
-
-    # add expression deviation 
-    exp = exp.view(exp.shape[0], -1, 3)
-    kp_transformed = kp_t + exp
-
-    return {'value': kp_transformed}
-
-
-
-def make_animation(source_image, source_semantics, target_semantics,
-                            generator, kp_detector, he_estimator, mapping, 
-                            yaw_c_seq=None, pitch_c_seq=None, roll_c_seq=None,
-                            use_exp=True, use_half=False):
-    with torch.no_grad():
-        predictions = []
-
-        kp_canonical = kp_detector(source_image)
-        he_source = mapping(source_semantics)
-        kp_source = keypoint_transformation(kp_canonical, he_source)
-    
-        for frame_idx in tqdm(range(target_semantics.shape[1]), 'Face Renderer:'):
-            # still check the dimension
-            # print(target_semantics.shape, source_semantics.shape)
-            target_semantics_frame = target_semantics[:, frame_idx]
-            he_driving = mapping(target_semantics_frame)
-            if yaw_c_seq is not None:
-                he_driving['yaw_in'] = yaw_c_seq[:, frame_idx]
-            if pitch_c_seq is not None:
-                he_driving['pitch_in'] = pitch_c_seq[:, frame_idx] 
-            if roll_c_seq is not None:
-                he_driving['roll_in'] = roll_c_seq[:, frame_idx] 
-            
-            kp_driving = keypoint_transformation(kp_canonical, he_driving)
-                
-            kp_norm = kp_driving
-            out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)
-            '''
-            source_image_new = out['prediction'].squeeze(1)
-            kp_canonical_new =  kp_detector(source_image_new)
-            he_source_new = he_estimator(source_image_new) 
-            kp_source_new = keypoint_transformation(kp_canonical_new, he_source_new, wo_exp=True)
-            kp_driving_new = keypoint_transformation(kp_canonical_new, he_driving, wo_exp=True)
-            out = generator(source_image_new, kp_source=kp_source_new, kp_driving=kp_driving_new)
-            '''
-            predictions.append(out['prediction'])
-        predictions_ts = torch.stack(predictions, dim=1)
-    return predictions_ts
-
-class AnimateModel(torch.nn.Module):
-    """
-    Merge all generator related updates into single model for better multi-gpu usage
-    """
-
-    def __init__(self, generator, kp_extractor, mapping):
-        super(AnimateModel, self).__init__()
-        self.kp_extractor = kp_extractor
-        self.generator = generator
-        self.mapping = mapping
-
-        self.kp_extractor.eval()
-        self.generator.eval()
-        self.mapping.eval()
-
-    def forward(self, x):
-        
-        source_image = x['source_image']
-        source_semantics = x['source_semantics']
-        target_semantics = x['target_semantics']
-        yaw_c_seq = x['yaw_c_seq']
-        pitch_c_seq = x['pitch_c_seq']
-        roll_c_seq = x['roll_c_seq']
-
-        predictions_video = make_animation(source_image, source_semantics, target_semantics,
-                                        self.generator, self.kp_extractor,
-                                        self.mapping, use_exp = True,
-                                        yaw_c_seq=yaw_c_seq, pitch_c_seq=pitch_c_seq, roll_c_seq=roll_c_seq)
-        
-        return predictions_video
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/data/data_utils/sentence_split.py b/spaces/fclong/summary/fengshen/data/data_utils/sentence_split.py
deleted file mode 100644
index 1a25e4b51b13f86f4a8a6b39f497f85c050856b6..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/data/data_utils/sentence_split.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import re
-
-
-class ChineseSentenceSplitter(object):
-    def merge_symmetry(self, sentences, symmetry=('“', '”')):
-        # '''合并对称符号，如双引号'''
-        effective_ = []
-        merged = True
-        for index in range(len(sentences)):
-            if symmetry[0] in sentences[index] and symmetry[1] not in sentences[index]:
-                merged = False
-                effective_.append(sentences[index])
-            elif symmetry[1] in sentences[index] and not merged:
-                merged = True
-                effective_[-1] += sentences[index]
-            elif symmetry[0] not in sentences[index] and symmetry[1] not in sentences[index] and not merged:
-                effective_[-1] += sentences[index]
-            else:
-                effective_.append(sentences[index])
-        return [i.strip() for i in effective_ if len(i.strip()) > 0]
-
-    def to_sentences(self, paragraph):
-        #  """由段落切分成句子"""
-        sentences = re.split(r"(？|。|[！]+|!|\…\…)", paragraph)
-        sentences.append("")
-        sentences = ["".join(i) for i in zip(sentences[0::2], sentences[1::2])]
-        sentences = [i.strip() for i in sentences if len(i.strip()) > 0]
-        for j in range(1, len(sentences)):
-            if sentences[j][0] == '”':
-                sentences[j-1] = sentences[j-1] + '”'
-                sentences[j] = sentences[j][1:]
-        return self.merge_symmetry(sentences)
-
-    def tokenize(self, text):
-        return self.to_sentences(text)
diff --git a/spaces/felixz/LLM-as-continuous-chat/app.py b/spaces/felixz/LLM-as-continuous-chat/app.py
deleted file mode 100644
index 9e43eb4da372cd9539332b7d8a3e6d4be35ff545..0000000000000000000000000000000000000000
--- a/spaces/felixz/LLM-as-continuous-chat/app.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import gradio as gr
-
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-
-# xl size run out of memory on 16GB vm
-# All the models have input length of 512 tokens and outputs of 512 tokens
-# small 80M param
-# base 250M
-# large 780M
-# xl
-# xxl
-model_name = "large"
-tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-" + model_name)
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-" + model_name)
-
-title = ""
-
-def get_examples ():
-    return [
-        ["Peter goes to the store to buy a soda. The soda costs $.25 an ounce. \
-He brought $2 with him and leaves with $.50. How many ounces of soda did he buy?",
-         "How much did Peter spend on soda? ** He spend $1.5 on soda because 2 - .5 = <<2-.5=1.5>>1.5 \
-     How many ounces of soda did Peter buy? ** He bought 6 ounces of soda because 1.5 / .25 = <<6=6>>6 #### 6"
-         ],
-        ["Krystian works in the library. He borrows an average of 40 books every day. \
-Every Friday, his number of borrowed books is about 40% higher than the daily average. How many books does he borrow in a week if the library is open from Monday to Friday?"
-            ,"How many books does Krystian borrow on Friday? ** The number of books borrowed \
-on Friday is higher by 40 * 40/100 = <<40*40/100=16>>16 books. How many books does Krystian borrow in a week? ** There are 5 days from Monday to Friday inclusive, so Krystian borrows an average of 5 * 40 = <<5*40=200>>200 books during that time. How many books does Krystian borrow in a week? ** With Friday's increase in borrowings, during one week Krystian borrows 200 + 16 = <<200+16=216>>216 books."]
-        , ["Jane had $60 but gave $30 to dave and went to movies and spend $2. How much money does Jane has left? Answer by reasoning step by step:", "$28"],
-        ["Cat is a friend of a Dog. Are cat and Dog friends?", "Yes"]
-    ]
-
-
-def text2text(input_text):
-    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
-
-    input_num_tokens = input_ids.shape[1]
-
-    print( "Number of input tokens: " + str(input_num_tokens))
-    print("Length of input: " + str(len(input_text)))
-
-    list_of_tokens = tokenizer.convert_ids_to_tokens(input_ids.view(-1).tolist())
-
-    print( "Tokens : " + ' '.join(list_of_tokens))
-
-    # Does not seem to care if it goes over 512... humm...
-    # To make it faster generate 100 tokens at a time
-    # sampling mode.. don't greedily take the highest probability token every time. Helps it chat with some variation
-    # temperature.. how random should the sampling be.
-    # top_p Which set of tokens to sample from. Filters out some low probability tokens before smapling.
-    # 
-    # input_ids should not be over 512 tokens. This method does not break over 512 tokens.. what is it doing?
-    outputs = model.generate(input_ids, max_new_tokens=100, do_sample=True, temperature=0.7, top_p=0.8)
-
-    # Remove <pad> and </s> eof sequence tokens
-    model_output_text = tokenizer.decode(outputs[0],skip_special_tokens=True)
-
-    print("Number of output tokens: " + str(outputs.shape[1]))
-    print("length of output: " + str(len(model_output_text)))
-    print("Output: " + model_output_text)
-    # Space is added because model seem to not add it automatically.
-    output_text = input_text + " " + model_output_text
-    return output_text
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # Flan T5 Large Demo (Chat Mode)
-        780M parameter Large language model fine tuned on diverse tasks.
-        Prompt the model in the Input box. Models output is appended to input. To get additional generation hit submit again.
-        """)
-    txt_in = gr.Textbox(label="Input", lines=8)
-    correct_label = gr.Label(label="Correct")
-    # txt_out = gr.Textbox(value="", label="Output", lines=4)
-
-
-    btn = gr.Button(value="Submit")
-    # Send back to inputs
-    btn.click(text2text, inputs=[txt_in], outputs=[txt_in])
-
-
-    gr.Examples(
-        examples=get_examples(),
-        inputs=[txt_in,correct_label]
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clash of Kings APK How to Join Alliances and Win Battles.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clash of Kings APK How to Join Alliances and Win Battles.md
deleted file mode 100644
index 95717a9b5d20f6641f272f1b6c59a75c9104e644..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clash of Kings APK How to Join Alliances and Win Battles.md	
+++ /dev/null
@@ -1,103 +0,0 @@
-
-<h1>Clash of Kings APK Update: What's New and How to Download</h1>
-<p>Are you a fan of strategy games that let you build your own empire and conquer the world? If yes, then you might have heard of Clash of Kings, one of the most popular mobile games in this genre. But did you know that there is a new APK update for this game that brings a lot of exciting features and improvements? In this article, we will tell you everything you need to know about the Clash of Kings APK update, including what's new, how to download it, and some tips and tricks to enjoy the game.</p>
-<h2>clash of kings apk update</h2><br /><p><b><b>Download File</b> &middot;&middot;&middot;&middot;&middot; <a href="https://gohhs.com/2uPs4C">https://gohhs.com/2uPs4C</a></b></p><br /><br />
-<h2>Introduction</h2>
-<h3>What is Clash of Kings?</h3>
-<p>Clash of Kings is a free mobile game launched by the Elex Wireless team from China. It is an RTS (real-time strategy) game that lets you create your own kingdom, train your army, research advanced technology, forge alliances with other players, and fight against enemies from all over the world. The game has over 50 million downloads on Google Play and has received positive reviews from players and critics alike.</p>
-<h3>Why download the latest APK update?</h3>
-<p>An APK (Android Package Kit) is a file format that contains all the elements needed to install an app on an Android device. Sometimes, developers release APK updates for their apps that are not available on the official app stores. These updates may contain new features, bug fixes, performance enhancements, or security patches that can improve your gaming experience. The latest APK update for Clash of Kings was released on June 7, 2023, and it has a lot of new things to offer.</p>
-<h2>Features of Clash of Kings APK Update</h2>
-<h3>New civilizations and commanders</h3>
-<p>One of the most exciting features of the Clash of Kings APK update is the addition of five new civilizations inspired by historical empires. These are Viking, Yamato, Crescent, Huaxia, and Dragon-Born. Each civilization has its own unique design, culture, and characteristics. You can choose your favorite one and explore its history and legends.</p>
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-<p>Along with the new civilizations, the update also introduces more than 50 new commanders with different traits and abilities. These commanders can lead your troops into battle and give you an edge over your enemies. You can also collect and upgrade them to make them more powerful.</p>
-<h3>Improved graphics and gameplay</h3>
-<p>The Clash of Kings APK update also brings some improvements to the graphics and gameplay of the game. The game now has more realistic and detailed graphics that will make you feel like you are in a medieval world. The game also has smoother and faster gameplay that will let you enjoy real-time battles with easy controls.</p>
-<h3>Enhanced cross-server battles and alliances</h3>
-<p>The game also offers more opportunities for cross-server battles and alliances with the update. You can now move to different kingdoms to form new alliances and participate in multi-level cross-server battle royals. You can also join various events and challenges with your allies and compete with other players from different servers.</p>
-<h2>How to Download Clash of Kings APK Update</h2>
-<h3>Requirements and compatibility</h3>
-<p>To download the Clash of Kings APK update, you need to have an Android device that meets the following requirements:</p>
-<ul>
-<li>Android version: 4.0.3 or higher</li>
-<li>RAM: 1 GB or more</li>
-<li>Storage space: 964 MB or more</li>
-<li>Internet connection: Wi-Fi or mobile data</li>
-</ul> <h3>Steps to download and install</h3>
-<p>To download and install the Clash of Kings APK update, you need to follow these steps:</p>
-<ol>
-<li>Go to the download link of the APK file. You can use one of the links provided by the web search results, such as  or . Make sure you choose a trusted and secure source.</li>
-<li>Tap on the download button and wait for the file to be downloaded on your device. You may need to enable the option to download files from unknown sources in your device settings.</li>
-<li>Once the file is downloaded, locate it in your file manager and tap on it to start the installation process. You may need to grant some permissions to the app.</li>
-<li>Follow the instructions on the screen and wait for the installation to be completed. You may need to uninstall the previous version of the game if you have it.</li>
-<li>Launch the game and enjoy the new features and improvements.</li>
-</ol>
-<h3>Tips and tricks to enjoy the game</h3>
-<p>Now that you have downloaded and installed the Clash of Kings APK update, you may want to know some tips and tricks to make the most out of the game. Here are some of them:  </p>
-<ul>
-<li>Join an active alliance and cooperate with your allies. You can get help from them in building, researching, attacking, and defending. You can also share resources, information, and rewards with them.</li>
-<li>Focus on upgrading your castle and your resource buildings. Your castle is the heart of your kingdom and determines your level and power. Your resource buildings provide you with food, wood, iron, and gold that you need for everything else.</li>
-<li>Balance your army composition and strategy. You have different types of units that have different strengths and weaknesses. You should have a mix of infantry, cavalry, archers, siege, and special units. You should also use different formations and tactics depending on the situation.</li>
-<li>Participate in events and quests. The game offers various events and quests that can give you rewards, such as gold, items, speed-ups, VIP points, etc. You should try to complete as many as you can to boost your progress and gain an advantage.</li>
-<li>Have fun and be respectful. The game is meant to be a fun and social experience where you can interact with other players from around the world. You should enjoy the game and be respectful to others. Don't cheat, spam, or harass other players.</li>
-</ul>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, Clash of Kings is a great game for strategy lovers who want to build their own empire and conquer the world. The game has a new APK update that brings a lot of new features and improvements, such as new civilizations, commanders, graphics, gameplay, cross-server battles, and alliances. To download the update, you need to have an Android device that meets the requirements and follow the steps provided in this article. You can also use some tips and tricks to enjoy the game more.</p>
-<h3>Call to action and feedback</h3>
-<p>If you are interested in playing Clash of Kings or updating your game to the latest version, you can use one of the links below to download the APK file. We hope you found this article helpful and informative. If you have any questions or feedback, please leave a comment below. We would love to hear from you.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Clash of Kings APK update:</p>
-<ol>
-<li>Is Clash of Kings APK update safe to download?</li>
-<p>Yes, as long as you download it from a trusted and secure source. However, you should always be careful when downloading files from unknown sources and scan them for viruses or malware before installing them.</p>
-<li>What are the benefits of downloading Clash of Kings APK update?</li>
-<p>The benefits of downloading Clash of Kings APK update are that you can enjoy new features and improvements that are not available on the official app stores. You can also get access to exclusive events and rewards that are only available for APK users.</p>
-<li>How do I update Clash of Kings APK?</li>
-<p>To update Clash of Kings APK, you need to download the latest version of the APK file from a reliable source and install it on your device. You may need to uninstall the previous version of the game if you have it.</p>
-<li>Can I play Clash of Kings APK update with my friends who have the official version?</li>
-<p>Yes, you can play Clash of Kings APK update with your friends who have the official version. The game is compatible with both versions and allows cross-platform play <li>What are the drawbacks of downloading Clash of Kings APK update?</li>
-<p>The drawbacks of downloading Clash of Kings APK update are that you may encounter some bugs or glitches that are not fixed yet. You may also face some compatibility issues with your device or other apps. You may also risk violating the terms and conditions of the game or the app store.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Como baixar e instalar Stickman Falling APK com MOD de dinheiro infinito.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Como baixar e instalar Stickman Falling APK com MOD de dinheiro infinito.md
deleted file mode 100644
index c4739f95186d74346b84b9faf9c350545a564742..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Como baixar e instalar Stickman Falling APK com MOD de dinheiro infinito.md	
+++ /dev/null
@@ -1,121 +0,0 @@
-<br />
-<h1>Stickman Falling Dinheiro Infinito APK: How to Download and Play</h1>
- <p>Do you love stickman games? Do you enjoy watching the stickman fall and crash in different ways? If yes, then you should try Stickman Falling, a fun and addictive game where you need to help the stickman survive the obstacles and reach the finish line. But what if you want to have more money and resources in the game? What if you want to remove all the ads and enjoy the game without interruptions? Well, there is a solution for that: Stickman Falling Dinheiro Infinito APK. In this article, we will tell you everything you need to know about this modded version of Stickman Falling, how to download and install it, and how to play it like a pro. Let's get started!</p>
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- <h2>What is Stickman Falling?</h2>
- <p>Stickman Falling is a casual game developed by Skygo. It is available for Android devices on Google Play Store. The game has more than 50 million downloads and a 4.1 rating out of 5. The game is simple but challenging: you need to control the stickman as he falls from different heights and angles, avoiding the obstacles and landing safely. You can choose from various vehicles, such as cars, bikes, trucks, planes, helicopters, and more. You can also customize your stickman with different outfits, hats, glasses, and accessories. The game has realistic physics and graphics, as well as funny sound effects. The game is suitable for all ages and can be played offline.</p>
- <h3>Features of Stickman Falling</h3>
- <p>Some of the features of Stickman Falling are:</p>
-<ul>
-<li>More than 100 levels with different scenarios and difficulties.</li>
-<li>More than 20 vehicles with different characteristics and abilities.</li>
-<li>More than 30 items to customize your stickman.</li>
-<li>Daily rewards and achievements.</li>
-<li>Leaderboards and rankings.</li>
-<li>Easy controls and user-friendly interface.</li>
-</ul>
- <h3>How to play Stickman Falling</h3>
- <p>The gameplay of Stickman Falling is simple but fun. Here are the basic steps:</p>
-<ol>
-<li>Select a level from the map.</li>
-<li>Select a vehicle from the garage.</li>
-<li>Tap the screen to start the fall.</li>
-<li>Tilt your device to steer the vehicle.</li>
-<li>Tap the screen again to use boosters or brakes.</li>
-<li>Avoid the obstacles and land safely.</li>
-<li>Earn coins and diamonds for completing the level.</li>
-</ol>
- <h2>What is Stickman Falling Dinheiro Infinito APK?</h2>
- <p>Stickman Falling Dinheiro Infinito APK is a modified version of Stickman Falling that gives you unlimited money and resources in the game. It also removes all ads from the game, so you can enjoy it without interruptions. With this mod, you can unlock all the vehicles, items, levels, and features in the game without spending any real money. You can also enjoy faster gameplay and smoother performance.</p>
- <h3>Benefits of Stickman Falling Dinheiro Infinito APK</h3>
- <p>Some of the benefits of Stickman Falling Dinheiro Infinito APK are:</p>
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-<ul>
-<li>You can have unlimited money and resources in the game.</li>
-<li>You can unlock all the vehicles, items, levels, and features in the game.</li>
-<li>You can remove all ads from the game.</li>
-<li>You can enjoy faster gameplay and smoother performance.</li>
-<li>You can play offline without any restrictions.</li>
-</ul>
- <h3>How to download and install Stickman Falling Dinheiro Infinito APK</h3>
- <p>To download and install Stickman Falling Dinheiro Inf nito APK, you need to follow these steps:</p>
-<ol>
-<li>Go to the link  and download the APK file.</li>
-<li>Enable the installation of unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Locate the downloaded APK file on your device and tap on it.</li>
-<li>Follow the instructions on the screen and install the app.</li>
-<li>Launch the app and enjoy the game.</li>
-</ol>
- <h2>Tips and tricks for Stickman Falling</h2>
- <p>Stickman Falling is a fun and addictive game, but it can also be challenging and frustrating at times. To help you master the game and have more fun, here are some tips and tricks you can use:</p>
- <h3>Choose the right vehicle</h3>
- <p>The vehicle you choose can make a big difference in your performance and score. Each vehicle has different characteristics and abilities, such as speed, durability, handling, and weight. Some vehicles are better suited for certain levels and obstacles than others. For example, a bike might be faster and more agile, but it can also break easily and flip over. A truck might be more durable and stable, but it can also be slower and harder to steer. You should experiment with different vehicles and find the one that suits your style and preference.</p>
- <h3>Use the boosters wisely</h3>
- <p>Boosters are items that can help you in your fall. They can give you extra speed, protection, or power. However, they are not always available and they have limited uses. You should use them wisely and strategically, depending on the situation. For example, you can use a booster to accelerate when you need to avoid an obstacle or reach the finish line faster. You can also use a booster to protect yourself when you are about to crash or hit something hard. You can also use a booster to break through a barrier or a wall. You should save your boosters for when you really need them and not waste them unnecessarily.</p>
- <h3>Avoid the spikes and saws</h3>
- <p>The spikes and saws are some of the most dangerous obstacles in the game. They can cause a lot of damage to your vehicle and your stickman. They can also make you lose coins and diamonds. You should avoid them at all costs and try to steer clear of them. If you see them coming, you should either change your direction or use a booster to escape them. You should also watch out for other obstacles that can push you or bounce you into them, such as barrels, balls, or springs.</p>
- <h3>Collect coins and diamonds</h3>
- <p>Coins and diamonds are the currency of the game. You can use them to buy new vehicles, items, and features in the game. You can also use them to upgrade your existing vehicles and items. You can earn coins and diamonds by completing levels, achieving goals, watching ads, or using Stickman Falling Dinheiro Infinito APK. You should collect as many coins and diamonds as you can and spend them wisely.</p>
- <h2>Conclusion</h2>
- <p>Stickman Falling is a fun and addictive game that will keep you entertained for hours. You can enjoy watching the stickman fall and crash in different ways, while controlling his vehicle and avoiding the obstacles. You can also customize your stickman and your vehicle with various items and features. If you want to have more money and resources in the game, you can download Stickman Falling Dinheiro Infinito APK, which gives you unlimited access to everything in the game. You can also use some tips and tricks to improve your skills and score in the game. We hope this article was helpful for you and that you will enjoy playing Stickman Falling.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Stickman Falling:</p>
-<ul>
-<li><b>Is Stickman Falling free?</b><br>
-Yes, Stickman Falling is free to download and play on Android devices. However, it contains ads that can be removed by purchasing the premium version or using Stickman Falling Dinheiro Infinito APK.</li>
-<li><b>Is Stickman Falling safe?</b><br>
-Yes, Stickman Falling is safe to play on Android devices. It does not contain any viruses or malware that can harm your device or data. However, if you download Stickman Falling Dinheiro Infinito APK from an unknown source, you should be careful and scan it before installing it.</li>
-<li><b>Is Stickman Falling offline?</b><br>
-Yes, Stickman Falling can be played offline without any internet connection. However, some features of the game might require an internet connection, such as watching ads or accessing leaderboards.</li>
-<li><b>How do I update Stickman Falling?</b><br>
-You can update Stickman Falling by going to Google Play Store and checking for any available updates. You can also enable the automatic update option on your device settings. If you download Stickman Falling Dinheiro Infinito APK from an external source, you might need to check the website regularly for any new versions.</li>
-<li><b>How do I contact the developer of Stickman Falling?</b><br>
-You can contact the developer of Stickman Falling by sending an email to skygo@skygo.com. You can also visit their website or follow them on Facebook, Twitter, or Instagram for more information and updates.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/models/__init__.py b/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/models/__init__.py
deleted file mode 100644
index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/models/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .musicgen import MusicGen
-from .lm import LMModel
-from .encodec import CompressionModel, EncodecModel
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/merge-descriptors/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/merge-descriptors/README.md
deleted file mode 100644
index d593c0ebd7c15b5749e913412ab3b729d114b81e..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/merge-descriptors/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Merge Descriptors
-
-[![NPM Version][npm-image]][npm-url]
-[![NPM Downloads][downloads-image]][downloads-url]
-[![Build Status][travis-image]][travis-url]
-[![Test Coverage][coveralls-image]][coveralls-url]
-
-Merge objects using descriptors.
-
-```js
-var thing = {
-  get name() {
-    return 'jon'
-  }
-}
-
-var animal = {
-
-}
-
-merge(animal, thing)
-
-animal.name === 'jon'
-```
-
-## API
-
-### merge(destination, source)
-
-Redefines `destination`'s descriptors with `source`'s.
-
-### merge(destination, source, false)
-
-Defines `source`'s descriptors on `destination` if `destination` does not have
-a descriptor by the same name.
-
-## License
-
-[MIT](LICENSE)
-
-[npm-image]: https://img.shields.io/npm/v/merge-descriptors.svg
-[npm-url]: https://npmjs.org/package/merge-descriptors
-[travis-image]: https://img.shields.io/travis/component/merge-descriptors/master.svg
-[travis-url]: https://travis-ci.org/component/merge-descriptors
-[coveralls-image]: https://img.shields.io/coveralls/component/merge-descriptors/master.svg
-[coveralls-url]: https://coveralls.io/r/component/merge-descriptors?branch=master
-[downloads-image]: https://img.shields.io/npm/dm/merge-descriptors.svg
-[downloads-url]: https://npmjs.org/package/merge-descriptors
diff --git a/spaces/flax-community/multilingual-image-captioning/sections/bias.md b/spaces/flax-community/multilingual-image-captioning/sections/bias.md
deleted file mode 100644
index e4f45c99e227c796cf9f0d578e03302f20b1b84f..0000000000000000000000000000000000000000
--- a/spaces/flax-community/multilingual-image-captioning/sections/bias.md
+++ /dev/null
@@ -1,9 +0,0 @@
-### Limitations
-- Our model has a major limitation in that the training data provided was limited to a sequence length of 64 tokens. Hence, it does not perform very well with longer sequence lengths. Sometimes, it yields up empty captions. We are working on it as of this writing by doubling the maximum sequence length of translation and training.
-- The dataset has all `Person` type named entites masked as `<PERSON>`. While that is good for biases as we explain below, the dataset contains too many `<PERSON>` tags and the model results in `<PERSON><PERSON><PERSON>` sometimes for Person-related images.
-- Our captions are sometimes generic. Stating what is present in the image instead of generation well-formed and convoluted captions. Despite the training, the BLEU scores we achieve are not very great, which could be a reason for this. With higher BLEU scores, we can expect less-generic models.
-- English captions are sometimes better than other languages. This can be due to the fact that we limit sequence length of other languages to 64 (and now 128) while English text works fine. This could also be due to poor-quality translations which we wish to address in our next attempt.
-### Biases
-Due to the gender, racial, color and stereotypical biases in data, person identification by an image captioning model suffers. Also, the gender-activity bias, owing to the word-by-word prediction, influences other words in the caption prediction, resulting in the well-known problem of label bias.
-
-One of the reasons why we chose Conceptual 12M over COCO captioning dataset for training our Multi-lingual Image Captioning model was that in former all named entities of type Person were substituted by a special token <PERSON>. Because of this, the gendered terms in our captions became quite infrequent. We'll present a few captions from our model to analyse how our model performed on different images on which different pre-trained image captioning model usually gives gender prediction biases.
\ No newline at end of file
diff --git a/spaces/flowers-team/SocialAISchool/scripts/LLM_test.py b/spaces/flowers-team/SocialAISchool/scripts/LLM_test.py
deleted file mode 100644
index 0cf38fb88020192fd7280133246f99491e4f3fdb..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/scripts/LLM_test.py
+++ /dev/null
@@ -1,926 +0,0 @@
-import argparse
-import json
-import requests
-import time
-import warnings
-from n_tokens import estimate_price
-import pickle
-
-import numpy as np
-import torch
-from pathlib import Path
-
-# from utils.babyai_utils.baby_agent import load_agent
-from utils import *
-from textworld_utils.utils import generate_text_obs
-from models import *
-import subprocess
-import os
-
-from matplotlib import pyplot as plt
-
-from gym_minigrid.wrappers import *
-from gym_minigrid.window import Window
-from datetime import datetime
-
-from imageio import mimsave
-
-def new_episode_marker():
-    return "New episode.\n"
-
-def success_marker():
-    return "Success!\n"
-
-def failure_marker():
-    return "Failure!\n"
-
-def action_query():
-    return "Act :"
-
-def get_parsed_action(text_action):
-    """
-    Parses the text generated by a model and extracts the action
-    """
-
-    if "move forward" in text_action:
-        return "move forward"
-
-    elif "done" in text_action:
-        return "done"
-
-    elif "turn left" in text_action:
-        return "turn left"
-
-    elif "turn right" in text_action:
-        return "turn right"
-
-    elif "toggle" in text_action:
-        return "toggle"
-
-    elif "no_op" in text_action:
-        return "no_op"
-    else:
-        warnings.warn(f"Undefined action {text_action}")
-        return "no_op"
-
-
-def action_to_prompt_action_text(action):
-    if np.allclose(action, [int(env.actions.forward), np.nan, np.nan], equal_nan=True):
-        # 2
-        text_action = "move forward"
-
-    elif np.allclose(action, [int(env.actions.left), np.nan, np.nan], equal_nan=True):
-        # 0
-        text_action = "turn left"
-
-    elif np.allclose(action, [int(env.actions.right), np.nan, np.nan], equal_nan=True):
-        # 1
-        text_action = "turn right"
-
-    elif np.allclose(action, [int(env.actions.toggle), np.nan, np.nan], equal_nan=True):
-        # 3
-        text_action = "toggle"
-
-    elif np.allclose(action, [int(env.actions.done), np.nan, np.nan], equal_nan=True):
-        # 4
-        text_action = "done"
-
-    elif np.allclose(action, [np.nan, np.nan, np.nan], equal_nan=True):
-        text_action = "no_op"
-
-    else:
-        warnings.warn(f"Undefined action {action}")
-        return "no_op"
-
-    return f"{action_query()} {text_action}\n"
-
-
-
-def text_action_to_action(text_action):
-
-    # text_action = get_parsed_action(text_action)
-
-    if "move forward" == text_action:
-        action = [int(env.actions.forward), np.nan, np.nan]
-
-    elif "turn left" == text_action:
-        action = [int(env.actions.left), np.nan, np.nan]
-
-    elif "turn right" == text_action:
-        action = [int(env.actions.right), np.nan, np.nan]
-
-    elif "toggle" == text_action:
-        action = [int(env.actions.toggle), np.nan, np.nan]
-
-    elif "done" == text_action:
-        action = [int(env.actions.done), np.nan, np.nan]
-
-    elif "no_op" == text_action:
-        action = [np.nan, np.nan, np.nan]
-
-    return action
-
-
-def prompt_preprocessor(llm_prompt):
-    # remove peer observations
-    lines = llm_prompt.split("\n")
-    new_lines = []
-    for line in lines:
-        if line.startswith("#"):
-            continue
-
-        elif line.startswith("Conversation"):
-            continue
-
-        elif "peer" in line:
-            caretaker = True
-            if caretaker:
-                # show only the location of the caretaker
-
-                # this is very ugly, todo: refactor this
-                assert "there is a" in line
-                start_index = line.index('there is a') + 11
-                new_line = line[:start_index] + 'caretaker'
-
-                new_lines.append(new_line)
-
-            else:
-                # no caretaker at all
-                if line.startswith("Obs :") and "peer" in line:
-                    # remove only the peer descriptions
-                    line = "Obs :"
-                    new_lines.append(line)
-                else:
-                    assert "peer" in line
-
-        elif "Caretaker:" in line:
-            line = line.replace("Caretaker:", "Caretaker says: ")
-            new_lines.append(line)
-
-        else:
-            new_lines.append(line)
-
-    return "\n".join(new_lines)
-
-# def generate_text_obs(obs, info):
-#
-#     text_observation = obs_to_text(info)
-#
-#     llm_prompt = "Obs : "
-#     llm_prompt += "".join(text_observation)
-#
-#     # add utterances
-#     if obs["utterance_history"] != "Conversation: \n":
-#         utt_hist = obs['utterance_history']
-#         utt_hist = utt_hist.replace("Conversation: \n","")
-#         llm_prompt += utt_hist
-#
-#     return llm_prompt
-
-# def obs_to_text(info):
-#     image, vis_mask = info["image"], info["vis_mask"]
-#     carrying = info["carrying"]
-#     agent_pos_vx, agent_pos_vy = info["agent_pos_vx"], info["agent_pos_vy"]
-#     npc_actions_dict = info["npc_actions_dict"]
-#
-#     # (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state)
-#     # State, 0: open, 1: closed, 2: locked
-#     IDX_TO_COLOR = dict(zip(COLOR_TO_IDX.values(), COLOR_TO_IDX.keys()))
-#     IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys()))
-#
-#     list_textual_descriptions = []
-#
-#     if carrying is not None:
-#         list_textual_descriptions.append("You carry a {} {}".format(carrying.color, carrying.type))
-#
-#     # agent_pos_vx, agent_pos_vy = self.get_view_coords(self.agent_pos[0], self.agent_pos[1])
-#
-#     view_field_dictionary = dict()
-#
-#     for i in range(image.shape[0]):
-#         for j in range(image.shape[1]):
-#             if image[i][j][0] != 0 and image[i][j][0] != 1 and image[i][j][0] != 2:
-#                 if i not in view_field_dictionary.keys():
-#                     view_field_dictionary[i] = dict()
-#                     view_field_dictionary[i][j] = image[i][j]
-#                 else:
-#                     view_field_dictionary[i][j] = image[i][j]
-#
-#     # Find the wall if any
-#     #  We describe a wall only if there is no objects between the agent and the wall in straight line
-#
-#     # Find wall in front
-#     add_wall_descr = False
-#     if add_wall_descr:
-#         j = agent_pos_vy - 1
-#         object_seen = False
-#         while j >= 0 and not object_seen:
-#             if image[agent_pos_vx][j][0] != 0 and image[agent_pos_vx][j][0] != 1:
-#                 if image[agent_pos_vx][j][0] == 2:
-#                     list_textual_descriptions.append(
-#                         f"A wall is {agent_pos_vy - j} steps in front of you. \n")  # forward
-#                     object_seen = True
-#                 else:
-#                     object_seen = True
-#             j -= 1
-#         # Find wall left
-#         i = agent_pos_vx - 1
-#         object_seen = False
-#         while i >= 0 and not object_seen:
-#             if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1:
-#                 if image[i][agent_pos_vy][0] == 2:
-#                     list_textual_descriptions.append(
-#                         f"A wall is {agent_pos_vx - i} steps to the left. \n")  # left
-#                     object_seen = True
-#                 else:
-#                     object_seen = True
-#             i -= 1
-#         # Find wall right
-#         i = agent_pos_vx + 1
-#         object_seen = False
-#         while i < image.shape[0] and not object_seen:
-#             if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1:
-#                 if image[i][agent_pos_vy][0] == 2:
-#                     list_textual_descriptions.append(
-#                         f"A wall is {i - agent_pos_vx} steps to the right. \n")  # right
-#                     object_seen = True
-#                 else:
-#                     object_seen = True
-#             i += 1
-#
-#     # list_textual_descriptions.append("You see the following objects: ")
-#     # returns the position of seen objects relative to you
-#     for i in view_field_dictionary.keys():
-#         for j in view_field_dictionary[i].keys():
-#             if i != agent_pos_vx or j != agent_pos_vy:
-#                 object = view_field_dictionary[i][j]
-#
-#                 # # don't show npc
-#                 # if IDX_TO_OBJECT[object[0]] == "npc":
-#                 #     continue
-#
-#                 front_dist = agent_pos_vy - j
-#                 left_right_dist = i - agent_pos_vx
-#
-#                 loc_descr = ""
-#                 if front_dist == 1 and left_right_dist == 0:
-#                     loc_descr += "Right in front of you "
-#
-#                 elif left_right_dist == 1 and front_dist == 0:
-#                     loc_descr += "Just to the right of you"
-#
-#                 elif left_right_dist == -1 and front_dist == 0:
-#                     loc_descr += "Just to the left of you"
-#
-#                 else:
-#                     front_str = str(front_dist) + " steps in front of you " if front_dist > 0 else ""
-#
-#                     loc_descr += front_str
-#
-#                     suff = "s" if abs(left_right_dist) > 0 else ""
-#                     and_ = "and" if loc_descr != "" else ""
-#
-#                     if left_right_dist < 0:
-#                         left_right_str = f"{and_} {-left_right_dist} step{suff} to the left"
-#                         loc_descr += left_right_str
-#
-#                     elif left_right_dist > 0:
-#                         left_right_str = f"{and_} {left_right_dist} step{suff} to the right"
-#                         loc_descr += left_right_str
-#
-#                     else:
-#                         left_right_str = ""
-#                         loc_descr += left_right_str
-#
-#                 loc_descr += f" there is a "
-#
-#                 obj_type = IDX_TO_OBJECT[object[0]]
-#                 if obj_type == "npc":
-#                     IDX_TO_STATE = {0: 'friendly', 1: 'antagonistic'}
-#
-#                     description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} peer. "
-#
-#                     # gaze
-#                     gaze_dir = {
-#                         0: "towards you",
-#                         1: "to the left of you",
-#                         2: "in the same direction as you",
-#                         3: "to the right of you",
-#                     }
-#                     description += f"It is looking {gaze_dir[object[3]]}. "
-#
-#                     # point
-#                     point_dir = {
-#                         0: "towards you",
-#                         1: "to the left of you",
-#                         2: "in the same direction as you",
-#                         3: "to the right of you",
-#                     }
-#
-#                     if object[4] != 255:
-#                         description += f"It is pointing {point_dir[object[4]]}. "
-#
-#                     # last action
-#                     last_action = {v: k for k, v in npc_actions_dict.items()}[object[5]]
-#
-#                     last_action = {
-#                         "go_forward": "foward",
-#                         "rotate_left": "turn left",
-#                         "rotate_right": "turn right",
-#                         "toggle_action": "toggle",
-#                         "point_stop_point": "stop pointing",
-#                         "point_E": "",
-#                         "point_S": "",
-#                         "point_W": "",
-#                         "point_N": "",
-#                         "stop_point": "stop pointing",
-#                         "no_op": ""
-#                     }[last_action]
-#
-#                     if last_action not in ["no_op", ""]:
-#                         description += f"It's last action is {last_action}. "
-#
-#                 elif obj_type in ["switch", "apple", "generatorplatform", "marble", "marbletee", "fence"]:
-#                     # todo: this assumes that Switch.no_light == True
-#                     description = f"{IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
-#                     assert object[2:].mean() == 0
-#
-#                 elif obj_type == "lockablebox":
-#                     IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'}
-#                     description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
-#                     assert object[3:].mean() == 0
-#
-#                 elif obj_type == "applegenerator":
-#                     IDX_TO_STATE = {1: 'square', 2: 'round'}
-#                     description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
-#                     assert object[3:].mean() == 0
-#
-#                 elif obj_type == "remotedoor":
-#                     IDX_TO_STATE = {0: 'open', 1: 'closed'}
-#                     description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
-#                     assert object[3:].mean() == 0
-#
-#                 elif obj_type == "door":
-#                     IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'}
-#                     description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
-#                     assert object[3:].mean() == 0
-#
-#                 elif obj_type == "lever":
-#                     IDX_TO_STATE = {1: 'activated', 0: 'unactivated'}
-#                     if object[3] == 255:
-#                         countdown_txt = ""
-#                     else:
-#                         countdown_txt = f"with {object[3]} timesteps left. "
-#
-#                     description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} {countdown_txt}"
-#
-#                     assert object[4:].mean() == 0
-#                 else:
-#                     raise ValueError(f"Undefined object type {obj_type}")
-#
-#                 full_destr = loc_descr + description + "\n"
-#
-#                 list_textual_descriptions.append(full_destr)
-#
-#     if len(list_textual_descriptions) == 0:
-#         list_textual_descriptions.append("\n")
-#
-#     return list_textual_descriptions
-
-def plt_2_rgb(env):
-    # data = np.frombuffer(env.window.fig.canvas.tostring_rgb(), dtype=np.uint8)
-    # data = data.reshape(env.window.fig.canvas.get_width_height()[::-1] + (3,))
-
-    width, height = env.window.fig.get_size_inches() * env.window.fig.get_dpi()
-    data = np.fromstring(env.window.fig.canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
-    return data
-
-
-def reset(env):
-    env.reset()
-    # a dirty trick just to get obs and info
-    return env.step([np.nan, np.nan, np.nan])
-    # return step("no_op")
-
-def generate(text_input, model):
-    # return "(a) move forward"
-    if model == "dummy":
-        print("dummy action forward")
-        return "move forward"
-
-    elif model == "interactive":
-        return input("Enter action:")
-
-    elif model == "random":
-        print("random agent")
-
-        print("PROMPT:")
-        print(text_input)
-        return random.choice([
-            "move forward",
-            "turn left",
-            "turn right",
-            "toggle",
-        ])
-
-    elif model in ["gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613", "gpt-4-0613", "gpt-4-0314"]:
-        while True:
-            try:
-                c = openai.ChatCompletion.create(
-                    model=model,
-                    messages=[
-                        # {"role": "system", "content": ""},
-                        # {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
-                        # {"role": "user", "content": "Continue the following text in the most logical way.\n"+text_input}
-
-                        # {"role": "system", "content":
-                        #     "You are an agent and can use the following actions: 'move forward', 'toggle', 'turn left', 'turn right', 'done'."
-                        #     # "The caretaker will say the color of the box which you should open. Turn until you find this box and toggle it when it is right in front of it."
-                        #     # "Then an apple will appear and you can toggle it to succeed."
-                        #  },
-                        {"role": "user", "content": text_input}
-                    ],
-                    max_tokens=3,
-                    n=1,
-                    temperature=0.0,
-                    request_timeout=30,
-                )
-                break
-            except Exception as e:
-                print(e)
-                print("Pausing")
-                time.sleep(10)
-                continue
-        print("->LLM generation: ", c['choices'][0]['message']['content'])
-        return c['choices'][0]['message']['content']
-
-    elif re.match(r"text-.*-\d{3}", model) or model in ["gpt-3.5-turbo-instruct-0914"]:
-        while True:
-            try:
-                response = openai.Completion.create(
-                    model=model,
-                    prompt=text_input,
-                    # temperature=0.7,
-                    temperature=0.0,
-                    max_tokens=3,
-                    top_p=1,
-                    frequency_penalty=0,
-                    presence_penalty=0,
-                    timeout=30
-                )
-                break
-
-            except Exception as e:
-                print(e)
-                print("Pausing")
-                time.sleep(10)
-                continue
-
-        choices = response["choices"]
-        assert len(choices) == 1
-        return choices[0]["text"].strip().lower()  # remove newline from the end
-
-    elif model in ["gpt2_large", "api_bloom"]:
-        # HF_TOKEN = os.getenv("HF_TOKEN")
-        if model == "gpt2_large":
-            API_URL = "https://api-inference.huggingface.co/models/gpt2-large"
-
-        elif model == "api_bloom":
-            API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
-
-        else:
-            raise ValueError(f"Undefined model {model}.")
-
-        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-        def query(text_prompt, n_tokens=3):
-
-            input = text_prompt
-
-            # make n_tokens request and append the output each time - one request generates one token
-
-            for _ in range(n_tokens):
-                # prepare request
-                payload = {
-                    "inputs": input,
-                    "parameters": {
-                        "do_sample": False,
-                        'temperature': 0,
-                        'wait_for_model': True,
-                        # "max_length": 500,  # for gpt2
-                        # "max_new_tokens": 250  # fot gpt2-xl
-                    },
-                }
-                data = json.dumps(payload)
-
-                # request
-                response = requests.request("POST", API_URL, headers=headers, data=data)
-                response_json = json.loads(response.content.decode("utf-8"))
-
-                if type(response_json) is list and len(response_json) == 1:
-                    # generated_text contains the input + the response
-                    response_full_text = response_json[0]['generated_text']
-
-                    # we use this as the next input
-                    input = response_full_text
-
-                else:
-                    print("Invalid request to huggingface api")
-                    from IPython import embed; embed()
-
-            # remove the prompt from the beginning
-            assert response_full_text.startswith(text_prompt)
-            response_text = response_full_text[len(text_prompt):]
-
-            return response_text
-
-        response = query(text_input).strip().lower()
-        return response
-
-    elif model in ["bloom_560m"]:
-        # from transformers import BloomForCausalLM
-        # from transformers import BloomTokenizerFast
-        #
-        # tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
-        # model = BloomForCausalLM.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
-
-        inputs = hf_tokenizer(text_input, return_tensors="pt")
-        # 3 words
-        result_length = inputs['input_ids'].shape[-1]+3
-        full_output = hf_tokenizer.decode(hf_model.generate(inputs["input_ids"], max_length=result_length)[0])
-
-        assert full_output.startswith(text_input)
-        response = full_output[len(text_input):]
-
-        response = response.strip().lower()
-
-        return response
-
-    else:
-        raise ValueError("Unknown model.")
-
-
-def estimate_tokens_selenium(prompt):
-    # selenium is used because python3.9 is needed for tiktoken
-
-    from selenium import webdriver
-    from selenium.webdriver.common.by import By
-    from selenium.webdriver.support.ui import WebDriverWait
-    from selenium.webdriver.support import expected_conditions as EC
-    import time
-
-    # Initialize the WebDriver instance
-    options = webdriver.ChromeOptions()
-    options.add_argument('headless')
-
-    # set up the driver
-    driver = webdriver.Chrome(options=options)
-
-    # Navigate to the website
-    driver.get('https://platform.openai.com/tokenizer')
-
-    text_input = driver.find_element(By.XPATH, '/html/body/div[1]/div[1]/div/div[2]/div[3]/textarea')
-    text_input.clear()
-    text_input.send_keys(prompt)
-
-    n_tokens = 0
-    while n_tokens == 0:
-        time.sleep(1)
-        # Wait for the response to be loaded
-        wait = WebDriverWait(driver, 10)
-        response = wait.until(
-            EC.presence_of_element_located((By.CSS_SELECTOR, 'div.tokenizer-stat:nth-child(1) > div:nth-child(2)')))
-        n_tokens = int(response.text.replace(",", ""))
-
-
-    # Close the WebDriver instance
-    driver.quit()
-    return n_tokens
-
-
-def load_in_context_examples(in_context_episodes):
-    in_context_examples = ""
-    print(f'Loading {len(in_context_episodes)} examples.')
-    for episode_data in in_context_episodes:
-
-        in_context_examples += new_episode_marker()
-
-        for step_i, step_data in enumerate(episode_data):
-
-            action = step_data["action"]
-            info = step_data["info"]
-            obs = step_data["obs"]
-            reward = step_data["reward"]
-            done = step_data["done"]
-
-            if step_i == 0:
-                # step 0 is the initial state of the environment
-                assert action is None
-                prompt_action_text = ""
-
-            else:
-                prompt_action_text = action_to_prompt_action_text(action)
-
-            text_obs = generate_text_obs(obs, info)
-            step_text = prompt_preprocessor(prompt_action_text + text_obs)
-
-            in_context_examples += step_text
-
-            if done:
-                if reward > 0:
-                    in_context_examples += success_marker()
-                else:
-                    in_context_examples += failure_marker()
-
-            else:
-                # in all envs reward is given in the end
-                # in the initial step rewards is None
-                assert reward == 0 or (step_i == 0 and reward is None)
-
-    print("-------------------------- IN CONTEXT EXAMPLES --------------------------")
-    print(in_context_examples)
-    print("-------------------------------------------------------------------------")
-
-    return in_context_examples
-
-
-if __name__ == "__main__":
-
-    # Parse arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model", required=False,
-                        help="text-ada-001")
-    parser.add_argument("--seed", type=int, default=0,
-                        help="Seed of the first episode. The seed for the following episodes will be used in order: seed, seed + 1, ... seed + (n_episodes-1) (default: 0)")
-    parser.add_argument("--max-steps", type=int, default=15,
-                        help="max num of steps")
-    parser.add_argument("--shift", type=int, default=0,
-                        help="number of times the environment is reset at the beginning (default: 0)")
-    parser.add_argument("--argmax", action="store_true", default=False,
-                        help="select the action with highest probability (default: False)")
-    parser.add_argument("--pause", type=float, default=0.5,
-                        help="pause duration between two consequent actions of the agent (default: 0.5)")
-    parser.add_argument("--env-name", type=str,
-                        default="SocialAI-AsocialBoxInformationSeekingParamEnv-v1",
-                        # default="SocialAI-ColorBoxesLLMCSParamEnv-v1",
-                        required=False,
-                        help="env name")
-    parser.add_argument("--in-context-path", type=str,
-                        # old
-                        # default='llm_data/in_context_asocial_box.txt'
-                        # default='llm_data/in_context_color_boxes.txt',
-                        # new
-                        # asocial box
-                        default='llm_data/in_context_examples/in_context_asocialbox_SocialAI-AsocialBoxInformationSeekingParamEnv-v1_2023_07_19_19_28_48/episodes.pkl',
-                        # colorbox
-                        # default='llm_data/in_context_examples/in_context_colorbox_SocialAI-ColorBoxesLLMCSParamEnv-v1_2023_07_20_13_11_54/episodes.pkl',
-                        required=False,
-                        help="path to in context examples")
-    parser.add_argument("--episodes", type=int, default=10,
-                        help="number of episodes to visualize")
-    parser.add_argument("--env-args", nargs='*', default=None)
-    parser.add_argument("--agent_view", default=False, help="draw the agent sees (partially observable view)", action='store_true' )
-    parser.add_argument("--tile_size", type=int, help="size at which to render tiles", default=32 )
-    parser.add_argument("--mask-unobserved", default=False, help="mask cells that are not observed by the agent", action='store_true' )
-    parser.add_argument("--log", type=str, default="llm_log/episodes_log", help="log from the run", required=False)
-    parser.add_argument("--feed-full-ep", default=False, help="weather to append the whole episode to the prompt", action='store_true')
-    parser.add_argument("--last-n", type=int, help="how many last steps to provide in observation (if not feed-full-ep)", default=3)
-    parser.add_argument("--skip-check", default=False, help="Don't estimate the price.", action="store_true")
-
-    args = parser.parse_args()
-
-    # Set seed for all randomness sources
-
-    seed(args.seed)
-
-    model = args.model
-
-
-    in_context_examples_path = args.in_context_path
-
-    # test for paper: remove later
-    if "asocialbox" in in_context_examples_path:
-        assert args.env_name == "SocialAI-AsocialBoxInformationSeekingParamEnv-v1"
-    elif "colorbox" in in_context_examples_path:
-        assert args.env_name == "SocialAI-ColorBoxesLLMCSParamEnv-v1"
-
-
-    print("env name:", args.env_name)
-    print("examples:", in_context_examples_path)
-    print("model:", args.model)
-
-    # datetime
-    now = datetime.now()
-    datetime_string = now.strftime("%d_%m_%Y_%H:%M:%S")
-    print(datetime_string)
-
-    # log filenames
-
-    log_folder = args.log+"_"+datetime_string+"/"
-    os.mkdir(log_folder)
-    evaluation_log_filename = log_folder+"evaluation_log.json"
-    prompt_log_filename = log_folder + "prompt_log.txt"
-    ep_h_log_filename = log_folder+"episode_history_query.txt"
-    gif_savename = log_folder + "demo.gif"
-
-
-    env_args = env_args_str_to_dict(args.env_args)
-    env = make_env(args.env_name, args.seed, env_args)
-
-    # env = gym.make(args.env_name, **env_args)
-    print(f"Environment {args.env_name} and args: {env_args_str_to_dict(args.env_args)}\n")
-
-    # Define agent
-    print("Agent loaded\n")
-
-    # prepare models
-    model_instance = None
-
-    if "text" in args.model or "gpt-3" in args.model or "gpt-4" in args.model:
-        import openai
-        openai.api_key = os.getenv("OPENAI_API_KEY")
-
-    elif args.model in ["gpt2_large", "api_bloom"]:
-        HF_TOKEN = os.getenv("HF_TOKEN")
-
-    elif args.model in ["bloom_560m"]:
-        from transformers import BloomForCausalLM
-        from transformers import BloomTokenizerFast
-
-        hf_tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
-        hf_model = BloomForCausalLM.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
-
-    elif args.model in ["bloom"]:
-        from transformers import BloomForCausalLM
-        from transformers import BloomTokenizerFast
-
-        hf_tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom", cache_dir=".cache/huggingface/")
-        hf_model = BloomForCausalLM.from_pretrained("bigscience/bloom", cache_dir=".cache/huggingface/")
-
-        model_instance = (hf_tokenizer, hf_model)
-
-    with open(in_context_examples_path, "rb") as f:
-        in_context_episodes = pickle.load(f)
-
-    in_context_examples = load_in_context_examples(in_context_episodes)
-
-    with open(prompt_log_filename, "a+") as f:
-        f.write(datetime_string)
-
-    with open(ep_h_log_filename, "a+") as f:
-        f.write(datetime_string)
-
-    full_episode_history = args.feed_full_ep
-    last_n=args.last_n
-
-    if full_episode_history:
-        print("Full episode history.")
-    else:
-        print(f"Last {args.last_n} steps.")
-
-    if not args.skip_check and not args.model in ["dummy", "random", "interactive"]:
-        print(f"Estimating price for model {args.model}.")
-        in_context_n_tokens = estimate_tokens_selenium(in_context_examples)
-
-        n_in_context_steps = sum([len(ep) for ep in in_context_episodes])
-        tokens_per_step = in_context_n_tokens / n_in_context_steps
-
-        _, price = estimate_price(
-            num_of_episodes=args.episodes,
-            in_context_len=in_context_n_tokens,
-            tokens_per_step=tokens_per_step,
-            n_steps=args.max_steps,
-            last_n=last_n,
-            model=args.model,
-            feed_episode_history=full_episode_history
-        )
-        input(f"You will spend: {price} dollars. ok?")
-
-        # prepare frames list to save to gif
-    frames = []
-
-    assert args.max_steps <= 20
-
-    success_rates = []
-    # episodes start
-    for episode in range(args.episodes):
-        print("Episode:", episode)
-        episode_history_text = new_episode_marker()
-
-        success = False
-        episode_seed = args.seed + episode
-        env = make_env(args.env_name, episode_seed, env_args)
-
-        with open(prompt_log_filename, "a+") as f:
-            f.write("\n\n")
-
-        observations = []
-        actions = []
-        for i in range(int(args.max_steps)):
-
-            if i == 0:
-                obs, reward, done, info = reset(env)
-                prompt_action_text = ""
-
-            else:
-                with open(prompt_log_filename, "a+") as f:
-                    f.write("\nnew prompt: -----------------------------------\n")
-                    f.write(llm_prompt)
-
-                # querry the model
-                generation = generate(llm_prompt, args.model)
-
-                # parse the action
-                text_action = get_parsed_action(generation)
-
-                # get the raw action
-                action = text_action_to_action(text_action)
-
-                # execute the action
-                obs, reward, done, info = env.step(action)
-
-                prompt_action_text = f"{action_query()} {text_action}\n"
-
-                assert action_to_prompt_action_text(action) == prompt_action_text
-
-                actions.append(prompt_action_text)
-
-            text_obs = generate_text_obs(obs, info)
-            observations.append(text_obs)
-
-            step_text = prompt_preprocessor(prompt_action_text + text_obs)
-            print("Step text:")
-            print(step_text)
-
-            episode_history_text += step_text  # append to history of this episode
-
-            if full_episode_history:
-                # feed full episode history
-                llm_prompt = in_context_examples + episode_history_text + action_query()
-
-            else:
-                n = min(last_n, len(observations))
-                obs = observations[-n:]
-                act = (actions + [action_query()])[-n:]
-
-                episode_text = "".join([o+a for o, a in zip(obs, act)])
-
-                llm_prompt = in_context_examples + new_episode_marker() + episode_text
-
-            llm_prompt = prompt_preprocessor(llm_prompt)
-
-            # save the image
-            env.render(mode="human")
-            rgb_img = plt_2_rgb(env)
-            frames.append(rgb_img)
-
-            if env.current_env.box.blocked and not env.current_env.box.is_open:
-                # target box is blocked -> apple can't be obtained
-                # break to save compute
-                break
-
-            if done:
-                # quadruple last frame to pause between episodes
-                for i in range(3):
-                    same_img = np.copy(rgb_img)
-                    # toggle a pixel between frames to avoid cropping when going from gif to mp4
-                    same_img[0, 0, 2] = 0 if (i % 2) == 0 else 255
-                    frames.append(same_img)
-
-                if reward > 0:
-                    print("Success!")
-
-
-                    episode_history_text += success_marker()
-                    success = True
-                else:
-                    episode_history_text += failure_marker()
-
-                with open(ep_h_log_filename, "a+") as f:
-                    f.write("\nnew prompt: -----------------------------------\n")
-                    f.write(episode_history_text)
-
-                break
-
-            else:
-                with open(ep_h_log_filename, "a+") as f:
-                    f.write("\nnew prompt: -----------------------------------\n")
-                    f.write(episode_history_text)
-
-        print(f"{'Success' if success else 'Failure'}")
-        success_rates.append(success)
-
-    mean_success_rate =  np.mean(success_rates)
-    print("Success rate:", mean_success_rate)
-    print(f"Saving gif to {gif_savename}.")
-    mimsave(gif_savename, frames, duration=args.pause)
-
-    print("Done.")
-
-    log_data_dict = vars(args)
-    log_data_dict["success_rates"] = success_rates
-    log_data_dict["mean_success_rate"] = mean_success_rate
-
-    print("Evaluation log: ", evaluation_log_filename)
-    with open(evaluation_log_filename, "w") as f:
-        f.write(json.dumps(log_data_dict))
diff --git a/spaces/freddyaboulton/gradio_pdf/src/backend/gradio_pdf/templates/example/index.js b/spaces/freddyaboulton/gradio_pdf/src/backend/gradio_pdf/templates/example/index.js
deleted file mode 100644
index 2a747d30019883117bd611067771bb31404023e7..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/gradio_pdf/src/backend/gradio_pdf/templates/example/index.js
+++ /dev/null
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-            setFillColorSpace: 51,
-            setStrokeColor: 52,
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-            setStrokeRGBColor: 58,
-            setFillRGBColor: 59,
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-            setFillCMYKColor: 61,
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-            paintXObject: 66,
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-            beginMarkedContentProps: 70,
-            endMarkedContent: 71,
-            beginCompat: 72,
-            endCompat: 73,
-            paintFormXObjectBegin: 74,
-            paintFormXObjectEnd: 75,
-            beginGroup: 76,
-            endGroup: 77,
-            beginAnnotation: 80,
-            endAnnotation: 81,
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-            paintImageMaskXObjectGroup: 84,
-            paintImageXObject: 85,
-            paintInlineImageXObject: 86,
-            paintInlineImageXObjectGroup: 87,
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-            constructPath: 91
-          };
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-          };
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-            }
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-          class b extends S {
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-            }
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-            constructor(Y) {
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-              bt.push(String.fromCharCode.apply(null, Zt));
-            }
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-          }
-          function tt(ot) {
-            typeof ot != "string" && H("Invalid argument for stringToBytes");
-            const Y = ot.length, G = new Uint8Array(Y);
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-          }
-          function Q(ot) {
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-          }
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-          }
-          function ct(ot) {
-            const Y = /* @__PURE__ */ Object.create(null);
-            for (const [G, bt] of ot)
-              Y[G] = bt;
-            return Y;
-          }
-          function yt() {
-            const ot = new Uint8Array(4);
-            return ot[0] = 1, new Uint32Array(ot.buffer, 0, 1)[0] === 1;
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-            static get isOffscreenCanvasSupported() {
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-            }
-            static get platform() {
-              return typeof navigator > "u" ? xt(this, "platform", {
-                isWin: !1,
-                isMac: !1
-              }) : xt(this, "platform", {
-                isWin: navigator.platform.includes("Win"),
-                isMac: navigator.platform.includes("Mac")
-              });
-            }
-            static get isCSSRoundSupported() {
-              var Y, G;
-              return xt(this, "isCSSRoundSupported", (G = (Y = globalThis.CSS) == null ? void 0 : Y.supports) == null ? void 0 : G.call(Y, "width: round(1.5px, 1px)"));
-            }
-          }
-          d.FeatureTest = Ft;
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-          class St {
-            static makeHexColor(Y, G, bt) {
-              return `#${Bt[Y]}${Bt[G]}${Bt[bt]}`;
-            }
-            static scaleMinMax(Y, G) {
-              let bt;
-              Y[0] ? (Y[0] < 0 && (bt = G[0], G[0] = G[1], G[1] = bt), G[0] *= Y[0], G[1] *= Y[0], Y[3] < 0 && (bt = G[2], G[2] = G[3], G[3] = bt), G[2] *= Y[3], G[3] *= Y[3]) : (bt = G[0], G[0] = G[2], G[2] = bt, bt = G[1], G[1] = G[3], G[3] = bt, Y[1] < 0 && (bt = G[2], G[2] = G[3], G[3] = bt), G[2] *= Y[1], G[3] *= Y[1], Y[2] < 0 && (bt = G[0], G[0] = G[1], G[1] = bt), G[0] *= Y[2], G[1] *= Y[2]), G[0] += Y[4], G[1] += Y[4], G[2] += Y[5], G[3] += Y[5];
-            }
-            static transform(Y, G) {
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-            }
-            static applyTransform(Y, G) {
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-              return [bt, At];
-            }
-            static applyInverseTransform(Y, G) {
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-              return [At, te];
-            }
-            static getAxialAlignedBoundingBox(Y, G) {
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-            }
-            static inverseTransform(Y) {
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-            }
-            static singularValueDecompose2dScale(Y) {
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-              return [Math.sqrt(Lt), Math.sqrt(Tt)];
-            }
-            static normalizeRect(Y) {
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-              return Y[0] > Y[2] && (G[0] = Y[2], G[2] = Y[0]), Y[1] > Y[3] && (G[1] = Y[3], G[3] = Y[1]), G;
-            }
-            static intersect(Y, G) {
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-                  if (Math.abs(Nt) < 1e-12)
-                    continue;
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-                  continue;
-                }
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-              return Tt[0][j] = Y, Tt[1][j] = G, Tt[0][j + 1] = $, Tt[1][j + 1] = vt, Tt[0].length = Tt[1].length = j + 2, [Math.min(...Tt[0]), Math.min(...Tt[1]), Math.max(...Tt[0]), Math.max(...Tt[1])];
-            }
-          }
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-                    fatal: !0
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-                  return bt.decode(At);
-                } catch (bt) {
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-                }
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-          }
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-          function Ct(ot = /* @__PURE__ */ new Date()) {
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-          }
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-              return t(this, $t);
-            }
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-          let Gt = null, Ht = null;
-          function Xt(ot) {
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-          }
-          function Vt() {
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-              return crypto.randomUUID();
-            const ot = new Uint8Array(32);
-            if (typeof crypto < "u" && typeof (crypto == null ? void 0 : crypto.getRandomValues) == "function")
-              crypto.getRandomValues(ot);
-            else
-              for (let Y = 0; Y < 32; Y++)
-                ot[Y] = Math.floor(Math.random() * 255);
-            return N(ot);
-          }
-          const Wt = "pdfjs_internal_id_";
-          d.AnnotationPrefix = Wt;
-        },
-        /* 2 */
-        /***/
-        (__unused_webpack_module, exports, __w_pdfjs_require__) => {
-          var dt, et, l, P, he, X, Ee, B, F, g, O, I, x, v, A, u, we, w, C, Be, a, c;
-          Object.defineProperty(exports, "__esModule", {
-            value: !0
-          }), exports.RenderTask = exports.PDFWorkerUtil = exports.PDFWorker = exports.PDFPageProxy = exports.PDFDocumentProxy = exports.PDFDocumentLoadingTask = exports.PDFDataRangeTransport = exports.LoopbackPort = exports.DefaultStandardFontDataFactory = exports.DefaultFilterFactory = exports.DefaultCanvasFactory = exports.DefaultCMapReaderFactory = void 0, Object.defineProperty(exports, "SVGGraphics", {
-            enumerable: !0,
-            get: function() {
-              return _displaySvg.SVGGraphics;
-            }
-          }), exports.build = void 0, exports.getDocument = getDocument, exports.version = void 0;
-          var _util = __w_pdfjs_require__(1), _annotation_storage = __w_pdfjs_require__(3), _display_utils = __w_pdfjs_require__(6), _font_loader = __w_pdfjs_require__(9), _displayNode_utils = __w_pdfjs_require__(10), _canvas = __w_pdfjs_require__(11), _worker_options = __w_pdfjs_require__(14), _message_handler = __w_pdfjs_require__(15), _metadata = __w_pdfjs_require__(16), _optional_content_config = __w_pdfjs_require__(17), _transport_stream = __w_pdfjs_require__(18), _displayFetch_stream = __w_pdfjs_require__(19), _displayNetwork = __w_pdfjs_require__(22), _displayNode_stream = __w_pdfjs_require__(23), _displaySvg = __w_pdfjs_require__(24), _xfa_text = __w_pdfjs_require__(25);
-          const DEFAULT_RANGE_CHUNK_SIZE = 65536, RENDERING_CANCELLED_TIMEOUT = 100, DELAYED_CLEANUP_TIMEOUT = 5e3, DefaultCanvasFactory = _util.isNodeJS ? _displayNode_utils.NodeCanvasFactory : _display_utils.DOMCanvasFactory;
-          exports.DefaultCanvasFactory = DefaultCanvasFactory;
-          const DefaultCMapReaderFactory = _util.isNodeJS ? _displayNode_utils.NodeCMapReaderFactory : _display_utils.DOMCMapReaderFactory;
-          exports.DefaultCMapReaderFactory = DefaultCMapReaderFactory;
-          const DefaultFilterFactory = _util.isNodeJS ? _displayNode_utils.NodeFilterFactory : _display_utils.DOMFilterFactory;
-          exports.DefaultFilterFactory = DefaultFilterFactory;
-          const DefaultStandardFontDataFactory = _util.isNodeJS ? _displayNode_utils.NodeStandardFontDataFactory : _display_utils.DOMStandardFontDataFactory;
-          exports.DefaultStandardFontDataFactory = DefaultStandardFontDataFactory;
-          function getDocument(p) {
-            if (typeof p == "string" || p instanceof URL ? p = {
-              url: p
-            } : (0, _util.isArrayBuffer)(p) && (p = {
-              data: p
-            }), typeof p != "object")
-              throw new Error("Invalid parameter in getDocument, need parameter object.");
-            if (!p.url && !p.data && !p.range)
-              throw new Error("Invalid parameter object: need either .data, .range or .url");
-            const r = new PDFDocumentLoadingTask(), {
-              docId: T
-            } = r, m = p.url ? getUrlProp(p.url) : null, U = p.data ? getDataProp(p.data) : null, z = p.httpHeaders || null, E = p.withCredentials === !0, V = p.password ?? null, st = p.range instanceof PDFDataRangeTransport ? p.range : null, at = Number.isInteger(p.rangeChunkSize) && p.rangeChunkSize > 0 ? p.rangeChunkSize : DEFAULT_RANGE_CHUNK_SIZE;
-            let H = p.worker instanceof PDFWorker ? p.worker : null;
-            const lt = p.verbosity, gt = typeof p.docBaseUrl == "string" && !(0, _display_utils.isDataScheme)(p.docBaseUrl) ? p.docBaseUrl : null, wt = typeof p.cMapUrl == "string" ? p.cMapUrl : null, xt = p.cMapPacked !== !1, S = p.CMapReaderFactory || DefaultCMapReaderFactory, i = typeof p.standardFontDataUrl == "string" ? p.standardFontDataUrl : null, n = p.StandardFontDataFactory || DefaultStandardFontDataFactory, s = p.stopAtErrors !== !0, o = Number.isInteger(p.maxImageSize) && p.maxImageSize > -1 ? p.maxImageSize : -1, h = p.isEvalSupported !== !1, b = typeof p.isOffscreenCanvasSupported == "boolean" ? p.isOffscreenCanvasSupported : !_util.isNodeJS, M = Number.isInteger(p.canvasMaxAreaInBytes) ? p.canvasMaxAreaInBytes : -1, N = typeof p.disableFontFace == "boolean" ? p.disableFontFace : _util.isNodeJS, tt = p.fontExtraProperties === !0, Q = p.enableXfa === !0, nt = p.ownerDocument || globalThis.document, ct = p.disableRange === !0, yt = p.disableStream === !0, ut = p.disableAutoFetch === !0, Ft = p.pdfBug === !0, Bt = st ? st.length : p.length ?? NaN, St = typeof p.useSystemFonts == "boolean" ? p.useSystemFonts : !_util.isNodeJS && !N, Dt = typeof p.useWorkerFetch == "boolean" ? p.useWorkerFetch : S === _display_utils.DOMCMapReaderFactory && n === _display_utils.DOMStandardFontDataFactory && wt && i && (0, _display_utils.isValidFetchUrl)(wt, document.baseURI) && (0, _display_utils.isValidFetchUrl)(i, document.baseURI), ft = p.canvasFactory || new DefaultCanvasFactory({
-              ownerDocument: nt
-            }), K = p.filterFactory || new DefaultFilterFactory({
-              docId: T,
-              ownerDocument: nt
-            }), J = null;
-            (0, _util.setVerbosityLevel)(lt);
-            const ht = {
-              canvasFactory: ft,
-              filterFactory: K
-            };
-            if (Dt || (ht.cMapReaderFactory = new S({
-              baseUrl: wt,
-              isCompressed: xt
-            }), ht.standardFontDataFactory = new n({
-              baseUrl: i
-            })), !H) {
-              const jt = {
-                verbosity: lt,
-                port: _worker_options.GlobalWorkerOptions.workerPort
-              };
-              H = jt.port ? PDFWorker.fromPort(jt) : new PDFWorker(jt), r._worker = H;
-            }
-            const Et = {
-              docId: T,
-              apiVersion: "3.11.174",
-              data: U,
-              password: V,
-              disableAutoFetch: ut,
-              rangeChunkSize: at,
-              length: Bt,
-              docBaseUrl: gt,
-              enableXfa: Q,
-              evaluatorOptions: {
-                maxImageSize: o,
-                disableFontFace: N,
-                ignoreErrors: s,
-                isEvalSupported: h,
-                isOffscreenCanvasSupported: b,
-                canvasMaxAreaInBytes: M,
-                fontExtraProperties: tt,
-                useSystemFonts: St,
-                cMapUrl: Dt ? wt : null,
-                standardFontDataUrl: Dt ? i : null
-              }
-            }, Ct = {
-              ignoreErrors: s,
-              isEvalSupported: h,
-              disableFontFace: N,
-              fontExtraProperties: tt,
-              enableXfa: Q,
-              ownerDocument: nt,
-              disableAutoFetch: ut,
-              pdfBug: Ft,
-              styleElement: J
-            };
-            return H.promise.then(function() {
-              if (r.destroyed)
-                throw new Error("Loading aborted");
-              const jt = _fetchDocument(H, Et), Gt = new Promise(function(Ht) {
-                let Xt;
-                st ? Xt = new _transport_stream.PDFDataTransportStream({
-                  length: Bt,
-                  initialData: st.initialData,
-                  progressiveDone: st.progressiveDone,
-                  contentDispositionFilename: st.contentDispositionFilename,
-                  disableRange: ct,
-                  disableStream: yt
-                }, st) : U || (Xt = ((Wt) => _util.isNodeJS ? new _displayNode_stream.PDFNodeStream(Wt) : (0, _display_utils.isValidFetchUrl)(Wt.url) ? new _displayFetch_stream.PDFFetchStream(Wt) : new _displayNetwork.PDFNetworkStream(Wt))({
-                  url: m,
-                  length: Bt,
-                  httpHeaders: z,
-                  withCredentials: E,
-                  rangeChunkSize: at,
-                  disableRange: ct,
-                  disableStream: yt
-                })), Ht(Xt);
-              });
-              return Promise.all([jt, Gt]).then(function([Ht, Xt]) {
-                if (r.destroyed)
-                  throw new Error("Loading aborted");
-                const Vt = new _message_handler.MessageHandler(T, Ht, H.port), Wt = new WorkerTransport(Vt, r, Xt, Ct, ht);
-                r._transport = Wt, Vt.send("Ready", null);
-              });
-            }).catch(r._capability.reject), r;
-          }
-          async function _fetchDocument(p, r) {
-            if (p.destroyed)
-              throw new Error("Worker was destroyed");
-            const T = await p.messageHandler.sendWithPromise("GetDocRequest", r, r.data ? [r.data.buffer] : null);
-            if (p.destroyed)
-              throw new Error("Worker was destroyed");
-            return T;
-          }
-          function getUrlProp(p) {
-            if (p instanceof URL)
-              return p.href;
-            try {
-              return new URL(p, window.location).href;
-            } catch {
-              if (_util.isNodeJS && typeof p == "string")
-                return p;
-            }
-            throw new Error("Invalid PDF url data: either string or URL-object is expected in the url property.");
-          }
-          function getDataProp(p) {
-            if (_util.isNodeJS && typeof Buffer < "u" && p instanceof Buffer)
-              throw new Error("Please provide binary data as `Uint8Array`, rather than `Buffer`.");
-            if (p instanceof Uint8Array && p.byteLength === p.buffer.byteLength)
-              return p;
-            if (typeof p == "string")
-              return (0, _util.stringToBytes)(p);
-            if (typeof p == "object" && !isNaN(p == null ? void 0 : p.length) || (0, _util.isArrayBuffer)(p))
-              return new Uint8Array(p);
-            throw new Error("Invalid PDF binary data: either TypedArray, string, or array-like object is expected in the data property.");
-          }
-          const d = class d {
-            constructor() {
-              this._capability = new _util.PromiseCapability(), this._transport = null, this._worker = null, this.docId = `d${ge(d, dt)._++}`, this.destroyed = !1, this.onPassword = null, this.onProgress = null;
-            }
-            get promise() {
-              return this._capability.promise;
-            }
-            async destroy() {
-              var r, T, m;
-              this.destroyed = !0;
-              try {
-                (r = this._worker) != null && r.port && (this._worker._pendingDestroy = !0), await ((T = this._transport) == null ? void 0 : T.destroy());
-              } catch (U) {
-                throw (m = this._worker) != null && m.port && delete this._worker._pendingDestroy, U;
-              }
-              this._transport = null, this._worker && (this._worker.destroy(), this._worker = null);
-            }
-          };
-          dt = new WeakMap(), L(d, dt, 0);
-          let PDFDocumentLoadingTask = d;
-          exports.PDFDocumentLoadingTask = PDFDocumentLoadingTask;
-          class PDFDataRangeTransport {
-            constructor(r, T, m = !1, U = null) {
-              this.length = r, this.initialData = T, this.progressiveDone = m, this.contentDispositionFilename = U, this._rangeListeners = [], this._progressListeners = [], this._progressiveReadListeners = [], this._progressiveDoneListeners = [], this._readyCapability = new _util.PromiseCapability();
-            }
-            addRangeListener(r) {
-              this._rangeListeners.push(r);
-            }
-            addProgressListener(r) {
-              this._progressListeners.push(r);
-            }
-            addProgressiveReadListener(r) {
-              this._progressiveReadListeners.push(r);
-            }
-            addProgressiveDoneListener(r) {
-              this._progressiveDoneListeners.push(r);
-            }
-            onDataRange(r, T) {
-              for (const m of this._rangeListeners)
-                m(r, T);
-            }
-            onDataProgress(r, T) {
-              this._readyCapability.promise.then(() => {
-                for (const m of this._progressListeners)
-                  m(r, T);
-              });
-            }
-            onDataProgressiveRead(r) {
-              this._readyCapability.promise.then(() => {
-                for (const T of this._progressiveReadListeners)
-                  T(r);
-              });
-            }
-            onDataProgressiveDone() {
-              this._readyCapability.promise.then(() => {
-                for (const r of this._progressiveDoneListeners)
-                  r();
-              });
-            }
-            transportReady() {
-              this._readyCapability.resolve();
-            }
-            requestDataRange(r, T) {
-              (0, _util.unreachable)("Abstract method PDFDataRangeTransport.requestDataRange");
-            }
-            abort() {
-            }
-          }
-          exports.PDFDataRangeTransport = PDFDataRangeTransport;
-          class PDFDocumentProxy {
-            constructor(r, T) {
-              this._pdfInfo = r, this._transport = T, Object.defineProperty(this, "getJavaScript", {
-                value: () => ((0, _display_utils.deprecated)("`PDFDocumentProxy.getJavaScript`, please use `PDFDocumentProxy.getJSActions` instead."), this.getJSActions().then((m) => {
-                  if (!m)
-                    return m;
-                  const U = [];
-                  for (const z in m)
-                    U.push(...m[z]);
-                  return U;
-                }))
-              });
-            }
-            get annotationStorage() {
-              return this._transport.annotationStorage;
-            }
-            get filterFactory() {
-              return this._transport.filterFactory;
-            }
-            get numPages() {
-              return this._pdfInfo.numPages;
-            }
-            get fingerprints() {
-              return this._pdfInfo.fingerprints;
-            }
-            get isPureXfa() {
-              return (0, _util.shadow)(this, "isPureXfa", !!this._transport._htmlForXfa);
-            }
-            get allXfaHtml() {
-              return this._transport._htmlForXfa;
-            }
-            getPage(r) {
-              return this._transport.getPage(r);
-            }
-            getPageIndex(r) {
-              return this._transport.getPageIndex(r);
-            }
-            getDestinations() {
-              return this._transport.getDestinations();
-            }
-            getDestination(r) {
-              return this._transport.getDestination(r);
-            }
-            getPageLabels() {
-              return this._transport.getPageLabels();
-            }
-            getPageLayout() {
-              return this._transport.getPageLayout();
-            }
-            getPageMode() {
-              return this._transport.getPageMode();
-            }
-            getViewerPreferences() {
-              return this._transport.getViewerPreferences();
-            }
-            getOpenAction() {
-              return this._transport.getOpenAction();
-            }
-            getAttachments() {
-              return this._transport.getAttachments();
-            }
-            getJSActions() {
-              return this._transport.getDocJSActions();
-            }
-            getOutline() {
-              return this._transport.getOutline();
-            }
-            getOptionalContentConfig() {
-              return this._transport.getOptionalContentConfig();
-            }
-            getPermissions() {
-              return this._transport.getPermissions();
-            }
-            getMetadata() {
-              return this._transport.getMetadata();
-            }
-            getMarkInfo() {
-              return this._transport.getMarkInfo();
-            }
-            getData() {
-              return this._transport.getData();
-            }
-            saveDocument() {
-              return this._transport.saveDocument();
-            }
-            getDownloadInfo() {
-              return this._transport.downloadInfoCapability.promise;
-            }
-            cleanup(r = !1) {
-              return this._transport.startCleanup(r || this.isPureXfa);
-            }
-            destroy() {
-              return this.loadingTask.destroy();
-            }
-            get loadingParams() {
-              return this._transport.loadingParams;
-            }
-            get loadingTask() {
-              return this._transport.loadingTask;
-            }
-            getFieldObjects() {
-              return this._transport.getFieldObjects();
-            }
-            hasJSActions() {
-              return this._transport.hasJSActions();
-            }
-            getCalculationOrderIds() {
-              return this._transport.getCalculationOrderIds();
-            }
-          }
-          exports.PDFDocumentProxy = PDFDocumentProxy;
-          class PDFPageProxy {
-            constructor(r, T, m, U = !1) {
-              L(this, P);
-              L(this, X);
-              L(this, et, null);
-              L(this, l, !1);
-              this._pageIndex = r, this._pageInfo = T, this._transport = m, this._stats = U ? new _display_utils.StatTimer() : null, this._pdfBug = U, this.commonObjs = m.commonObjs, this.objs = new PDFObjects(), this._maybeCleanupAfterRender = !1, this._intentStates = /* @__PURE__ */ new Map(), this.destroyed = !1;
-            }
-            get pageNumber() {
-              return this._pageIndex + 1;
-            }
-            get rotate() {
-              return this._pageInfo.rotate;
-            }
-            get ref() {
-              return this._pageInfo.ref;
-            }
-            get userUnit() {
-              return this._pageInfo.userUnit;
-            }
-            get view() {
-              return this._pageInfo.view;
-            }
-            getViewport({
-              scale: r,
-              rotation: T = this.rotate,
-              offsetX: m = 0,
-              offsetY: U = 0,
-              dontFlip: z = !1
-            } = {}) {
-              return new _display_utils.PageViewport({
-                viewBox: this.view,
-                scale: r,
-                rotation: T,
-                offsetX: m,
-                offsetY: U,
-                dontFlip: z
-              });
-            }
-            getAnnotations({
-              intent: r = "display"
-            } = {}) {
-              const T = this._transport.getRenderingIntent(r);
-              return this._transport.getAnnotations(this._pageIndex, T.renderingIntent);
-            }
-            getJSActions() {
-              return this._transport.getPageJSActions(this._pageIndex);
-            }
-            get filterFactory() {
-              return this._transport.filterFactory;
-            }
-            get isPureXfa() {
-              return (0, _util.shadow)(this, "isPureXfa", !!this._transport._htmlForXfa);
-            }
-            async getXfa() {
-              var r;
-              return ((r = this._transport._htmlForXfa) == null ? void 0 : r.children[this._pageIndex]) || null;
-            }
-            render({
-              canvasContext: r,
-              viewport: T,
-              intent: m = "display",
-              annotationMode: U = _util.AnnotationMode.ENABLE,
-              transform: z = null,
-              background: E = null,
-              optionalContentConfigPromise: V = null,
-              annotationCanvasMap: st = null,
-              pageColors: at = null,
-              printAnnotationStorage: H = null
-            }) {
-              var n, s;
-              (n = this._stats) == null || n.time("Overall");
-              const lt = this._transport.getRenderingIntent(m, U, H);
-              Z(this, l, !1), W(this, X, Ee).call(this), V || (V = this._transport.getOptionalContentConfig());
-              let gt = this._intentStates.get(lt.cacheKey);
-              gt || (gt = /* @__PURE__ */ Object.create(null), this._intentStates.set(lt.cacheKey, gt)), gt.streamReaderCancelTimeout && (clearTimeout(gt.streamReaderCancelTimeout), gt.streamReaderCancelTimeout = null);
-              const wt = !!(lt.renderingIntent & _util.RenderingIntentFlag.PRINT);
-              gt.displayReadyCapability || (gt.displayReadyCapability = new _util.PromiseCapability(), gt.operatorList = {
-                fnArray: [],
-                argsArray: [],
-                lastChunk: !1,
-                separateAnnots: null
-              }, (s = this._stats) == null || s.time("Page Request"), this._pumpOperatorList(lt));
-              const xt = (o) => {
-                var h, b;
-                gt.renderTasks.delete(S), (this._maybeCleanupAfterRender || wt) && Z(this, l, !0), W(this, P, he).call(this, !wt), o ? (S.capability.reject(o), this._abortOperatorList({
-                  intentState: gt,
-                  reason: o instanceof Error ? o : new Error(o)
-                })) : S.capability.resolve(), (h = this._stats) == null || h.timeEnd("Rendering"), (b = this._stats) == null || b.timeEnd("Overall");
-              }, S = new InternalRenderTask({
-                callback: xt,
-                params: {
-                  canvasContext: r,
-                  viewport: T,
-                  transform: z,
-                  background: E
-                },
-                objs: this.objs,
-                commonObjs: this.commonObjs,
-                annotationCanvasMap: st,
-                operatorList: gt.operatorList,
-                pageIndex: this._pageIndex,
-                canvasFactory: this._transport.canvasFactory,
-                filterFactory: this._transport.filterFactory,
-                useRequestAnimationFrame: !wt,
-                pdfBug: this._pdfBug,
-                pageColors: at
-              });
-              (gt.renderTasks || (gt.renderTasks = /* @__PURE__ */ new Set())).add(S);
-              const i = S.task;
-              return Promise.all([gt.displayReadyCapability.promise, V]).then(([o, h]) => {
-                var b;
-                if (this.destroyed) {
-                  xt();
-                  return;
-                }
-                (b = this._stats) == null || b.time("Rendering"), S.initializeGraphics({
-                  transparency: o,
-                  optionalContentConfig: h
-                }), S.operatorListChanged();
-              }).catch(xt), i;
-            }
-            getOperatorList({
-              intent: r = "display",
-              annotationMode: T = _util.AnnotationMode.ENABLE,
-              printAnnotationStorage: m = null
-            } = {}) {
-              var st;
-              function U() {
-                E.operatorList.lastChunk && (E.opListReadCapability.resolve(E.operatorList), E.renderTasks.delete(V));
-              }
-              const z = this._transport.getRenderingIntent(r, T, m, !0);
-              let E = this._intentStates.get(z.cacheKey);
-              E || (E = /* @__PURE__ */ Object.create(null), this._intentStates.set(z.cacheKey, E));
-              let V;
-              return E.opListReadCapability || (V = /* @__PURE__ */ Object.create(null), V.operatorListChanged = U, E.opListReadCapability = new _util.PromiseCapability(), (E.renderTasks || (E.renderTasks = /* @__PURE__ */ new Set())).add(V), E.operatorList = {
-                fnArray: [],
-                argsArray: [],
-                lastChunk: !1,
-                separateAnnots: null
-              }, (st = this._stats) == null || st.time("Page Request"), this._pumpOperatorList(z)), E.opListReadCapability.promise;
-            }
-            streamTextContent({
-              includeMarkedContent: r = !1,
-              disableNormalization: T = !1
-            } = {}) {
-              return this._transport.messageHandler.sendWithStream("GetTextContent", {
-                pageIndex: this._pageIndex,
-                includeMarkedContent: r === !0,
-                disableNormalization: T === !0
-              }, {
-                highWaterMark: 100,
-                size(U) {
-                  return U.items.length;
-                }
-              });
-            }
-            getTextContent(r = {}) {
-              if (this._transport._htmlForXfa)
-                return this.getXfa().then((m) => _xfa_text.XfaText.textContent(m));
-              const T = this.streamTextContent(r);
-              return new Promise(function(m, U) {
-                function z() {
-                  E.read().then(function({
-                    value: st,
-                    done: at
-                  }) {
-                    if (at) {
-                      m(V);
-                      return;
-                    }
-                    Object.assign(V.styles, st.styles), V.items.push(...st.items), z();
-                  }, U);
-                }
-                const E = T.getReader(), V = {
-                  items: [],
-                  styles: /* @__PURE__ */ Object.create(null)
-                };
-                z();
-              });
-            }
-            getStructTree() {
-              return this._transport.getStructTree(this._pageIndex);
-            }
-            _destroy() {
-              this.destroyed = !0;
-              const r = [];
-              for (const T of this._intentStates.values())
-                if (this._abortOperatorList({
-                  intentState: T,
-                  reason: new Error("Page was destroyed."),
-                  force: !0
-                }), !T.opListReadCapability)
-                  for (const m of T.renderTasks)
-                    r.push(m.completed), m.cancel();
-              return this.objs.clear(), Z(this, l, !1), W(this, X, Ee).call(this), Promise.all(r);
-            }
-            cleanup(r = !1) {
-              Z(this, l, !0);
-              const T = W(this, P, he).call(this, !1);
-              return r && T && this._stats && (this._stats = new _display_utils.StatTimer()), T;
-            }
-            _startRenderPage(r, T) {
-              var U, z;
-              const m = this._intentStates.get(T);
-              m && ((U = this._stats) == null || U.timeEnd("Page Request"), (z = m.displayReadyCapability) == null || z.resolve(r));
-            }
-            _renderPageChunk(r, T) {
-              for (let m = 0, U = r.length; m < U; m++)
-                T.operatorList.fnArray.push(r.fnArray[m]), T.operatorList.argsArray.push(r.argsArray[m]);
-              T.operatorList.lastChunk = r.lastChunk, T.operatorList.separateAnnots = r.separateAnnots;
-              for (const m of T.renderTasks)
-                m.operatorListChanged();
-              r.lastChunk && W(this, P, he).call(this, !0);
-            }
-            _pumpOperatorList({
-              renderingIntent: r,
-              cacheKey: T,
-              annotationStorageSerializable: m
-            }) {
-              const {
-                map: U,
-                transfers: z
-              } = m, V = this._transport.messageHandler.sendWithStream("GetOperatorList", {
-                pageIndex: this._pageIndex,
-                intent: r,
-                cacheKey: T,
-                annotationStorage: U
-              }, z).getReader(), st = this._intentStates.get(T);
-              st.streamReader = V;
-              const at = () => {
-                V.read().then(({
-                  value: H,
-                  done: lt
-                }) => {
-                  if (lt) {
-                    st.streamReader = null;
-                    return;
-                  }
-                  this._transport.destroyed || (this._renderPageChunk(H, st), at());
-                }, (H) => {
-                  if (st.streamReader = null, !this._transport.destroyed) {
-                    if (st.operatorList) {
-                      st.operatorList.lastChunk = !0;
-                      for (const lt of st.renderTasks)
-                        lt.operatorListChanged();
-                      W(this, P, he).call(this, !0);
-                    }
-                    if (st.displayReadyCapability)
-                      st.displayReadyCapability.reject(H);
-                    else if (st.opListReadCapability)
-                      st.opListReadCapability.reject(H);
-                    else
-                      throw H;
-                  }
-                });
-              };
-              at();
-            }
-            _abortOperatorList({
-              intentState: r,
-              reason: T,
-              force: m = !1
-            }) {
-              if (r.streamReader) {
-                if (r.streamReaderCancelTimeout && (clearTimeout(r.streamReaderCancelTimeout), r.streamReaderCancelTimeout = null), !m) {
-                  if (r.renderTasks.size > 0)
-                    return;
-                  if (T instanceof _display_utils.RenderingCancelledException) {
-                    let U = RENDERING_CANCELLED_TIMEOUT;
-                    T.extraDelay > 0 && T.extraDelay < 1e3 && (U += T.extraDelay), r.streamReaderCancelTimeout = setTimeout(() => {
-                      r.streamReaderCancelTimeout = null, this._abortOperatorList({
-                        intentState: r,
-                        reason: T,
-                        force: !0
-                      });
-                    }, U);
-                    return;
-                  }
-                }
-                if (r.streamReader.cancel(new _util.AbortException(T.message)).catch(() => {
-                }), r.streamReader = null, !this._transport.destroyed) {
-                  for (const [U, z] of this._intentStates)
-                    if (z === r) {
-                      this._intentStates.delete(U);
-                      break;
-                    }
-                  this.cleanup();
-                }
-              }
-            }
-            get stats() {
-              return this._stats;
-            }
-          }
-          et = new WeakMap(), l = new WeakMap(), P = new WeakSet(), he = function(r = !1) {
-            if (W(this, X, Ee).call(this), !t(this, l) || this.destroyed)
-              return !1;
-            if (r)
-              return Z(this, et, setTimeout(() => {
-                Z(this, et, null), W(this, P, he).call(this, !1);
-              }, DELAYED_CLEANUP_TIMEOUT)), !1;
-            for (const {
-              renderTasks: T,
-              operatorList: m
-            } of this._intentStates.values())
-              if (T.size > 0 || !m.lastChunk)
-                return !1;
-            return this._intentStates.clear(), this.objs.clear(), Z(this, l, !1), !0;
-          }, X = new WeakSet(), Ee = function() {
-            t(this, et) && (clearTimeout(t(this, et)), Z(this, et, null));
-          }, exports.PDFPageProxy = PDFPageProxy;
-          class LoopbackPort {
-            constructor() {
-              L(this, B, /* @__PURE__ */ new Set());
-              L(this, F, Promise.resolve());
-            }
-            postMessage(r, T) {
-              const m = {
-                data: structuredClone(r, T ? {
-                  transfer: T
-                } : null)
-              };
-              t(this, F).then(() => {
-                for (const U of t(this, B))
-                  U.call(this, m);
-              });
-            }
-            addEventListener(r, T) {
-              t(this, B).add(T);
-            }
-            removeEventListener(r, T) {
-              t(this, B).delete(T);
-            }
-            terminate() {
-              t(this, B).clear();
-            }
-          }
-          B = new WeakMap(), F = new WeakMap(), exports.LoopbackPort = LoopbackPort;
-          const PDFWorkerUtil = {
-            isWorkerDisabled: !1,
-            fallbackWorkerSrc: null,
-            fakeWorkerId: 0
-          };
-          exports.PDFWorkerUtil = PDFWorkerUtil;
-          {
-            if (_util.isNodeJS && typeof commonjsRequire == "function")
-              PDFWorkerUtil.isWorkerDisabled = !0, PDFWorkerUtil.fallbackWorkerSrc = "./pdf.worker.js";
-            else if (typeof document == "object") {
-              const p = (g = document == null ? void 0 : document.currentScript) == null ? void 0 : g.src;
-              p && (PDFWorkerUtil.fallbackWorkerSrc = p.replace(/(\.(?:min\.)?js)(\?.*)?$/i, ".worker$1$2"));
-            }
-            PDFWorkerUtil.isSameOrigin = function(p, r) {
-              let T;
-              try {
-                if (T = new URL(p), !T.origin || T.origin === "null")
-                  return !1;
-              } catch {
-                return !1;
-              }
-              const m = new URL(r, T);
-              return T.origin === m.origin;
-            }, PDFWorkerUtil.createCDNWrapper = function(p) {
-              const r = `importScripts("${p}");`;
-              return URL.createObjectURL(new Blob([r]));
-            };
-          }
-          const _PDFWorker = class _PDFWorker {
-            constructor({
-              name: p = null,
-              port: r = null,
-              verbosity: T = (0, _util.getVerbosityLevel)()
-            } = {}) {
-              var m;
-              if (this.name = p, this.destroyed = !1, this.verbosity = T, this._readyCapability = new _util.PromiseCapability(), this._port = null, this._webWorker = null, this._messageHandler = null, r) {
-                if ((m = t(_PDFWorker, O)) != null && m.has(r))
-                  throw new Error("Cannot use more than one PDFWorker per port.");
-                (t(_PDFWorker, O) || Z(_PDFWorker, O, /* @__PURE__ */ new WeakMap())).set(r, this), this._initializeFromPort(r);
-                return;
-              }
-              this._initialize();
-            }
-            get promise() {
-              return this._readyCapability.promise;
-            }
-            get port() {
-              return this._port;
-            }
-            get messageHandler() {
-              return this._messageHandler;
-            }
-            _initializeFromPort(p) {
-              this._port = p, this._messageHandler = new _message_handler.MessageHandler("main", "worker", p), this._messageHandler.on("ready", function() {
-              }), this._readyCapability.resolve(), this._messageHandler.send("configure", {
-                verbosity: this.verbosity
-              });
-            }
-            _initialize() {
-              if (!PDFWorkerUtil.isWorkerDisabled && !_PDFWorker._mainThreadWorkerMessageHandler) {
-                let {
-                  workerSrc: p
-                } = _PDFWorker;
-                try {
-                  PDFWorkerUtil.isSameOrigin(window.location.href, p) || (p = PDFWorkerUtil.createCDNWrapper(new URL(p, window.location).href));
-                  const r = new Worker(p), T = new _message_handler.MessageHandler("main", "worker", r), m = () => {
-                    r.removeEventListener("error", U), T.destroy(), r.terminate(), this.destroyed ? this._readyCapability.reject(new Error("Worker was destroyed")) : this._setupFakeWorker();
-                  }, U = () => {
-                    this._webWorker || m();
-                  };
-                  r.addEventListener("error", U), T.on("test", (E) => {
-                    if (r.removeEventListener("error", U), this.destroyed) {
-                      m();
-                      return;
-                    }
-                    E ? (this._messageHandler = T, this._port = r, this._webWorker = r, this._readyCapability.resolve(), T.send("configure", {
-                      verbosity: this.verbosity
-                    })) : (this._setupFakeWorker(), T.destroy(), r.terminate());
-                  }), T.on("ready", (E) => {
-                    if (r.removeEventListener("error", U), this.destroyed) {
-                      m();
-                      return;
-                    }
-                    try {
-                      z();
-                    } catch {
-                      this._setupFakeWorker();
-                    }
-                  });
-                  const z = () => {
-                    const E = new Uint8Array();
-                    T.send("test", E, [E.buffer]);
-                  };
-                  z();
-                  return;
-                } catch {
-                  (0, _util.info)("The worker has been disabled.");
-                }
-              }
-              this._setupFakeWorker();
-            }
-            _setupFakeWorker() {
-              PDFWorkerUtil.isWorkerDisabled || ((0, _util.warn)("Setting up fake worker."), PDFWorkerUtil.isWorkerDisabled = !0), _PDFWorker._setupFakeWorkerGlobal.then((p) => {
-                if (this.destroyed) {
-                  this._readyCapability.reject(new Error("Worker was destroyed"));
-                  return;
-                }
-                const r = new LoopbackPort();
-                this._port = r;
-                const T = `fake${PDFWorkerUtil.fakeWorkerId++}`, m = new _message_handler.MessageHandler(T + "_worker", T, r);
-                p.setup(m, r);
-                const U = new _message_handler.MessageHandler(T, T + "_worker", r);
-                this._messageHandler = U, this._readyCapability.resolve(), U.send("configure", {
-                  verbosity: this.verbosity
-                });
-              }).catch((p) => {
-                this._readyCapability.reject(new Error(`Setting up fake worker failed: "${p.message}".`));
-              });
-            }
-            destroy() {
-              var p;
-              this.destroyed = !0, this._webWorker && (this._webWorker.terminate(), this._webWorker = null), (p = t(_PDFWorker, O)) == null || p.delete(this._port), this._port = null, this._messageHandler && (this._messageHandler.destroy(), this._messageHandler = null);
-            }
-            static fromPort(p) {
-              var T;
-              if (!(p != null && p.port))
-                throw new Error("PDFWorker.fromPort - invalid method signature.");
-              const r = (T = t(this, O)) == null ? void 0 : T.get(p.port);
-              if (r) {
-                if (r._pendingDestroy)
-                  throw new Error("PDFWorker.fromPort - the worker is being destroyed.\nPlease remember to await `PDFDocumentLoadingTask.destroy()`-calls.");
-                return r;
-              }
-              return new _PDFWorker(p);
-            }
-            static get workerSrc() {
-              if (_worker_options.GlobalWorkerOptions.workerSrc)
-                return _worker_options.GlobalWorkerOptions.workerSrc;
-              if (PDFWorkerUtil.fallbackWorkerSrc !== null)
-                return _util.isNodeJS || (0, _display_utils.deprecated)('No "GlobalWorkerOptions.workerSrc" specified.'), PDFWorkerUtil.fallbackWorkerSrc;
-              throw new Error('No "GlobalWorkerOptions.workerSrc" specified.');
-            }
-            static get _mainThreadWorkerMessageHandler() {
-              var p;
-              try {
-                return ((p = globalThis.pdfjsWorker) == null ? void 0 : p.WorkerMessageHandler) || null;
-              } catch {
-                return null;
-              }
-            }
-            static get _setupFakeWorkerGlobal() {
-              const loader = async () => {
-                const mainWorkerMessageHandler = this._mainThreadWorkerMessageHandler;
-                if (mainWorkerMessageHandler)
-                  return mainWorkerMessageHandler;
-                if (_util.isNodeJS && typeof commonjsRequire == "function") {
-                  const worker = eval("require")(this.workerSrc);
-                  return worker.WorkerMessageHandler;
-                }
-                return await (0, _display_utils.loadScript)(this.workerSrc), window.pdfjsWorker.WorkerMessageHandler;
-              };
-              return (0, _util.shadow)(this, "_setupFakeWorkerGlobal", loader());
-            }
-          };
-          O = new WeakMap(), L(_PDFWorker, O, void 0);
-          let PDFWorker = _PDFWorker;
-          exports.PDFWorker = PDFWorker;
-          class WorkerTransport {
-            constructor(r, T, m, U, z) {
-              L(this, u);
-              L(this, I, /* @__PURE__ */ new Map());
-              L(this, x, /* @__PURE__ */ new Map());
-              L(this, v, /* @__PURE__ */ new Map());
-              L(this, A, null);
-              this.messageHandler = r, this.loadingTask = T, this.commonObjs = new PDFObjects(), this.fontLoader = new _font_loader.FontLoader({
-                ownerDocument: U.ownerDocument,
-                styleElement: U.styleElement
-              }), this._params = U, this.canvasFactory = z.canvasFactory, this.filterFactory = z.filterFactory, this.cMapReaderFactory = z.cMapReaderFactory, this.standardFontDataFactory = z.standardFontDataFactory, this.destroyed = !1, this.destroyCapability = null, this._networkStream = m, this._fullReader = null, this._lastProgress = null, this.downloadInfoCapability = new _util.PromiseCapability(), this.setupMessageHandler();
-            }
-            get annotationStorage() {
-              return (0, _util.shadow)(this, "annotationStorage", new _annotation_storage.AnnotationStorage());
-            }
-            getRenderingIntent(r, T = _util.AnnotationMode.ENABLE, m = null, U = !1) {
-              let z = _util.RenderingIntentFlag.DISPLAY, E = _annotation_storage.SerializableEmpty;
-              switch (r) {
-                case "any":
-                  z = _util.RenderingIntentFlag.ANY;
-                  break;
-                case "display":
-                  break;
-                case "print":
-                  z = _util.RenderingIntentFlag.PRINT;
-                  break;
-                default:
-                  (0, _util.warn)(`getRenderingIntent - invalid intent: ${r}`);
-              }
-              switch (T) {
-                case _util.AnnotationMode.DISABLE:
-                  z += _util.RenderingIntentFlag.ANNOTATIONS_DISABLE;
-                  break;
-                case _util.AnnotationMode.ENABLE:
-                  break;
-                case _util.AnnotationMode.ENABLE_FORMS:
-                  z += _util.RenderingIntentFlag.ANNOTATIONS_FORMS;
-                  break;
-                case _util.AnnotationMode.ENABLE_STORAGE:
-                  z += _util.RenderingIntentFlag.ANNOTATIONS_STORAGE, E = (z & _util.RenderingIntentFlag.PRINT && m instanceof _annotation_storage.PrintAnnotationStorage ? m : this.annotationStorage).serializable;
-                  break;
-                default:
-                  (0, _util.warn)(`getRenderingIntent - invalid annotationMode: ${T}`);
-              }
-              return U && (z += _util.RenderingIntentFlag.OPLIST), {
-                renderingIntent: z,
-                cacheKey: `${z}_${E.hash}`,
-                annotationStorageSerializable: E
-              };
-            }
-            destroy() {
-              var m;
-              if (this.destroyCapability)
-                return this.destroyCapability.promise;
-              this.destroyed = !0, this.destroyCapability = new _util.PromiseCapability(), (m = t(this, A)) == null || m.reject(new Error("Worker was destroyed during onPassword callback"));
-              const r = [];
-              for (const U of t(this, x).values())
-                r.push(U._destroy());
-              t(this, x).clear(), t(this, v).clear(), this.hasOwnProperty("annotationStorage") && this.annotationStorage.resetModified();
-              const T = this.messageHandler.sendWithPromise("Terminate", null);
-              return r.push(T), Promise.all(r).then(() => {
-                var U;
-                this.commonObjs.clear(), this.fontLoader.clear(), t(this, I).clear(), this.filterFactory.destroy(), (U = this._networkStream) == null || U.cancelAllRequests(new _util.AbortException("Worker was terminated.")), this.messageHandler && (this.messageHandler.destroy(), this.messageHandler = null), this.destroyCapability.resolve();
-              }, this.destroyCapability.reject), this.destroyCapability.promise;
-            }
-            setupMessageHandler() {
-              const {
-                messageHandler: r,
-                loadingTask: T
-              } = this;
-              r.on("GetReader", (m, U) => {
-                (0, _util.assert)(this._networkStream, "GetReader - no `IPDFStream` instance available."), this._fullReader = this._networkStream.getFullReader(), this._fullReader.onProgress = (z) => {
-                  this._lastProgress = {
-                    loaded: z.loaded,
-                    total: z.total
-                  };
-                }, U.onPull = () => {
-                  this._fullReader.read().then(function({
-                    value: z,
-                    done: E
-                  }) {
-                    if (E) {
-                      U.close();
-                      return;
-                    }
-                    (0, _util.assert)(z instanceof ArrayBuffer, "GetReader - expected an ArrayBuffer."), U.enqueue(new Uint8Array(z), 1, [z]);
-                  }).catch((z) => {
-                    U.error(z);
-                  });
-                }, U.onCancel = (z) => {
-                  this._fullReader.cancel(z), U.ready.catch((E) => {
-                    if (!this.destroyed)
-                      throw E;
-                  });
-                };
-              }), r.on("ReaderHeadersReady", (m) => {
-                const U = new _util.PromiseCapability(), z = this._fullReader;
-                return z.headersReady.then(() => {
-                  var E;
-                  (!z.isStreamingSupported || !z.isRangeSupported) && (this._lastProgress && ((E = T.onProgress) == null || E.call(T, this._lastProgress)), z.onProgress = (V) => {
-                    var st;
-                    (st = T.onProgress) == null || st.call(T, {
-                      loaded: V.loaded,
-                      total: V.total
-                    });
-                  }), U.resolve({
-                    isStreamingSupported: z.isStreamingSupported,
-                    isRangeSupported: z.isRangeSupported,
-                    contentLength: z.contentLength
-                  });
-                }, U.reject), U.promise;
-              }), r.on("GetRangeReader", (m, U) => {
-                (0, _util.assert)(this._networkStream, "GetRangeReader - no `IPDFStream` instance available.");
-                const z = this._networkStream.getRangeReader(m.begin, m.end);
-                if (!z) {
-                  U.close();
-                  return;
-                }
-                U.onPull = () => {
-                  z.read().then(function({
-                    value: E,
-                    done: V
-                  }) {
-                    if (V) {
-                      U.close();
-                      return;
-                    }
-                    (0, _util.assert)(E instanceof ArrayBuffer, "GetRangeReader - expected an ArrayBuffer."), U.enqueue(new Uint8Array(E), 1, [E]);
-                  }).catch((E) => {
-                    U.error(E);
-                  });
-                }, U.onCancel = (E) => {
-                  z.cancel(E), U.ready.catch((V) => {
-                    if (!this.destroyed)
-                      throw V;
-                  });
-                };
-              }), r.on("GetDoc", ({
-                pdfInfo: m
-              }) => {
-                this._numPages = m.numPages, this._htmlForXfa = m.htmlForXfa, delete m.htmlForXfa, T._capability.resolve(new PDFDocumentProxy(m, this));
-              }), r.on("DocException", function(m) {
-                let U;
-                switch (m.name) {
-                  case "PasswordException":
-                    U = new _util.PasswordException(m.message, m.code);
-                    break;
-                  case "InvalidPDFException":
-                    U = new _util.InvalidPDFException(m.message);
-                    break;
-                  case "MissingPDFException":
-                    U = new _util.MissingPDFException(m.message);
-                    break;
-                  case "UnexpectedResponseException":
-                    U = new _util.UnexpectedResponseException(m.message, m.status);
-                    break;
-                  case "UnknownErrorException":
-                    U = new _util.UnknownErrorException(m.message, m.details);
-                    break;
-                  default:
-                    (0, _util.unreachable)("DocException - expected a valid Error.");
-                }
-                T._capability.reject(U);
-              }), r.on("PasswordRequest", (m) => {
-                if (Z(this, A, new _util.PromiseCapability()), T.onPassword) {
-                  const U = (z) => {
-                    z instanceof Error ? t(this, A).reject(z) : t(this, A).resolve({
-                      password: z
-                    });
-                  };
-                  try {
-                    T.onPassword(U, m.code);
-                  } catch (z) {
-                    t(this, A).reject(z);
-                  }
-                } else
-                  t(this, A).reject(new _util.PasswordException(m.message, m.code));
-                return t(this, A).promise;
-              }), r.on("DataLoaded", (m) => {
-                var U;
-                (U = T.onProgress) == null || U.call(T, {
-                  loaded: m.length,
-                  total: m.length
-                }), this.downloadInfoCapability.resolve(m);
-              }), r.on("StartRenderPage", (m) => {
-                if (this.destroyed)
-                  return;
-                t(this, x).get(m.pageIndex)._startRenderPage(m.transparency, m.cacheKey);
-              }), r.on("commonobj", ([m, U, z]) => {
-                var E;
-                if (!this.destroyed && !this.commonObjs.has(m))
-                  switch (U) {
-                    case "Font":
-                      const V = this._params;
-                      if ("error" in z) {
-                        const H = z.error;
-                        (0, _util.warn)(`Error during font loading: ${H}`), this.commonObjs.resolve(m, H);
-                        break;
-                      }
-                      const st = V.pdfBug && ((E = globalThis.FontInspector) != null && E.enabled) ? (H, lt) => globalThis.FontInspector.fontAdded(H, lt) : null, at = new _font_loader.FontFaceObject(z, {
-                        isEvalSupported: V.isEvalSupported,
-                        disableFontFace: V.disableFontFace,
-                        ignoreErrors: V.ignoreErrors,
-                        inspectFont: st
-                      });
-                      this.fontLoader.bind(at).catch((H) => r.sendWithPromise("FontFallback", {
-                        id: m
-                      })).finally(() => {
-                        !V.fontExtraProperties && at.data && (at.data = null), this.commonObjs.resolve(m, at);
-                      });
-                      break;
-                    case "FontPath":
-                    case "Image":
-                    case "Pattern":
-                      this.commonObjs.resolve(m, z);
-                      break;
-                    default:
-                      throw new Error(`Got unknown common object type ${U}`);
-                  }
-              }), r.on("obj", ([m, U, z, E]) => {
-                var st;
-                if (this.destroyed)
-                  return;
-                const V = t(this, x).get(U);
-                if (!V.objs.has(m))
-                  switch (z) {
-                    case "Image":
-                      if (V.objs.resolve(m, E), E) {
-                        let at;
-                        if (E.bitmap) {
-                          const {
-                            width: H,
-                            height: lt
-                          } = E;
-                          at = H * lt * 4;
-                        } else
-                          at = ((st = E.data) == null ? void 0 : st.length) || 0;
-                        at > _util.MAX_IMAGE_SIZE_TO_CACHE && (V._maybeCleanupAfterRender = !0);
-                      }
-                      break;
-                    case "Pattern":
-                      V.objs.resolve(m, E);
-                      break;
-                    default:
-                      throw new Error(`Got unknown object type ${z}`);
-                  }
-              }), r.on("DocProgress", (m) => {
-                var U;
-                this.destroyed || (U = T.onProgress) == null || U.call(T, {
-                  loaded: m.loaded,
-                  total: m.total
-                });
-              }), r.on("FetchBuiltInCMap", (m) => this.destroyed ? Promise.reject(new Error("Worker was destroyed.")) : this.cMapReaderFactory ? this.cMapReaderFactory.fetch(m) : Promise.reject(new Error("CMapReaderFactory not initialized, see the `useWorkerFetch` parameter."))), r.on("FetchStandardFontData", (m) => this.destroyed ? Promise.reject(new Error("Worker was destroyed.")) : this.standardFontDataFactory ? this.standardFontDataFactory.fetch(m) : Promise.reject(new Error("StandardFontDataFactory not initialized, see the `useWorkerFetch` parameter.")));
-            }
-            getData() {
-              return this.messageHandler.sendWithPromise("GetData", null);
-            }
-            saveDocument() {
-              var m;
-              this.annotationStorage.size <= 0 && (0, _util.warn)("saveDocument called while `annotationStorage` is empty, please use the getData-method instead.");
-              const {
-                map: r,
-                transfers: T
-              } = this.annotationStorage.serializable;
-              return this.messageHandler.sendWithPromise("SaveDocument", {
-                isPureXfa: !!this._htmlForXfa,
-                numPages: this._numPages,
-                annotationStorage: r,
-                filename: ((m = this._fullReader) == null ? void 0 : m.filename) ?? null
-              }, T).finally(() => {
-                this.annotationStorage.resetModified();
-              });
-            }
-            getPage(r) {
-              if (!Number.isInteger(r) || r <= 0 || r > this._numPages)
-                return Promise.reject(new Error("Invalid page request."));
-              const T = r - 1, m = t(this, v).get(T);
-              if (m)
-                return m;
-              const U = this.messageHandler.sendWithPromise("GetPage", {
-                pageIndex: T
-              }).then((z) => {
-                if (this.destroyed)
-                  throw new Error("Transport destroyed");
-                const E = new PDFPageProxy(T, z, this, this._params.pdfBug);
-                return t(this, x).set(T, E), E;
-              });
-              return t(this, v).set(T, U), U;
-            }
-            getPageIndex(r) {
-              return typeof r != "object" || r === null || !Number.isInteger(r.num) || r.num < 0 || !Number.isInteger(r.gen) || r.gen < 0 ? Promise.reject(new Error("Invalid pageIndex request.")) : this.messageHandler.sendWithPromise("GetPageIndex", {
-                num: r.num,
-                gen: r.gen
-              });
-            }
-            getAnnotations(r, T) {
-              return this.messageHandler.sendWithPromise("GetAnnotations", {
-                pageIndex: r,
-                intent: T
-              });
-            }
-            getFieldObjects() {
-              return W(this, u, we).call(this, "GetFieldObjects");
-            }
-            hasJSActions() {
-              return W(this, u, we).call(this, "HasJSActions");
-            }
-            getCalculationOrderIds() {
-              return this.messageHandler.sendWithPromise("GetCalculationOrderIds", null);
-            }
-            getDestinations() {
-              return this.messageHandler.sendWithPromise("GetDestinations", null);
-            }
-            getDestination(r) {
-              return typeof r != "string" ? Promise.reject(new Error("Invalid destination request.")) : this.messageHandler.sendWithPromise("GetDestination", {
-                id: r
-              });
-            }
-            getPageLabels() {
-              return this.messageHandler.sendWithPromise("GetPageLabels", null);
-            }
-            getPageLayout() {
-              return this.messageHandler.sendWithPromise("GetPageLayout", null);
-            }
-            getPageMode() {
-              return this.messageHandler.sendWithPromise("GetPageMode", null);
-            }
-            getViewerPreferences() {
-              return this.messageHandler.sendWithPromise("GetViewerPreferences", null);
-            }
-            getOpenAction() {
-              return this.messageHandler.sendWithPromise("GetOpenAction", null);
-            }
-            getAttachments() {
-              return this.messageHandler.sendWithPromise("GetAttachments", null);
-            }
-            getDocJSActions() {
-              return W(this, u, we).call(this, "GetDocJSActions");
-            }
-            getPageJSActions(r) {
-              return this.messageHandler.sendWithPromise("GetPageJSActions", {
-                pageIndex: r
-              });
-            }
-            getStructTree(r) {
-              return this.messageHandler.sendWithPromise("GetStructTree", {
-                pageIndex: r
-              });
-            }
-            getOutline() {
-              return this.messageHandler.sendWithPromise("GetOutline", null);
-            }
-            getOptionalContentConfig() {
-              return this.messageHandler.sendWithPromise("GetOptionalContentConfig", null).then((r) => new _optional_content_config.OptionalContentConfig(r));
-            }
-            getPermissions() {
-              return this.messageHandler.sendWithPromise("GetPermissions", null);
-            }
-            getMetadata() {
-              const r = "GetMetadata", T = t(this, I).get(r);
-              if (T)
-                return T;
-              const m = this.messageHandler.sendWithPromise(r, null).then((U) => {
-                var z, E;
-                return {
-                  info: U[0],
-                  metadata: U[1] ? new _metadata.Metadata(U[1]) : null,
-                  contentDispositionFilename: ((z = this._fullReader) == null ? void 0 : z.filename) ?? null,
-                  contentLength: ((E = this._fullReader) == null ? void 0 : E.contentLength) ?? null
-                };
-              });
-              return t(this, I).set(r, m), m;
-            }
-            getMarkInfo() {
-              return this.messageHandler.sendWithPromise("GetMarkInfo", null);
-            }
-            async startCleanup(r = !1) {
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-                enableXfa: T
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-                optionalContentConfig: T
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-                transform: z,
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-              L(this, F, !1);
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-              L(this, O, null);
-              L(this, I, null);
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-            static deleteAnnotationElement(s) {
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-                parent: s.parent,
-                uiManager: s._uiManager
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-            }
-            setInForeground() {
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-            }
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-              this._focusEventsAllowed && (t(this, _) ? Z(this, _, !1) : this.parent.setSelected(this));
-            }
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-            }
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-            }
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-            }
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-            }
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-                capture: !0
-              }), s.addEventListener("keydown", (h) => {
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-            }
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-            }
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-              decorative: o
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-              let Vt = parseInt(J[8], 10);
-              Vt = Vt >= 0 && Vt <= 23 ? Vt : 0;
-              let Wt = parseInt(J[9], 10) || 0;
-              return Wt = Wt >= 0 && Wt <= 59 ? Wt : 0, Xt === "-" ? (jt += Vt, Gt += Wt) : Xt === "+" && (jt -= Vt, Gt -= Wt), new Date(Date.UTC(ht, Et, Ct, jt, Gt, Ht));
-            }
-          }
-          d.PDFDateString = r;
-          function T(ft, {
-            scale: K = 1,
-            rotation: J = 0
-          }) {
-            const {
-              width: ht,
-              height: Et
-            } = ft.attributes.style, Ct = [0, 0, parseInt(ht), parseInt(Et)];
-            return new x({
-              viewBox: Ct,
-              scale: K,
-              rotation: J
-            });
-          }
-          function m(ft) {
-            if (ft.startsWith("#")) {
-              const K = parseInt(ft.slice(1), 16);
-              return [(K & 16711680) >> 16, (K & 65280) >> 8, K & 255];
-            }
-            return ft.startsWith("rgb(") ? ft.slice(4, -1).split(",").map((K) => parseInt(K)) : ft.startsWith("rgba(") ? ft.slice(5, -1).split(",").map((K) => parseInt(K)).slice(0, 3) : ((0, P.warn)(`Not a valid color format: "${ft}"`), [0, 0, 0]);
-          }
-          function U(ft) {
-            const K = document.createElement("span");
-            K.style.visibility = "hidden", document.body.append(K);
-            for (const J of ft.keys()) {
-              K.style.color = J;
-              const ht = window.getComputedStyle(K).color;
-              ft.set(J, m(ht));
-            }
-            K.remove();
-          }
-          function z(ft) {
-            const {
-              a: K,
-              b: J,
-              c: ht,
-              d: Et,
-              e: Ct,
-              f: jt
-            } = ft.getTransform();
-            return [K, J, ht, Et, Ct, jt];
-          }
-          function E(ft) {
-            const {
-              a: K,
-              b: J,
-              c: ht,
-              d: Et,
-              e: Ct,
-              f: jt
-            } = ft.getTransform().invertSelf();
-            return [K, J, ht, Et, Ct, jt];
-          }
-          function V(ft, K, J = !1, ht = !0) {
-            if (K instanceof x) {
-              const {
-                pageWidth: Et,
-                pageHeight: Ct
-              } = K.rawDims, {
-                style: jt
-              } = ft, Gt = P.FeatureTest.isCSSRoundSupported, Ht = `var(--scale-factor) * ${Et}px`, Xt = `var(--scale-factor) * ${Ct}px`, Vt = Gt ? `round(${Ht}, 1px)` : `calc(${Ht})`, Wt = Gt ? `round(${Xt}, 1px)` : `calc(${Xt})`;
-              !J || K.rotation % 180 === 0 ? (jt.width = Vt, jt.height = Wt) : (jt.width = Wt, jt.height = Vt);
-            }
-            ht && ft.setAttribute("data-main-rotation", K.rotation);
-          }
-        },
-        /* 7 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.BaseStandardFontDataFactory = d.BaseSVGFactory = d.BaseFilterFactory = d.BaseCanvasFactory = d.BaseCMapReaderFactory = void 0;
-          var l = et(1);
-          class P {
-            constructor() {
-              this.constructor === P && (0, l.unreachable)("Cannot initialize BaseFilterFactory.");
-            }
-            addFilter(g) {
-              return "none";
-            }
-            addHCMFilter(g, O) {
-              return "none";
-            }
-            addHighlightHCMFilter(g, O, I, x) {
-              return "none";
-            }
-            destroy(g = !1) {
-            }
-          }
-          d.BaseFilterFactory = P;
-          class rt {
-            constructor() {
-              this.constructor === rt && (0, l.unreachable)("Cannot initialize BaseCanvasFactory.");
-            }
-            create(g, O) {
-              if (g <= 0 || O <= 0)
-                throw new Error("Invalid canvas size");
-              const I = this._createCanvas(g, O);
-              return {
-                canvas: I,
-                context: I.getContext("2d")
-              };
-            }
-            reset(g, O, I) {
-              if (!g.canvas)
-                throw new Error("Canvas is not specified");
-              if (O <= 0 || I <= 0)
-                throw new Error("Invalid canvas size");
-              g.canvas.width = O, g.canvas.height = I;
-            }
-            destroy(g) {
-              if (!g.canvas)
-                throw new Error("Canvas is not specified");
-              g.canvas.width = 0, g.canvas.height = 0, g.canvas = null, g.context = null;
-            }
-            _createCanvas(g, O) {
-              (0, l.unreachable)("Abstract method `_createCanvas` called.");
-            }
-          }
-          d.BaseCanvasFactory = rt;
-          class X {
-            constructor({
-              baseUrl: g = null,
-              isCompressed: O = !0
-            }) {
-              this.constructor === X && (0, l.unreachable)("Cannot initialize BaseCMapReaderFactory."), this.baseUrl = g, this.isCompressed = O;
-            }
-            async fetch({
-              name: g
-            }) {
-              if (!this.baseUrl)
-                throw new Error('The CMap "baseUrl" parameter must be specified, ensure that the "cMapUrl" and "cMapPacked" API parameters are provided.');
-              if (!g)
-                throw new Error("CMap name must be specified.");
-              const O = this.baseUrl + g + (this.isCompressed ? ".bcmap" : ""), I = this.isCompressed ? l.CMapCompressionType.BINARY : l.CMapCompressionType.NONE;
-              return this._fetchData(O, I).catch((x) => {
-                throw new Error(`Unable to load ${this.isCompressed ? "binary " : ""}CMap at: ${O}`);
-              });
-            }
-            _fetchData(g, O) {
-              (0, l.unreachable)("Abstract method `_fetchData` called.");
-            }
-          }
-          d.BaseCMapReaderFactory = X;
-          class pt {
-            constructor({
-              baseUrl: g = null
-            }) {
-              this.constructor === pt && (0, l.unreachable)("Cannot initialize BaseStandardFontDataFactory."), this.baseUrl = g;
-            }
-            async fetch({
-              filename: g
-            }) {
-              if (!this.baseUrl)
-                throw new Error('The standard font "baseUrl" parameter must be specified, ensure that the "standardFontDataUrl" API parameter is provided.');
-              if (!g)
-                throw new Error("Font filename must be specified.");
-              const O = `${this.baseUrl}${g}`;
-              return this._fetchData(O).catch((I) => {
-                throw new Error(`Unable to load font data at: ${O}`);
-              });
-            }
-            _fetchData(g) {
-              (0, l.unreachable)("Abstract method `_fetchData` called.");
-            }
-          }
-          d.BaseStandardFontDataFactory = pt;
-          class B {
-            constructor() {
-              this.constructor === B && (0, l.unreachable)("Cannot initialize BaseSVGFactory.");
-            }
-            create(g, O, I = !1) {
-              if (g <= 0 || O <= 0)
-                throw new Error("Invalid SVG dimensions");
-              const x = this._createSVG("svg:svg");
-              return x.setAttribute("version", "1.1"), I || (x.setAttribute("width", `${g}px`), x.setAttribute("height", `${O}px`)), x.setAttribute("preserveAspectRatio", "none"), x.setAttribute("viewBox", `0 0 ${g} ${O}`), x;
-            }
-            createElement(g) {
-              if (typeof g != "string")
-                throw new Error("Invalid SVG element type");
-              return this._createSVG(g);
-            }
-            _createSVG(g) {
-              (0, l.unreachable)("Abstract method `_createSVG` called.");
-            }
-          }
-          d.BaseSVGFactory = B;
-        },
-        /* 8 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.MurmurHash3_64 = void 0;
-          var l = et(1);
-          const P = 3285377520, rt = 4294901760, X = 65535;
-          class pt {
-            constructor(F) {
-              this.h1 = F ? F & 4294967295 : P, this.h2 = F ? F & 4294967295 : P;
-            }
-            update(F) {
-              let g, O;
-              if (typeof F == "string") {
-                g = new Uint8Array(F.length * 2), O = 0;
-                for (let k = 0, p = F.length; k < p; k++) {
-                  const r = F.charCodeAt(k);
-                  r <= 255 ? g[O++] = r : (g[O++] = r >>> 8, g[O++] = r & 255);
-                }
-              } else if ((0, l.isArrayBuffer)(F))
-                g = F.slice(), O = g.byteLength;
-              else
-                throw new Error("Wrong data format in MurmurHash3_64_update. Input must be a string or array.");
-              const I = O >> 2, x = O - I * 4, v = new Uint32Array(g.buffer, 0, I);
-              let A = 0, u = 0, _ = this.h1, w = this.h2;
-              const C = 3432918353, y = 461845907, a = C & X, c = y & X;
-              for (let k = 0; k < I; k++)
-                k & 1 ? (A = v[k], A = A * C & rt | A * a & X, A = A << 15 | A >>> 17, A = A * y & rt | A * c & X, _ ^= A, _ = _ << 13 | _ >>> 19, _ = _ * 5 + 3864292196) : (u = v[k], u = u * C & rt | u * a & X, u = u << 15 | u >>> 17, u = u * y & rt | u * c & X, w ^= u, w = w << 13 | w >>> 19, w = w * 5 + 3864292196);
-              switch (A = 0, x) {
-                case 3:
-                  A ^= g[I * 4 + 2] << 16;
-                case 2:
-                  A ^= g[I * 4 + 1] << 8;
-                case 1:
-                  A ^= g[I * 4], A = A * C & rt | A * a & X, A = A << 15 | A >>> 17, A = A * y & rt | A * c & X, I & 1 ? _ ^= A : w ^= A;
-              }
-              this.h1 = _, this.h2 = w;
-            }
-            hexdigest() {
-              let F = this.h1, g = this.h2;
-              return F ^= g >>> 1, F = F * 3981806797 & rt | F * 36045 & X, g = g * 4283543511 & rt | ((g << 16 | F >>> 16) * 2950163797 & rt) >>> 16, F ^= g >>> 1, F = F * 444984403 & rt | F * 60499 & X, g = g * 3301882366 & rt | ((g << 16 | F >>> 16) * 3120437893 & rt) >>> 16, F ^= g >>> 1, (F >>> 0).toString(16).padStart(8, "0") + (g >>> 0).toString(16).padStart(8, "0");
-            }
-          }
-          d.MurmurHash3_64 = pt;
-        },
-        /* 9 */
-        /***/
-        (dt, d, et) => {
-          var X;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.FontLoader = d.FontFaceObject = void 0;
-          var l = et(1);
-          class P {
-            constructor({
-              ownerDocument: B = globalThis.document,
-              styleElement: F = null
-            }) {
-              L(this, X, /* @__PURE__ */ new Set());
-              this._document = B, this.nativeFontFaces = /* @__PURE__ */ new Set(), this.styleElement = null, this.loadingRequests = [], this.loadTestFontId = 0;
-            }
-            addNativeFontFace(B) {
-              this.nativeFontFaces.add(B), this._document.fonts.add(B);
-            }
-            removeNativeFontFace(B) {
-              this.nativeFontFaces.delete(B), this._document.fonts.delete(B);
-            }
-            insertRule(B) {
-              this.styleElement || (this.styleElement = this._document.createElement("style"), this._document.documentElement.getElementsByTagName("head")[0].append(this.styleElement));
-              const F = this.styleElement.sheet;
-              F.insertRule(B, F.cssRules.length);
-            }
-            clear() {
-              for (const B of this.nativeFontFaces)
-                this._document.fonts.delete(B);
-              this.nativeFontFaces.clear(), t(this, X).clear(), this.styleElement && (this.styleElement.remove(), this.styleElement = null);
-            }
-            async loadSystemFont(B) {
-              if (!(!B || t(this, X).has(B.loadedName))) {
-                if ((0, l.assert)(!this.disableFontFace, "loadSystemFont shouldn't be called when `disableFontFace` is set."), this.isFontLoadingAPISupported) {
-                  const {
-                    loadedName: F,
-                    src: g,
-                    style: O
-                  } = B, I = new FontFace(F, g, O);
-                  this.addNativeFontFace(I);
-                  try {
-                    await I.load(), t(this, X).add(F);
-                  } catch {
-                    (0, l.warn)(`Cannot load system font: ${B.baseFontName}, installing it could help to improve PDF rendering.`), this.removeNativeFontFace(I);
-                  }
-                  return;
-                }
-                (0, l.unreachable)("Not implemented: loadSystemFont without the Font Loading API.");
-              }
-            }
-            async bind(B) {
-              if (B.attached || B.missingFile && !B.systemFontInfo)
-                return;
-              if (B.attached = !0, B.systemFontInfo) {
-                await this.loadSystemFont(B.systemFontInfo);
-                return;
-              }
-              if (this.isFontLoadingAPISupported) {
-                const g = B.createNativeFontFace();
-                if (g) {
-                  this.addNativeFontFace(g);
-                  try {
-                    await g.loaded;
-                  } catch (O) {
-                    throw (0, l.warn)(`Failed to load font '${g.family}': '${O}'.`), B.disableFontFace = !0, O;
-                  }
-                }
-                return;
-              }
-              const F = B.createFontFaceRule();
-              if (F) {
-                if (this.insertRule(F), this.isSyncFontLoadingSupported)
-                  return;
-                await new Promise((g) => {
-                  const O = this._queueLoadingCallback(g);
-                  this._prepareFontLoadEvent(B, O);
-                });
-              }
-            }
-            get isFontLoadingAPISupported() {
-              var F;
-              const B = !!((F = this._document) != null && F.fonts);
-              return (0, l.shadow)(this, "isFontLoadingAPISupported", B);
-            }
-            get isSyncFontLoadingSupported() {
-              let B = !1;
-              return (l.isNodeJS || typeof navigator < "u" && /Mozilla\/5.0.*?rv:\d+.*? Gecko/.test(navigator.userAgent)) && (B = !0), (0, l.shadow)(this, "isSyncFontLoadingSupported", B);
-            }
-            _queueLoadingCallback(B) {
-              function F() {
-                for ((0, l.assert)(!O.done, "completeRequest() cannot be called twice."), O.done = !0; g.length > 0 && g[0].done; ) {
-                  const I = g.shift();
-                  setTimeout(I.callback, 0);
-                }
-              }
-              const {
-                loadingRequests: g
-              } = this, O = {
-                done: !1,
-                complete: F,
-                callback: B
-              };
-              return g.push(O), O;
-            }
-            get _loadTestFont() {
-              const B = atob("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");
-              return (0, l.shadow)(this, "_loadTestFont", B);
-            }
-            _prepareFontLoadEvent(B, F) {
-              function g(m, U) {
-                return m.charCodeAt(U) << 24 | m.charCodeAt(U + 1) << 16 | m.charCodeAt(U + 2) << 8 | m.charCodeAt(U + 3) & 255;
-              }
-              function O(m, U, z, E) {
-                const V = m.substring(0, U), st = m.substring(U + z);
-                return V + E + st;
-              }
-              let I, x;
-              const v = this._document.createElement("canvas");
-              v.width = 1, v.height = 1;
-              const A = v.getContext("2d");
-              let u = 0;
-              function _(m, U) {
-                if (++u > 30) {
-                  (0, l.warn)("Load test font never loaded."), U();
-                  return;
-                }
-                if (A.font = "30px " + m, A.fillText(".", 0, 20), A.getImageData(0, 0, 1, 1).data[3] > 0) {
-                  U();
-                  return;
-                }
-                setTimeout(_.bind(null, m, U));
-              }
-              const w = `lt${Date.now()}${this.loadTestFontId++}`;
-              let C = this._loadTestFont;
-              C = O(C, 976, w.length, w);
-              const a = 16, c = 1482184792;
-              let k = g(C, a);
-              for (I = 0, x = w.length - 3; I < x; I += 4)
-                k = k - c + g(w, I) | 0;
-              I < w.length && (k = k - c + g(w + "XXX", I) | 0), C = O(C, a, 4, (0, l.string32)(k));
-              const p = `url(data:font/opentype;base64,${btoa(C)});`, r = `@font-face {font-family:"${w}";src:${p}}`;
-              this.insertRule(r);
-              const T = this._document.createElement("div");
-              T.style.visibility = "hidden", T.style.width = T.style.height = "10px", T.style.position = "absolute", T.style.top = T.style.left = "0px";
-              for (const m of [B.loadedName, w]) {
-                const U = this._document.createElement("span");
-                U.textContent = "Hi", U.style.fontFamily = m, T.append(U);
-              }
-              this._document.body.append(T), _(w, () => {
-                T.remove(), F.complete();
-              });
-            }
-          }
-          X = new WeakMap(), d.FontLoader = P;
-          class rt {
-            constructor(B, {
-              isEvalSupported: F = !0,
-              disableFontFace: g = !1,
-              ignoreErrors: O = !1,
-              inspectFont: I = null
-            }) {
-              this.compiledGlyphs = /* @__PURE__ */ Object.create(null);
-              for (const x in B)
-                this[x] = B[x];
-              this.isEvalSupported = F !== !1, this.disableFontFace = g === !0, this.ignoreErrors = O === !0, this._inspectFont = I;
-            }
-            createNativeFontFace() {
-              var F;
-              if (!this.data || this.disableFontFace)
-                return null;
-              let B;
-              if (!this.cssFontInfo)
-                B = new FontFace(this.loadedName, this.data, {});
-              else {
-                const g = {
-                  weight: this.cssFontInfo.fontWeight
-                };
-                this.cssFontInfo.italicAngle && (g.style = `oblique ${this.cssFontInfo.italicAngle}deg`), B = new FontFace(this.cssFontInfo.fontFamily, this.data, g);
-              }
-              return (F = this._inspectFont) == null || F.call(this, this), B;
-            }
-            createFontFaceRule() {
-              var O;
-              if (!this.data || this.disableFontFace)
-                return null;
-              const B = (0, l.bytesToString)(this.data), F = `url(data:${this.mimetype};base64,${btoa(B)});`;
-              let g;
-              if (!this.cssFontInfo)
-                g = `@font-face {font-family:"${this.loadedName}";src:${F}}`;
-              else {
-                let I = `font-weight: ${this.cssFontInfo.fontWeight};`;
-                this.cssFontInfo.italicAngle && (I += `font-style: oblique ${this.cssFontInfo.italicAngle}deg;`), g = `@font-face {font-family:"${this.cssFontInfo.fontFamily}";${I}src:${F}}`;
-              }
-              return (O = this._inspectFont) == null || O.call(this, this, F), g;
-            }
-            getPathGenerator(B, F) {
-              if (this.compiledGlyphs[F] !== void 0)
-                return this.compiledGlyphs[F];
-              let g;
-              try {
-                g = B.get(this.loadedName + "_path_" + F);
-              } catch (O) {
-                if (!this.ignoreErrors)
-                  throw O;
-                return (0, l.warn)(`getPathGenerator - ignoring character: "${O}".`), this.compiledGlyphs[F] = function(I, x) {
-                };
-              }
-              if (this.isEvalSupported && l.FeatureTest.isEvalSupported) {
-                const O = [];
-                for (const I of g) {
-                  const x = I.args !== void 0 ? I.args.join(",") : "";
-                  O.push("c.", I.cmd, "(", x, `);
-`);
-                }
-                return this.compiledGlyphs[F] = new Function("c", "size", O.join(""));
-              }
-              return this.compiledGlyphs[F] = function(O, I) {
-                for (const x of g)
-                  x.cmd === "scale" && (x.args = [I, -I]), O[x.cmd].apply(O, x.args);
-              };
-            }
-          }
-          d.FontFaceObject = rt;
-        },
-        /* 10 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.NodeStandardFontDataFactory = d.NodeFilterFactory = d.NodeCanvasFactory = d.NodeCMapReaderFactory = void 0;
-          var l = et(7);
-          et(1);
-          const P = function(F) {
-            return new Promise((g, O) => {
-              require$$5.readFile(F, (x, v) => {
-                if (x || !v) {
-                  O(new Error(x));
-                  return;
-                }
-                g(new Uint8Array(v));
-              });
-            });
-          };
-          class rt extends l.BaseFilterFactory {
-          }
-          d.NodeFilterFactory = rt;
-          class X extends l.BaseCanvasFactory {
-            _createCanvas(g, O) {
-              return require$$5.createCanvas(g, O);
-            }
-          }
-          d.NodeCanvasFactory = X;
-          class pt extends l.BaseCMapReaderFactory {
-            _fetchData(g, O) {
-              return P(g).then((I) => ({
-                cMapData: I,
-                compressionType: O
-              }));
-            }
-          }
-          d.NodeCMapReaderFactory = pt;
-          class B extends l.BaseStandardFontDataFactory {
-            _fetchData(g) {
-              return P(g);
-            }
-          }
-          d.NodeStandardFontDataFactory = B;
-        },
-        /* 11 */
-        /***/
-        (dt, d, et) => {
-          var H, Qe, gt, Ze;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.CanvasGraphics = void 0;
-          var l = et(1), P = et(6), rt = et(12), X = et(13);
-          const pt = 16, B = 100, F = 4096, g = 15, O = 10, I = 1e3, x = 16;
-          function v(S, i) {
-            if (S._removeMirroring)
-              throw new Error("Context is already forwarding operations.");
-            S.__originalSave = S.save, S.__originalRestore = S.restore, S.__originalRotate = S.rotate, S.__originalScale = S.scale, S.__originalTranslate = S.translate, S.__originalTransform = S.transform, S.__originalSetTransform = S.setTransform, S.__originalResetTransform = S.resetTransform, S.__originalClip = S.clip, S.__originalMoveTo = S.moveTo, S.__originalLineTo = S.lineTo, S.__originalBezierCurveTo = S.bezierCurveTo, S.__originalRect = S.rect, S.__originalClosePath = S.closePath, S.__originalBeginPath = S.beginPath, S._removeMirroring = () => {
-              S.save = S.__originalSave, S.restore = S.__originalRestore, S.rotate = S.__originalRotate, S.scale = S.__originalScale, S.translate = S.__originalTranslate, S.transform = S.__originalTransform, S.setTransform = S.__originalSetTransform, S.resetTransform = S.__originalResetTransform, S.clip = S.__originalClip, S.moveTo = S.__originalMoveTo, S.lineTo = S.__originalLineTo, S.bezierCurveTo = S.__originalBezierCurveTo, S.rect = S.__originalRect, S.closePath = S.__originalClosePath, S.beginPath = S.__originalBeginPath, delete S._removeMirroring;
-            }, S.save = function() {
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-            }, S.translate = function(s, o) {
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-            }, S.scale = function(s, o) {
-              i.scale(s, o), this.__originalScale(s, o);
-            }, S.transform = function(s, o, h, b, M, N) {
-              i.transform(s, o, h, b, M, N), this.__originalTransform(s, o, h, b, M, N);
-            }, S.setTransform = function(s, o, h, b, M, N) {
-              i.setTransform(s, o, h, b, M, N), this.__originalSetTransform(s, o, h, b, M, N);
-            }, S.resetTransform = function() {
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-              i.clip(s), this.__originalClip(s);
-            }, S.moveTo = function(n, s) {
-              i.moveTo(n, s), this.__originalMoveTo(n, s);
-            }, S.lineTo = function(n, s) {
-              i.lineTo(n, s), this.__originalLineTo(n, s);
-            }, S.bezierCurveTo = function(n, s, o, h, b, M) {
-              i.bezierCurveTo(n, s, o, h, b, M), this.__originalBezierCurveTo(n, s, o, h, b, M);
-            }, S.rect = function(n, s, o, h) {
-              i.rect(n, s, o, h), this.__originalRect(n, s, o, h);
-            }, S.closePath = function() {
-              i.closePath(), this.__originalClosePath();
-            }, S.beginPath = function() {
-              i.beginPath(), this.__originalBeginPath();
-            };
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-            }
-            delete(i) {
-              delete this.cache[i];
-            }
-            clear() {
-              for (const i in this.cache) {
-                const n = this.cache[i];
-                this.canvasFactory.destroy(n), delete this.cache[i];
-              }
-            }
-          }
-          function u(S, i, n, s, o, h, b, M, N, tt) {
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-            if (nt === 0 && ct === 0) {
-              const Dt = b * Q + ut, ft = Math.round(Dt), K = M * yt + Ft, J = Math.round(K), ht = (b + N) * Q + ut, Et = Math.abs(Math.round(ht) - ft) || 1, Ct = (M + tt) * yt + Ft, jt = Math.abs(Math.round(Ct) - J) || 1;
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-            }
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-            let b = new Uint8Array(h * (n + 1)), M, N, tt;
-            const Q = i + 7 & -8;
-            let nt = new Uint8Array(Q * n), ct = 0;
-            for (const St of S.data) {
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-            let yt = 0;
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-            for (nt[ct] !== 0 && (b[N] = 2, ++yt), M = 1; M < n; M++) {
-              ct = M * Q, tt = M * h, nt[ct - Q] !== nt[ct] && (b[tt] = nt[ct] ? 1 : 8, ++yt);
-              let St = (nt[ct] ? 4 : 0) + (nt[ct - Q] ? 8 : 0);
-              for (N = 1; N < i; N++)
-                St = (St >> 2) + (nt[ct + 1] ? 4 : 0) + (nt[ct - Q + 1] ? 8 : 0), o[St] && (b[tt + N] = o[St], ++yt), ct++;
-              if (nt[ct - Q] !== nt[ct] && (b[tt + N] = nt[ct] ? 2 : 4, ++yt), yt > s)
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-              let St = M * h;
-              const Dt = St + i;
-              for (; St < Dt && !b[St]; )
-                St++;
-              if (St === Dt)
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-              Ft.moveTo(St % h, M);
-              const ft = St;
-              let K = b[St];
-              do {
-                const J = ut[K];
-                do
-                  St += J;
-                while (!b[St]);
-                const ht = b[St];
-                ht !== 5 && ht !== 10 ? (K = ht, b[St] = 0) : (K = ht & 51 * K >> 4, b[St] &= K >> 2 | K << 2), Ft.lineTo(St % h, St / h | 0), b[St] || --yt;
-              } while (ft !== St);
-              --M;
-            }
-            return nt = null, b = null, function(St) {
-              St.save(), St.scale(1 / i, -1 / n), St.translate(0, -n), St.fill(Ft), St.beginPath(), St.restore();
-            };
-          }
-          class w {
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-              this.alphaIsShape = !1, this.fontSize = 0, this.fontSizeScale = 1, this.textMatrix = l.IDENTITY_MATRIX, this.textMatrixScale = 1, this.fontMatrix = l.FONT_IDENTITY_MATRIX, this.leading = 0, this.x = 0, this.y = 0, this.lineX = 0, this.lineY = 0, this.charSpacing = 0, this.wordSpacing = 0, this.textHScale = 1, this.textRenderingMode = l.TextRenderingMode.FILL, this.textRise = 0, this.fillColor = "#000000", this.strokeColor = "#000000", this.patternFill = !1, this.fillAlpha = 1, this.strokeAlpha = 1, this.lineWidth = 1, this.activeSMask = null, this.transferMaps = "none", this.startNewPathAndClipBox([0, 0, i, n]);
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-              return i.clipBox = this.clipBox.slice(), i;
-            }
-            setCurrentPoint(i, n) {
-              this.x = i, this.y = n;
-            }
-            updatePathMinMax(i, n, s) {
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-            }
-            updateRectMinMax(i, n) {
-              const s = l.Util.applyTransform(n, i), o = l.Util.applyTransform(n.slice(2), i);
-              this.minX = Math.min(this.minX, s[0], o[0]), this.minY = Math.min(this.minY, s[1], o[1]), this.maxX = Math.max(this.maxX, s[0], o[0]), this.maxY = Math.max(this.maxY, s[1], o[1]);
-            }
-            updateScalingPathMinMax(i, n) {
-              l.Util.scaleMinMax(i, n), this.minX = Math.min(this.minX, n[0]), this.maxX = Math.max(this.maxX, n[1]), this.minY = Math.min(this.minY, n[2]), this.maxY = Math.max(this.maxY, n[3]);
-            }
-            updateCurvePathMinMax(i, n, s, o, h, b, M, N, tt, Q) {
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-              if (Q) {
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-                return;
-              }
-              this.updateRectMinMax(i, nt);
-            }
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-              const s = [this.minX, this.minY, this.maxX, this.maxY];
-              if (i === rt.PathType.STROKE) {
-                n || (0, l.unreachable)("Stroke bounding box must include transform.");
-                const o = l.Util.singularValueDecompose2dScale(n), h = o[0] * this.lineWidth / 2, b = o[1] * this.lineWidth / 2;
-                s[0] -= h, s[1] -= b, s[2] += h, s[3] += b;
-              }
-              return s;
-            }
-            updateClipFromPath() {
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-              this.startNewPathAndClipBox(i || [0, 0, 0, 0]);
-            }
-            isEmptyClip() {
-              return this.minX === 1 / 0;
-            }
-            startNewPathAndClipBox(i) {
-              this.clipBox = i, this.minX = 1 / 0, this.minY = 1 / 0, this.maxX = 0, this.maxY = 0;
-            }
-            getClippedPathBoundingBox(i = rt.PathType.FILL, n = null) {
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-            }
-          }
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-              return;
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-            const n = i.height, s = i.width, o = n % x, h = (n - o) / x, b = o === 0 ? h : h + 1, M = S.createImageData(s, x);
-            let N = 0, tt;
-            const Q = i.data, nt = M.data;
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-              for (ct = 0; ct < b; ct++) {
-                for (ut = ct < h ? x : o, tt = 0, yt = 0; yt < ut; yt++) {
-                  const ht = Bt - N;
-                  let Et = 0;
-                  const Ct = ht > ft ? s : ht * 8 - 7, jt = Ct & -8;
-                  let Gt = 0, Ht = 0;
-                  for (; Et < jt; Et += 8)
-                    Ht = Q[N++], St[tt++] = Ht & 128 ? K : J, St[tt++] = Ht & 64 ? K : J, St[tt++] = Ht & 32 ? K : J, St[tt++] = Ht & 16 ? K : J, St[tt++] = Ht & 8 ? K : J, St[tt++] = Ht & 4 ? K : J, St[tt++] = Ht & 2 ? K : J, St[tt++] = Ht & 1 ? K : J;
-                  for (; Et < Ct; Et++)
-                    Gt === 0 && (Ht = Q[N++], Gt = 128), St[tt++] = Ht & Gt ? K : J, Gt >>= 1;
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-                  St[tt++] = 0;
-                S.putImageData(M, 0, ct * x);
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-              ct < b && (Ft = s * o * 4, nt.set(Q.subarray(N, N + Ft)), S.putImageData(M, 0, yt));
-            } else if (i.kind === l.ImageKind.RGB_24BPP)
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-                  nt[tt++] = Q[N++], nt[tt++] = Q[N++], nt[tt++] = Q[N++], nt[tt++] = 255;
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-              }
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-              throw new Error(`bad image kind: ${i.kind}`);
-          }
-          function y(S, i) {
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-              S.drawImage(i.bitmap, 0, 0);
-              return;
-            }
-            const n = i.height, s = i.width, o = n % x, h = (n - o) / x, b = o === 0 ? h : h + 1, M = S.createImageData(s, x);
-            let N = 0;
-            const tt = i.data, Q = M.data;
-            for (let nt = 0; nt < b; nt++) {
-              const ct = nt < h ? x : o;
-              ({
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-              } = (0, X.convertBlackAndWhiteToRGBA)({
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-                srcPos: N,
-                dest: Q,
-                width: s,
-                height: ct,
-                nonBlackColor: 0
-              })), S.putImageData(M, 0, nt * x);
-            }
-          }
-          function a(S, i) {
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-              } = S;
-              i !== "none" && i !== "" && (S.filter = "none");
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-              const b = S[h];
-              if (b === 0)
-                S[h - 3] = i, S[h - 2] = n, S[h - 1] = s;
-              else if (b < 255) {
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-              }
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-          function p(S, i, n) {
-            const s = S.length, o = 1 / 255;
-            for (let h = 3; h < s; h += 4) {
-              const b = n ? n[S[h]] : S[h];
-              i[h] = i[h] * b * o | 0;
-            }
-          }
-          function r(S, i, n) {
-            const s = S.length;
-            for (let o = 3; o < s; o += 4) {
-              const h = S[o - 3] * 77 + S[o - 2] * 152 + S[o - 1] * 28;
-              i[o] = n ? i[o] * n[h >> 8] >> 8 : i[o] * h >> 16;
-            }
-          }
-          function T(S, i, n, s, o, h, b, M, N, tt, Q) {
-            const nt = !!h, ct = nt ? h[0] : 0, yt = nt ? h[1] : 0, ut = nt ? h[2] : 0, Ft = o === "Luminosity" ? r : p, St = Math.min(s, Math.ceil(1048576 / n));
-            for (let Dt = 0; Dt < s; Dt += St) {
-              const ft = Math.min(St, s - Dt), K = S.getImageData(M - tt, Dt + (N - Q), n, ft), J = i.getImageData(M, Dt + N, n, ft);
-              nt && k(K.data, ct, yt, ut), Ft(K.data, J.data, b), i.putImageData(J, M, Dt + N);
-            }
-          }
-          function m(S, i, n, s) {
-            const o = s[0], h = s[1], b = s[2] - o, M = s[3] - h;
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-          }
-          function U(S, i) {
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-            const s = Math.fround((globalThis.devicePixelRatio || 1) * P.PixelsPerInch.PDF_TO_CSS_UNITS);
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-              optionalContentConfig: b,
-              markedContentStack: M = null
-            }, N, tt) {
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-            }
-            getObject(i, n = null) {
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-            }
-            beginDrawing({
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-              viewport: n,
-              transparency: s = !1,
-              background: o = null
-            }) {
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-                const N = this.cachedCanvases.getCanvas("transparent", h, b);
-                this.compositeCtx = this.ctx, this.transparentCanvas = N.canvas, this.ctx = N.context, this.ctx.save(), this.ctx.transform(...(0, P.getCurrentTransform)(this.compositeCtx));
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-              this.ctx.save(), c(this.ctx), i && (this.ctx.transform(...i), this.outputScaleX = i[0], this.outputScaleY = i[0]), this.ctx.transform(...n.transform), this.viewportScale = n.scale, this.baseTransform = (0, P.getCurrentTransform)(this.ctx);
-            }
-            executeOperatorList(i, n, s, o) {
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-              let M = n || 0;
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-              let nt = 0;
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-                  this[ut].apply(this, h[M]);
-                else
-                  for (const Ft of h[M]) {
-                    const Bt = Ft.startsWith("g_") ? ct : yt;
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-                  }
-                if (M++, M === N)
-                  return M;
-                if (tt && ++nt > O) {
-                  if (Date.now() > Q)
-                    return s(), M;
-                  nt = 0;
-                }
-              }
-            }
-            endDrawing() {
-              W(this, H, Qe).call(this), this.cachedCanvases.clear(), this.cachedPatterns.clear();
-              for (const i of this._cachedBitmapsMap.values()) {
-                for (const n of i.values())
-                  typeof HTMLCanvasElement < "u" && n instanceof HTMLCanvasElement && (n.width = n.height = 0);
-                i.clear();
-              }
-              this._cachedBitmapsMap.clear(), W(this, gt, Ze).call(this);
-            }
-            _scaleImage(i, n) {
-              const s = i.width, o = i.height;
-              let h = Math.max(Math.hypot(n[0], n[1]), 1), b = Math.max(Math.hypot(n[2], n[3]), 1), M = s, N = o, tt = "prescale1", Q, nt;
-              for (; h > 2 && M > 1 || b > 2 && N > 1; ) {
-                let ct = M, yt = N;
-                h > 2 && M > 1 && (ct = M >= 16384 ? Math.floor(M / 2) - 1 || 1 : Math.ceil(M / 2), h /= M / ct), b > 2 && N > 1 && (yt = N >= 16384 ? Math.floor(N / 2) - 1 || 1 : Math.ceil(N) / 2, b /= N / yt), Q = this.cachedCanvases.getCanvas(tt, ct, yt), nt = Q.context, nt.clearRect(0, 0, ct, yt), nt.drawImage(i, 0, 0, M, N, 0, 0, ct, yt), i = Q.canvas, M = ct, N = yt, tt = tt === "prescale1" ? "prescale2" : "prescale1";
-              }
-              return {
-                img: i,
-                paintWidth: M,
-                paintHeight: N
-              };
-            }
-            _createMaskCanvas(i) {
-              const n = this.ctx, {
-                width: s,
-                height: o
-              } = i, h = this.current.fillColor, b = this.current.patternFill, M = (0, P.getCurrentTransform)(n);
-              let N, tt, Q, nt;
-              if ((i.bitmap || i.data) && i.count > 1) {
-                const Et = i.bitmap || i.data.buffer;
-                tt = JSON.stringify(b ? M : [M.slice(0, 4), h]), N = this._cachedBitmapsMap.get(Et), N || (N = /* @__PURE__ */ new Map(), this._cachedBitmapsMap.set(Et, N));
-                const Ct = N.get(tt);
-                if (Ct && !b) {
-                  const jt = Math.round(Math.min(M[0], M[2]) + M[4]), Gt = Math.round(Math.min(M[1], M[3]) + M[5]);
-                  return {
-                    canvas: Ct,
-                    offsetX: jt,
-                    offsetY: Gt
-                  };
-                }
-                Q = Ct;
-              }
-              Q || (nt = this.cachedCanvases.getCanvas("maskCanvas", s, o), y(nt.context, i));
-              let ct = l.Util.transform(M, [1 / s, 0, 0, -1 / o, 0, 0]);
-              ct = l.Util.transform(ct, [1, 0, 0, 1, 0, -o]);
-              const yt = l.Util.applyTransform([0, 0], ct), ut = l.Util.applyTransform([s, o], ct), Ft = l.Util.normalizeRect([yt[0], yt[1], ut[0], ut[1]]), Bt = Math.round(Ft[2] - Ft[0]) || 1, St = Math.round(Ft[3] - Ft[1]) || 1, Dt = this.cachedCanvases.getCanvas("fillCanvas", Bt, St), ft = Dt.context, K = Math.min(yt[0], ut[0]), J = Math.min(yt[1], ut[1]);
-              ft.translate(-K, -J), ft.transform(...ct), Q || (Q = this._scaleImage(nt.canvas, (0, P.getCurrentTransformInverse)(ft)), Q = Q.img, N && b && N.set(tt, Q)), ft.imageSmoothingEnabled = U((0, P.getCurrentTransform)(ft), i.interpolate), u(ft, Q, 0, 0, Q.width, Q.height, 0, 0, s, o), ft.globalCompositeOperation = "source-in";
-              const ht = l.Util.transform((0, P.getCurrentTransformInverse)(ft), [1, 0, 0, 1, -K, -J]);
-              return ft.fillStyle = b ? h.getPattern(n, this, ht, rt.PathType.FILL) : h, ft.fillRect(0, 0, s, o), N && !b && (this.cachedCanvases.delete("fillCanvas"), N.set(tt, Dt.canvas)), {
-                canvas: Dt.canvas,
-                offsetX: Math.round(K),
-                offsetY: Math.round(J)
-              };
-            }
-            setLineWidth(i) {
-              i !== this.current.lineWidth && (this._cachedScaleForStroking[0] = -1), this.current.lineWidth = i, this.ctx.lineWidth = i;
-            }
-            setLineCap(i) {
-              this.ctx.lineCap = z[i];
-            }
-            setLineJoin(i) {
-              this.ctx.lineJoin = E[i];
-            }
-            setMiterLimit(i) {
-              this.ctx.miterLimit = i;
-            }
-            setDash(i, n) {
-              const s = this.ctx;
-              s.setLineDash !== void 0 && (s.setLineDash(i), s.lineDashOffset = n);
-            }
-            setRenderingIntent(i) {
-            }
-            setFlatness(i) {
-            }
-            setGState(i) {
-              for (const [n, s] of i)
-                switch (n) {
-                  case "LW":
-                    this.setLineWidth(s);
-                    break;
-                  case "LC":
-                    this.setLineCap(s);
-                    break;
-                  case "LJ":
-                    this.setLineJoin(s);
-                    break;
-                  case "ML":
-                    this.setMiterLimit(s);
-                    break;
-                  case "D":
-                    this.setDash(s[0], s[1]);
-                    break;
-                  case "RI":
-                    this.setRenderingIntent(s);
-                    break;
-                  case "FL":
-                    this.setFlatness(s);
-                    break;
-                  case "Font":
-                    this.setFont(s[0], s[1]);
-                    break;
-                  case "CA":
-                    this.current.strokeAlpha = s;
-                    break;
-                  case "ca":
-                    this.current.fillAlpha = s, this.ctx.globalAlpha = s;
-                    break;
-                  case "BM":
-                    this.ctx.globalCompositeOperation = s;
-                    break;
-                  case "SMask":
-                    this.current.activeSMask = s ? this.tempSMask : null, this.tempSMask = null, this.checkSMaskState();
-                    break;
-                  case "TR":
-                    this.ctx.filter = this.current.transferMaps = this.filterFactory.addFilter(s);
-                    break;
-                }
-            }
-            get inSMaskMode() {
-              return !!this.suspendedCtx;
-            }
-            checkSMaskState() {
-              const i = this.inSMaskMode;
-              this.current.activeSMask && !i ? this.beginSMaskMode() : !this.current.activeSMask && i && this.endSMaskMode();
-            }
-            beginSMaskMode() {
-              if (this.inSMaskMode)
-                throw new Error("beginSMaskMode called while already in smask mode");
-              const i = this.ctx.canvas.width, n = this.ctx.canvas.height, s = "smaskGroupAt" + this.groupLevel, o = this.cachedCanvases.getCanvas(s, i, n);
-              this.suspendedCtx = this.ctx, this.ctx = o.context;
-              const h = this.ctx;
-              h.setTransform(...(0, P.getCurrentTransform)(this.suspendedCtx)), a(this.suspendedCtx, h), v(h, this.suspendedCtx), this.setGState([["BM", "source-over"], ["ca", 1], ["CA", 1]]);
-            }
-            endSMaskMode() {
-              if (!this.inSMaskMode)
-                throw new Error("endSMaskMode called while not in smask mode");
-              this.ctx._removeMirroring(), a(this.ctx, this.suspendedCtx), this.ctx = this.suspendedCtx, this.suspendedCtx = null;
-            }
-            compose(i) {
-              if (!this.current.activeSMask)
-                return;
-              i ? (i[0] = Math.floor(i[0]), i[1] = Math.floor(i[1]), i[2] = Math.ceil(i[2]), i[3] = Math.ceil(i[3])) : i = [0, 0, this.ctx.canvas.width, this.ctx.canvas.height];
-              const n = this.current.activeSMask, s = this.suspendedCtx;
-              m(s, n, this.ctx, i), this.ctx.save(), this.ctx.setTransform(1, 0, 0, 1, 0, 0), this.ctx.clearRect(0, 0, this.ctx.canvas.width, this.ctx.canvas.height), this.ctx.restore();
-            }
-            save() {
-              this.inSMaskMode ? (a(this.ctx, this.suspendedCtx), this.suspendedCtx.save()) : this.ctx.save();
-              const i = this.current;
-              this.stateStack.push(i), this.current = i.clone();
-            }
-            restore() {
-              this.stateStack.length === 0 && this.inSMaskMode && this.endSMaskMode(), this.stateStack.length !== 0 && (this.current = this.stateStack.pop(), this.inSMaskMode ? (this.suspendedCtx.restore(), a(this.suspendedCtx, this.ctx)) : this.ctx.restore(), this.checkSMaskState(), this.pendingClip = null, this._cachedScaleForStroking[0] = -1, this._cachedGetSinglePixelWidth = null);
-            }
-            transform(i, n, s, o, h, b) {
-              this.ctx.transform(i, n, s, o, h, b), this._cachedScaleForStroking[0] = -1, this._cachedGetSinglePixelWidth = null;
-            }
-            constructPath(i, n, s) {
-              const o = this.ctx, h = this.current;
-              let b = h.x, M = h.y, N, tt;
-              const Q = (0, P.getCurrentTransform)(o), nt = Q[0] === 0 && Q[3] === 0 || Q[1] === 0 && Q[2] === 0, ct = nt ? s.slice(0) : null;
-              for (let yt = 0, ut = 0, Ft = i.length; yt < Ft; yt++)
-                switch (i[yt] | 0) {
-                  case l.OPS.rectangle:
-                    b = n[ut++], M = n[ut++];
-                    const Bt = n[ut++], St = n[ut++], Dt = b + Bt, ft = M + St;
-                    o.moveTo(b, M), Bt === 0 || St === 0 ? o.lineTo(Dt, ft) : (o.lineTo(Dt, M), o.lineTo(Dt, ft), o.lineTo(b, ft)), nt || h.updateRectMinMax(Q, [b, M, Dt, ft]), o.closePath();
-                    break;
-                  case l.OPS.moveTo:
-                    b = n[ut++], M = n[ut++], o.moveTo(b, M), nt || h.updatePathMinMax(Q, b, M);
-                    break;
-                  case l.OPS.lineTo:
-                    b = n[ut++], M = n[ut++], o.lineTo(b, M), nt || h.updatePathMinMax(Q, b, M);
-                    break;
-                  case l.OPS.curveTo:
-                    N = b, tt = M, b = n[ut + 4], M = n[ut + 5], o.bezierCurveTo(n[ut], n[ut + 1], n[ut + 2], n[ut + 3], b, M), h.updateCurvePathMinMax(Q, N, tt, n[ut], n[ut + 1], n[ut + 2], n[ut + 3], b, M, ct), ut += 6;
-                    break;
-                  case l.OPS.curveTo2:
-                    N = b, tt = M, o.bezierCurveTo(b, M, n[ut], n[ut + 1], n[ut + 2], n[ut + 3]), h.updateCurvePathMinMax(Q, N, tt, b, M, n[ut], n[ut + 1], n[ut + 2], n[ut + 3], ct), b = n[ut + 2], M = n[ut + 3], ut += 4;
-                    break;
-                  case l.OPS.curveTo3:
-                    N = b, tt = M, b = n[ut + 2], M = n[ut + 3], o.bezierCurveTo(n[ut], n[ut + 1], b, M, b, M), h.updateCurvePathMinMax(Q, N, tt, n[ut], n[ut + 1], b, M, b, M, ct), ut += 4;
-                    break;
-                  case l.OPS.closePath:
-                    o.closePath();
-                    break;
-                }
-              nt && h.updateScalingPathMinMax(Q, ct), h.setCurrentPoint(b, M);
-            }
-            closePath() {
-              this.ctx.closePath();
-            }
-            stroke(i = !0) {
-              const n = this.ctx, s = this.current.strokeColor;
-              n.globalAlpha = this.current.strokeAlpha, this.contentVisible && (typeof s == "object" && (s != null && s.getPattern) ? (n.save(), n.strokeStyle = s.getPattern(n, this, (0, P.getCurrentTransformInverse)(n), rt.PathType.STROKE), this.rescaleAndStroke(!1), n.restore()) : this.rescaleAndStroke(!0)), i && this.consumePath(this.current.getClippedPathBoundingBox()), n.globalAlpha = this.current.fillAlpha;
-            }
-            closeStroke() {
-              this.closePath(), this.stroke();
-            }
-            fill(i = !0) {
-              const n = this.ctx, s = this.current.fillColor, o = this.current.patternFill;
-              let h = !1;
-              o && (n.save(), n.fillStyle = s.getPattern(n, this, (0, P.getCurrentTransformInverse)(n), rt.PathType.FILL), h = !0);
-              const b = this.current.getClippedPathBoundingBox();
-              this.contentVisible && b !== null && (this.pendingEOFill ? (n.fill("evenodd"), this.pendingEOFill = !1) : n.fill()), h && n.restore(), i && this.consumePath(b);
-            }
-            eoFill() {
-              this.pendingEOFill = !0, this.fill();
-            }
-            fillStroke() {
-              this.fill(!1), this.stroke(!1), this.consumePath();
-            }
-            eoFillStroke() {
-              this.pendingEOFill = !0, this.fillStroke();
-            }
-            closeFillStroke() {
-              this.closePath(), this.fillStroke();
-            }
-            closeEOFillStroke() {
-              this.pendingEOFill = !0, this.closePath(), this.fillStroke();
-            }
-            endPath() {
-              this.consumePath();
-            }
-            clip() {
-              this.pendingClip = V;
-            }
-            eoClip() {
-              this.pendingClip = st;
-            }
-            beginText() {
-              this.current.textMatrix = l.IDENTITY_MATRIX, this.current.textMatrixScale = 1, this.current.x = this.current.lineX = 0, this.current.y = this.current.lineY = 0;
-            }
-            endText() {
-              const i = this.pendingTextPaths, n = this.ctx;
-              if (i === void 0) {
-                n.beginPath();
-                return;
-              }
-              n.save(), n.beginPath();
-              for (const s of i)
-                n.setTransform(...s.transform), n.translate(s.x, s.y), s.addToPath(n, s.fontSize);
-              n.restore(), n.clip(), n.beginPath(), delete this.pendingTextPaths;
-            }
-            setCharSpacing(i) {
-              this.current.charSpacing = i;
-            }
-            setWordSpacing(i) {
-              this.current.wordSpacing = i;
-            }
-            setHScale(i) {
-              this.current.textHScale = i / 100;
-            }
-            setLeading(i) {
-              this.current.leading = -i;
-            }
-            setFont(i, n) {
-              var Q;
-              const s = this.commonObjs.get(i), o = this.current;
-              if (!s)
-                throw new Error(`Can't find font for ${i}`);
-              if (o.fontMatrix = s.fontMatrix || l.FONT_IDENTITY_MATRIX, (o.fontMatrix[0] === 0 || o.fontMatrix[3] === 0) && (0, l.warn)("Invalid font matrix for font " + i), n < 0 ? (n = -n, o.fontDirection = -1) : o.fontDirection = 1, this.current.font = s, this.current.fontSize = n, s.isType3Font)
-                return;
-              const h = s.loadedName || "sans-serif", b = ((Q = s.systemFontInfo) == null ? void 0 : Q.css) || `"${h}", ${s.fallbackName}`;
-              let M = "normal";
-              s.black ? M = "900" : s.bold && (M = "bold");
-              const N = s.italic ? "italic" : "normal";
-              let tt = n;
-              n < pt ? tt = pt : n > B && (tt = B), this.current.fontSizeScale = n / tt, this.ctx.font = `${N} ${M} ${tt}px ${b}`;
-            }
-            setTextRenderingMode(i) {
-              this.current.textRenderingMode = i;
-            }
-            setTextRise(i) {
-              this.current.textRise = i;
-            }
-            moveText(i, n) {
-              this.current.x = this.current.lineX += i, this.current.y = this.current.lineY += n;
-            }
-            setLeadingMoveText(i, n) {
-              this.setLeading(-n), this.moveText(i, n);
-            }
-            setTextMatrix(i, n, s, o, h, b) {
-              this.current.textMatrix = [i, n, s, o, h, b], this.current.textMatrixScale = Math.hypot(i, n), this.current.x = this.current.lineX = 0, this.current.y = this.current.lineY = 0;
-            }
-            nextLine() {
-              this.moveText(0, this.current.leading);
-            }
-            paintChar(i, n, s, o) {
-              const h = this.ctx, b = this.current, M = b.font, N = b.textRenderingMode, tt = b.fontSize / b.fontSizeScale, Q = N & l.TextRenderingMode.FILL_STROKE_MASK, nt = !!(N & l.TextRenderingMode.ADD_TO_PATH_FLAG), ct = b.patternFill && !M.missingFile;
-              let yt;
-              (M.disableFontFace || nt || ct) && (yt = M.getPathGenerator(this.commonObjs, i)), M.disableFontFace || ct ? (h.save(), h.translate(n, s), h.beginPath(), yt(h, tt), o && h.setTransform(...o), (Q === l.TextRenderingMode.FILL || Q === l.TextRenderingMode.FILL_STROKE) && h.fill(), (Q === l.TextRenderingMode.STROKE || Q === l.TextRenderingMode.FILL_STROKE) && h.stroke(), h.restore()) : ((Q === l.TextRenderingMode.FILL || Q === l.TextRenderingMode.FILL_STROKE) && h.fillText(i, n, s), (Q === l.TextRenderingMode.STROKE || Q === l.TextRenderingMode.FILL_STROKE) && h.strokeText(i, n, s)), nt && (this.pendingTextPaths || (this.pendingTextPaths = [])).push({
-                transform: (0, P.getCurrentTransform)(h),
-                x: n,
-                y: s,
-                fontSize: tt,
-                addToPath: yt
-              });
-            }
-            get isFontSubpixelAAEnabled() {
-              const {
-                context: i
-              } = this.cachedCanvases.getCanvas("isFontSubpixelAAEnabled", 10, 10);
-              i.scale(1.5, 1), i.fillText("I", 0, 10);
-              const n = i.getImageData(0, 0, 10, 10).data;
-              let s = !1;
-              for (let o = 3; o < n.length; o += 4)
-                if (n[o] > 0 && n[o] < 255) {
-                  s = !0;
-                  break;
-                }
-              return (0, l.shadow)(this, "isFontSubpixelAAEnabled", s);
-            }
-            showText(i) {
-              const n = this.current, s = n.font;
-              if (s.isType3Font)
-                return this.showType3Text(i);
-              const o = n.fontSize;
-              if (o === 0)
-                return;
-              const h = this.ctx, b = n.fontSizeScale, M = n.charSpacing, N = n.wordSpacing, tt = n.fontDirection, Q = n.textHScale * tt, nt = i.length, ct = s.vertical, yt = ct ? 1 : -1, ut = s.defaultVMetrics, Ft = o * n.fontMatrix[0], Bt = n.textRenderingMode === l.TextRenderingMode.FILL && !s.disableFontFace && !n.patternFill;
-              h.save(), h.transform(...n.textMatrix), h.translate(n.x, n.y + n.textRise), tt > 0 ? h.scale(Q, -1) : h.scale(Q, 1);
-              let St;
-              if (n.patternFill) {
-                h.save();
-                const ht = n.fillColor.getPattern(h, this, (0, P.getCurrentTransformInverse)(h), rt.PathType.FILL);
-                St = (0, P.getCurrentTransform)(h), h.restore(), h.fillStyle = ht;
-              }
-              let Dt = n.lineWidth;
-              const ft = n.textMatrixScale;
-              if (ft === 0 || Dt === 0) {
-                const ht = n.textRenderingMode & l.TextRenderingMode.FILL_STROKE_MASK;
-                (ht === l.TextRenderingMode.STROKE || ht === l.TextRenderingMode.FILL_STROKE) && (Dt = this.getSinglePixelWidth());
-              } else
-                Dt /= ft;
-              if (b !== 1 && (h.scale(b, b), Dt /= b), h.lineWidth = Dt, s.isInvalidPDFjsFont) {
-                const ht = [];
-                let Et = 0;
-                for (const Ct of i)
-                  ht.push(Ct.unicode), Et += Ct.width;
-                h.fillText(ht.join(""), 0, 0), n.x += Et * Ft * Q, h.restore(), this.compose();
-                return;
-              }
-              let K = 0, J;
-              for (J = 0; J < nt; ++J) {
-                const ht = i[J];
-                if (typeof ht == "number") {
-                  K += yt * ht * o / 1e3;
-                  continue;
-                }
-                let Et = !1;
-                const Ct = (ht.isSpace ? N : 0) + M, jt = ht.fontChar, Gt = ht.accent;
-                let Ht, Xt, Vt = ht.width;
-                if (ct) {
-                  const $t = ht.vmetric || ut, ot = -(ht.vmetric ? $t[1] : Vt * 0.5) * Ft, Y = $t[2] * Ft;
-                  Vt = $t ? -$t[0] : Vt, Ht = ot / b, Xt = (K + Y) / b;
-                } else
-                  Ht = K / b, Xt = 0;
-                if (s.remeasure && Vt > 0) {
-                  const $t = h.measureText(jt).width * 1e3 / o * b;
-                  if (Vt < $t && this.isFontSubpixelAAEnabled) {
-                    const ot = Vt / $t;
-                    Et = !0, h.save(), h.scale(ot, 1), Ht /= ot;
-                  } else
-                    Vt !== $t && (Ht += (Vt - $t) / 2e3 * o / b);
-                }
-                if (this.contentVisible && (ht.isInFont || s.missingFile)) {
-                  if (Bt && !Gt)
-                    h.fillText(jt, Ht, Xt);
-                  else if (this.paintChar(jt, Ht, Xt, St), Gt) {
-                    const $t = Ht + o * Gt.offset.x / b, ot = Xt - o * Gt.offset.y / b;
-                    this.paintChar(Gt.fontChar, $t, ot, St);
-                  }
-                }
-                const Wt = ct ? Vt * Ft - Ct * tt : Vt * Ft + Ct * tt;
-                K += Wt, Et && h.restore();
-              }
-              ct ? n.y -= K : n.x += K * Q, h.restore(), this.compose();
-            }
-            showType3Text(i) {
-              const n = this.ctx, s = this.current, o = s.font, h = s.fontSize, b = s.fontDirection, M = o.vertical ? 1 : -1, N = s.charSpacing, tt = s.wordSpacing, Q = s.textHScale * b, nt = s.fontMatrix || l.FONT_IDENTITY_MATRIX, ct = i.length, yt = s.textRenderingMode === l.TextRenderingMode.INVISIBLE;
-              let ut, Ft, Bt, St;
-              if (!(yt || h === 0)) {
-                for (this._cachedScaleForStroking[0] = -1, this._cachedGetSinglePixelWidth = null, n.save(), n.transform(...s.textMatrix), n.translate(s.x, s.y), n.scale(Q, b), ut = 0; ut < ct; ++ut) {
-                  if (Ft = i[ut], typeof Ft == "number") {
-                    St = M * Ft * h / 1e3, this.ctx.translate(St, 0), s.x += St * Q;
-                    continue;
-                  }
-                  const Dt = (Ft.isSpace ? tt : 0) + N, ft = o.charProcOperatorList[Ft.operatorListId];
-                  if (!ft) {
-                    (0, l.warn)(`Type3 character "${Ft.operatorListId}" is not available.`);
-                    continue;
-                  }
-                  this.contentVisible && (this.processingType3 = Ft, this.save(), n.scale(h, h), n.transform(...nt), this.executeOperatorList(ft), this.restore()), Bt = l.Util.applyTransform([Ft.width, 0], nt)[0] * h + Dt, n.translate(Bt, 0), s.x += Bt * Q;
-                }
-                n.restore(), this.processingType3 = null;
-              }
-            }
-            setCharWidth(i, n) {
-            }
-            setCharWidthAndBounds(i, n, s, o, h, b) {
-              this.ctx.rect(s, o, h - s, b - o), this.ctx.clip(), this.endPath();
-            }
-            getColorN_Pattern(i) {
-              let n;
-              if (i[0] === "TilingPattern") {
-                const s = i[1], o = this.baseTransform || (0, P.getCurrentTransform)(this.ctx), h = {
-                  createCanvasGraphics: (b) => new xt(b, this.commonObjs, this.objs, this.canvasFactory, this.filterFactory, {
-                    optionalContentConfig: this.optionalContentConfig,
-                    markedContentStack: this.markedContentStack
-                  })
-                };
-                n = new rt.TilingPattern(i, s, this.ctx, h, o);
-              } else
-                n = this._getPattern(i[1], i[2]);
-              return n;
-            }
-            setStrokeColorN() {
-              this.current.strokeColor = this.getColorN_Pattern(arguments);
-            }
-            setFillColorN() {
-              this.current.fillColor = this.getColorN_Pattern(arguments), this.current.patternFill = !0;
-            }
-            setStrokeRGBColor(i, n, s) {
-              const o = l.Util.makeHexColor(i, n, s);
-              this.ctx.strokeStyle = o, this.current.strokeColor = o;
-            }
-            setFillRGBColor(i, n, s) {
-              const o = l.Util.makeHexColor(i, n, s);
-              this.ctx.fillStyle = o, this.current.fillColor = o, this.current.patternFill = !1;
-            }
-            _getPattern(i, n = null) {
-              let s;
-              return this.cachedPatterns.has(i) ? s = this.cachedPatterns.get(i) : (s = (0, rt.getShadingPattern)(this.getObject(i)), this.cachedPatterns.set(i, s)), n && (s.matrix = n), s;
-            }
-            shadingFill(i) {
-              if (!this.contentVisible)
-                return;
-              const n = this.ctx;
-              this.save();
-              const s = this._getPattern(i);
-              n.fillStyle = s.getPattern(n, this, (0, P.getCurrentTransformInverse)(n), rt.PathType.SHADING);
-              const o = (0, P.getCurrentTransformInverse)(n);
-              if (o) {
-                const {
-                  width: h,
-                  height: b
-                } = n.canvas, [M, N, tt, Q] = l.Util.getAxialAlignedBoundingBox([0, 0, h, b], o);
-                this.ctx.fillRect(M, N, tt - M, Q - N);
-              } else
-                this.ctx.fillRect(-1e10, -1e10, 2e10, 2e10);
-              this.compose(this.current.getClippedPathBoundingBox()), this.restore();
-            }
-            beginInlineImage() {
-              (0, l.unreachable)("Should not call beginInlineImage");
-            }
-            beginImageData() {
-              (0, l.unreachable)("Should not call beginImageData");
-            }
-            paintFormXObjectBegin(i, n) {
-              if (this.contentVisible && (this.save(), this.baseTransformStack.push(this.baseTransform), Array.isArray(i) && i.length === 6 && this.transform(...i), this.baseTransform = (0, P.getCurrentTransform)(this.ctx), n)) {
-                const s = n[2] - n[0], o = n[3] - n[1];
-                this.ctx.rect(n[0], n[1], s, o), this.current.updateRectMinMax((0, P.getCurrentTransform)(this.ctx), n), this.clip(), this.endPath();
-              }
-            }
-            paintFormXObjectEnd() {
-              this.contentVisible && (this.restore(), this.baseTransform = this.baseTransformStack.pop());
-            }
-            beginGroup(i) {
-              if (!this.contentVisible)
-                return;
-              this.save(), this.inSMaskMode && (this.endSMaskMode(), this.current.activeSMask = null);
-              const n = this.ctx;
-              i.isolated || (0, l.info)("TODO: Support non-isolated groups."), i.knockout && (0, l.warn)("Knockout groups not supported.");
-              const s = (0, P.getCurrentTransform)(n);
-              if (i.matrix && n.transform(...i.matrix), !i.bbox)
-                throw new Error("Bounding box is required.");
-              let o = l.Util.getAxialAlignedBoundingBox(i.bbox, (0, P.getCurrentTransform)(n));
-              const h = [0, 0, n.canvas.width, n.canvas.height];
-              o = l.Util.intersect(o, h) || [0, 0, 0, 0];
-              const b = Math.floor(o[0]), M = Math.floor(o[1]);
-              let N = Math.max(Math.ceil(o[2]) - b, 1), tt = Math.max(Math.ceil(o[3]) - M, 1), Q = 1, nt = 1;
-              N > F && (Q = N / F, N = F), tt > F && (nt = tt / F, tt = F), this.current.startNewPathAndClipBox([0, 0, N, tt]);
-              let ct = "groupAt" + this.groupLevel;
-              i.smask && (ct += "_smask_" + this.smaskCounter++ % 2);
-              const yt = this.cachedCanvases.getCanvas(ct, N, tt), ut = yt.context;
-              ut.scale(1 / Q, 1 / nt), ut.translate(-b, -M), ut.transform(...s), i.smask ? this.smaskStack.push({
-                canvas: yt.canvas,
-                context: ut,
-                offsetX: b,
-                offsetY: M,
-                scaleX: Q,
-                scaleY: nt,
-                subtype: i.smask.subtype,
-                backdrop: i.smask.backdrop,
-                transferMap: i.smask.transferMap || null,
-                startTransformInverse: null
-              }) : (n.setTransform(1, 0, 0, 1, 0, 0), n.translate(b, M), n.scale(Q, nt), n.save()), a(n, ut), this.ctx = ut, this.setGState([["BM", "source-over"], ["ca", 1], ["CA", 1]]), this.groupStack.push(n), this.groupLevel++;
-            }
-            endGroup(i) {
-              if (!this.contentVisible)
-                return;
-              this.groupLevel--;
-              const n = this.ctx, s = this.groupStack.pop();
-              if (this.ctx = s, this.ctx.imageSmoothingEnabled = !1, i.smask)
-                this.tempSMask = this.smaskStack.pop(), this.restore();
-              else {
-                this.ctx.restore();
-                const o = (0, P.getCurrentTransform)(this.ctx);
-                this.restore(), this.ctx.save(), this.ctx.setTransform(...o);
-                const h = l.Util.getAxialAlignedBoundingBox([0, 0, n.canvas.width, n.canvas.height], o);
-                this.ctx.drawImage(n.canvas, 0, 0), this.ctx.restore(), this.compose(h);
-              }
-            }
-            beginAnnotation(i, n, s, o, h) {
-              if (W(this, H, Qe).call(this), c(this.ctx), this.ctx.save(), this.save(), this.baseTransform && this.ctx.setTransform(...this.baseTransform), Array.isArray(n) && n.length === 4) {
-                const b = n[2] - n[0], M = n[3] - n[1];
-                if (h && this.annotationCanvasMap) {
-                  s = s.slice(), s[4] -= n[0], s[5] -= n[1], n = n.slice(), n[0] = n[1] = 0, n[2] = b, n[3] = M;
-                  const [N, tt] = l.Util.singularValueDecompose2dScale((0, P.getCurrentTransform)(this.ctx)), {
-                    viewportScale: Q
-                  } = this, nt = Math.ceil(b * this.outputScaleX * Q), ct = Math.ceil(M * this.outputScaleY * Q);
-                  this.annotationCanvas = this.canvasFactory.create(nt, ct);
-                  const {
-                    canvas: yt,
-                    context: ut
-                  } = this.annotationCanvas;
-                  this.annotationCanvasMap.set(i, yt), this.annotationCanvas.savedCtx = this.ctx, this.ctx = ut, this.ctx.save(), this.ctx.setTransform(N, 0, 0, -tt, 0, M * tt), c(this.ctx);
-                } else
-                  c(this.ctx), this.ctx.rect(n[0], n[1], b, M), this.ctx.clip(), this.endPath();
-              }
-              this.current = new w(this.ctx.canvas.width, this.ctx.canvas.height), this.transform(...s), this.transform(...o);
-            }
-            endAnnotation() {
-              this.annotationCanvas && (this.ctx.restore(), W(this, gt, Ze).call(this), this.ctx = this.annotationCanvas.savedCtx, delete this.annotationCanvas.savedCtx, delete this.annotationCanvas);
-            }
-            paintImageMaskXObject(i) {
-              if (!this.contentVisible)
-                return;
-              const n = i.count;
-              i = this.getObject(i.data, i), i.count = n;
-              const s = this.ctx, o = this.processingType3;
-              if (o && (o.compiled === void 0 && (o.compiled = _(i)), o.compiled)) {
-                o.compiled(s);
-                return;
-              }
-              const h = this._createMaskCanvas(i), b = h.canvas;
-              s.save(), s.setTransform(1, 0, 0, 1, 0, 0), s.drawImage(b, h.offsetX, h.offsetY), s.restore(), this.compose();
-            }
-            paintImageMaskXObjectRepeat(i, n, s = 0, o = 0, h, b) {
-              if (!this.contentVisible)
-                return;
-              i = this.getObject(i.data, i);
-              const M = this.ctx;
-              M.save();
-              const N = (0, P.getCurrentTransform)(M);
-              M.transform(n, s, o, h, 0, 0);
-              const tt = this._createMaskCanvas(i);
-              M.setTransform(1, 0, 0, 1, tt.offsetX - N[4], tt.offsetY - N[5]);
-              for (let Q = 0, nt = b.length; Q < nt; Q += 2) {
-                const ct = l.Util.transform(N, [n, s, o, h, b[Q], b[Q + 1]]), [yt, ut] = l.Util.applyTransform([0, 0], ct);
-                M.drawImage(tt.canvas, yt, ut);
-              }
-              M.restore(), this.compose();
-            }
-            paintImageMaskXObjectGroup(i) {
-              if (!this.contentVisible)
-                return;
-              const n = this.ctx, s = this.current.fillColor, o = this.current.patternFill;
-              for (const h of i) {
-                const {
-                  data: b,
-                  width: M,
-                  height: N,
-                  transform: tt
-                } = h, Q = this.cachedCanvases.getCanvas("maskCanvas", M, N), nt = Q.context;
-                nt.save();
-                const ct = this.getObject(b, h);
-                y(nt, ct), nt.globalCompositeOperation = "source-in", nt.fillStyle = o ? s.getPattern(nt, this, (0, P.getCurrentTransformInverse)(n), rt.PathType.FILL) : s, nt.fillRect(0, 0, M, N), nt.restore(), n.save(), n.transform(...tt), n.scale(1, -1), u(n, Q.canvas, 0, 0, M, N, 0, -1, 1, 1), n.restore();
-              }
-              this.compose();
-            }
-            paintImageXObject(i) {
-              if (!this.contentVisible)
-                return;
-              const n = this.getObject(i);
-              if (!n) {
-                (0, l.warn)("Dependent image isn't ready yet");
-                return;
-              }
-              this.paintInlineImageXObject(n);
-            }
-            paintImageXObjectRepeat(i, n, s, o) {
-              if (!this.contentVisible)
-                return;
-              const h = this.getObject(i);
-              if (!h) {
-                (0, l.warn)("Dependent image isn't ready yet");
-                return;
-              }
-              const b = h.width, M = h.height, N = [];
-              for (let tt = 0, Q = o.length; tt < Q; tt += 2)
-                N.push({
-                  transform: [n, 0, 0, s, o[tt], o[tt + 1]],
-                  x: 0,
-                  y: 0,
-                  w: b,
-                  h: M
-                });
-              this.paintInlineImageXObjectGroup(h, N);
-            }
-            applyTransferMapsToCanvas(i) {
-              return this.current.transferMaps !== "none" && (i.filter = this.current.transferMaps, i.drawImage(i.canvas, 0, 0), i.filter = "none"), i.canvas;
-            }
-            applyTransferMapsToBitmap(i) {
-              if (this.current.transferMaps === "none")
-                return i.bitmap;
-              const {
-                bitmap: n,
-                width: s,
-                height: o
-              } = i, h = this.cachedCanvases.getCanvas("inlineImage", s, o), b = h.context;
-              return b.filter = this.current.transferMaps, b.drawImage(n, 0, 0), b.filter = "none", h.canvas;
-            }
-            paintInlineImageXObject(i) {
-              if (!this.contentVisible)
-                return;
-              const n = i.width, s = i.height, o = this.ctx;
-              if (this.save(), !l.isNodeJS) {
-                const {
-                  filter: M
-                } = o;
-                M !== "none" && M !== "" && (o.filter = "none");
-              }
-              o.scale(1 / n, -1 / s);
-              let h;
-              if (i.bitmap)
-                h = this.applyTransferMapsToBitmap(i);
-              else if (typeof HTMLElement == "function" && i instanceof HTMLElement || !i.data)
-                h = i;
-              else {
-                const N = this.cachedCanvases.getCanvas("inlineImage", n, s).context;
-                C(N, i), h = this.applyTransferMapsToCanvas(N);
-              }
-              const b = this._scaleImage(h, (0, P.getCurrentTransformInverse)(o));
-              o.imageSmoothingEnabled = U((0, P.getCurrentTransform)(o), i.interpolate), u(o, b.img, 0, 0, b.paintWidth, b.paintHeight, 0, -s, n, s), this.compose(), this.restore();
-            }
-            paintInlineImageXObjectGroup(i, n) {
-              if (!this.contentVisible)
-                return;
-              const s = this.ctx;
-              let o;
-              if (i.bitmap)
-                o = i.bitmap;
-              else {
-                const h = i.width, b = i.height, N = this.cachedCanvases.getCanvas("inlineImage", h, b).context;
-                C(N, i), o = this.applyTransferMapsToCanvas(N);
-              }
-              for (const h of n)
-                s.save(), s.transform(...h.transform), s.scale(1, -1), u(s, o, h.x, h.y, h.w, h.h, 0, -1, 1, 1), s.restore();
-              this.compose();
-            }
-            paintSolidColorImageMask() {
-              this.contentVisible && (this.ctx.fillRect(0, 0, 1, 1), this.compose());
-            }
-            markPoint(i) {
-            }
-            markPointProps(i, n) {
-            }
-            beginMarkedContent(i) {
-              this.markedContentStack.push({
-                visible: !0
-              });
-            }
-            beginMarkedContentProps(i, n) {
-              i === "OC" ? this.markedContentStack.push({
-                visible: this.optionalContentConfig.isVisible(n)
-              }) : this.markedContentStack.push({
-                visible: !0
-              }), this.contentVisible = this.isContentVisible();
-            }
-            endMarkedContent() {
-              this.markedContentStack.pop(), this.contentVisible = this.isContentVisible();
-            }
-            beginCompat() {
-            }
-            endCompat() {
-            }
-            consumePath(i) {
-              const n = this.current.isEmptyClip();
-              this.pendingClip && this.current.updateClipFromPath(), this.pendingClip || this.compose(i);
-              const s = this.ctx;
-              this.pendingClip && (n || (this.pendingClip === st ? s.clip("evenodd") : s.clip()), this.pendingClip = null), this.current.startNewPathAndClipBox(this.current.clipBox), s.beginPath();
-            }
-            getSinglePixelWidth() {
-              if (!this._cachedGetSinglePixelWidth) {
-                const i = (0, P.getCurrentTransform)(this.ctx);
-                if (i[1] === 0 && i[2] === 0)
-                  this._cachedGetSinglePixelWidth = 1 / Math.min(Math.abs(i[0]), Math.abs(i[3]));
-                else {
-                  const n = Math.abs(i[0] * i[3] - i[2] * i[1]), s = Math.hypot(i[0], i[2]), o = Math.hypot(i[1], i[3]);
-                  this._cachedGetSinglePixelWidth = Math.max(s, o) / n;
-                }
-              }
-              return this._cachedGetSinglePixelWidth;
-            }
-            getScaleForStroking() {
-              if (this._cachedScaleForStroking[0] === -1) {
-                const {
-                  lineWidth: i
-                } = this.current, {
-                  a: n,
-                  b: s,
-                  c: o,
-                  d: h
-                } = this.ctx.getTransform();
-                let b, M;
-                if (s === 0 && o === 0) {
-                  const N = Math.abs(n), tt = Math.abs(h);
-                  if (N === tt)
-                    if (i === 0)
-                      b = M = 1 / N;
-                    else {
-                      const Q = N * i;
-                      b = M = Q < 1 ? 1 / Q : 1;
-                    }
-                  else if (i === 0)
-                    b = 1 / N, M = 1 / tt;
-                  else {
-                    const Q = N * i, nt = tt * i;
-                    b = Q < 1 ? 1 / Q : 1, M = nt < 1 ? 1 / nt : 1;
-                  }
-                } else {
-                  const N = Math.abs(n * h - s * o), tt = Math.hypot(n, s), Q = Math.hypot(o, h);
-                  if (i === 0)
-                    b = Q / N, M = tt / N;
-                  else {
-                    const nt = i * N;
-                    b = Q > nt ? Q / nt : 1, M = tt > nt ? tt / nt : 1;
-                  }
-                }
-                this._cachedScaleForStroking[0] = b, this._cachedScaleForStroking[1] = M;
-              }
-              return this._cachedScaleForStroking;
-            }
-            rescaleAndStroke(i) {
-              const {
-                ctx: n
-              } = this, {
-                lineWidth: s
-              } = this.current, [o, h] = this.getScaleForStroking();
-              if (n.lineWidth = s || 1, o === 1 && h === 1) {
-                n.stroke();
-                return;
-              }
-              const b = n.getLineDash();
-              if (i && n.save(), n.scale(o, h), b.length > 0) {
-                const M = Math.max(o, h);
-                n.setLineDash(b.map((N) => N / M)), n.lineDashOffset /= M;
-              }
-              n.stroke(), i && n.restore();
-            }
-            isContentVisible() {
-              for (let i = this.markedContentStack.length - 1; i >= 0; i--)
-                if (!this.markedContentStack[i].visible)
-                  return !1;
-              return !0;
-            }
-          };
-          H = new WeakSet(), Qe = function() {
-            for (; this.stateStack.length || this.inSMaskMode; )
-              this.restore();
-            this.ctx.restore(), this.transparentCanvas && (this.ctx = this.compositeCtx, this.ctx.save(), this.ctx.setTransform(1, 0, 0, 1, 0, 0), this.ctx.drawImage(this.transparentCanvas, 0, 0), this.ctx.restore(), this.transparentCanvas = null);
-          }, gt = new WeakSet(), Ze = function() {
-            if (this.pageColors) {
-              const i = this.filterFactory.addHCMFilter(this.pageColors.foreground, this.pageColors.background);
-              if (i !== "none") {
-                const n = this.ctx.filter;
-                this.ctx.filter = i, this.ctx.drawImage(this.ctx.canvas, 0, 0), this.ctx.filter = n;
-              }
-            }
-          };
-          let at = xt;
-          d.CanvasGraphics = at;
-          for (const S in l.OPS)
-            at.prototype[S] !== void 0 && (at.prototype[l.OPS[S]] = at.prototype[S]);
-        },
-        /* 12 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.TilingPattern = d.PathType = void 0, d.getShadingPattern = x;
-          var l = et(1), P = et(6);
-          const rt = {
-            FILL: "Fill",
-            STROKE: "Stroke",
-            SHADING: "Shading"
-          };
-          d.PathType = rt;
-          function X(_, w) {
-            if (!w)
-              return;
-            const C = w[2] - w[0], y = w[3] - w[1], a = new Path2D();
-            a.rect(w[0], w[1], C, y), _.clip(a);
-          }
-          class pt {
-            constructor() {
-              this.constructor === pt && (0, l.unreachable)("Cannot initialize BaseShadingPattern.");
-            }
-            getPattern() {
-              (0, l.unreachable)("Abstract method `getPattern` called.");
-            }
-          }
-          class B extends pt {
-            constructor(w) {
-              super(), this._type = w[1], this._bbox = w[2], this._colorStops = w[3], this._p0 = w[4], this._p1 = w[5], this._r0 = w[6], this._r1 = w[7], this.matrix = null;
-            }
-            _createGradient(w) {
-              let C;
-              this._type === "axial" ? C = w.createLinearGradient(this._p0[0], this._p0[1], this._p1[0], this._p1[1]) : this._type === "radial" && (C = w.createRadialGradient(this._p0[0], this._p0[1], this._r0, this._p1[0], this._p1[1], this._r1));
-              for (const y of this._colorStops)
-                C.addColorStop(y[0], y[1]);
-              return C;
-            }
-            getPattern(w, C, y, a) {
-              let c;
-              if (a === rt.STROKE || a === rt.FILL) {
-                const k = C.current.getClippedPathBoundingBox(a, (0, P.getCurrentTransform)(w)) || [0, 0, 0, 0], p = Math.ceil(k[2] - k[0]) || 1, r = Math.ceil(k[3] - k[1]) || 1, T = C.cachedCanvases.getCanvas("pattern", p, r, !0), m = T.context;
-                m.clearRect(0, 0, m.canvas.width, m.canvas.height), m.beginPath(), m.rect(0, 0, m.canvas.width, m.canvas.height), m.translate(-k[0], -k[1]), y = l.Util.transform(y, [1, 0, 0, 1, k[0], k[1]]), m.transform(...C.baseTransform), this.matrix && m.transform(...this.matrix), X(m, this._bbox), m.fillStyle = this._createGradient(m), m.fill(), c = w.createPattern(T.canvas, "no-repeat");
-                const U = new DOMMatrix(y);
-                c.setTransform(U);
-              } else
-                X(w, this._bbox), c = this._createGradient(w);
-              return c;
-            }
-          }
-          function F(_, w, C, y, a, c, k, p) {
-            const r = w.coords, T = w.colors, m = _.data, U = _.width * 4;
-            let z;
-            r[C + 1] > r[y + 1] && (z = C, C = y, y = z, z = c, c = k, k = z), r[y + 1] > r[a + 1] && (z = y, y = a, a = z, z = k, k = p, p = z), r[C + 1] > r[y + 1] && (z = C, C = y, y = z, z = c, c = k, k = z);
-            const E = (r[C] + w.offsetX) * w.scaleX, V = (r[C + 1] + w.offsetY) * w.scaleY, st = (r[y] + w.offsetX) * w.scaleX, at = (r[y + 1] + w.offsetY) * w.scaleY, H = (r[a] + w.offsetX) * w.scaleX, lt = (r[a + 1] + w.offsetY) * w.scaleY;
-            if (V >= lt)
-              return;
-            const gt = T[c], wt = T[c + 1], xt = T[c + 2], S = T[k], i = T[k + 1], n = T[k + 2], s = T[p], o = T[p + 1], h = T[p + 2], b = Math.round(V), M = Math.round(lt);
-            let N, tt, Q, nt, ct, yt, ut, Ft;
-            for (let Bt = b; Bt <= M; Bt++) {
-              if (Bt < at) {
-                const J = Bt < V ? 0 : (V - Bt) / (V - at);
-                N = E - (E - st) * J, tt = gt - (gt - S) * J, Q = wt - (wt - i) * J, nt = xt - (xt - n) * J;
-              } else {
-                let J;
-                Bt > lt ? J = 1 : at === lt ? J = 0 : J = (at - Bt) / (at - lt), N = st - (st - H) * J, tt = S - (S - s) * J, Q = i - (i - o) * J, nt = n - (n - h) * J;
-              }
-              let St;
-              Bt < V ? St = 0 : Bt > lt ? St = 1 : St = (V - Bt) / (V - lt), ct = E - (E - H) * St, yt = gt - (gt - s) * St, ut = wt - (wt - o) * St, Ft = xt - (xt - h) * St;
-              const Dt = Math.round(Math.min(N, ct)), ft = Math.round(Math.max(N, ct));
-              let K = U * Bt + Dt * 4;
-              for (let J = Dt; J <= ft; J++)
-                St = (N - J) / (N - ct), St < 0 ? St = 0 : St > 1 && (St = 1), m[K++] = tt - (tt - yt) * St | 0, m[K++] = Q - (Q - ut) * St | 0, m[K++] = nt - (nt - Ft) * St | 0, m[K++] = 255;
-            }
-          }
-          function g(_, w, C) {
-            const y = w.coords, a = w.colors;
-            let c, k;
-            switch (w.type) {
-              case "lattice":
-                const p = w.verticesPerRow, r = Math.floor(y.length / p) - 1, T = p - 1;
-                for (c = 0; c < r; c++) {
-                  let m = c * p;
-                  for (let U = 0; U < T; U++, m++)
-                    F(_, C, y[m], y[m + 1], y[m + p], a[m], a[m + 1], a[m + p]), F(_, C, y[m + p + 1], y[m + 1], y[m + p], a[m + p + 1], a[m + 1], a[m + p]);
-                }
-                break;
-              case "triangles":
-                for (c = 0, k = y.length; c < k; c += 3)
-                  F(_, C, y[c], y[c + 1], y[c + 2], a[c], a[c + 1], a[c + 2]);
-                break;
-              default:
-                throw new Error("illegal figure");
-            }
-          }
-          class O extends pt {
-            constructor(w) {
-              super(), this._coords = w[2], this._colors = w[3], this._figures = w[4], this._bounds = w[5], this._bbox = w[7], this._background = w[8], this.matrix = null;
-            }
-            _createMeshCanvas(w, C, y) {
-              const p = Math.floor(this._bounds[0]), r = Math.floor(this._bounds[1]), T = Math.ceil(this._bounds[2]) - p, m = Math.ceil(this._bounds[3]) - r, U = Math.min(Math.ceil(Math.abs(T * w[0] * 1.1)), 3e3), z = Math.min(Math.ceil(Math.abs(m * w[1] * 1.1)), 3e3), E = T / U, V = m / z, st = {
-                coords: this._coords,
-                colors: this._colors,
-                offsetX: -p,
-                offsetY: -r,
-                scaleX: 1 / E,
-                scaleY: 1 / V
-              }, at = U + 2 * 2, H = z + 2 * 2, lt = y.getCanvas("mesh", at, H, !1), gt = lt.context, wt = gt.createImageData(U, z);
-              if (C) {
-                const S = wt.data;
-                for (let i = 0, n = S.length; i < n; i += 4)
-                  S[i] = C[0], S[i + 1] = C[1], S[i + 2] = C[2], S[i + 3] = 255;
-              }
-              for (const S of this._figures)
-                g(wt, S, st);
-              return gt.putImageData(wt, 2, 2), {
-                canvas: lt.canvas,
-                offsetX: p - 2 * E,
-                offsetY: r - 2 * V,
-                scaleX: E,
-                scaleY: V
-              };
-            }
-            getPattern(w, C, y, a) {
-              X(w, this._bbox);
-              let c;
-              if (a === rt.SHADING)
-                c = l.Util.singularValueDecompose2dScale((0, P.getCurrentTransform)(w));
-              else if (c = l.Util.singularValueDecompose2dScale(C.baseTransform), this.matrix) {
-                const p = l.Util.singularValueDecompose2dScale(this.matrix);
-                c = [c[0] * p[0], c[1] * p[1]];
-              }
-              const k = this._createMeshCanvas(c, a === rt.SHADING ? null : this._background, C.cachedCanvases);
-              return a !== rt.SHADING && (w.setTransform(...C.baseTransform), this.matrix && w.transform(...this.matrix)), w.translate(k.offsetX, k.offsetY), w.scale(k.scaleX, k.scaleY), w.createPattern(k.canvas, "no-repeat");
-            }
-          }
-          class I extends pt {
-            getPattern() {
-              return "hotpink";
-            }
-          }
-          function x(_) {
-            switch (_[0]) {
-              case "RadialAxial":
-                return new B(_);
-              case "Mesh":
-                return new O(_);
-              case "Dummy":
-                return new I();
-            }
-            throw new Error(`Unknown IR type: ${_[0]}`);
-          }
-          const v = {
-            COLORED: 1,
-            UNCOLORED: 2
-          }, u = class u {
-            constructor(w, C, y, a, c) {
-              this.operatorList = w[2], this.matrix = w[3] || [1, 0, 0, 1, 0, 0], this.bbox = w[4], this.xstep = w[5], this.ystep = w[6], this.paintType = w[7], this.tilingType = w[8], this.color = C, this.ctx = y, this.canvasGraphicsFactory = a, this.baseTransform = c;
-            }
-            createPatternCanvas(w) {
-              const C = this.operatorList, y = this.bbox, a = this.xstep, c = this.ystep, k = this.paintType, p = this.tilingType, r = this.color, T = this.canvasGraphicsFactory;
-              (0, l.info)("TilingType: " + p);
-              const m = y[0], U = y[1], z = y[2], E = y[3], V = l.Util.singularValueDecompose2dScale(this.matrix), st = l.Util.singularValueDecompose2dScale(this.baseTransform), at = [V[0] * st[0], V[1] * st[1]], H = this.getSizeAndScale(a, this.ctx.canvas.width, at[0]), lt = this.getSizeAndScale(c, this.ctx.canvas.height, at[1]), gt = w.cachedCanvases.getCanvas("pattern", H.size, lt.size, !0), wt = gt.context, xt = T.createCanvasGraphics(wt);
-              xt.groupLevel = w.groupLevel, this.setFillAndStrokeStyleToContext(xt, k, r);
-              let S = m, i = U, n = z, s = E;
-              return m < 0 && (S = 0, n += Math.abs(m)), U < 0 && (i = 0, s += Math.abs(U)), wt.translate(-(H.scale * S), -(lt.scale * i)), xt.transform(H.scale, 0, 0, lt.scale, 0, 0), wt.save(), this.clipBbox(xt, S, i, n, s), xt.baseTransform = (0, P.getCurrentTransform)(xt.ctx), xt.executeOperatorList(C), xt.endDrawing(), {
-                canvas: gt.canvas,
-                scaleX: H.scale,
-                scaleY: lt.scale,
-                offsetX: S,
-                offsetY: i
-              };
-            }
-            getSizeAndScale(w, C, y) {
-              w = Math.abs(w);
-              const a = Math.max(u.MAX_PATTERN_SIZE, C);
-              let c = Math.ceil(w * y);
-              return c >= a ? c = a : y = c / w, {
-                scale: y,
-                size: c
-              };
-            }
-            clipBbox(w, C, y, a, c) {
-              const k = a - C, p = c - y;
-              w.ctx.rect(C, y, k, p), w.current.updateRectMinMax((0, P.getCurrentTransform)(w.ctx), [C, y, a, c]), w.clip(), w.endPath();
-            }
-            setFillAndStrokeStyleToContext(w, C, y) {
-              const a = w.ctx, c = w.current;
-              switch (C) {
-                case v.COLORED:
-                  const k = this.ctx;
-                  a.fillStyle = k.fillStyle, a.strokeStyle = k.strokeStyle, c.fillColor = k.fillStyle, c.strokeColor = k.strokeStyle;
-                  break;
-                case v.UNCOLORED:
-                  const p = l.Util.makeHexColor(y[0], y[1], y[2]);
-                  a.fillStyle = p, a.strokeStyle = p, c.fillColor = p, c.strokeColor = p;
-                  break;
-                default:
-                  throw new l.FormatError(`Unsupported paint type: ${C}`);
-              }
-            }
-            getPattern(w, C, y, a) {
-              let c = y;
-              a !== rt.SHADING && (c = l.Util.transform(c, C.baseTransform), this.matrix && (c = l.Util.transform(c, this.matrix)));
-              const k = this.createPatternCanvas(C);
-              let p = new DOMMatrix(c);
-              p = p.translate(k.offsetX, k.offsetY), p = p.scale(1 / k.scaleX, 1 / k.scaleY);
-              const r = w.createPattern(k.canvas, "repeat");
-              return r.setTransform(p), r;
-            }
-          };
-          ee(u, "MAX_PATTERN_SIZE", 3e3);
-          let A = u;
-          d.TilingPattern = A;
-        },
-        /* 13 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.convertBlackAndWhiteToRGBA = rt, d.convertToRGBA = P, d.grayToRGBA = pt;
-          var l = et(1);
-          function P(B) {
-            switch (B.kind) {
-              case l.ImageKind.GRAYSCALE_1BPP:
-                return rt(B);
-              case l.ImageKind.RGB_24BPP:
-                return X(B);
-            }
-            return null;
-          }
-          function rt({
-            src: B,
-            srcPos: F = 0,
-            dest: g,
-            width: O,
-            height: I,
-            nonBlackColor: x = 4294967295,
-            inverseDecode: v = !1
-          }) {
-            const A = l.FeatureTest.isLittleEndian ? 4278190080 : 255, [u, _] = v ? [x, A] : [A, x], w = O >> 3, C = O & 7, y = B.length;
-            g = new Uint32Array(g.buffer);
-            let a = 0;
-            for (let c = 0; c < I; c++) {
-              for (const p = F + w; F < p; F++) {
-                const r = F < y ? B[F] : 255;
-                g[a++] = r & 128 ? _ : u, g[a++] = r & 64 ? _ : u, g[a++] = r & 32 ? _ : u, g[a++] = r & 16 ? _ : u, g[a++] = r & 8 ? _ : u, g[a++] = r & 4 ? _ : u, g[a++] = r & 2 ? _ : u, g[a++] = r & 1 ? _ : u;
-              }
-              if (C === 0)
-                continue;
-              const k = F < y ? B[F++] : 255;
-              for (let p = 0; p < C; p++)
-                g[a++] = k & 1 << 7 - p ? _ : u;
-            }
-            return {
-              srcPos: F,
-              destPos: a
-            };
-          }
-          function X({
-            src: B,
-            srcPos: F = 0,
-            dest: g,
-            destPos: O = 0,
-            width: I,
-            height: x
-          }) {
-            let v = 0;
-            const A = B.length >> 2, u = new Uint32Array(B.buffer, F, A);
-            if (l.FeatureTest.isLittleEndian) {
-              for (; v < A - 2; v += 3, O += 4) {
-                const _ = u[v], w = u[v + 1], C = u[v + 2];
-                g[O] = _ | 4278190080, g[O + 1] = _ >>> 24 | w << 8 | 4278190080, g[O + 2] = w >>> 16 | C << 16 | 4278190080, g[O + 3] = C >>> 8 | 4278190080;
-              }
-              for (let _ = v * 4, w = B.length; _ < w; _ += 3)
-                g[O++] = B[_] | B[_ + 1] << 8 | B[_ + 2] << 16 | 4278190080;
-            } else {
-              for (; v < A - 2; v += 3, O += 4) {
-                const _ = u[v], w = u[v + 1], C = u[v + 2];
-                g[O] = _ | 255, g[O + 1] = _ << 24 | w >>> 8 | 255, g[O + 2] = w << 16 | C >>> 16 | 255, g[O + 3] = C << 8 | 255;
-              }
-              for (let _ = v * 4, w = B.length; _ < w; _ += 3)
-                g[O++] = B[_] << 24 | B[_ + 1] << 16 | B[_ + 2] << 8 | 255;
-            }
-            return {
-              srcPos: F,
-              destPos: O
-            };
-          }
-          function pt(B, F) {
-            if (l.FeatureTest.isLittleEndian)
-              for (let g = 0, O = B.length; g < O; g++)
-                F[g] = B[g] * 65793 | 4278190080;
-            else
-              for (let g = 0, O = B.length; g < O; g++)
-                F[g] = B[g] * 16843008 | 255;
-          }
-        },
-        /* 14 */
-        /***/
-        (dt, d) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.GlobalWorkerOptions = void 0;
-          const et = /* @__PURE__ */ Object.create(null);
-          d.GlobalWorkerOptions = et, et.workerPort = null, et.workerSrc = "";
-        },
-        /* 15 */
-        /***/
-        (dt, d, et) => {
-          var B, xi, g, ki, I, xe;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.MessageHandler = void 0;
-          var l = et(1);
-          const P = {
-            UNKNOWN: 0,
-            DATA: 1,
-            ERROR: 2
-          }, rt = {
-            UNKNOWN: 0,
-            CANCEL: 1,
-            CANCEL_COMPLETE: 2,
-            CLOSE: 3,
-            ENQUEUE: 4,
-            ERROR: 5,
-            PULL: 6,
-            PULL_COMPLETE: 7,
-            START_COMPLETE: 8
-          };
-          function X(v) {
-            switch (v instanceof Error || typeof v == "object" && v !== null || (0, l.unreachable)('wrapReason: Expected "reason" to be a (possibly cloned) Error.'), v.name) {
-              case "AbortException":
-                return new l.AbortException(v.message);
-              case "MissingPDFException":
-                return new l.MissingPDFException(v.message);
-              case "PasswordException":
-                return new l.PasswordException(v.message, v.code);
-              case "UnexpectedResponseException":
-                return new l.UnexpectedResponseException(v.message, v.status);
-              case "UnknownErrorException":
-                return new l.UnknownErrorException(v.message, v.details);
-              default:
-                return new l.UnknownErrorException(v.message, v.toString());
-            }
-          }
-          class pt {
-            constructor(A, u, _) {
-              L(this, B);
-              L(this, g);
-              L(this, I);
-              this.sourceName = A, this.targetName = u, this.comObj = _, this.callbackId = 1, this.streamId = 1, this.streamSinks = /* @__PURE__ */ Object.create(null), this.streamControllers = /* @__PURE__ */ Object.create(null), this.callbackCapabilities = /* @__PURE__ */ Object.create(null), this.actionHandler = /* @__PURE__ */ Object.create(null), this._onComObjOnMessage = (w) => {
-                const C = w.data;
-                if (C.targetName !== this.sourceName)
-                  return;
-                if (C.stream) {
-                  W(this, g, ki).call(this, C);
-                  return;
-                }
-                if (C.callback) {
-                  const a = C.callbackId, c = this.callbackCapabilities[a];
-                  if (!c)
-                    throw new Error(`Cannot resolve callback ${a}`);
-                  if (delete this.callbackCapabilities[a], C.callback === P.DATA)
-                    c.resolve(C.data);
-                  else if (C.callback === P.ERROR)
-                    c.reject(X(C.reason));
-                  else
-                    throw new Error("Unexpected callback case");
-                  return;
-                }
-                const y = this.actionHandler[C.action];
-                if (!y)
-                  throw new Error(`Unknown action from worker: ${C.action}`);
-                if (C.callbackId) {
-                  const a = this.sourceName, c = C.sourceName;
-                  new Promise(function(k) {
-                    k(y(C.data));
-                  }).then(function(k) {
-                    _.postMessage({
-                      sourceName: a,
-                      targetName: c,
-                      callback: P.DATA,
-                      callbackId: C.callbackId,
-                      data: k
-                    });
-                  }, function(k) {
-                    _.postMessage({
-                      sourceName: a,
-                      targetName: c,
-                      callback: P.ERROR,
-                      callbackId: C.callbackId,
-                      reason: X(k)
-                    });
-                  });
-                  return;
-                }
-                if (C.streamId) {
-                  W(this, B, xi).call(this, C);
-                  return;
-                }
-                y(C.data);
-              }, _.addEventListener("message", this._onComObjOnMessage);
-            }
-            on(A, u) {
-              const _ = this.actionHandler;
-              if (_[A])
-                throw new Error(`There is already an actionName called "${A}"`);
-              _[A] = u;
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-              this.comObj.postMessage({
-                sourceName: this.sourceName,
-                targetName: this.targetName,
-                action: A,
-                data: u
-              }, _);
-            }
-            sendWithPromise(A, u, _) {
-              const w = this.callbackId++, C = new l.PromiseCapability();
-              this.callbackCapabilities[w] = C;
-              try {
-                this.comObj.postMessage({
-                  sourceName: this.sourceName,
-                  targetName: this.targetName,
-                  action: A,
-                  callbackId: w,
-                  data: u
-                }, _);
-              } catch (y) {
-                C.reject(y);
-              }
-              return C.promise;
-            }
-            sendWithStream(A, u, _, w) {
-              const C = this.streamId++, y = this.sourceName, a = this.targetName, c = this.comObj;
-              return new ReadableStream({
-                start: (k) => {
-                  const p = new l.PromiseCapability();
-                  return this.streamControllers[C] = {
-                    controller: k,
-                    startCall: p,
-                    pullCall: null,
-                    cancelCall: null,
-                    isClosed: !1
-                  }, c.postMessage({
-                    sourceName: y,
-                    targetName: a,
-                    action: A,
-                    streamId: C,
-                    data: u,
-                    desiredSize: k.desiredSize
-                  }, w), p.promise;
-                },
-                pull: (k) => {
-                  const p = new l.PromiseCapability();
-                  return this.streamControllers[C].pullCall = p, c.postMessage({
-                    sourceName: y,
-                    targetName: a,
-                    stream: rt.PULL,
-                    streamId: C,
-                    desiredSize: k.desiredSize
-                  }), p.promise;
-                },
-                cancel: (k) => {
-                  (0, l.assert)(k instanceof Error, "cancel must have a valid reason");
-                  const p = new l.PromiseCapability();
-                  return this.streamControllers[C].cancelCall = p, this.streamControllers[C].isClosed = !0, c.postMessage({
-                    sourceName: y,
-                    targetName: a,
-                    stream: rt.CANCEL,
-                    streamId: C,
-                    reason: X(k)
-                  }), p.promise;
-                }
-              }, _);
-            }
-            destroy() {
-              this.comObj.removeEventListener("message", this._onComObjOnMessage);
-            }
-          }
-          B = new WeakSet(), xi = function(A) {
-            const u = A.streamId, _ = this.sourceName, w = A.sourceName, C = this.comObj, y = this, a = this.actionHandler[A.action], c = {
-              enqueue(k, p = 1, r) {
-                if (this.isCancelled)
-                  return;
-                const T = this.desiredSize;
-                this.desiredSize -= p, T > 0 && this.desiredSize <= 0 && (this.sinkCapability = new l.PromiseCapability(), this.ready = this.sinkCapability.promise), C.postMessage({
-                  sourceName: _,
-                  targetName: w,
-                  stream: rt.ENQUEUE,
-                  streamId: u,
-                  chunk: k
-                }, r);
-              },
-              close() {
-                this.isCancelled || (this.isCancelled = !0, C.postMessage({
-                  sourceName: _,
-                  targetName: w,
-                  stream: rt.CLOSE,
-                  streamId: u
-                }), delete y.streamSinks[u]);
-              },
-              error(k) {
-                (0, l.assert)(k instanceof Error, "error must have a valid reason"), !this.isCancelled && (this.isCancelled = !0, C.postMessage({
-                  sourceName: _,
-                  targetName: w,
-                  stream: rt.ERROR,
-                  streamId: u,
-                  reason: X(k)
-                }));
-              },
-              sinkCapability: new l.PromiseCapability(),
-              onPull: null,
-              onCancel: null,
-              isCancelled: !1,
-              desiredSize: A.desiredSize,
-              ready: null
-            };
-            c.sinkCapability.resolve(), c.ready = c.sinkCapability.promise, this.streamSinks[u] = c, new Promise(function(k) {
-              k(a(A.data, c));
-            }).then(function() {
-              C.postMessage({
-                sourceName: _,
-                targetName: w,
-                stream: rt.START_COMPLETE,
-                streamId: u,
-                success: !0
-              });
-            }, function(k) {
-              C.postMessage({
-                sourceName: _,
-                targetName: w,
-                stream: rt.START_COMPLETE,
-                streamId: u,
-                reason: X(k)
-              });
-            });
-          }, g = new WeakSet(), ki = function(A) {
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-              case rt.START_COMPLETE:
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-                break;
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-                A.success ? y.pullCall.resolve() : y.pullCall.reject(X(A.reason));
-                break;
-              case rt.PULL:
-                if (!a) {
-                  C.postMessage({
-                    sourceName: _,
-                    targetName: w,
-                    stream: rt.PULL_COMPLETE,
-                    streamId: u,
-                    success: !0
-                  });
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-                  C.postMessage({
-                    sourceName: _,
-                    targetName: w,
-                    stream: rt.PULL_COMPLETE,
-                    streamId: u,
-                    success: !0
-                  });
-                }, function(c) {
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-                    targetName: w,
-                    stream: rt.PULL_COMPLETE,
-                    streamId: u,
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-                break;
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-                  break;
-                new Promise(function(c) {
-                  var k;
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-                }).then(function() {
-                  C.postMessage({
-                    sourceName: _,
-                    targetName: w,
-                    stream: rt.CANCEL_COMPLETE,
-                    streamId: u,
-                    success: !0
-                  });
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-                  C.postMessage({
-                    sourceName: _,
-                    targetName: w,
-                    stream: rt.CANCEL_COMPLETE,
-                    streamId: u,
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-                throw new Error("Unexpected stream case");
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-          var rt, X;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
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-          var l = et(1);
-          class P {
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-            get(B) {
-              return t(this, rt).get(B) ?? null;
-            }
-            getAll() {
-              return (0, l.objectFromMap)(t(this, rt));
-            }
-            has(B) {
-              return t(this, rt).has(B);
-            }
-          }
-          rt = new WeakMap(), X = new WeakMap(), d.Metadata = P;
-        },
-        /* 17 */
-        /***/
-        (dt, d, et) => {
-          var B, F, g, O, I, x, ti;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.OptionalContentConfig = void 0;
-          var l = et(1), P = et(8);
-          const rt = Symbol("INTERNAL");
-          class X {
-            constructor(u, _) {
-              L(this, B, !0);
-              this.name = u, this.intent = _;
-            }
-            get visible() {
-              return t(this, B);
-            }
-            _setVisible(u, _) {
-              u !== rt && (0, l.unreachable)("Internal method `_setVisible` called."), Z(this, B, _);
-            }
-          }
-          B = new WeakMap();
-          class pt {
-            constructor(u) {
-              L(this, x);
-              L(this, F, null);
-              L(this, g, /* @__PURE__ */ new Map());
-              L(this, O, null);
-              L(this, I, null);
-              if (this.name = null, this.creator = null, u !== null) {
-                this.name = u.name, this.creator = u.creator, Z(this, I, u.order);
-                for (const _ of u.groups)
-                  t(this, g).set(_.id, new X(_.name, _.intent));
-                if (u.baseState === "OFF")
-                  for (const _ of t(this, g).values())
-                    _._setVisible(rt, !1);
-                for (const _ of u.on)
-                  t(this, g).get(_)._setVisible(rt, !0);
-                for (const _ of u.off)
-                  t(this, g).get(_)._setVisible(rt, !1);
-                Z(this, O, this.getHash());
-              }
-            }
-            isVisible(u) {
-              if (t(this, g).size === 0)
-                return !0;
-              if (!u)
-                return (0, l.warn)("Optional content group not defined."), !0;
-              if (u.type === "OCG")
-                return t(this, g).has(u.id) ? t(this, g).get(u.id).visible : ((0, l.warn)(`Optional content group not found: ${u.id}`), !0);
-              if (u.type === "OCMD") {
-                if (u.expression)
-                  return W(this, x, ti).call(this, u.expression);
-                if (!u.policy || u.policy === "AnyOn") {
-                  for (const _ of u.ids) {
-                    if (!t(this, g).has(_))
-                      return (0, l.warn)(`Optional content group not found: ${_}`), !0;
-                    if (t(this, g).get(_).visible)
-                      return !0;
-                  }
-                  return !1;
-                } else if (u.policy === "AllOn") {
-                  for (const _ of u.ids) {
-                    if (!t(this, g).has(_))
-                      return (0, l.warn)(`Optional content group not found: ${_}`), !0;
-                    if (!t(this, g).get(_).visible)
-                      return !1;
-                  }
-                  return !0;
-                } else if (u.policy === "AnyOff") {
-                  for (const _ of u.ids) {
-                    if (!t(this, g).has(_))
-                      return (0, l.warn)(`Optional content group not found: ${_}`), !0;
-                    if (!t(this, g).get(_).visible)
-                      return !0;
-                  }
-                  return !1;
-                } else if (u.policy === "AllOff") {
-                  for (const _ of u.ids) {
-                    if (!t(this, g).has(_))
-                      return (0, l.warn)(`Optional content group not found: ${_}`), !0;
-                    if (t(this, g).get(_).visible)
-                      return !1;
-                  }
-                  return !0;
-                }
-                return (0, l.warn)(`Unknown optional content policy ${u.policy}.`), !0;
-              }
-              return (0, l.warn)(`Unknown group type ${u.type}.`), !0;
-            }
-            setVisibility(u, _ = !0) {
-              if (!t(this, g).has(u)) {
-                (0, l.warn)(`Optional content group not found: ${u}`);
-                return;
-              }
-              t(this, g).get(u)._setVisible(rt, !!_), Z(this, F, null);
-            }
-            get hasInitialVisibility() {
-              return t(this, O) === null || this.getHash() === t(this, O);
-            }
-            getOrder() {
-              return t(this, g).size ? t(this, I) ? t(this, I).slice() : [...t(this, g).keys()] : null;
-            }
-            getGroups() {
-              return t(this, g).size > 0 ? (0, l.objectFromMap)(t(this, g)) : null;
-            }
-            getGroup(u) {
-              return t(this, g).get(u) || null;
-            }
-            getHash() {
-              if (t(this, F) !== null)
-                return t(this, F);
-              const u = new P.MurmurHash3_64();
-              for (const [_, w] of t(this, g))
-                u.update(`${_}:${w.visible}`);
-              return Z(this, F, u.hexdigest());
-            }
-          }
-          F = new WeakMap(), g = new WeakMap(), O = new WeakMap(), I = new WeakMap(), x = new WeakSet(), ti = function(u) {
-            const _ = u.length;
-            if (_ < 2)
-              return !0;
-            const w = u[0];
-            for (let C = 1; C < _; C++) {
-              const y = u[C];
-              let a;
-              if (Array.isArray(y))
-                a = W(this, x, ti).call(this, y);
-              else if (t(this, g).has(y))
-                a = t(this, g).get(y).visible;
-              else
-                return (0, l.warn)(`Optional content group not found: ${y}`), !0;
-              switch (w) {
-                case "And":
-                  if (!a)
-                    return !1;
-                  break;
-                case "Or":
-                  if (a)
-                    return !0;
-                  break;
-                case "Not":
-                  return !a;
-                default:
-                  return !0;
-              }
-            }
-            return w === "And";
-          }, d.OptionalContentConfig = pt;
-        },
-        /* 18 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.PDFDataTransportStream = void 0;
-          var l = et(1), P = et(6);
-          class rt {
-            constructor({
-              length: F,
-              initialData: g,
-              progressiveDone: O = !1,
-              contentDispositionFilename: I = null,
-              disableRange: x = !1,
-              disableStream: v = !1
-            }, A) {
-              if ((0, l.assert)(A, 'PDFDataTransportStream - missing required "pdfDataRangeTransport" argument.'), this._queuedChunks = [], this._progressiveDone = O, this._contentDispositionFilename = I, (g == null ? void 0 : g.length) > 0) {
-                const u = g instanceof Uint8Array && g.byteLength === g.buffer.byteLength ? g.buffer : new Uint8Array(g).buffer;
-                this._queuedChunks.push(u);
-              }
-              this._pdfDataRangeTransport = A, this._isStreamingSupported = !v, this._isRangeSupported = !x, this._contentLength = F, this._fullRequestReader = null, this._rangeReaders = [], this._pdfDataRangeTransport.addRangeListener((u, _) => {
-                this._onReceiveData({
-                  begin: u,
-                  chunk: _
-                });
-              }), this._pdfDataRangeTransport.addProgressListener((u, _) => {
-                this._onProgress({
-                  loaded: u,
-                  total: _
-                });
-              }), this._pdfDataRangeTransport.addProgressiveReadListener((u) => {
-                this._onReceiveData({
-                  chunk: u
-                });
-              }), this._pdfDataRangeTransport.addProgressiveDoneListener(() => {
-                this._onProgressiveDone();
-              }), this._pdfDataRangeTransport.transportReady();
-            }
-            _onReceiveData({
-              begin: F,
-              chunk: g
-            }) {
-              const O = g instanceof Uint8Array && g.byteLength === g.buffer.byteLength ? g.buffer : new Uint8Array(g).buffer;
-              if (F === void 0)
-                this._fullRequestReader ? this._fullRequestReader._enqueue(O) : this._queuedChunks.push(O);
-              else {
-                const I = this._rangeReaders.some(function(x) {
-                  return x._begin !== F ? !1 : (x._enqueue(O), !0);
-                });
-                (0, l.assert)(I, "_onReceiveData - no `PDFDataTransportStreamRangeReader` instance found.");
-              }
-            }
-            get _progressiveDataLength() {
-              var F;
-              return ((F = this._fullRequestReader) == null ? void 0 : F._loaded) ?? 0;
-            }
-            _onProgress(F) {
-              var g, O, I, x;
-              F.total === void 0 ? (O = (g = this._rangeReaders[0]) == null ? void 0 : g.onProgress) == null || O.call(g, {
-                loaded: F.loaded
-              }) : (x = (I = this._fullRequestReader) == null ? void 0 : I.onProgress) == null || x.call(I, {
-                loaded: F.loaded,
-                total: F.total
-              });
-            }
-            _onProgressiveDone() {
-              var F;
-              (F = this._fullRequestReader) == null || F.progressiveDone(), this._progressiveDone = !0;
-            }
-            _removeRangeReader(F) {
-              const g = this._rangeReaders.indexOf(F);
-              g >= 0 && this._rangeReaders.splice(g, 1);
-            }
-            getFullReader() {
-              (0, l.assert)(!this._fullRequestReader, "PDFDataTransportStream.getFullReader can only be called once.");
-              const F = this._queuedChunks;
-              return this._queuedChunks = null, new X(this, F, this._progressiveDone, this._contentDispositionFilename);
-            }
-            getRangeReader(F, g) {
-              if (g <= this._progressiveDataLength)
-                return null;
-              const O = new pt(this, F, g);
-              return this._pdfDataRangeTransport.requestDataRange(F, g), this._rangeReaders.push(O), O;
-            }
-            cancelAllRequests(F) {
-              var g;
-              (g = this._fullRequestReader) == null || g.cancel(F);
-              for (const O of this._rangeReaders.slice(0))
-                O.cancel(F);
-              this._pdfDataRangeTransport.abort();
-            }
-          }
-          d.PDFDataTransportStream = rt;
-          class X {
-            constructor(F, g, O = !1, I = null) {
-              this._stream = F, this._done = O || !1, this._filename = (0, P.isPdfFile)(I) ? I : null, this._queuedChunks = g || [], this._loaded = 0;
-              for (const x of this._queuedChunks)
-                this._loaded += x.byteLength;
-              this._requests = [], this._headersReady = Promise.resolve(), F._fullRequestReader = this, this.onProgress = null;
-            }
-            _enqueue(F) {
-              this._done || (this._requests.length > 0 ? this._requests.shift().resolve({
-                value: F,
-                done: !1
-              }) : this._queuedChunks.push(F), this._loaded += F.byteLength);
-            }
-            get headersReady() {
-              return this._headersReady;
-            }
-            get filename() {
-              return this._filename;
-            }
-            get isRangeSupported() {
-              return this._stream._isRangeSupported;
-            }
-            get isStreamingSupported() {
-              return this._stream._isStreamingSupported;
-            }
-            get contentLength() {
-              return this._stream._contentLength;
-            }
-            async read() {
-              if (this._queuedChunks.length > 0)
-                return {
-                  value: this._queuedChunks.shift(),
-                  done: !1
-                };
-              if (this._done)
-                return {
-                  value: void 0,
-                  done: !0
-                };
-              const F = new l.PromiseCapability();
-              return this._requests.push(F), F.promise;
-            }
-            cancel(F) {
-              this._done = !0;
-              for (const g of this._requests)
-                g.resolve({
-                  value: void 0,
-                  done: !0
-                });
-              this._requests.length = 0;
-            }
-            progressiveDone() {
-              this._done || (this._done = !0);
-            }
-          }
-          class pt {
-            constructor(F, g, O) {
-              this._stream = F, this._begin = g, this._end = O, this._queuedChunk = null, this._requests = [], this._done = !1, this.onProgress = null;
-            }
-            _enqueue(F) {
-              if (!this._done) {
-                if (this._requests.length === 0)
-                  this._queuedChunk = F;
-                else {
-                  this._requests.shift().resolve({
-                    value: F,
-                    done: !1
-                  });
-                  for (const O of this._requests)
-                    O.resolve({
-                      value: void 0,
-                      done: !0
-                    });
-                  this._requests.length = 0;
-                }
-                this._done = !0, this._stream._removeRangeReader(this);
-              }
-            }
-            get isStreamingSupported() {
-              return !1;
-            }
-            async read() {
-              if (this._queuedChunk) {
-                const g = this._queuedChunk;
-                return this._queuedChunk = null, {
-                  value: g,
-                  done: !1
-                };
-              }
-              if (this._done)
-                return {
-                  value: void 0,
-                  done: !0
-                };
-              const F = new l.PromiseCapability();
-              return this._requests.push(F), F.promise;
-            }
-            cancel(F) {
-              this._done = !0;
-              for (const g of this._requests)
-                g.resolve({
-                  value: void 0,
-                  done: !0
-                });
-              this._requests.length = 0, this._stream._removeRangeReader(this);
-            }
-          }
-        },
-        /* 19 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.PDFFetchStream = void 0;
-          var l = et(1), P = et(20);
-          function rt(O, I, x) {
-            return {
-              method: "GET",
-              headers: O,
-              signal: x.signal,
-              mode: "cors",
-              credentials: I ? "include" : "same-origin",
-              redirect: "follow"
-            };
-          }
-          function X(O) {
-            const I = new Headers();
-            for (const x in O) {
-              const v = O[x];
-              v !== void 0 && I.append(x, v);
-            }
-            return I;
-          }
-          function pt(O) {
-            return O instanceof Uint8Array ? O.buffer : O instanceof ArrayBuffer ? O : ((0, l.warn)(`getArrayBuffer - unexpected data format: ${O}`), new Uint8Array(O).buffer);
-          }
-          class B {
-            constructor(I) {
-              this.source = I, this.isHttp = /^https?:/i.test(I.url), this.httpHeaders = this.isHttp && I.httpHeaders || {}, this._fullRequestReader = null, this._rangeRequestReaders = [];
-            }
-            get _progressiveDataLength() {
-              var I;
-              return ((I = this._fullRequestReader) == null ? void 0 : I._loaded) ?? 0;
-            }
-            getFullReader() {
-              return (0, l.assert)(!this._fullRequestReader, "PDFFetchStream.getFullReader can only be called once."), this._fullRequestReader = new F(this), this._fullRequestReader;
-            }
-            getRangeReader(I, x) {
-              if (x <= this._progressiveDataLength)
-                return null;
-              const v = new g(this, I, x);
-              return this._rangeRequestReaders.push(v), v;
-            }
-            cancelAllRequests(I) {
-              var x;
-              (x = this._fullRequestReader) == null || x.cancel(I);
-              for (const v of this._rangeRequestReaders.slice(0))
-                v.cancel(I);
-            }
-          }
-          d.PDFFetchStream = B;
-          class F {
-            constructor(I) {
-              this._stream = I, this._reader = null, this._loaded = 0, this._filename = null;
-              const x = I.source;
-              this._withCredentials = x.withCredentials || !1, this._contentLength = x.length, this._headersCapability = new l.PromiseCapability(), this._disableRange = x.disableRange || !1, this._rangeChunkSize = x.rangeChunkSize, !this._rangeChunkSize && !this._disableRange && (this._disableRange = !0), this._abortController = new AbortController(), this._isStreamingSupported = !x.disableStream, this._isRangeSupported = !x.disableRange, this._headers = X(this._stream.httpHeaders);
-              const v = x.url;
-              fetch(v, rt(this._headers, this._withCredentials, this._abortController)).then((A) => {
-                if (!(0, P.validateResponseStatus)(A.status))
-                  throw (0, P.createResponseStatusError)(A.status, v);
-                this._reader = A.body.getReader(), this._headersCapability.resolve();
-                const u = (C) => A.headers.get(C), {
-                  allowRangeRequests: _,
-                  suggestedLength: w
-                } = (0, P.validateRangeRequestCapabilities)({
-                  getResponseHeader: u,
-                  isHttp: this._stream.isHttp,
-                  rangeChunkSize: this._rangeChunkSize,
-                  disableRange: this._disableRange
-                });
-                this._isRangeSupported = _, this._contentLength = w || this._contentLength, this._filename = (0, P.extractFilenameFromHeader)(u), !this._isStreamingSupported && this._isRangeSupported && this.cancel(new l.AbortException("Streaming is disabled."));
-              }).catch(this._headersCapability.reject), this.onProgress = null;
-            }
-            get headersReady() {
-              return this._headersCapability.promise;
-            }
-            get filename() {
-              return this._filename;
-            }
-            get contentLength() {
-              return this._contentLength;
-            }
-            get isRangeSupported() {
-              return this._isRangeSupported;
-            }
-            get isStreamingSupported() {
-              return this._isStreamingSupported;
-            }
-            async read() {
-              var v;
-              await this._headersCapability.promise;
-              const {
-                value: I,
-                done: x
-              } = await this._reader.read();
-              return x ? {
-                value: I,
-                done: x
-              } : (this._loaded += I.byteLength, (v = this.onProgress) == null || v.call(this, {
-                loaded: this._loaded,
-                total: this._contentLength
-              }), {
-                value: pt(I),
-                done: !1
-              });
-            }
-            cancel(I) {
-              var x;
-              (x = this._reader) == null || x.cancel(I), this._abortController.abort();
-            }
-          }
-          class g {
-            constructor(I, x, v) {
-              this._stream = I, this._reader = null, this._loaded = 0;
-              const A = I.source;
-              this._withCredentials = A.withCredentials || !1, this._readCapability = new l.PromiseCapability(), this._isStreamingSupported = !A.disableStream, this._abortController = new AbortController(), this._headers = X(this._stream.httpHeaders), this._headers.append("Range", `bytes=${x}-${v - 1}`);
-              const u = A.url;
-              fetch(u, rt(this._headers, this._withCredentials, this._abortController)).then((_) => {
-                if (!(0, P.validateResponseStatus)(_.status))
-                  throw (0, P.createResponseStatusError)(_.status, u);
-                this._readCapability.resolve(), this._reader = _.body.getReader();
-              }).catch(this._readCapability.reject), this.onProgress = null;
-            }
-            get isStreamingSupported() {
-              return this._isStreamingSupported;
-            }
-            async read() {
-              var v;
-              await this._readCapability.promise;
-              const {
-                value: I,
-                done: x
-              } = await this._reader.read();
-              return x ? {
-                value: I,
-                done: x
-              } : (this._loaded += I.byteLength, (v = this.onProgress) == null || v.call(this, {
-                loaded: this._loaded
-              }), {
-                value: pt(I),
-                done: !1
-              });
-            }
-            cancel(I) {
-              var x;
-              (x = this._reader) == null || x.cancel(I), this._abortController.abort();
-            }
-          }
-        },
-        /* 20 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.createResponseStatusError = B, d.extractFilenameFromHeader = pt, d.validateRangeRequestCapabilities = X, d.validateResponseStatus = F;
-          var l = et(1), P = et(21), rt = et(6);
-          function X({
-            getResponseHeader: g,
-            isHttp: O,
-            rangeChunkSize: I,
-            disableRange: x
-          }) {
-            const v = {
-              allowRangeRequests: !1,
-              suggestedLength: void 0
-            }, A = parseInt(g("Content-Length"), 10);
-            return !Number.isInteger(A) || (v.suggestedLength = A, A <= 2 * I) || x || !O || g("Accept-Ranges") !== "bytes" || (g("Content-Encoding") || "identity") !== "identity" || (v.allowRangeRequests = !0), v;
-          }
-          function pt(g) {
-            const O = g("Content-Disposition");
-            if (O) {
-              let I = (0, P.getFilenameFromContentDispositionHeader)(O);
-              if (I.includes("%"))
-                try {
-                  I = decodeURIComponent(I);
-                } catch {
-                }
-              if ((0, rt.isPdfFile)(I))
-                return I;
-            }
-            return null;
-          }
-          function B(g, O) {
-            return g === 404 || g === 0 && O.startsWith("file:") ? new l.MissingPDFException('Missing PDF "' + O + '".') : new l.UnexpectedResponseException(`Unexpected server response (${g}) while retrieving PDF "${O}".`, g);
-          }
-          function F(g) {
-            return g === 200 || g === 206;
-          }
-        },
-        /* 21 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.getFilenameFromContentDispositionHeader = P;
-          var l = et(1);
-          function P(rt) {
-            let X = !0, pt = B("filename\\*", "i").exec(rt);
-            if (pt) {
-              pt = pt[1];
-              let A = I(pt);
-              return A = unescape(A), A = x(A), A = v(A), g(A);
-            }
-            if (pt = O(rt), pt) {
-              const A = v(pt);
-              return g(A);
-            }
-            if (pt = B("filename", "i").exec(rt), pt) {
-              pt = pt[1];
-              let A = I(pt);
-              return A = v(A), g(A);
-            }
-            function B(A, u) {
-              return new RegExp("(?:^|;)\\s*" + A + '\\s*=\\s*([^";\\s][^;\\s]*|"(?:[^"\\\\]|\\\\"?)+"?)', u);
-            }
-            function F(A, u) {
-              if (A) {
-                if (!/^[\x00-\xFF]+$/.test(u))
-                  return u;
-                try {
-                  const _ = new TextDecoder(A, {
-                    fatal: !0
-                  }), w = (0, l.stringToBytes)(u);
-                  u = _.decode(w), X = !1;
-                } catch {
-                }
-              }
-              return u;
-            }
-            function g(A) {
-              return X && /[\x80-\xff]/.test(A) && (A = F("utf-8", A), X && (A = F("iso-8859-1", A))), A;
-            }
-            function O(A) {
-              const u = [];
-              let _;
-              const w = B("filename\\*((?!0\\d)\\d+)(\\*?)", "ig");
-              for (; (_ = w.exec(A)) !== null; ) {
-                let [, y, a, c] = _;
-                if (y = parseInt(y, 10), y in u) {
-                  if (y === 0)
-                    break;
-                  continue;
-                }
-                u[y] = [a, c];
-              }
-              const C = [];
-              for (let y = 0; y < u.length && y in u; ++y) {
-                let [a, c] = u[y];
-                c = I(c), a && (c = unescape(c), y === 0 && (c = x(c))), C.push(c);
-              }
-              return C.join("");
-            }
-            function I(A) {
-              if (A.startsWith('"')) {
-                const u = A.slice(1).split('\\"');
-                for (let _ = 0; _ < u.length; ++_) {
-                  const w = u[_].indexOf('"');
-                  w !== -1 && (u[_] = u[_].slice(0, w), u.length = _ + 1), u[_] = u[_].replaceAll(/\\(.)/g, "$1");
-                }
-                A = u.join('"');
-              }
-              return A;
-            }
-            function x(A) {
-              const u = A.indexOf("'");
-              if (u === -1)
-                return A;
-              const _ = A.slice(0, u), C = A.slice(u + 1).replace(/^[^']*'/, "");
-              return F(_, C);
-            }
-            function v(A) {
-              return !A.startsWith("=?") || /[\x00-\x19\x80-\xff]/.test(A) ? A : A.replaceAll(/=\?([\w-]*)\?([QqBb])\?((?:[^?]|\?(?!=))*)\?=/g, function(u, _, w, C) {
-                if (w === "q" || w === "Q")
-                  return C = C.replaceAll("_", " "), C = C.replaceAll(/=([0-9a-fA-F]{2})/g, function(y, a) {
-                    return String.fromCharCode(parseInt(a, 16));
-                  }), F(_, C);
-                try {
-                  C = atob(C);
-                } catch {
-                }
-                return F(_, C);
-              });
-            }
-            return "";
-          }
-        },
-        /* 22 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.PDFNetworkStream = void 0;
-          var l = et(1), P = et(20);
-          const rt = 200, X = 206;
-          function pt(I) {
-            const x = I.response;
-            return typeof x != "string" ? x : (0, l.stringToBytes)(x).buffer;
-          }
-          class B {
-            constructor(x, v = {}) {
-              this.url = x, this.isHttp = /^https?:/i.test(x), this.httpHeaders = this.isHttp && v.httpHeaders || /* @__PURE__ */ Object.create(null), this.withCredentials = v.withCredentials || !1, this.currXhrId = 0, this.pendingRequests = /* @__PURE__ */ Object.create(null);
-            }
-            requestRange(x, v, A) {
-              const u = {
-                begin: x,
-                end: v
-              };
-              for (const _ in A)
-                u[_] = A[_];
-              return this.request(u);
-            }
-            requestFull(x) {
-              return this.request(x);
-            }
-            request(x) {
-              const v = new XMLHttpRequest(), A = this.currXhrId++, u = this.pendingRequests[A] = {
-                xhr: v
-              };
-              v.open("GET", this.url), v.withCredentials = this.withCredentials;
-              for (const _ in this.httpHeaders) {
-                const w = this.httpHeaders[_];
-                w !== void 0 && v.setRequestHeader(_, w);
-              }
-              return this.isHttp && "begin" in x && "end" in x ? (v.setRequestHeader("Range", `bytes=${x.begin}-${x.end - 1}`), u.expectedStatus = X) : u.expectedStatus = rt, v.responseType = "arraybuffer", x.onError && (v.onerror = function(_) {
-                x.onError(v.status);
-              }), v.onreadystatechange = this.onStateChange.bind(this, A), v.onprogress = this.onProgress.bind(this, A), u.onHeadersReceived = x.onHeadersReceived, u.onDone = x.onDone, u.onError = x.onError, u.onProgress = x.onProgress, v.send(null), A;
-            }
-            onProgress(x, v) {
-              var u;
-              const A = this.pendingRequests[x];
-              A && ((u = A.onProgress) == null || u.call(A, v));
-            }
-            onStateChange(x, v) {
-              var y, a, c;
-              const A = this.pendingRequests[x];
-              if (!A)
-                return;
-              const u = A.xhr;
-              if (u.readyState >= 2 && A.onHeadersReceived && (A.onHeadersReceived(), delete A.onHeadersReceived), u.readyState !== 4 || !(x in this.pendingRequests))
-                return;
-              if (delete this.pendingRequests[x], u.status === 0 && this.isHttp) {
-                (y = A.onError) == null || y.call(A, u.status);
-                return;
-              }
-              const _ = u.status || rt;
-              if (!(_ === rt && A.expectedStatus === X) && _ !== A.expectedStatus) {
-                (a = A.onError) == null || a.call(A, u.status);
-                return;
-              }
-              const C = pt(u);
-              if (_ === X) {
-                const k = u.getResponseHeader("Content-Range"), p = /bytes (\d+)-(\d+)\/(\d+)/.exec(k);
-                A.onDone({
-                  begin: parseInt(p[1], 10),
-                  chunk: C
-                });
-              } else
-                C ? A.onDone({
-                  begin: 0,
-                  chunk: C
-                }) : (c = A.onError) == null || c.call(A, u.status);
-            }
-            getRequestXhr(x) {
-              return this.pendingRequests[x].xhr;
-            }
-            isPendingRequest(x) {
-              return x in this.pendingRequests;
-            }
-            abortRequest(x) {
-              const v = this.pendingRequests[x].xhr;
-              delete this.pendingRequests[x], v.abort();
-            }
-          }
-          class F {
-            constructor(x) {
-              this._source = x, this._manager = new B(x.url, {
-                httpHeaders: x.httpHeaders,
-                withCredentials: x.withCredentials
-              }), this._rangeChunkSize = x.rangeChunkSize, this._fullRequestReader = null, this._rangeRequestReaders = [];
-            }
-            _onRangeRequestReaderClosed(x) {
-              const v = this._rangeRequestReaders.indexOf(x);
-              v >= 0 && this._rangeRequestReaders.splice(v, 1);
-            }
-            getFullReader() {
-              return (0, l.assert)(!this._fullRequestReader, "PDFNetworkStream.getFullReader can only be called once."), this._fullRequestReader = new g(this._manager, this._source), this._fullRequestReader;
-            }
-            getRangeReader(x, v) {
-              const A = new O(this._manager, x, v);
-              return A.onClosed = this._onRangeRequestReaderClosed.bind(this), this._rangeRequestReaders.push(A), A;
-            }
-            cancelAllRequests(x) {
-              var v;
-              (v = this._fullRequestReader) == null || v.cancel(x);
-              for (const A of this._rangeRequestReaders.slice(0))
-                A.cancel(x);
-            }
-          }
-          d.PDFNetworkStream = F;
-          class g {
-            constructor(x, v) {
-              this._manager = x;
-              const A = {
-                onHeadersReceived: this._onHeadersReceived.bind(this),
-                onDone: this._onDone.bind(this),
-                onError: this._onError.bind(this),
-                onProgress: this._onProgress.bind(this)
-              };
-              this._url = v.url, this._fullRequestId = x.requestFull(A), this._headersReceivedCapability = new l.PromiseCapability(), this._disableRange = v.disableRange || !1, this._contentLength = v.length, this._rangeChunkSize = v.rangeChunkSize, !this._rangeChunkSize && !this._disableRange && (this._disableRange = !0), this._isStreamingSupported = !1, this._isRangeSupported = !1, this._cachedChunks = [], this._requests = [], this._done = !1, this._storedError = void 0, this._filename = null, this.onProgress = null;
-            }
-            _onHeadersReceived() {
-              const x = this._fullRequestId, v = this._manager.getRequestXhr(x), A = (w) => v.getResponseHeader(w), {
-                allowRangeRequests: u,
-                suggestedLength: _
-              } = (0, P.validateRangeRequestCapabilities)({
-                getResponseHeader: A,
-                isHttp: this._manager.isHttp,
-                rangeChunkSize: this._rangeChunkSize,
-                disableRange: this._disableRange
-              });
-              u && (this._isRangeSupported = !0), this._contentLength = _ || this._contentLength, this._filename = (0, P.extractFilenameFromHeader)(A), this._isRangeSupported && this._manager.abortRequest(x), this._headersReceivedCapability.resolve();
-            }
-            _onDone(x) {
-              if (x && (this._requests.length > 0 ? this._requests.shift().resolve({
-                value: x.chunk,
-                done: !1
-              }) : this._cachedChunks.push(x.chunk)), this._done = !0, !(this._cachedChunks.length > 0)) {
-                for (const v of this._requests)
-                  v.resolve({
-                    value: void 0,
-                    done: !0
-                  });
-                this._requests.length = 0;
-              }
-            }
-            _onError(x) {
-              this._storedError = (0, P.createResponseStatusError)(x, this._url), this._headersReceivedCapability.reject(this._storedError);
-              for (const v of this._requests)
-                v.reject(this._storedError);
-              this._requests.length = 0, this._cachedChunks.length = 0;
-            }
-            _onProgress(x) {
-              var v;
-              (v = this.onProgress) == null || v.call(this, {
-                loaded: x.loaded,
-                total: x.lengthComputable ? x.total : this._contentLength
-              });
-            }
-            get filename() {
-              return this._filename;
-            }
-            get isRangeSupported() {
-              return this._isRangeSupported;
-            }
-            get isStreamingSupported() {
-              return this._isStreamingSupported;
-            }
-            get contentLength() {
-              return this._contentLength;
-            }
-            get headersReady() {
-              return this._headersReceivedCapability.promise;
-            }
-            async read() {
-              if (this._storedError)
-                throw this._storedError;
-              if (this._cachedChunks.length > 0)
-                return {
-                  value: this._cachedChunks.shift(),
-                  done: !1
-                };
-              if (this._done)
-                return {
-                  value: void 0,
-                  done: !0
-                };
-              const x = new l.PromiseCapability();
-              return this._requests.push(x), x.promise;
-            }
-            cancel(x) {
-              this._done = !0, this._headersReceivedCapability.reject(x);
-              for (const v of this._requests)
-                v.resolve({
-                  value: void 0,
-                  done: !0
-                });
-              this._requests.length = 0, this._manager.isPendingRequest(this._fullRequestId) && this._manager.abortRequest(this._fullRequestId), this._fullRequestReader = null;
-            }
-          }
-          class O {
-            constructor(x, v, A) {
-              this._manager = x;
-              const u = {
-                onDone: this._onDone.bind(this),
-                onError: this._onError.bind(this),
-                onProgress: this._onProgress.bind(this)
-              };
-              this._url = x.url, this._requestId = x.requestRange(v, A, u), this._requests = [], this._queuedChunk = null, this._done = !1, this._storedError = void 0, this.onProgress = null, this.onClosed = null;
-            }
-            _close() {
-              var x;
-              (x = this.onClosed) == null || x.call(this, this);
-            }
-            _onDone(x) {
-              const v = x.chunk;
-              this._requests.length > 0 ? this._requests.shift().resolve({
-                value: v,
-                done: !1
-              }) : this._queuedChunk = v, this._done = !0;
-              for (const A of this._requests)
-                A.resolve({
-                  value: void 0,
-                  done: !0
-                });
-              this._requests.length = 0, this._close();
-            }
-            _onError(x) {
-              this._storedError = (0, P.createResponseStatusError)(x, this._url);
-              for (const v of this._requests)
-                v.reject(this._storedError);
-              this._requests.length = 0, this._queuedChunk = null;
-            }
-            _onProgress(x) {
-              var v;
-              this.isStreamingSupported || (v = this.onProgress) == null || v.call(this, {
-                loaded: x.loaded
-              });
-            }
-            get isStreamingSupported() {
-              return !1;
-            }
-            async read() {
-              if (this._storedError)
-                throw this._storedError;
-              if (this._queuedChunk !== null) {
-                const v = this._queuedChunk;
-                return this._queuedChunk = null, {
-                  value: v,
-                  done: !1
-                };
-              }
-              if (this._done)
-                return {
-                  value: void 0,
-                  done: !0
-                };
-              const x = new l.PromiseCapability();
-              return this._requests.push(x), x.promise;
-            }
-            cancel(x) {
-              this._done = !0;
-              for (const v of this._requests)
-                v.resolve({
-                  value: void 0,
-                  done: !0
-                });
-              this._requests.length = 0, this._manager.isPendingRequest(this._requestId) && this._manager.abortRequest(this._requestId), this._close();
-            }
-          }
-        },
-        /* 23 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.PDFNodeStream = void 0;
-          var l = et(1), P = et(20);
-          const rt = /^file:\/\/\/[a-zA-Z]:\//;
-          function X(A) {
-            const u = require$$5, _ = u.parse(A);
-            return _.protocol === "file:" || _.host ? _ : /^[a-z]:[/\\]/i.test(A) ? u.parse(`file:///${A}`) : (_.host || (_.protocol = "file:"), _);
-          }
-          class pt {
-            constructor(u) {
-              this.source = u, this.url = X(u.url), this.isHttp = this.url.protocol === "http:" || this.url.protocol === "https:", this.isFsUrl = this.url.protocol === "file:", this.httpHeaders = this.isHttp && u.httpHeaders || {}, this._fullRequestReader = null, this._rangeRequestReaders = [];
-            }
-            get _progressiveDataLength() {
-              var u;
-              return ((u = this._fullRequestReader) == null ? void 0 : u._loaded) ?? 0;
-            }
-            getFullReader() {
-              return (0, l.assert)(!this._fullRequestReader, "PDFNodeStream.getFullReader can only be called once."), this._fullRequestReader = this.isFsUrl ? new x(this) : new O(this), this._fullRequestReader;
-            }
-            getRangeReader(u, _) {
-              if (_ <= this._progressiveDataLength)
-                return null;
-              const w = this.isFsUrl ? new v(this, u, _) : new I(this, u, _);
-              return this._rangeRequestReaders.push(w), w;
-            }
-            cancelAllRequests(u) {
-              var _;
-              (_ = this._fullRequestReader) == null || _.cancel(u);
-              for (const w of this._rangeRequestReaders.slice(0))
-                w.cancel(u);
-            }
-          }
-          d.PDFNodeStream = pt;
-          class B {
-            constructor(u) {
-              this._url = u.url, this._done = !1, this._storedError = null, this.onProgress = null;
-              const _ = u.source;
-              this._contentLength = _.length, this._loaded = 0, this._filename = null, this._disableRange = _.disableRange || !1, this._rangeChunkSize = _.rangeChunkSize, !this._rangeChunkSize && !this._disableRange && (this._disableRange = !0), this._isStreamingSupported = !_.disableStream, this._isRangeSupported = !_.disableRange, this._readableStream = null, this._readCapability = new l.PromiseCapability(), this._headersCapability = new l.PromiseCapability();
-            }
-            get headersReady() {
-              return this._headersCapability.promise;
-            }
-            get filename() {
-              return this._filename;
-            }
-            get contentLength() {
-              return this._contentLength;
-            }
-            get isRangeSupported() {
-              return this._isRangeSupported;
-            }
-            get isStreamingSupported() {
-              return this._isStreamingSupported;
-            }
-            async read() {
-              var w;
-              if (await this._readCapability.promise, this._done)
-                return {
-                  value: void 0,
-                  done: !0
-                };
-              if (this._storedError)
-                throw this._storedError;
-              const u = this._readableStream.read();
-              return u === null ? (this._readCapability = new l.PromiseCapability(), this.read()) : (this._loaded += u.length, (w = this.onProgress) == null || w.call(this, {
-                loaded: this._loaded,
-                total: this._contentLength
-              }), {
-                value: new Uint8Array(u).buffer,
-                done: !1
-              });
-            }
-            cancel(u) {
-              if (!this._readableStream) {
-                this._error(u);
-                return;
-              }
-              this._readableStream.destroy(u);
-            }
-            _error(u) {
-              this._storedError = u, this._readCapability.resolve();
-            }
-            _setReadableStream(u) {
-              this._readableStream = u, u.on("readable", () => {
-                this._readCapability.resolve();
-              }), u.on("end", () => {
-                u.destroy(), this._done = !0, this._readCapability.resolve();
-              }), u.on("error", (_) => {
-                this._error(_);
-              }), !this._isStreamingSupported && this._isRangeSupported && this._error(new l.AbortException("streaming is disabled")), this._storedError && this._readableStream.destroy(this._storedError);
-            }
-          }
-          class F {
-            constructor(u) {
-              this._url = u.url, this._done = !1, this._storedError = null, this.onProgress = null, this._loaded = 0, this._readableStream = null, this._readCapability = new l.PromiseCapability();
-              const _ = u.source;
-              this._isStreamingSupported = !_.disableStream;
-            }
-            get isStreamingSupported() {
-              return this._isStreamingSupported;
-            }
-            async read() {
-              var w;
-              if (await this._readCapability.promise, this._done)
-                return {
-                  value: void 0,
-                  done: !0
-                };
-              if (this._storedError)
-                throw this._storedError;
-              const u = this._readableStream.read();
-              return u === null ? (this._readCapability = new l.PromiseCapability(), this.read()) : (this._loaded += u.length, (w = this.onProgress) == null || w.call(this, {
-                loaded: this._loaded
-              }), {
-                value: new Uint8Array(u).buffer,
-                done: !1
-              });
-            }
-            cancel(u) {
-              if (!this._readableStream) {
-                this._error(u);
-                return;
-              }
-              this._readableStream.destroy(u);
-            }
-            _error(u) {
-              this._storedError = u, this._readCapability.resolve();
-            }
-            _setReadableStream(u) {
-              this._readableStream = u, u.on("readable", () => {
-                this._readCapability.resolve();
-              }), u.on("end", () => {
-                u.destroy(), this._done = !0, this._readCapability.resolve();
-              }), u.on("error", (_) => {
-                this._error(_);
-              }), this._storedError && this._readableStream.destroy(this._storedError);
-            }
-          }
-          function g(A, u) {
-            return {
-              protocol: A.protocol,
-              auth: A.auth,
-              host: A.hostname,
-              port: A.port,
-              path: A.path,
-              method: "GET",
-              headers: u
-            };
-          }
-          class O extends B {
-            constructor(u) {
-              super(u);
-              const _ = (w) => {
-                if (w.statusCode === 404) {
-                  const c = new l.MissingPDFException(`Missing PDF "${this._url}".`);
-                  this._storedError = c, this._headersCapability.reject(c);
-                  return;
-                }
-                this._headersCapability.resolve(), this._setReadableStream(w);
-                const C = (c) => this._readableStream.headers[c.toLowerCase()], {
-                  allowRangeRequests: y,
-                  suggestedLength: a
-                } = (0, P.validateRangeRequestCapabilities)({
-                  getResponseHeader: C,
-                  isHttp: u.isHttp,
-                  rangeChunkSize: this._rangeChunkSize,
-                  disableRange: this._disableRange
-                });
-                this._isRangeSupported = y, this._contentLength = a || this._contentLength, this._filename = (0, P.extractFilenameFromHeader)(C);
-              };
-              if (this._request = null, this._url.protocol === "http:") {
-                const w = require$$5;
-                this._request = w.request(g(this._url, u.httpHeaders), _);
-              } else {
-                const w = require$$5;
-                this._request = w.request(g(this._url, u.httpHeaders), _);
-              }
-              this._request.on("error", (w) => {
-                this._storedError = w, this._headersCapability.reject(w);
-              }), this._request.end();
-            }
-          }
-          class I extends F {
-            constructor(u, _, w) {
-              super(u), this._httpHeaders = {};
-              for (const y in u.httpHeaders) {
-                const a = u.httpHeaders[y];
-                a !== void 0 && (this._httpHeaders[y] = a);
-              }
-              this._httpHeaders.Range = `bytes=${_}-${w - 1}`;
-              const C = (y) => {
-                if (y.statusCode === 404) {
-                  const a = new l.MissingPDFException(`Missing PDF "${this._url}".`);
-                  this._storedError = a;
-                  return;
-                }
-                this._setReadableStream(y);
-              };
-              if (this._request = null, this._url.protocol === "http:") {
-                const y = require$$5;
-                this._request = y.request(g(this._url, this._httpHeaders), C);
-              } else {
-                const y = require$$5;
-                this._request = y.request(g(this._url, this._httpHeaders), C);
-              }
-              this._request.on("error", (y) => {
-                this._storedError = y;
-              }), this._request.end();
-            }
-          }
-          class x extends B {
-            constructor(u) {
-              super(u);
-              let _ = decodeURIComponent(this._url.path);
-              rt.test(this._url.href) && (_ = _.replace(/^\//, ""));
-              const w = require$$5;
-              w.lstat(_, (C, y) => {
-                if (C) {
-                  C.code === "ENOENT" && (C = new l.MissingPDFException(`Missing PDF "${_}".`)), this._storedError = C, this._headersCapability.reject(C);
-                  return;
-                }
-                this._contentLength = y.size, this._setReadableStream(w.createReadStream(_)), this._headersCapability.resolve();
-              });
-            }
-          }
-          class v extends F {
-            constructor(u, _, w) {
-              super(u);
-              let C = decodeURIComponent(this._url.path);
-              rt.test(this._url.href) && (C = C.replace(/^\//, ""));
-              const y = require$$5;
-              this._setReadableStream(y.createReadStream(C, {
-                start: _,
-                end: w - 1
-              }));
-            }
-          }
-        },
-        /* 24 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.SVGGraphics = void 0;
-          var l = et(6), P = et(1);
-          const rt = {
-            fontStyle: "normal",
-            fontWeight: "normal",
-            fillColor: "#000000"
-          }, X = "http://www.w3.org/XML/1998/namespace", pt = "http://www.w3.org/1999/xlink", B = ["butt", "round", "square"], F = ["miter", "round", "bevel"], g = function(y, a = "", c = !1) {
-            if (URL.createObjectURL && typeof Blob < "u" && !c)
-              return URL.createObjectURL(new Blob([y], {
-                type: a
-              }));
-            const k = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=";
-            let p = `data:${a};base64,`;
-            for (let r = 0, T = y.length; r < T; r += 3) {
-              const m = y[r] & 255, U = y[r + 1] & 255, z = y[r + 2] & 255, E = m >> 2, V = (m & 3) << 4 | U >> 4, st = r + 1 < T ? (U & 15) << 2 | z >> 6 : 64, at = r + 2 < T ? z & 63 : 64;
-              p += k[E] + k[V] + k[st] + k[at];
-            }
-            return p;
-          }, O = function() {
-            const y = new Uint8Array([137, 80, 78, 71, 13, 10, 26, 10]), a = 12, c = new Int32Array(256);
-            for (let z = 0; z < 256; z++) {
-              let E = z;
-              for (let V = 0; V < 8; V++)
-                E = E & 1 ? 3988292384 ^ E >> 1 & 2147483647 : E >> 1 & 2147483647;
-              c[z] = E;
-            }
-            function k(z, E, V) {
-              let st = -1;
-              for (let at = E; at < V; at++) {
-                const H = (st ^ z[at]) & 255, lt = c[H];
-                st = st >>> 8 ^ lt;
-              }
-              return st ^ -1;
-            }
-            function p(z, E, V, st) {
-              let at = st;
-              const H = E.length;
-              V[at] = H >> 24 & 255, V[at + 1] = H >> 16 & 255, V[at + 2] = H >> 8 & 255, V[at + 3] = H & 255, at += 4, V[at] = z.charCodeAt(0) & 255, V[at + 1] = z.charCodeAt(1) & 255, V[at + 2] = z.charCodeAt(2) & 255, V[at + 3] = z.charCodeAt(3) & 255, at += 4, V.set(E, at), at += E.length;
-              const lt = k(V, st + 4, at);
-              V[at] = lt >> 24 & 255, V[at + 1] = lt >> 16 & 255, V[at + 2] = lt >> 8 & 255, V[at + 3] = lt & 255;
-            }
-            function r(z, E, V) {
-              let st = 1, at = 0;
-              for (let H = E; H < V; ++H)
-                st = (st + (z[H] & 255)) % 65521, at = (at + st) % 65521;
-              return at << 16 | st;
-            }
-            function T(z) {
-              if (!P.isNodeJS)
-                return m(z);
-              try {
-                const E = parseInt(process.versions.node) >= 8 ? z : Buffer.from(z), V = require$$5.deflateSync(E, {
-                  level: 9
-                });
-                return V instanceof Uint8Array ? V : new Uint8Array(V);
-              } catch (E) {
-                (0, P.warn)("Not compressing PNG because zlib.deflateSync is unavailable: " + E);
-              }
-              return m(z);
-            }
-            function m(z) {
-              let E = z.length;
-              const V = 65535, st = Math.ceil(E / V), at = new Uint8Array(2 + E + st * 5 + 4);
-              let H = 0;
-              at[H++] = 120, at[H++] = 156;
-              let lt = 0;
-              for (; E > V; )
-                at[H++] = 0, at[H++] = 255, at[H++] = 255, at[H++] = 0, at[H++] = 0, at.set(z.subarray(lt, lt + V), H), H += V, lt += V, E -= V;
-              at[H++] = 1, at[H++] = E & 255, at[H++] = E >> 8 & 255, at[H++] = ~E & 65535 & 255, at[H++] = (~E & 65535) >> 8 & 255, at.set(z.subarray(lt), H), H += z.length - lt;
-              const gt = r(z, 0, z.length);
-              return at[H++] = gt >> 24 & 255, at[H++] = gt >> 16 & 255, at[H++] = gt >> 8 & 255, at[H++] = gt & 255, at;
-            }
-            function U(z, E, V, st) {
-              const at = z.width, H = z.height;
-              let lt, gt, wt;
-              const xt = z.data;
-              switch (E) {
-                case P.ImageKind.GRAYSCALE_1BPP:
-                  gt = 0, lt = 1, wt = at + 7 >> 3;
-                  break;
-                case P.ImageKind.RGB_24BPP:
-                  gt = 2, lt = 8, wt = at * 3;
-                  break;
-                case P.ImageKind.RGBA_32BPP:
-                  gt = 6, lt = 8, wt = at * 4;
-                  break;
-                default:
-                  throw new Error("invalid format");
-              }
-              const S = new Uint8Array((1 + wt) * H);
-              let i = 0, n = 0;
-              for (let N = 0; N < H; ++N)
-                S[i++] = 0, S.set(xt.subarray(n, n + wt), i), n += wt, i += wt;
-              if (E === P.ImageKind.GRAYSCALE_1BPP && st) {
-                i = 0;
-                for (let N = 0; N < H; N++) {
-                  i++;
-                  for (let tt = 0; tt < wt; tt++)
-                    S[i++] ^= 255;
-                }
-              }
-              const s = new Uint8Array([at >> 24 & 255, at >> 16 & 255, at >> 8 & 255, at & 255, H >> 24 & 255, H >> 16 & 255, H >> 8 & 255, H & 255, lt, gt, 0, 0, 0]), o = T(S), h = y.length + a * 3 + s.length + o.length, b = new Uint8Array(h);
-              let M = 0;
-              return b.set(y, M), M += y.length, p("IHDR", s, b, M), M += a + s.length, p("IDATA", o, b, M), M += a + o.length, p("IEND", new Uint8Array(0), b, M), g(b, "image/png", V);
-            }
-            return function(E, V, st) {
-              const at = E.kind === void 0 ? P.ImageKind.GRAYSCALE_1BPP : E.kind;
-              return U(E, at, V, st);
-            };
-          }();
-          class I {
-            constructor() {
-              this.fontSizeScale = 1, this.fontWeight = rt.fontWeight, this.fontSize = 0, this.textMatrix = P.IDENTITY_MATRIX, this.fontMatrix = P.FONT_IDENTITY_MATRIX, this.leading = 0, this.textRenderingMode = P.TextRenderingMode.FILL, this.textMatrixScale = 1, this.x = 0, this.y = 0, this.lineX = 0, this.lineY = 0, this.charSpacing = 0, this.wordSpacing = 0, this.textHScale = 1, this.textRise = 0, this.fillColor = rt.fillColor, this.strokeColor = "#000000", this.fillAlpha = 1, this.strokeAlpha = 1, this.lineWidth = 1, this.lineJoin = "", this.lineCap = "", this.miterLimit = 0, this.dashArray = [], this.dashPhase = 0, this.dependencies = [], this.activeClipUrl = null, this.clipGroup = null, this.maskId = "";
-            }
-            clone() {
-              return Object.create(this);
-            }
-            setCurrentPoint(a, c) {
-              this.x = a, this.y = c;
-            }
-          }
-          function x(y) {
-            let a = [];
-            const c = [];
-            for (const k of y) {
-              if (k.fn === "save") {
-                a.push({
-                  fnId: 92,
-                  fn: "group",
-                  items: []
-                }), c.push(a), a = a.at(-1).items;
-                continue;
-              }
-              k.fn === "restore" ? a = c.pop() : a.push(k);
-            }
-            return a;
-          }
-          function v(y) {
-            if (Number.isInteger(y))
-              return y.toString();
-            const a = y.toFixed(10);
-            let c = a.length - 1;
-            if (a[c] !== "0")
-              return a;
-            do
-              c--;
-            while (a[c] === "0");
-            return a.substring(0, a[c] === "." ? c : c + 1);
-          }
-          function A(y) {
-            if (y[4] === 0 && y[5] === 0) {
-              if (y[1] === 0 && y[2] === 0)
-                return y[0] === 1 && y[3] === 1 ? "" : `scale(${v(y[0])} ${v(y[3])})`;
-              if (y[0] === y[3] && y[1] === -y[2]) {
-                const a = Math.acos(y[0]) * 180 / Math.PI;
-                return `rotate(${v(a)})`;
-              }
-            } else if (y[0] === 1 && y[1] === 0 && y[2] === 0 && y[3] === 1)
-              return `translate(${v(y[4])} ${v(y[5])})`;
-            return `matrix(${v(y[0])} ${v(y[1])} ${v(y[2])} ${v(y[3])} ${v(y[4])} ${v(y[5])})`;
-          }
-          let u = 0, _ = 0, w = 0;
-          class C {
-            constructor(a, c, k = !1) {
-              (0, l.deprecated)("The SVG back-end is no longer maintained and *may* be removed in the future."), this.svgFactory = new l.DOMSVGFactory(), this.current = new I(), this.transformMatrix = P.IDENTITY_MATRIX, this.transformStack = [], this.extraStack = [], this.commonObjs = a, this.objs = c, this.pendingClip = null, this.pendingEOFill = !1, this.embedFonts = !1, this.embeddedFonts = /* @__PURE__ */ Object.create(null), this.cssStyle = null, this.forceDataSchema = !!k, this._operatorIdMapping = [];
-              for (const p in P.OPS)
-                this._operatorIdMapping[P.OPS[p]] = p;
-            }
-            getObject(a, c = null) {
-              return typeof a == "string" ? a.startsWith("g_") ? this.commonObjs.get(a) : this.objs.get(a) : c;
-            }
-            save() {
-              this.transformStack.push(this.transformMatrix);
-              const a = this.current;
-              this.extraStack.push(a), this.current = a.clone();
-            }
-            restore() {
-              this.transformMatrix = this.transformStack.pop(), this.current = this.extraStack.pop(), this.pendingClip = null, this.tgrp = null;
-            }
-            group(a) {
-              this.save(), this.executeOpTree(a), this.restore();
-            }
-            loadDependencies(a) {
-              const c = a.fnArray, k = a.argsArray;
-              for (let p = 0, r = c.length; p < r; p++)
-                if (c[p] === P.OPS.dependency)
-                  for (const T of k[p]) {
-                    const m = T.startsWith("g_") ? this.commonObjs : this.objs, U = new Promise((z) => {
-                      m.get(T, z);
-                    });
-                    this.current.dependencies.push(U);
-                  }
-              return Promise.all(this.current.dependencies);
-            }
-            transform(a, c, k, p, r, T) {
-              const m = [a, c, k, p, r, T];
-              this.transformMatrix = P.Util.transform(this.transformMatrix, m), this.tgrp = null;
-            }
-            getSVG(a, c) {
-              this.viewport = c;
-              const k = this._initialize(c);
-              return this.loadDependencies(a).then(() => (this.transformMatrix = P.IDENTITY_MATRIX, this.executeOpTree(this.convertOpList(a)), k));
-            }
-            convertOpList(a) {
-              const c = this._operatorIdMapping, k = a.argsArray, p = a.fnArray, r = [];
-              for (let T = 0, m = p.length; T < m; T++) {
-                const U = p[T];
-                r.push({
-                  fnId: U,
-                  fn: c[U],
-                  args: k[T]
-                });
-              }
-              return x(r);
-            }
-            executeOpTree(a) {
-              for (const c of a) {
-                const k = c.fn, p = c.fnId, r = c.args;
-                switch (p | 0) {
-                  case P.OPS.beginText:
-                    this.beginText();
-                    break;
-                  case P.OPS.dependency:
-                    break;
-                  case P.OPS.setLeading:
-                    this.setLeading(r);
-                    break;
-                  case P.OPS.setLeadingMoveText:
-                    this.setLeadingMoveText(r[0], r[1]);
-                    break;
-                  case P.OPS.setFont:
-                    this.setFont(r);
-                    break;
-                  case P.OPS.showText:
-                    this.showText(r[0]);
-                    break;
-                  case P.OPS.showSpacedText:
-                    this.showText(r[0]);
-                    break;
-                  case P.OPS.endText:
-                    this.endText();
-                    break;
-                  case P.OPS.moveText:
-                    this.moveText(r[0], r[1]);
-                    break;
-                  case P.OPS.setCharSpacing:
-                    this.setCharSpacing(r[0]);
-                    break;
-                  case P.OPS.setWordSpacing:
-                    this.setWordSpacing(r[0]);
-                    break;
-                  case P.OPS.setHScale:
-                    this.setHScale(r[0]);
-                    break;
-                  case P.OPS.setTextMatrix:
-                    this.setTextMatrix(r[0], r[1], r[2], r[3], r[4], r[5]);
-                    break;
-                  case P.OPS.setTextRise:
-                    this.setTextRise(r[0]);
-                    break;
-                  case P.OPS.setTextRenderingMode:
-                    this.setTextRenderingMode(r[0]);
-                    break;
-                  case P.OPS.setLineWidth:
-                    this.setLineWidth(r[0]);
-                    break;
-                  case P.OPS.setLineJoin:
-                    this.setLineJoin(r[0]);
-                    break;
-                  case P.OPS.setLineCap:
-                    this.setLineCap(r[0]);
-                    break;
-                  case P.OPS.setMiterLimit:
-                    this.setMiterLimit(r[0]);
-                    break;
-                  case P.OPS.setFillRGBColor:
-                    this.setFillRGBColor(r[0], r[1], r[2]);
-                    break;
-                  case P.OPS.setStrokeRGBColor:
-                    this.setStrokeRGBColor(r[0], r[1], r[2]);
-                    break;
-                  case P.OPS.setStrokeColorN:
-                    this.setStrokeColorN(r);
-                    break;
-                  case P.OPS.setFillColorN:
-                    this.setFillColorN(r);
-                    break;
-                  case P.OPS.shadingFill:
-                    this.shadingFill(r[0]);
-                    break;
-                  case P.OPS.setDash:
-                    this.setDash(r[0], r[1]);
-                    break;
-                  case P.OPS.setRenderingIntent:
-                    this.setRenderingIntent(r[0]);
-                    break;
-                  case P.OPS.setFlatness:
-                    this.setFlatness(r[0]);
-                    break;
-                  case P.OPS.setGState:
-                    this.setGState(r[0]);
-                    break;
-                  case P.OPS.fill:
-                    this.fill();
-                    break;
-                  case P.OPS.eoFill:
-                    this.eoFill();
-                    break;
-                  case P.OPS.stroke:
-                    this.stroke();
-                    break;
-                  case P.OPS.fillStroke:
-                    this.fillStroke();
-                    break;
-                  case P.OPS.eoFillStroke:
-                    this.eoFillStroke();
-                    break;
-                  case P.OPS.clip:
-                    this.clip("nonzero");
-                    break;
-                  case P.OPS.eoClip:
-                    this.clip("evenodd");
-                    break;
-                  case P.OPS.paintSolidColorImageMask:
-                    this.paintSolidColorImageMask();
-                    break;
-                  case P.OPS.paintImageXObject:
-                    this.paintImageXObject(r[0]);
-                    break;
-                  case P.OPS.paintInlineImageXObject:
-                    this.paintInlineImageXObject(r[0]);
-                    break;
-                  case P.OPS.paintImageMaskXObject:
-                    this.paintImageMaskXObject(r[0]);
-                    break;
-                  case P.OPS.paintFormXObjectBegin:
-                    this.paintFormXObjectBegin(r[0], r[1]);
-                    break;
-                  case P.OPS.paintFormXObjectEnd:
-                    this.paintFormXObjectEnd();
-                    break;
-                  case P.OPS.closePath:
-                    this.closePath();
-                    break;
-                  case P.OPS.closeStroke:
-                    this.closeStroke();
-                    break;
-                  case P.OPS.closeFillStroke:
-                    this.closeFillStroke();
-                    break;
-                  case P.OPS.closeEOFillStroke:
-                    this.closeEOFillStroke();
-                    break;
-                  case P.OPS.nextLine:
-                    this.nextLine();
-                    break;
-                  case P.OPS.transform:
-                    this.transform(r[0], r[1], r[2], r[3], r[4], r[5]);
-                    break;
-                  case P.OPS.constructPath:
-                    this.constructPath(r[0], r[1]);
-                    break;
-                  case P.OPS.endPath:
-                    this.endPath();
-                    break;
-                  case 92:
-                    this.group(c.items);
-                    break;
-                  default:
-                    (0, P.warn)(`Unimplemented operator ${k}`);
-                    break;
-                }
-              }
-            }
-            setWordSpacing(a) {
-              this.current.wordSpacing = a;
-            }
-            setCharSpacing(a) {
-              this.current.charSpacing = a;
-            }
-            nextLine() {
-              this.moveText(0, this.current.leading);
-            }
-            setTextMatrix(a, c, k, p, r, T) {
-              const m = this.current;
-              m.textMatrix = m.lineMatrix = [a, c, k, p, r, T], m.textMatrixScale = Math.hypot(a, c), m.x = m.lineX = 0, m.y = m.lineY = 0, m.xcoords = [], m.ycoords = [], m.tspan = this.svgFactory.createElement("svg:tspan"), m.tspan.setAttributeNS(null, "font-family", m.fontFamily), m.tspan.setAttributeNS(null, "font-size", `${v(m.fontSize)}px`), m.tspan.setAttributeNS(null, "y", v(-m.y)), m.txtElement = this.svgFactory.createElement("svg:text"), m.txtElement.append(m.tspan);
-            }
-            beginText() {
-              const a = this.current;
-              a.x = a.lineX = 0, a.y = a.lineY = 0, a.textMatrix = P.IDENTITY_MATRIX, a.lineMatrix = P.IDENTITY_MATRIX, a.textMatrixScale = 1, a.tspan = this.svgFactory.createElement("svg:tspan"), a.txtElement = this.svgFactory.createElement("svg:text"), a.txtgrp = this.svgFactory.createElement("svg:g"), a.xcoords = [], a.ycoords = [];
-            }
-            moveText(a, c) {
-              const k = this.current;
-              k.x = k.lineX += a, k.y = k.lineY += c, k.xcoords = [], k.ycoords = [], k.tspan = this.svgFactory.createElement("svg:tspan"), k.tspan.setAttributeNS(null, "font-family", k.fontFamily), k.tspan.setAttributeNS(null, "font-size", `${v(k.fontSize)}px`), k.tspan.setAttributeNS(null, "y", v(-k.y));
-            }
-            showText(a) {
-              const c = this.current, k = c.font, p = c.fontSize;
-              if (p === 0)
-                return;
-              const r = c.fontSizeScale, T = c.charSpacing, m = c.wordSpacing, U = c.fontDirection, z = c.textHScale * U, E = k.vertical, V = E ? 1 : -1, st = k.defaultVMetrics, at = p * c.fontMatrix[0];
-              let H = 0;
-              for (const wt of a) {
-                if (wt === null) {
-                  H += U * m;
-                  continue;
-                } else if (typeof wt == "number") {
-                  H += V * wt * p / 1e3;
-                  continue;
-                }
-                const xt = (wt.isSpace ? m : 0) + T, S = wt.fontChar;
-                let i, n, s = wt.width;
-                if (E) {
-                  let h;
-                  const b = wt.vmetric || st;
-                  h = wt.vmetric ? b[1] : s * 0.5, h = -h * at;
-                  const M = b[2] * at;
-                  s = b ? -b[0] : s, i = h / r, n = (H + M) / r;
-                } else
-                  i = H / r, n = 0;
-                (wt.isInFont || k.missingFile) && (c.xcoords.push(c.x + i), E && c.ycoords.push(-c.y + n), c.tspan.textContent += S);
-                const o = E ? s * at - xt * U : s * at + xt * U;
-                H += o;
-              }
-              c.tspan.setAttributeNS(null, "x", c.xcoords.map(v).join(" ")), E ? c.tspan.setAttributeNS(null, "y", c.ycoords.map(v).join(" ")) : c.tspan.setAttributeNS(null, "y", v(-c.y)), E ? c.y -= H : c.x += H * z, c.tspan.setAttributeNS(null, "font-family", c.fontFamily), c.tspan.setAttributeNS(null, "font-size", `${v(c.fontSize)}px`), c.fontStyle !== rt.fontStyle && c.tspan.setAttributeNS(null, "font-style", c.fontStyle), c.fontWeight !== rt.fontWeight && c.tspan.setAttributeNS(null, "font-weight", c.fontWeight);
-              const lt = c.textRenderingMode & P.TextRenderingMode.FILL_STROKE_MASK;
-              if (lt === P.TextRenderingMode.FILL || lt === P.TextRenderingMode.FILL_STROKE ? (c.fillColor !== rt.fillColor && c.tspan.setAttributeNS(null, "fill", c.fillColor), c.fillAlpha < 1 && c.tspan.setAttributeNS(null, "fill-opacity", c.fillAlpha)) : c.textRenderingMode === P.TextRenderingMode.ADD_TO_PATH ? c.tspan.setAttributeNS(null, "fill", "transparent") : c.tspan.setAttributeNS(null, "fill", "none"), lt === P.TextRenderingMode.STROKE || lt === P.TextRenderingMode.FILL_STROKE) {
-                const wt = 1 / (c.textMatrixScale || 1);
-                this._setStrokeAttributes(c.tspan, wt);
-              }
-              let gt = c.textMatrix;
-              c.textRise !== 0 && (gt = gt.slice(), gt[5] += c.textRise), c.txtElement.setAttributeNS(null, "transform", `${A(gt)} scale(${v(z)}, -1)`), c.txtElement.setAttributeNS(X, "xml:space", "preserve"), c.txtElement.append(c.tspan), c.txtgrp.append(c.txtElement), this._ensureTransformGroup().append(c.txtElement);
-            }
-            setLeadingMoveText(a, c) {
-              this.setLeading(-c), this.moveText(a, c);
-            }
-            addFontStyle(a) {
-              if (!a.data)
-                throw new Error('addFontStyle: No font data available, ensure that the "fontExtraProperties" API parameter is set.');
-              this.cssStyle || (this.cssStyle = this.svgFactory.createElement("svg:style"), this.cssStyle.setAttributeNS(null, "type", "text/css"), this.defs.append(this.cssStyle));
-              const c = g(a.data, a.mimetype, this.forceDataSchema);
-              this.cssStyle.textContent += `@font-face { font-family: "${a.loadedName}"; src: url(${c}); }
-`;
-            }
-            setFont(a) {
-              const c = this.current, k = this.commonObjs.get(a[0]);
-              let p = a[1];
-              c.font = k, this.embedFonts && !k.missingFile && !this.embeddedFonts[k.loadedName] && (this.addFontStyle(k), this.embeddedFonts[k.loadedName] = k), c.fontMatrix = k.fontMatrix || P.FONT_IDENTITY_MATRIX;
-              let r = "normal";
-              k.black ? r = "900" : k.bold && (r = "bold");
-              const T = k.italic ? "italic" : "normal";
-              p < 0 ? (p = -p, c.fontDirection = -1) : c.fontDirection = 1, c.fontSize = p, c.fontFamily = k.loadedName, c.fontWeight = r, c.fontStyle = T, c.tspan = this.svgFactory.createElement("svg:tspan"), c.tspan.setAttributeNS(null, "y", v(-c.y)), c.xcoords = [], c.ycoords = [];
-            }
-            endText() {
-              var c;
-              const a = this.current;
-              a.textRenderingMode & P.TextRenderingMode.ADD_TO_PATH_FLAG && ((c = a.txtElement) != null && c.hasChildNodes()) && (a.element = a.txtElement, this.clip("nonzero"), this.endPath());
-            }
-            setLineWidth(a) {
-              a > 0 && (this.current.lineWidth = a);
-            }
-            setLineCap(a) {
-              this.current.lineCap = B[a];
-            }
-            setLineJoin(a) {
-              this.current.lineJoin = F[a];
-            }
-            setMiterLimit(a) {
-              this.current.miterLimit = a;
-            }
-            setStrokeAlpha(a) {
-              this.current.strokeAlpha = a;
-            }
-            setStrokeRGBColor(a, c, k) {
-              this.current.strokeColor = P.Util.makeHexColor(a, c, k);
-            }
-            setFillAlpha(a) {
-              this.current.fillAlpha = a;
-            }
-            setFillRGBColor(a, c, k) {
-              this.current.fillColor = P.Util.makeHexColor(a, c, k), this.current.tspan = this.svgFactory.createElement("svg:tspan"), this.current.xcoords = [], this.current.ycoords = [];
-            }
-            setStrokeColorN(a) {
-              this.current.strokeColor = this._makeColorN_Pattern(a);
-            }
-            setFillColorN(a) {
-              this.current.fillColor = this._makeColorN_Pattern(a);
-            }
-            shadingFill(a) {
-              const {
-                width: c,
-                height: k
-              } = this.viewport, p = P.Util.inverseTransform(this.transformMatrix), [r, T, m, U] = P.Util.getAxialAlignedBoundingBox([0, 0, c, k], p), z = this.svgFactory.createElement("svg:rect");
-              z.setAttributeNS(null, "x", r), z.setAttributeNS(null, "y", T), z.setAttributeNS(null, "width", m - r), z.setAttributeNS(null, "height", U - T), z.setAttributeNS(null, "fill", this._makeShadingPattern(a)), this.current.fillAlpha < 1 && z.setAttributeNS(null, "fill-opacity", this.current.fillAlpha), this._ensureTransformGroup().append(z);
-            }
-            _makeColorN_Pattern(a) {
-              return a[0] === "TilingPattern" ? this._makeTilingPattern(a) : this._makeShadingPattern(a);
-            }
-            _makeTilingPattern(a) {
-              const c = a[1], k = a[2], p = a[3] || P.IDENTITY_MATRIX, [r, T, m, U] = a[4], z = a[5], E = a[6], V = a[7], st = `shading${w++}`, [at, H, lt, gt] = P.Util.normalizeRect([...P.Util.applyTransform([r, T], p), ...P.Util.applyTransform([m, U], p)]), [wt, xt] = P.Util.singularValueDecompose2dScale(p), S = z * wt, i = E * xt, n = this.svgFactory.createElement("svg:pattern");
-              n.setAttributeNS(null, "id", st), n.setAttributeNS(null, "patternUnits", "userSpaceOnUse"), n.setAttributeNS(null, "width", S), n.setAttributeNS(null, "height", i), n.setAttributeNS(null, "x", `${at}`), n.setAttributeNS(null, "y", `${H}`);
-              const s = this.svg, o = this.transformMatrix, h = this.current.fillColor, b = this.current.strokeColor, M = this.svgFactory.create(lt - at, gt - H);
-              if (this.svg = M, this.transformMatrix = p, V === 2) {
-                const N = P.Util.makeHexColor(...c);
-                this.current.fillColor = N, this.current.strokeColor = N;
-              }
-              return this.executeOpTree(this.convertOpList(k)), this.svg = s, this.transformMatrix = o, this.current.fillColor = h, this.current.strokeColor = b, n.append(M.childNodes[0]), this.defs.append(n), `url(#${st})`;
-            }
-            _makeShadingPattern(a) {
-              switch (typeof a == "string" && (a = this.objs.get(a)), a[0]) {
-                case "RadialAxial":
-                  const c = `shading${w++}`, k = a[3];
-                  let p;
-                  switch (a[1]) {
-                    case "axial":
-                      const r = a[4], T = a[5];
-                      p = this.svgFactory.createElement("svg:linearGradient"), p.setAttributeNS(null, "id", c), p.setAttributeNS(null, "gradientUnits", "userSpaceOnUse"), p.setAttributeNS(null, "x1", r[0]), p.setAttributeNS(null, "y1", r[1]), p.setAttributeNS(null, "x2", T[0]), p.setAttributeNS(null, "y2", T[1]);
-                      break;
-                    case "radial":
-                      const m = a[4], U = a[5], z = a[6], E = a[7];
-                      p = this.svgFactory.createElement("svg:radialGradient"), p.setAttributeNS(null, "id", c), p.setAttributeNS(null, "gradientUnits", "userSpaceOnUse"), p.setAttributeNS(null, "cx", U[0]), p.setAttributeNS(null, "cy", U[1]), p.setAttributeNS(null, "r", E), p.setAttributeNS(null, "fx", m[0]), p.setAttributeNS(null, "fy", m[1]), p.setAttributeNS(null, "fr", z);
-                      break;
-                    default:
-                      throw new Error(`Unknown RadialAxial type: ${a[1]}`);
-                  }
-                  for (const r of k) {
-                    const T = this.svgFactory.createElement("svg:stop");
-                    T.setAttributeNS(null, "offset", r[0]), T.setAttributeNS(null, "stop-color", r[1]), p.append(T);
-                  }
-                  return this.defs.append(p), `url(#${c})`;
-                case "Mesh":
-                  return (0, P.warn)("Unimplemented pattern Mesh"), null;
-                case "Dummy":
-                  return "hotpink";
-                default:
-                  throw new Error(`Unknown IR type: ${a[0]}`);
-              }
-            }
-            setDash(a, c) {
-              this.current.dashArray = a, this.current.dashPhase = c;
-            }
-            constructPath(a, c) {
-              const k = this.current;
-              let p = k.x, r = k.y, T = [], m = 0;
-              for (const U of a)
-                switch (U | 0) {
-                  case P.OPS.rectangle:
-                    p = c[m++], r = c[m++];
-                    const z = c[m++], E = c[m++], V = p + z, st = r + E;
-                    T.push("M", v(p), v(r), "L", v(V), v(r), "L", v(V), v(st), "L", v(p), v(st), "Z");
-                    break;
-                  case P.OPS.moveTo:
-                    p = c[m++], r = c[m++], T.push("M", v(p), v(r));
-                    break;
-                  case P.OPS.lineTo:
-                    p = c[m++], r = c[m++], T.push("L", v(p), v(r));
-                    break;
-                  case P.OPS.curveTo:
-                    p = c[m + 4], r = c[m + 5], T.push("C", v(c[m]), v(c[m + 1]), v(c[m + 2]), v(c[m + 3]), v(p), v(r)), m += 6;
-                    break;
-                  case P.OPS.curveTo2:
-                    T.push("C", v(p), v(r), v(c[m]), v(c[m + 1]), v(c[m + 2]), v(c[m + 3])), p = c[m + 2], r = c[m + 3], m += 4;
-                    break;
-                  case P.OPS.curveTo3:
-                    p = c[m + 2], r = c[m + 3], T.push("C", v(c[m]), v(c[m + 1]), v(p), v(r), v(p), v(r)), m += 4;
-                    break;
-                  case P.OPS.closePath:
-                    T.push("Z");
-                    break;
-                }
-              T = T.join(" "), k.path && a.length > 0 && a[0] !== P.OPS.rectangle && a[0] !== P.OPS.moveTo ? T = k.path.getAttributeNS(null, "d") + T : (k.path = this.svgFactory.createElement("svg:path"), this._ensureTransformGroup().append(k.path)), k.path.setAttributeNS(null, "d", T), k.path.setAttributeNS(null, "fill", "none"), k.element = k.path, k.setCurrentPoint(p, r);
-            }
-            endPath() {
-              const a = this.current;
-              if (a.path = null, !this.pendingClip)
-                return;
-              if (!a.element) {
-                this.pendingClip = null;
-                return;
-              }
-              const c = `clippath${u++}`, k = this.svgFactory.createElement("svg:clipPath");
-              k.setAttributeNS(null, "id", c), k.setAttributeNS(null, "transform", A(this.transformMatrix));
-              const p = a.element.cloneNode(!0);
-              if (this.pendingClip === "evenodd" ? p.setAttributeNS(null, "clip-rule", "evenodd") : p.setAttributeNS(null, "clip-rule", "nonzero"), this.pendingClip = null, k.append(p), this.defs.append(k), a.activeClipUrl) {
-                a.clipGroup = null;
-                for (const r of this.extraStack)
-                  r.clipGroup = null;
-                k.setAttributeNS(null, "clip-path", a.activeClipUrl);
-              }
-              a.activeClipUrl = `url(#${c})`, this.tgrp = null;
-            }
-            clip(a) {
-              this.pendingClip = a;
-            }
-            closePath() {
-              const a = this.current;
-              if (a.path) {
-                const c = `${a.path.getAttributeNS(null, "d")}Z`;
-                a.path.setAttributeNS(null, "d", c);
-              }
-            }
-            setLeading(a) {
-              this.current.leading = -a;
-            }
-            setTextRise(a) {
-              this.current.textRise = a;
-            }
-            setTextRenderingMode(a) {
-              this.current.textRenderingMode = a;
-            }
-            setHScale(a) {
-              this.current.textHScale = a / 100;
-            }
-            setRenderingIntent(a) {
-            }
-            setFlatness(a) {
-            }
-            setGState(a) {
-              for (const [c, k] of a)
-                switch (c) {
-                  case "LW":
-                    this.setLineWidth(k);
-                    break;
-                  case "LC":
-                    this.setLineCap(k);
-                    break;
-                  case "LJ":
-                    this.setLineJoin(k);
-                    break;
-                  case "ML":
-                    this.setMiterLimit(k);
-                    break;
-                  case "D":
-                    this.setDash(k[0], k[1]);
-                    break;
-                  case "RI":
-                    this.setRenderingIntent(k);
-                    break;
-                  case "FL":
-                    this.setFlatness(k);
-                    break;
-                  case "Font":
-                    this.setFont(k);
-                    break;
-                  case "CA":
-                    this.setStrokeAlpha(k);
-                    break;
-                  case "ca":
-                    this.setFillAlpha(k);
-                    break;
-                  default:
-                    (0, P.warn)(`Unimplemented graphic state operator ${c}`);
-                    break;
-                }
-            }
-            fill() {
-              const a = this.current;
-              a.element && (a.element.setAttributeNS(null, "fill", a.fillColor), a.element.setAttributeNS(null, "fill-opacity", a.fillAlpha), this.endPath());
-            }
-            stroke() {
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-              a.element && (this._setStrokeAttributes(a.element), a.element.setAttributeNS(null, "fill", "none"), this.endPath());
-            }
-            _setStrokeAttributes(a, c = 1) {
-              const k = this.current;
-              let p = k.dashArray;
-              c !== 1 && p.length > 0 && (p = p.map(function(r) {
-                return c * r;
-              })), a.setAttributeNS(null, "stroke", k.strokeColor), a.setAttributeNS(null, "stroke-opacity", k.strokeAlpha), a.setAttributeNS(null, "stroke-miterlimit", v(k.miterLimit)), a.setAttributeNS(null, "stroke-linecap", k.lineCap), a.setAttributeNS(null, "stroke-linejoin", k.lineJoin), a.setAttributeNS(null, "stroke-width", v(c * k.lineWidth) + "px"), a.setAttributeNS(null, "stroke-dasharray", p.map(v).join(" ")), a.setAttributeNS(null, "stroke-dashoffset", v(c * k.dashPhase) + "px");
-            }
-            eoFill() {
-              var a;
-              (a = this.current.element) == null || a.setAttributeNS(null, "fill-rule", "evenodd"), this.fill();
-            }
-            fillStroke() {
-              this.stroke(), this.fill();
-            }
-            eoFillStroke() {
-              var a;
-              (a = this.current.element) == null || a.setAttributeNS(null, "fill-rule", "evenodd"), this.fillStroke();
-            }
-            closeStroke() {
-              this.closePath(), this.stroke();
-            }
-            closeFillStroke() {
-              this.closePath(), this.fillStroke();
-            }
-            closeEOFillStroke() {
-              this.closePath(), this.eoFillStroke();
-            }
-            paintSolidColorImageMask() {
-              const a = this.svgFactory.createElement("svg:rect");
-              a.setAttributeNS(null, "x", "0"), a.setAttributeNS(null, "y", "0"), a.setAttributeNS(null, "width", "1px"), a.setAttributeNS(null, "height", "1px"), a.setAttributeNS(null, "fill", this.current.fillColor), this._ensureTransformGroup().append(a);
-            }
-            paintImageXObject(a) {
-              const c = this.getObject(a);
-              if (!c) {
-                (0, P.warn)(`Dependent image with object ID ${a} is not ready yet`);
-                return;
-              }
-              this.paintInlineImageXObject(c);
-            }
-            paintInlineImageXObject(a, c) {
-              const k = a.width, p = a.height, r = O(a, this.forceDataSchema, !!c), T = this.svgFactory.createElement("svg:rect");
-              T.setAttributeNS(null, "x", "0"), T.setAttributeNS(null, "y", "0"), T.setAttributeNS(null, "width", v(k)), T.setAttributeNS(null, "height", v(p)), this.current.element = T, this.clip("nonzero");
-              const m = this.svgFactory.createElement("svg:image");
-              m.setAttributeNS(pt, "xlink:href", r), m.setAttributeNS(null, "x", "0"), m.setAttributeNS(null, "y", v(-p)), m.setAttributeNS(null, "width", v(k) + "px"), m.setAttributeNS(null, "height", v(p) + "px"), m.setAttributeNS(null, "transform", `scale(${v(1 / k)} ${v(-1 / p)})`), c ? c.append(m) : this._ensureTransformGroup().append(m);
-            }
-            paintImageMaskXObject(a) {
-              const c = this.getObject(a.data, a);
-              if (c.bitmap) {
-                (0, P.warn)("paintImageMaskXObject: ImageBitmap support is not implemented, ensure that the `isOffscreenCanvasSupported` API parameter is disabled.");
-                return;
-              }
-              const k = this.current, p = c.width, r = c.height, T = k.fillColor;
-              k.maskId = `mask${_++}`;
-              const m = this.svgFactory.createElement("svg:mask");
-              m.setAttributeNS(null, "id", k.maskId);
-              const U = this.svgFactory.createElement("svg:rect");
-              U.setAttributeNS(null, "x", "0"), U.setAttributeNS(null, "y", "0"), U.setAttributeNS(null, "width", v(p)), U.setAttributeNS(null, "height", v(r)), U.setAttributeNS(null, "fill", T), U.setAttributeNS(null, "mask", `url(#${k.maskId})`), this.defs.append(m), this._ensureTransformGroup().append(U), this.paintInlineImageXObject(c, m);
-            }
-            paintFormXObjectBegin(a, c) {
-              if (Array.isArray(a) && a.length === 6 && this.transform(a[0], a[1], a[2], a[3], a[4], a[5]), c) {
-                const k = c[2] - c[0], p = c[3] - c[1], r = this.svgFactory.createElement("svg:rect");
-                r.setAttributeNS(null, "x", c[0]), r.setAttributeNS(null, "y", c[1]), r.setAttributeNS(null, "width", v(k)), r.setAttributeNS(null, "height", v(p)), this.current.element = r, this.clip("nonzero"), this.endPath();
-              }
-            }
-            paintFormXObjectEnd() {
-            }
-            _initialize(a) {
-              const c = this.svgFactory.create(a.width, a.height), k = this.svgFactory.createElement("svg:defs");
-              c.append(k), this.defs = k;
-              const p = this.svgFactory.createElement("svg:g");
-              return p.setAttributeNS(null, "transform", A(a.transform)), c.append(p), this.svg = p, c;
-            }
-            _ensureClipGroup() {
-              if (!this.current.clipGroup) {
-                const a = this.svgFactory.createElement("svg:g");
-                a.setAttributeNS(null, "clip-path", this.current.activeClipUrl), this.svg.append(a), this.current.clipGroup = a;
-              }
-              return this.current.clipGroup;
-            }
-            _ensureTransformGroup() {
-              return this.tgrp || (this.tgrp = this.svgFactory.createElement("svg:g"), this.tgrp.setAttributeNS(null, "transform", A(this.transformMatrix)), this.current.activeClipUrl ? this._ensureClipGroup().append(this.tgrp) : this.svg.append(this.tgrp)), this.tgrp;
-            }
-          }
-          d.SVGGraphics = C;
-        },
-        /* 25 */
-        /***/
-        (dt, d) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
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-          class et {
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-          }
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-              } = y;
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-            }) {
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-              if (this._isReadableStream) {
-                const y = () => {
-                  this._reader.read().then(({
-                    value: a,
-                    done: c
-                  }) => {
-                    if (c) {
-                      w.resolve();
-                      return;
-                    }
-                    Object.assign(C, a.styles), this._processItems(a.items, C), y();
-                  }, w.reject);
-                };
-                this._reader = this._textContentSource.getReader(), y();
-              } else if (this._textContentSource) {
-                const {
-                  items: y,
-                  styles: a
-                } = this._textContentSource;
-                this._processItems(y, a), w.resolve();
-              } else
-                throw new Error('No "textContentSource" parameter specified.');
-              w.promise.then(() => {
-                C = null, x(this);
-              }, this._capability.reject);
-            }
-          }
-          d.TextLayerRenderTask = v;
-          function A(_) {
-            !_.textContentSource && (_.textContent || _.textContentStream) && ((0, P.deprecated)("The TextLayerRender `textContent`/`textContentStream` parameters will be removed in the future, please use `textContentSource` instead."), _.textContentSource = _.textContent || _.textContentStream);
-            const {
-              container: w,
-              viewport: C
-            } = _, y = getComputedStyle(w), a = y.getPropertyValue("visibility"), c = parseFloat(y.getPropertyValue("--scale-factor"));
-            a === "visible" && (!c || Math.abs(c - C.scale) > 1e-5) && console.error("The `--scale-factor` CSS-variable must be set, to the same value as `viewport.scale`, either on the `container`-element itself or higher up in the DOM.");
-            const k = new v(_);
-            return k._render(), k;
-          }
-          function u({
-            container: _,
-            viewport: w,
-            textDivs: C,
-            textDivProperties: y,
-            isOffscreenCanvasSupported: a,
-            mustRotate: c = !0,
-            mustRescale: k = !0
-          }) {
-            if (c && (0, P.setLayerDimensions)(_, {
-              rotation: w.rotation
-            }), k) {
-              const p = F(0, a), T = {
-                prevFontSize: null,
-                prevFontFamily: null,
-                div: null,
-                scale: w.scale * (globalThis.devicePixelRatio || 1),
-                properties: null,
-                ctx: p
-              };
-              for (const m of C)
-                T.properties = y.get(m), T.div = m, I(T);
-            }
-          }
-        },
-        /* 27 */
-        /***/
-        (dt, d, et) => {
-          var g, O, I, x, v, A, u, _, w, C, y, ei, c, ke, p, ii, T, si;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.AnnotationEditorLayer = void 0;
-          var l = et(1), P = et(4), rt = et(28), X = et(33), pt = et(6), B = et(34);
-          const U = class U {
-            constructor({
-              uiManager: E,
-              pageIndex: V,
-              div: st,
-              accessibilityManager: at,
-              annotationLayer: H,
-              viewport: lt,
-              l10n: gt
-            }) {
-              L(this, y);
-              L(this, c);
-              L(this, p);
-              L(this, T);
-              L(this, g, void 0);
-              L(this, O, !1);
-              L(this, I, null);
-              L(this, x, this.pointerup.bind(this));
-              L(this, v, this.pointerdown.bind(this));
-              L(this, A, /* @__PURE__ */ new Map());
-              L(this, u, !1);
-              L(this, _, !1);
-              L(this, w, !1);
-              L(this, C, void 0);
-              const wt = [rt.FreeTextEditor, X.InkEditor, B.StampEditor];
-              if (!U._initialized) {
-                U._initialized = !0;
-                for (const xt of wt)
-                  xt.initialize(gt);
-              }
-              E.registerEditorTypes(wt), Z(this, C, E), this.pageIndex = V, this.div = st, Z(this, g, at), Z(this, I, H), this.viewport = lt, t(this, C).addLayer(this);
-            }
-            get isEmpty() {
-              return t(this, A).size === 0;
-            }
-            updateToolbar(E) {
-              t(this, C).updateToolbar(E);
-            }
-            updateMode(E = t(this, C).getMode()) {
-              W(this, T, si).call(this), E === l.AnnotationEditorType.INK ? (this.addInkEditorIfNeeded(!1), this.disableClick()) : this.enableClick(), E !== l.AnnotationEditorType.NONE && (this.div.classList.toggle("freeTextEditing", E === l.AnnotationEditorType.FREETEXT), this.div.classList.toggle("inkEditing", E === l.AnnotationEditorType.INK), this.div.classList.toggle("stampEditing", E === l.AnnotationEditorType.STAMP), this.div.hidden = !1);
-            }
-            addInkEditorIfNeeded(E) {
-              if (!E && t(this, C).getMode() !== l.AnnotationEditorType.INK)
-                return;
-              if (!E) {
-                for (const st of t(this, A).values())
-                  if (st.isEmpty()) {
-                    st.setInBackground();
-                    return;
-                  }
-              }
-              W(this, c, ke).call(this, {
-                offsetX: 0,
-                offsetY: 0
-              }, !1).setInBackground();
-            }
-            setEditingState(E) {
-              t(this, C).setEditingState(E);
-            }
-            addCommands(E) {
-              t(this, C).addCommands(E);
-            }
-            enable() {
-              this.div.style.pointerEvents = "auto";
-              const E = /* @__PURE__ */ new Set();
-              for (const st of t(this, A).values())
-                st.enableEditing(), st.annotationElementId && E.add(st.annotationElementId);
-              if (!t(this, I))
-                return;
-              const V = t(this, I).getEditableAnnotations();
-              for (const st of V) {
-                if (st.hide(), t(this, C).isDeletedAnnotationElement(st.data.id) || E.has(st.data.id))
-                  continue;
-                const at = this.deserialize(st);
-                at && (this.addOrRebuild(at), at.enableEditing());
-              }
-            }
-            disable() {
-              var V;
-              Z(this, w, !0), this.div.style.pointerEvents = "none";
-              const E = /* @__PURE__ */ new Set();
-              for (const st of t(this, A).values()) {
-                if (st.disableEditing(), !st.annotationElementId || st.serialize() !== null) {
-                  E.add(st.annotationElementId);
-                  continue;
-                }
-                (V = this.getEditableAnnotation(st.annotationElementId)) == null || V.show(), st.remove();
-              }
-              if (t(this, I)) {
-                const st = t(this, I).getEditableAnnotations();
-                for (const at of st) {
-                  const {
-                    id: H
-                  } = at.data;
-                  E.has(H) || t(this, C).isDeletedAnnotationElement(H) || at.show();
-                }
-              }
-              W(this, T, si).call(this), this.isEmpty && (this.div.hidden = !0), Z(this, w, !1);
-            }
-            getEditableAnnotation(E) {
-              var V;
-              return ((V = t(this, I)) == null ? void 0 : V.getEditableAnnotation(E)) || null;
-            }
-            setActiveEditor(E) {
-              t(this, C).getActive() !== E && t(this, C).setActiveEditor(E);
-            }
-            enableClick() {
-              this.div.addEventListener("pointerdown", t(this, v)), this.div.addEventListener("pointerup", t(this, x));
-            }
-            disableClick() {
-              this.div.removeEventListener("pointerdown", t(this, v)), this.div.removeEventListener("pointerup", t(this, x));
-            }
-            attach(E) {
-              t(this, A).set(E.id, E);
-              const {
-                annotationElementId: V
-              } = E;
-              V && t(this, C).isDeletedAnnotationElement(V) && t(this, C).removeDeletedAnnotationElement(E);
-            }
-            detach(E) {
-              var V;
-              t(this, A).delete(E.id), (V = t(this, g)) == null || V.removePointerInTextLayer(E.contentDiv), !t(this, w) && E.annotationElementId && t(this, C).addDeletedAnnotationElement(E);
-            }
-            remove(E) {
-              this.detach(E), t(this, C).removeEditor(E), E.div.contains(document.activeElement) && setTimeout(() => {
-                t(this, C).focusMainContainer();
-              }, 0), E.div.remove(), E.isAttachedToDOM = !1, t(this, _) || this.addInkEditorIfNeeded(!1);
-            }
-            changeParent(E) {
-              var V;
-              E.parent !== this && (E.annotationElementId && (t(this, C).addDeletedAnnotationElement(E.annotationElementId), P.AnnotationEditor.deleteAnnotationElement(E), E.annotationElementId = null), this.attach(E), (V = E.parent) == null || V.detach(E), E.setParent(this), E.div && E.isAttachedToDOM && (E.div.remove(), this.div.append(E.div)));
-            }
-            add(E) {
-              if (this.changeParent(E), t(this, C).addEditor(E), this.attach(E), !E.isAttachedToDOM) {
-                const V = E.render();
-                this.div.append(V), E.isAttachedToDOM = !0;
-              }
-              E.fixAndSetPosition(), E.onceAdded(), t(this, C).addToAnnotationStorage(E);
-            }
-            moveEditorInDOM(E) {
-              var st;
-              if (!E.isAttachedToDOM)
-                return;
-              const {
-                activeElement: V
-              } = document;
-              E.div.contains(V) && (E._focusEventsAllowed = !1, setTimeout(() => {
-                E.div.contains(document.activeElement) ? E._focusEventsAllowed = !0 : (E.div.addEventListener("focusin", () => {
-                  E._focusEventsAllowed = !0;
-                }, {
-                  once: !0
-                }), V.focus());
-              }, 0)), E._structTreeParentId = (st = t(this, g)) == null ? void 0 : st.moveElementInDOM(this.div, E.div, E.contentDiv, !0);
-            }
-            addOrRebuild(E) {
-              E.needsToBeRebuilt() ? E.rebuild() : this.add(E);
-            }
-            addUndoableEditor(E) {
-              const V = () => E._uiManager.rebuild(E), st = () => {
-                E.remove();
-              };
-              this.addCommands({
-                cmd: V,
-                undo: st,
-                mustExec: !1
-              });
-            }
-            getNextId() {
-              return t(this, C).getId();
-            }
-            pasteEditor(E, V) {
-              t(this, C).updateToolbar(E), t(this, C).updateMode(E);
-              const {
-                offsetX: st,
-                offsetY: at
-              } = W(this, p, ii).call(this), H = this.getNextId(), lt = W(this, y, ei).call(this, {
-                parent: this,
-                id: H,
-                x: st,
-                y: at,
-                uiManager: t(this, C),
-                isCentered: !0,
-                ...V
-              });
-              lt && this.add(lt);
-            }
-            deserialize(E) {
-              switch (E.annotationType ?? E.annotationEditorType) {
-                case l.AnnotationEditorType.FREETEXT:
-                  return rt.FreeTextEditor.deserialize(E, this, t(this, C));
-                case l.AnnotationEditorType.INK:
-                  return X.InkEditor.deserialize(E, this, t(this, C));
-                case l.AnnotationEditorType.STAMP:
-                  return B.StampEditor.deserialize(E, this, t(this, C));
-              }
-              return null;
-            }
-            addNewEditor() {
-              W(this, c, ke).call(this, W(this, p, ii).call(this), !0);
-            }
-            setSelected(E) {
-              t(this, C).setSelected(E);
-            }
-            toggleSelected(E) {
-              t(this, C).toggleSelected(E);
-            }
-            isSelected(E) {
-              return t(this, C).isSelected(E);
-            }
-            unselect(E) {
-              t(this, C).unselect(E);
-            }
-            pointerup(E) {
-              const {
-                isMac: V
-              } = l.FeatureTest.platform;
-              if (!(E.button !== 0 || E.ctrlKey && V) && E.target === this.div && t(this, u)) {
-                if (Z(this, u, !1), !t(this, O)) {
-                  Z(this, O, !0);
-                  return;
-                }
-                if (t(this, C).getMode() === l.AnnotationEditorType.STAMP) {
-                  t(this, C).unselectAll();
-                  return;
-                }
-                W(this, c, ke).call(this, E, !1);
-              }
-            }
-            pointerdown(E) {
-              if (t(this, u)) {
-                Z(this, u, !1);
-                return;
-              }
-              const {
-                isMac: V
-              } = l.FeatureTest.platform;
-              if (E.button !== 0 || E.ctrlKey && V || E.target !== this.div)
-                return;
-              Z(this, u, !0);
-              const st = t(this, C).getActive();
-              Z(this, O, !st || st.isEmpty());
-            }
-            findNewParent(E, V, st) {
-              const at = t(this, C).findParent(V, st);
-              return at === null || at === this ? !1 : (at.changeParent(E), !0);
-            }
-            destroy() {
-              var E, V;
-              ((E = t(this, C).getActive()) == null ? void 0 : E.parent) === this && (t(this, C).commitOrRemove(), t(this, C).setActiveEditor(null));
-              for (const st of t(this, A).values())
-                (V = t(this, g)) == null || V.removePointerInTextLayer(st.contentDiv), st.setParent(null), st.isAttachedToDOM = !1, st.div.remove();
-              this.div = null, t(this, A).clear(), t(this, C).removeLayer(this);
-            }
-            render({
-              viewport: E
-            }) {
-              this.viewport = E, (0, pt.setLayerDimensions)(this.div, E);
-              for (const V of t(this, C).getEditors(this.pageIndex))
-                this.add(V);
-              this.updateMode();
-            }
-            update({
-              viewport: E
-            }) {
-              t(this, C).commitOrRemove(), this.viewport = E, (0, pt.setLayerDimensions)(this.div, {
-                rotation: E.rotation
-              }), this.updateMode();
-            }
-            get pageDimensions() {
-              const {
-                pageWidth: E,
-                pageHeight: V
-              } = this.viewport.rawDims;
-              return [E, V];
-            }
-          };
-          g = new WeakMap(), O = new WeakMap(), I = new WeakMap(), x = new WeakMap(), v = new WeakMap(), A = new WeakMap(), u = new WeakMap(), _ = new WeakMap(), w = new WeakMap(), C = new WeakMap(), y = new WeakSet(), ei = function(E) {
-            switch (t(this, C).getMode()) {
-              case l.AnnotationEditorType.FREETEXT:
-                return new rt.FreeTextEditor(E);
-              case l.AnnotationEditorType.INK:
-                return new X.InkEditor(E);
-              case l.AnnotationEditorType.STAMP:
-                return new B.StampEditor(E);
-            }
-            return null;
-          }, c = new WeakSet(), ke = function(E, V) {
-            const st = this.getNextId(), at = W(this, y, ei).call(this, {
-              parent: this,
-              id: st,
-              x: E.offsetX,
-              y: E.offsetY,
-              uiManager: t(this, C),
-              isCentered: V
-            });
-            return at && this.add(at), at;
-          }, p = new WeakSet(), ii = function() {
-            const {
-              x: E,
-              y: V,
-              width: st,
-              height: at
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-              offsetX: i,
-              offsetY: n
-            };
-          }, T = new WeakSet(), si = function() {
-            Z(this, _, !0);
-            for (const E of t(this, A).values())
-              E.isEmpty() && E.remove();
-            Z(this, _, !1);
-          }, ee(U, "_initialized", !1);
-          let F = U;
-          d.AnnotationEditorLayer = F;
-        },
-        /* 28 */
-        /***/
-        (dt, d, et) => {
-          var B, F, g, O, I, x, v, A, u, _, Fi, C, Mi, a, Ri, k, ye, r, ni, m, Di, z, ri;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.FreeTextEditor = void 0;
-          var l = et(1), P = et(5), rt = et(4), X = et(29);
-          const V = class V extends rt.AnnotationEditor {
-            constructor(H) {
-              super({
-                ...H,
-                name: "freeTextEditor"
-              });
-              L(this, _);
-              L(this, C);
-              L(this, a);
-              L(this, k);
-              L(this, r);
-              L(this, m);
-              L(this, z);
-              L(this, B, this.editorDivBlur.bind(this));
-              L(this, F, this.editorDivFocus.bind(this));
-              L(this, g, this.editorDivInput.bind(this));
-              L(this, O, this.editorDivKeydown.bind(this));
-              L(this, I, void 0);
-              L(this, x, "");
-              L(this, v, `${this.id}-editor`);
-              L(this, A, void 0);
-              L(this, u, null);
-              Z(this, I, H.color || V._defaultColor || rt.AnnotationEditor._defaultLineColor), Z(this, A, H.fontSize || V._defaultFontSize);
-            }
-            static get _keyboardManager() {
-              const H = V.prototype, lt = (xt) => xt.isEmpty(), gt = P.AnnotationEditorUIManager.TRANSLATE_SMALL, wt = P.AnnotationEditorUIManager.TRANSLATE_BIG;
-              return (0, l.shadow)(this, "_keyboardManager", new P.KeyboardManager([[["ctrl+s", "mac+meta+s", "ctrl+p", "mac+meta+p"], H.commitOrRemove, {
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-              }], [["ctrl+Enter", "mac+meta+Enter", "Escape", "mac+Escape"], H.commitOrRemove], [["ArrowLeft", "mac+ArrowLeft"], H._translateEmpty, {
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-                checker: lt
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-                checker: lt
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-                args: [gt, 0],
-                checker: lt
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-                args: [wt, 0],
-                checker: lt
-              }], [["ArrowUp", "mac+ArrowUp"], H._translateEmpty, {
-                args: [0, -gt],
-                checker: lt
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-                args: [0, -wt],
-                checker: lt
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-                args: [0, wt],
-                checker: lt
-              }]]));
-            }
-            static initialize(H) {
-              rt.AnnotationEditor.initialize(H, {
-                strings: ["free_text2_default_content", "editor_free_text2_aria_label"]
-              });
-              const lt = getComputedStyle(document.documentElement);
-              this._internalPadding = parseFloat(lt.getPropertyValue("--freetext-padding"));
-            }
-            static updateDefaultParams(H, lt) {
-              switch (H) {
-                case l.AnnotationEditorParamsType.FREETEXT_SIZE:
-                  V._defaultFontSize = lt;
-                  break;
-                case l.AnnotationEditorParamsType.FREETEXT_COLOR:
-                  V._defaultColor = lt;
-                  break;
-              }
-            }
-            updateParams(H, lt) {
-              switch (H) {
-                case l.AnnotationEditorParamsType.FREETEXT_SIZE:
-                  W(this, _, Fi).call(this, lt);
-                  break;
-                case l.AnnotationEditorParamsType.FREETEXT_COLOR:
-                  W(this, C, Mi).call(this, lt);
-                  break;
-              }
-            }
-            static get defaultPropertiesToUpdate() {
-              return [[l.AnnotationEditorParamsType.FREETEXT_SIZE, V._defaultFontSize], [l.AnnotationEditorParamsType.FREETEXT_COLOR, V._defaultColor || rt.AnnotationEditor._defaultLineColor]];
-            }
-            get propertiesToUpdate() {
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-            }
-            _translateEmpty(H, lt) {
-              this._uiManager.translateSelectedEditors(H, lt, !0);
-            }
-            getInitialTranslation() {
-              const H = this.parentScale;
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-            }
-            rebuild() {
-              this.parent && (super.rebuild(), this.div !== null && (this.isAttachedToDOM || this.parent.add(this)));
-            }
-            enableEditMode() {
-              this.isInEditMode() || (this.parent.setEditingState(!1), this.parent.updateToolbar(l.AnnotationEditorType.FREETEXT), super.enableEditMode(), this.overlayDiv.classList.remove("enabled"), this.editorDiv.contentEditable = !0, this._isDraggable = !1, this.div.removeAttribute("aria-activedescendant"), this.editorDiv.addEventListener("keydown", t(this, O)), this.editorDiv.addEventListener("focus", t(this, F)), this.editorDiv.addEventListener("blur", t(this, B)), this.editorDiv.addEventListener("input", t(this, g)));
-            }
-            disableEditMode() {
-              this.isInEditMode() && (this.parent.setEditingState(!0), super.disableEditMode(), this.overlayDiv.classList.add("enabled"), this.editorDiv.contentEditable = !1, this.div.setAttribute("aria-activedescendant", t(this, v)), this._isDraggable = !0, this.editorDiv.removeEventListener("keydown", t(this, O)), this.editorDiv.removeEventListener("focus", t(this, F)), this.editorDiv.removeEventListener("blur", t(this, B)), this.editorDiv.removeEventListener("input", t(this, g)), this.div.focus({
-                preventScroll: !0
-              }), this.isEditing = !1, this.parent.div.classList.add("freeTextEditing"));
-            }
-            focusin(H) {
-              this._focusEventsAllowed && (super.focusin(H), H.target !== this.editorDiv && this.editorDiv.focus());
-            }
-            onceAdded() {
-              var H;
-              if (this.width) {
-                W(this, z, ri).call(this);
-                return;
-              }
-              this.enableEditMode(), this.editorDiv.focus(), (H = this._initialOptions) != null && H.isCentered && this.center(), this._initialOptions = null;
-            }
-            isEmpty() {
-              return !this.editorDiv || this.editorDiv.innerText.trim() === "";
-            }
-            remove() {
-              this.isEditing = !1, this.parent && (this.parent.setEditingState(!0), this.parent.div.classList.add("freeTextEditing")), super.remove();
-            }
-            commit() {
-              if (!this.isInEditMode())
-                return;
-              super.commit(), this.disableEditMode();
-              const H = t(this, x), lt = Z(this, x, W(this, a, Ri).call(this).trimEnd());
-              if (H === lt)
-                return;
-              const gt = (wt) => {
-                if (Z(this, x, wt), !wt) {
-                  this.remove();
-                  return;
-                }
-                W(this, r, ni).call(this), this._uiManager.rebuild(this), W(this, k, ye).call(this);
-              };
-              this.addCommands({
-                cmd: () => {
-                  gt(lt);
-                },
-                undo: () => {
-                  gt(H);
-                },
-                mustExec: !1
-              }), W(this, k, ye).call(this);
-            }
-            shouldGetKeyboardEvents() {
-              return this.isInEditMode();
-            }
-            enterInEditMode() {
-              this.enableEditMode(), this.editorDiv.focus();
-            }
-            dblclick(H) {
-              this.enterInEditMode();
-            }
-            keydown(H) {
-              H.target === this.div && H.key === "Enter" && (this.enterInEditMode(), H.preventDefault());
-            }
-            editorDivKeydown(H) {
-              V._keyboardManager.exec(this, H);
-            }
-            editorDivFocus(H) {
-              this.isEditing = !0;
-            }
-            editorDivBlur(H) {
-              this.isEditing = !1;
-            }
-            editorDivInput(H) {
-              this.parent.div.classList.toggle("freeTextEditing", this.isEmpty());
-            }
-            disableEditing() {
-              this.editorDiv.setAttribute("role", "comment"), this.editorDiv.removeAttribute("aria-multiline");
-            }
-            enableEditing() {
-              this.editorDiv.setAttribute("role", "textbox"), this.editorDiv.setAttribute("aria-multiline", !0);
-            }
-            render() {
-              if (this.div)
-                return this.div;
-              let H, lt;
-              this.width && (H = this.x, lt = this.y), super.render(), this.editorDiv = document.createElement("div"), this.editorDiv.className = "internal", this.editorDiv.setAttribute("id", t(this, v)), this.enableEditing(), rt.AnnotationEditor._l10nPromise.get("editor_free_text2_aria_label").then((wt) => {
-                var xt;
-                return (xt = this.editorDiv) == null ? void 0 : xt.setAttribute("aria-label", wt);
-              }), rt.AnnotationEditor._l10nPromise.get("free_text2_default_content").then((wt) => {
-                var xt;
-                return (xt = this.editorDiv) == null ? void 0 : xt.setAttribute("default-content", wt);
-              }), this.editorDiv.contentEditable = !0;
-              const {
-                style: gt
-              } = this.editorDiv;
-              if (gt.fontSize = `calc(${t(this, A)}px * var(--scale-factor))`, gt.color = t(this, I), this.div.append(this.editorDiv), this.overlayDiv = document.createElement("div"), this.overlayDiv.classList.add("overlay", "enabled"), this.div.append(this.overlayDiv), (0, P.bindEvents)(this, this.div, ["dblclick", "keydown"]), this.width) {
-                const [wt, xt] = this.parentDimensions;
-                if (this.annotationElementId) {
-                  const {
-                    position: S
-                  } = t(this, u);
-                  let [i, n] = this.getInitialTranslation();
-                  [i, n] = this.pageTranslationToScreen(i, n);
-                  const [s, o] = this.pageDimensions, [h, b] = this.pageTranslation;
-                  let M, N;
-                  switch (this.rotation) {
-                    case 0:
-                      M = H + (S[0] - h) / s, N = lt + this.height - (S[1] - b) / o;
-                      break;
-                    case 90:
-                      M = H + (S[0] - h) / s, N = lt - (S[1] - b) / o, [i, n] = [n, -i];
-                      break;
-                    case 180:
-                      M = H - this.width + (S[0] - h) / s, N = lt - (S[1] - b) / o, [i, n] = [-i, -n];
-                      break;
-                    case 270:
-                      M = H + (S[0] - h - this.height * o) / s, N = lt + (S[1] - b - this.width * s) / o, [i, n] = [-n, i];
-                      break;
-                  }
-                  this.setAt(M * wt, N * xt, i, n);
-                } else
-                  this.setAt(H * wt, lt * xt, this.width * wt, this.height * xt);
-                W(this, r, ni).call(this), this._isDraggable = !0, this.editorDiv.contentEditable = !1;
-              } else
-                this._isDraggable = !1, this.editorDiv.contentEditable = !0;
-              return this.div;
-            }
-            get contentDiv() {
-              return this.editorDiv;
-            }
-            static deserialize(H, lt, gt) {
-              let wt = null;
-              if (H instanceof X.FreeTextAnnotationElement) {
-                const {
-                  data: {
-                    defaultAppearanceData: {
-                      fontSize: S,
-                      fontColor: i
-                    },
-                    rect: n,
-                    rotation: s,
-                    id: o
-                  },
-                  textContent: h,
-                  textPosition: b,
-                  parent: {
-                    page: {
-                      pageNumber: M
-                    }
-                  }
-                } = H;
-                if (!h || h.length === 0)
-                  return null;
-                wt = H = {
-                  annotationType: l.AnnotationEditorType.FREETEXT,
-                  color: Array.from(i),
-                  fontSize: S,
-                  value: h.join(`
-`),
-                  position: b,
-                  pageIndex: M - 1,
-                  rect: n,
-                  rotation: s,
-                  id: o,
-                  deleted: !1
-                };
-              }
-              const xt = super.deserialize(H, lt, gt);
-              return Z(xt, A, H.fontSize), Z(xt, I, l.Util.makeHexColor(...H.color)), Z(xt, x, H.value), xt.annotationElementId = H.id || null, Z(xt, u, wt), xt;
-            }
-            serialize(H = !1) {
-              if (this.isEmpty())
-                return null;
-              if (this.deleted)
-                return {
-                  pageIndex: this.pageIndex,
-                  id: this.annotationElementId,
-                  deleted: !0
-                };
-              const lt = V._internalPadding * this.parentScale, gt = this.getRect(lt, lt), wt = rt.AnnotationEditor._colorManager.convert(this.isAttachedToDOM ? getComputedStyle(this.editorDiv).color : t(this, I)), xt = {
-                annotationType: l.AnnotationEditorType.FREETEXT,
-                color: wt,
-                fontSize: t(this, A),
-                value: t(this, x),
-                pageIndex: this.pageIndex,
-                rect: gt,
-                rotation: this.rotation,
-                structTreeParentId: this._structTreeParentId
-              };
-              return H ? xt : this.annotationElementId && !W(this, m, Di).call(this, xt) ? null : (xt.id = this.annotationElementId, xt);
-            }
-          };
-          B = new WeakMap(), F = new WeakMap(), g = new WeakMap(), O = new WeakMap(), I = new WeakMap(), x = new WeakMap(), v = new WeakMap(), A = new WeakMap(), u = new WeakMap(), _ = new WeakSet(), Fi = function(H) {
-            const lt = (wt) => {
-              this.editorDiv.style.fontSize = `calc(${wt}px * var(--scale-factor))`, this.translate(0, -(wt - t(this, A)) * this.parentScale), Z(this, A, wt), W(this, k, ye).call(this);
-            }, gt = t(this, A);
-            this.addCommands({
-              cmd: () => {
-                lt(H);
-              },
-              undo: () => {
-                lt(gt);
-              },
-              mustExec: !0,
-              type: l.AnnotationEditorParamsType.FREETEXT_SIZE,
-              overwriteIfSameType: !0,
-              keepUndo: !0
-            });
-          }, C = new WeakSet(), Mi = function(H) {
-            const lt = t(this, I);
-            this.addCommands({
-              cmd: () => {
-                Z(this, I, this.editorDiv.style.color = H);
-              },
-              undo: () => {
-                Z(this, I, this.editorDiv.style.color = lt);
-              },
-              mustExec: !0,
-              type: l.AnnotationEditorParamsType.FREETEXT_COLOR,
-              overwriteIfSameType: !0,
-              keepUndo: !0
-            });
-          }, a = new WeakSet(), Ri = function() {
-            const H = this.editorDiv.getElementsByTagName("div");
-            if (H.length === 0)
-              return this.editorDiv.innerText;
-            const lt = [];
-            for (const gt of H)
-              lt.push(gt.innerText.replace(/\r\n?|\n/, ""));
-            return lt.join(`
-`);
-          }, k = new WeakSet(), ye = function() {
-            const [H, lt] = this.parentDimensions;
-            let gt;
-            if (this.isAttachedToDOM)
-              gt = this.div.getBoundingClientRect();
-            else {
-              const {
-                currentLayer: wt,
-                div: xt
-              } = this, S = xt.style.display;
-              xt.style.display = "hidden", wt.div.append(this.div), gt = xt.getBoundingClientRect(), xt.remove(), xt.style.display = S;
-            }
-            this.rotation % 180 === this.parentRotation % 180 ? (this.width = gt.width / H, this.height = gt.height / lt) : (this.width = gt.height / H, this.height = gt.width / lt), this.fixAndSetPosition();
-          }, r = new WeakSet(), ni = function() {
-            if (this.editorDiv.replaceChildren(), !!t(this, x))
-              for (const H of t(this, x).split(`
-`)) {
-                const lt = document.createElement("div");
-                lt.append(H ? document.createTextNode(H) : document.createElement("br")), this.editorDiv.append(lt);
-              }
-          }, m = new WeakSet(), Di = function(H) {
-            const {
-              value: lt,
-              fontSize: gt,
-              color: wt,
-              rect: xt,
-              pageIndex: S
-            } = t(this, u);
-            return H.value !== lt || H.fontSize !== gt || H.rect.some((i, n) => Math.abs(i - xt[n]) >= 1) || H.color.some((i, n) => i !== wt[n]) || H.pageIndex !== S;
-          }, z = new WeakSet(), ri = function(H = !1) {
-            if (!this.annotationElementId)
-              return;
-            if (W(this, k, ye).call(this), !H && (this.width === 0 || this.height === 0)) {
-              setTimeout(() => W(this, z, ri).call(this, !0), 0);
-              return;
-            }
-            const lt = V._internalPadding * this.parentScale;
-            t(this, u).rect = this.getRect(lt, lt);
-          }, ee(V, "_freeTextDefaultContent", ""), ee(V, "_internalPadding", 0), ee(V, "_defaultColor", null), ee(V, "_defaultFontSize", 10), ee(V, "_type", "freetext");
-          let pt = V;
-          d.FreeTextEditor = pt;
-        },
-        /* 29 */
-        /***/
-        (dt, d, et) => {
-          var n, o, ce, b, Ii, N, tt, Q, nt, ct, yt, ut, Ft, Bt, St, Dt, ft, K, J, ht, Et, Ct, jt, Li, Ht, Fe, Vt, ai, $t, oi, Y, G, bt, At, te, Zt, $, li, Lt, Tt, Ot, Nt, Oi, _t, ci;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.StampAnnotationElement = d.InkAnnotationElement = d.FreeTextAnnotationElement = d.AnnotationLayer = void 0;
-          var l = et(1), P = et(6), rt = et(3), X = et(30), pt = et(31), B = et(32);
-          const F = 1e3, g = 9, O = /* @__PURE__ */ new WeakSet();
-          function I(It) {
-            return {
-              width: It[2] - It[0],
-              height: It[3] - It[1]
-            };
-          }
-          class x {
-            static create(R) {
-              switch (R.data.annotationType) {
-                case l.AnnotationType.LINK:
-                  return new A(R);
-                case l.AnnotationType.TEXT:
-                  return new u(R);
-                case l.AnnotationType.WIDGET:
-                  switch (R.data.fieldType) {
-                    case "Tx":
-                      return new w(R);
-                    case "Btn":
-                      return R.data.radioButton ? new a(R) : R.data.checkBox ? new y(R) : new c(R);
-                    case "Ch":
-                      return new k(R);
-                    case "Sig":
-                      return new C(R);
-                  }
-                  return new _(R);
-                case l.AnnotationType.POPUP:
-                  return new p(R);
-                case l.AnnotationType.FREETEXT:
-                  return new T(R);
-                case l.AnnotationType.LINE:
-                  return new m(R);
-                case l.AnnotationType.SQUARE:
-                  return new U(R);
-                case l.AnnotationType.CIRCLE:
-                  return new z(R);
-                case l.AnnotationType.POLYLINE:
-                  return new E(R);
-                case l.AnnotationType.CARET:
-                  return new st(R);
-                case l.AnnotationType.INK:
-                  return new at(R);
-                case l.AnnotationType.POLYGON:
-                  return new V(R);
-                case l.AnnotationType.HIGHLIGHT:
-                  return new H(R);
-                case l.AnnotationType.UNDERLINE:
-                  return new lt(R);
-                case l.AnnotationType.SQUIGGLY:
-                  return new gt(R);
-                case l.AnnotationType.STRIKEOUT:
-                  return new wt(R);
-                case l.AnnotationType.STAMP:
-                  return new xt(R);
-                case l.AnnotationType.FILEATTACHMENT:
-                  return new S(R);
-                default:
-                  return new v(R);
-              }
-            }
-          }
-          const s = class s {
-            constructor(R, {
-              isRenderable: e = !1,
-              ignoreBorder: f = !1,
-              createQuadrilaterals: D = !1
-            } = {}) {
-              L(this, n, !1);
-              this.isRenderable = e, this.data = R.data, this.layer = R.layer, this.linkService = R.linkService, this.downloadManager = R.downloadManager, this.imageResourcesPath = R.imageResourcesPath, this.renderForms = R.renderForms, this.svgFactory = R.svgFactory, this.annotationStorage = R.annotationStorage, this.enableScripting = R.enableScripting, this.hasJSActions = R.hasJSActions, this._fieldObjects = R.fieldObjects, this.parent = R.parent, e && (this.container = this._createContainer(f)), D && this._createQuadrilaterals();
-            }
-            static _hasPopupData({
-              titleObj: R,
-              contentsObj: e,
-              richText: f
-            }) {
-              return !!(R != null && R.str || e != null && e.str || f != null && f.str);
-            }
-            get hasPopupData() {
-              return s._hasPopupData(this.data);
-            }
-            _createContainer(R) {
-              const {
-                data: e,
-                parent: {
-                  page: f,
-                  viewport: D
-                }
-              } = this, j = document.createElement("section");
-              j.setAttribute("data-annotation-id", e.id), this instanceof _ || (j.tabIndex = F), j.style.zIndex = this.parent.zIndex++, this.data.popupRef && j.setAttribute("aria-haspopup", "dialog"), e.noRotate && j.classList.add("norotate");
-              const {
-                pageWidth: q,
-                pageHeight: it,
-                pageX: mt,
-                pageY: kt
-              } = D.rawDims;
-              if (!e.rect || this instanceof p) {
-                const {
-                  rotation: Ut
-                } = e;
-                return !e.hasOwnCanvas && Ut !== 0 && this.setRotation(Ut, j), j;
-              }
-              const {
-                width: Pt,
-                height: zt
-              } = I(e.rect), Mt = l.Util.normalizeRect([e.rect[0], f.view[3] - e.rect[1] + f.view[1], e.rect[2], f.view[3] - e.rect[3] + f.view[1]]);
-              if (!R && e.borderStyle.width > 0) {
-                j.style.borderWidth = `${e.borderStyle.width}px`;
-                const Ut = e.borderStyle.horizontalCornerRadius, qt = e.borderStyle.verticalCornerRadius;
-                if (Ut > 0 || qt > 0) {
-                  const Qt = `calc(${Ut}px * var(--scale-factor)) / calc(${qt}px * var(--scale-factor))`;
-                  j.style.borderRadius = Qt;
-                } else if (this instanceof a) {
-                  const Qt = `calc(${Pt}px * var(--scale-factor)) / calc(${zt}px * var(--scale-factor))`;
-                  j.style.borderRadius = Qt;
-                }
-                switch (e.borderStyle.style) {
-                  case l.AnnotationBorderStyleType.SOLID:
-                    j.style.borderStyle = "solid";
-                    break;
-                  case l.AnnotationBorderStyleType.DASHED:
-                    j.style.borderStyle = "dashed";
-                    break;
-                  case l.AnnotationBorderStyleType.BEVELED:
-                    (0, l.warn)("Unimplemented border style: beveled");
-                    break;
-                  case l.AnnotationBorderStyleType.INSET:
-                    (0, l.warn)("Unimplemented border style: inset");
-                    break;
-                  case l.AnnotationBorderStyleType.UNDERLINE:
-                    j.style.borderBottomStyle = "solid";
-                    break;
-                }
-                const Kt = e.borderColor || null;
-                Kt ? (Z(this, n, !0), j.style.borderColor = l.Util.makeHexColor(Kt[0] | 0, Kt[1] | 0, Kt[2] | 0)) : j.style.borderWidth = 0;
-              }
-              j.style.left = `${100 * (Mt[0] - mt) / q}%`, j.style.top = `${100 * (Mt[1] - kt) / it}%`;
-              const {
-                rotation: Rt
-              } = e;
-              return e.hasOwnCanvas || Rt === 0 ? (j.style.width = `${100 * Pt / q}%`, j.style.height = `${100 * zt / it}%`) : this.setRotation(Rt, j), j;
-            }
-            setRotation(R, e = this.container) {
-              if (!this.data.rect)
-                return;
-              const {
-                pageWidth: f,
-                pageHeight: D
-              } = this.parent.viewport.rawDims, {
-                width: j,
-                height: q
-              } = I(this.data.rect);
-              let it, mt;
-              R % 180 === 0 ? (it = 100 * j / f, mt = 100 * q / D) : (it = 100 * q / f, mt = 100 * j / D), e.style.width = `${it}%`, e.style.height = `${mt}%`, e.setAttribute("data-main-rotation", (360 - R) % 360);
-            }
-            get _commonActions() {
-              const R = (e, f, D) => {
-                const j = D.detail[e], q = j[0], it = j.slice(1);
-                D.target.style[f] = X.ColorConverters[`${q}_HTML`](it), this.annotationStorage.setValue(this.data.id, {
-                  [f]: X.ColorConverters[`${q}_rgb`](it)
-                });
-              };
-              return (0, l.shadow)(this, "_commonActions", {
-                display: (e) => {
-                  const {
-                    display: f
-                  } = e.detail, D = f % 2 === 1;
-                  this.container.style.visibility = D ? "hidden" : "visible", this.annotationStorage.setValue(this.data.id, {
-                    noView: D,
-                    noPrint: f === 1 || f === 2
-                  });
-                },
-                print: (e) => {
-                  this.annotationStorage.setValue(this.data.id, {
-                    noPrint: !e.detail.print
-                  });
-                },
-                hidden: (e) => {
-                  const {
-                    hidden: f
-                  } = e.detail;
-                  this.container.style.visibility = f ? "hidden" : "visible", this.annotationStorage.setValue(this.data.id, {
-                    noPrint: f,
-                    noView: f
-                  });
-                },
-                focus: (e) => {
-                  setTimeout(() => e.target.focus({
-                    preventScroll: !1
-                  }), 0);
-                },
-                userName: (e) => {
-                  e.target.title = e.detail.userName;
-                },
-                readonly: (e) => {
-                  e.target.disabled = e.detail.readonly;
-                },
-                required: (e) => {
-                  this._setRequired(e.target, e.detail.required);
-                },
-                bgColor: (e) => {
-                  R("bgColor", "backgroundColor", e);
-                },
-                fillColor: (e) => {
-                  R("fillColor", "backgroundColor", e);
-                },
-                fgColor: (e) => {
-                  R("fgColor", "color", e);
-                },
-                textColor: (e) => {
-                  R("textColor", "color", e);
-                },
-                borderColor: (e) => {
-                  R("borderColor", "borderColor", e);
-                },
-                strokeColor: (e) => {
-                  R("strokeColor", "borderColor", e);
-                },
-                rotation: (e) => {
-                  const f = e.detail.rotation;
-                  this.setRotation(f), this.annotationStorage.setValue(this.data.id, {
-                    rotation: f
-                  });
-                }
-              });
-            }
-            _dispatchEventFromSandbox(R, e) {
-              const f = this._commonActions;
-              for (const D of Object.keys(e.detail)) {
-                const j = R[D] || f[D];
-                j == null || j(e);
-              }
-            }
-            _setDefaultPropertiesFromJS(R) {
-              if (!this.enableScripting)
-                return;
-              const e = this.annotationStorage.getRawValue(this.data.id);
-              if (!e)
-                return;
-              const f = this._commonActions;
-              for (const [D, j] of Object.entries(e)) {
-                const q = f[D];
-                if (q) {
-                  const it = {
-                    detail: {
-                      [D]: j
-                    },
-                    target: R
-                  };
-                  q(it), delete e[D];
-                }
-              }
-            }
-            _createQuadrilaterals() {
-              if (!this.container)
-                return;
-              const {
-                quadPoints: R
-              } = this.data;
-              if (!R)
-                return;
-              const [e, f, D, j] = this.data.rect;
-              if (R.length === 1) {
-                const [, {
-                  x: qt,
-                  y: Kt
-                }, {
-                  x: Qt,
-                  y: se
-                }] = R[0];
-                if (D === qt && j === Kt && e === Qt && f === se)
-                  return;
-              }
-              const {
-                style: q
-              } = this.container;
-              let it;
-              if (t(this, n)) {
-                const {
-                  borderColor: qt,
-                  borderWidth: Kt
-                } = q;
-                q.borderWidth = 0, it = ["url('data:image/svg+xml;utf8,", '<svg xmlns="http://www.w3.org/2000/svg"', ' preserveAspectRatio="none" viewBox="0 0 1 1">', `<g fill="transparent" stroke="${qt}" stroke-width="${Kt}">`], this.container.classList.add("hasBorder");
-              }
-              const mt = D - e, kt = j - f, {
-                svgFactory: Pt
-              } = this, zt = Pt.createElement("svg");
-              zt.classList.add("quadrilateralsContainer"), zt.setAttribute("width", 0), zt.setAttribute("height", 0);
-              const Mt = Pt.createElement("defs");
-              zt.append(Mt);
-              const Rt = Pt.createElement("clipPath"), Ut = `clippath_${this.data.id}`;
-              Rt.setAttribute("id", Ut), Rt.setAttribute("clipPathUnits", "objectBoundingBox"), Mt.append(Rt);
-              for (const [, {
-                x: qt,
-                y: Kt
-              }, {
-                x: Qt,
-                y: se
-              }] of R) {
-                const ie = Pt.createElement("rect"), ne = (Qt - e) / mt, oe = (j - Kt) / kt, le = (qt - Qt) / mt, _i = (Kt - se) / kt;
-                ie.setAttribute("x", ne), ie.setAttribute("y", oe), ie.setAttribute("width", le), ie.setAttribute("height", _i), Rt.append(ie), it == null || it.push(`<rect vector-effect="non-scaling-stroke" x="${ne}" y="${oe}" width="${le}" height="${_i}"/>`);
-              }
-              t(this, n) && (it.push("</g></svg>')"), q.backgroundImage = it.join("")), this.container.append(zt), this.container.style.clipPath = `url(#${Ut})`;
-            }
-            _createPopup() {
-              const {
-                container: R,
-                data: e
-              } = this;
-              R.setAttribute("aria-haspopup", "dialog");
-              const f = new p({
-                data: {
-                  color: e.color,
-                  titleObj: e.titleObj,
-                  modificationDate: e.modificationDate,
-                  contentsObj: e.contentsObj,
-                  richText: e.richText,
-                  parentRect: e.rect,
-                  borderStyle: 0,
-                  id: `popup_${e.id}`,
-                  rotation: e.rotation
-                },
-                parent: this.parent,
-                elements: [this]
-              });
-              this.parent.div.append(f.render());
-            }
-            render() {
-              (0, l.unreachable)("Abstract method `AnnotationElement.render` called");
-            }
-            _getElementsByName(R, e = null) {
-              const f = [];
-              if (this._fieldObjects) {
-                const D = this._fieldObjects[R];
-                if (D)
-                  for (const {
-                    page: j,
-                    id: q,
-                    exportValues: it
-                  } of D) {
-                    if (j === -1 || q === e)
-                      continue;
-                    const mt = typeof it == "string" ? it : null, kt = document.querySelector(`[data-element-id="${q}"]`);
-                    if (kt && !O.has(kt)) {
-                      (0, l.warn)(`_getElementsByName - element not allowed: ${q}`);
-                      continue;
-                    }
-                    f.push({
-                      id: q,
-                      exportValue: mt,
-                      domElement: kt
-                    });
-                  }
-                return f;
-              }
-              for (const D of document.getElementsByName(R)) {
-                const {
-                  exportValue: j
-                } = D, q = D.getAttribute("data-element-id");
-                q !== e && O.has(D) && f.push({
-                  id: q,
-                  exportValue: j,
-                  domElement: D
-                });
-              }
-              return f;
-            }
-            show() {
-              var R;
-              this.container && (this.container.hidden = !1), (R = this.popup) == null || R.maybeShow();
-            }
-            hide() {
-              var R;
-              this.container && (this.container.hidden = !0), (R = this.popup) == null || R.forceHide();
-            }
-            getElementsToTriggerPopup() {
-              return this.container;
-            }
-            addHighlightArea() {
-              const R = this.getElementsToTriggerPopup();
-              if (Array.isArray(R))
-                for (const e of R)
-                  e.classList.add("highlightArea");
-              else
-                R.classList.add("highlightArea");
-            }
-            _editOnDoubleClick() {
-              const {
-                annotationEditorType: R,
-                data: {
-                  id: e
-                }
-              } = this;
-              this.container.addEventListener("dblclick", () => {
-                var f;
-                (f = this.linkService.eventBus) == null || f.dispatch("switchannotationeditormode", {
-                  source: this,
-                  mode: R,
-                  editId: e
-                });
-              });
-            }
-          };
-          n = new WeakMap();
-          let v = s;
-          class A extends v {
-            constructor(e, f = null) {
-              super(e, {
-                isRenderable: !0,
-                ignoreBorder: !!(f != null && f.ignoreBorder),
-                createQuadrilaterals: !0
-              });
-              L(this, o);
-              L(this, b);
-              this.isTooltipOnly = e.data.isTooltipOnly;
-            }
-            render() {
-              const {
-                data: e,
-                linkService: f
-              } = this, D = document.createElement("a");
-              D.setAttribute("data-element-id", e.id);
-              let j = !1;
-              return e.url ? (f.addLinkAttributes(D, e.url, e.newWindow), j = !0) : e.action ? (this._bindNamedAction(D, e.action), j = !0) : e.attachment ? (this._bindAttachment(D, e.attachment), j = !0) : e.setOCGState ? (W(this, b, Ii).call(this, D, e.setOCGState), j = !0) : e.dest ? (this._bindLink(D, e.dest), j = !0) : (e.actions && (e.actions.Action || e.actions["Mouse Up"] || e.actions["Mouse Down"]) && this.enableScripting && this.hasJSActions && (this._bindJSAction(D, e), j = !0), e.resetForm ? (this._bindResetFormAction(D, e.resetForm), j = !0) : this.isTooltipOnly && !j && (this._bindLink(D, ""), j = !0)), this.container.classList.add("linkAnnotation"), j && this.container.append(D), this.container;
-            }
-            _bindLink(e, f) {
-              e.href = this.linkService.getDestinationHash(f), e.onclick = () => (f && this.linkService.goToDestination(f), !1), (f || f === "") && W(this, o, ce).call(this);
-            }
-            _bindNamedAction(e, f) {
-              e.href = this.linkService.getAnchorUrl(""), e.onclick = () => (this.linkService.executeNamedAction(f), !1), W(this, o, ce).call(this);
-            }
-            _bindAttachment(e, f) {
-              e.href = this.linkService.getAnchorUrl(""), e.onclick = () => {
-                var D;
-                return (D = this.downloadManager) == null || D.openOrDownloadData(this.container, f.content, f.filename), !1;
-              }, W(this, o, ce).call(this);
-            }
-            _bindJSAction(e, f) {
-              e.href = this.linkService.getAnchorUrl("");
-              const D = /* @__PURE__ */ new Map([["Action", "onclick"], ["Mouse Up", "onmouseup"], ["Mouse Down", "onmousedown"]]);
-              for (const j of Object.keys(f.actions)) {
-                const q = D.get(j);
-                q && (e[q] = () => {
-                  var it;
-                  return (it = this.linkService.eventBus) == null || it.dispatch("dispatcheventinsandbox", {
-                    source: this,
-                    detail: {
-                      id: f.id,
-                      name: j
-                    }
-                  }), !1;
-                });
-              }
-              e.onclick || (e.onclick = () => !1), W(this, o, ce).call(this);
-            }
-            _bindResetFormAction(e, f) {
-              const D = e.onclick;
-              if (D || (e.href = this.linkService.getAnchorUrl("")), W(this, o, ce).call(this), !this._fieldObjects) {
-                (0, l.warn)('_bindResetFormAction - "resetForm" action not supported, ensure that the `fieldObjects` parameter is provided.'), D || (e.onclick = () => !1);
-                return;
-              }
-              e.onclick = () => {
-                var zt;
-                D == null || D();
-                const {
-                  fields: j,
-                  refs: q,
-                  include: it
-                } = f, mt = [];
-                if (j.length !== 0 || q.length !== 0) {
-                  const Mt = new Set(q);
-                  for (const Rt of j) {
-                    const Ut = this._fieldObjects[Rt] || [];
-                    for (const {
-                      id: qt
-                    } of Ut)
-                      Mt.add(qt);
-                  }
-                  for (const Rt of Object.values(this._fieldObjects))
-                    for (const Ut of Rt)
-                      Mt.has(Ut.id) === it && mt.push(Ut);
-                } else
-                  for (const Mt of Object.values(this._fieldObjects))
-                    mt.push(...Mt);
-                const kt = this.annotationStorage, Pt = [];
-                for (const Mt of mt) {
-                  const {
-                    id: Rt
-                  } = Mt;
-                  switch (Pt.push(Rt), Mt.type) {
-                    case "text": {
-                      const qt = Mt.defaultValue || "";
-                      kt.setValue(Rt, {
-                        value: qt
-                      });
-                      break;
-                    }
-                    case "checkbox":
-                    case "radiobutton": {
-                      const qt = Mt.defaultValue === Mt.exportValues;
-                      kt.setValue(Rt, {
-                        value: qt
-                      });
-                      break;
-                    }
-                    case "combobox":
-                    case "listbox": {
-                      const qt = Mt.defaultValue || "";
-                      kt.setValue(Rt, {
-                        value: qt
-                      });
-                      break;
-                    }
-                    default:
-                      continue;
-                  }
-                  const Ut = document.querySelector(`[data-element-id="${Rt}"]`);
-                  if (Ut) {
-                    if (!O.has(Ut)) {
-                      (0, l.warn)(`_bindResetFormAction - element not allowed: ${Rt}`);
-                      continue;
-                    }
-                  } else
-                    continue;
-                  Ut.dispatchEvent(new Event("resetform"));
-                }
-                return this.enableScripting && ((zt = this.linkService.eventBus) == null || zt.dispatch("dispatcheventinsandbox", {
-                  source: this,
-                  detail: {
-                    id: "app",
-                    ids: Pt,
-                    name: "ResetForm"
-                  }
-                })), !1;
-              };
-            }
-          }
-          o = new WeakSet(), ce = function() {
-            this.container.setAttribute("data-internal-link", "");
-          }, b = new WeakSet(), Ii = function(e, f) {
-            e.href = this.linkService.getAnchorUrl(""), e.onclick = () => (this.linkService.executeSetOCGState(f), !1), W(this, o, ce).call(this);
-          };
-          class u extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0
-              });
-            }
-            render() {
-              this.container.classList.add("textAnnotation");
-              const R = document.createElement("img");
-              return R.src = this.imageResourcesPath + "annotation-" + this.data.name.toLowerCase() + ".svg", R.alt = "[{{type}} Annotation]", R.dataset.l10nId = "text_annotation_type", R.dataset.l10nArgs = JSON.stringify({
-                type: this.data.name
-              }), !this.data.popupRef && this.hasPopupData && this._createPopup(), this.container.append(R), this.container;
-            }
-          }
-          class _ extends v {
-            render() {
-              return this.data.alternativeText && (this.container.title = this.data.alternativeText), this.container;
-            }
-            showElementAndHideCanvas(R) {
-              var e;
-              this.data.hasOwnCanvas && (((e = R.previousSibling) == null ? void 0 : e.nodeName) === "CANVAS" && (R.previousSibling.hidden = !0), R.hidden = !1);
-            }
-            _getKeyModifier(R) {
-              const {
-                isWin: e,
-                isMac: f
-              } = l.FeatureTest.platform;
-              return e && R.ctrlKey || f && R.metaKey;
-            }
-            _setEventListener(R, e, f, D, j) {
-              f.includes("mouse") ? R.addEventListener(f, (q) => {
-                var it;
-                (it = this.linkService.eventBus) == null || it.dispatch("dispatcheventinsandbox", {
-                  source: this,
-                  detail: {
-                    id: this.data.id,
-                    name: D,
-                    value: j(q),
-                    shift: q.shiftKey,
-                    modifier: this._getKeyModifier(q)
-                  }
-                });
-              }) : R.addEventListener(f, (q) => {
-                var it;
-                if (f === "blur") {
-                  if (!e.focused || !q.relatedTarget)
-                    return;
-                  e.focused = !1;
-                } else if (f === "focus") {
-                  if (e.focused)
-                    return;
-                  e.focused = !0;
-                }
-                j && ((it = this.linkService.eventBus) == null || it.dispatch("dispatcheventinsandbox", {
-                  source: this,
-                  detail: {
-                    id: this.data.id,
-                    name: D,
-                    value: j(q)
-                  }
-                }));
-              });
-            }
-            _setEventListeners(R, e, f, D) {
-              var j, q, it;
-              for (const [mt, kt] of f)
-                (kt === "Action" || (j = this.data.actions) != null && j[kt]) && ((kt === "Focus" || kt === "Blur") && (e || (e = {
-                  focused: !1
-                })), this._setEventListener(R, e, mt, kt, D), kt === "Focus" && !((q = this.data.actions) != null && q.Blur) ? this._setEventListener(R, e, "blur", "Blur", null) : kt === "Blur" && !((it = this.data.actions) != null && it.Focus) && this._setEventListener(R, e, "focus", "Focus", null));
-            }
-            _setBackgroundColor(R) {
-              const e = this.data.backgroundColor || null;
-              R.style.backgroundColor = e === null ? "transparent" : l.Util.makeHexColor(e[0], e[1], e[2]);
-            }
-            _setTextStyle(R) {
-              const e = ["left", "center", "right"], {
-                fontColor: f
-              } = this.data.defaultAppearanceData, D = this.data.defaultAppearanceData.fontSize || g, j = R.style;
-              let q;
-              const it = 2, mt = (kt) => Math.round(10 * kt) / 10;
-              if (this.data.multiLine) {
-                const kt = Math.abs(this.data.rect[3] - this.data.rect[1] - it), Pt = Math.round(kt / (l.LINE_FACTOR * D)) || 1, zt = kt / Pt;
-                q = Math.min(D, mt(zt / l.LINE_FACTOR));
-              } else {
-                const kt = Math.abs(this.data.rect[3] - this.data.rect[1] - it);
-                q = Math.min(D, mt(kt / l.LINE_FACTOR));
-              }
-              j.fontSize = `calc(${q}px * var(--scale-factor))`, j.color = l.Util.makeHexColor(f[0], f[1], f[2]), this.data.textAlignment !== null && (j.textAlign = e[this.data.textAlignment]);
-            }
-            _setRequired(R, e) {
-              e ? R.setAttribute("required", !0) : R.removeAttribute("required"), R.setAttribute("aria-required", e);
-            }
-          }
-          class w extends _ {
-            constructor(R) {
-              const e = R.renderForms || !R.data.hasAppearance && !!R.data.fieldValue;
-              super(R, {
-                isRenderable: e
-              });
-            }
-            setPropertyOnSiblings(R, e, f, D) {
-              const j = this.annotationStorage;
-              for (const q of this._getElementsByName(R.name, R.id))
-                q.domElement && (q.domElement[e] = f), j.setValue(q.id, {
-                  [D]: f
-                });
-            }
-            render() {
-              var D, j;
-              const R = this.annotationStorage, e = this.data.id;
-              this.container.classList.add("textWidgetAnnotation");
-              let f = null;
-              if (this.renderForms) {
-                const q = R.getValue(e, {
-                  value: this.data.fieldValue
-                });
-                let it = q.value || "";
-                const mt = R.getValue(e, {
-                  charLimit: this.data.maxLen
-                }).charLimit;
-                mt && it.length > mt && (it = it.slice(0, mt));
-                let kt = q.formattedValue || ((D = this.data.textContent) == null ? void 0 : D.join(`
-`)) || null;
-                kt && this.data.comb && (kt = kt.replaceAll(/\s+/g, ""));
-                const Pt = {
-                  userValue: it,
-                  formattedValue: kt,
-                  lastCommittedValue: null,
-                  commitKey: 1,
-                  focused: !1
-                };
-                this.data.multiLine ? (f = document.createElement("textarea"), f.textContent = kt ?? it, this.data.doNotScroll && (f.style.overflowY = "hidden")) : (f = document.createElement("input"), f.type = "text", f.setAttribute("value", kt ?? it), this.data.doNotScroll && (f.style.overflowX = "hidden")), this.data.hasOwnCanvas && (f.hidden = !0), O.add(f), f.setAttribute("data-element-id", e), f.disabled = this.data.readOnly, f.name = this.data.fieldName, f.tabIndex = F, this._setRequired(f, this.data.required), mt && (f.maxLength = mt), f.addEventListener("input", (Mt) => {
-                  R.setValue(e, {
-                    value: Mt.target.value
-                  }), this.setPropertyOnSiblings(f, "value", Mt.target.value, "value"), Pt.formattedValue = null;
-                }), f.addEventListener("resetform", (Mt) => {
-                  const Rt = this.data.defaultFieldValue ?? "";
-                  f.value = Pt.userValue = Rt, Pt.formattedValue = null;
-                });
-                let zt = (Mt) => {
-                  const {
-                    formattedValue: Rt
-                  } = Pt;
-                  Rt != null && (Mt.target.value = Rt), Mt.target.scrollLeft = 0;
-                };
-                if (this.enableScripting && this.hasJSActions) {
-                  f.addEventListener("focus", (Rt) => {
-                    if (Pt.focused)
-                      return;
-                    const {
-                      target: Ut
-                    } = Rt;
-                    Pt.userValue && (Ut.value = Pt.userValue), Pt.lastCommittedValue = Ut.value, Pt.commitKey = 1, Pt.focused = !0;
-                  }), f.addEventListener("updatefromsandbox", (Rt) => {
-                    this.showElementAndHideCanvas(Rt.target);
-                    const Ut = {
-                      value(qt) {
-                        Pt.userValue = qt.detail.value ?? "", R.setValue(e, {
-                          value: Pt.userValue.toString()
-                        }), qt.target.value = Pt.userValue;
-                      },
-                      formattedValue(qt) {
-                        const {
-                          formattedValue: Kt
-                        } = qt.detail;
-                        Pt.formattedValue = Kt, Kt != null && qt.target !== document.activeElement && (qt.target.value = Kt), R.setValue(e, {
-                          formattedValue: Kt
-                        });
-                      },
-                      selRange(qt) {
-                        qt.target.setSelectionRange(...qt.detail.selRange);
-                      },
-                      charLimit: (qt) => {
-                        var ie;
-                        const {
-                          charLimit: Kt
-                        } = qt.detail, {
-                          target: Qt
-                        } = qt;
-                        if (Kt === 0) {
-                          Qt.removeAttribute("maxLength");
-                          return;
-                        }
-                        Qt.setAttribute("maxLength", Kt);
-                        let se = Pt.userValue;
-                        !se || se.length <= Kt || (se = se.slice(0, Kt), Qt.value = Pt.userValue = se, R.setValue(e, {
-                          value: se
-                        }), (ie = this.linkService.eventBus) == null || ie.dispatch("dispatcheventinsandbox", {
-                          source: this,
-                          detail: {
-                            id: e,
-                            name: "Keystroke",
-                            value: se,
-                            willCommit: !0,
-                            commitKey: 1,
-                            selStart: Qt.selectionStart,
-                            selEnd: Qt.selectionEnd
-                          }
-                        }));
-                      }
-                    };
-                    this._dispatchEventFromSandbox(Ut, Rt);
-                  }), f.addEventListener("keydown", (Rt) => {
-                    var Kt;
-                    Pt.commitKey = 1;
-                    let Ut = -1;
-                    if (Rt.key === "Escape" ? Ut = 0 : Rt.key === "Enter" && !this.data.multiLine ? Ut = 2 : Rt.key === "Tab" && (Pt.commitKey = 3), Ut === -1)
-                      return;
-                    const {
-                      value: qt
-                    } = Rt.target;
-                    Pt.lastCommittedValue !== qt && (Pt.lastCommittedValue = qt, Pt.userValue = qt, (Kt = this.linkService.eventBus) == null || Kt.dispatch("dispatcheventinsandbox", {
-                      source: this,
-                      detail: {
-                        id: e,
-                        name: "Keystroke",
-                        value: qt,
-                        willCommit: !0,
-                        commitKey: Ut,
-                        selStart: Rt.target.selectionStart,
-                        selEnd: Rt.target.selectionEnd
-                      }
-                    }));
-                  });
-                  const Mt = zt;
-                  zt = null, f.addEventListener("blur", (Rt) => {
-                    var qt;
-                    if (!Pt.focused || !Rt.relatedTarget)
-                      return;
-                    Pt.focused = !1;
-                    const {
-                      value: Ut
-                    } = Rt.target;
-                    Pt.userValue = Ut, Pt.lastCommittedValue !== Ut && ((qt = this.linkService.eventBus) == null || qt.dispatch("dispatcheventinsandbox", {
-                      source: this,
-                      detail: {
-                        id: e,
-                        name: "Keystroke",
-                        value: Ut,
-                        willCommit: !0,
-                        commitKey: Pt.commitKey,
-                        selStart: Rt.target.selectionStart,
-                        selEnd: Rt.target.selectionEnd
-                      }
-                    })), Mt(Rt);
-                  }), (j = this.data.actions) != null && j.Keystroke && f.addEventListener("beforeinput", (Rt) => {
-                    var oe;
-                    Pt.lastCommittedValue = null;
-                    const {
-                      data: Ut,
-                      target: qt
-                    } = Rt, {
-                      value: Kt,
-                      selectionStart: Qt,
-                      selectionEnd: se
-                    } = qt;
-                    let ie = Qt, ne = se;
-                    switch (Rt.inputType) {
-                      case "deleteWordBackward": {
-                        const le = Kt.substring(0, Qt).match(/\w*[^\w]*$/);
-                        le && (ie -= le[0].length);
-                        break;
-                      }
-                      case "deleteWordForward": {
-                        const le = Kt.substring(Qt).match(/^[^\w]*\w*/);
-                        le && (ne += le[0].length);
-                        break;
-                      }
-                      case "deleteContentBackward":
-                        Qt === se && (ie -= 1);
-                        break;
-                      case "deleteContentForward":
-                        Qt === se && (ne += 1);
-                        break;
-                    }
-                    Rt.preventDefault(), (oe = this.linkService.eventBus) == null || oe.dispatch("dispatcheventinsandbox", {
-                      source: this,
-                      detail: {
-                        id: e,
-                        name: "Keystroke",
-                        value: Kt,
-                        change: Ut || "",
-                        willCommit: !1,
-                        selStart: ie,
-                        selEnd: ne
-                      }
-                    });
-                  }), this._setEventListeners(f, Pt, [["focus", "Focus"], ["blur", "Blur"], ["mousedown", "Mouse Down"], ["mouseenter", "Mouse Enter"], ["mouseleave", "Mouse Exit"], ["mouseup", "Mouse Up"]], (Rt) => Rt.target.value);
-                }
-                if (zt && f.addEventListener("blur", zt), this.data.comb) {
-                  const Rt = (this.data.rect[2] - this.data.rect[0]) / mt;
-                  f.classList.add("comb"), f.style.letterSpacing = `calc(${Rt}px * var(--scale-factor) - 1ch)`;
-                }
-              } else
-                f = document.createElement("div"), f.textContent = this.data.fieldValue, f.style.verticalAlign = "middle", f.style.display = "table-cell";
-              return this._setTextStyle(f), this._setBackgroundColor(f), this._setDefaultPropertiesFromJS(f), this.container.append(f), this.container;
-            }
-          }
-          class C extends _ {
-            constructor(R) {
-              super(R, {
-                isRenderable: !!R.data.hasOwnCanvas
-              });
-            }
-          }
-          class y extends _ {
-            constructor(R) {
-              super(R, {
-                isRenderable: R.renderForms
-              });
-            }
-            render() {
-              const R = this.annotationStorage, e = this.data, f = e.id;
-              let D = R.getValue(f, {
-                value: e.exportValue === e.fieldValue
-              }).value;
-              typeof D == "string" && (D = D !== "Off", R.setValue(f, {
-                value: D
-              })), this.container.classList.add("buttonWidgetAnnotation", "checkBox");
-              const j = document.createElement("input");
-              return O.add(j), j.setAttribute("data-element-id", f), j.disabled = e.readOnly, this._setRequired(j, this.data.required), j.type = "checkbox", j.name = e.fieldName, D && j.setAttribute("checked", !0), j.setAttribute("exportValue", e.exportValue), j.tabIndex = F, j.addEventListener("change", (q) => {
-                const {
-                  name: it,
-                  checked: mt
-                } = q.target;
-                for (const kt of this._getElementsByName(it, f)) {
-                  const Pt = mt && kt.exportValue === e.exportValue;
-                  kt.domElement && (kt.domElement.checked = Pt), R.setValue(kt.id, {
-                    value: Pt
-                  });
-                }
-                R.setValue(f, {
-                  value: mt
-                });
-              }), j.addEventListener("resetform", (q) => {
-                const it = e.defaultFieldValue || "Off";
-                q.target.checked = it === e.exportValue;
-              }), this.enableScripting && this.hasJSActions && (j.addEventListener("updatefromsandbox", (q) => {
-                const it = {
-                  value(mt) {
-                    mt.target.checked = mt.detail.value !== "Off", R.setValue(f, {
-                      value: mt.target.checked
-                    });
-                  }
-                };
-                this._dispatchEventFromSandbox(it, q);
-              }), this._setEventListeners(j, null, [["change", "Validate"], ["change", "Action"], ["focus", "Focus"], ["blur", "Blur"], ["mousedown", "Mouse Down"], ["mouseenter", "Mouse Enter"], ["mouseleave", "Mouse Exit"], ["mouseup", "Mouse Up"]], (q) => q.target.checked)), this._setBackgroundColor(j), this._setDefaultPropertiesFromJS(j), this.container.append(j), this.container;
-            }
-          }
-          class a extends _ {
-            constructor(R) {
-              super(R, {
-                isRenderable: R.renderForms
-              });
-            }
-            render() {
-              this.container.classList.add("buttonWidgetAnnotation", "radioButton");
-              const R = this.annotationStorage, e = this.data, f = e.id;
-              let D = R.getValue(f, {
-                value: e.fieldValue === e.buttonValue
-              }).value;
-              typeof D == "string" && (D = D !== e.buttonValue, R.setValue(f, {
-                value: D
-              }));
-              const j = document.createElement("input");
-              if (O.add(j), j.setAttribute("data-element-id", f), j.disabled = e.readOnly, this._setRequired(j, this.data.required), j.type = "radio", j.name = e.fieldName, D && j.setAttribute("checked", !0), j.tabIndex = F, j.addEventListener("change", (q) => {
-                const {
-                  name: it,
-                  checked: mt
-                } = q.target;
-                for (const kt of this._getElementsByName(it, f))
-                  R.setValue(kt.id, {
-                    value: !1
-                  });
-                R.setValue(f, {
-                  value: mt
-                });
-              }), j.addEventListener("resetform", (q) => {
-                const it = e.defaultFieldValue;
-                q.target.checked = it != null && it === e.buttonValue;
-              }), this.enableScripting && this.hasJSActions) {
-                const q = e.buttonValue;
-                j.addEventListener("updatefromsandbox", (it) => {
-                  const mt = {
-                    value: (kt) => {
-                      const Pt = q === kt.detail.value;
-                      for (const zt of this._getElementsByName(kt.target.name)) {
-                        const Mt = Pt && zt.id === f;
-                        zt.domElement && (zt.domElement.checked = Mt), R.setValue(zt.id, {
-                          value: Mt
-                        });
-                      }
-                    }
-                  };
-                  this._dispatchEventFromSandbox(mt, it);
-                }), this._setEventListeners(j, null, [["change", "Validate"], ["change", "Action"], ["focus", "Focus"], ["blur", "Blur"], ["mousedown", "Mouse Down"], ["mouseenter", "Mouse Enter"], ["mouseleave", "Mouse Exit"], ["mouseup", "Mouse Up"]], (it) => it.target.checked);
-              }
-              return this._setBackgroundColor(j), this._setDefaultPropertiesFromJS(j), this.container.append(j), this.container;
-            }
-          }
-          class c extends A {
-            constructor(R) {
-              super(R, {
-                ignoreBorder: R.data.hasAppearance
-              });
-            }
-            render() {
-              const R = super.render();
-              R.classList.add("buttonWidgetAnnotation", "pushButton"), this.data.alternativeText && (R.title = this.data.alternativeText);
-              const e = R.lastChild;
-              return this.enableScripting && this.hasJSActions && e && (this._setDefaultPropertiesFromJS(e), e.addEventListener("updatefromsandbox", (f) => {
-                this._dispatchEventFromSandbox({}, f);
-              })), R;
-            }
-          }
-          class k extends _ {
-            constructor(R) {
-              super(R, {
-                isRenderable: R.renderForms
-              });
-            }
-            render() {
-              this.container.classList.add("choiceWidgetAnnotation");
-              const R = this.annotationStorage, e = this.data.id, f = R.getValue(e, {
-                value: this.data.fieldValue
-              }), D = document.createElement("select");
-              O.add(D), D.setAttribute("data-element-id", e), D.disabled = this.data.readOnly, this._setRequired(D, this.data.required), D.name = this.data.fieldName, D.tabIndex = F;
-              let j = this.data.combo && this.data.options.length > 0;
-              this.data.combo || (D.size = this.data.options.length, this.data.multiSelect && (D.multiple = !0)), D.addEventListener("resetform", (Pt) => {
-                const zt = this.data.defaultFieldValue;
-                for (const Mt of D.options)
-                  Mt.selected = Mt.value === zt;
-              });
-              for (const Pt of this.data.options) {
-                const zt = document.createElement("option");
-                zt.textContent = Pt.displayValue, zt.value = Pt.exportValue, f.value.includes(Pt.exportValue) && (zt.setAttribute("selected", !0), j = !1), D.append(zt);
-              }
-              let q = null;
-              if (j) {
-                const Pt = document.createElement("option");
-                Pt.value = " ", Pt.setAttribute("hidden", !0), Pt.setAttribute("selected", !0), D.prepend(Pt), q = () => {
-                  Pt.remove(), D.removeEventListener("input", q), q = null;
-                }, D.addEventListener("input", q);
-              }
-              const it = (Pt) => {
-                const zt = Pt ? "value" : "textContent", {
-                  options: Mt,
-                  multiple: Rt
-                } = D;
-                return Rt ? Array.prototype.filter.call(Mt, (Ut) => Ut.selected).map((Ut) => Ut[zt]) : Mt.selectedIndex === -1 ? null : Mt[Mt.selectedIndex][zt];
-              };
-              let mt = it(!1);
-              const kt = (Pt) => {
-                const zt = Pt.target.options;
-                return Array.prototype.map.call(zt, (Mt) => ({
-                  displayValue: Mt.textContent,
-                  exportValue: Mt.value
-                }));
-              };
-              return this.enableScripting && this.hasJSActions ? (D.addEventListener("updatefromsandbox", (Pt) => {
-                const zt = {
-                  value(Mt) {
-                    q == null || q();
-                    const Rt = Mt.detail.value, Ut = new Set(Array.isArray(Rt) ? Rt : [Rt]);
-                    for (const qt of D.options)
-                      qt.selected = Ut.has(qt.value);
-                    R.setValue(e, {
-                      value: it(!0)
-                    }), mt = it(!1);
-                  },
-                  multipleSelection(Mt) {
-                    D.multiple = !0;
-                  },
-                  remove(Mt) {
-                    const Rt = D.options, Ut = Mt.detail.remove;
-                    Rt[Ut].selected = !1, D.remove(Ut), Rt.length > 0 && Array.prototype.findIndex.call(Rt, (Kt) => Kt.selected) === -1 && (Rt[0].selected = !0), R.setValue(e, {
-                      value: it(!0),
-                      items: kt(Mt)
-                    }), mt = it(!1);
-                  },
-                  clear(Mt) {
-                    for (; D.length !== 0; )
-                      D.remove(0);
-                    R.setValue(e, {
-                      value: null,
-                      items: []
-                    }), mt = it(!1);
-                  },
-                  insert(Mt) {
-                    const {
-                      index: Rt,
-                      displayValue: Ut,
-                      exportValue: qt
-                    } = Mt.detail.insert, Kt = D.children[Rt], Qt = document.createElement("option");
-                    Qt.textContent = Ut, Qt.value = qt, Kt ? Kt.before(Qt) : D.append(Qt), R.setValue(e, {
-                      value: it(!0),
-                      items: kt(Mt)
-                    }), mt = it(!1);
-                  },
-                  items(Mt) {
-                    const {
-                      items: Rt
-                    } = Mt.detail;
-                    for (; D.length !== 0; )
-                      D.remove(0);
-                    for (const Ut of Rt) {
-                      const {
-                        displayValue: qt,
-                        exportValue: Kt
-                      } = Ut, Qt = document.createElement("option");
-                      Qt.textContent = qt, Qt.value = Kt, D.append(Qt);
-                    }
-                    D.options.length > 0 && (D.options[0].selected = !0), R.setValue(e, {
-                      value: it(!0),
-                      items: kt(Mt)
-                    }), mt = it(!1);
-                  },
-                  indices(Mt) {
-                    const Rt = new Set(Mt.detail.indices);
-                    for (const Ut of Mt.target.options)
-                      Ut.selected = Rt.has(Ut.index);
-                    R.setValue(e, {
-                      value: it(!0)
-                    }), mt = it(!1);
-                  },
-                  editable(Mt) {
-                    Mt.target.disabled = !Mt.detail.editable;
-                  }
-                };
-                this._dispatchEventFromSandbox(zt, Pt);
-              }), D.addEventListener("input", (Pt) => {
-                var Mt;
-                const zt = it(!0);
-                R.setValue(e, {
-                  value: zt
-                }), Pt.preventDefault(), (Mt = this.linkService.eventBus) == null || Mt.dispatch("dispatcheventinsandbox", {
-                  source: this,
-                  detail: {
-                    id: e,
-                    name: "Keystroke",
-                    value: mt,
-                    changeEx: zt,
-                    willCommit: !1,
-                    commitKey: 1,
-                    keyDown: !1
-                  }
-                });
-              }), this._setEventListeners(D, null, [["focus", "Focus"], ["blur", "Blur"], ["mousedown", "Mouse Down"], ["mouseenter", "Mouse Enter"], ["mouseleave", "Mouse Exit"], ["mouseup", "Mouse Up"], ["input", "Action"], ["input", "Validate"]], (Pt) => Pt.target.value)) : D.addEventListener("input", function(Pt) {
-                R.setValue(e, {
-                  value: it(!0)
-                });
-              }), this.data.combo && this._setTextStyle(D), this._setBackgroundColor(D), this._setDefaultPropertiesFromJS(D), this.container.append(D), this.container;
-            }
-          }
-          class p extends v {
-            constructor(R) {
-              const {
-                data: e,
-                elements: f
-              } = R;
-              super(R, {
-                isRenderable: v._hasPopupData(e)
-              }), this.elements = f;
-            }
-            render() {
-              this.container.classList.add("popupAnnotation");
-              const R = new r({
-                container: this.container,
-                color: this.data.color,
-                titleObj: this.data.titleObj,
-                modificationDate: this.data.modificationDate,
-                contentsObj: this.data.contentsObj,
-                richText: this.data.richText,
-                rect: this.data.rect,
-                parentRect: this.data.parentRect || null,
-                parent: this.parent,
-                elements: this.elements,
-                open: this.data.open
-              }), e = [];
-              for (const f of this.elements)
-                f.popup = R, e.push(f.data.id), f.addHighlightArea();
-              return this.container.setAttribute("aria-controls", e.map((f) => `${l.AnnotationPrefix}${f}`).join(",")), this.container;
-            }
-          }
-          class r {
-            constructor({
-              container: R,
-              color: e,
-              elements: f,
-              titleObj: D,
-              modificationDate: j,
-              contentsObj: q,
-              richText: it,
-              parent: mt,
-              rect: kt,
-              parentRect: Pt,
-              open: zt
-            }) {
-              L(this, jt);
-              L(this, Ht);
-              L(this, Vt);
-              L(this, $t);
-              L(this, N, null);
-              L(this, tt, W(this, jt, Li).bind(this));
-              L(this, Q, W(this, $t, oi).bind(this));
-              L(this, nt, W(this, Vt, ai).bind(this));
-              L(this, ct, W(this, Ht, Fe).bind(this));
-              L(this, yt, null);
-              L(this, ut, null);
-              L(this, Ft, null);
-              L(this, Bt, null);
-              L(this, St, null);
-              L(this, Dt, null);
-              L(this, ft, !1);
-              L(this, K, null);
-              L(this, J, null);
-              L(this, ht, null);
-              L(this, Et, null);
-              L(this, Ct, !1);
-              var Rt;
-              Z(this, ut, R), Z(this, Et, D), Z(this, Ft, q), Z(this, ht, it), Z(this, St, mt), Z(this, yt, e), Z(this, J, kt), Z(this, Dt, Pt), Z(this, Bt, f);
-              const Mt = P.PDFDateString.toDateObject(j);
-              Mt && Z(this, N, mt.l10n.get("annotation_date_string", {
-                date: Mt.toLocaleDateString(),
-                time: Mt.toLocaleTimeString()
-              })), this.trigger = f.flatMap((Ut) => Ut.getElementsToTriggerPopup());
-              for (const Ut of this.trigger)
-                Ut.addEventListener("click", t(this, ct)), Ut.addEventListener("mouseenter", t(this, nt)), Ut.addEventListener("mouseleave", t(this, Q)), Ut.classList.add("popupTriggerArea");
-              for (const Ut of f)
-                (Rt = Ut.container) == null || Rt.addEventListener("keydown", t(this, tt));
-              t(this, ut).hidden = !0, zt && W(this, Ht, Fe).call(this);
-            }
-            render() {
-              if (t(this, K))
-                return;
-              const {
-                page: {
-                  view: R
-                },
-                viewport: {
-                  rawDims: {
-                    pageWidth: e,
-                    pageHeight: f,
-                    pageX: D,
-                    pageY: j
-                  }
-                }
-              } = t(this, St), q = Z(this, K, document.createElement("div"));
-              if (q.className = "popup", t(this, yt)) {
-                const ie = q.style.outlineColor = l.Util.makeHexColor(...t(this, yt));
-                CSS.supports("background-color", "color-mix(in srgb, red 30%, white)") ? q.style.backgroundColor = `color-mix(in srgb, ${ie} 30%, white)` : q.style.backgroundColor = l.Util.makeHexColor(...t(this, yt).map((oe) => Math.floor(0.7 * (255 - oe) + oe)));
-              }
-              const it = document.createElement("span");
-              it.className = "header";
-              const mt = document.createElement("h1");
-              if (it.append(mt), {
-                dir: mt.dir,
-                str: mt.textContent
-              } = t(this, Et), q.append(it), t(this, N)) {
-                const ie = document.createElement("span");
-                ie.classList.add("popupDate"), t(this, N).then((ne) => {
-                  ie.textContent = ne;
-                }), it.append(ie);
-              }
-              const kt = t(this, Ft), Pt = t(this, ht);
-              if (Pt != null && Pt.str && (!(kt != null && kt.str) || kt.str === Pt.str))
-                B.XfaLayer.render({
-                  xfaHtml: Pt.html,
-                  intent: "richText",
-                  div: q
-                }), q.lastChild.classList.add("richText", "popupContent");
-              else {
-                const ie = this._formatContents(kt);
-                q.append(ie);
-              }
-              let zt = !!t(this, Dt), Mt = zt ? t(this, Dt) : t(this, J);
-              for (const ie of t(this, Bt))
-                if (!Mt || l.Util.intersect(ie.data.rect, Mt) !== null) {
-                  Mt = ie.data.rect, zt = !0;
-                  break;
-                }
-              const Rt = l.Util.normalizeRect([Mt[0], R[3] - Mt[1] + R[1], Mt[2], R[3] - Mt[3] + R[1]]), Ut = 5, qt = zt ? Mt[2] - Mt[0] + Ut : 0, Kt = Rt[0] + qt, Qt = Rt[1], {
-                style: se
-              } = t(this, ut);
-              se.left = `${100 * (Kt - D) / e}%`, se.top = `${100 * (Qt - j) / f}%`, t(this, ut).append(q);
-            }
-            _formatContents({
-              str: R,
-              dir: e
-            }) {
-              const f = document.createElement("p");
-              f.classList.add("popupContent"), f.dir = e;
-              const D = R.split(/(?:\r\n?|\n)/);
-              for (let j = 0, q = D.length; j < q; ++j) {
-                const it = D[j];
-                f.append(document.createTextNode(it)), j < q - 1 && f.append(document.createElement("br"));
-              }
-              return f;
-            }
-            forceHide() {
-              Z(this, Ct, this.isVisible), t(this, Ct) && (t(this, ut).hidden = !0);
-            }
-            maybeShow() {
-              t(this, Ct) && (Z(this, Ct, !1), t(this, ut).hidden = !1);
-            }
-            get isVisible() {
-              return t(this, ut).hidden === !1;
-            }
-          }
-          N = new WeakMap(), tt = new WeakMap(), Q = new WeakMap(), nt = new WeakMap(), ct = new WeakMap(), yt = new WeakMap(), ut = new WeakMap(), Ft = new WeakMap(), Bt = new WeakMap(), St = new WeakMap(), Dt = new WeakMap(), ft = new WeakMap(), K = new WeakMap(), J = new WeakMap(), ht = new WeakMap(), Et = new WeakMap(), Ct = new WeakMap(), jt = new WeakSet(), Li = function(R) {
-            R.altKey || R.shiftKey || R.ctrlKey || R.metaKey || (R.key === "Enter" || R.key === "Escape" && t(this, ft)) && W(this, Ht, Fe).call(this);
-          }, Ht = new WeakSet(), Fe = function() {
-            Z(this, ft, !t(this, ft)), t(this, ft) ? (W(this, Vt, ai).call(this), t(this, ut).addEventListener("click", t(this, ct)), t(this, ut).addEventListener("keydown", t(this, tt))) : (W(this, $t, oi).call(this), t(this, ut).removeEventListener("click", t(this, ct)), t(this, ut).removeEventListener("keydown", t(this, tt)));
-          }, Vt = new WeakSet(), ai = function() {
-            t(this, K) || this.render(), this.isVisible ? t(this, ft) && t(this, ut).classList.add("focused") : (t(this, ut).hidden = !1, t(this, ut).style.zIndex = parseInt(t(this, ut).style.zIndex) + 1e3);
-          }, $t = new WeakSet(), oi = function() {
-            t(this, ut).classList.remove("focused"), !(t(this, ft) || !this.isVisible) && (t(this, ut).hidden = !0, t(this, ut).style.zIndex = parseInt(t(this, ut).style.zIndex) - 1e3);
-          };
-          class T extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              }), this.textContent = R.data.textContent, this.textPosition = R.data.textPosition, this.annotationEditorType = l.AnnotationEditorType.FREETEXT;
-            }
-            render() {
-              if (this.container.classList.add("freeTextAnnotation"), this.textContent) {
-                const R = document.createElement("div");
-                R.classList.add("annotationTextContent"), R.setAttribute("role", "comment");
-                for (const e of this.textContent) {
-                  const f = document.createElement("span");
-                  f.textContent = e, R.append(f);
-                }
-                this.container.append(R);
-              }
-              return !this.data.popupRef && this.hasPopupData && this._createPopup(), this._editOnDoubleClick(), this.container;
-            }
-          }
-          d.FreeTextAnnotationElement = T;
-          class m extends v {
-            constructor(e) {
-              super(e, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              });
-              L(this, Y, null);
-            }
-            render() {
-              this.container.classList.add("lineAnnotation");
-              const e = this.data, {
-                width: f,
-                height: D
-              } = I(e.rect), j = this.svgFactory.create(f, D, !0), q = Z(this, Y, this.svgFactory.createElement("svg:line"));
-              return q.setAttribute("x1", e.rect[2] - e.lineCoordinates[0]), q.setAttribute("y1", e.rect[3] - e.lineCoordinates[1]), q.setAttribute("x2", e.rect[2] - e.lineCoordinates[2]), q.setAttribute("y2", e.rect[3] - e.lineCoordinates[3]), q.setAttribute("stroke-width", e.borderStyle.width || 1), q.setAttribute("stroke", "transparent"), q.setAttribute("fill", "transparent"), j.append(q), this.container.append(j), !e.popupRef && this.hasPopupData && this._createPopup(), this.container;
-            }
-            getElementsToTriggerPopup() {
-              return t(this, Y);
-            }
-            addHighlightArea() {
-              this.container.classList.add("highlightArea");
-            }
-          }
-          Y = new WeakMap();
-          class U extends v {
-            constructor(e) {
-              super(e, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              });
-              L(this, G, null);
-            }
-            render() {
-              this.container.classList.add("squareAnnotation");
-              const e = this.data, {
-                width: f,
-                height: D
-              } = I(e.rect), j = this.svgFactory.create(f, D, !0), q = e.borderStyle.width, it = Z(this, G, this.svgFactory.createElement("svg:rect"));
-              return it.setAttribute("x", q / 2), it.setAttribute("y", q / 2), it.setAttribute("width", f - q), it.setAttribute("height", D - q), it.setAttribute("stroke-width", q || 1), it.setAttribute("stroke", "transparent"), it.setAttribute("fill", "transparent"), j.append(it), this.container.append(j), !e.popupRef && this.hasPopupData && this._createPopup(), this.container;
-            }
-            getElementsToTriggerPopup() {
-              return t(this, G);
-            }
-            addHighlightArea() {
-              this.container.classList.add("highlightArea");
-            }
-          }
-          G = new WeakMap();
-          class z extends v {
-            constructor(e) {
-              super(e, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              });
-              L(this, bt, null);
-            }
-            render() {
-              this.container.classList.add("circleAnnotation");
-              const e = this.data, {
-                width: f,
-                height: D
-              } = I(e.rect), j = this.svgFactory.create(f, D, !0), q = e.borderStyle.width, it = Z(this, bt, this.svgFactory.createElement("svg:ellipse"));
-              return it.setAttribute("cx", f / 2), it.setAttribute("cy", D / 2), it.setAttribute("rx", f / 2 - q / 2), it.setAttribute("ry", D / 2 - q / 2), it.setAttribute("stroke-width", q || 1), it.setAttribute("stroke", "transparent"), it.setAttribute("fill", "transparent"), j.append(it), this.container.append(j), !e.popupRef && this.hasPopupData && this._createPopup(), this.container;
-            }
-            getElementsToTriggerPopup() {
-              return t(this, bt);
-            }
-            addHighlightArea() {
-              this.container.classList.add("highlightArea");
-            }
-          }
-          bt = new WeakMap();
-          class E extends v {
-            constructor(e) {
-              super(e, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              });
-              L(this, At, null);
-              this.containerClassName = "polylineAnnotation", this.svgElementName = "svg:polyline";
-            }
-            render() {
-              this.container.classList.add(this.containerClassName);
-              const e = this.data, {
-                width: f,
-                height: D
-              } = I(e.rect), j = this.svgFactory.create(f, D, !0);
-              let q = [];
-              for (const mt of e.vertices) {
-                const kt = mt.x - e.rect[0], Pt = e.rect[3] - mt.y;
-                q.push(kt + "," + Pt);
-              }
-              q = q.join(" ");
-              const it = Z(this, At, this.svgFactory.createElement(this.svgElementName));
-              return it.setAttribute("points", q), it.setAttribute("stroke-width", e.borderStyle.width || 1), it.setAttribute("stroke", "transparent"), it.setAttribute("fill", "transparent"), j.append(it), this.container.append(j), !e.popupRef && this.hasPopupData && this._createPopup(), this.container;
-            }
-            getElementsToTriggerPopup() {
-              return t(this, At);
-            }
-            addHighlightArea() {
-              this.container.classList.add("highlightArea");
-            }
-          }
-          At = new WeakMap();
-          class V extends E {
-            constructor(R) {
-              super(R), this.containerClassName = "polygonAnnotation", this.svgElementName = "svg:polygon";
-            }
-          }
-          class st extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              });
-            }
-            render() {
-              return this.container.classList.add("caretAnnotation"), !this.data.popupRef && this.hasPopupData && this._createPopup(), this.container;
-            }
-          }
-          class at extends v {
-            constructor(e) {
-              super(e, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              });
-              L(this, te, []);
-              this.containerClassName = "inkAnnotation", this.svgElementName = "svg:polyline", this.annotationEditorType = l.AnnotationEditorType.INK;
-            }
-            render() {
-              this.container.classList.add(this.containerClassName);
-              const e = this.data, {
-                width: f,
-                height: D
-              } = I(e.rect), j = this.svgFactory.create(f, D, !0);
-              for (const q of e.inkLists) {
-                let it = [];
-                for (const kt of q) {
-                  const Pt = kt.x - e.rect[0], zt = e.rect[3] - kt.y;
-                  it.push(`${Pt},${zt}`);
-                }
-                it = it.join(" ");
-                const mt = this.svgFactory.createElement(this.svgElementName);
-                t(this, te).push(mt), mt.setAttribute("points", it), mt.setAttribute("stroke-width", e.borderStyle.width || 1), mt.setAttribute("stroke", "transparent"), mt.setAttribute("fill", "transparent"), !e.popupRef && this.hasPopupData && this._createPopup(), j.append(mt);
-              }
-              return this.container.append(j), this.container;
-            }
-            getElementsToTriggerPopup() {
-              return t(this, te);
-            }
-            addHighlightArea() {
-              this.container.classList.add("highlightArea");
-            }
-          }
-          te = new WeakMap(), d.InkAnnotationElement = at;
-          class H extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0,
-                ignoreBorder: !0,
-                createQuadrilaterals: !0
-              });
-            }
-            render() {
-              return !this.data.popupRef && this.hasPopupData && this._createPopup(), this.container.classList.add("highlightAnnotation"), this.container;
-            }
-          }
-          class lt extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0,
-                ignoreBorder: !0,
-                createQuadrilaterals: !0
-              });
-            }
-            render() {
-              return !this.data.popupRef && this.hasPopupData && this._createPopup(), this.container.classList.add("underlineAnnotation"), this.container;
-            }
-          }
-          class gt extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0,
-                ignoreBorder: !0,
-                createQuadrilaterals: !0
-              });
-            }
-            render() {
-              return !this.data.popupRef && this.hasPopupData && this._createPopup(), this.container.classList.add("squigglyAnnotation"), this.container;
-            }
-          }
-          class wt extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0,
-                ignoreBorder: !0,
-                createQuadrilaterals: !0
-              });
-            }
-            render() {
-              return !this.data.popupRef && this.hasPopupData && this._createPopup(), this.container.classList.add("strikeoutAnnotation"), this.container;
-            }
-          }
-          class xt extends v {
-            constructor(R) {
-              super(R, {
-                isRenderable: !0,
-                ignoreBorder: !0
-              });
-            }
-            render() {
-              return this.container.classList.add("stampAnnotation"), !this.data.popupRef && this.hasPopupData && this._createPopup(), this.container;
-            }
-          }
-          d.StampAnnotationElement = xt;
-          class S extends v {
-            constructor(e) {
-              var j;
-              super(e, {
-                isRenderable: !0
-              });
-              L(this, $);
-              L(this, Zt, null);
-              const {
-                filename: f,
-                content: D
-              } = this.data.file;
-              this.filename = (0, P.getFilenameFromUrl)(f, !0), this.content = D, (j = this.linkService.eventBus) == null || j.dispatch("fileattachmentannotation", {
-                source: this,
-                filename: f,
-                content: D
-              });
-            }
-            render() {
-              this.container.classList.add("fileAttachmentAnnotation");
-              const {
-                container: e,
-                data: f
-              } = this;
-              let D;
-              f.hasAppearance || f.fillAlpha === 0 ? D = document.createElement("div") : (D = document.createElement("img"), D.src = `${this.imageResourcesPath}annotation-${/paperclip/i.test(f.name) ? "paperclip" : "pushpin"}.svg`, f.fillAlpha && f.fillAlpha < 1 && (D.style = `filter: opacity(${Math.round(f.fillAlpha * 100)}%);`)), D.addEventListener("dblclick", W(this, $, li).bind(this)), Z(this, Zt, D);
-              const {
-                isMac: j
-              } = l.FeatureTest.platform;
-              return e.addEventListener("keydown", (q) => {
-                q.key === "Enter" && (j ? q.metaKey : q.ctrlKey) && W(this, $, li).call(this);
-              }), !f.popupRef && this.hasPopupData ? this._createPopup() : D.classList.add("popupTriggerArea"), e.append(D), e;
-            }
-            getElementsToTriggerPopup() {
-              return t(this, Zt);
-            }
-            addHighlightArea() {
-              this.container.classList.add("highlightArea");
-            }
-          }
-          Zt = new WeakMap(), $ = new WeakSet(), li = function() {
-            var e;
-            (e = this.downloadManager) == null || e.openOrDownloadData(this.container, this.content, this.filename);
-          };
-          class i {
-            constructor({
-              div: R,
-              accessibilityManager: e,
-              annotationCanvasMap: f,
-              l10n: D,
-              page: j,
-              viewport: q
-            }) {
-              L(this, Nt);
-              L(this, _t);
-              L(this, Lt, null);
-              L(this, Tt, null);
-              L(this, Ot, /* @__PURE__ */ new Map());
-              this.div = R, Z(this, Lt, e), Z(this, Tt, f), this.l10n = D, this.page = j, this.viewport = q, this.zIndex = 0, this.l10n || (this.l10n = pt.NullL10n);
-            }
-            async render(R) {
-              const {
-                annotations: e
-              } = R, f = this.div;
-              (0, P.setLayerDimensions)(f, this.viewport);
-              const D = /* @__PURE__ */ new Map(), j = {
-                data: null,
-                layer: f,
-                linkService: R.linkService,
-                downloadManager: R.downloadManager,
-                imageResourcesPath: R.imageResourcesPath || "",
-                renderForms: R.renderForms !== !1,
-                svgFactory: new P.DOMSVGFactory(),
-                annotationStorage: R.annotationStorage || new rt.AnnotationStorage(),
-                enableScripting: R.enableScripting === !0,
-                hasJSActions: R.hasJSActions,
-                fieldObjects: R.fieldObjects,
-                parent: this,
-                elements: null
-              };
-              for (const q of e) {
-                if (q.noHTML)
-                  continue;
-                const it = q.annotationType === l.AnnotationType.POPUP;
-                if (it) {
-                  const Pt = D.get(q.id);
-                  if (!Pt)
-                    continue;
-                  j.elements = Pt;
-                } else {
-                  const {
-                    width: Pt,
-                    height: zt
-                  } = I(q.rect);
-                  if (Pt <= 0 || zt <= 0)
-                    continue;
-                }
-                j.data = q;
-                const mt = x.create(j);
-                if (!mt.isRenderable)
-                  continue;
-                if (!it && q.popupRef) {
-                  const Pt = D.get(q.popupRef);
-                  Pt ? Pt.push(mt) : D.set(q.popupRef, [mt]);
-                }
-                mt.annotationEditorType > 0 && t(this, Ot).set(mt.data.id, mt);
-                const kt = mt.render();
-                q.hidden && (kt.style.visibility = "hidden"), W(this, Nt, Oi).call(this, kt, q.id);
-              }
-              W(this, _t, ci).call(this), await this.l10n.translate(f);
-            }
-            update({
-              viewport: R
-            }) {
-              const e = this.div;
-              this.viewport = R, (0, P.setLayerDimensions)(e, {
-                rotation: R.rotation
-              }), W(this, _t, ci).call(this), e.hidden = !1;
-            }
-            getEditableAnnotations() {
-              return Array.from(t(this, Ot).values());
-            }
-            getEditableAnnotation(R) {
-              return t(this, Ot).get(R);
-            }
-          }
-          Lt = new WeakMap(), Tt = new WeakMap(), Ot = new WeakMap(), Nt = new WeakSet(), Oi = function(R, e) {
-            var D;
-            const f = R.firstChild || R;
-            f.id = `${l.AnnotationPrefix}${e}`, this.div.append(R), (D = t(this, Lt)) == null || D.moveElementInDOM(this.div, R, f, !1);
-          }, _t = new WeakSet(), ci = function() {
-            if (!t(this, Tt))
-              return;
-            const R = this.div;
-            for (const [e, f] of t(this, Tt)) {
-              const D = R.querySelector(`[data-annotation-id="${e}"]`);
-              if (!D)
-                continue;
-              const {
-                firstChild: j
-              } = D;
-              j ? j.nodeName === "CANVAS" ? j.replaceWith(f) : j.before(f) : D.append(f);
-            }
-            t(this, Tt).clear();
-          }, d.AnnotationLayer = i;
-        },
-        /* 30 */
-        /***/
-        (dt, d) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.ColorConverters = void 0;
-          function et(rt) {
-            return Math.floor(Math.max(0, Math.min(1, rt)) * 255).toString(16).padStart(2, "0");
-          }
-          function l(rt) {
-            return Math.max(0, Math.min(255, 255 * rt));
-          }
-          class P {
-            static CMYK_G([X, pt, B, F]) {
-              return ["G", 1 - Math.min(1, 0.3 * X + 0.59 * B + 0.11 * pt + F)];
-            }
-            static G_CMYK([X]) {
-              return ["CMYK", 0, 0, 0, 1 - X];
-            }
-            static G_RGB([X]) {
-              return ["RGB", X, X, X];
-            }
-            static G_rgb([X]) {
-              return X = l(X), [X, X, X];
-            }
-            static G_HTML([X]) {
-              const pt = et(X);
-              return `#${pt}${pt}${pt}`;
-            }
-            static RGB_G([X, pt, B]) {
-              return ["G", 0.3 * X + 0.59 * pt + 0.11 * B];
-            }
-            static RGB_rgb(X) {
-              return X.map(l);
-            }
-            static RGB_HTML(X) {
-              return `#${X.map(et).join("")}`;
-            }
-            static T_HTML() {
-              return "#00000000";
-            }
-            static T_rgb() {
-              return [null];
-            }
-            static CMYK_RGB([X, pt, B, F]) {
-              return ["RGB", 1 - Math.min(1, X + F), 1 - Math.min(1, B + F), 1 - Math.min(1, pt + F)];
-            }
-            static CMYK_rgb([X, pt, B, F]) {
-              return [l(1 - Math.min(1, X + F)), l(1 - Math.min(1, B + F)), l(1 - Math.min(1, pt + F))];
-            }
-            static CMYK_HTML(X) {
-              const pt = this.CMYK_RGB(X).slice(1);
-              return this.RGB_HTML(pt);
-            }
-            static RGB_CMYK([X, pt, B]) {
-              const F = 1 - X, g = 1 - pt, O = 1 - B, I = Math.min(F, g, O);
-              return ["CMYK", F, g, O, I];
-            }
-          }
-          d.ColorConverters = P;
-        },
-        /* 31 */
-        /***/
-        (dt, d) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.NullL10n = void 0, d.getL10nFallback = l;
-          const et = {
-            of_pages: "of {{pagesCount}}",
-            page_of_pages: "({{pageNumber}} of {{pagesCount}})",
-            document_properties_kb: "{{size_kb}} KB ({{size_b}} bytes)",
-            document_properties_mb: "{{size_mb}} MB ({{size_b}} bytes)",
-            document_properties_date_string: "{{date}}, {{time}}",
-            document_properties_page_size_unit_inches: "in",
-            document_properties_page_size_unit_millimeters: "mm",
-            document_properties_page_size_orientation_portrait: "portrait",
-            document_properties_page_size_orientation_landscape: "landscape",
-            document_properties_page_size_name_a3: "A3",
-            document_properties_page_size_name_a4: "A4",
-            document_properties_page_size_name_letter: "Letter",
-            document_properties_page_size_name_legal: "Legal",
-            document_properties_page_size_dimension_string: "{{width}} × {{height}} {{unit}} ({{orientation}})",
-            document_properties_page_size_dimension_name_string: "{{width}} × {{height}} {{unit}} ({{name}}, {{orientation}})",
-            document_properties_linearized_yes: "Yes",
-            document_properties_linearized_no: "No",
-            additional_layers: "Additional Layers",
-            page_landmark: "Page {{page}}",
-            thumb_page_title: "Page {{page}}",
-            thumb_page_canvas: "Thumbnail of Page {{page}}",
-            find_reached_top: "Reached top of document, continued from bottom",
-            find_reached_bottom: "Reached end of document, continued from top",
-            "find_match_count[one]": "{{current}} of {{total}} match",
-            "find_match_count[other]": "{{current}} of {{total}} matches",
-            "find_match_count_limit[one]": "More than {{limit}} match",
-            "find_match_count_limit[other]": "More than {{limit}} matches",
-            find_not_found: "Phrase not found",
-            page_scale_width: "Page Width",
-            page_scale_fit: "Page Fit",
-            page_scale_auto: "Automatic Zoom",
-            page_scale_actual: "Actual Size",
-            page_scale_percent: "{{scale}}%",
-            loading_error: "An error occurred while loading the PDF.",
-            invalid_file_error: "Invalid or corrupted PDF file.",
-            missing_file_error: "Missing PDF file.",
-            unexpected_response_error: "Unexpected server response.",
-            rendering_error: "An error occurred while rendering the page.",
-            annotation_date_string: "{{date}}, {{time}}",
-            printing_not_supported: "Warning: Printing is not fully supported by this browser.",
-            printing_not_ready: "Warning: The PDF is not fully loaded for printing.",
-            web_fonts_disabled: "Web fonts are disabled: unable to use embedded PDF fonts.",
-            free_text2_default_content: "Start typing…",
-            editor_free_text2_aria_label: "Text Editor",
-            editor_ink2_aria_label: "Draw Editor",
-            editor_ink_canvas_aria_label: "User-created image",
-            editor_alt_text_button_label: "Alt text",
-            editor_alt_text_edit_button_label: "Edit alt text",
-            editor_alt_text_decorative_tooltip: "Marked as decorative"
-          };
-          et.print_progress_percent = "{{progress}}%";
-          function l(X, pt) {
-            switch (X) {
-              case "find_match_count":
-                X = `find_match_count[${pt.total === 1 ? "one" : "other"}]`;
-                break;
-              case "find_match_count_limit":
-                X = `find_match_count_limit[${pt.limit === 1 ? "one" : "other"}]`;
-                break;
-            }
-            return et[X] || "";
-          }
-          function P(X, pt) {
-            return pt ? X.replaceAll(/\{\{\s*(\w+)\s*\}\}/g, (B, F) => F in pt ? pt[F] : "{{" + F + "}}") : X;
-          }
-          const rt = {
-            async getLanguage() {
-              return "en-us";
-            },
-            async getDirection() {
-              return "ltr";
-            },
-            async get(X, pt = null, B = l(X, pt)) {
-              return P(B, pt);
-            },
-            async translate(X) {
-            }
-          };
-          d.NullL10n = rt;
-        },
-        /* 32 */
-        /***/
-        (dt, d, et) => {
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.XfaLayer = void 0;
-          var l = et(25);
-          class P {
-            static setupStorage(X, pt, B, F, g) {
-              const O = F.getValue(pt, {
-                value: null
-              });
-              switch (B.name) {
-                case "textarea":
-                  if (O.value !== null && (X.textContent = O.value), g === "print")
-                    break;
-                  X.addEventListener("input", (I) => {
-                    F.setValue(pt, {
-                      value: I.target.value
-                    });
-                  });
-                  break;
-                case "input":
-                  if (B.attributes.type === "radio" || B.attributes.type === "checkbox") {
-                    if (O.value === B.attributes.xfaOn ? X.setAttribute("checked", !0) : O.value === B.attributes.xfaOff && X.removeAttribute("checked"), g === "print")
-                      break;
-                    X.addEventListener("change", (I) => {
-                      F.setValue(pt, {
-                        value: I.target.checked ? I.target.getAttribute("xfaOn") : I.target.getAttribute("xfaOff")
-                      });
-                    });
-                  } else {
-                    if (O.value !== null && X.setAttribute("value", O.value), g === "print")
-                      break;
-                    X.addEventListener("input", (I) => {
-                      F.setValue(pt, {
-                        value: I.target.value
-                      });
-                    });
-                  }
-                  break;
-                case "select":
-                  if (O.value !== null) {
-                    X.setAttribute("value", O.value);
-                    for (const I of B.children)
-                      I.attributes.value === O.value ? I.attributes.selected = !0 : I.attributes.hasOwnProperty("selected") && delete I.attributes.selected;
-                  }
-                  X.addEventListener("input", (I) => {
-                    const x = I.target.options, v = x.selectedIndex === -1 ? "" : x[x.selectedIndex].value;
-                    F.setValue(pt, {
-                      value: v
-                    });
-                  });
-                  break;
-              }
-            }
-            static setAttributes({
-              html: X,
-              element: pt,
-              storage: B = null,
-              intent: F,
-              linkService: g
-            }) {
-              const {
-                attributes: O
-              } = pt, I = X instanceof HTMLAnchorElement;
-              O.type === "radio" && (O.name = `${O.name}-${F}`);
-              for (const [x, v] of Object.entries(O))
-                if (v != null)
-                  switch (x) {
-                    case "class":
-                      v.length && X.setAttribute(x, v.join(" "));
-                      break;
-                    case "dataId":
-                      break;
-                    case "id":
-                      X.setAttribute("data-element-id", v);
-                      break;
-                    case "style":
-                      Object.assign(X.style, v);
-                      break;
-                    case "textContent":
-                      X.textContent = v;
-                      break;
-                    default:
-                      (!I || x !== "href" && x !== "newWindow") && X.setAttribute(x, v);
-                  }
-              I && g.addLinkAttributes(X, O.href, O.newWindow), B && O.dataId && this.setupStorage(X, O.dataId, pt, B);
-            }
-            static render(X) {
-              var A;
-              const pt = X.annotationStorage, B = X.linkService, F = X.xfaHtml, g = X.intent || "display", O = document.createElement(F.name);
-              F.attributes && this.setAttributes({
-                html: O,
-                element: F,
-                intent: g,
-                linkService: B
-              });
-              const I = [[F, -1, O]], x = X.div;
-              if (x.append(O), X.viewport) {
-                const u = `matrix(${X.viewport.transform.join(",")})`;
-                x.style.transform = u;
-              }
-              g !== "richText" && x.setAttribute("class", "xfaLayer xfaFont");
-              const v = [];
-              for (; I.length > 0; ) {
-                const [u, _, w] = I.at(-1);
-                if (_ + 1 === u.children.length) {
-                  I.pop();
-                  continue;
-                }
-                const C = u.children[++I.at(-1)[1]];
-                if (C === null)
-                  continue;
-                const {
-                  name: y
-                } = C;
-                if (y === "#text") {
-                  const c = document.createTextNode(C.value);
-                  v.push(c), w.append(c);
-                  continue;
-                }
-                const a = (A = C == null ? void 0 : C.attributes) != null && A.xmlns ? document.createElementNS(C.attributes.xmlns, y) : document.createElement(y);
-                if (w.append(a), C.attributes && this.setAttributes({
-                  html: a,
-                  element: C,
-                  storage: pt,
-                  intent: g,
-                  linkService: B
-                }), C.children && C.children.length > 0)
-                  I.push([C, -1, a]);
-                else if (C.value) {
-                  const c = document.createTextNode(C.value);
-                  l.XfaText.shouldBuildText(y) && v.push(c), a.append(c);
-                }
-              }
-              for (const u of x.querySelectorAll(".xfaNonInteractive input, .xfaNonInteractive textarea"))
-                u.setAttribute("readOnly", !0);
-              return {
-                textDivs: v
-              };
-            }
-            static update(X) {
-              const pt = `matrix(${X.viewport.transform.join(",")})`;
-              X.div.style.transform = pt, X.div.hidden = !1;
-            }
-          }
-          d.XfaLayer = P;
-        },
-        /* 33 */
-        /***/
-        (dt, d, et) => {
-          var F, g, O, I, x, v, A, u, _, w, C, y, a, c, k, Ni, r, Bi, m, Ui, z, ji, V, hi, at, Hi, lt, di, wt, Wi, S, Gi, n, zi, o, Xi, b, Vi, N, ae, Q, ui, ct, Me, ut, Re, Bt, fe, Dt, fi, K, De, ht, qi, Ct, pi, Gt, $i, Xt, Yi, Wt, gi, ot, Ie, G, pe;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.InkEditor = void 0;
-          var l = et(1), P = et(4), rt = et(29), X = et(6), pt = et(5);
-          const At = class At extends P.AnnotationEditor {
-            constructor($) {
-              super({
-                ...$,
-                name: "inkEditor"
-              });
-              L(this, k);
-              L(this, r);
-              L(this, m);
-              L(this, z);
-              L(this, V);
-              L(this, at);
-              L(this, lt);
-              L(this, wt);
-              L(this, S);
-              L(this, n);
-              L(this, o);
-              L(this, b);
-              L(this, N);
-              L(this, Q);
-              L(this, ct);
-              L(this, ut);
-              L(this, Bt);
-              L(this, Dt);
-              L(this, K);
-              L(this, Xt);
-              L(this, Wt);
-              L(this, ot);
-              L(this, G);
-              L(this, F, 0);
-              L(this, g, 0);
-              L(this, O, this.canvasPointermove.bind(this));
-              L(this, I, this.canvasPointerleave.bind(this));
-              L(this, x, this.canvasPointerup.bind(this));
-              L(this, v, this.canvasPointerdown.bind(this));
-              L(this, A, new Path2D());
-              L(this, u, !1);
-              L(this, _, !1);
-              L(this, w, !1);
-              L(this, C, null);
-              L(this, y, 0);
-              L(this, a, 0);
-              L(this, c, null);
-              this.color = $.color || null, this.thickness = $.thickness || null, this.opacity = $.opacity || null, this.paths = [], this.bezierPath2D = [], this.allRawPaths = [], this.currentPath = [], this.scaleFactor = 1, this.translationX = this.translationY = 0, this.x = 0, this.y = 0, this._willKeepAspectRatio = !0;
-            }
-            static initialize($) {
-              P.AnnotationEditor.initialize($, {
-                strings: ["editor_ink_canvas_aria_label", "editor_ink2_aria_label"]
-              });
-            }
-            static updateDefaultParams($, vt) {
-              switch ($) {
-                case l.AnnotationEditorParamsType.INK_THICKNESS:
-                  At._defaultThickness = vt;
-                  break;
-                case l.AnnotationEditorParamsType.INK_COLOR:
-                  At._defaultColor = vt;
-                  break;
-                case l.AnnotationEditorParamsType.INK_OPACITY:
-                  At._defaultOpacity = vt / 100;
-                  break;
-              }
-            }
-            updateParams($, vt) {
-              switch ($) {
-                case l.AnnotationEditorParamsType.INK_THICKNESS:
-                  W(this, k, Ni).call(this, vt);
-                  break;
-                case l.AnnotationEditorParamsType.INK_COLOR:
-                  W(this, r, Bi).call(this, vt);
-                  break;
-                case l.AnnotationEditorParamsType.INK_OPACITY:
-                  W(this, m, Ui).call(this, vt);
-                  break;
-              }
-            }
-            static get defaultPropertiesToUpdate() {
-              return [[l.AnnotationEditorParamsType.INK_THICKNESS, At._defaultThickness], [l.AnnotationEditorParamsType.INK_COLOR, At._defaultColor || P.AnnotationEditor._defaultLineColor], [l.AnnotationEditorParamsType.INK_OPACITY, Math.round(At._defaultOpacity * 100)]];
-            }
-            get propertiesToUpdate() {
-              return [[l.AnnotationEditorParamsType.INK_THICKNESS, this.thickness || At._defaultThickness], [l.AnnotationEditorParamsType.INK_COLOR, this.color || At._defaultColor || P.AnnotationEditor._defaultLineColor], [l.AnnotationEditorParamsType.INK_OPACITY, Math.round(100 * (this.opacity ?? At._defaultOpacity))]];
-            }
-            rebuild() {
-              this.parent && (super.rebuild(), this.div !== null && (this.canvas || (W(this, ct, Me).call(this), W(this, ut, Re).call(this)), this.isAttachedToDOM || (this.parent.add(this), W(this, Bt, fe).call(this)), W(this, G, pe).call(this)));
-            }
-            remove() {
-              this.canvas !== null && (this.isEmpty() || this.commit(), this.canvas.width = this.canvas.height = 0, this.canvas.remove(), this.canvas = null, t(this, C).disconnect(), Z(this, C, null), super.remove());
-            }
-            setParent($) {
-              !this.parent && $ ? this._uiManager.removeShouldRescale(this) : this.parent && $ === null && this._uiManager.addShouldRescale(this), super.setParent($);
-            }
-            onScaleChanging() {
-              const [$, vt] = this.parentDimensions, Lt = this.width * $, Tt = this.height * vt;
-              this.setDimensions(Lt, Tt);
-            }
-            enableEditMode() {
-              t(this, u) || this.canvas === null || (super.enableEditMode(), this._isDraggable = !1, this.canvas.addEventListener("pointerdown", t(this, v)));
-            }
-            disableEditMode() {
-              !this.isInEditMode() || this.canvas === null || (super.disableEditMode(), this._isDraggable = !this.isEmpty(), this.div.classList.remove("editing"), this.canvas.removeEventListener("pointerdown", t(this, v)));
-            }
-            onceAdded() {
-              this._isDraggable = !this.isEmpty();
-            }
-            isEmpty() {
-              return this.paths.length === 0 || this.paths.length === 1 && this.paths[0].length === 0;
-            }
-            commit() {
-              t(this, u) || (super.commit(), this.isEditing = !1, this.disableEditMode(), this.setInForeground(), Z(this, u, !0), this.div.classList.add("disabled"), W(this, G, pe).call(this, !0), this.makeResizable(), this.parent.addInkEditorIfNeeded(!0), this.moveInDOM(), this.div.focus({
-                preventScroll: !0
-              }));
-            }
-            focusin($) {
-              this._focusEventsAllowed && (super.focusin($), this.enableEditMode());
-            }
-            canvasPointerdown($) {
-              $.button !== 0 || !this.isInEditMode() || t(this, u) || (this.setInForeground(), $.preventDefault(), $.type !== "mouse" && this.div.focus(), W(this, at, Hi).call(this, $.offsetX, $.offsetY));
-            }
-            canvasPointermove($) {
-              $.preventDefault(), W(this, lt, di).call(this, $.offsetX, $.offsetY);
-            }
-            canvasPointerup($) {
-              $.preventDefault(), W(this, Q, ui).call(this, $);
-            }
-            canvasPointerleave($) {
-              W(this, Q, ui).call(this, $);
-            }
-            get isResizable() {
-              return !this.isEmpty() && t(this, u);
-            }
-            render() {
-              if (this.div)
-                return this.div;
-              let $, vt;
-              this.width && ($ = this.x, vt = this.y), super.render(), P.AnnotationEditor._l10nPromise.get("editor_ink2_aria_label").then((Jt) => {
-                var _t;
-                return (_t = this.div) == null ? void 0 : _t.setAttribute("aria-label", Jt);
-              });
-              const [Lt, Tt, Ot, Nt] = W(this, z, ji).call(this);
-              if (this.setAt(Lt, Tt, 0, 0), this.setDims(Ot, Nt), W(this, ct, Me).call(this), this.width) {
-                const [Jt, _t] = this.parentDimensions;
-                this.setAspectRatio(this.width * Jt, this.height * _t), this.setAt($ * Jt, vt * _t, this.width * Jt, this.height * _t), Z(this, w, !0), W(this, Bt, fe).call(this), this.setDims(this.width * Jt, this.height * _t), W(this, N, ae).call(this), this.div.classList.add("disabled");
-              } else
-                this.div.classList.add("editing"), this.enableEditMode();
-              return W(this, ut, Re).call(this), this.div;
-            }
-            setDimensions($, vt) {
-              const Lt = Math.round($), Tt = Math.round(vt);
-              if (t(this, y) === Lt && t(this, a) === Tt)
-                return;
-              Z(this, y, Lt), Z(this, a, Tt), this.canvas.style.visibility = "hidden";
-              const [Ot, Nt] = this.parentDimensions;
-              this.width = $ / Ot, this.height = vt / Nt, this.fixAndSetPosition(), t(this, u) && W(this, Dt, fi).call(this, $, vt), W(this, Bt, fe).call(this), W(this, N, ae).call(this), this.canvas.style.visibility = "visible", this.fixDims();
-            }
-            static deserialize($, vt, Lt) {
-              var j, q, it;
-              if ($ instanceof rt.InkAnnotationElement)
-                return null;
-              const Tt = super.deserialize($, vt, Lt);
-              Tt.thickness = $.thickness, Tt.color = l.Util.makeHexColor(...$.color), Tt.opacity = $.opacity;
-              const [Ot, Nt] = Tt.pageDimensions, Jt = Tt.width * Ot, _t = Tt.height * Nt, Yt = Tt.parentScale, It = $.thickness / 2;
-              Z(Tt, u, !0), Z(Tt, y, Math.round(Jt)), Z(Tt, a, Math.round(_t));
-              const {
-                paths: R,
-                rect: e,
-                rotation: f
-              } = $;
-              for (let {
-                bezier: mt
-              } of R) {
-                mt = W(j = At, Gt, $i).call(j, mt, e, f);
-                const kt = [];
-                Tt.paths.push(kt);
-                let Pt = Yt * (mt[0] - It), zt = Yt * (mt[1] - It);
-                for (let Rt = 2, Ut = mt.length; Rt < Ut; Rt += 6) {
-                  const qt = Yt * (mt[Rt] - It), Kt = Yt * (mt[Rt + 1] - It), Qt = Yt * (mt[Rt + 2] - It), se = Yt * (mt[Rt + 3] - It), ie = Yt * (mt[Rt + 4] - It), ne = Yt * (mt[Rt + 5] - It);
-                  kt.push([[Pt, zt], [qt, Kt], [Qt, se], [ie, ne]]), Pt = ie, zt = ne;
-                }
-                const Mt = W(this, ht, qi).call(this, kt);
-                Tt.bezierPath2D.push(Mt);
-              }
-              const D = W(q = Tt, Wt, gi).call(q);
-              return Z(Tt, g, Math.max(P.AnnotationEditor.MIN_SIZE, D[2] - D[0])), Z(Tt, F, Math.max(P.AnnotationEditor.MIN_SIZE, D[3] - D[1])), W(it = Tt, Dt, fi).call(it, Jt, _t), Tt;
-            }
-            serialize() {
-              if (this.isEmpty())
-                return null;
-              const $ = this.getRect(0, 0), vt = P.AnnotationEditor._colorManager.convert(this.ctx.strokeStyle);
-              return {
-                annotationType: l.AnnotationEditorType.INK,
-                color: vt,
-                thickness: this.thickness,
-                opacity: this.opacity,
-                paths: W(this, Xt, Yi).call(this, this.scaleFactor / this.parentScale, this.translationX, this.translationY, $),
-                pageIndex: this.pageIndex,
-                rect: $,
-                rotation: this.rotation,
-                structTreeParentId: this._structTreeParentId
-              };
-            }
-          };
-          F = new WeakMap(), g = new WeakMap(), O = new WeakMap(), I = new WeakMap(), x = new WeakMap(), v = new WeakMap(), A = new WeakMap(), u = new WeakMap(), _ = new WeakMap(), w = new WeakMap(), C = new WeakMap(), y = new WeakMap(), a = new WeakMap(), c = new WeakMap(), k = new WeakSet(), Ni = function($) {
-            const vt = this.thickness;
-            this.addCommands({
-              cmd: () => {
-                this.thickness = $, W(this, G, pe).call(this);
-              },
-              undo: () => {
-                this.thickness = vt, W(this, G, pe).call(this);
-              },
-              mustExec: !0,
-              type: l.AnnotationEditorParamsType.INK_THICKNESS,
-              overwriteIfSameType: !0,
-              keepUndo: !0
-            });
-          }, r = new WeakSet(), Bi = function($) {
-            const vt = this.color;
-            this.addCommands({
-              cmd: () => {
-                this.color = $, W(this, N, ae).call(this);
-              },
-              undo: () => {
-                this.color = vt, W(this, N, ae).call(this);
-              },
-              mustExec: !0,
-              type: l.AnnotationEditorParamsType.INK_COLOR,
-              overwriteIfSameType: !0,
-              keepUndo: !0
-            });
-          }, m = new WeakSet(), Ui = function($) {
-            $ /= 100;
-            const vt = this.opacity;
-            this.addCommands({
-              cmd: () => {
-                this.opacity = $, W(this, N, ae).call(this);
-              },
-              undo: () => {
-                this.opacity = vt, W(this, N, ae).call(this);
-              },
-              mustExec: !0,
-              type: l.AnnotationEditorParamsType.INK_OPACITY,
-              overwriteIfSameType: !0,
-              keepUndo: !0
-            });
-          }, z = new WeakSet(), ji = function() {
-            const {
-              parentRotation: $,
-              parentDimensions: [vt, Lt]
-            } = this;
-            switch ($) {
-              case 90:
-                return [0, Lt, Lt, vt];
-              case 180:
-                return [vt, Lt, vt, Lt];
-              case 270:
-                return [vt, 0, Lt, vt];
-              default:
-                return [0, 0, vt, Lt];
-            }
-          }, V = new WeakSet(), hi = function() {
-            const {
-              ctx: $,
-              color: vt,
-              opacity: Lt,
-              thickness: Tt,
-              parentScale: Ot,
-              scaleFactor: Nt
-            } = this;
-            $.lineWidth = Tt * Ot / Nt, $.lineCap = "round", $.lineJoin = "round", $.miterLimit = 10, $.strokeStyle = `${vt}${(0, pt.opacityToHex)(Lt)}`;
-          }, at = new WeakSet(), Hi = function($, vt) {
-            this.canvas.addEventListener("contextmenu", X.noContextMenu), this.canvas.addEventListener("pointerleave", t(this, I)), this.canvas.addEventListener("pointermove", t(this, O)), this.canvas.addEventListener("pointerup", t(this, x)), this.canvas.removeEventListener("pointerdown", t(this, v)), this.isEditing = !0, t(this, w) || (Z(this, w, !0), W(this, Bt, fe).call(this), this.thickness || (this.thickness = At._defaultThickness), this.color || (this.color = At._defaultColor || P.AnnotationEditor._defaultLineColor), this.opacity ?? (this.opacity = At._defaultOpacity)), this.currentPath.push([$, vt]), Z(this, _, !1), W(this, V, hi).call(this), Z(this, c, () => {
-              W(this, n, zi).call(this), t(this, c) && window.requestAnimationFrame(t(this, c));
-            }), window.requestAnimationFrame(t(this, c));
-          }, lt = new WeakSet(), di = function($, vt) {
-            const [Lt, Tt] = this.currentPath.at(-1);
-            if (this.currentPath.length > 1 && $ === Lt && vt === Tt)
-              return;
-            const Ot = this.currentPath;
-            let Nt = t(this, A);
-            if (Ot.push([$, vt]), Z(this, _, !0), Ot.length <= 2) {
-              Nt.moveTo(...Ot[0]), Nt.lineTo($, vt);
-              return;
-            }
-            Ot.length === 3 && (Z(this, A, Nt = new Path2D()), Nt.moveTo(...Ot[0])), W(this, o, Xi).call(this, Nt, ...Ot.at(-3), ...Ot.at(-2), $, vt);
-          }, wt = new WeakSet(), Wi = function() {
-            if (this.currentPath.length === 0)
-              return;
-            const $ = this.currentPath.at(-1);
-            t(this, A).lineTo(...$);
-          }, S = new WeakSet(), Gi = function($, vt) {
-            Z(this, c, null), $ = Math.min(Math.max($, 0), this.canvas.width), vt = Math.min(Math.max(vt, 0), this.canvas.height), W(this, lt, di).call(this, $, vt), W(this, wt, Wi).call(this);
-            let Lt;
-            if (this.currentPath.length !== 1)
-              Lt = W(this, b, Vi).call(this);
-            else {
-              const _t = [$, vt];
-              Lt = [[_t, _t.slice(), _t.slice(), _t]];
-            }
-            const Tt = t(this, A), Ot = this.currentPath;
-            this.currentPath = [], Z(this, A, new Path2D());
-            const Nt = () => {
-              this.allRawPaths.push(Ot), this.paths.push(Lt), this.bezierPath2D.push(Tt), this.rebuild();
-            }, Jt = () => {
-              this.allRawPaths.pop(), this.paths.pop(), this.bezierPath2D.pop(), this.paths.length === 0 ? this.remove() : (this.canvas || (W(this, ct, Me).call(this), W(this, ut, Re).call(this)), W(this, G, pe).call(this));
-            };
-            this.addCommands({
-              cmd: Nt,
-              undo: Jt,
-              mustExec: !0
-            });
-          }, n = new WeakSet(), zi = function() {
-            if (!t(this, _))
-              return;
-            Z(this, _, !1);
-            const $ = Math.ceil(this.thickness * this.parentScale), vt = this.currentPath.slice(-3), Lt = vt.map((Nt) => Nt[0]), Tt = vt.map((Nt) => Nt[1]);
-            Math.min(...Lt) - $, Math.max(...Lt) + $, Math.min(...Tt) - $, Math.max(...Tt) + $;
-            const {
-              ctx: Ot
-            } = this;
-            Ot.save(), Ot.clearRect(0, 0, this.canvas.width, this.canvas.height);
-            for (const Nt of this.bezierPath2D)
-              Ot.stroke(Nt);
-            Ot.stroke(t(this, A)), Ot.restore();
-          }, o = new WeakSet(), Xi = function($, vt, Lt, Tt, Ot, Nt, Jt) {
-            const _t = (vt + Tt) / 2, Yt = (Lt + Ot) / 2, It = (Tt + Nt) / 2, R = (Ot + Jt) / 2;
-            $.bezierCurveTo(_t + 2 * (Tt - _t) / 3, Yt + 2 * (Ot - Yt) / 3, It + 2 * (Tt - It) / 3, R + 2 * (Ot - R) / 3, It, R);
-          }, b = new WeakSet(), Vi = function() {
-            const $ = this.currentPath;
-            if ($.length <= 2)
-              return [[$[0], $[0], $.at(-1), $.at(-1)]];
-            const vt = [];
-            let Lt, [Tt, Ot] = $[0];
-            for (Lt = 1; Lt < $.length - 2; Lt++) {
-              const [e, f] = $[Lt], [D, j] = $[Lt + 1], q = (e + D) / 2, it = (f + j) / 2, mt = [Tt + 2 * (e - Tt) / 3, Ot + 2 * (f - Ot) / 3], kt = [q + 2 * (e - q) / 3, it + 2 * (f - it) / 3];
-              vt.push([[Tt, Ot], mt, kt, [q, it]]), [Tt, Ot] = [q, it];
-            }
-            const [Nt, Jt] = $[Lt], [_t, Yt] = $[Lt + 1], It = [Tt + 2 * (Nt - Tt) / 3, Ot + 2 * (Jt - Ot) / 3], R = [_t + 2 * (Nt - _t) / 3, Yt + 2 * (Jt - Yt) / 3];
-            return vt.push([[Tt, Ot], It, R, [_t, Yt]]), vt;
-          }, N = new WeakSet(), ae = function() {
-            if (this.isEmpty()) {
-              W(this, K, De).call(this);
-              return;
-            }
-            W(this, V, hi).call(this);
-            const {
-              canvas: $,
-              ctx: vt
-            } = this;
-            vt.setTransform(1, 0, 0, 1, 0, 0), vt.clearRect(0, 0, $.width, $.height), W(this, K, De).call(this);
-            for (const Lt of this.bezierPath2D)
-              vt.stroke(Lt);
-          }, Q = new WeakSet(), ui = function($) {
-            this.canvas.removeEventListener("pointerleave", t(this, I)), this.canvas.removeEventListener("pointermove", t(this, O)), this.canvas.removeEventListener("pointerup", t(this, x)), this.canvas.addEventListener("pointerdown", t(this, v)), setTimeout(() => {
-              this.canvas.removeEventListener("contextmenu", X.noContextMenu);
-            }, 10), W(this, S, Gi).call(this, $.offsetX, $.offsetY), this.addToAnnotationStorage(), this.setInBackground();
-          }, ct = new WeakSet(), Me = function() {
-            this.canvas = document.createElement("canvas"), this.canvas.width = this.canvas.height = 0, this.canvas.className = "inkEditorCanvas", P.AnnotationEditor._l10nPromise.get("editor_ink_canvas_aria_label").then(($) => {
-              var vt;
-              return (vt = this.canvas) == null ? void 0 : vt.setAttribute("aria-label", $);
-            }), this.div.append(this.canvas), this.ctx = this.canvas.getContext("2d");
-          }, ut = new WeakSet(), Re = function() {
-            Z(this, C, new ResizeObserver(($) => {
-              const vt = $[0].contentRect;
-              vt.width && vt.height && this.setDimensions(vt.width, vt.height);
-            })), t(this, C).observe(this.div);
-          }, Bt = new WeakSet(), fe = function() {
-            if (!t(this, w))
-              return;
-            const [$, vt] = this.parentDimensions;
-            this.canvas.width = Math.ceil(this.width * $), this.canvas.height = Math.ceil(this.height * vt), W(this, K, De).call(this);
-          }, Dt = new WeakSet(), fi = function($, vt) {
-            const Lt = W(this, ot, Ie).call(this), Tt = ($ - Lt) / t(this, g), Ot = (vt - Lt) / t(this, F);
-            this.scaleFactor = Math.min(Tt, Ot);
-          }, K = new WeakSet(), De = function() {
-            const $ = W(this, ot, Ie).call(this) / 2;
-            this.ctx.setTransform(this.scaleFactor, 0, 0, this.scaleFactor, this.translationX * this.scaleFactor + $, this.translationY * this.scaleFactor + $);
-          }, ht = new WeakSet(), qi = function($) {
-            const vt = new Path2D();
-            for (let Lt = 0, Tt = $.length; Lt < Tt; Lt++) {
-              const [Ot, Nt, Jt, _t] = $[Lt];
-              Lt === 0 && vt.moveTo(...Ot), vt.bezierCurveTo(Nt[0], Nt[1], Jt[0], Jt[1], _t[0], _t[1]);
-            }
-            return vt;
-          }, Ct = new WeakSet(), pi = function($, vt, Lt) {
-            const [Tt, Ot, Nt, Jt] = vt;
-            switch (Lt) {
-              case 0:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2)
-                  $[_t] += Tt, $[_t + 1] = Jt - $[_t + 1];
-                break;
-              case 90:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2) {
-                  const It = $[_t];
-                  $[_t] = $[_t + 1] + Tt, $[_t + 1] = It + Ot;
-                }
-                break;
-              case 180:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2)
-                  $[_t] = Nt - $[_t], $[_t + 1] += Ot;
-                break;
-              case 270:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2) {
-                  const It = $[_t];
-                  $[_t] = Nt - $[_t + 1], $[_t + 1] = Jt - It;
-                }
-                break;
-              default:
-                throw new Error("Invalid rotation");
-            }
-            return $;
-          }, Gt = new WeakSet(), $i = function($, vt, Lt) {
-            const [Tt, Ot, Nt, Jt] = vt;
-            switch (Lt) {
-              case 0:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2)
-                  $[_t] -= Tt, $[_t + 1] = Jt - $[_t + 1];
-                break;
-              case 90:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2) {
-                  const It = $[_t];
-                  $[_t] = $[_t + 1] - Ot, $[_t + 1] = It - Tt;
-                }
-                break;
-              case 180:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2)
-                  $[_t] = Nt - $[_t], $[_t + 1] -= Ot;
-                break;
-              case 270:
-                for (let _t = 0, Yt = $.length; _t < Yt; _t += 2) {
-                  const It = $[_t];
-                  $[_t] = Jt - $[_t + 1], $[_t + 1] = Nt - It;
-                }
-                break;
-              default:
-                throw new Error("Invalid rotation");
-            }
-            return $;
-          }, Xt = new WeakSet(), Yi = function($, vt, Lt, Tt) {
-            var Yt, It;
-            const Ot = [], Nt = this.thickness / 2, Jt = $ * vt + Nt, _t = $ * Lt + Nt;
-            for (const R of this.paths) {
-              const e = [], f = [];
-              for (let D = 0, j = R.length; D < j; D++) {
-                const [q, it, mt, kt] = R[D], Pt = $ * q[0] + Jt, zt = $ * q[1] + _t, Mt = $ * it[0] + Jt, Rt = $ * it[1] + _t, Ut = $ * mt[0] + Jt, qt = $ * mt[1] + _t, Kt = $ * kt[0] + Jt, Qt = $ * kt[1] + _t;
-                D === 0 && (e.push(Pt, zt), f.push(Pt, zt)), e.push(Mt, Rt, Ut, qt, Kt, Qt), f.push(Mt, Rt), D === j - 1 && f.push(Kt, Qt);
-              }
-              Ot.push({
-                bezier: W(Yt = At, Ct, pi).call(Yt, e, Tt, this.rotation),
-                points: W(It = At, Ct, pi).call(It, f, Tt, this.rotation)
-              });
-            }
-            return Ot;
-          }, Wt = new WeakSet(), gi = function() {
-            let $ = 1 / 0, vt = -1 / 0, Lt = 1 / 0, Tt = -1 / 0;
-            for (const Ot of this.paths)
-              for (const [Nt, Jt, _t, Yt] of Ot) {
-                const It = l.Util.bezierBoundingBox(...Nt, ...Jt, ..._t, ...Yt);
-                $ = Math.min($, It[0]), Lt = Math.min(Lt, It[1]), vt = Math.max(vt, It[2]), Tt = Math.max(Tt, It[3]);
-              }
-            return [$, Lt, vt, Tt];
-          }, ot = new WeakSet(), Ie = function() {
-            return t(this, u) ? Math.ceil(this.thickness * this.parentScale) : 0;
-          }, G = new WeakSet(), pe = function($ = !1) {
-            if (this.isEmpty())
-              return;
-            if (!t(this, u)) {
-              W(this, N, ae).call(this);
-              return;
-            }
-            const vt = W(this, Wt, gi).call(this), Lt = W(this, ot, Ie).call(this);
-            Z(this, g, Math.max(P.AnnotationEditor.MIN_SIZE, vt[2] - vt[0])), Z(this, F, Math.max(P.AnnotationEditor.MIN_SIZE, vt[3] - vt[1]));
-            const Tt = Math.ceil(Lt + t(this, g) * this.scaleFactor), Ot = Math.ceil(Lt + t(this, F) * this.scaleFactor), [Nt, Jt] = this.parentDimensions;
-            this.width = Tt / Nt, this.height = Ot / Jt, this.setAspectRatio(Tt, Ot);
-            const _t = this.translationX, Yt = this.translationY;
-            this.translationX = -vt[0], this.translationY = -vt[1], W(this, Bt, fe).call(this), W(this, N, ae).call(this), Z(this, y, Tt), Z(this, a, Ot), this.setDims(Tt, Ot);
-            const It = $ ? Lt / this.scaleFactor / 2 : 0;
-            this.translate(_t - this.translationX - It, Yt - this.translationY - It);
-          }, L(At, ht), L(At, Ct), L(At, Gt), ee(At, "_defaultColor", null), ee(At, "_defaultOpacity", 1), ee(At, "_defaultThickness", 1), ee(At, "_type", "ink");
-          let B = At;
-          d.InkEditor = B;
-        },
-        /* 34 */
-        /***/
-        (dt, d, et) => {
-          var B, F, g, O, I, x, v, A, u, _, w, ve, y, Se, c, Le, p, mi, T, Ki, U, Ji, E, bi, st, Oe, H, Qi;
-          Object.defineProperty(d, "__esModule", {
-            value: !0
-          }), d.StampEditor = void 0;
-          var l = et(1), P = et(4), rt = et(6), X = et(29);
-          const gt = class gt extends P.AnnotationEditor {
-            constructor(S) {
-              super({
-                ...S,
-                name: "stampEditor"
-              });
-              L(this, w);
-              L(this, y);
-              L(this, c);
-              L(this, p);
-              L(this, T);
-              L(this, U);
-              L(this, E);
-              L(this, st);
-              L(this, H);
-              L(this, B, null);
-              L(this, F, null);
-              L(this, g, null);
-              L(this, O, null);
-              L(this, I, null);
-              L(this, x, null);
-              L(this, v, null);
-              L(this, A, null);
-              L(this, u, !1);
-              L(this, _, !1);
-              Z(this, O, S.bitmapUrl), Z(this, I, S.bitmapFile);
-            }
-            static initialize(S) {
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-            }
-            static isHandlingMimeForPasting(S) {
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-            }
-            static paste(S, i) {
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-                bitmapFile: S.getAsFile()
-              });
-            }
-            remove() {
-              var S, i;
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-            }
-            rebuild() {
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-                t(this, F) && W(this, c, Le).call(this);
-                return;
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-              super.rebuild(), this.div !== null && (t(this, F) && W(this, c, Le).call(this), this.isAttachedToDOM || this.parent.add(this));
-            }
-            onceAdded() {
-              this._isDraggable = !0, this.div.focus();
-            }
-            isEmpty() {
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-            get isResizable() {
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-            render() {
-              if (this.div)
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-              if (this.width && (S = this.x, i = this.y), super.render(), this.div.hidden = !0, t(this, B) ? W(this, p, mi).call(this) : W(this, c, Le).call(this), this.width) {
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-                this.setAt(S * n, i * s, this.width * n, this.height * s);
-              }
-              return this.div;
-            }
-            static deserialize(S, i, n) {
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-                rect: o,
-                bitmapUrl: h,
-                bitmapId: b,
-                isSvg: M,
-                accessibilityData: N
-              } = S;
-              b && n.imageManager.isValidId(b) ? Z(s, F, b) : Z(s, O, h), Z(s, u, M);
-              const [tt, Q] = s.pageDimensions;
-              return s.width = (o[2] - o[0]) / tt, s.height = (o[3] - o[1]) / Q, N && (s.altTextData = N), s;
-            }
-            serialize(S = !1, i = null) {
-              if (this.isEmpty())
-                return null;
-              const n = {
-                annotationType: l.AnnotationEditorType.STAMP,
-                bitmapId: t(this, F),
-                pageIndex: this.pageIndex,
-                rect: this.getRect(0, 0),
-                rotation: this.rotation,
-                isSvg: t(this, u),
-                structTreeParentId: this._structTreeParentId
-              };
-              if (S)
-                return n.bitmapUrl = W(this, st, Oe).call(this, !0), n.accessibilityData = this.altTextData, n;
-              const {
-                decorative: s,
-                altText: o
-              } = this.altTextData;
-              if (!s && o && (n.accessibilityData = {
-                type: "Figure",
-                alt: o
-              }), i === null)
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-              i.stamps || (i.stamps = /* @__PURE__ */ new Map());
-              const h = t(this, u) ? (n.rect[2] - n.rect[0]) * (n.rect[3] - n.rect[1]) : null;
-              if (!i.stamps.has(t(this, F)))
-                i.stamps.set(t(this, F), {
-                  area: h,
-                  serialized: n
-                }), n.bitmap = W(this, st, Oe).call(this, !1);
-              else if (t(this, u)) {
-                const b = i.stamps.get(t(this, F));
-                h > b.area && (b.area = h, b.serialized.bitmap.close(), b.serialized.bitmap = W(this, st, Oe).call(this, !1));
-              }
-              return n;
-            }
-          };
-          B = new WeakMap(), F = new WeakMap(), g = new WeakMap(), O = new WeakMap(), I = new WeakMap(), x = new WeakMap(), v = new WeakMap(), A = new WeakMap(), u = new WeakMap(), _ = new WeakMap(), w = new WeakSet(), ve = function(S, i = !1) {
-            if (!S) {
-              this.remove();
-              return;
-            }
-            Z(this, B, S.bitmap), i || (Z(this, F, S.id), Z(this, u, S.isSvg)), W(this, p, mi).call(this);
-          }, y = new WeakSet(), Se = function() {
-            Z(this, g, null), this._uiManager.enableWaiting(!1), t(this, x) && this.div.focus();
-          }, c = new WeakSet(), Le = function() {
-            if (t(this, F)) {
-              this._uiManager.enableWaiting(!0), this._uiManager.imageManager.getFromId(t(this, F)).then((i) => W(this, w, ve).call(this, i, !0)).finally(() => W(this, y, Se).call(this));
-              return;
-            }
-            if (t(this, O)) {
-              const i = t(this, O);
-              Z(this, O, null), this._uiManager.enableWaiting(!0), Z(this, g, this._uiManager.imageManager.getFromUrl(i).then((n) => W(this, w, ve).call(this, n)).finally(() => W(this, y, Se).call(this)));
-              return;
-            }
-            if (t(this, I)) {
-              const i = t(this, I);
-              Z(this, I, null), this._uiManager.enableWaiting(!0), Z(this, g, this._uiManager.imageManager.getFromFile(i).then((n) => W(this, w, ve).call(this, n)).finally(() => W(this, y, Se).call(this)));
-              return;
-            }
-            const S = document.createElement("input");
-            S.type = "file", S.accept = gt.supportedTypesStr, Z(this, g, new Promise((i) => {
-              S.addEventListener("change", async () => {
-                if (!S.files || S.files.length === 0)
-                  this.remove();
-                else {
-                  this._uiManager.enableWaiting(!0);
-                  const n = await this._uiManager.imageManager.getFromFile(S.files[0]);
-                  W(this, w, ve).call(this, n);
-                }
-                i();
-              }), S.addEventListener("cancel", () => {
-                this.remove(), i();
-              });
-            }).finally(() => W(this, y, Se).call(this))), S.click();
-          }, p = new WeakSet(), mi = function() {
-            const {
-              div: S
-            } = this;
-            let {
-              width: i,
-              height: n
-            } = t(this, B);
-            const [s, o] = this.pageDimensions, h = 0.75;
-            if (this.width)
-              i = this.width * s, n = this.height * o;
-            else if (i > h * s || n > h * o) {
-              const tt = Math.min(h * s / i, h * o / n);
-              i *= tt, n *= tt;
-            }
-            const [b, M] = this.parentDimensions;
-            this.setDims(i * b / s, n * M / o), this._uiManager.enableWaiting(!1);
-            const N = Z(this, x, document.createElement("canvas"));
-            S.append(N), S.hidden = !1, W(this, E, bi).call(this, i, n), W(this, H, Qi).call(this), t(this, _) || (this.parent.addUndoableEditor(this), Z(this, _, !0)), this._uiManager._eventBus.dispatch("reporttelemetry", {
-              source: this,
-              details: {
-                type: "editing",
-                subtype: this.editorType,
-                data: {
-                  action: "inserted_image"
-                }
-              }
-            }), this.addAltTextButton();
-          }, T = new WeakSet(), Ki = function(S, i) {
-            var h;
-            const [n, s] = this.parentDimensions;
-            this.width = S / n, this.height = i / s, this.setDims(S, i), (h = this._initialOptions) != null && h.isCentered ? this.center() : this.fixAndSetPosition(), this._initialOptions = null, t(this, A) !== null && clearTimeout(t(this, A)), Z(this, A, setTimeout(() => {
-              Z(this, A, null), W(this, E, bi).call(this, S, i);
-            }, 200));
-          }, U = new WeakSet(), Ji = function(S, i) {
-            const {
-              width: n,
-              height: s
-            } = t(this, B);
-            let o = n, h = s, b = t(this, B);
-            for (; o > 2 * S || h > 2 * i; ) {
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-              o > 2 * S && (o = o >= 16384 ? Math.floor(o / 2) - 1 : Math.ceil(o / 2)), h > 2 * i && (h = h >= 16384 ? Math.floor(h / 2) - 1 : Math.ceil(h / 2));
-              const tt = new OffscreenCanvas(o, h);
-              tt.getContext("2d").drawImage(b, 0, 0, M, N, 0, 0, o, h), b = tt.transferToImageBitmap();
-            }
-            return b;
-          }, E = new WeakSet(), bi = function(S, i) {
-            S = Math.ceil(S), i = Math.ceil(i);
-            const n = t(this, x);
-            if (!n || n.width === S && n.height === i)
-              return;
-            n.width = S, n.height = i;
-            const s = t(this, u) ? t(this, B) : W(this, U, Ji).call(this, S, i), o = n.getContext("2d");
-            o.filter = this._uiManager.hcmFilter, o.drawImage(s, 0, 0, s.width, s.height, 0, 0, S, i);
-          }, st = new WeakSet(), Oe = function(S) {
-            if (S) {
-              if (t(this, u)) {
-                const s = this._uiManager.imageManager.getSvgUrl(t(this, F));
-                if (s)
-                  return s;
-              }
-              const i = document.createElement("canvas");
-              return {
-                width: i.width,
-                height: i.height
-              } = t(this, B), i.getContext("2d").drawImage(t(this, B), 0, 0), i.toDataURL();
-            }
-            if (t(this, u)) {
-              const [i, n] = this.pageDimensions, s = Math.round(this.width * i * rt.PixelsPerInch.PDF_TO_CSS_UNITS), o = Math.round(this.height * n * rt.PixelsPerInch.PDF_TO_CSS_UNITS), h = new OffscreenCanvas(s, o);
-              return h.getContext("2d").drawImage(t(this, B), 0, 0, t(this, B).width, t(this, B).height, 0, 0, s, o), h.transferToImageBitmap();
-            }
-            return structuredClone(t(this, B));
-          }, H = new WeakSet(), Qi = function() {
-            Z(this, v, new ResizeObserver((S) => {
-              const i = S[0].contentRect;
-              i.width && i.height && W(this, T, Ki).call(this, i.width, i.height);
-            })), t(this, v).observe(this.div);
-          }, ee(gt, "_type", "stamp");
-          let pt = gt;
-          d.StampEditor = pt;
-        }
-        /******/
-      ], __webpack_module_cache__ = {};
-      function __w_pdfjs_require__(dt) {
-        var d = __webpack_module_cache__[dt];
-        if (d !== void 0)
-          return d.exports;
-        var et = __webpack_module_cache__[dt] = {
-          /******/
-          // no module.id needed
-          /******/
-          // no module.loaded needed
-          /******/
-          exports: {}
-          /******/
-        };
-        return __webpack_modules__[dt](et, et.exports, __w_pdfjs_require__), et.exports;
-      }
-      var __webpack_exports__ = {};
-      return (() => {
-        var dt = __webpack_exports__;
-        Object.defineProperty(dt, "__esModule", {
-          value: !0
-        }), Object.defineProperty(dt, "AbortException", {
-          enumerable: !0,
-          get: function() {
-            return d.AbortException;
-          }
-        }), Object.defineProperty(dt, "AnnotationEditorLayer", {
-          enumerable: !0,
-          get: function() {
-            return rt.AnnotationEditorLayer;
-          }
-        }), Object.defineProperty(dt, "AnnotationEditorParamsType", {
-          enumerable: !0,
-          get: function() {
-            return d.AnnotationEditorParamsType;
-          }
-        }), Object.defineProperty(dt, "AnnotationEditorType", {
-          enumerable: !0,
-          get: function() {
-            return d.AnnotationEditorType;
-          }
-        }), Object.defineProperty(dt, "AnnotationEditorUIManager", {
-          enumerable: !0,
-          get: function() {
-            return X.AnnotationEditorUIManager;
-          }
-        }), Object.defineProperty(dt, "AnnotationLayer", {
-          enumerable: !0,
-          get: function() {
-            return pt.AnnotationLayer;
-          }
-        }), Object.defineProperty(dt, "AnnotationMode", {
-          enumerable: !0,
-          get: function() {
-            return d.AnnotationMode;
-          }
-        }), Object.defineProperty(dt, "CMapCompressionType", {
-          enumerable: !0,
-          get: function() {
-            return d.CMapCompressionType;
-          }
-        }), Object.defineProperty(dt, "DOMSVGFactory", {
-          enumerable: !0,
-          get: function() {
-            return l.DOMSVGFactory;
-          }
-        }), Object.defineProperty(dt, "FeatureTest", {
-          enumerable: !0,
-          get: function() {
-            return d.FeatureTest;
-          }
-        }), Object.defineProperty(dt, "GlobalWorkerOptions", {
-          enumerable: !0,
-          get: function() {
-            return B.GlobalWorkerOptions;
-          }
-        }), Object.defineProperty(dt, "ImageKind", {
-          enumerable: !0,
-          get: function() {
-            return d.ImageKind;
-          }
-        }), Object.defineProperty(dt, "InvalidPDFException", {
-          enumerable: !0,
-          get: function() {
-            return d.InvalidPDFException;
-          }
-        }), Object.defineProperty(dt, "MissingPDFException", {
-          enumerable: !0,
-          get: function() {
-            return d.MissingPDFException;
-          }
-        }), Object.defineProperty(dt, "OPS", {
-          enumerable: !0,
-          get: function() {
-            return d.OPS;
-          }
-        }), Object.defineProperty(dt, "PDFDataRangeTransport", {
-          enumerable: !0,
-          get: function() {
-            return et.PDFDataRangeTransport;
-          }
-        }), Object.defineProperty(dt, "PDFDateString", {
-          enumerable: !0,
-          get: function() {
-            return l.PDFDateString;
-          }
-        }), Object.defineProperty(dt, "PDFWorker", {
-          enumerable: !0,
-          get: function() {
-            return et.PDFWorker;
-          }
-        }), Object.defineProperty(dt, "PasswordResponses", {
-          enumerable: !0,
-          get: function() {
-            return d.PasswordResponses;
-          }
-        }), Object.defineProperty(dt, "PermissionFlag", {
-          enumerable: !0,
-          get: function() {
-            return d.PermissionFlag;
-          }
-        }), Object.defineProperty(dt, "PixelsPerInch", {
-          enumerable: !0,
-          get: function() {
-            return l.PixelsPerInch;
-          }
-        }), Object.defineProperty(dt, "PromiseCapability", {
-          enumerable: !0,
-          get: function() {
-            return d.PromiseCapability;
-          }
-        }), Object.defineProperty(dt, "RenderingCancelledException", {
-          enumerable: !0,
-          get: function() {
-            return l.RenderingCancelledException;
-          }
-        }), Object.defineProperty(dt, "SVGGraphics", {
-          enumerable: !0,
-          get: function() {
-            return et.SVGGraphics;
-          }
-        }), Object.defineProperty(dt, "UnexpectedResponseException", {
-          enumerable: !0,
-          get: function() {
-            return d.UnexpectedResponseException;
-          }
-        }), Object.defineProperty(dt, "Util", {
-          enumerable: !0,
-          get: function() {
-            return d.Util;
-          }
-        }), Object.defineProperty(dt, "VerbosityLevel", {
-          enumerable: !0,
-          get: function() {
-            return d.VerbosityLevel;
-          }
-        }), Object.defineProperty(dt, "XfaLayer", {
-          enumerable: !0,
-          get: function() {
-            return F.XfaLayer;
-          }
-        }), Object.defineProperty(dt, "build", {
-          enumerable: !0,
-          get: function() {
-            return et.build;
-          }
-        }), Object.defineProperty(dt, "createValidAbsoluteUrl", {
-          enumerable: !0,
-          get: function() {
-            return d.createValidAbsoluteUrl;
-          }
-        }), Object.defineProperty(dt, "getDocument", {
-          enumerable: !0,
-          get: function() {
-            return et.getDocument;
-          }
-        }), Object.defineProperty(dt, "getFilenameFromUrl", {
-          enumerable: !0,
-          get: function() {
-            return l.getFilenameFromUrl;
-          }
-        }), Object.defineProperty(dt, "getPdfFilenameFromUrl", {
-          enumerable: !0,
-          get: function() {
-            return l.getPdfFilenameFromUrl;
-          }
-        }), Object.defineProperty(dt, "getXfaPageViewport", {
-          enumerable: !0,
-          get: function() {
-            return l.getXfaPageViewport;
-          }
-        }), Object.defineProperty(dt, "isDataScheme", {
-          enumerable: !0,
-          get: function() {
-            return l.isDataScheme;
-          }
-        }), Object.defineProperty(dt, "isPdfFile", {
-          enumerable: !0,
-          get: function() {
-            return l.isPdfFile;
-          }
-        }), Object.defineProperty(dt, "loadScript", {
-          enumerable: !0,
-          get: function() {
-            return l.loadScript;
-          }
-        }), Object.defineProperty(dt, "noContextMenu", {
-          enumerable: !0,
-          get: function() {
-            return l.noContextMenu;
-          }
-        }), Object.defineProperty(dt, "normalizeUnicode", {
-          enumerable: !0,
-          get: function() {
-            return d.normalizeUnicode;
-          }
-        }), Object.defineProperty(dt, "renderTextLayer", {
-          enumerable: !0,
-          get: function() {
-            return P.renderTextLayer;
-          }
-        }), Object.defineProperty(dt, "setLayerDimensions", {
-          enumerable: !0,
-          get: function() {
-            return l.setLayerDimensions;
-          }
-        }), Object.defineProperty(dt, "shadow", {
-          enumerable: !0,
-          get: function() {
-            return d.shadow;
-          }
-        }), Object.defineProperty(dt, "updateTextLayer", {
-          enumerable: !0,
-          get: function() {
-            return P.updateTextLayer;
-          }
-        }), Object.defineProperty(dt, "version", {
-          enumerable: !0,
-          get: function() {
-            return et.version;
-          }
-        });
-        var d = __w_pdfjs_require__(1), et = __w_pdfjs_require__(2), l = __w_pdfjs_require__(6), P = __w_pdfjs_require__(26), rt = __w_pdfjs_require__(27), X = __w_pdfjs_require__(5), pt = __w_pdfjs_require__(29), B = __w_pdfjs_require__(14), F = __w_pdfjs_require__(32);
-      })(), __webpack_exports__;
-    })()
-  ));
-})(pdf);
-var pdfExports = pdf.exports;
-const pdfjsLib = /* @__PURE__ */ getDefaultExportFromCjs(pdfExports), Example_svelte_svelte_type_style_lang = "", {
-  SvelteComponent,
-  append,
-  attr,
-  binding_callbacks,
-  detach,
-  element,
-  init,
-  insert,
-  noop,
-  safe_not_equal,
-  set_style,
-  toggle_class
-} = window.__gradio__svelte__internal;
-function create_fragment(dt) {
-  let d, et;
-  return {
-    c() {
-      d = element("div"), et = element("canvas"), set_style(d, "justify-content", "center"), set_style(d, "align-items", "center"), set_style(d, "display", "flex"), set_style(d, "flex-direction", "column"), attr(d, "class", "svelte-1gecy8w"), toggle_class(
-        d,
-        "table",
-        /*type*/
-        dt[0] === "table"
-      ), toggle_class(
-        d,
-        "gallery",
-        /*type*/
-        dt[0] === "gallery"
-      ), toggle_class(
-        d,
-        "selected",
-        /*selected*/
-        dt[1]
-      );
-    },
-    m(l, P) {
-      insert(l, d, P), append(d, et), dt[5](et);
-    },
-    p(l, [P]) {
-      P & /*type*/
-      1 && toggle_class(
-        d,
-        "table",
-        /*type*/
-        l[0] === "table"
-      ), P & /*type*/
-      1 && toggle_class(
-        d,
-        "gallery",
-        /*type*/
-        l[0] === "gallery"
-      ), P & /*selected*/
-      2 && toggle_class(
-        d,
-        "selected",
-        /*selected*/
-        l[1]
-      );
-    },
-    i: noop,
-    o: noop,
-    d(l) {
-      l && detach(d), dt[5](null);
-    }
-  };
-}
-function instance(dt, d, et) {
-  let { value: l } = d, { samples_dir: P } = d, { type: rt } = d, { selected: X = !1 } = d;
-  pdfjsLib.GlobalWorkerOptions.workerSrc = "https://cdn.bootcss.com/pdf.js/3.11.174/pdf.worker.js";
-  let pt, B;
-  async function F(I) {
-    pt = await pdfjsLib.getDocument(I).promise, g();
-  }
-  function g() {
-    pt.getPage(1).then((I) => {
-      const x = B.getContext("2d");
-      x.clearRect(0, 0, B.width, B.height);
-      const v = I.getViewport({ scale: 0.2 }), A = { canvasContext: x, viewport: v };
-      et(2, B.width = v.width, B), et(2, B.height = v.height, B), I.render(A);
-    });
-  }
-  function O(I) {
-    binding_callbacks[I ? "unshift" : "push"](() => {
-      B = I, et(2, B);
-    });
-  }
-  return dt.$$set = (I) => {
-    "value" in I && et(3, l = I.value), "samples_dir" in I && et(4, P = I.samples_dir), "type" in I && et(0, rt = I.type), "selected" in I && et(1, X = I.selected);
-  }, dt.$$.update = () => {
-    dt.$$.dirty & /*samples_dir, value*/
-    24 && F(P + l);
-  }, [rt, X, B, l, P, O];
-}
-class Example extends SvelteComponent {
-  constructor(d) {
-    super(), init(this, d, instance, create_fragment, safe_not_equal, {
-      value: 3,
-      samples_dir: 4,
-      type: 0,
-      selected: 1
-    });
-  }
-}
-export {
-  Example as default
-};
diff --git a/spaces/geekyrakshit/enhance-me/test.py b/spaces/geekyrakshit/enhance-me/test.py
deleted file mode 100644
index 0ec36e500901a098606425c0ae12945f5066ce80..0000000000000000000000000000000000000000
--- a/spaces/geekyrakshit/enhance-me/test.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from enhance_me.commons import download_unpaired_low_light_dataset
-
-
-download_unpaired_low_light_dataset()
diff --git a/spaces/gilmar/health_insurance_app/app.py b/spaces/gilmar/health_insurance_app/app.py
deleted file mode 100644
index 4c491c6aa07cb150609e42e6731d2df6910b1149..0000000000000000000000000000000000000000
--- a/spaces/gilmar/health_insurance_app/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import joblib
-import gradio as gr
-import pandas as pd
-from models import HealthInsurance
-
-def load_data():
-    global _model 
-    global _column_transformer
-    global _bins_annual_premium_type
-
-    _model = joblib.load(filename = 'parameters/random_forrest.gz')
-    _column_transformer = joblib.load(filename = 'parameters/column_transformer.joblib')
-    _bins_annual_premium_type = joblib.load(filename = 'parameters/bins_annual_premium_type.joblib')
-
-def predict(df):
-
-    health_insurance = HealthInsurance(_model,_column_transformer,
-                                            _bins_annual_premium_type)
-    
-    df_predicted = health_insurance.predict(df)  
-
-    return df_predicted[['score','previously_insured',
-                                    'annual_premium','vintage','gender',
-                                    'age','region_code','policy_sales_channel',
-                                    'driving_license','vehicle_age',
-                                    'vehicle_damage']] 
-
-def create_input_table():
-    return gr.Dataframe(headers = ['previously_insured',
-                                    'annual_premium','vintage','gender',
-                                    'age','region_code','policy_sales_channel',
-                                    'driving_license','vehicle_age',
-                                    'vehicle_damage'],
-                        datatype = ['number','number','number','str','number',
-                                    'number','number','number','str','str'],
-                        row_count= 1,
-                        col_count= (10,'fixed'),
-                        type = 'pandas',
-                        label = 'Input')
-
-def create_output_table():
-    return gr.Dataframe(headers = ['score','previously_insured',
-                                    'annual_premium','vintage','gender',
-                                    'age','region_code','policy_sales_channel',
-                                    'driving_license','vehicle_age',
-                                    'vehicle_damage'],
-                        type = 'pandas',
-                        label = 'Output',
-                        wrap = True,
-                        interactive =False)
-
-def create_file_object():
-    return gr.File(label = 'File upload',
-                    type = 'bytes')
-
-def convert_file_to_pandas(file):
-
-    df = pd.read_excel(io = file)
-
-    return df
-
-def build_interface():
-
-    with gr.Blocks() as interface:
-
-        file_object = create_file_object()
-        input_table = create_input_table()
-        output_table = create_output_table()
-
-        greet_btn = gr.Button("Submit")
-        greet_btn.click(fn=predict, inputs=input_table, outputs=output_table)
-
-        file_object.change(fn = convert_file_to_pandas, 
-                           inputs = file_object, 
-                           outputs = input_table)
-    
-    interface.launch()
-
-
-if __name__ == "__main__":
-    load_data()
-    build_interface()
\ No newline at end of file
diff --git a/spaces/gossminn/fillmorle-app/sftp/predictor/span_predictor.orig.py b/spaces/gossminn/fillmorle-app/sftp/predictor/span_predictor.orig.py
deleted file mode 100644
index e84fb18f87fd57d51e1f800351ad065cc32b87e0..0000000000000000000000000000000000000000
--- a/spaces/gossminn/fillmorle-app/sftp/predictor/span_predictor.orig.py
+++ /dev/null
@@ -1,362 +0,0 @@
-import os
-from time import time
-from typing import *
-import json
-
-import numpy as np
-import torch
-from allennlp.common.util import JsonDict, sanitize
-from allennlp.data import DatasetReader, Instance
-from allennlp.data.data_loaders import SimpleDataLoader
-from allennlp.data.samplers import MaxTokensBatchSampler
-from allennlp.data.tokenizers import SpacyTokenizer
-from allennlp.models import Model
-from allennlp.nn import util as nn_util
-from allennlp.predictors import Predictor
-from concrete import (
-    MentionArgument, SituationMentionSet, SituationMention, TokenRefSequence,
-    EntityMention, EntityMentionSet, Entity, EntitySet, AnnotationMetadata, Communication
-)
-from concrete.util import CommunicationReader, AnalyticUUIDGeneratorFactory, CommunicationWriterZip
-from concrete.validate import validate_communication
-
-from ..data_reader import concrete_doc, concrete_doc_tokenized
-from ..utils import Span, re_index_span, VIRTUAL_ROOT
-
-
-class PredictionReturn(NamedTuple):
-    span: Union[Span, dict, Communication]
-    sentence: List[str]
-    meta: Dict[str, Any]
-
-
-class ForceDecodingReturn(NamedTuple):
-    span: np.ndarray
-    label: List[str]
-    distribution: np.ndarray
-
-
-@Predictor.register('span')
-class SpanPredictor(Predictor):
-    @staticmethod
-    def format_convert(
-            sentence: Union[List[str], List[List[str]]],
-            prediction: Union[Span, List[Span]],
-            output_format: str
-    ):
-        if output_format == 'span':
-            return prediction
-        elif output_format == 'json':
-            if isinstance(prediction, list):
-                return [SpanPredictor.format_convert(sent, pred, 'json') for sent, pred in zip(sentence, prediction)]
-            return prediction.to_json()
-        elif output_format == 'concrete':
-            if isinstance(prediction, Span):
-                sentence, prediction = [sentence], [prediction]
-            return concrete_doc_tokenized(sentence, prediction)
-
-    def predict_concrete(
-            self,
-            concrete_path: str,
-            output_path: Optional[str] = None,
-            max_tokens: int = 2048,
-            ontology_mapping: Optional[Dict[str, str]] = None,
-    ):
-        os.makedirs(os.path.dirname(output_path), exist_ok=True)
-        writer = CommunicationWriterZip(output_path)
-
-        for comm, fn in CommunicationReader(concrete_path):
-            assert len(comm.sectionList) == 1
-            concrete_sentences = comm.sectionList[0].sentenceList
-            json_sentences = list()
-            for con_sent in concrete_sentences:
-                json_sentences.append(
-                    [t.text for t in con_sent.tokenization.tokenList.tokenList]
-                )
-            predictions = self.predict_batch_sentences(json_sentences, max_tokens, ontology_mapping=ontology_mapping)
-
-            # Merge predictions into concrete
-            aug = AnalyticUUIDGeneratorFactory(comm).create()
-            situation_mention_set = SituationMentionSet(next(aug), AnnotationMetadata('Span Finder', time()), list())
-            comm.situationMentionSetList = [situation_mention_set]
-            situation_mention_set.mentionList = sm_list = list()
-            entity_mention_set = EntityMentionSet(next(aug), AnnotationMetadata('Span Finder', time()), list())
-            comm.entityMentionSetList = [entity_mention_set]
-            entity_mention_set.mentionList = em_list = list()
-            entity_set = EntitySet(
-                next(aug), AnnotationMetadata('Span Finder', time()), list(), None, entity_mention_set.uuid
-            )
-            comm.entitySetList = [entity_set]
-
-            em_dict = dict()
-            for con_sent, pred in zip(concrete_sentences, predictions):
-                for event in pred.span:
-                    def raw_text_span(start_idx, end_idx, **_):
-                        si_char = con_sent.tokenization.tokenList.tokenList[start_idx].textSpan.start
-                        ei_char = con_sent.tokenization.tokenList.tokenList[end_idx].textSpan.ending
-                        return comm.text[si_char:ei_char]
-                    sm = SituationMention(
-                        next(aug),
-                        text=raw_text_span(event.start_idx, event.end_idx),
-                        situationKind=event.label,
-                        situationType='EVENT',
-                        confidence=event.confidence,
-                        argumentList=list(),
-                        tokens=TokenRefSequence(
-                            tokenIndexList=list(range(event.start_idx, event.end_idx+1)),
-                            tokenizationId=con_sent.tokenization.uuid
-                        )
-                    )
-
-                    for arg in event:
-                        em = em_dict.get((arg.start_idx, arg.end_idx + 1))
-                        if em is None:
-                            em = EntityMention(
-                                next(aug),
-                                tokens=TokenRefSequence(
-                                    tokenIndexList=list(range(arg.start_idx, arg.end_idx+1)),
-                                    tokenizationId=con_sent.tokenization.uuid,
-                                ),
-                                text=raw_text_span(arg.start_idx, arg.end_idx)
-                            )
-                            em_list.append(em)
-                            entity_set.entityList.append(Entity(next(aug), id=em.text, mentionIdList=[em.uuid]))
-                            em_dict[(arg.start_idx, arg.end_idx+1)] = em
-                        sm.argumentList.append(MentionArgument(
-                            role=arg.label,
-                            entityMentionId=em.uuid,
-                            confidence=arg.confidence
-                        ))
-                    sm_list.append(sm)
-            validate_communication(comm)
-            writer.write(comm, fn)
-        writer.close()
-
-    def predict_sentence(
-            self,
-            sentence: Union[str, List[str]],
-            ontology_mapping: Optional[Dict[str, str]] = None,
-            output_format: str = 'span',
-    ) -> PredictionReturn:
-        """
-        Predict spans on a single sentence (no batch). If not tokenized, will tokenize it with SpacyTokenizer.
-        :param sentence: If tokenized, should be a list of tokens in string. If not, should be a string.
-        :param ontology_mapping:
-        :param output_format: span, json or concrete.
-        """
-        prediction = self.predict_json(self._prepare_sentence(sentence))
-        prediction['prediction'] = self.format_convert(
-            prediction['sentence'],
-            Span.from_json(prediction['prediction']).map_ontology(ontology_mapping),
-            output_format
-        )
-        return PredictionReturn(prediction['prediction'], prediction['sentence'], prediction.get('meta', dict()))
-
-    def predict_batch_sentences(
-            self,
-            sentences: List[Union[List[str], str]],
-            max_tokens: int = 512,
-            ontology_mapping: Optional[Dict[str, str]] = None,
-            output_format: str = 'span',
-    ) -> List[PredictionReturn]:
-        """
-        Predict spans on a batch of sentences. If not tokenized, will tokenize it with SpacyTokenizer.
-        :param sentences: A list of sentences. Refer to `predict_sentence`.
-        :param max_tokens: Maximum tokens in a batch.
-        :param ontology_mapping: If not None, will try to map the output from one ontology to another.
-            If the predicted frame is not in the mapping, the prediction will be ignored.
-        :param output_format: span, json or concrete.
-        :return: A list of predictions.
-        """
-        sentences = list(map(self._prepare_sentence, sentences))
-        for i_sent, sent in enumerate(sentences):
-            sent['meta'] = {"idx": i_sent}
-        instances = list(map(self._json_to_instance, sentences))
-        outputs = list()
-        for ins_indices in MaxTokensBatchSampler(max_tokens, ["tokens"], 0.0).get_batch_indices(instances):
-            batch_ins = list(
-                SimpleDataLoader([instances[ins_idx] for ins_idx in ins_indices], len(ins_indices), vocab=self.vocab)
-            )[0]
-            batch_inputs = nn_util.move_to_device(batch_ins, device=self.cuda_device)
-            batch_outputs = self._model(**batch_inputs)
-            for meta, prediction, inputs in zip(
-                batch_outputs['meta'], batch_outputs['prediction'], batch_outputs['inputs']
-            ):
-                prediction.map_ontology(ontology_mapping)
-                prediction = self.format_convert(inputs['sentence'], prediction, output_format)
-                outputs.append(PredictionReturn(prediction, inputs['sentence'], {"input_idx": meta['idx']}))
-
-        outputs.sort(key=lambda x: x.meta['input_idx'])
-        return outputs
-
-    def predict_instance(self, instance: Instance) -> JsonDict:
-        outputs = self._model.forward_on_instance(instance)
-        outputs = sanitize(outputs)
-        return {
-            'prediction': outputs['prediction'],
-            'sentence': outputs['inputs']['sentence'],
-            'meta': outputs.get('meta', {})
-        }
-
-    def __init__(
-            self,
-            model: Model,
-            dataset_reader: DatasetReader,
-            frozen: bool = True,
-    ):
-        super(SpanPredictor, self).__init__(model=model, dataset_reader=dataset_reader, frozen=frozen)
-        self.spacy_tokenizer = SpacyTokenizer(language='en_core_web_sm')
-
-    def economize(
-            self,
-            max_decoding_spans: Optional[int] = None,
-            max_recursion_depth: Optional[int] = None,
-    ):
-        if max_decoding_spans:
-            self._model._max_decoding_spans = max_decoding_spans
-        if max_recursion_depth:
-            self._model._max_recursion_depth = max_recursion_depth
-
-    def _json_to_instance(self, json_dict: JsonDict) -> Instance:
-        return self._dataset_reader.text_to_instance(**json_dict)
-
-    @staticmethod
-    def to_nested(prediction: List[dict]):
-        first_layer, idx2children = list(), dict()
-        for idx, pred in enumerate(prediction):
-            children = list()
-            pred['children'] = idx2children[idx+1] = children
-            if pred['parent'] == 0:
-                first_layer.append(pred)
-            else:
-                idx2children[pred['parent']].append(pred)
-            del pred['parent']
-        return first_layer
-
-    def _prepare_sentence(self, sentence: Union[str, List[str]]) -> Dict[str, List[str]]:
-        if isinstance(sentence, str):
-            while '  ' in sentence:
-                sentence = sentence.replace('  ', ' ')
-            sentence = sentence.replace(chr(65533), '')
-            if sentence == '':
-                sentence = [""]
-            sentence = list(map(str, self.spacy_tokenizer.tokenize(sentence)))
-        return {"tokens": sentence}
-
-    @staticmethod
-    def json_to_concrete(
-            predictions: List[dict],
-    ):
-        sentences = list()
-        for pred in predictions:
-            tokenization, event = list(), list()
-            sent = {'text': ' '.join(pred['inputs']), 'tokenization': tokenization, 'event': event}
-            sentences.append(sent)
-            start_idx = 0
-            for token in pred['inputs']:
-                tokenization.append((start_idx, len(token)-1+start_idx))
-                start_idx += len(token) + 1
-            for pred_event in pred['prediction']:
-                arg_list = list()
-                one_event = {'argument': arg_list}
-                event.append(one_event)
-                for key in ['start_idx', 'end_idx', 'label']:
-                    one_event[key] = pred_event[key]
-                for pred_arg in pred_event['children']:
-                    arg_list.append({key: pred_arg[key] for key in ['start_idx', 'end_idx', 'label']})
-
-        concrete_comm = concrete_doc(sentences)
-        return concrete_comm
-
-    def force_decode(
-            self,
-            sentence: List[str],
-            parent_span: Tuple[int, int] = (-1, -1),
-            parent_label: str = VIRTUAL_ROOT,
-            child_spans: Optional[List[Tuple[int, int]]] = None,
-    ) -> ForceDecodingReturn:
-        """
-        Force decoding. There are 2 modes:
-        1. Given parent span and its label, find all it children (direct children, not including other descendents)
-            and type them.
-        2. Given parent span, parent label, and children spans, type all children.
-        :param sentence: Tokens.
-        :param parent_span: [start_idx, end_idx], both inclusive.
-        :param parent_label: Parent label in string.
-        :param child_spans: Optional. If provided, will turn to mode 2; else mode 1.
-        :return:
-            - span: children spans.
-            - label: most probable labels of children.
-            - distribution: distribution over children labels.
-        """
-        instance = self._dataset_reader.text_to_instance(self._prepare_sentence(sentence)['tokens'])
-        model_input = nn_util.move_to_device(
-            list(SimpleDataLoader([instance], 1, vocab=self.vocab))[0], device=self.cuda_device
-        )
-        offsets = instance.fields['raw_inputs'].metadata['offsets']
-
-        with torch.no_grad():
-            tokens = model_input['tokens']
-            parent_span = re_index_span(parent_span, offsets)
-            if parent_span[1] >= self._dataset_reader.max_length:
-                return ForceDecodingReturn(
-                    np.zeros([0, 2], dtype=np.int),
-                    [],
-                    np.zeros([0, self.vocab.get_vocab_size('span_label')], dtype=np.float64)
-                )
-            if child_spans is not None:
-                token_vec = self._model.word_embedding(tokens)
-                child_pieces = [re_index_span(bdr, offsets) for bdr in child_spans]
-                child_pieces = list(filter(lambda x: x[1] < self._dataset_reader.max_length-1, child_pieces))
-                span_tensor = torch.tensor(
-                    [parent_span] + child_pieces, dtype=torch.int64, device=self.device
-                ).unsqueeze(0)
-                parent_indices = span_tensor.new_zeros(span_tensor.shape[0:2])
-                span_labels = parent_indices.new_full(
-                    parent_indices.shape, self._model.vocab.get_token_index(parent_label, 'span_label')
-                )
-                span_vec = self._model._span_extractor(token_vec, span_tensor)
-                typing_out = self._model._span_typing(span_vec, parent_indices, span_labels)
-                distribution = typing_out['distribution'][0, 1:].cpu().numpy()
-                boundary = np.array(child_spans)
-            else:
-                parent_label_tensor = torch.tensor(
-                    [self._model.vocab.get_token_index(parent_label, 'span_label')], device=self.device
-                )
-                parent_boundary_tensor = torch.tensor([parent_span], device=self.device)
-                boundary, _, num_children, distribution = self._model.one_step_prediction(
-                    tokens, parent_boundary_tensor, parent_label_tensor
-                )
-                boundary, distribution = boundary[0].cpu().tolist(), distribution[0].cpu().numpy()
-                boundary = np.array([re_index_span(bdr, offsets, True) for bdr in boundary])
-
-            labels = [
-                self.vocab.get_token_from_index(label_idx, 'span_label') for label_idx in distribution.argmax(1)
-            ]
-            return ForceDecodingReturn(boundary, labels, distribution)
-
-    @property
-    def vocab(self):
-        return self._model.vocab
-
-    @property
-    def device(self):
-        return self.cuda_device if self.cuda_device > -1 else 'cpu'
-
-    @staticmethod
-    def read_ontology_mapping(file_path: str):
-        """
-        Read the ontology mapping file. The file format can be read in docs.
-        """
-        if file_path is None:
-            return None
-        if file_path.endswith('.json'):
-            return json.load(open(file_path))
-        mapping = dict()
-        for line in open(file_path).readlines():
-            parent_label, original_label, new_label = line.replace('\n', '').split('\t')
-            if parent_label == '*':
-                mapping[original_label] = new_label
-            else:
-                mapping[(parent_label, original_label)] = new_label
-        return mapping
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Descargar gratis bb multi unlocker key v 19.0 y disfruta de la libertad de usar cualquier operador en tu Alcatel.md b/spaces/gotiQspiryo/whisper-ui/examples/Descargar gratis bb multi unlocker key v 19.0 y disfruta de la libertad de usar cualquier operador en tu Alcatel.md
deleted file mode 100644
index e717aed308afca34677b723373058e94bbf3f593..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Descargar gratis bb multi unlocker key v 19.0 y disfruta de la libertad de usar cualquier operador en tu Alcatel.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Groupmail Business Cracked Fulliso The Ultimate Guide to Email Marketing Campaigns.md b/spaces/gotiQspiryo/whisper-ui/examples/Groupmail Business Cracked Fulliso The Ultimate Guide to Email Marketing Campaigns.md
deleted file mode 100644
index 70cfd274c4243268fb6b76cacef0038022f53367..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Groupmail Business Cracked Fulliso The Ultimate Guide to Email Marketing Campaigns.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Groupmail Business Cracked Fulliso</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://urlgoal.com/2uyM8E">https://urlgoal.com/2uyM8E</a></b></p><br /><br />
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Los Galos Discografia Descargar.md b/spaces/gotiQspiryo/whisper-ui/examples/Los Galos Discografia Descargar.md
deleted file mode 100644
index e5d8301388b6e294ceb0e8d82dc0cee3f4705740..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Los Galos Discografia Descargar.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-<h2>los galos discografia descargar</h2><br /><p><b><b>Download</b> &#9675;&#9675;&#9675; <a href="https://urlgoal.com/2uyNhS">https://urlgoal.com/2uyNhS</a></b></p><br /><br />
-<br />
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-
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diff --git a/spaces/gradio/HuBERT/examples/m2m_100/tokenizers/tokenize_thai.py b/spaces/gradio/HuBERT/examples/m2m_100/tokenizers/tokenize_thai.py
deleted file mode 100644
index 9c72cb89056f6fc92a8963415e5f3a1e61b33a5b..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/m2m_100/tokenizers/tokenize_thai.py
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-from pythainlp import word_tokenize
-
-
-for line in sys.stdin:
-    print(" ".join(word_tokenize(line.strip())))
diff --git a/spaces/gradio/gpt-neo/data/train_tokenizer.py b/spaces/gradio/gpt-neo/data/train_tokenizer.py
deleted file mode 100644
index 2ed56a8bfee88368fff7fd221ff7498031d292bb..0000000000000000000000000000000000000000
--- a/spaces/gradio/gpt-neo/data/train_tokenizer.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import os
-import random
-import argparse
-import shutil
-from glob import glob
-from pathlib import Path
-
-from lm_dataformat import Reader
-from tokenizers import (Tokenizer, decoders, models, pre_tokenizers,
-                        processors, trainers)
-from tokenizers.normalizers import NFKC
-from tqdm import tqdm
-
-# parser
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--base_dir", type=str, help="Path to where your files are located. Files ending in .zst are treated as \
-                    archives, all others as raw text.")
-parser.add_argument("--output_dir", type=str, default="tokenizers", help="Where to put the tokenizer")
-parser.add_argument("--file_type", type=str, choices=["xz", "txt"], default="xz", help="Extension of file to parse")
-parser.add_argument("--vocab_size", type=int, help="Size of vocabulary", required = True)
-args = parser.parse_args()
-
-# main script
-
-data_path = Path(args.base_dir)
-archives = glob(str(data_path / f"*.{args.file_type}"))
-
-out_path = Path(args.output_dir)
-
-if os.path.exists(out_path):
-    shutil.rmtree(out_path)
-
-if not out_path.is_dir():
-    out_path.mkdir()
-
-    for arch in tqdm(archives):
-        name = os.path.basename(arch).split(".")[0] + ".txt"
-        fp = out_path / name
-
-        if args.file_type == 'xz':
-            g = Reader(arch).stream_data()
-
-            with open(fp, "w") as f:
-                for s in g:
-                    f.write(s)
-                    f.write("\n\n")
-        elif args.file_type == 'txt':
-            shutil.copyfile(str(arch), str(fp))
-
-data_files = glob(str(out_path / "*.txt"))
-data_files = random.sample(data_files, int(0.2 * len(data_files)))
-
-assert len(data_files) > 0, 'No data files found'
-
-# Initialize a tokenizer
-tokenizer = Tokenizer(models.BPE())
-
-# Customize pre-tokenization and decoding
-tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True)
-tokenizer.decoder = decoders.ByteLevel()
-tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)
-tokenizer.normalizer = NFKC()
-
-# And then train
-trainer = trainers.BpeTrainer(vocab_size=args.vocab_size, min_frequency=2, special_tokens=["<|endoftext|>", "<|padding|>"])
-tokenizer.train(trainer, data_files)
-
-# And Save it
-tokenizer_path = out_path / "byte-level-bpe.tokenizer.json"
-tokenizer.save(str(tokenizer_path), pretty=True)
-
-print(f'tokenizer saved at {str(tokenizer_path)}')
\ No newline at end of file
diff --git a/spaces/gradio/longformer/tvm/_ffi/_ctypes/ndarray.py b/spaces/gradio/longformer/tvm/_ffi/_ctypes/ndarray.py
deleted file mode 100644
index 9367160b811b3a593711b8534f265a23002a0230..0000000000000000000000000000000000000000
--- a/spaces/gradio/longformer/tvm/_ffi/_ctypes/ndarray.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-# pylint: disable=invalid-name
-"""Runtime NDArray api"""
-from __future__ import absolute_import
-
-import ctypes
-from ..base import _LIB, check_call, c_str
-from ..runtime_ctypes import TVMArrayHandle, TVMNDArrayContainerHandle
-from .types import RETURN_SWITCH, C_TO_PY_ARG_SWITCH, _wrap_arg_func, _return_handle
-
-
-TVMPyCapsuleDestructor = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
-_c_str_dltensor = c_str('dltensor')
-_c_str_used_dltensor = c_str('used_dltensor')
-
-
-# used for PyCapsule manipulation
-if hasattr(ctypes, 'pythonapi'):
-    ctypes.pythonapi.PyCapsule_GetName.restype = ctypes.c_char_p
-    ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
-    ctypes.pythonapi.PyCapsule_New.restype = ctypes.py_object
-
-
-def _from_dlpack(dltensor):
-    dltensor = ctypes.py_object(dltensor)
-    if ctypes.pythonapi.PyCapsule_IsValid(dltensor, _c_str_dltensor):
-        ptr = ctypes.pythonapi.PyCapsule_GetPointer(dltensor, _c_str_dltensor)
-        # enforce type to make sure it works for all ctypes
-        ptr = ctypes.cast(ptr, ctypes.c_void_p)
-        handle = TVMArrayHandle()
-        check_call(_LIB.TVMArrayFromDLPack(ptr, ctypes.byref(handle)))
-        ctypes.pythonapi.PyCapsule_SetName(dltensor, _c_str_used_dltensor)
-        ctypes.pythonapi.PyCapsule_SetDestructor(dltensor, TVMPyCapsuleDestructor(0))
-        return _make_array(handle, False, False)
-    raise ValueError("Expect a dltensor field, PyCapsule can only be consumed once")
-
-
-def _dlpack_deleter(pycapsule):
-    pycapsule = ctypes.cast(pycapsule, ctypes.py_object)
-    if ctypes.pythonapi.PyCapsule_IsValid(pycapsule, _c_str_dltensor):
-        ptr = ctypes.pythonapi.PyCapsule_GetPointer(pycapsule, _c_str_dltensor)
-        # enforce type to make sure it works for all ctypes
-        ptr = ctypes.cast(ctypes.c_void_p, ptr)
-        _LIB.TVMDLManagedTensorCallDeleter(ptr)
-        ctypes.pythonapi.PyCapsule_SetDestructor(dltensor, TVMPyCapsuleDestructor(0))
-
-_c_dlpack_deleter = TVMPyCapsuleDestructor(_dlpack_deleter)
-
-
-class NDArrayBase(object):
-    """A simple Device/CPU Array object in runtime."""
-    __slots__ = ["handle", "is_view"]
-    # pylint: disable=no-member
-    def __init__(self, handle, is_view=False):
-        """Initialize the function with handle
-
-        Parameters
-        ----------
-        handle : TVMArrayHandle
-            the handle to the underlying C++ TVMArray
-        """
-        self.handle = handle
-        self.is_view = is_view
-
-    def __del__(self):
-        if not self.is_view and _LIB:
-            check_call(_LIB.TVMArrayFree(self.handle))
-
-    @property
-    def _tvm_handle(self):
-        return ctypes.cast(self.handle, ctypes.c_void_p).value
-
-    def to_dlpack(self):
-        """Produce an array from a DLPack Tensor without copying memory
-
-        Returns
-        -------
-        dlpack : DLPack tensor view of the array data
-        """
-        handle = ctypes.c_void_p()
-        check_call(_LIB.TVMArrayToDLPack(self.handle, ctypes.byref(handle)))
-        return ctypes.pythonapi.PyCapsule_New(handle, _c_str_dltensor, _c_dlpack_deleter)
-
-
-def _make_array(handle, is_view, is_container):
-    global _TVM_ND_CLS
-    handle = ctypes.cast(handle, TVMArrayHandle)
-    fcreate = _CLASS_NDARRAY
-    if is_container and _TVM_ND_CLS:
-        array_type_info = ctypes.cast(handle, TVMNDArrayContainerHandle).array_type_info.value
-        if array_type_info > 0:
-            fcreate = _TVM_ND_CLS[array_type_info]
-    return fcreate(handle, is_view)
-
-_TVM_COMPATS = ()
-
-def _reg_extension(cls, fcreate):
-    global _TVM_COMPATS
-    _TVM_COMPATS += (cls,)
-    if fcreate:
-        fret = lambda x: fcreate(_return_handle(x))
-        RETURN_SWITCH[cls._tvm_tcode] = fret
-        C_TO_PY_ARG_SWITCH[cls._tvm_tcode] = _wrap_arg_func(fret, cls._tvm_tcode)
-
-_TVM_ND_CLS = {}
-
-def _reg_ndarray(cls, fcreate):
-    global _TVM_ND_CLS
-    _TVM_ND_CLS[cls._array_type_code] = fcreate
-
-_CLASS_NDARRAY = None
-
-def _set_class_ndarray(cls):
-    global _CLASS_NDARRAY
-    _CLASS_NDARRAY = cls
diff --git a/spaces/haakohu/DeepPrivacy/README.md b/spaces/haakohu/DeepPrivacy/README.md
deleted file mode 100644
index 59eb5ced25b603d665a121cb783442d118d7eb97..0000000000000000000000000000000000000000
--- a/spaces/haakohu/DeepPrivacy/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: DeepPrivacy - A Generative Adversarial Network for Face Anonymization
-emoji: 🌍
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/haakohu/deep_privacy2_face/dp2/detection/models/vit_pose/backbone.py b/spaces/haakohu/deep_privacy2_face/dp2/detection/models/vit_pose/backbone.py
deleted file mode 100644
index f78712dd6131b02ea674fac452852274ae0dc5c2..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2_face/dp2/detection/models/vit_pose/backbone.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# Code adapted from: https://github.com/gpastal24/ViTPose-Pytorch
-import torch
-from functools import partial
-import torch.nn as nn
-import torch.utils.checkpoint as checkpoint
-
-from timm.models.layers import drop_path, to_2tuple, trunc_normal_
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    """
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-    def extra_repr(self):
-        return 'p={}'.format(self.drop_prob)
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-            self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
-            proj_drop=0., attn_head_dim=None,):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.dim = dim
-
-        if attn_head_dim is not None:
-            head_dim = attn_head_dim
-        all_head_dim = head_dim * self.num_heads
-
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=qkv_bias)
-
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(all_head_dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x)
-        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None,
-                 drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU,
-                 norm_layer=nn.LayerNorm, attn_head_dim=None
-                 ):
-        super().__init__()
-
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop, attn_head_dim=attn_head_dim
-        )
-
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, ratio=1):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (ratio ** 2)
-        self.patch_shape = (int(img_size[0] // patch_size[0] * ratio), int(img_size[1] // patch_size[1] * ratio))
-        self.origin_patch_shape = (int(img_size[0] // patch_size[0]), int(img_size[1] // patch_size[1]))
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=(
-            patch_size[0] // ratio), padding=4 + 2 * (ratio//2-1))
-
-    def forward(self, x, **kwargs):
-        B, C, H, W = x.shape
-        x = self.proj(x)
-        Hp, Wp = x.shape[2], x.shape[3]
-
-        x = x.flatten(2).transpose(1, 2)
-        return x, (Hp, Wp)
-
-
-class HybridEmbed(nn.Module):
-    """ CNN Feature Map Embedding
-    Extract feature map from CNN, flatten, project to embedding dim.
-    """
-
-    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
-        super().__init__()
-        assert isinstance(backbone, nn.Module)
-        img_size = to_2tuple(img_size)
-        self.img_size = img_size
-        self.backbone = backbone
-        if feature_size is None:
-            with torch.no_grad():
-                training = backbone.training
-                if training:
-                    backbone.eval()
-                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
-                feature_size = o.shape[-2:]
-                feature_dim = o.shape[1]
-                backbone.train(training)
-        else:
-            feature_size = to_2tuple(feature_size)
-            feature_dim = self.backbone.feature_info.channels()[-1]
-        self.num_patches = feature_size[0] * feature_size[1]
-        self.proj = nn.Linear(feature_dim, embed_dim)
-
-    def forward(self, x):
-        x = self.backbone(x)[-1]
-        x = x.flatten(2).transpose(1, 2)
-        x = self.proj(x)
-        return x
-
-
-# @BACKBONES.register_module()
-class ViT(nn.Module):
-
-    def __init__(self,
-                 img_size=224, patch_size=16, in_chans=3, num_classes=80, embed_dim=768, depth=12,
-                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
-                 drop_path_rate=0., hybrid_backbone=None, norm_layer=None, use_checkpoint=False,
-                 frozen_stages=-1, ratio=1, last_norm=True,
-                 patch_padding='pad', freeze_attn=False, freeze_ffn=False,
-                 ):
-        # Protect mutable default arguments
-        super(ViT, self).__init__()
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
-        self.num_classes = num_classes
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        self.frozen_stages = frozen_stages
-        self.use_checkpoint = use_checkpoint
-        self.patch_padding = patch_padding
-        self.freeze_attn = freeze_attn
-        self.freeze_ffn = freeze_ffn
-        self.depth = depth
-
-        if hybrid_backbone is not None:
-            self.patch_embed = HybridEmbed(
-                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
-        else:
-            self.patch_embed = PatchEmbed(
-                img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, ratio=ratio)
-        num_patches = self.patch_embed.num_patches
-
-        # since the pretraining model has class token
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
-
-        self.blocks = nn.ModuleList([
-            Block(
-                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
-            )
-            for i in range(depth)])
-
-        self.last_norm = norm_layer(embed_dim) if last_norm else nn.Identity()
-
-        if self.pos_embed is not None:
-            trunc_normal_(self.pos_embed, std=.02)
-
-        self._freeze_stages()
-
-    def _freeze_stages(self):
-        """Freeze parameters."""
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        for i in range(1, self.frozen_stages + 1):
-            m = self.blocks[i]
-            m.eval()
-            for param in m.parameters():
-                param.requires_grad = False
-
-        if self.freeze_attn:
-            for i in range(0, self.depth):
-                m = self.blocks[i]
-                m.attn.eval()
-                m.norm1.eval()
-                for param in m.attn.parameters():
-                    param.requires_grad = False
-                for param in m.norm1.parameters():
-                    param.requires_grad = False
-
-        if self.freeze_ffn:
-            self.pos_embed.requires_grad = False
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-            for i in range(0, self.depth):
-                m = self.blocks[i]
-                m.mlp.eval()
-                m.norm2.eval()
-                for param in m.mlp.parameters():
-                    param.requires_grad = False
-                for param in m.norm2.parameters():
-                    param.requires_grad = False
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        super().init_weights(pretrained, patch_padding=self.patch_padding)
-
-        if pretrained is None:
-            def _init_weights(m):
-                if isinstance(m, nn.Linear):
-                    trunc_normal_(m.weight, std=.02)
-                    if isinstance(m, nn.Linear) and m.bias is not None:
-                        nn.init.constant_(m.bias, 0)
-                elif isinstance(m, nn.LayerNorm):
-                    nn.init.constant_(m.bias, 0)
-                    nn.init.constant_(m.weight, 1.0)
-
-            self.apply(_init_weights)
-
-    def get_num_layers(self):
-        return len(self.blocks)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def forward_features(self, x):
-        B, C, H, W = x.shape
-        x, (Hp, Wp) = self.patch_embed(x)
-
-        if self.pos_embed is not None:
-            # fit for multiple GPU training
-            # since the first element for pos embed (sin-cos manner) is zero, it will cause no difference
-            x = x + self.pos_embed[:, 1:] + self.pos_embed[:, :1]
-
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-
-        x = self.last_norm(x)
-
-        xp = x.permute(0, 2, 1).reshape(B, -1, Hp, Wp).contiguous()
-
-        return xp
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        return x
-
-    def train(self, mode=True):
-        """Convert the model into training mode."""
-        super().train(mode)
-        self._freeze_stages()
diff --git a/spaces/happiestminds/trackbot/db_schema.sql b/spaces/happiestminds/trackbot/db_schema.sql
deleted file mode 100644
index f1f3e9a7fed89f6a6b54756d0853d905ec2c5547..0000000000000000000000000000000000000000
--- a/spaces/happiestminds/trackbot/db_schema.sql
+++ /dev/null
@@ -1,108 +0,0 @@
--- SQLite Database Schema for Shipping Application
-
--- Static Tables
-CREATE TABLE Manufacturers (
-  ManufacturerID INTEGER PRIMARY KEY,
-  Name TEXT NOT NULL
-);
-
-CREATE TABLE Showrooms (
-  ShowroomID INTEGER PRIMARY KEY,
-  Name TEXT NOT NULL,
-  DestinationLocationID INTEGER,
-  FOREIGN KEY (DestinationLocationID) REFERENCES Locations(LocationID)
-);
-
-CREATE TABLE ShippingModes (
-  ModeID INTEGER PRIMARY KEY,
-  Name TEXT NOT NULL
-);
-
--- Table for Shipment Status
-CREATE TABLE Status (
-  StatusID INTEGER PRIMARY KEY,
-  Description TEXT NOT NULL CHECK (Description IN ('CREATED', 'IN-TRANSIT', 'AT-WAREHOUSE', 'DELIVERED'))
-);
-
--- Table for Locations
-CREATE TABLE Locations (
-  LocationID INTEGER PRIMARY KEY,
-  Name TEXT NOT NULL,
-  GMapsURL TEXT
-);
-
--- New table for Customers
-CREATE TABLE Customers (
-  CustomerID INTEGER PRIMARY KEY,
-  Name TEXT NOT NULL,
-  Email TEXT,
-  ContactNumber TEXT
-  --, HashedPassword TEXT -- optional, for authentication
-);
-
--- Dynamic Tables
-CREATE TABLE Consignments (
-  ConsignmentID INTEGER PRIMARY KEY,
-  ManufacturerID INTEGER,
-  SourceLocationID INTEGER,
-  DestinationLocationID INTEGER,
-  CurrentLocationID INTEGER,
-  ETA DATETIME,
-  FOREIGN KEY (ManufacturerID) REFERENCES Manufacturers(ManufacturerID),
-  FOREIGN KEY (SourceLocationID) REFERENCES Locations(LocationID),
-  FOREIGN KEY (DestinationLocationID) REFERENCES Locations(LocationID),
-  FOREIGN KEY (CurrentLocationID) REFERENCES Locations(LocationID)
-);
-
-CREATE TABLE Vehicles (
-  VIN TEXT PRIMARY KEY,
-  ConsignmentID INTEGER,
-  FOREIGN KEY (ConsignmentID) REFERENCES Consignments(ConsignmentID)
-);
-
-CREATE TABLE Consignment_Showroom (
-  ConsignmentID INTEGER,
-  ShowroomID INTEGER,
-  PRIMARY KEY (ConsignmentID, ShowroomID),
-  FOREIGN KEY (ConsignmentID) REFERENCES Consignments(ConsignmentID),
-  FOREIGN KEY (ShowroomID) REFERENCES Showrooms(ShowroomID)
-);
-
-CREATE TABLE Consignment_ShippingMode (
-  ConsignmentID INTEGER,
-  ModeID INTEGER,
-  PRIMARY KEY (ConsignmentID, ModeID),
-  FOREIGN KEY (ConsignmentID) REFERENCES Consignments(ConsignmentID),
-  FOREIGN KEY (ModeID) REFERENCES ShippingModes(ModeID)
-);
-
--- Modified table to include Location for each status update
-CREATE TABLE Consignment_Status (
-  ConsignmentID INTEGER,
-  StatusID INTEGER,
-  LocationID INTEGER,
-  Timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
-  FOREIGN KEY (ConsignmentID) REFERENCES Consignments(ConsignmentID),
-  FOREIGN KEY (StatusID) REFERENCES Status(StatusID),
-  FOREIGN KEY (LocationID) REFERENCES Locations(LocationID)
-);
-
--- Table for Consignment Events
-CREATE TABLE Consignment_Events (
-  EventID INTEGER PRIMARY KEY,
-  ConsignmentID INTEGER,
-  StatusID INTEGER,
-  Timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
-  FOREIGN KEY (ConsignmentID) REFERENCES Consignments(ConsignmentID),
-  FOREIGN KEY (StatusID) REFERENCES Status(StatusID)
-);
-
--- New table to associate Customers with their Vehicles
-CREATE TABLE Customer_Vehicles (
-  CustomerID INTEGER,
-  VIN TEXT,
-  PRIMARY KEY (CustomerID, VIN),
-  FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID),
-  FOREIGN KEY (VIN) REFERENCES Vehicles(VIN)
-);
-
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/env.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/env.py
deleted file mode 100644
index 6769cae4cfb71ae05c605cb9e30eb12ee58c6ee7..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/env.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import importlib
-import importlib.util
-import logging
-import numpy as np
-import os
-import random
-import sys
-from datetime import datetime
-import torch
-
-__all__ = ["seed_all_rng"]
-
-
-def seed_all_rng(seed=None):
-    """
-    Set the random seed for the RNG in torch, numpy and python.
-
-    Args:
-        seed (int): if None, will use a strong random seed.
-    """
-    if seed is None:
-        seed = (
-            os.getpid()
-            + int(datetime.now().strftime("%S%f"))
-            + int.from_bytes(os.urandom(2), "big")
-        )
-        logger = logging.getLogger(__name__)
-        logger.info("Using a generated random seed {}".format(seed))
-    np.random.seed(seed)
-    torch.set_rng_state(torch.manual_seed(seed).get_state())
-    random.seed(seed)
-
-
-# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
-def _import_file(module_name, file_path, make_importable=False):
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    if make_importable:
-        sys.modules[module_name] = module
-    return module
-
-
-def _configure_libraries():
-    """
-    Configurations for some libraries.
-    """
-    # An environment option to disable `import cv2` globally,
-    # in case it leads to negative performance impact
-    disable_cv2 = int(os.environ.get("DETECTRON2_DISABLE_CV2", False))
-    if disable_cv2:
-        sys.modules["cv2"] = None
-    else:
-        # Disable opencl in opencv since its interaction with cuda often has negative effects
-        # This envvar is supported after OpenCV 3.4.0
-        os.environ["OPENCV_OPENCL_RUNTIME"] = "disabled"
-        try:
-            import cv2
-
-            if int(cv2.__version__.split(".")[0]) >= 3:
-                cv2.ocl.setUseOpenCL(False)
-        except ImportError:
-            pass
-
-    def get_version(module, digit=2):
-        return tuple(map(int, module.__version__.split(".")[:digit]))
-
-    # fmt: off
-    assert get_version(torch) >= (1, 4), "Requires torch>=1.4"
-    import fvcore
-    assert get_version(fvcore, 3) >= (0, 1, 1), "Requires fvcore>=0.1.1"
-    import yaml
-    assert get_version(yaml) >= (5, 1), "Requires pyyaml>=5.1"
-    # fmt: on
-
-
-_ENV_SETUP_DONE = False
-
-
-def setup_environment():
-    """Perform environment setup work. The default setup is a no-op, but this
-    function allows the user to specify a Python source file or a module in
-    the $DETECTRON2_ENV_MODULE environment variable, that performs
-    custom setup work that may be necessary to their computing environment.
-    """
-    global _ENV_SETUP_DONE
-    if _ENV_SETUP_DONE:
-        return
-    _ENV_SETUP_DONE = True
-
-    _configure_libraries()
-
-    custom_module_path = os.environ.get("DETECTRON2_ENV_MODULE")
-
-    if custom_module_path:
-        setup_custom_environment(custom_module_path)
-    else:
-        # The default setup is a no-op
-        pass
-
-
-def setup_custom_environment(custom_module):
-    """
-    Load custom environment setup by importing a Python source file or a
-    module, and run the setup function.
-    """
-    if custom_module.endswith(".py"):
-        module = _import_file("detectron2.utils.env.custom_module", custom_module)
-    else:
-        module = importlib.import_module(custom_module)
-    assert hasattr(module, "setup_environment") and callable(module.setup_environment), (
-        "Custom environment module defined in {} does not have the "
-        "required callable attribute 'setup_environment'."
-    ).format(custom_module)
-    module.setup_environment()
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/PointRend/point_rend/color_augmentation.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/PointRend/point_rend/color_augmentation.py
deleted file mode 100644
index 27344c470adac143186e61c8a5b0f39900937634..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/PointRend/point_rend/color_augmentation.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-import random
-import cv2
-from fvcore.transforms.transform import Transform
-
-
-class ColorAugSSDTransform(Transform):
-    """
-    A color related data augmentation used in Single Shot Multibox Detector (SSD).
-
-    Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy,
-       Scott Reed, Cheng-Yang Fu, Alexander C. Berg.
-       SSD: Single Shot MultiBox Detector. ECCV 2016.
-
-    Implementation based on:
-
-     https://github.com/weiliu89/caffe/blob
-       /4817bf8b4200b35ada8ed0dc378dceaf38c539e4
-       /src/caffe/util/im_transforms.cpp
-
-     https://github.com/chainer/chainercv/blob
-       /7159616642e0be7c5b3ef380b848e16b7e99355b/chainercv
-       /links/model/ssd/transforms.py
-    """
-
-    def __init__(
-        self,
-        img_format,
-        brightness_delta=32,
-        contrast_low=0.5,
-        contrast_high=1.5,
-        saturation_low=0.5,
-        saturation_high=1.5,
-        hue_delta=18,
-    ):
-        super().__init__()
-        assert img_format in ["BGR", "RGB"]
-        self.is_rgb = img_format == "RGB"
-        del img_format
-        self._set_attributes(locals())
-
-    def apply_coords(self, coords):
-        return coords
-
-    def apply_segmentation(self, segmentation):
-        return segmentation
-
-    def apply_image(self, img, interp=None):
-        if self.is_rgb:
-            img = img[:, :, [2, 1, 0]]
-        img = self.brightness(img)
-        if random.randrange(2):
-            img = self.contrast(img)
-            img = self.saturation(img)
-            img = self.hue(img)
-        else:
-            img = self.saturation(img)
-            img = self.hue(img)
-            img = self.contrast(img)
-        if self.is_rgb:
-            img = img[:, :, [2, 1, 0]]
-        return img
-
-    def convert(self, img, alpha=1, beta=0):
-        img = img.astype(np.float32) * alpha + beta
-        img = np.clip(img, 0, 255)
-        return img.astype(np.uint8)
-
-    def brightness(self, img):
-        if random.randrange(2):
-            return self.convert(
-                img, beta=random.uniform(-self.brightness_delta, self.brightness_delta)
-            )
-        return img
-
-    def contrast(self, img):
-        if random.randrange(2):
-            return self.convert(img, alpha=random.uniform(self.contrast_low, self.contrast_high))
-        return img
-
-    def saturation(self, img):
-        if random.randrange(2):
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-            img[:, :, 1] = self.convert(
-                img[:, :, 1], alpha=random.uniform(self.saturation_low, self.saturation_high)
-            )
-            return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
-        return img
-
-    def hue(self, img):
-        if random.randrange(2):
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-            img[:, :, 0] = (
-                img[:, :, 0].astype(int) + random.randint(-self.hue_delta, self.hue_delta)
-            ) % 180
-            return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
-        return img
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/benchmark.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/benchmark.py
deleted file mode 100644
index 9eec59f476882e4045ec3c682ffe515413a3be15..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/benchmark.py
+++ /dev/null
@@ -1,167 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-A script to benchmark builtin models.
-
-Note: this script has an extra dependency of psutil.
-"""
-
-import itertools
-import logging
-import psutil
-import torch
-import tqdm
-from fvcore.common.timer import Timer
-from torch.nn.parallel import DistributedDataParallel
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import (
-    DatasetFromList,
-    build_detection_test_loader,
-    build_detection_train_loader,
-)
-from detectron2.engine import SimpleTrainer, default_argument_parser, hooks, launch
-from detectron2.modeling import build_model
-from detectron2.solver import build_optimizer
-from detectron2.utils import comm
-from detectron2.utils.events import CommonMetricPrinter
-from detectron2.utils.logger import setup_logger
-
-logger = logging.getLogger("detectron2")
-
-
-def setup(args):
-    cfg = get_cfg()
-    cfg.merge_from_file(args.config_file)
-    cfg.SOLVER.BASE_LR = 0.001  # Avoid NaNs. Not useful in this script anyway.
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    setup_logger(distributed_rank=comm.get_rank())
-    return cfg
-
-
-def benchmark_data(args):
-    cfg = setup(args)
-
-    timer = Timer()
-    dataloader = build_detection_train_loader(cfg)
-    logger.info("Initialize loader using {} seconds.".format(timer.seconds()))
-
-    timer.reset()
-    itr = iter(dataloader)
-    for i in range(10):  # warmup
-        next(itr)
-        if i == 0:
-            startup_time = timer.seconds()
-    timer = Timer()
-    max_iter = 1000
-    for _ in tqdm.trange(max_iter):
-        next(itr)
-    logger.info(
-        "{} iters ({} images) in {} seconds.".format(
-            max_iter, max_iter * cfg.SOLVER.IMS_PER_BATCH, timer.seconds()
-        )
-    )
-    logger.info("Startup time: {} seconds".format(startup_time))
-    vram = psutil.virtual_memory()
-    logger.info(
-        "RAM Usage: {:.2f}/{:.2f} GB".format(
-            (vram.total - vram.available) / 1024 ** 3, vram.total / 1024 ** 3
-        )
-    )
-
-    # test for a few more rounds
-    for _ in range(10):
-        timer = Timer()
-        max_iter = 1000
-        for _ in tqdm.trange(max_iter):
-            next(itr)
-        logger.info(
-            "{} iters ({} images) in {} seconds.".format(
-                max_iter, max_iter * cfg.SOLVER.IMS_PER_BATCH, timer.seconds()
-            )
-        )
-
-
-def benchmark_train(args):
-    cfg = setup(args)
-    model = build_model(cfg)
-    logger.info("Model:\n{}".format(model))
-    if comm.get_world_size() > 1:
-        model = DistributedDataParallel(
-            model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
-        )
-    optimizer = build_optimizer(cfg, model)
-    checkpointer = DetectionCheckpointer(model, optimizer=optimizer)
-    checkpointer.load(cfg.MODEL.WEIGHTS)
-
-    cfg.defrost()
-    cfg.DATALOADER.NUM_WORKERS = 0
-    data_loader = build_detection_train_loader(cfg)
-    dummy_data = list(itertools.islice(data_loader, 100))
-
-    def f():
-        data = DatasetFromList(dummy_data, copy=False)
-        while True:
-            yield from data
-
-    max_iter = 400
-    trainer = SimpleTrainer(model, f(), optimizer)
-    trainer.register_hooks(
-        [hooks.IterationTimer(), hooks.PeriodicWriter([CommonMetricPrinter(max_iter)])]
-    )
-    trainer.train(1, max_iter)
-
-
-@torch.no_grad()
-def benchmark_eval(args):
-    cfg = setup(args)
-    model = build_model(cfg)
-    model.eval()
-    logger.info("Model:\n{}".format(model))
-    DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
-
-    cfg.defrost()
-    cfg.DATALOADER.NUM_WORKERS = 0
-    data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
-    dummy_data = list(itertools.islice(data_loader, 100))
-
-    def f():
-        while True:
-            yield from DatasetFromList(dummy_data, copy=False)
-
-    for _ in range(5):  # warmup
-        model(dummy_data[0])
-
-    max_iter = 400
-    timer = Timer()
-    with tqdm.tqdm(total=max_iter) as pbar:
-        for idx, d in enumerate(f()):
-            if idx == max_iter:
-                break
-            model(d)
-            pbar.update()
-    logger.info("{} iters in {} seconds.".format(max_iter, timer.seconds()))
-
-
-if __name__ == "__main__":
-    parser = default_argument_parser()
-    parser.add_argument("--task", choices=["train", "eval", "data"], required=True)
-    args = parser.parse_args()
-    assert not args.eval_only
-
-    if args.task == "data":
-        f = benchmark_data
-    elif args.task == "train":
-        """
-        Note: training speed may not be representative.
-        The training cost of a R-CNN model varies with the content of the data
-        and the quality of the model.
-        """
-        f = benchmark_train
-    elif args.task == "eval":
-        f = benchmark_eval
-        # only benchmark single-GPU inference.
-        assert args.num_gpus == 1 and args.num_machines == 1
-    launch(f, args.num_gpus, args.num_machines, args.machine_rank, args.dist_url, args=(args,))
diff --git a/spaces/hezhaoqia/vits-simple-api/vits/vits.py b/spaces/hezhaoqia/vits-simple-api/vits/vits.py
deleted file mode 100644
index 21c06c33feaae3d84e6312fc5caf9d77fd1070d1..0000000000000000000000000000000000000000
--- a/spaces/hezhaoqia/vits-simple-api/vits/vits.py
+++ /dev/null
@@ -1,243 +0,0 @@
-import librosa
-from vits import commons
-import re
-import numpy as np
-import torch
-from torch import no_grad, LongTensor, inference_mode, FloatTensor
-from utils.nlp import sentence_split
-from vits.mel_processing import spectrogram_torch
-from vits.text import text_to_sequence
-from vits.models import SynthesizerTrn
-from utils import utils
-
-
-class VITS:
-    def __init__(self, model, config, model_=None, model_type=None, device=torch.device("cpu")):
-        self.model_type = model_type
-        self.hps_ms = utils.get_hparams_from_file(config)
-        self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
-        self.n_symbols = len(getattr(self.hps_ms, 'symbols', []))
-        self.speakers = getattr(self.hps_ms, 'speakers', ['0'])
-        if not isinstance(self.speakers, list):
-            self.speakers = [item[0] for item in sorted(list(self.speakers.items()), key=lambda x: x[1])]
-        self.use_f0 = getattr(self.hps_ms.data, 'use_f0', False)
-        self.emotion_embedding = getattr(self.hps_ms.data, 'emotion_embedding',
-                                         getattr(self.hps_ms.model, 'emotion_embedding', False))
-        self.bert_embedding = getattr(self.hps_ms.data, 'bert_embedding',
-                                      getattr(self.hps_ms.model, 'bert_embedding', False))
-        self.hps_ms.model.emotion_embedding = self.emotion_embedding
-        self.hps_ms.model.bert_embedding = self.bert_embedding
-
-        self.net_g_ms = SynthesizerTrn(
-            self.n_symbols,
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            n_speakers=self.n_speakers,
-            **self.hps_ms.model)
-        _ = self.net_g_ms.eval()
-        self.device = device
-
-        # load model
-        self.load_model(model, model_)
-
-    def load_model(self, model, model_=None):
-        utils.load_checkpoint(model, self.net_g_ms)
-        self.net_g_ms.to(self.device)
-        if self.model_type == "hubert":
-            self.hubert = model_
-        elif self.model_type == "w2v2":
-            self.emotion_reference = model_
-
-    def get_cleaned_text(self, text, hps, cleaned=False):
-        if cleaned:
-            text_norm = text_to_sequence(text, hps.symbols, [])
-        else:
-            if self.bert_embedding:
-                text_norm, char_embed = text_to_sequence(text, hps.symbols, hps.data.text_cleaners,
-                                                         bert_embedding=self.bert_embedding)
-                text_norm = LongTensor(text_norm)
-                return text_norm, char_embed
-            else:
-                text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-        if hps.data.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        text_norm = LongTensor(text_norm)
-        return text_norm
-
-    def get_cleaner(self):
-        return getattr(self.hps_ms.data, 'text_cleaners', [None])[0]
-
-    def get_speakers(self, escape=False):
-        return self.speakers
-
-    def infer(self, params):
-        with no_grad():
-            x_tst = params.get("stn_tst").unsqueeze(0).to(self.device)
-            x_tst_lengths = LongTensor([params.get("stn_tst").size(0)]).to(self.device)
-            x_tst_prosody = torch.FloatTensor(params.get("char_embeds")).unsqueeze(0).to(
-                self.device) if self.bert_embedding else None
-            sid = params.get("sid").to(self.device) if not self.bert_embedding else None
-            emotion = params.get("emotion").to(self.device) if self.emotion_embedding else None
-
-            audio = self.net_g_ms.infer(x=x_tst,
-                                        x_lengths=x_tst_lengths,
-                                        sid=sid,
-                                        noise_scale=params.get("noise_scale"),
-                                        noise_scale_w=params.get("noise_scale_w"),
-                                        length_scale=params.get("length_scale"),
-                                        emotion_embedding=emotion,
-                                        bert=x_tst_prosody)[0][0, 0].data.float().cpu().numpy()
-
-        torch.cuda.empty_cache()
-
-        return audio
-
-    def get_infer_param(self, length_scale, noise_scale, noise_scale_w, text=None, speaker_id=None, audio_path=None,
-                        emotion=None, cleaned=False, f0_scale=1):
-        emo = None
-        char_embeds = None
-        if self.model_type != "hubert":
-            if self.bert_embedding:
-                stn_tst, char_embeds = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned)
-                sid = None
-            else:
-                stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned)
-                sid = LongTensor([speaker_id])
-
-        if self.model_type == "w2v2":
-            # if emotion_reference.endswith('.npy'):
-            #     emotion = np.load(emotion_reference)
-            #     emotion = FloatTensor(emotion).unsqueeze(0)
-            # else:
-            #     audio16000, sampling_rate = librosa.load(
-            #         emotion_reference, sr=16000, mono=True)
-            #     emotion = self.w2v2(audio16000, sampling_rate)[
-            #         'hidden_states']
-            #     emotion_reference = re.sub(
-            #         r'\..*$', '', emotion_reference)
-            #     np.save(emotion_reference, emotion.squeeze(0))
-            #     emotion = FloatTensor(emotion)
-            emo = torch.FloatTensor(self.emotion_reference[emotion]).unsqueeze(0)
-
-
-        elif self.model_type == "hubert":
-            if self.use_f0:
-                audio, sampling_rate = librosa.load(audio_path, sr=self.hps_ms.data.sampling_rate, mono=True)
-                audio16000 = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            else:
-                audio16000, sampling_rate = librosa.load(audio_path, sr=16000, mono=True)
-
-            with inference_mode():
-                units = self.hubert.units(FloatTensor(audio16000).unsqueeze(0).unsqueeze(0)).squeeze(0).numpy()
-                if self.use_f0:
-                    f0 = librosa.pyin(audio,
-                                      sr=sampling_rate,
-                                      fmin=librosa.note_to_hz('C0'),
-                                      fmax=librosa.note_to_hz('C7'),
-                                      frame_length=1780)[0]
-                    target_length = len(units[:, 0])
-                    f0 = np.nan_to_num(np.interp(np.arange(0, len(f0) * target_length, len(f0)) / target_length,
-                                                 np.arange(0, len(f0)), f0)) * f0_scale
-                    units[:, 0] = f0 / 10
-
-            stn_tst = FloatTensor(units)
-            sid = LongTensor([speaker_id])
-        params = {"length_scale": length_scale, "noise_scale": noise_scale,
-                  "noise_scale_w": noise_scale_w, "stn_tst": stn_tst,
-                  "sid": sid, "emotion": emo, "char_embeds": char_embeds}
-
-        return params
-
-    def get_tasks(self, voice):
-        text = voice.get("text", None)
-        speaker_id = voice.get("id", 0)
-        length = voice.get("length", 1)
-        noise = voice.get("noise", 0.667)
-        noisew = voice.get("noisew", 0.8)
-        max = voice.get("max", 50)
-        lang = voice.get("lang", "auto")
-        speaker_lang = voice.get("speaker_lang", None)
-        audio_path = voice.get("audio_path", None)
-        emotion = voice.get("emotion", 0)
-
-        # 去除所有多余的空白字符
-        if text is not None: text = re.sub(r'\s+', ' ', text).strip()
-
-        tasks = []
-        if self.model_type == "vits":
-            sentence_list = sentence_split(text, max, lang, speaker_lang)
-            for sentence in sentence_list:
-                params = self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length,
-                                              noise_scale=noise, noise_scale_w=noisew)
-                tasks.append(params)
-
-        elif self.model_type == "hubert":
-            params = self.get_infer_param(speaker_id=speaker_id, length_scale=length, noise_scale=noise,
-                                          noise_scale_w=noisew, audio_path=audio_path)
-            tasks.append(params)
-
-        elif self.model_type == "w2v2":
-            sentence_list = sentence_split(text, max, lang, speaker_lang)
-            for sentence in sentence_list:
-                params = self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length,
-                                              noise_scale=noise, noise_scale_w=noisew, emotion=emotion)
-                tasks.append(params)
-
-        return tasks
-
-    def get_audio(self, voice, auto_break=False):
-        tasks = self.get_tasks(voice)
-        # 停顿0.75s，避免语音分段合成再拼接后的连接突兀
-        brk = np.zeros(int(0.75 * 22050), dtype=np.int16)
-
-        audios = []
-        for task in tasks:
-            if auto_break:
-                chunk = np.concatenate((self.infer(task), brk), axis=0)
-            else:
-                chunk = self.infer(task)
-            audios.append(chunk)
-
-        audio = np.concatenate(audios, axis=0)
-        return audio
-
-    def get_stream_audio(self, voice, auto_break=False):
-        tasks = self.get_tasks(voice)
-
-        brk = np.zeros(int(0.75 * 22050), dtype=np.int16)
-
-        for task in tasks:
-            if auto_break:
-                chunk = np.concatenate((self.infer(task), brk), axis=0)
-            else:
-                chunk = self.infer(task)
-
-            yield chunk
-
-    def voice_conversion(self, voice):
-        audio_path = voice.get("audio_path")
-        original_id = voice.get("original_id")
-        target_id = voice.get("target_id")
-
-        audio = utils.load_audio_to_torch(
-            audio_path, self.hps_ms.data.sampling_rate)
-
-        y = audio.unsqueeze(0)
-
-        spec = spectrogram_torch(y, self.hps_ms.data.filter_length,
-                                 self.hps_ms.data.sampling_rate, self.hps_ms.data.hop_length,
-                                 self.hps_ms.data.win_length,
-                                 center=False)
-        spec_lengths = LongTensor([spec.size(-1)])
-        sid_src = LongTensor([original_id])
-
-        with no_grad():
-            sid_tgt = LongTensor([target_id])
-            audio = self.net_g_ms.voice_conversion(spec.to(self.device),
-                                                   spec_lengths.to(self.device),
-                                                   sid_src=sid_src.to(self.device),
-                                                   sid_tgt=sid_tgt.to(self.device))[0][0, 0].data.cpu().float().numpy()
-
-        torch.cuda.empty_cache()
-
-        return audio
diff --git a/spaces/hhalim/EleutherAI-gpt-j-6B/app.py b/spaces/hhalim/EleutherAI-gpt-j-6B/app.py
deleted file mode 100644
index b4ab9549994514c1b64784efe8b81534bb3fde6e..0000000000000000000000000000000000000000
--- a/spaces/hhalim/EleutherAI-gpt-j-6B/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/EleutherAI/gpt-j-6B").launch()
\ No newline at end of file
diff --git a/spaces/huaiji3y/bingo-Public/src/components/ui/input.tsx b/spaces/huaiji3y/bingo-Public/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/huggingface-course/amazon-reviews-demo/README.md b/spaces/huggingface-course/amazon-reviews-demo/README.md
deleted file mode 100644
index cfb6b4baae95773877d22b9c02ff3ab222449717..0000000000000000000000000000000000000000
--- a/spaces/huggingface-course/amazon-reviews-demo/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Amazon Reviews Demo
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-app_file: app.py 
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/hysts/ViTPose_video/model.py b/spaces/hysts/ViTPose_video/model.py
deleted file mode 100644
index 56123e755f8e891d1a85428d6959a597ec488390..0000000000000000000000000000000000000000
--- a/spaces/hysts/ViTPose_video/model.py
+++ /dev/null
@@ -1,271 +0,0 @@
-from __future__ import annotations
-
-import os
-import shlex
-import subprocess
-import sys
-import tempfile
-
-if os.getenv('SYSTEM') == 'spaces':
-    import mim
-
-    mim.uninstall('mmcv-full', confirm_yes=True)
-    mim.install('mmcv-full==1.5.0', is_yes=True)
-
-    subprocess.call(shlex.split('pip uninstall -y opencv-python'))
-    subprocess.call(shlex.split('pip uninstall -y opencv-python-headless'))
-    subprocess.call(
-        shlex.split('pip install opencv-python-headless==4.8.0.74'))
-
-import cv2
-import huggingface_hub
-import numpy as np
-import torch
-import torch.nn as nn
-
-sys.path.insert(0, 'ViTPose/')
-
-from mmdet.apis import inference_detector, init_detector
-from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
-                         process_mmdet_results, vis_pose_result)
-
-
-class DetModel:
-    MODEL_DICT = {
-        'YOLOX-tiny': {
-            'config':
-            'mmdet_configs/configs/yolox/yolox_tiny_8x8_300e_coco.py',
-            'model':
-            'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth',
-        },
-        'YOLOX-s': {
-            'config':
-            'mmdet_configs/configs/yolox/yolox_s_8x8_300e_coco.py',
-            'model':
-            'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth',
-        },
-        'YOLOX-l': {
-            'config':
-            'mmdet_configs/configs/yolox/yolox_l_8x8_300e_coco.py',
-            'model':
-            'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth',
-        },
-        'YOLOX-x': {
-            'config':
-            'mmdet_configs/configs/yolox/yolox_x_8x8_300e_coco.py',
-            'model':
-            'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth',
-        },
-    }
-
-    def __init__(self):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self._load_all_models_once()
-        self.model_name = 'YOLOX-l'
-        self.model = self._load_model(self.model_name)
-
-    def _load_all_models_once(self) -> None:
-        for name in self.MODEL_DICT:
-            self._load_model(name)
-
-    def _load_model(self, name: str) -> nn.Module:
-        d = self.MODEL_DICT[name]
-        return init_detector(d['config'], d['model'], device=self.device)
-
-    def set_model(self, name: str) -> None:
-        if name == self.model_name:
-            return
-        self.model_name = name
-        self.model = self._load_model(name)
-
-    def detect_and_visualize(
-            self, image: np.ndarray,
-            score_threshold: float) -> tuple[list[np.ndarray], np.ndarray]:
-        out = self.detect(image)
-        vis = self.visualize_detection_results(image, out, score_threshold)
-        return out, vis
-
-    def detect(self, image: np.ndarray) -> list[np.ndarray]:
-        image = image[:, :, ::-1]  # RGB -> BGR
-        out = inference_detector(self.model, image)
-        return out
-
-    def visualize_detection_results(
-            self,
-            image: np.ndarray,
-            detection_results: list[np.ndarray],
-            score_threshold: float = 0.3) -> np.ndarray:
-        person_det = [detection_results[0]] + [np.array([]).reshape(0, 5)] * 79
-
-        image = image[:, :, ::-1]  # RGB -> BGR
-        vis = self.model.show_result(image,
-                                     person_det,
-                                     score_thr=score_threshold,
-                                     bbox_color=None,
-                                     text_color=(200, 200, 200),
-                                     mask_color=None)
-        return vis[:, :, ::-1]  # BGR -> RGB
-
-
-class PoseModel:
-    MODEL_DICT = {
-        'ViTPose-B (single-task train)': {
-            'config':
-            'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py',
-            'model': 'models/vitpose-b.pth',
-        },
-        'ViTPose-L (single-task train)': {
-            'config':
-            'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py',
-            'model': 'models/vitpose-l.pth',
-        },
-        'ViTPose-B (multi-task train, COCO)': {
-            'config':
-            'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py',
-            'model': 'models/vitpose-b-multi-coco.pth',
-        },
-        'ViTPose-L (multi-task train, COCO)': {
-            'config':
-            'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py',
-            'model': 'models/vitpose-l-multi-coco.pth',
-        },
-    }
-
-    def __init__(self):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.model_name = 'ViTPose-B (multi-task train, COCO)'
-        self.model = self._load_model(self.model_name)
-
-    def _load_all_models_once(self) -> None:
-        for name in self.MODEL_DICT:
-            self._load_model(name)
-
-    def _load_model(self, name: str) -> nn.Module:
-        d = self.MODEL_DICT[name]
-        ckpt_path = huggingface_hub.hf_hub_download('public-data/ViTPose',
-                                                    d['model'])
-        model = init_pose_model(d['config'], ckpt_path, device=self.device)
-        return model
-
-    def set_model(self, name: str) -> None:
-        if name == self.model_name:
-            return
-        self.model_name = name
-        self.model = self._load_model(name)
-
-    def predict_pose_and_visualize(
-        self,
-        image: np.ndarray,
-        det_results: list[np.ndarray],
-        box_score_threshold: float,
-        kpt_score_threshold: float,
-        vis_dot_radius: int,
-        vis_line_thickness: int,
-    ) -> tuple[list[dict[str, np.ndarray]], np.ndarray]:
-        out = self.predict_pose(image, det_results, box_score_threshold)
-        vis = self.visualize_pose_results(image, out, kpt_score_threshold,
-                                          vis_dot_radius, vis_line_thickness)
-        return out, vis
-
-    def predict_pose(
-            self,
-            image: np.ndarray,
-            det_results: list[np.ndarray],
-            box_score_threshold: float = 0.5) -> list[dict[str, np.ndarray]]:
-        image = image[:, :, ::-1]  # RGB -> BGR
-        person_results = process_mmdet_results(det_results, 1)
-        out, _ = inference_top_down_pose_model(self.model,
-                                               image,
-                                               person_results=person_results,
-                                               bbox_thr=box_score_threshold,
-                                               format='xyxy')
-        return out
-
-    def visualize_pose_results(self,
-                               image: np.ndarray,
-                               pose_results: list[dict[str, np.ndarray]],
-                               kpt_score_threshold: float = 0.3,
-                               vis_dot_radius: int = 4,
-                               vis_line_thickness: int = 1) -> np.ndarray:
-        image = image[:, :, ::-1]  # RGB -> BGR
-        vis = vis_pose_result(self.model,
-                              image,
-                              pose_results,
-                              kpt_score_thr=kpt_score_threshold,
-                              radius=vis_dot_radius,
-                              thickness=vis_line_thickness)
-        return vis[:, :, ::-1]  # BGR -> RGB
-
-
-class AppModel:
-    def __init__(self):
-        self.det_model = DetModel()
-        self.pose_model = PoseModel()
-
-    def run(
-        self, video_path: str, det_model_name: str, pose_model_name: str,
-        box_score_threshold: float, max_num_frames: int,
-        kpt_score_threshold: float, vis_dot_radius: int,
-        vis_line_thickness: int
-    ) -> tuple[str, list[list[dict[str, np.ndarray]]]]:
-        if video_path is None:
-            return
-        self.det_model.set_model(det_model_name)
-        self.pose_model.set_model(pose_model_name)
-
-        cap = cv2.VideoCapture(video_path)
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        fps = cap.get(cv2.CAP_PROP_FPS)
-
-        preds_all = []
-
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-        writer = cv2.VideoWriter(out_file.name, fourcc, fps, (width, height))
-        for _ in range(max_num_frames):
-            ok, frame = cap.read()
-            if not ok:
-                break
-            rgb_frame = frame[:, :, ::-1]
-            det_preds = self.det_model.detect(rgb_frame)
-            preds, vis = self.pose_model.predict_pose_and_visualize(
-                rgb_frame, det_preds, box_score_threshold, kpt_score_threshold,
-                vis_dot_radius, vis_line_thickness)
-            preds_all.append(preds)
-            writer.write(vis[:, :, ::-1])
-        cap.release()
-        writer.release()
-
-        return out_file.name, preds_all
-
-    def visualize_pose_results(self, video_path: str,
-                               pose_preds_all: list[list[dict[str,
-                                                              np.ndarray]]],
-                               kpt_score_threshold: float, vis_dot_radius: int,
-                               vis_line_thickness: int) -> str:
-        if video_path is None or pose_preds_all is None:
-            return
-        cap = cv2.VideoCapture(video_path)
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        fps = cap.get(cv2.CAP_PROP_FPS)
-
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-        writer = cv2.VideoWriter(out_file.name, fourcc, fps, (width, height))
-        for pose_preds in pose_preds_all:
-            ok, frame = cap.read()
-            if not ok:
-                break
-            rgb_frame = frame[:, :, ::-1]
-            vis = self.pose_model.visualize_pose_results(
-                rgb_frame, pose_preds, kpt_score_threshold, vis_dot_radius,
-                vis_line_thickness)
-            writer.write(vis[:, :, ::-1])
-        cap.release()
-        writer.release()
-
-        return out_file.name
diff --git a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/ms1mv2_r50.py b/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/ms1mv2_r50.py
deleted file mode 100644
index 236721a526489b2cac7ba66a22bfc3d650e744cd..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/ms1mv2_r50.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.5, 0.0)
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/faces_emore"
-config.num_classes = 85742
-config.num_image = 5822653
-config.num_epoch = 20
-config.warmup_epoch = 0
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/hzrr/dal_audio_inference/README.md b/spaces/hzrr/dal_audio_inference/README.md
deleted file mode 100644
index 73d11d315c7fd003333228566808a21fa9792fa2..0000000000000000000000000000000000000000
--- a/spaces/hzrr/dal_audio_inference/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Dal Audio Inference
-emoji: 📚
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/illrapper/ill/Dockerfile b/spaces/illrapper/ill/Dockerfile
deleted file mode 100644
index a4c8b4f88ec3000f75b1413a72ba55e294692201..0000000000000000000000000000000000000000
--- a/spaces/illrapper/ill/Dockerfile
+++ /dev/null
@@ -1,2 +0,0 @@
-FROM huggingface/autotrain-advanced:latest
-CMD autotrain setup && autotrain app --port 7860
diff --git a/spaces/inamXcontru/PoeticTTS/Avatar Pc Game Activation Keygen Download Crack _HOT_.md b/spaces/inamXcontru/PoeticTTS/Avatar Pc Game Activation Keygen Download Crack _HOT_.md
deleted file mode 100644
index 571792ee0e0efe4ecd94bf059c4b93f77b054d34..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Avatar Pc Game Activation Keygen Download Crack _HOT_.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-<br />
-<p>It's also worth mentioning that keygens are much more valuable to bad actors than cracks, becausea keygen can be used on the <strong>real</strong> application, vs the bad actor having to distribute a <strong>modified</strong>,cracked version of the application.</p>
-<p>But remember, a crack != a keygen, so your application's licensing always runsthe risk of being circumvented via code modification. But license keys cannotbe forged when you utilize a licensing system built on modern cryptography.</p>
-<h2>Avatar Pc Game Activation Keygen Download Crack</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://gohhs.com/2uz5xG">https://gohhs.com/2uz5xG</a></b></p><br /><br />
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diff --git a/spaces/inamXcontru/PoeticTTS/Bounty Hunter In Italian Free Download.md b/spaces/inamXcontru/PoeticTTS/Bounty Hunter In Italian Free Download.md
deleted file mode 100644
index 4945a085ff2a8d698db29f2772da299a880293d6..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Bounty Hunter In Italian Free Download.md	
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/CxprogrammerdownloadFixed Fullversion.md b/spaces/inplisQlawa/anything-midjourney-v4-1/CxprogrammerdownloadFixed Fullversion.md
deleted file mode 100644
index 25ab8fb4cc06a83103cbc1c35a00c803f26f865f..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/CxprogrammerdownloadFixed Fullversion.md	
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-<br />
-<h1>CX-Programmer: The Best Software for Omron PLCs</h1>
-<p>If you are looking for a reliable and easy-to-use software for programming and debugging your Omron PLCs, you should consider CX-Programmer. CX-Programmer is part of the CX-One software suite, which integrates various tools for PLC development, such as CX-Server, CX-Process Tool, CX-Motion, CX-Position, CX-Simulator, CX-Integrator, CX-Designer, CX-Drive and CX-Protocol.</p>
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-<p>In this article, we will show you some of the features and benefits of using CX-Programmer for your Omron PLC projects. We will also tell you how to download the full version of CX-Programmer and install it on your computer.</p>
-
-<h2>What is CX-Programmer?</h2>
-<p>CX-Programmer is the programming software for all Omron's PLC series, from the compact CP1 series to the modular CJ2 and CS1 series. It supports both standard function blocks in IEC 61131-3 structured text or conventional ladder language, as well as custom function blocks created by the user. CX-Programmer also allows you to create and edit symbols, data types, structures and arrays for your PLC program.</p>
-<p>CX-Programmer has a user-friendly interface that makes programming and debugging your PLC a simple drag & drop configuration. You can use smart input features, such as predictive-text browser, auto incrementing addresses and automatic rung connections, to reduce keystrokes and errors. You can also use the watch window to monitor and modify the values of symbols and memory areas during online operation.</p>
-
-<h2>Why Use CX-Programmer?</h2>
-<p>CX-Programmer offers many advantages over other PLC programming software, such as:</p>
-<p></p>
-<ul>
-<li>It is fully integrated with the CX-One software suite, which means you can access other tools for PLC development without switching applications.</li>
-<li>It supports all Omron's PLC series, which means you can use the same software for different models and platforms.</li>
-<li>It has a wide variety of features to speed up the development of your PLC program, such as structures and arrays, timers and counters, smart input, position control verification and more.</li>
-<li>It has a powerful debugging function that allows you to test and troubleshoot your PLC program online or offline, using breakpoints, single-step execution, forced output and more.</li>
-<li>It has a flexible licensing system that allows you to install the software on multiple computers with a single license number.</li>
-</ul>
-
-<h2>How to Download and Install CX-Programmer?</h2>
-<p>If you want to download the full version of CX-Programmer, you can follow these steps:</p>
-<ol>
-<li>Go to <a href="https://plc247.com/download-cx-one-v4-51-cx-programmer-v9-73-full-version/">this link</a> and click on the GoogleDrive link to download the CX-One V4.51 software package.</li>
-<li>Extract the zip file using the password plc247.com.</li>
-<li>Run the setup.exe file and follow the instructions to install the software on your computer.</li>
-<li>Enter the license number when prompted. You can find the license number in the readme.txt file inside the zip file.</li>
-<li>Enjoy using CX-Programmer for your Omron PLC projects.</li>
-</ol>
-
-<h2>Conclusion</h2>
-<p>CX-Programmer is a great software for Omron PLCs that can help you create and debug your PLC programs with ease. It has many features and benefits that make it stand out from other PLC programming software. You can download the full version of CX-Programmer from plc247.com and install it on your computer with a single license number. If you have any questions or problems during the download or installation process, please contact us at Farishadi55@gmail.com or Whatsapp at 085786414566. We will be happy to assist you.</p>
-<h2>How to Use CX-Programmer?</h2>
-<p>Using CX-Programmer is very easy and intuitive. You can create and edit your PLC program using the graphical editor, where you can drag and drop function blocks, symbols and instructions. You can also use the text editor, where you can write your program in structured text or ladder language. You can switch between the two editors at any time.</p>
-<p>To use CX-Programmer, you need to connect your computer to your Omron PLC using a USB cable, an Ethernet cable or a wireless adapter. You can then transfer your program to the PLC or upload the existing program from the PLC. You can also monitor and modify the PLC status and data online or offline.</p>
-<p>CX-Programmer has a built-in simulator that allows you to test your program without connecting to a real PLC. You can simulate the input and output signals, check the logic and timing of your program, and debug any errors or faults. You can also use the position control verification function to display graphs of positions or speeds against time, verifying the action prior to transferring.</p>
-
-<h2>Where to Find More Information about CX-Programmer?</h2>
-<p>If you want to learn more about CX-Programmer and its features, you can visit the official website of Omron Europe, where you can find detailed information, manuals, tutorials and videos. You can also contact Omron's technical support team if you have any questions or issues with the software.</p>
-<p>Another source of information is plc247.com, a website dedicated to PLC programming and development. Here you can find articles, guides, tips and tricks on how to use CX-Programmer and other Omron software. You can also download the latest version of CX-One and CX-Programmer from this website.</p>
-<p>You can also join the online community of Omron PLC users and developers on forums, blogs and social media platforms. Here you can share your experience, ask for help, give feedback and exchange ideas with other PLC enthusiasts.</p>
-
-<h2>Conclusion</h2>
-<p>CX-Programmer is a powerful and versatile software for Omron PLCs that can help you create and debug your PLC programs with ease. It has many features and benefits that make it stand out from other PLC programming software. You can download the full version of CX-Programmer from plc247.com and install it on your computer with a single license number. If you have any questions or problems during the download or installation process, please contact us at Farishadi55@gmail.com or Whatsapp at 085786414566. We will be happy to assist you.</p>
-<h2>What are the Requirements and Compatibility of CX-Programmer?</h2>
-<p>CX-Programmer has some minimum requirements for your computer system and your Omron PLC. To use CX-Programmer, you need to have:</p>
-<ul>
-<li>A computer with Windows Vista, 7, 8, 8.1 or 10 operating system (32/64 bit).</li>
-<li>A USB port, an Ethernet port or a wireless adapter for connecting to your Omron PLC.</li>
-<li>An Omron PLC from the CP1, CP2E, CP2H, CP2L, CP2N, CP2W, CJ1W, CJ2H, CJ2M or CS1 series.</li>
-<li>A CX-One license number or a CX-Programmer license number.</li>
-</ul>
-<p>CX-Programmer is compatible with most of the Omron PLC series and models. However, some features may not be available for some PLCs. For example, the position control verification function is only available for CJ2H-CPU6x-EIP and CJ2M-CPU3x PLCs. You can check the compatibility of CX-Programmer with your Omron PLC on the official website of Omron Europe.</p>
-
-<h2>What are the Alternatives to CX-Programmer?</h2>
-<p>CX-Programmer is one of the best software for Omron PLCs, but it is not the only one. There are some alternatives that you can use if you want to try something different or if you have a different PLC brand. Some of the alternatives to CX-Programmer are:</p>
-<ul>
-<li>RSLogix 5000: This is a software for programming and debugging Allen-Bradley PLCs. It supports ladder logic, structured text, function block diagram and sequential function chart languages. It also has features such as tag-based addressing, online editing and simulation.</li>
-<li>STEP 7: This is a software for programming and debugging Siemens PLCs. It supports ladder logic, statement list and function block diagram languages. It also has features such as symbolic addressing, online testing and diagnostics.</li>
-<li>CODESYS: This is a software for programming and debugging various PLC brands that support the IEC 61131-3 standard. It supports all five languages of the standard: ladder logic, structured text, function block diagram, instruction list and sequential function chart. It also has features such as object-oriented programming, visualization and simulation.</li>
-</ul>
-
-<h2>Conclusion</h2>
-<p>CX-Programmer is a powerful and versatile software for Omron PLCs that can help you create and debug your PLC programs with ease. It has many features and benefits that make it stand out from other PLC programming software. You can download the full version of CX-Programmer from plc247.com and install it on your computer with a single license number. If you have any questions or problems during the download or installation process, please contact us at Farishadi55@gmail.com or Whatsapp at 085786414566. We will be happy to assist you.</p> 3cee63e6c2<br />
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deleted file mode 100644
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diff --git a/spaces/jackli888/stable-diffusion-webui/modules/codeformer_model.py b/spaces/jackli888/stable-diffusion-webui/modules/codeformer_model.py
deleted file mode 100644
index fc8fbc3e947f64a7f23f0da7243d6e0ad7cbeb79..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/codeformer_model.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import os
-import sys
-import traceback
-
-import cv2
-import torch
-
-import modules.face_restoration
-import modules.shared
-from modules import shared, devices, modelloader
-from modules.paths import models_path
-
-# codeformer people made a choice to include modified basicsr library to their project which makes
-# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
-# I am making a choice to include some files from codeformer to work around this issue.
-model_dir = "Codeformer"
-model_path = os.path.join(models_path, model_dir)
-model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-
-have_codeformer = False
-codeformer = None
-
-
-def setup_model(dirname):
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
-    path = modules.paths.paths.get("CodeFormer", None)
-    if path is None:
-        return
-
-    try:
-        from torchvision.transforms.functional import normalize
-        from modules.codeformer.codeformer_arch import CodeFormer
-        from basicsr.utils.download_util import load_file_from_url
-        from basicsr.utils import imwrite, img2tensor, tensor2img
-        from facelib.utils.face_restoration_helper import FaceRestoreHelper
-        from facelib.detection.retinaface import retinaface
-        from modules.shared import cmd_opts
-
-        net_class = CodeFormer
-
-        class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
-            def name(self):
-                return "CodeFormer"
-
-            def __init__(self, dirname):
-                self.net = None
-                self.face_helper = None
-                self.cmd_dir = dirname
-
-            def create_models(self):
-
-                if self.net is not None and self.face_helper is not None:
-                    self.net.to(devices.device_codeformer)
-                    return self.net, self.face_helper
-                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth')
-                if len(model_paths) != 0:
-                    ckpt_path = model_paths[0]
-                else:
-                    print("Unable to load codeformer model.")
-                    return None, None
-                net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
-                checkpoint = torch.load(ckpt_path)['params_ema']
-                net.load_state_dict(checkpoint)
-                net.eval()
-
-                if hasattr(retinaface, 'device'):
-                    retinaface.device = devices.device_codeformer
-                face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
-
-                self.net = net
-                self.face_helper = face_helper
-
-                return net, face_helper
-
-            def send_model_to(self, device):
-                self.net.to(device)
-                self.face_helper.face_det.to(device)
-                self.face_helper.face_parse.to(device)
-
-            def restore(self, np_image, w=None):
-                np_image = np_image[:, :, ::-1]
-
-                original_resolution = np_image.shape[0:2]
-
-                self.create_models()
-                if self.net is None or self.face_helper is None:
-                    return np_image
-
-                self.send_model_to(devices.device_codeformer)
-
-                self.face_helper.clean_all()
-                self.face_helper.read_image(np_image)
-                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
-                self.face_helper.align_warp_face()
-
-                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
-                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-                    cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
-
-                    try:
-                        with torch.no_grad():
-                            output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
-                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                        del output
-                        torch.cuda.empty_cache()
-                    except Exception as error:
-                        print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
-                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
-
-                    restored_face = restored_face.astype('uint8')
-                    self.face_helper.add_restored_face(restored_face)
-
-                self.face_helper.get_inverse_affine(None)
-
-                restored_img = self.face_helper.paste_faces_to_input_image()
-                restored_img = restored_img[:, :, ::-1]
-
-                if original_resolution != restored_img.shape[0:2]:
-                    restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
-
-                self.face_helper.clean_all()
-
-                if shared.opts.face_restoration_unload:
-                    self.send_model_to(devices.cpu)
-
-                return restored_img
-
-        global have_codeformer
-        have_codeformer = True
-
-        global codeformer
-        codeformer = FaceRestorerCodeFormer(dirname)
-        shared.face_restorers.append(codeformer)
-
-    except Exception:
-        print("Error setting up CodeFormer:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-   # sys.path = stored_sys_path
diff --git a/spaces/jbilcke-hf/image-caption-server/style.css b/spaces/jbilcke-hf/image-caption-server/style.css
deleted file mode 100644
index c031280ed2fae5d64d3024157cbdbc57508db86b..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/image-caption-server/style.css
+++ /dev/null
@@ -1,10 +0,0 @@
-h1 {
-  text-align: center;
-}
-
-#duplicate-button {
-  margin: auto;
-  color: #fff;
-  background: #1565c0;
-  border-radius: 100vh;
-}
diff --git a/spaces/jbraun19/Webcam-Object-Recognition-Yolo-n-Coco/config.py b/spaces/jbraun19/Webcam-Object-Recognition-Yolo-n-Coco/config.py
deleted file mode 100644
index 30a0a8149d5b1bb1a8f3f2868018e62ec45eefef..0000000000000000000000000000000000000000
--- a/spaces/jbraun19/Webcam-Object-Recognition-Yolo-n-Coco/config.py
+++ /dev/null
@@ -1,17 +0,0 @@
-yolo_config = {
-    # Basic
-    'img_size': (416, 416, 3),
-    'anchors': [12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401],
-    'strides': [8, 16, 32],
-    'xyscale': [1.2, 1.1, 1.05],
-
-    # Training
-    'iou_loss_thresh': 0.5,
-    'batch_size': 8,
-    'num_gpu': 1,  # 2,
-
-    # Inference
-    'max_boxes': 100,
-    'iou_threshold': 0.413,
-    'score_threshold': 0.3,
-}
diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer_preprocess_audio.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer_preprocess_audio.py
deleted file mode 100644
index fd4d01d476d77391322aef9d9d5a005adb1f5c15..0000000000000000000000000000000000000000
--- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer_preprocess_audio.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from synthesizer.preprocess import preprocess_dataset
-from synthesizer.hparams import hparams
-from utils.argutils import print_args
-from pathlib import Path
-import argparse
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Preprocesses audio files from datasets, encodes them as mel spectrograms "
-                    "and writes them to  the disk. Audio files are also saved, to be used by the "
-                    "vocoder for training.",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument("datasets_root", type=Path, help=\
-        "Path to the directory containing your LibriSpeech/TTS datasets.")
-    parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
-        "Path to the output directory that will contain the mel spectrograms, the audios and the "
-        "embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
-    parser.add_argument("-n", "--n_processes", type=int, default=None, help=\
-        "Number of processes in parallel.")
-    parser.add_argument("-s", "--skip_existing", action="store_true", help=\
-        "Whether to overwrite existing files with the same name. Useful if the preprocessing was "
-        "interrupted.")
-    parser.add_argument("--hparams", type=str, default="", help=\
-        "Hyperparameter overrides as a comma-separated list of name-value pairs")
-    parser.add_argument("--no_trim", action="store_true", help=\
-        "Preprocess audio without trimming silences (not recommended).")
-    parser.add_argument("--no_alignments", action="store_true", help=\
-        "Use this option when dataset does not include alignments\
-        (these are used to split long audio files into sub-utterances.)")
-    parser.add_argument("--datasets_name", type=str, default="LibriSpeech", help=\
-        "Name of the dataset directory to process.")
-    parser.add_argument("--subfolders", type=str, default="train-clean-100, train-clean-360", help=\
-        "Comma-separated list of subfolders to process inside your dataset directory")
-    args = parser.parse_args()
-
-    # Process the arguments
-    if not hasattr(args, "out_dir"):
-        args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
-
-    # Create directories
-    assert args.datasets_root.exists()
-    args.out_dir.mkdir(exist_ok=True, parents=True)
-
-    # Verify webrtcvad is available
-    if not args.no_trim:
-        try:
-            import webrtcvad
-        except:
-            raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
-                "noise removal and is recommended. Please install and try again. If installation fails, "
-                "use --no_trim to disable this error message.")
-    del args.no_trim
-
-    # Preprocess the dataset
-    print_args(args, parser)
-    args.hparams = hparams.parse(args.hparams)
-    preprocess_dataset(**vars(args))
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/__init__.py
deleted file mode 100644
index ba2d4855905903e30e9d05392d98a88bc26f4a18..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/__init__.py
+++ /dev/null
@@ -1,79 +0,0 @@
-#
-# A block cipher is instantiated as a combination of:
-# 1. A base cipher (such as AES)
-# 2. A mode of operation (such as CBC)
-#
-# Both items are implemented as C modules.
-#
-# The API of #1 is (replace "AES" with the name of the actual cipher):
-# - AES_start_operaion(key) --> base_cipher_state
-# - AES_encrypt(base_cipher_state, in, out, length)
-# - AES_decrypt(base_cipher_state, in, out, length)
-# - AES_stop_operation(base_cipher_state)
-#
-# Where base_cipher_state is AES_State, a struct with BlockBase (set of
-# pointers to encrypt/decrypt/stop) followed by cipher-specific data.
-#
-# The API of #2 is (replace "CBC" with the name of the actual mode):
-# - CBC_start_operation(base_cipher_state) --> mode_state
-# - CBC_encrypt(mode_state, in, out, length)
-# - CBC_decrypt(mode_state, in, out, length)
-# - CBC_stop_operation(mode_state)
-#
-# where mode_state is a a pointer to base_cipher_state plus mode-specific data.
-
-import os
-
-from Crypto.Cipher._mode_ecb import _create_ecb_cipher
-from Crypto.Cipher._mode_cbc import _create_cbc_cipher
-from Crypto.Cipher._mode_cfb import _create_cfb_cipher
-from Crypto.Cipher._mode_ofb import _create_ofb_cipher
-from Crypto.Cipher._mode_ctr import _create_ctr_cipher
-from Crypto.Cipher._mode_openpgp import _create_openpgp_cipher
-from Crypto.Cipher._mode_ccm import _create_ccm_cipher
-from Crypto.Cipher._mode_eax import _create_eax_cipher
-from Crypto.Cipher._mode_siv import _create_siv_cipher
-from Crypto.Cipher._mode_gcm import _create_gcm_cipher
-from Crypto.Cipher._mode_ocb import _create_ocb_cipher
-
-_modes = { 1:_create_ecb_cipher,
-           2:_create_cbc_cipher,
-           3:_create_cfb_cipher,
-           5:_create_ofb_cipher,
-           6:_create_ctr_cipher,
-           7:_create_openpgp_cipher,
-           9:_create_eax_cipher
-           }
-
-_extra_modes = { 8:_create_ccm_cipher,
-                10:_create_siv_cipher,
-                11:_create_gcm_cipher,
-                12:_create_ocb_cipher
-                }
-
-def _create_cipher(factory, key, mode, *args, **kwargs):
-
-    kwargs["key"] = key
-
-    modes = dict(_modes)
-    if kwargs.pop("add_aes_modes", False):
-        modes.update(_extra_modes)
-    if not mode in modes:
-        raise ValueError("Mode not supported")
-
-    if args:
-        if mode in (8, 9, 10, 11, 12):
-            if len(args) > 1:
-                raise TypeError("Too many arguments for this mode")
-            kwargs["nonce"] = args[0]
-        elif mode in (2, 3, 5, 7):
-            if len(args) > 1:
-                raise TypeError("Too many arguments for this mode")
-            kwargs["IV"] = args[0]
-        elif mode == 6:
-            if len(args) > 0:
-                raise TypeError("Too many arguments for this mode")
-        elif mode == 1:
-            raise TypeError("IV is not meaningful for the ECB mode")
-
-    return modes[mode](factory, **kwargs)
diff --git a/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/modules/image_degradation/bsrgan_light.py b/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index ec1200882368fe48194ed94b9a57c97276aa9e83..0000000000000000000000000000000000000000
--- a/spaces/johnslegers/stable-diffusion-gui-test/ldmlib/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldmlib.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-
-    wd2 = wd2/4
-    wd = wd/4
-
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(80, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        # elif i == 1:
-        #     image = add_blur(image, sf=sf)
-
-        if i == 0:
-            pass
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.8:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-        #
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image": image}
-    return example
-
-
-
-
-if __name__ == '__main__':
-    print("hey")
-    img = util.imread_uint('utils/test.png', 3)
-    img = img[:448, :448]
-    h = img.shape[0] // 4
-    print("resizing to", h)
-    sf = 4
-    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-    for i in range(20):
-        print(i)
-        img_hq = img
-        img_lq = deg_fn(img)["image"]
-        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
-        print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
-        print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-        util.imsave(img_concat, str(i) + '.png')
diff --git a/spaces/joushe/moe-tts/text/korean.py b/spaces/joushe/moe-tts/text/korean.py
deleted file mode 100644
index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000
--- a/spaces/joushe/moe-tts/text/korean.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄳ', 'ㄱㅅ'),
-    ('ㄵ', 'ㄴㅈ'),
-    ('ㄶ', 'ㄴㅎ'),
-    ('ㄺ', 'ㄹㄱ'),
-    ('ㄻ', 'ㄹㅁ'),
-    ('ㄼ', 'ㄹㅂ'),
-    ('ㄽ', 'ㄹㅅ'),
-    ('ㄾ', 'ㄹㅌ'),
-    ('ㄿ', 'ㄹㅍ'),
-    ('ㅀ', 'ㄹㅎ'),
-    ('ㅄ', 'ㅂㅅ'),
-    ('ㅘ', 'ㅗㅏ'),
-    ('ㅙ', 'ㅗㅐ'),
-    ('ㅚ', 'ㅗㅣ'),
-    ('ㅝ', 'ㅜㅓ'),
-    ('ㅞ', 'ㅜㅔ'),
-    ('ㅟ', 'ㅜㅣ'),
-    ('ㅢ', 'ㅡㅣ'),
-    ('ㅑ', 'ㅣㅏ'),
-    ('ㅒ', 'ㅣㅐ'),
-    ('ㅕ', 'ㅣㅓ'),
-    ('ㅖ', 'ㅣㅔ'),
-    ('ㅛ', 'ㅣㅗ'),
-    ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', '에이'),
-    ('b', '비'),
-    ('c', '시'),
-    ('d', '디'),
-    ('e', '이'),
-    ('f', '에프'),
-    ('g', '지'),
-    ('h', '에이치'),
-    ('i', '아이'),
-    ('j', '제이'),
-    ('k', '케이'),
-    ('l', '엘'),
-    ('m', '엠'),
-    ('n', '엔'),
-    ('o', '오'),
-    ('p', '피'),
-    ('q', '큐'),
-    ('r', '아르'),
-    ('s', '에스'),
-    ('t', '티'),
-    ('u', '유'),
-    ('v', '브이'),
-    ('w', '더블유'),
-    ('x', '엑스'),
-    ('y', '와이'),
-    ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('t͡ɕ','ʧ'),
-    ('d͡ʑ','ʥ'),
-    ('ɲ','n^'),
-    ('ɕ','ʃ'),
-    ('ʷ','w'),
-    ('ɭ','l`'),
-    ('ʎ','ɾ'),
-    ('ɣ','ŋ'),
-    ('ɰ','ɯ'),
-    ('ʝ','j'),
-    ('ʌ','ə'),
-    ('ɡ','g'),
-    ('\u031a','#'),
-    ('\u0348','='),
-    ('\u031e',''),
-    ('\u0320',''),
-    ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
-    for regex, replacement in _latin_to_hangul:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def divide_hangul(text):
-    text = j2hcj(h2j(text))
-    for regex, replacement in _hangul_divided:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def hangul_number(num, sino=True):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    num = re.sub(',', '', num)
-
-    if num == '0':
-        return '영'
-    if not sino and num == '20':
-        return '스무'
-
-    digits = '123456789'
-    names = '일이삼사오육칠팔구'
-    digit2name = {d: n for d, n in zip(digits, names)}
-
-    modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-    decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-    digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-    digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-    spelledout = []
-    for i, digit in enumerate(num):
-        i = len(num) - i - 1
-        if sino:
-            if i == 0:
-                name = digit2name.get(digit, '')
-            elif i == 1:
-                name = digit2name.get(digit, '') + '십'
-                name = name.replace('일십', '십')
-        else:
-            if i == 0:
-                name = digit2mod.get(digit, '')
-            elif i == 1:
-                name = digit2dec.get(digit, '')
-        if digit == '0':
-            if i % 4 == 0:
-                last_three = spelledout[-min(3, len(spelledout)):]
-                if ''.join(last_three) == '':
-                    spelledout.append('')
-                    continue
-            else:
-                spelledout.append('')
-                continue
-        if i == 2:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 3:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 4:
-            name = digit2name.get(digit, '') + '만'
-            name = name.replace('일만', '만')
-        elif i == 5:
-            name = digit2name.get(digit, '') + '십'
-            name = name.replace('일십', '십')
-        elif i == 6:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 7:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 8:
-            name = digit2name.get(digit, '') + '억'
-        elif i == 9:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 10:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 11:
-            name = digit2name.get(digit, '') + '천'
-        elif i == 12:
-            name = digit2name.get(digit, '') + '조'
-        elif i == 13:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 14:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 15:
-            name = digit2name.get(digit, '') + '천'
-        spelledout.append(name)
-    return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-    for token in tokens:
-        num, classifier = token
-        if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-            spelledout = hangul_number(num, sino=False)
-        else:
-            spelledout = hangul_number(num, sino=True)
-        text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-    # digit by digit for remaining digits
-    digits = '0123456789'
-    names = '영일이삼사오육칠팔구'
-    for d, n in zip(digits, names):
-        text = text.replace(d, n)
-    return text
-
-
-def korean_to_lazy_ipa(text):
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
-    for regex, replacement in _ipa_to_lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def korean_to_ipa(text):
-    text = korean_to_lazy_ipa(text)
-    return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
diff --git a/spaces/jurgendn/table-extraction/components/data_module.py b/spaces/jurgendn/table-extraction/components/data_module.py
deleted file mode 100644
index 8bee662576ea355f22d843e333dc4c5d2db29ed5..0000000000000000000000000000000000000000
--- a/spaces/jurgendn/table-extraction/components/data_module.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from typing import Callable, List, Optional, Union
-
-import torch
-from pytorch_lightning import LightningDataModule
-from torch.utils.data import DataLoader, Dataset
-
-
-class SampleDataset(Dataset):
-
-    def __init__(self,
-                 x: Union[List, torch.Tensor],
-                 y: Union[List, torch.Tensor],
-                 transforms: Optional[Callable] = None) -> None:
-        super(SampleDataset, self).__init__()
-        self.x = x
-        self.y = y
-
-        if transforms is None:
-            # Replace None with some default transforms
-            # If image, could be an Resize and ToTensor
-            self.transforms = lambda x: x
-        else:
-            self.transforms = transforms
-
-    def __len__(self):
-        return len(self.x)
-
-    def __getitem__(self, index: int):
-        x = self.x[index]
-        y = self.y[index]
-
-        x = self.transforms(x)
-        return x, y
-
-
-class SampleDataModule(LightningDataModule):
-
-    def __init__(self,
-                 x: Union[List, torch.Tensor],
-                 y: Union[List, torch.Tensor],
-                 transforms: Optional[Callable] = None,
-                 val_ratio: float = 0,
-                 batch_size: int = 32) -> None:
-        super(SampleDataModule, self).__init__()
-        assert 0 <= val_ratio < 1
-        assert isinstance(batch_size, int)
-        self.x = x
-        self.y = y
-
-        self.transforms = transforms
-        self.val_ratio = val_ratio
-        self.batch_size = batch_size
-
-        self.setup()
-        self.prepare_data()
-
-    def setup(self, stage: Optional[str] = None) -> None:
-        pass
-
-    def prepare_data(self) -> None:
-        n_samples: int = len(self.x)
-        train_size: int = n_samples - int(n_samples * self.val_ratio)
-
-        self.train_dataset = SampleDataset(x=self.x[:train_size],
-                                           y=self.y[:train_size],
-                                           transforms=self.transforms)
-        if train_size < n_samples:
-            self.val_dataset = SampleDataset(x=self.x[train_size:],
-                                             y=self.y[train_size:],
-                                             transforms=self.transforms)
-        else:
-            self.val_dataset = SampleDataset(x=self.x[-self.batch_size:],
-                                             y=self.y[-self.batch_size:],
-                                             transforms=self.transforms)
-
-    def train_dataloader(self) -> DataLoader:
-        return DataLoader(dataset=self.train_dataset,
-                          batch_size=self.batch_size)
-
-    def val_dataloader(self) -> DataLoader:
-        return DataLoader(dataset=self.val_dataset, batch_size=self.batch_size)
diff --git a/spaces/jykoh/gill/gill/layers.py b/spaces/jykoh/gill/gill/layers.py
deleted file mode 100644
index 378c89cd9cd44f081ccdce38fb8f9bd7291b1df1..0000000000000000000000000000000000000000
--- a/spaces/jykoh/gill/gill/layers.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import torch
-from torch import nn
-
-
-class TextFcLayer(nn.Module):
-  """Layers used in mapping text embeddings to visual outputs."""
-
-  def __init__(self, in_dim: int, out_dim: int, num_input_tokens: int = 1, num_output_tokens: int = 1, mode: str = 'linear'):
-    super().__init__()
-
-    self.num_input_tokens = num_input_tokens
-    self.num_output_tokens = num_output_tokens
-    self.mode = mode
-
-    if mode == 'linear':
-      self.model = nn.Linear(in_dim, out_dim)
-    elif mode == 'gill_mapper':  # TODO(jykoh): Rename to GILLMapper
-      hidden_dim = 512
-      self.fc = nn.Linear(in_dim, hidden_dim)
-      self.tfm = nn.Transformer(batch_first=True, norm_first=True,
-                                d_model=hidden_dim, num_encoder_layers=4, num_decoder_layers=4,
-                                dim_feedforward=hidden_dim * 4, dropout=0.0, nhead=4)
-      self.model = nn.Linear(hidden_dim, out_dim)
-      self.query_embs = nn.Parameter(torch.randn(1, num_output_tokens, hidden_dim))
-    else:
-      raise NotImplementedError(mode)
-
-  def forward(self, x: torch.Tensor, input_embs: torch.Tensor) -> torch.Tensor:
-    outputs = None
-    
-    if self.mode == 'gill_mapper':
-      x = x + input_embs
-
-    if isinstance(self.model, nn.ModuleList):
-      assert len(self.model) == x.shape[1] == self.num_input_tokens, (len(self.model), x.shape, self.num_input_tokens)
-      outputs = []
-      for i in range(self.num_input_tokens):
-        outputs.append(self.model[i](x[:, i, :]))  # (N, D)
-      outputs = torch.stack(outputs, dim=1)  # (N, T, D)
-    else:
-      if self.mode == 'gill_mapper':
-        x = self.fc(x)
-        x = self.tfm(x, self.query_embs.repeat(x.shape[0], 1, 1))
-      outputs = self.model(x)
-
-      if outputs.shape[1] != self.num_output_tokens and self.mode == 'linear':
-        if self.mode == 'linear':
-          outputs = outputs[:, :self.num_output_tokens, :]
-        else:
-          raise NotImplementedError
-    
-    assert outputs.shape[1] == 1 or (outputs.shape[1] * outputs.shape[2] == self.num_output_tokens * 768), (outputs.shape, self.num_output_tokens)
-    return outputs  # (N, T, D)
-
diff --git a/spaces/kadirnar/yolor/yolor/helpers.py b/spaces/kadirnar/yolor/yolor/helpers.py
deleted file mode 100644
index c0e196019b018863a4c6ae1857f8d2bb4b5da85c..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/yolor/yolor/helpers.py
+++ /dev/null
@@ -1,125 +0,0 @@
-from yolor.utils.general import non_max_suppression, scale_coords
-from yolor.utils.download import attempt_download_from_hub
-from yolor.utils.datasets import LoadStreams, LoadImages
-from yolor.utils.plots import plot_one_box
-from yolor.models.models import Darknet
-from yolor.detect import load_classes
-import torch.backends.cudnn as cudnn
-import random
-import torch
-import cv2
-
-import sys
-from pathlib import Path
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  
-    
-class Yolor:
-    def __init__(
-        self, 
-        cfg: str = 'yolor/cfg/yolor_p6.cfg',
-        weights: str = 'yolor/yolor_p6.pt',
-        imgsz: int = 640,
-        half : bool = False,
-        device: str = 'cuda:0',
-        hf_model: bool = False
-    ):
-        self.cfg = cfg
-        self.imgsz = imgsz
-        self.half = half
-        self.webcam = False
-        self.conf = 0.25
-        self.iou = 0.45
-        self.classes = None
-        self.class_name = 'yolor/data/coco.names'
-        self.save = True
-        self.show = False
-        self.save_path = 'output.jpg'
-        
-        if hf_model:
-            self.weights = attempt_download_from_hub(weights)  
-        else:
-            self.weights = weights
-        
-        self.device = torch.device(device)
-        self.model = self.load_model()
-        
-    def load_model(self):
-        model = Darknet(self.cfg, self.imgsz).to(self.device)
-        model.load_state_dict(torch.load(self.weights, map_location=self.device)['model'])
-        model.to(self.device).eval()
-        if self.half:
-            model.half()  # to FP16
-            
-        return model
-    
-    
-    def predict(self, source):
-        if self.webcam:
-            cudnn.benchmark = True  # set True to speed up constant image size inference
-            dataset = LoadStreams(source, img_size=self.imgsz)
-        else:
-            save_img = True
-            dataset = LoadImages(source, img_size=self.imgsz, auto_size=64)
-
-        # Get names and colors
-        names = load_classes(self.class_name)
-        colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
-
-        # Run inference
-        img = torch.zeros((1, 3, self.imgsz, self.imgsz), device=self.device)  # init img
-        for path, img, im0s, vid_cap in dataset:
-            img = torch.from_numpy(img).to(self.device)
-            img = img.half() if self.half else img.float()  # uint8 to fp16/32
-            img /= 255.0  # 0 - 255 to 0.0 - 1.0
-            if img.ndimension() == 3:
-                img = img.unsqueeze(0)
-
-            # Inference
-            pred = self.model(img, augment=False)[0]
-
-            # Apply NMS
-            pred = non_max_suppression(pred, self.conf, self.iou, classes=self.classes, agnostic=False)
-            for i, det in enumerate(pred):  # detections per image
-                if self.webcam:  # batch_size >= 1
-                    p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
-                else:
-                    p, s, im0 = path, '', im0s
-
-                s += '%gx%g ' % img.shape[2:]  # print string
-                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-                if det is not None and len(det):
-                    # Rescale boxes from img_size to im0 size
-                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
-                    # Print results
-                    for c in det[:, -1].unique():
-                        n = (det[:, -1] == c).sum()  # detections per class
-                        s += '%g %ss, ' % (n, names[int(c)])  # add to string
-
-                    # Write results
-                    for *xyxy, conf, cls in det:
-                        if self.save or self.show:  # Add bbox to image
-                            label = '%s %.2f' % (names[int(cls)], conf)
-                            plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
-                            
-
-                # Stream results
-                if self.show:
-                    cv2.imshow(p, im0)
-                    if cv2.waitKey(1) == ord('q'):  # q to quit
-                        raise StopIteration
-
-                # Save results (image with detections)
-                if save_img:
-                    if dataset.mode == 'images':  
-                        cv2.imwrite(self.save_path, im0)       
-        
-        return self.save_path
-
-if __name__ == '__main__':
-    yolor = Yolor(cfg='yolor/cfg/yolor_p6.cfg', weights='yolor_p6.pt', imgsz=640, device='cuda:0', hf_model=False)
-    yolor.predict('yolor/data/highway.jpg')
diff --git a/spaces/kamezawash/rembg/rembg/__init__.py b/spaces/kamezawash/rembg/rembg/__init__.py
deleted file mode 100644
index afdc5405843f1d962cef9f0165280385c1b45ab3..0000000000000000000000000000000000000000
--- a/spaces/kamezawash/rembg/rembg/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import sys
-import warnings
-
-if not (sys.version_info.major == 3 and sys.version_info.minor == 9):
-    warnings.warn("This library is only for Python 3.9", RuntimeWarning)
-
-from . import _version
-
-__version__ = _version.get_versions()["version"]
-
-from .bg import remove
diff --git a/spaces/keras-dreambooth/dreambooth_fantasy/app.py b/spaces/keras-dreambooth/dreambooth_fantasy/app.py
deleted file mode 100644
index 64811a907cd1d7ed2bef858c5e8974f273e099bc..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/dreambooth_fantasy/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from huggingface_hub import from_pretrained_keras
-import keras_cv
-import gradio as gr
-from tensorflow import keras
-
-keras.mixed_precision.set_global_policy("mixed_float16")
-# load keras model
-resolution = 512
-dreambooth_model = keras_cv.models.StableDiffusion(
-        img_width=resolution, img_height=resolution, jit_compile=True, 
-    )
-loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_fantasy")
-dreambooth_model._diffusion_model = loaded_diffusion_model
-
-
-def generate_images(prompt: str, negative_prompt:str, num_imgs_to_gen: int, num_steps: int):
-    """
-    This function is used to generate images using our fine-tuned keras dreambooth stable diffusion model.
-    Args:
-        prompt (str): The text input given by the user based on which images will be generated.
-        num_imgs_to_gen (int): The number of images to be generated using given prompt.
-        num_steps (int): The number of denoising steps
-    Returns:
-        generated_img (List): List of images that were generated using the model
-    """
-    generated_img = dreambooth_model.text_to_image(
-        prompt, 
-        negative_prompt=negative_prompt,
-        batch_size=num_imgs_to_gen,
-        num_steps=num_steps,
-    )
-   
-    return generated_img
-    
-with gr.Blocks() as demo:
-    gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Keras Dreambooth - Dreambooth Fantasy Demo</h2>")    
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(lines=1, value="sks fantasy_world that is colorful with green scenary", label="Base Prompt")
-            negative_prompt = gr.Textbox(lines=1, value="deformed", label="Negative Prompt")
-            samples = gr.Slider(minimum=1, maximum=10, default=1, step=1, label="Number of Image")
-            num_steps = gr.Slider(label="Inference Steps",value=50)
-            run = gr.Button(value="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Outputs").style(grid=(1,2))
-
-    run.click(generate_images, inputs=[prompt,negative_prompt, samples, num_steps], outputs=gallery)
-    
-    gr.Examples([["A phot of sks fantasy_world that is colorful with green scenary","blue sky", 3, 75]],
-                [prompt,negative_prompt, samples,num_steps], gallery, generate_images)
-    gr.Markdown('\n Demo created by: <a href=\"https://huggingface.co/mwitiderrick/\">Derrick Mwiti</a>')
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/keras-io/cct/README.md b/spaces/keras-io/cct/README.md
deleted file mode 100644
index db89e95707841a6861c5ca15c40183eee09392b6..0000000000000000000000000000000000000000
--- a/spaces/keras-io/cct/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Cct
-emoji: 🌖
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/keras-io/ctc_asr/app.py b/spaces/keras-io/ctc_asr/app.py
deleted file mode 100644
index 2c818de7f56723303b8e414974f4caedbc800434..0000000000000000000000000000000000000000
--- a/spaces/keras-io/ctc_asr/app.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-import tensorflow_io as tfio
-from huggingface_hub import from_pretrained_keras
-
-
-model = from_pretrained_keras("keras-io/ctc_asr", compile=False)
-
-characters = [x for x in "abcdefghijklmnopqrstuvwxyz'?! "]
-# Mapping characters to integers
-char_to_num = keras.layers.StringLookup(vocabulary=characters, oov_token="")
-# Mapping integers back to original characters
-num_to_char = keras.layers.StringLookup(
-    vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True
-)
-
-# An integer scalar Tensor. The window length in samples.
-frame_length = 256
-# An integer scalar Tensor. The number of samples to step.
-frame_step = 160
-# An integer scalar Tensor. The size of the FFT to apply.
-# If not provided, uses the smallest power of 2 enclosing frame_length.
-fft_length = 384
-
-SAMPLE_RATE = 22050
-
-
-def decode_batch_predictions(pred):
-    input_len = np.ones(pred.shape[0]) * pred.shape[1]
-    # Use greedy search. For complex tasks, you can use beam search
-    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0]
-    # Iterate over the results and get back the text
-    output_text = []
-    for result in results:
-        result = tf.strings.reduce_join(num_to_char(result)).numpy().decode("utf-8")
-        output_text.append(result)
-    return output_text
-
-
-def load_16k_audio_wav(filename):
-    # Read file content
-    file_content = tf.io.read_file(filename)
-
-    # Decode audio wave
-    audio_wav, sample_rate = tf.audio.decode_wav(file_content, desired_channels=1)
-    audio_wav = tf.squeeze(audio_wav, axis=-1)
-    sample_rate = tf.cast(sample_rate, dtype=tf.int64)
-
-    # Resample to 16k
-    audio_wav = tfio.audio.resample(
-        audio_wav, rate_in=sample_rate, rate_out=SAMPLE_RATE
-    )
-
-    return audio_wav
-
-
-def mic_to_tensor(recorded_audio_file):
-    sample_rate, audio = recorded_audio_file
-
-    audio_wav = tf.constant(audio, dtype=tf.float32)
-    if tf.rank(audio_wav) > 1:
-        audio_wav = tf.reduce_mean(audio_wav, axis=1)
-    audio_wav = tfio.audio.resample(
-        audio_wav, rate_in=sample_rate, rate_out=SAMPLE_RATE
-    )
-
-    audio_wav = tf.divide(audio_wav, tf.reduce_max(tf.abs(audio_wav)))
-
-    return audio_wav
-
-
-def tensor_to_predictions(audio_tensor):
-    # 3. Change type to float
-    audio_tensor = tf.cast(audio_tensor, tf.float32)
-
-    # 4. Get the spectrogram
-    spectrogram = tf.signal.stft(
-        audio_tensor,
-        frame_length=frame_length,
-        frame_step=frame_step,
-        fft_length=fft_length,
-    )
-
-    # 5. We only need the magnitude, which can be derived by applying tf.abs
-    spectrogram = tf.abs(spectrogram)
-    spectrogram = tf.math.pow(spectrogram, 0.5)
-
-    # 6. normalisation
-    means = tf.math.reduce_mean(spectrogram, 1, keepdims=True)
-    stddevs = tf.math.reduce_std(spectrogram, 1, keepdims=True)
-    spectrogram = (spectrogram - means) / (stddevs + 1e-10)
-
-    spectrogram = tf.expand_dims(spectrogram, axis=0)
-
-    batch_predictions = model.predict(spectrogram)
-    batch_predictions = decode_batch_predictions(batch_predictions)
-    return batch_predictions
-
-
-def clear_inputs_and_outputs():
-    return [None, None, None]
-
-
-def predict(recorded_audio_file, uploaded_audio_file):
-    # 1. Read wav file
-    if recorded_audio_file:
-        audio_tensor = mic_to_tensor(recorded_audio_file)
-    else:
-        audio_tensor = load_16k_audio_wav(uploaded_audio_file)
-
-    prediction = tensor_to_predictions(audio_tensor)[0]
-    return prediction
-
-
-# gr.Interface(
-#     infer,
-#     inputs=gr.Audio(source="microphone", type="filepath"),
-#     outputs=gr.Textbox(lines=5, label="Input Text"),
-#     #title=title,
-#     #description=description,
-#     #article=article,
-#     #examples=examples,
-#     enable_queue=True,
-# ).launch(debug=True)
-
-# Main function
-if __name__ == "__main__":
-    demo = gr.Blocks()
-
-    with demo:
-        gr.Markdown(
-            """
-            <center><h1>Automatic Speech Recognition using CTC</h1></center> \
-            This space is a demo of Automatic Speech Recognition using Keras trained on LJSpeech dataset.<br> \
-    In this space, you can record your voice or upload a wav file and the model will predict the words spoken in English<br><br>
-            """
-        )
-        with gr.Row():
-            ## Input
-            with gr.Column():
-                mic_input = gr.Audio(source="microphone", label="Record your own voice")
-                upl_input = gr.Audio(
-                    source="upload", type="filepath", label="Upload a wav file"
-                )
-
-                with gr.Row():
-                    clr_btn = gr.Button(value="Clear", variant="secondary")
-                    prd_btn = gr.Button(value="Predict")
-
-            # Outputs
-            with gr.Column():
-                lbl_output = gr.Label(label="Text")
-
-        # Credits
-        with gr.Row():
-            gr.Markdown(
-                """
-                <h4>Credits</h4>
-                Author: <a href="https://twitter.com/anuragcomm"> Anurag Singh</a>.<br>
-                Based on the following Keras example <a href="https://keras.io/examples/audio/ctc_asr">Automatic Speech Recognition using CTC</a> by <a href="https://rbouadjenek.github.io/">Mohamed Reda Bouadjenek</a> and <a href="https://www.linkedin.com/in/parkerhuynh/">Ngoc Dung Huynh</a><br>
-                Check out the model <a href="https://huggingface.co/keras-io/ctc_asr">here</a>
-                """
-            )
-
-        clr_btn.click(
-            fn=clear_inputs_and_outputs,
-            inputs=[],
-            outputs=[mic_input, upl_input, lbl_output],
-        )
-        prd_btn.click(
-            fn=predict,
-            inputs=[mic_input, upl_input],
-            outputs=[lbl_output],
-        )
-
-    demo.launch(debug=True)
diff --git a/spaces/keras-io/structured-data-classification-grn-vsn/app.py b/spaces/keras-io/structured-data-classification-grn-vsn/app.py
deleted file mode 100644
index 4abab1b5fd1c3712365ee414383ea5f5b99852a5..0000000000000000000000000000000000000000
--- a/spaces/keras-io/structured-data-classification-grn-vsn/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import gradio as gr
-from utils.constants import CSV_HEADER, NUMERIC_FEATURE_NAMES, NUMBER_INPUT_COLS
-from utils.preprocess import create_max_values_map, create_dropdown_default_values_map, create_sample_test_data, CATEGORICAL_FEATURES_WITH_VOCABULARY
-from utils.predict import batch_predict, user_input_predict
-
-inputs_list = []
-max_values_map = create_max_values_map()
-dropdown_default_values_map = create_dropdown_default_values_map()
-sample_input_df_val = create_sample_test_data()
-
-
-demo = gr.Blocks()
-
-with demo:
-    
-    gr.Markdown("# **Binary Classification using Gated Residual and Variable Selection Networks** \n")
-    gr.Markdown("This space demonstrates the use of Gated Residual Networks (GRN) and Variable Selection Networks (VSN), proposed by Bryan Lim et al. in <a href=\"https://arxiv.org/abs/1912.09363/\">Temporal Fusion Transformers (TFT) for Interpretable Multi-horizon Time Series Forecasting</a> for structured data classification")
-    gr.Markdown("The model used by this space was trained on the [United States Census Income Dataset](https://archive.ics.uci.edu/ml/datasets/Census-Income+%28KDD%29) and helps to predict if the income of a person will be >$500K or not.")
-    gr.Markdown("Play around and see yourself 🤗 ")
-    
-    with gr.Tabs():
-        
-        with gr.TabItem("Predict using Example inputs"):
-            gr.Markdown("**Input DataFrame** \n")
-            input_df = gr.Dataframe(headers=CSV_HEADER,value=sample_input_df_val,)
-            gr.Markdown("**Output DataFrame** \n")
-            output_df = gr.Dataframe()
-            gr.Markdown("**Make Predictions**")
-            with gr.Row():
-                compute_button = gr.Button("Predict")
-        
-        with gr.TabItem("Tweak inputs Yourself & Predict"):
-            with gr.Tabs():
-                
-                with gr.TabItem("Numerical Inputs"):
-                    gr.Markdown("Set values for numerical inputs here.")
-                    for num_variable in NUMERIC_FEATURE_NAMES:
-                        with gr.Column():
-                            if num_variable in NUMBER_INPUT_COLS:
-                                numeric_input = gr.Number(label=num_variable)
-                            else:
-                                curr_max_val = max_values_map["max_"+num_variable]
-                                numeric_input = gr.Slider(0,curr_max_val, label=num_variable,step=1)
-                            inputs_list.append(numeric_input)
-                
-                with gr.TabItem("Categorical Inputs"):
-                    gr.Markdown("Choose values for categorical inputs here.")
-                    for cat_variable in CATEGORICAL_FEATURES_WITH_VOCABULARY.keys():
-                        with gr.Column():
-                            categorical_input = gr.Dropdown(CATEGORICAL_FEATURES_WITH_VOCABULARY[cat_variable], label=cat_variable, value=str(dropdown_default_values_map["max_"+cat_variable]))
-                            inputs_list.append(categorical_input)
-                            
-                    predict_button = gr.Button("Predict")
-                    final_output = gr.Label()
-    
-    predict_button.click(user_input_predict, inputs=inputs_list, outputs=final_output)
-    compute_button.click(batch_predict, inputs=input_df, outputs=output_df)
-    gr.Markdown('\n Demo created by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a> <br> Based on this <a href=\"https://keras.io/examples/structured_data/classification_with_grn_and_vsn/\">Keras example</a> by <a href=\"https://www.linkedin.com/in/khalid-salama-24403144/\">Khalid Salama</a> <br> Demo Powered by this <a href=\"https://huggingface.co/keras-io/structured-data-classification-grn-vsn/\">GRN-VSN model</a>')
-    
-    
-demo.launch()
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/models/arcface_torch/utils/utils_os.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/models/arcface_torch/utils/utils_os.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kokofixcomputers/chat-ui/src/routes/admin/export/+server.ts b/spaces/kokofixcomputers/chat-ui/src/routes/admin/export/+server.ts
deleted file mode 100644
index e8806ab8b719c399455229289d88f00ddb3d8e43..0000000000000000000000000000000000000000
--- a/spaces/kokofixcomputers/chat-ui/src/routes/admin/export/+server.ts
+++ /dev/null
@@ -1,107 +0,0 @@
-import {
-	PARQUET_EXPORT_DATASET,
-	PARQUET_EXPORT_HF_TOKEN,
-	PARQUET_EXPORT_SECRET,
-} from "$env/static/private";
-import { collections } from "$lib/server/database";
-import type { Message } from "$lib/types/Message";
-import { error } from "@sveltejs/kit";
-import { pathToFileURL } from "node:url";
-import { unlink } from "node:fs/promises";
-import { uploadFile } from "@huggingface/hub";
-import parquet from "parquetjs";
-import { z } from "zod";
-
-// Triger like this:
-// curl -X POST "http://localhost:5173/chat/admin/export" -H "Authorization: Bearer <PARQUET_EXPORT_SECRET>" -H "Content-Type: application/json" -d '{"model": "OpenAssistant/oasst-sft-6-llama-30b-xor"}'
-
-export async function POST({ request }) {
-	if (!PARQUET_EXPORT_SECRET || !PARQUET_EXPORT_DATASET || !PARQUET_EXPORT_HF_TOKEN) {
-		throw error(500, "Parquet export is not configured.");
-	}
-
-	if (request.headers.get("Authorization") !== `Bearer ${PARQUET_EXPORT_SECRET}`) {
-		throw error(403);
-	}
-
-	const { model } = z
-		.object({
-			model: z.string(),
-		})
-		.parse(await request.json());
-
-	const schema = new parquet.ParquetSchema({
-		title: { type: "UTF8" },
-		created_at: { type: "TIMESTAMP_MILLIS" },
-		updated_at: { type: "TIMESTAMP_MILLIS" },
-		messages: { repeated: true, fields: { from: { type: "UTF8" }, content: { type: "UTF8" } } },
-	});
-
-	const fileName = `/tmp/conversations-${new Date().toJSON().slice(0, 10)}-${Date.now()}.parquet`;
-
-	const writer = await parquet.ParquetWriter.openFile(schema, fileName);
-
-	let count = 0;
-	console.log("Exporting conversations for model", model);
-
-	for await (const conversation of collections.settings.aggregate<{
-		title: string;
-		created_at: Date;
-		updated_at: Date;
-		messages: Message[];
-	}>([
-		{ $match: { shareConversationsWithModelAuthors: true } },
-		{
-			$lookup: {
-				from: "conversations",
-				localField: "sessionId",
-				foreignField: "sessionId",
-				as: "conversations",
-				pipeline: [{ $match: { model } }],
-			},
-		},
-		{ $unwind: "$conversations" },
-		{
-			$project: {
-				title: "$conversations.title",
-				created_at: "$conversations.createdAt",
-				updated_at: "$conversations.updatedAt",
-				messages: "$conversations.messages",
-			},
-		},
-	])) {
-		await writer.appendRow({
-			title: conversation.title,
-			created_at: conversation.created_at,
-			updated_at: conversation.updated_at,
-			messages: conversation.messages.map((message: Message) => ({
-				from: message.from,
-				content: message.content,
-			})),
-		});
-		++count;
-
-		if (count % 1_000 === 0) {
-			console.log("Exported", count, "conversations");
-		}
-	}
-
-	await writer.close();
-
-	console.log("Uploading", fileName, "to Hugging Face Hub");
-
-	await uploadFile({
-		file: pathToFileURL(fileName),
-		credentials: { accessToken: PARQUET_EXPORT_HF_TOKEN },
-		repo: {
-			type: "dataset",
-			name: PARQUET_EXPORT_DATASET,
-		},
-	});
-
-	console.log("Upload done");
-
-	await unlink(fileName);
-
-	return new Response();
-}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/PyAccess.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/PyAccess.py
deleted file mode 100644
index 39747b4f311807822d225d091f1616eeaed388ff..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/PyAccess.py
+++ /dev/null
@@ -1,360 +0,0 @@
-#
-# The Python Imaging Library
-# Pillow fork
-#
-# Python implementation of the PixelAccess Object
-#
-# Copyright (c) 1997-2009 by Secret Labs AB.  All rights reserved.
-# Copyright (c) 1995-2009 by Fredrik Lundh.
-# Copyright (c) 2013 Eric Soroos
-#
-# See the README file for information on usage and redistribution
-#
-
-# Notes:
-#
-#  * Implements the pixel access object following Access.c
-#  * Taking only the tuple form, which is used from python.
-#    * Fill.c uses the integer form, but it's still going to use the old
-#      Access.c implementation.
-#
-
-import logging
-import sys
-
-try:
-    from cffi import FFI
-
-    defs = """
-    struct Pixel_RGBA {
-        unsigned char r,g,b,a;
-    };
-    struct Pixel_I16 {
-        unsigned char l,r;
-    };
-    """
-    ffi = FFI()
-    ffi.cdef(defs)
-except ImportError as ex:
-    # Allow error import for doc purposes, but error out when accessing
-    # anything in core.
-    from ._util import DeferredError
-
-    FFI = ffi = DeferredError(ex)
-
-logger = logging.getLogger(__name__)
-
-
-class PyAccess:
-    def __init__(self, img, readonly=False):
-        vals = dict(img.im.unsafe_ptrs)
-        self.readonly = readonly
-        self.image8 = ffi.cast("unsigned char **", vals["image8"])
-        self.image32 = ffi.cast("int **", vals["image32"])
-        self.image = ffi.cast("unsigned char **", vals["image"])
-        self.xsize, self.ysize = img.im.size
-        self._img = img
-
-        # Keep pointer to im object to prevent dereferencing.
-        self._im = img.im
-        if self._im.mode in ("P", "PA"):
-            self._palette = img.palette
-
-        # Debugging is polluting test traces, only useful here
-        # when hacking on PyAccess
-        # logger.debug("%s", vals)
-        self._post_init()
-
-    def _post_init(self):
-        pass
-
-    def __setitem__(self, xy, color):
-        """
-        Modifies the pixel at x,y. The color is given as a single
-        numerical value for single band images, and a tuple for
-        multi-band images
-
-        :param xy: The pixel coordinate, given as (x, y). See
-           :ref:`coordinate-system`.
-        :param color: The pixel value.
-        """
-        if self.readonly:
-            msg = "Attempt to putpixel a read only image"
-            raise ValueError(msg)
-        (x, y) = xy
-        if x < 0:
-            x = self.xsize + x
-        if y < 0:
-            y = self.ysize + y
-        (x, y) = self.check_xy((x, y))
-
-        if (
-            self._im.mode in ("P", "PA")
-            and isinstance(color, (list, tuple))
-            and len(color) in [3, 4]
-        ):
-            # RGB or RGBA value for a P or PA image
-            if self._im.mode == "PA":
-                alpha = color[3] if len(color) == 4 else 255
-                color = color[:3]
-            color = self._palette.getcolor(color, self._img)
-            if self._im.mode == "PA":
-                color = (color, alpha)
-
-        return self.set_pixel(x, y, color)
-
-    def __getitem__(self, xy):
-        """
-        Returns the pixel at x,y. The pixel is returned as a single
-        value for single band images or a tuple for multiple band
-        images
-
-        :param xy: The pixel coordinate, given as (x, y). See
-          :ref:`coordinate-system`.
-        :returns: a pixel value for single band images, a tuple of
-          pixel values for multiband images.
-        """
-        (x, y) = xy
-        if x < 0:
-            x = self.xsize + x
-        if y < 0:
-            y = self.ysize + y
-        (x, y) = self.check_xy((x, y))
-        return self.get_pixel(x, y)
-
-    putpixel = __setitem__
-    getpixel = __getitem__
-
-    def check_xy(self, xy):
-        (x, y) = xy
-        if not (0 <= x < self.xsize and 0 <= y < self.ysize):
-            msg = "pixel location out of range"
-            raise ValueError(msg)
-        return xy
-
-
-class _PyAccess32_2(PyAccess):
-    """PA, LA, stored in first and last bytes of a 32 bit word"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.r, pixel.a
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        # tuple
-        pixel.r = min(color[0], 255)
-        pixel.a = min(color[1], 255)
-
-
-class _PyAccess32_3(PyAccess):
-    """RGB and friends, stored in the first three bytes of a 32 bit word"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.r, pixel.g, pixel.b
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        # tuple
-        pixel.r = min(color[0], 255)
-        pixel.g = min(color[1], 255)
-        pixel.b = min(color[2], 255)
-        pixel.a = 255
-
-
-class _PyAccess32_4(PyAccess):
-    """RGBA etc, all 4 bytes of a 32 bit word"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.r, pixel.g, pixel.b, pixel.a
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        # tuple
-        pixel.r = min(color[0], 255)
-        pixel.g = min(color[1], 255)
-        pixel.b = min(color[2], 255)
-        pixel.a = min(color[3], 255)
-
-
-class _PyAccess8(PyAccess):
-    """1, L, P, 8 bit images stored as uint8"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = self.image8
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        try:
-            # integer
-            self.pixels[y][x] = min(color, 255)
-        except TypeError:
-            # tuple
-            self.pixels[y][x] = min(color[0], 255)
-
-
-class _PyAccessI16_N(PyAccess):
-    """I;16 access, native bitendian without conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("unsigned short **", self.image)
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        try:
-            # integer
-            self.pixels[y][x] = min(color, 65535)
-        except TypeError:
-            # tuple
-            self.pixels[y][x] = min(color[0], 65535)
-
-
-class _PyAccessI16_L(PyAccess):
-    """I;16L access, with conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_I16 **", self.image)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.l + pixel.r * 256
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        try:
-            color = min(color, 65535)
-        except TypeError:
-            color = min(color[0], 65535)
-
-        pixel.l = color & 0xFF  # noqa: E741
-        pixel.r = color >> 8
-
-
-class _PyAccessI16_B(PyAccess):
-    """I;16B access, with conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_I16 **", self.image)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.l * 256 + pixel.r
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        try:
-            color = min(color, 65535)
-        except Exception:
-            color = min(color[0], 65535)
-
-        pixel.l = color >> 8  # noqa: E741
-        pixel.r = color & 0xFF
-
-
-class _PyAccessI32_N(PyAccess):
-    """Signed Int32 access, native endian"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = self.image32
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        self.pixels[y][x] = color
-
-
-class _PyAccessI32_Swap(PyAccess):
-    """I;32L/B access, with byteswapping conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = self.image32
-
-    def reverse(self, i):
-        orig = ffi.new("int *", i)
-        chars = ffi.cast("unsigned char *", orig)
-        chars[0], chars[1], chars[2], chars[3] = chars[3], chars[2], chars[1], chars[0]
-        return ffi.cast("int *", chars)[0]
-
-    def get_pixel(self, x, y):
-        return self.reverse(self.pixels[y][x])
-
-    def set_pixel(self, x, y, color):
-        self.pixels[y][x] = self.reverse(color)
-
-
-class _PyAccessF(PyAccess):
-    """32 bit float access"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("float **", self.image32)
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        try:
-            # not a tuple
-            self.pixels[y][x] = color
-        except TypeError:
-            # tuple
-            self.pixels[y][x] = color[0]
-
-
-mode_map = {
-    "1": _PyAccess8,
-    "L": _PyAccess8,
-    "P": _PyAccess8,
-    "I;16N": _PyAccessI16_N,
-    "LA": _PyAccess32_2,
-    "La": _PyAccess32_2,
-    "PA": _PyAccess32_2,
-    "RGB": _PyAccess32_3,
-    "LAB": _PyAccess32_3,
-    "HSV": _PyAccess32_3,
-    "YCbCr": _PyAccess32_3,
-    "RGBA": _PyAccess32_4,
-    "RGBa": _PyAccess32_4,
-    "RGBX": _PyAccess32_4,
-    "CMYK": _PyAccess32_4,
-    "F": _PyAccessF,
-    "I": _PyAccessI32_N,
-}
-
-if sys.byteorder == "little":
-    mode_map["I;16"] = _PyAccessI16_N
-    mode_map["I;16L"] = _PyAccessI16_N
-    mode_map["I;16B"] = _PyAccessI16_B
-
-    mode_map["I;32L"] = _PyAccessI32_N
-    mode_map["I;32B"] = _PyAccessI32_Swap
-else:
-    mode_map["I;16"] = _PyAccessI16_L
-    mode_map["I;16L"] = _PyAccessI16_L
-    mode_map["I;16B"] = _PyAccessI16_N
-
-    mode_map["I;32L"] = _PyAccessI32_Swap
-    mode_map["I;32B"] = _PyAccessI32_N
-
-
-def new(img, readonly=False):
-    access_type = mode_map.get(img.mode, None)
-    if not access_type:
-        logger.debug("PyAccess Not Implemented: %s", img.mode)
-        return None
-    return access_type(img, readonly)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/utils/mimebundle.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/utils/mimebundle.py
deleted file mode 100644
index 1e00542fb4617e01a6bece351494e512835779c8..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/utils/mimebundle.py
+++ /dev/null
@@ -1,196 +0,0 @@
-from .html import spec_to_html
-
-
-def spec_to_mimebundle(
-    spec,
-    format,
-    mode=None,
-    vega_version=None,
-    vegaembed_version=None,
-    vegalite_version=None,
-    engine=None,
-    **kwargs,
-):
-    """Convert a vega-lite specification to a mimebundle
-
-    The mimebundle type is controlled by the ``format`` argument, which can be
-    one of the following ['html', 'json', 'png', 'svg', 'pdf', 'vega', 'vega-lite']
-
-    Parameters
-    ----------
-    spec : dict
-        a dictionary representing a vega-lite plot spec
-    format : string {'html', 'json', 'png', 'svg', 'pdf', 'vega', 'vega-lite'}
-        the file format to be saved.
-    mode : string {'vega-lite'}
-        The rendering mode.
-    vega_version : string
-        The version of vega.js to use
-    vegaembed_version : string
-        The version of vegaembed.js to use
-    vegalite_version : string
-        The version of vegalite.js to use. Only required if mode=='vega-lite'
-    engine: string {'vl-convert', 'altair_saver'}
-        the conversion engine to use for 'png', 'svg', 'pdf', and 'vega' formats
-    **kwargs :
-        Additional arguments will be passed to the generating function
-
-    Returns
-    -------
-    output : dict
-        a mime-bundle representing the image
-
-    Note
-    ----
-    The png, svg, pdf, and vega outputs require the altair_saver package
-    """
-    if mode != "vega-lite":
-        raise ValueError("mode must be 'vega-lite'")
-
-    if format in ["png", "svg", "pdf", "vega"]:
-        return _spec_to_mimebundle_with_engine(
-            spec, format, mode, engine=engine, **kwargs
-        )
-    if format == "html":
-        html = spec_to_html(
-            spec,
-            mode=mode,
-            vega_version=vega_version,
-            vegaembed_version=vegaembed_version,
-            vegalite_version=vegalite_version,
-            **kwargs,
-        )
-        return {"text/html": html}
-    if format == "vega-lite":
-        if vegalite_version is None:
-            raise ValueError("Must specify vegalite_version")
-        return {"application/vnd.vegalite.v{}+json".format(vegalite_version[0]): spec}
-    if format == "json":
-        return {"application/json": spec}
-    raise ValueError(
-        "format must be one of "
-        "['html', 'json', 'png', 'svg', 'pdf', 'vega', 'vega-lite']"
-    )
-
-
-def _spec_to_mimebundle_with_engine(spec, format, mode, **kwargs):
-    """Helper for Vega-Lite to mimebundle conversions that require an engine
-
-    Parameters
-    ----------
-    spec : dict
-        a dictionary representing a vega-lite plot spec
-    format : string {'png', 'svg', 'pdf', 'vega'}
-        the format of the mimebundle to be returned
-    mode : string {'vega-lite'}
-        The rendering mode.
-    engine: string {'vl-convert', 'altair_saver'}
-        the conversion engine to use
-    **kwargs :
-        Additional arguments will be passed to the conversion function
-    """
-    # Normalize the engine string (if any) by lower casing
-    # and removing underscores and hyphens
-    engine = kwargs.pop("engine", None)
-    normalized_engine = _validate_normalize_engine(engine, format)
-
-    if normalized_engine == "vlconvert":
-        import vl_convert as vlc
-        from ..vegalite import SCHEMA_VERSION
-
-        # Compute VlConvert's vl_version string (of the form 'v5_2')
-        # from SCHEMA_VERSION (of the form 'v5.2.0')
-        vl_version = "_".join(SCHEMA_VERSION.split(".")[:2])
-        if format == "vega":
-            vg = vlc.vegalite_to_vega(spec, vl_version=vl_version)
-            return {"application/vnd.vega.v5+json": vg}
-        elif format == "svg":
-            svg = vlc.vegalite_to_svg(spec, vl_version=vl_version)
-            return {"image/svg+xml": svg}
-        elif format == "png":
-            png = vlc.vegalite_to_png(
-                spec,
-                vl_version=vl_version,
-                scale=kwargs.get("scale_factor", 1.0),
-            )
-            return {"image/png": png}
-        else:
-            # This should be validated above
-            # but raise exception for the sake of future development
-            raise ValueError("Unexpected format {fmt!r}".format(fmt=format))
-    elif normalized_engine == "altairsaver":
-        import altair_saver
-
-        return altair_saver.render(spec, format, mode=mode, **kwargs)
-    else:
-        # This should be validated above
-        # but raise exception for the sake of future development
-        raise ValueError(
-            "Unexpected normalized_engine {eng!r}".format(eng=normalized_engine)
-        )
-
-
-def _validate_normalize_engine(engine, format):
-    """Helper to validate and normalize the user-provided engine
-
-    engine : {None, 'vl-convert', 'altair_saver'}
-        the user-provided engine string
-    format : string {'png', 'svg', 'pdf', 'vega'}
-        the format of the mimebundle to be returned
-    """
-    # Try to import vl_convert
-    try:
-        import vl_convert as vlc
-    except ImportError:
-        vlc = None
-
-    # Try to import altair_saver
-    try:
-        import altair_saver
-    except ImportError:
-        altair_saver = None
-
-    # Normalize engine string by lower casing and removing underscores and hyphens
-    normalized_engine = (
-        None if engine is None else engine.lower().replace("-", "").replace("_", "")
-    )
-
-    # Validate or infer default value of normalized_engine
-    if normalized_engine == "vlconvert":
-        if vlc is None:
-            raise ValueError(
-                "The 'vl-convert' conversion engine requires the vl-convert-python package"
-            )
-        if format == "pdf":
-            raise ValueError(
-                "The 'vl-convert' conversion engine does not support the {fmt!r} format.\n"
-                "Use the 'altair_saver' engine instead".format(fmt=format)
-            )
-    elif normalized_engine == "altairsaver":
-        if altair_saver is None:
-            raise ValueError(
-                "The 'altair_saver' conversion engine requires the altair_saver package"
-            )
-    elif normalized_engine is None:
-        if vlc is not None and format != "pdf":
-            normalized_engine = "vlconvert"
-        elif altair_saver is not None:
-            normalized_engine = "altairsaver"
-        else:
-            if format == "pdf":
-                raise ValueError(
-                    "Saving charts in {fmt!r} format requires the altair_saver package: "
-                    "see http://github.com/altair-viz/altair_saver/".format(fmt=format)
-                )
-            else:
-                raise ValueError(
-                    "Saving charts in {fmt!r} format requires the vl-convert-python or altair_saver package: "
-                    "see http://github.com/altair-viz/altair_saver/".format(fmt=format)
-                )
-    else:
-        raise ValueError(
-            "Invalid conversion engine {engine!r}. Expected one of {valid!r}".format(
-                engine=engine, valid=("vl-convert", "altair_saver")
-            )
-        )
-    return normalized_engine
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_S_.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_S_.py
deleted file mode 100644
index 667eb0e53473c1566d4b45e5621d8897ebd7b9fe..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I_S_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .T_S_I_V_ import table_T_S_I_V_
-
-
-class table_T_S_I_S_(table_T_S_I_V_):
-    pass
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_writers.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_writers.py
deleted file mode 100644
index 939cdb912a9debaea07fbf3a9ac04549c44d077c..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_writers.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Code to read HTTP data
-#
-# Strategy: each writer takes an event + a write-some-bytes function, which is
-# calls.
-#
-# WRITERS is a dict describing how to pick a reader. It maps states to either:
-# - a writer
-# - or, for body writers, a dict of framin-dependent writer factories
-
-from typing import Any, Callable, Dict, List, Tuple, Type, Union
-
-from ._events import Data, EndOfMessage, Event, InformationalResponse, Request, Response
-from ._headers import Headers
-from ._state import CLIENT, IDLE, SEND_BODY, SEND_RESPONSE, SERVER
-from ._util import LocalProtocolError, Sentinel
-
-__all__ = ["WRITERS"]
-
-Writer = Callable[[bytes], Any]
-
-
-def write_headers(headers: Headers, write: Writer) -> None:
-    # "Since the Host field-value is critical information for handling a
-    # request, a user agent SHOULD generate Host as the first header field
-    # following the request-line." - RFC 7230
-    raw_items = headers._full_items
-    for raw_name, name, value in raw_items:
-        if name == b"host":
-            write(b"%s: %s\r\n" % (raw_name, value))
-    for raw_name, name, value in raw_items:
-        if name != b"host":
-            write(b"%s: %s\r\n" % (raw_name, value))
-    write(b"\r\n")
-
-
-def write_request(request: Request, write: Writer) -> None:
-    if request.http_version != b"1.1":
-        raise LocalProtocolError("I only send HTTP/1.1")
-    write(b"%s %s HTTP/1.1\r\n" % (request.method, request.target))
-    write_headers(request.headers, write)
-
-
-# Shared between InformationalResponse and Response
-def write_any_response(
-    response: Union[InformationalResponse, Response], write: Writer
-) -> None:
-    if response.http_version != b"1.1":
-        raise LocalProtocolError("I only send HTTP/1.1")
-    status_bytes = str(response.status_code).encode("ascii")
-    # We don't bother sending ascii status messages like "OK"; they're
-    # optional and ignored by the protocol. (But the space after the numeric
-    # status code is mandatory.)
-    #
-    # XX FIXME: could at least make an effort to pull out the status message
-    # from stdlib's http.HTTPStatus table. Or maybe just steal their enums
-    # (either by import or copy/paste). We already accept them as status codes
-    # since they're of type IntEnum < int.
-    write(b"HTTP/1.1 %s %s\r\n" % (status_bytes, response.reason))
-    write_headers(response.headers, write)
-
-
-class BodyWriter:
-    def __call__(self, event: Event, write: Writer) -> None:
-        if type(event) is Data:
-            self.send_data(event.data, write)
-        elif type(event) is EndOfMessage:
-            self.send_eom(event.headers, write)
-        else:  # pragma: no cover
-            assert False
-
-    def send_data(self, data: bytes, write: Writer) -> None:
-        pass
-
-    def send_eom(self, headers: Headers, write: Writer) -> None:
-        pass
-
-
-#
-# These are all careful not to do anything to 'data' except call len(data) and
-# write(data). This allows us to transparently pass-through funny objects,
-# like placeholder objects referring to files on disk that will be sent via
-# sendfile(2).
-#
-class ContentLengthWriter(BodyWriter):
-    def __init__(self, length: int) -> None:
-        self._length = length
-
-    def send_data(self, data: bytes, write: Writer) -> None:
-        self._length -= len(data)
-        if self._length < 0:
-            raise LocalProtocolError("Too much data for declared Content-Length")
-        write(data)
-
-    def send_eom(self, headers: Headers, write: Writer) -> None:
-        if self._length != 0:
-            raise LocalProtocolError("Too little data for declared Content-Length")
-        if headers:
-            raise LocalProtocolError("Content-Length and trailers don't mix")
-
-
-class ChunkedWriter(BodyWriter):
-    def send_data(self, data: bytes, write: Writer) -> None:
-        # if we encoded 0-length data in the naive way, it would look like an
-        # end-of-message.
-        if not data:
-            return
-        write(b"%x\r\n" % len(data))
-        write(data)
-        write(b"\r\n")
-
-    def send_eom(self, headers: Headers, write: Writer) -> None:
-        write(b"0\r\n")
-        write_headers(headers, write)
-
-
-class Http10Writer(BodyWriter):
-    def send_data(self, data: bytes, write: Writer) -> None:
-        write(data)
-
-    def send_eom(self, headers: Headers, write: Writer) -> None:
-        if headers:
-            raise LocalProtocolError("can't send trailers to HTTP/1.0 client")
-        # no need to close the socket ourselves, that will be taken care of by
-        # Connection: close machinery
-
-
-WritersType = Dict[
-    Union[Tuple[Type[Sentinel], Type[Sentinel]], Type[Sentinel]],
-    Union[
-        Dict[str, Type[BodyWriter]],
-        Callable[[Union[InformationalResponse, Response], Writer], None],
-        Callable[[Request, Writer], None],
-    ],
-]
-
-WRITERS: WritersType = {
-    (CLIENT, IDLE): write_request,
-    (SERVER, IDLE): write_any_response,
-    (SERVER, SEND_RESPONSE): write_any_response,
-    SEND_BODY: {
-        "chunked": ChunkedWriter,
-        "content-length": ContentLengthWriter,
-        "http/1.0": Http10Writer,
-    },
-}
diff --git a/spaces/lcw777789564/panzuowenji/README.md b/spaces/lcw777789564/panzuowenji/README.md
deleted file mode 100644
index 6c8a3e3d2f305762c8a8735232c38a19a0731bb5..0000000000000000000000000000000000000000
--- a/spaces/lcw777789564/panzuowenji/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Panzuowenji
-emoji: 📊
-colorFrom: indigo
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/leogabraneth/text-generation-webui-main/modules/grammar.py b/spaces/leogabraneth/text-generation-webui-main/modules/grammar.py
deleted file mode 100644
index 5f6ad3a637d85bc31eecb141c149d562e53a90c2..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/modules/grammar.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from torch_grammar import GrammarSampler
-from transformers.generation.logits_process import LogitsProcessor
-
-from modules import shared
-
-sampler = None
-grammar = None
-grammar_string = ''
-
-
-class GrammarLogitsProcessor(LogitsProcessor):
-    def __init__(self, string):
-
-        global sampler, grammar, grammar_string
-
-        if string != grammar_string:
-            grammar_string = string
-            if string.strip() != '':
-                string = string.strip() + '\n'
-                sampler = GrammarSampler(string, 'root', shared.tokenizer)
-            else:
-                sampler = None
-
-        if sampler is not None:
-            grammar = sampler.logits_processor()
-        else:
-            grammar = None
-
-    def __call__(self, input_ids, scores):
-        if grammar is not None:
-            scores = grammar(input_ids, scores)
-
-        return scores
diff --git a/spaces/lgabrielb/fruit_classifier/app.py b/spaces/lgabrielb/fruit_classifier/app.py
deleted file mode 100644
index 3dcac7e96a46199b0dd185d7340289de0e8f07b4..0000000000000000000000000000000000000000
--- a/spaces/lgabrielb/fruit_classifier/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-
-learn = load_learner('export.pkl')
-labels = learn.dls.vocab
-
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-title = "Fruit Classifier"
-examples = ['banana.jpg', 'jabuticaba.jpeg', 'laranja.jpg',  'maca.jpg', 'melancia.jpeg']
-
-iface = gr.Interface(
-    fn=classify_image,
-    inputs=gr.inputs.Image(shape=(512, 512)),
-    outputs=gr.outputs.Label(num_top_classes=3),
-    title=title,
-    examples=examples,
-    )
-iface.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Hydrus 2d 3d Crack In 14 !EXCLUSIVE!.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Hydrus 2d 3d Crack In 14 !EXCLUSIVE!.md
deleted file mode 100644
index aa73177e0437edbf15d709d5ef8b0e01e42a5cd5..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Hydrus 2d 3d Crack In 14 !EXCLUSIVE!.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-
-<p>hydrus 2d / 3d pro crack is the fantastic software that helps to analyze the flow and transport of water and solute in the saturated media. this software can be used for the geological and hydrological analysis. it is fully compatible with all the important operating systems such as windows, linux and mac osx. this software is based on the richards equation and convection-dispersion equation. this software is used to analyze the flow and transport of water and solute in the saturated media. you can also try hydrus crack.</p>
-<h2>Hydrus 2d 3d Crack In 14</h2><br /><p><b><b>Download Zip</b> &gt;&gt;&gt; <a href="https://bytlly.com/2uGxfP">https://bytlly.com/2uGxfP</a></b></p><br /><br />
-<p>hydrus 2d / 3d crack is an easy to use and user-friendly software which can be used for geological and hydrological analysis. it is developed based on the richards equation and convection-dispersion equation. it provides an interactive user interface to design the flow domains, manage the nodes, and to analyze the sensitivity of the model parameters. you can also try hydrus crack.</p>
-<p>hydrus 2d / 3d crack is the software which is used to analyze the flow and heat transfer. it is developed based on the richards equation and convection-dispersion equation. this software provides an interactive user interface to design the flow domains, manage the nodes, and to analyze the sensitivity of the model parameters.</p>
-<p>hydrus-2d allows the simulation of two-dimensional flow systems using a mesh-based model, and many of the features of hydrus 2d are the same as those of hydrus 3d.. in hydrus-2d, hydrus-2d combines two- and three-dimensional hydrological modeling in a single model. the hydrus-2d model includes more than 90 pre-defined surface water bodies and more than 70 pre-defined subsurface. hydrus-2d can solve a wide range of two-dimensional flow processes, including such systems as well as hydrogeological, hydrologic,. 3d hydrus, hydrus 3d, 3d hydrus, hydrus 3d, hydrus-2d, hydrus-2d, hydrus model, model hydrus, model hydrus-2d, model hydrus-2d, hydrus model, model hydrus, model hydrus-2d, model hydrus-2d,. for further information, go to hydrus-2d, hydrus 3d, hydrus model, hydrus model hydrus-2d, model hydrus-2d, model hydrus-2d. hydrus 2d 3d crack in 14 hydrus 2d 3d crack in 14. hydrus 2d 3d crack in 14 hydrus 2d 3d crack in 14 hydrus 2d 3d crack in 14 <p>this is a game demo of hydrus, available for <b>pc</b> (windows 10/8/7/vista/xp) and mac os x. hydrus-3d, hydrus 3d, hydrus-3d, hydrus model, model hydrus, model hydrus-2d, model hydrus-2d, hydrus model, model hydrus, model hydrus-2d, model hydrus-2d. this demo shows a simple two-dimensional layout of a complex subsurface-flow wetland system, such as a constructed wetland. modeling of subsurface flow and transport of liquid and solid materials within the wetlands is provided by hydrus. the hydrus model combines two- and three-dimensional hydrological modeling in a single model. the hydrus-3d model includes more than 90 pre-defined surface water bodies and more than 70 pre-defined subsurface.</p>
-<p></p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Little Big Planet 3 Pc Download Free Full.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Little Big Planet 3 Pc Download Free Full.md
deleted file mode 100644
index 0304f75da1ea35db2e5d246d980e6096c36c3f1c..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Little Big Planet 3 Pc Download Free Full.md	
+++ /dev/null
@@ -1,96 +0,0 @@
-<br />
-<h1>Little Big Planet 3 PC Download Free Full</h1>
-
-<p>If you are a fan of creative and colorful platform games, you might be interested in <strong>Little Big Planet 3</strong>, the third installment of the popular series that features Sackboy and his new friends. This game was originally released for PlayStation 3 and PlayStation 4 in 2014, but did you know that you can also play it on your PC? In this article, we will show you how to download and play <strong>Little Big Planet 3</strong> on your PC for free, using an emulator called RPCS3. You will also learn more about the game and its features, as well as some tips and tricks to enjoy it better.</p>
-
-<h2>What is Little Big Planet 3?</h2>
-
-<p><strong>Little Big Planet 3</strong> is a puzzle-platformer game that combines elements of a sandbox game. It is developed by Sumo Digital, with the help of XDev and Media Molecule, the original creators of the series. The game follows the adventures of Sackboy, a cute and customizable character made of fabric, who travels to a planet called Bunkum to stop an evil force called the Titans from destroying creativity.</p>
-<h2>little big planet 3 pc download free full</h2><br /><p><b><b>Download</b> - <a href="https://bytlly.com/2uGych">https://bytlly.com/2uGych</a></b></p><br /><br />
-
-<p>The game has two main modes: Adventure and Create. In Adventure mode, you can play through the story levels, which are divided into four themes: Prologue, Manglewood, The Ziggurat, and Bunkum Lagoon. Each theme has its own hub world, where you can explore and find secrets, collectibles, and side missions. You can also play online with other players or join community levels created by other users.</p>
-
-<p>In Create mode, you can unleash your imagination and create your own levels, characters, objects, power-ups, stickers, vehicles, and more. You can use a variety of tools and materials to design your own world and share it with other players online. You can also play levels created by other users and rate them.</p>
-
-<p>The game introduces three new playable characters besides Sackboy: OddSock, Toggle, and Swoop. Each character has its own unique abilities and personality. OddSock is a dog-like character who can run fast and wall-jump. Toggle is a big and heavy character who can switch to a small and light version. Swoop is a bird-like character who can fly and carry objects or other characters. You can switch between these characters during gameplay or play as them in co-op mode with up to four players.</p>
-
-<h2>How to Download Little Big Planet 3 for PC</h2>
-
-<p>To download <strong>Little Big Planet 3</strong> for PC, you will need an emulator called RPCS3. This is a software that allows you to run PlayStation 3 games on your PC. It is still in development, so it may not work perfectly for all games, but it has made great progress in recent years and it can run <strong>Little Big Planet 3</strong> with good performance and graphics.</p>
-
-<p>Here are the steps to download and play <strong>Little Big Planet 3</strong> on your PC:</p>
-
-<ol>
-<li>Download RPCS3 from its official website: https://rpcs3.net/. You will need a 64-bit Windows system with at least 4 GB of RAM and a compatible graphics card.</li>
-<li>Extract the RPCS3 folder to a location of your choice on your PC.</li>
-<li>Download the PlayStation 3 firmware from this link: https://www.playstation.com/en-us/support/hardware/ps3/system-software/. You will need this to run RPCS3.</li>
-<li>Copy the firmware file (PS3UPDAT.PUP) to the \dev_hdd0\ folder inside the RPCS3 folder.</li>
-<li>Run rpcs3.exe as administrator. You will see a window asking you to install the firmware. Click on "Yes" and wait for it to finish.</li>
-<li>Download <strong>Little Big Planet 3</strong> from this link: https://www.gamesknit.com/littlebigplanet-3-version-for-pc/. You will need a torrent client such as uTorrent or BitTorrent to download it.</li>
-<li>Open the torrent file with your torrent client and wait for the download to finish. It may take some time depending on your internet speed and the number of seeders.</li>
-<li>Once the download is complete, go to the folder where you saved the game files. You will see a folder called "LittleBigPlanet 3". Open it and run setup.exe.</li>
-<li>Follow the installation instructions. Choose a destination folder for the game and agree to the terms and conditions.</li>
-<li>When the installation is done, do not launch the game yet. You need to apply a crack to make it work on RPCS3.</li>
-<li>Go back to the folder where you downloaded the game files. You will see another folder called "Crack". Open it and copy all the files inside.</li>
-<li>Paste them into the folder where you installed the game. Replace any existing files if prompted.</li>
-<li>Launch RPCS3 again. Click on "File" and then "Add Games". Browse to the folder where you installed the game and select it.</li>
-<li>You will see <strong>Little Big Planet 3</strong> appear on your game list on RPCS3. Right-click on it and select "Configure".</li>
-<li>You will see a window with various settings for the game. You can tweak them according to your preferences and system specifications, but here are some recommended settings:</li>
-<ul>
-<li>CPU: Enable SPU loop detection, SPU cache, Accurate xfloat, SPU block size = Safe.</li>
-<li>GPU: Renderer = Vulkan, Resolution scale = Native (or higher if your PC can handle it), Anisotropic filter = Auto.</li>
-<li>Audio: Audio backend = XAudio2 (or OpenAL if you have problems), Enable buffering, Buffering duration = 100ms.</li>
-<li>I/O: Keyboard handler = Basic (or Null if you use a controller), Mouse handler = Basic (or Null if you use a controller), Camera input = Null (or Basic if you use PlayStation Eye), Move handler = Null (or Basic if you use PlayStation Move).</li>
-</ul>
-<li>Click on "Save" to apply the settings.</li>
-<li>You are now ready to play <strong>Little Big Planet 3</strong> on your PC. Double-click on the game icon on RPCS3 and enjoy!</li>
-</ol>
-
-<h2>Tips and Tricks for Little Big Planet 3</h2>
-
-<p><strong>Little Big Planet 3</strong> is a game that requires creativity, exploration, cooperation, and skill. Here are some tips and tricks that can help you have more fun and success in this game:</p>
-
-<ul>
-<li>In Adventure mode, try to collect as many items as possible. They will help you customize your character, create your own levels, or unlock new features.</li>
-<li>In Adventure mode, look for hidden areas and secrets. They may contain bonus items or challenges that can reward you with more items or trophies.</li>
-<li>In Adventure mode, experiment with different characters and their abilities. They may help you solve puzzles or access new areas that Sackboy alone cannot reach.</li>
-<li>In Create mode, use tutorials and guides to learn how to use the tools and materials effectively. You can also watch videos or play levels created by other users to get inspiration or tips.</li>
-<li>In Create mode, test your levels before publishing them online. Make sure they are fun, balanced, original, and bug-free.</li>
-<li>In Create mode, share your levels online and get feedback from other users. You can also play levels created by other users and rate them or leave comments.</li>
-<li>In Online mode, communicate with other players using voice chat or text chat. You can also use gestures or stickers to express yourself or interact with others.</li>
-<li>In Online mode, cooperate with other players or compete against them in various modes and challenges. You can also join or create communities based on your interests or preferences.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p><strong>Little Big Planet 3</strong> is a wonderful game that offers endless possibilities for creativity and fun. You can play it on your PC for free using an emulator called RPCS3. We hope this article helped you download and play this game on your PC without any problems. If you have any questions or suggestions, feel free to leave them in the comments section below. Happy gaming!</p>
-<h2>What are the Benefits of Playing Little Big Planet 3 on PC?</h2>
-
-<p>Playing <strong>Little Big Planet 3</strong> on PC has some advantages over playing it on PlayStation 3 or PlayStation 4. Here are some of them:</p>
-<p></p>
-
-<ul>
-<li>You can play it for free. You don't need to buy the game or pay for a subscription to play online. You just need to download the emulator and the game files and follow the instructions in this article.</li>
-<li>You can play it with better graphics and performance. You can adjust the settings of the emulator and the game to suit your PC specifications and preferences. You can also use higher resolution, anti-aliasing, anisotropic filtering, and other enhancements to improve the visual quality of the game.</li>
-<li>You can play it with more options and features. You can use a keyboard and mouse, a controller, or any other input device that is compatible with your PC. You can also use mods, cheats, trainers, or other tools to customize or modify the game to your liking.</li>
-<li>You can play it with a larger and more active community. You can join or create online games with other players who use RPCS3 or other emulators. You can also access more levels, characters, objects, and other creations made by other users online.</li>
-</ul>
-
-<h2>What are the Drawbacks of Playing Little Big Planet 3 on PC?</h2>
-
-<p>Playing <strong>Little Big Planet 3</strong> on PC also has some disadvantages that you should be aware of. Here are some of them:</p>
-
-<ul>
-<li>You may encounter some bugs or glitches. The game and the emulator are not perfect and may have some errors or issues that can affect your gameplay experience. For example, you may experience crashes, freezes, slowdowns, audio problems, graphical glitches, or missing features.</li>
-<li>You may need a powerful PC. The game and the emulator require a lot of resources to run smoothly and without problems. You may need a high-end CPU, GPU, RAM, and SSD to play the game at its best quality and performance.</li>
-<li>You may face some legal or ethical issues. The game and the emulator are not officially supported or endorsed by Sony or Sumo Digital. You may be violating some copyrights or terms of service by downloading and playing the game on your PC. You may also be depriving the developers of their deserved income by not buying their product.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p><strong>Little Big Planet 3</strong> is a wonderful game that offers endless possibilities for creativity and fun. You can play it on your PC for free using an emulator called RPCS3. We hope this article helped you download and play this game on your PC without any problems. If you have any questions or suggestions, feel free to leave them in the comments section below. Happy gaming!</p>
-<h2>Conclusion</h2>
-
-<p><strong>Little Big Planet 3</strong> is a wonderful game that offers endless possibilities for creativity and fun. You can play it on your PC for free using an emulator called RPCS3. We hope this article helped you download and play this game on your PC without any problems. If you have any questions or suggestions, feel free to leave them in the comments section below. Happy gaming!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lj1995/vocal2guitar/uvr5_pack/lib_v5/nets_537238KB.py b/spaces/lj1995/vocal2guitar/uvr5_pack/lib_v5/nets_537238KB.py
deleted file mode 100644
index 1ceac4a470ca311d594818d52e5f96919cfddb26..0000000000000000000000000000000000000000
--- a/spaces/lj1995/vocal2guitar/uvr5_pack/lib_v5/nets_537238KB.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import torch
-import numpy as np
-from torch import nn
-import torch.nn.functional as F
-
-from uvr5_pack.lib_v5 import layers_537238KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 64)
-        self.stg1_high_band_net = BaseASPPNet(2, 64)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(32, 64)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(64, 128)
-
-        self.out = nn.Conv2d(128, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/modules/F0Predictor/crepe.py b/spaces/lllqqq/so-vits-svc-models-pcr/modules/F0Predictor/crepe.py
deleted file mode 100644
index c6fb45c79bcd306202a2c0282b3d73a8074ced5d..0000000000000000000000000000000000000000
--- a/spaces/lllqqq/so-vits-svc-models-pcr/modules/F0Predictor/crepe.py
+++ /dev/null
@@ -1,340 +0,0 @@
-from typing import Optional,Union
-try:
-    from typing import Literal
-except Exception as e:
-    from typing_extensions import Literal
-import numpy as np
-import torch
-import torchcrepe
-from torch import nn
-from torch.nn import functional as F
-import scipy
-
-#from:https://github.com/fishaudio/fish-diffusion
-
-def repeat_expand(
-    content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
-):
-    """Repeat content to target length.
-    This is a wrapper of torch.nn.functional.interpolate.
-
-    Args:
-        content (torch.Tensor): tensor
-        target_len (int): target length
-        mode (str, optional): interpolation mode. Defaults to "nearest".
-
-    Returns:
-        torch.Tensor: tensor
-    """
-
-    ndim = content.ndim
-
-    if content.ndim == 1:
-        content = content[None, None]
-    elif content.ndim == 2:
-        content = content[None]
-
-    assert content.ndim == 3
-
-    is_np = isinstance(content, np.ndarray)
-    if is_np:
-        content = torch.from_numpy(content)
-
-    results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
-
-    if is_np:
-        results = results.numpy()
-
-    if ndim == 1:
-        return results[0, 0]
-    elif ndim == 2:
-        return results[0]
-
-
-class BasePitchExtractor:
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        keep_zeros: bool = True,
-    ):
-        """Base pitch extractor.
-
-        Args:
-            hop_length (int, optional): Hop length. Defaults to 512.
-            f0_min (float, optional): Minimum f0. Defaults to 50.0.
-            f0_max (float, optional): Maximum f0. Defaults to 1100.0.
-            keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
-        """
-
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.keep_zeros = keep_zeros
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        raise NotImplementedError("BasePitchExtractor is not callable.")
-
-    def post_process(self, x, sampling_rate, f0, pad_to):
-        if isinstance(f0, np.ndarray):
-            f0 = torch.from_numpy(f0).float().to(x.device)
-
-        if pad_to is None:
-            return f0
-
-        f0 = repeat_expand(f0, pad_to)
-
-        if self.keep_zeros:
-            return f0
-        
-        vuv_vector = torch.zeros_like(f0)
-        vuv_vector[f0 > 0.0] = 1.0
-        vuv_vector[f0 <= 0.0] = 0.0
-        
-        # 去掉0频率, 并线性插值
-        nzindex = torch.nonzero(f0).squeeze()
-        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
-        time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
-        time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
-
-        if f0.shape[0] <= 0:
-            return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
-
-        if f0.shape[0] == 1:
-            return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
-    
-        # 大概可以用 torch 重写?
-        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
-        vuv_vector = vuv_vector.cpu().numpy()
-        vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
-        
-        return f0,vuv_vector
-
-
-class MaskedAvgPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of mean pooling that supports masked values.
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedAvgPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        # Apply the mask by setting masked elements to zero, or make NaNs zero
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        # Ensure mask has the same shape as the input tensor
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-        # Create a ones kernel with the same number of channels as the input tensor
-        ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
-
-        # Perform sum pooling
-        sum_pooled = nn.functional.conv1d(
-            masked_x,
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-
-        # Count the non-masked (valid) elements in each pooling window
-        valid_count = nn.functional.conv1d(
-            mask.float(),
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-        valid_count = valid_count.clamp(min=1)  # Avoid division by zero
-
-        # Perform masked average pooling
-        avg_pooled = sum_pooled / valid_count
-
-        # Fill zero values with NaNs
-        avg_pooled[avg_pooled == 0] = float("nan")
-
-        if ndim == 2:
-            return avg_pooled.squeeze(1)
-
-        return avg_pooled
-
-
-class MaskedMedianPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of median pooling that supports masked values.
-
-        This implementation is inspired by the median pooling implementation in
-        https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedMedianPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-
-        x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
-        mask = F.pad(
-            mask.float(), (self.padding, self.padding), mode="constant", value=0
-        )
-
-        x = x.unfold(2, self.kernel_size, self.stride)
-        mask = mask.unfold(2, self.kernel_size, self.stride)
-
-        x = x.contiguous().view(x.size()[:3] + (-1,))
-        mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
-
-        # Combine the mask with the input tensor
-        #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
-        x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
-
-        # Sort the masked tensor along the last dimension
-        x_sorted, _ = torch.sort(x_masked, dim=-1)
-
-        # Compute the count of non-masked (valid) values
-        valid_count = mask.sum(dim=-1)
-
-        # Calculate the index of the median value for each pooling window
-        median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
-
-        # Gather the median values using the calculated indices
-        median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
-
-        # Fill infinite values with NaNs
-        median_pooled[torch.isinf(median_pooled)] = float("nan")
-        
-        if ndim == 2:
-            return median_pooled.squeeze(1)
-
-        return median_pooled
-
-
-class CrepePitchExtractor(BasePitchExtractor):
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        threshold: float = 0.05,
-        keep_zeros: bool = False,
-        device = None,
-        model: Literal["full", "tiny"] = "full",
-        use_fast_filters: bool = True,
-        decoder="viterbi"
-    ):
-        super().__init__(hop_length, f0_min, f0_max, keep_zeros)
-        if decoder == "viterbi":
-            self.decoder = torchcrepe.decode.viterbi
-        elif decoder == "argmax":
-            self.decoder = torchcrepe.decode.argmax
-        elif decoder == "weighted_argmax":
-            self.decoder = torchcrepe.decode.weighted_argmax
-        else:
-            raise "Unknown decoder"
-        self.threshold = threshold
-        self.model = model
-        self.use_fast_filters = use_fast_filters
-        self.hop_length = hop_length
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        if self.use_fast_filters:
-            self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
-            self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        """Extract pitch using crepe.
-
-
-        Args:
-            x (torch.Tensor): Audio signal, shape (1, T).
-            sampling_rate (int, optional): Sampling rate. Defaults to 44100.
-            pad_to (int, optional): Pad to length. Defaults to None.
-
-        Returns:
-            torch.Tensor: Pitch, shape (T // hop_length,).
-        """
-
-        assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
-        assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
-
-        x = x.to(self.dev)
-        f0, pd = torchcrepe.predict(
-            x,
-            sampling_rate,
-            self.hop_length,
-            self.f0_min,
-            self.f0_max,
-            pad=True,
-            model=self.model,
-            batch_size=1024,
-            device=x.device,
-            return_periodicity=True,
-            decoder=self.decoder
-        )
-
-        # Filter, remove silence, set uv threshold, refer to the original warehouse readme
-        if self.use_fast_filters:
-            pd = self.median_filter(pd)
-        else:
-            pd = torchcrepe.filter.median(pd, 3)
-
-        pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
-        f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
-        
-        if self.use_fast_filters:
-            f0 = self.mean_filter(f0)
-        else:
-            f0 = torchcrepe.filter.mean(f0, 3)
-
-        f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
-
-        if torch.all(f0 == 0):
-            rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to)
-            return rtn,rtn
-        
-        return self.post_process(x, sampling_rate, f0, pad_to)
diff --git a/spaces/luisoala/raw2logit/figures/figure2.sh b/spaces/luisoala/raw2logit/figures/figure2.sh
deleted file mode 100644
index beefc0b306de0f3a3cf3d0bcc7fc59f03183e002..0000000000000000000000000000000000000000
--- a/spaces/luisoala/raw2logit/figures/figure2.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-python figures.py \
---experiment_name track-test \
---run_name track-all \
---output train_vs_val_loss \
diff --git a/spaces/magicr/BuboGPT/imagebind/models/multimodal_formers.py b/spaces/magicr/BuboGPT/imagebind/models/multimodal_formers.py
deleted file mode 100644
index f571fac4f6b2d4b351086eb70352bbc473bad4fa..0000000000000000000000000000000000000000
--- a/spaces/magicr/BuboGPT/imagebind/models/multimodal_formers.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import logging
-import os
-
-import torch
-from torch import nn, Tensor
-
-from bubogpt.common.dist_utils import download_cached_file
-from bubogpt.common.utils import is_url
-from bubogpt.models.Qformer import BertConfig, BertLMHeadModel
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class BaseQFormer(nn.Module):
-    def __init__(self, freeze_qformer=False):
-        super().__init__()
-        self.freeze_qformer = freeze_qformer
-        self.Qformer = None
-
-    def check_and_freeze(self):
-        assert self.Qformer is not None
-        if self.freeze_qformer:
-            for name, param in self.Qformer.named_parameters():
-                param.requires_grad = False
-            self.Qformer = self.Qformer.eval()
-            self.Qformer.train = disabled_train
-            self.query_tokens.requires_grad = False
-            logging.info("Freeze This QFormer")
-
-    def load_from_pretrained(self, url_or_filename):
-        if is_url(url_or_filename):
-            cached_file = download_cached_file(
-                url_or_filename, check_hash=False, progress=True
-            )
-            checkpoint = torch.load(cached_file, map_location="cpu")
-        elif os.path.isfile(url_or_filename):
-            checkpoint = torch.load(url_or_filename, map_location="cpu")
-        else:
-            raise RuntimeError("checkpoint url or path is invalid")
-
-        state_dict = checkpoint["model"]
-
-        msg = self.load_state_dict(state_dict, strict=False)
-
-        logging.info("Missing keys {}".format(msg.missing_keys))
-        logging.info("load checkpoint from %s" % url_or_filename)
-
-        return msg
-
-
-class SequenceGenericQFormer(BaseQFormer):
-    def __init__(self,
-                 num_query_token: int,
-                 encoder_width: int = 768,
-                 freeze_qformer: bool = False,
-                 q_former_model: str = "",
-                 cross_attention_freq: int = 2
-                 ):
-        super().__init__(freeze_qformer)
-        self.Qformer, self.query_tokens = self.init_Qformer(num_query_token, encoder_width, cross_attention_freq)
-        if q_former_model != "":
-            self.load_Qformer(q_former_model)
-        self.check_and_freeze()
-
-    def set_Qformer(self):
-        self.Qformer.cls = None
-        self.Qformer.bert.embeddings.word_embeddings = None
-        self.Qformer.bert.embeddings.position_embeddings = None
-        for layer in self.Qformer.bert.encoder.layer:
-            layer.output = None
-            layer.intermediate = None
-
-    def load_Qformer(self, q_former_model):
-        self.Qformer.cls = None
-        self.Qformer.bert.embeddings.word_embeddings = None
-        self.Qformer.bert.embeddings.position_embeddings = None
-        for layer in self.Qformer.bert.encoder.layer:
-            layer.output = None
-            layer.intermediate = None
-        self.load_from_pretrained(url_or_filename=q_former_model)
-
-    @classmethod
-    def init_Qformer(cls, num_query_token, encoder_width, cross_attention_freq=2):
-        encoder_config = BertConfig.from_pretrained("bert-base-uncased")
-        encoder_config.encoder_width = encoder_width
-        # insert cross-attention layer every other block
-        encoder_config.add_cross_attention = True
-        encoder_config.cross_attention_freq = cross_attention_freq
-        encoder_config.query_length = num_query_token
-        Qformer = BertLMHeadModel(config=encoder_config)
-        query_tokens = nn.Parameter(
-            torch.zeros(1, num_query_token, encoder_config.hidden_size)
-        )
-        query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
-        return Qformer, query_tokens
-
-    def forward(self, input_embeds: Tensor) -> Tensor:
-        input_atts = torch.ones(input_embeds.size()[:-1], dtype=torch.long).to(input_embeds.device)
-        query_tokens = self.query_tokens.expand(input_embeds.shape[0], -1, -1)
-        query_output = self.Qformer.bert(
-            query_embeds=query_tokens,
-            encoder_hidden_states=input_embeds,
-            encoder_attention_mask=input_atts,
-            return_dict=True,
-        )
-        return query_output.last_hidden_state
diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/options/train_options.py b/spaces/manhkhanhUIT/BOPBTL/Global/options/train_options.py
deleted file mode 100644
index 6cc3296657043568a3a961d793f2c69f568bab1a..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/BOPBTL/Global/options/train_options.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-from .base_options import BaseOptions
-
-class TrainOptions(BaseOptions):
-    def initialize(self):
-        BaseOptions.initialize(self)
-        # for displays
-        self.parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen')
-        self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
-        self.parser.add_argument('--save_latest_freq', type=int, default=10000, help='frequency of saving the latest results')
-        self.parser.add_argument('--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs')
-        self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
-        self.parser.add_argument('--debug', action='store_true', help='only do one epoch and displays at each iteration')
-
-        # for training
-        self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
-        # self.parser.add_argument('--load_pretrain', type=str, default='', help='load the pretrained model from the specified location')
-        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
-        self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
-        self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')
-        self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')
-        self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
-        self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
-        self.parser.add_argument('--training_dataset',type=str,default='',help='training use which dataset')
-
-        # for discriminators        
-        self.parser.add_argument('--num_D', type=int, default=2, help='number of discriminators to use')
-        self.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
-        self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')    
-        self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
-        self.parser.add_argument('--l2_feat', type=float, help='weight for feature mapping loss') 
-        self.parser.add_argument('--use_l1_feat', action='store_true', help='use l1 for feat mapping')               
-        self.parser.add_argument('--no_ganFeat_loss', action='store_true', help='if specified, do *not* use discriminator feature matching loss')
-        self.parser.add_argument('--no_vgg_loss', action='store_true', help='if specified, do *not* use VGG feature matching loss')        
-        self.parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')
-        self.parser.add_argument('--gan_type', type=str, default='lsgan', help='Choose the loss type of GAN')
-        self.parser.add_argument('--pool_size', type=int, default=0, help='the size of image buffer that stores previously generated images')
-        self.parser.add_argument('--norm_D',type=str, default='spectralinstance', help='instance normalization or batch normalization')
-        self.parser.add_argument('--init_D',type=str,default='xavier',help='normal|xavier|xavier_uniform|kaiming|orthogonal|none')
-
-        self.parser.add_argument('--no_TTUR',action='store_true',help='No TTUR')
-
-        self.parser.add_argument('--start_epoch',type=int,default=-1,help='write the start_epoch of iter.txt into this parameter')
-        self.parser.add_argument('--no_degradation',action='store_true',help='when train the mapping, enable this parameter --> no degradation will be added into clean image')
-        self.parser.add_argument('--no_load_VAE',action='store_true',help='when train the mapping, enable this parameter --> random initialize the encoder an decoder')
-        self.parser.add_argument('--use_v2_degradation',action='store_true',help='enable this parameter --> 4 kinds of degradations will be used to synthesize corruption')
-        self.parser.add_argument('--use_vae_which_epoch',type=str,default='200')
-
-
-        self.parser.add_argument('--use_focal_loss',action='store_true')
-
-        self.parser.add_argument('--mask_need_scale',action='store_true',help='enable this param means that the pixel range of mask is 0-255')
-        self.parser.add_argument('--positive_weight',type=float,default=1.0,help='(For scratch detection) Since the scratch number is less, and we use a weight strategy. This parameter means that we want to decrease the weight.')
-
-        self.parser.add_argument('--no_update_lr',action='store_true',help='use this means we do not update the LR while training')
-
-
-        self.isTrain = True
diff --git a/spaces/marmg/zshot/streamlit_apps_config.py b/spaces/marmg/zshot/streamlit_apps_config.py
deleted file mode 100644
index 91722e4d43eedd20ae5617c70fac321a6101cfdc..0000000000000000000000000000000000000000
--- a/spaces/marmg/zshot/streamlit_apps_config.py
+++ /dev/null
@@ -1,63 +0,0 @@
-#APP STYLE
-MAX_WIDTH = 1600
-PADDING_TOP = 0.25
-PADDING_BOTTOM = 0.25
-PADDING_RIGHT = 4
-PADDING_LEFT = 4
-COLOR = 'black'
-BACKGROUND_COLOR = 'white'
-
-HTML_WRAPPER = """<div class="scroll entities" style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 0.25rem; margin-bottom: 2.5rem; white-space:pre-wrap">{}</div>"""
-HTML_INDEX_WRAPPER = """<div ">{}</div>"""
-
-STYLE_CONFIG_OLD = f"""
-<style>
-    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');
-    *:not(text){{
-      font-family: Montserrat;
-    }}
-
-    .reportview-container .main .block-container{{
-        max-width: {MAX_WIDTH}px;
-        padding-top: {PADDING_TOP}rem;
-        padding-right: {PADDING_RIGHT}rem;
-        padding-left: {PADDING_LEFT}rem;
-        padding-bottom: {PADDING_BOTTOM}rem;
-    }}
-    .reportview-container .main {{
-        color: {COLOR};
-        background-color: {BACKGROUND_COLOR};
-    }}
-    div.scroll {{
-                margin:1px, 1px;
-                padding:1px;
-                width: 100%;
-                height: 500px;
-                overflow-x: hidden;
-                overflow-x: auto;
-    }}
-    .reportview-container .markdown-text-container{{
-        font-family: roboto !important;
-        color: dimgray !important;
-        line-height: normal !important;
-    }}
-    .reportview-container h2
-    {{
-        font-weight: 400 !important;
-        font-size: 1.5rem !important;
-        line-height: 1.6!important;
-    }}
-    .reportview-container h2
-    {{
-        font-weight: 300 !important;
-        font-size: 1.3rem !important;
-        line-height: 1.4!important;
-    }}
-
-
-</style>
-"""
-
-with open('./style.css') as f:
-    STYLE_CONFIG_NEW = f.read()
-STYLE_CONFIG = STYLE_CONFIG_OLD + '<style>{}</style>'.format(STYLE_CONFIG_NEW)
diff --git a/spaces/matthoffner/open-codetree/components/Playground/index.tsx b/spaces/matthoffner/open-codetree/components/Playground/index.tsx
deleted file mode 100644
index 7e1b541e39173b257f71174c6a421723d31821a7..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/open-codetree/components/Playground/index.tsx
+++ /dev/null
@@ -1,259 +0,0 @@
-import React, { useRef, useEffect, useState, ChangeEvent, FormEvent } from "react";
-import { Message, useChat } from "ai/react";
-import { useAppDispatch, useAppSelector } from "../../store/hook";
-import {
-  compiler_state,
-  initEsbuild,
-} from "../../store/features/compilerSlice";
-import { editor_state, set_monaco_input_value, update_editor_code } from "../../store/features/editorSlice";
-import { theme_state } from "../../store/features/themeSlice";
-import { ModalEnum, open_modal } from "../../store/features/modalSlice";
-
-import ConsoleLog from "./ConsoleLog";
-import Iframe from "./Iframe";
-import InputCodeTab from "./InputCodeTab";
-import Footer from "./Footer";
-import Pane from "./Pane";
-import { SendIcon } from "../../constants/icon";
-import ReactMarkdown from "react-markdown";
-import { ChatRequestOptions } from "ai";
-
-const DEFAULT_PROMPT = `You are assisting in a WASM-powered live editor, Codetree, where React code is instantly reflected in the browser. Generate React code with the following constraints:
-1. Always import both React and ReactDOM.
-2. All logic should be encapsulated within a single app component file.
-3. External dependencies or imports outside of the React core are not permitted.
-4. Always use inline styles for styling.
-5. Code snippets should conclude with rendering to the DOM's root element: ReactDOM.render(<App />, document.getElementById('root'));
-6. Ensure the code is optimized for a live browser environment and adheres strictly to React best practices.
-
-Write the code with the above guidelines in mind.`;
-
-const Playground = () => {
-  const dispatch = useAppDispatch();
-  const formRef = useRef<HTMLFormElement>(null);
-  const inputRef = useRef<HTMLTextAreaElement>(null);
-  const { theme } = useAppSelector(theme_state);
-  const { esbuildStatus, isCompiling, output } = useAppSelector(compiler_state);
-  const { logs, editorValue, isLogTabOpen } = useAppSelector(editor_state);
-  const [markdownCode, setMarkdownCode] = useState('');
-  const [prevMarkdownCode, setPrevMarkdownCode] = useState(markdownCode);
-  const [isSystemInputVisible, setSystemInputVisible] = useState(false);
-  const [isModelInputVisible, setModelInputVisible] = useState(false);
-  const [aiProvider, setAIProvider] = useState<string>("openai");
-  const [urlOption, setUrlOption] = useState<string | any>(null);
-  const [isChatVisible, setIsChatVisible] = useState(true);
-
-  const [systemMessage, setSystemMessage] = useState(
-    DEFAULT_PROMPT
-  );
-
-  const isValidCodeBlock = (markdownCode: string) => {
-    return markdownCode && markdownCode.length > 10 && markdownCode.includes('\n');
-  }
-
-  const modifiedHandleSubmit = async (e: FormEvent<HTMLFormElement>, chatRequestOptions?: ChatRequestOptions) => {
-    e.preventDefault();
-    await handleSubmit(e, { 
-      ...chatRequestOptions, 
-      aiProvider, 
-    } as any);
-  };
-
-  useEffect(() => {
-    const timer = setInterval(() => {
-      if (isValidCodeBlock(markdownCode) && markdownCode !== prevMarkdownCode) {
-        dispatch(update_editor_code({ type: 'javascript', content: markdownCode }));
-        setPrevMarkdownCode(markdownCode);
-      }
-    }, 2000);
-
-    return () => {
-      clearInterval(timer);
-    };
-  }, [markdownCode, prevMarkdownCode, dispatch]);
-
-  const { append, messages, input, setInput, handleSubmit, ...rest } = useChat({
-    body: {
-      systemMessage: systemMessage,
-      aiProvider: aiProvider,
-      url: urlOption
-    },
-    onError: (error) => {
-      console.error(error);
-    },
-  });
-
-  useEffect(() => {
-    if (!esbuildStatus.isReady) {
-      dispatch(initEsbuild());
-    }
-  }, [dispatch, esbuildStatus]);
-
-  useEffect(() => {
-    dispatch(open_modal(ModalEnum.TEMPLATE));
-  }, [dispatch]);
-
-  useEffect(() => {
-    if(isValidCodeBlock(markdownCode)) {
-      const newEditorValue = {
-        name: "React",
-        description: "By codetree",
-        public: true,
-        iconSrc: "/icons/reactjs.svg",
-        tabs: {
-          javascript: {
-            title: "JS/JSX",
-            entryPoints: "index.js",
-            monacoLanguage: "javascript",
-            data: markdownCode
-          },
-          html: {
-            title: "index.html",
-            entryPoints: "index.html",
-            monacoLanguage: "html",
-            data: ""
-          },
-          css: {
-            title: "main.css",
-            entryPoints: "main.css",
-            monacoLanguage: "css",
-            data: ""
-          }
-        }
-      };
-      
-      dispatch(set_monaco_input_value(newEditorValue as any));
-    }
-  }, [markdownCode, dispatch]);
-
-  return (
-    <div style={{ background: theme.background }}>
-      <div className="flex flex-col">
-          <div className="px-4 pb-2 pt-3 shadow-lg sm:pb-3 sm:pt-4">
-            <span className="text-lg font-semibold">System Prompt</span>
-            <button
-              onClick={() => setSystemInputVisible(!isSystemInputVisible)}
-              className="p-2 rounded-full hover:bg-gray-200 transition duration-300"
-            >
-              {isSystemInputVisible ? '−' : '+'}
-            </button>
-            <span className="text-lg font-semibold">Model</span>
-              <button
-                onClick={() => setModelInputVisible(!isModelInputVisible)}
-                className="p-2 rounded-full hover:bg-gray-200 transition duration-300"
-              >
-                {isModelInputVisible ? '−' : '+'}
-            </button>
-            <span className="text-lg font-semibold">Chat</span>
-              <button
-                onClick={() => setIsChatVisible(!isChatVisible)}
-                className="p-2 rounded-full hover:bg-gray-200 transition duration-300"
-              >
-                {isChatVisible ? '−' : '+'}
-            </button>
-            
-            {isSystemInputVisible && (
-              <textarea 
-                className="textarea my-4 border p-2 rounded-md shadow-sm w-full resize-none" 
-                value={systemMessage || ''} 
-                onChange={e => setSystemMessage(e.target.value)} 
-                placeholder="Enter custom system prompt here"
-                rows={2} // initial number of rows, can be adjusted
-                style={{ overflowWrap: 'break-word', whiteSpace: 'pre-wrap' }}
-              />
-            )}
-            {isModelInputVisible && (
-              <div className="my-4">
-                <select 
-                  value={aiProvider}
-                  onChange={(e) => setAIProvider(e.target.value)}
-                  className="border p-2 rounded-md shadow-sm w-full bg-transparent text-gray-700 mt-2"
-                >
-                  <option value="openai">gpt-4</option>
-                  <option value="meta-llama/Llama-2-70b-chat-hf">meta-llama/Llama-2-70b-chat-hf</option>
-                  <label className="block text-gray-700 text-sm font-bold mb-2" htmlFor="url-option">
-                    Model URL Option:
-                  </label>
-                  
-                </select>
-                <input 
-                  id="url-option"
-                  type="url"
-                  placeholder="Enter URL"
-                  value={urlOption}
-                  onChange={(e) => setUrlOption(e.target.value)}
-                  className="border p-2 rounded-md shadow-sm w-full bg-transparent text-gray-700 mt-2"
-                />
-              </div>
-            )}
-
-            {isChatVisible &&
-                <form ref={formRef} onSubmit={modifiedHandleSubmit} className="relative w-full">
-                <textarea ref={inputRef} onChange={(e) => setInput(e.target.value)}
-                placeholder="Enter your message"
-                onKeyDown={(e) => {
-                  if (e.key === "Enter" && !e.shiftKey) {
-                    formRef.current?.requestSubmit();
-                    e.preventDefault();
-                  }
-                }}
-                  spellCheck={false} className="textarea" value={input} />
-                <button 
-                    type="submit"
-                    className="absolute inset-y-0 right-3 my-auto flex h-8 w-8 items-center justify-center rounded-md transition-all"
-                >
-                  <SendIcon
-                      className={"h-4 w-4"}
-                  />
-                </button>
-              </form>
-            }
-            
-        </div>
-        {isChatVisible && (
-          <div className="flex flex-col items-start space-y-4 overflow-y-auto max-h-[20vh]">
-          {messages?.map((message, index) => (
-            <p key={index} className="messages-text">
-              <ReactMarkdown
-                className="prose mt-1 w-full break-words prose-p:leading-relaxed" 
-                components={{
-                  a: (props) => (
-                    <a {...props} target="_blank" rel="noopener noreferrer" />
-                  ),
-                  // @ts-ignore
-                  code: ({node, ...props}) => {
-                    const codeValue = props.children[0] || '';
-                    setMarkdownCode(codeValue as any);
-                    
-                    return null;
-                  }                    
-                }}
-              >
-                {message.content}
-              </ReactMarkdown>
-            </p>
-          ))}
-          </div>
-        )}
-      </div>
-      <Pane
-        panelA={<InputCodeTab editorValue={editorValue} />}
-        panelB={
-          <Iframe
-            tabs={editorValue.tabs}
-            output={output}
-            isCompiling={isCompiling}
-            esbuildStatus={esbuildStatus}
-          />
-        }
-        panelC={<ConsoleLog logs={logs} />}
-        lastPanelVisibility={isLogTabOpen}
-      />
-
-      <Footer />
-    </div>
-
-  );
-};
-
-export default Playground;
diff --git a/spaces/mayordp/DeepFakeAI/DeepFakeAI/capturer.py b/spaces/mayordp/DeepFakeAI/DeepFakeAI/capturer.py
deleted file mode 100644
index 9ba555c222d55166c9fb5faf0b32f1afd6a69d46..0000000000000000000000000000000000000000
--- a/spaces/mayordp/DeepFakeAI/DeepFakeAI/capturer.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from typing import Optional
-import cv2
-
-from DeepFakeAI.typing import Frame
-
-
-def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[Frame]:
-	capture = cv2.VideoCapture(video_path)
-	frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-	capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
-	has_frame, frame = capture.read()
-	capture.release()
-	if has_frame:
-		return frame
-	return None
-
-
-def get_video_frame_total(video_path : str) -> int:
-	capture = cv2.VideoCapture(video_path)
-	video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
-	capture.release()
-	return video_frame_total
diff --git a/spaces/mb1te/PSII_FINAL/Dockerfile b/spaces/mb1te/PSII_FINAL/Dockerfile
deleted file mode 100644
index 3e6e6935d49662f02e90c84ab5f03f8d524ac68a..0000000000000000000000000000000000000000
--- a/spaces/mb1te/PSII_FINAL/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM python:3.8-slim
-
-COPY requirements.txt requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-WORKDIR $HOME/app
-COPY --chown=user . $HOME/app
-
-CMD ["uvicorn", "app.server:app", "--host", "0.0.0.0", "--port", "8080"]
diff --git a/spaces/merve/hidden-bias/source/third_party/swoopy-drag.js b/spaces/merve/hidden-bias/source/third_party/swoopy-drag.js
deleted file mode 100644
index 3c740601b5111efdf47f0fd5da9d41de58ceb757..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/third_party/swoopy-drag.js
+++ /dev/null
@@ -1,193 +0,0 @@
-// https://github.com/1wheel/swoopy-drag Copyright (c) 2016 Adam Pearce
-
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('d3')) :
-  typeof define === 'function' && define.amd ? define(['exports', 'd3'], factory) :
-  (factory((global.d3 = global.d3 || {}),global.d3));
-}(this, function (exports,d3) { 'use strict';
-
-  function swoopyDrag(){
-    var x = function(d){ return d }
-    var y = function(d){ return d }
-
-    var annotations = []
-    var annotationSel
-
-    var draggable = false
-
-    var dispatch = d3.dispatch('drag')
-
-    var textDrag = d3.drag()
-        .on('drag', function(d){
-          var x = d3.event.x
-          var y = d3.event.y
-          d.textOffset = [x, y].map(Math.round)
-
-          d3.select(this).call(translate, d.textOffset)
-
-          dispatch.call('drag')
-        })
-        .subject(function(d){ return {x: d.textOffset[0], y: d.textOffset[1]} })
-
-    var circleDrag = d3.drag()
-        .on('drag', function(d){
-          var x = d3.event.x
-          var y = d3.event.y
-          d.pos = [x, y].map(Math.round)
-
-          var parentSel = d3.select(this.parentNode)
-
-          var path = ''
-          var points = parentSel.selectAll('circle').data()
-          if (points[0].type == 'A'){
-            path = calcCirclePath(points)
-          } else{
-            points.forEach(function(d){ path = path + d.type  + d.pos })
-          }
-
-          parentSel.select('path').attr('d', path).datum().path = path
-          d3.select(this).call(translate, d.pos)
-
-          dispatch.call('drag')
-        })
-        .subject(function(d){ return {x: d.pos[0], y: d.pos[1]} })
-
-
-    var rv = function(sel){
-      annotationSel = sel.html('').selectAll('g')
-          .data(annotations).enter()
-        .append('g')
-          .call(translate, function(d){ return [x(d), y(d)] })
-
-      var textSel = annotationSel.append('text')
-          .call(translate, ƒ('textOffset'))
-          .text(ƒ('text'))
-
-      annotationSel.append('path')
-          .attr('d', ƒ('path'))
-
-      if (!draggable) return
-
-      annotationSel.style('cursor', 'pointer')
-      textSel.call(textDrag)
-
-      annotationSel.selectAll('circle').data(function(d){
-        var points = []
-
-        if (~d.path.indexOf('A')){
-          //handle arc paths seperatly -- only one circle supported
-          var pathNode = d3.select(this).select('path').node()
-          var l = pathNode.getTotalLength()
-
-          points = [0, .5, 1].map(function(d){
-            var p = pathNode.getPointAtLength(d*l)
-            return {pos: [p.x, p.y], type: 'A'}
-          })
-        } else{
-          var i = 1
-          var type = 'M'
-          var commas = 0
-
-          for (var j = 1; j < d.path.length; j++){
-            var curChar = d.path[j]
-            if (curChar == ',') commas++
-            if (curChar == 'L' || curChar == 'C' || commas == 2){
-              points.push({pos: d.path.slice(i, j).split(','), type: type})
-              type = curChar
-              i = j + 1
-              commas = 0
-            }
-          }
-
-          points.push({pos: d.path.slice(i, j).split(','), type: type})
-        }
-
-        return points
-      }).enter().append('circle')
-          .attr('r', 8)
-          .attr('fill', 'rgba(0,0,0,0)')
-          .attr('stroke', '#333')
-          .attr('stroke-dasharray', '2 2')
-          .call(translate, ƒ('pos'))
-          .call(circleDrag)
-
-      dispatch.call('drag')
-    }
-
-
-    rv.annotations = function(_x){
-      if (typeof(_x) == 'undefined') return annotations
-      annotations = _x
-      return rv
-    }
-    rv.x = function(_x){
-      if (typeof(_x) == 'undefined') return x
-      x = _x
-      return rv
-    }
-    rv.y = function(_x){
-      if (typeof(_x) == 'undefined') return y
-      y = _x
-      return rv
-    }
-    rv.draggable = function(_x){
-      if (typeof(_x) == 'undefined') return draggable
-      draggable = _x
-      return rv
-    }
-    rv.on = function() {
-      var value = dispatch.on.apply(dispatch, arguments);
-      return value === dispatch ? rv : value;
-    }
-
-    return rv
-
-    //convert 3 points to an Arc Path 
-    function calcCirclePath(points){
-      var a = points[0].pos
-      var b = points[2].pos
-      var c = points[1].pos
-
-      var A = dist(b, c)
-      var B = dist(c, a)
-      var C = dist(a, b)
-
-      var angle = Math.acos((A*A + B*B - C*C)/(2*A*B))
-      
-      //calc radius of circle
-      var K = .5*A*B*Math.sin(angle)
-      var r = A*B*C/4/K
-      r = Math.round(r*1000)/1000
-
-      //large arc flag
-      var laf = +(Math.PI/2 > angle)
-
-      //sweep flag
-      var saf = +((b[0] - a[0])*(c[1] - a[1]) - (b[1] - a[1])*(c[0] - a[0]) < 0) 
-
-      return ['M', a, 'A', r, r, 0, laf, saf, b].join(' ')
-    }
-
-    function dist(a, b){
-      return Math.sqrt(
-        Math.pow(a[0] - b[0], 2) +
-        Math.pow(a[1] - b[1], 2))
-    }
-
-
-    //no jetpack dependency 
-    function translate(sel, pos){
-      sel.attr('transform', function(d){
-        var posStr = typeof(pos) == 'function' ? pos(d) : pos
-        return 'translate(' + posStr + ')' 
-      }) 
-    }
-
-    function ƒ(str){ return function(d){ return d[str] } } 
-  }
-
-  exports.swoopyDrag = swoopyDrag;
-
-  Object.defineProperty(exports, '__esModule', { value: true });
-
-}));
diff --git a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/style.css b/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/style.css
deleted file mode 100644
index 3a35c688d5bcaa0ff242d9e053734657ba580c01..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/style.css
+++ /dev/null
@@ -1,104 +0,0 @@
-body{
-  font-family: menlo, Consolas, 'Lucida Console', monospace; 
-  margin: 10px;
-  margin-left:  20px;
-  width:  1130px;
-  /*background: #f0f;*/
-}
-
-.tooltip {
-  top: -1000px;
-  position: fixed;
-  padding: 10px;
-  background: rgba(255, 255, 255, .90);
-  border: 1px solid lightgray;
-  pointer-events: none;
-}
-.tooltip-hidden{
-  opacity: 0;
-  transition: all .3s;
-  transition-delay: .1s;
-}
-
-@media (max-width: 590px){
-  div.tooltip{
-    bottom: -1px;
-    width: calc(100%);
-    left: -1px !important;
-    right: -1px !important;
-    top: auto !important;
-    width: auto !important;
-  }
-}
-
-svg{
-  overflow: visible;
-}
-
-.domain{
-  display: none;
-}
-
-.axis{
-  opacity:  .7;
-}
-
-text{
-  /*pointer-events: none;*/
-  text-shadow: 0 1.5px 0 #fff, 1.5px 0 0 #fff, 0 -1.5px 0 #fff, -1.5px 0 0 #fff;
-}
-
-
-#graph > div{
-  /*display: inline-block;*/
-}
-
-.active path{
-  stroke:  #f0f;
-  /*stroke-width:  2;*/
-  opacity:  1;
-}
-.active text{
-  fill: #f0f;
-  opacity: 1 !important;
-  font-size: 14px;
-
-}
-
-p{
-  max-width: 650px;
-}
-
-
-.bg-tick{
-  stroke: #eee;
-}
-
-.tick{
-  display: none;
-}
-
-text.tiny{
-  font-size: 9px;
-  font-family: monospace;
-}
-
-circle.sentence.active{
-  fill: #f0f;
-}
-
-div.sentence{
-  color:  #333;
-}
-div.sentence.active{
-  background: rgba(255,0,255,.1);
-} 
-
-.list{
-  /*border:  1px solid #555;*/
-  /*padding:  10px;*/
-}
-
-
-
-
diff --git a/spaces/mingyuan/MotionDiffuse/models/gaussian_diffusion.py b/spaces/mingyuan/MotionDiffuse/models/gaussian_diffusion.py
deleted file mode 100644
index 1c2d7a67a22a3d6413fa9336a7e6ae7bfa4b1476..0000000000000000000000000000000000000000
--- a/spaces/mingyuan/MotionDiffuse/models/gaussian_diffusion.py
+++ /dev/null
@@ -1,1145 +0,0 @@
-"""
-This code is borrowed from https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/gaussian_diffusion.py
-"""
-
-import enum
-import math
-
-import numpy as np
-import torch as th
-
-
-from abc import ABC, abstractmethod
-import torch.distributed as dist
-
-
-def create_named_schedule_sampler(name, diffusion):
-    """
-    Create a ScheduleSampler from a library of pre-defined samplers.
-    :param name: the name of the sampler.
-    :param diffusion: the diffusion object to sample for.
-    """
-    if name == "uniform":
-        return UniformSampler(diffusion)
-    elif name == "loss-second-moment":
-        return LossSecondMomentResampler(diffusion)
-    else:
-        raise NotImplementedError(f"unknown schedule sampler: {name}")
-
-
-class ScheduleSampler(ABC):
-    """
-    A distribution over timesteps in the diffusion process, intended to reduce
-    variance of the objective.
-    By default, samplers perform unbiased importance sampling, in which the
-    objective's mean is unchanged.
-    However, subclasses may override sample() to change how the resampled
-    terms are reweighted, allowing for actual changes in the objective.
-    """
-
-    @abstractmethod
-    def weights(self):
-        """
-        Get a numpy array of weights, one per diffusion step.
-        The weights needn't be normalized, but must be positive.
-        """
-
-    def sample(self, batch_size, device):
-        """
-        Importance-sample timesteps for a batch.
-        :param batch_size: the number of timesteps.
-        :param device: the torch device to save to.
-        :return: a tuple (timesteps, weights):
-                 - timesteps: a tensor of timestep indices.
-                 - weights: a tensor of weights to scale the resulting losses.
-        """
-        w = self.weights()
-        p = w / np.sum(w)
-        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
-        indices = th.from_numpy(indices_np).long().to(device)
-        weights_np = 1 / (len(p) * p[indices_np])
-        weights = th.from_numpy(weights_np).float().to(device)
-        return indices, weights
-
-
-class UniformSampler(ScheduleSampler):
-    def __init__(self, diffusion):
-        self.diffusion = diffusion
-        self._weights = np.ones([diffusion.num_timesteps])
-
-    def weights(self):
-        return self._weights
-
-
-class LossAwareSampler(ScheduleSampler):
-    def update_with_local_losses(self, local_ts, local_losses):
-        """
-        Update the reweighting using losses from a model.
-        Call this method from each rank with a batch of timesteps and the
-        corresponding losses for each of those timesteps.
-        This method will perform synchronization to make sure all of the ranks
-        maintain the exact same reweighting.
-        :param local_ts: an integer Tensor of timesteps.
-        :param local_losses: a 1D Tensor of losses.
-        """
-        batch_sizes = [
-            th.tensor([0], dtype=th.int32, device=local_ts.device)
-            for _ in range(dist.get_world_size())
-        ]
-        dist.all_gather(
-            batch_sizes,
-            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
-        )
-
-        # Pad all_gather batches to be the maximum batch size.
-        batch_sizes = [x.item() for x in batch_sizes]
-        max_bs = max(batch_sizes)
-
-        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
-        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
-        dist.all_gather(timestep_batches, local_ts)
-        dist.all_gather(loss_batches, local_losses)
-        timesteps = [
-            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
-        ]
-        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
-        self.update_with_all_losses(timesteps, losses)
-
-    @abstractmethod
-    def update_with_all_losses(self, ts, losses):
-        """
-        Update the reweighting using losses from a model.
-        Sub-classes should override this method to update the reweighting
-        using losses from the model.
-        This method directly updates the reweighting without synchronizing
-        between workers. It is called by update_with_local_losses from all
-        ranks with identical arguments. Thus, it should have deterministic
-        behavior to maintain state across workers.
-        :param ts: a list of int timesteps.
-        :param losses: a list of float losses, one per timestep.
-        """
-
-
-class LossSecondMomentResampler(LossAwareSampler):
-    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
-        self.diffusion = diffusion
-        self.history_per_term = history_per_term
-        self.uniform_prob = uniform_prob
-        self._loss_history = np.zeros(
-            [diffusion.num_timesteps, history_per_term], dtype=np.float64
-        )
-        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
-
-    def weights(self):
-        if not self._warmed_up():
-            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
-        weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
-        weights /= np.sum(weights)
-        weights *= 1 - self.uniform_prob
-        weights += self.uniform_prob / len(weights)
-        return weights
-
-    def update_with_all_losses(self, ts, losses):
-        for t, loss in zip(ts, losses):
-            if self._loss_counts[t] == self.history_per_term:
-                # Shift out the oldest loss term.
-                self._loss_history[t, :-1] = self._loss_history[t, 1:]
-                self._loss_history[t, -1] = loss
-            else:
-                self._loss_history[t, self._loss_counts[t]] = loss
-                self._loss_counts[t] += 1
-
-    def _warmed_up(self):
-        return (self._loss_counts == self.history_per_term).all()
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, th.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for th.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + th.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
-    )
-
-
-def approx_standard_normal_cdf(x):
-    """
-    A fast approximation of the cumulative distribution function of the
-    standard normal.
-    """
-    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
-
-
-def discretized_gaussian_log_likelihood(x, *, means, log_scales):
-    """
-    Compute the log-likelihood of a Gaussian distribution discretizing to a
-    given image.
-    :param x: the target images. It is assumed that this was uint8 values,
-              rescaled to the range [-1, 1].
-    :param means: the Gaussian mean Tensor.
-    :param log_scales: the Gaussian log stddev Tensor.
-    :return: a tensor like x of log probabilities (in nats).
-    """
-    assert x.shape == means.shape == log_scales.shape
-    centered_x = x - means
-    inv_stdv = th.exp(-log_scales)
-    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
-    cdf_plus = approx_standard_normal_cdf(plus_in)
-    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
-    cdf_min = approx_standard_normal_cdf(min_in)
-    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
-    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
-    cdf_delta = cdf_plus - cdf_min
-    log_probs = th.where(
-        x < -0.999,
-        log_cdf_plus,
-        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
-    )
-    assert log_probs.shape == x.shape
-    return log_probs
-
-
-def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
-    """
-    Get a pre-defined beta schedule for the given name.
-
-    The beta schedule library consists of beta schedules which remain similar
-    in the limit of num_diffusion_timesteps.
-    Beta schedules may be added, but should not be removed or changed once
-    they are committed to maintain backwards compatibility.
-    """
-    if schedule_name == "linear":
-        # Linear schedule from Ho et al, extended to work for any number of
-        # diffusion steps.
-        scale = 1000 / num_diffusion_timesteps
-        beta_start = scale * 0.0001
-        beta_end = scale * 0.02
-        return np.linspace(
-            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
-        )
-    elif schedule_name == "cosine":
-        return betas_for_alpha_bar(
-            num_diffusion_timesteps,
-            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
-        )
-    else:
-        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-class ModelMeanType(enum.Enum):
-    """
-    Which type of output the model predicts.
-    """
-
-    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
-    START_X = enum.auto()  # the model predicts x_0
-    EPSILON = enum.auto()  # the model predicts epsilon
-
-
-class ModelVarType(enum.Enum):
-    """
-    What is used as the model's output variance.
-
-    The LEARNED_RANGE option has been added to allow the model to predict
-    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
-    """
-
-    LEARNED = enum.auto()
-    FIXED_SMALL = enum.auto()
-    FIXED_LARGE = enum.auto()
-    LEARNED_RANGE = enum.auto()
-
-
-class LossType(enum.Enum):
-    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
-    RESCALED_MSE = (
-        enum.auto()
-    )  # use raw MSE loss (with RESCALED_KL when learning variances)
-    KL = enum.auto()  # use the variational lower-bound
-    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
-
-    def is_vb(self):
-        return self == LossType.KL or self == LossType.RESCALED_KL
-
-
-class GaussianDiffusion:
-    """
-    Utilities for training and sampling diffusion models.
-
-    Ported directly from here, and then adapted over time to further experimentation.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
-
-    :param betas: a 1-D numpy array of betas for each diffusion timestep,
-                  starting at T and going to 1.
-    :param model_mean_type: a ModelMeanType determining what the model outputs.
-    :param model_var_type: a ModelVarType determining how variance is output.
-    :param loss_type: a LossType determining the loss function to use.
-    :param rescale_timesteps: if True, pass floating point timesteps into the
-                              model so that they are always scaled like in the
-                              original paper (0 to 1000).
-    """
-
-    def __init__(
-        self,
-        *,
-        betas,
-        model_mean_type,
-        model_var_type,
-        loss_type,
-        rescale_timesteps=False,
-    ):
-        self.model_mean_type = model_mean_type
-        self.model_var_type = model_var_type
-        self.loss_type = loss_type
-        self.rescale_timesteps = rescale_timesteps
-
-        # Use float64 for accuracy.
-        betas = np.array(betas, dtype=np.float64)
-        self.betas = betas
-        assert len(betas.shape) == 1, "betas must be 1-D"
-        assert (betas > 0).all() and (betas <= 1).all()
-
-        self.num_timesteps = int(betas.shape[0])
-
-        alphas = 1.0 - betas
-        self.alphas_cumprod = np.cumprod(alphas, axis=0)
-        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
-        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
-        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
-        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
-        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
-        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        self.posterior_variance = (
-            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
-        )
-        # log calculation clipped because the posterior variance is 0 at the
-        # beginning of the diffusion chain.
-        self.posterior_log_variance_clipped = np.log(
-            np.append(self.posterior_variance[1], self.posterior_variance[1:])
-        )
-        self.posterior_mean_coef1 = (
-            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
-        )
-        self.posterior_mean_coef2 = (
-            (1.0 - self.alphas_cumprod_prev)
-            * np.sqrt(alphas)
-            / (1.0 - self.alphas_cumprod)
-        )
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (
-            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        )
-        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = _extract_into_tensor(
-            self.log_one_minus_alphas_cumprod, t, x_start.shape
-        )
-        return mean, variance, log_variance
-
-    def q_sample(self, x_start, t, noise=None):
-        """
-        Diffuse the data for a given number of diffusion steps.
-
-        In other words, sample from q(x_t | x_0).
-
-        :param x_start: the initial data batch.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :param noise: if specified, the split-out normal noise.
-        :return: A noisy version of x_start.
-        """
-        if noise is None:
-            noise = th.randn_like(x_start)
-        assert noise.shape == x_start.shape
-        return (
-            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
-            * noise
-        )
-
-    def q_posterior_mean_variance(self, x_start, x_t, t):
-        """
-        Compute the mean and variance of the diffusion posterior:
-
-            q(x_{t-1} | x_t, x_0)
-
-        """
-        assert x_start.shape == x_t.shape
-        posterior_mean = (
-            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
-            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = _extract_into_tensor(
-            self.posterior_log_variance_clipped, t, x_t.shape
-        )
-        assert (
-            posterior_mean.shape[0]
-            == posterior_variance.shape[0]
-            == posterior_log_variance_clipped.shape[0]
-            == x_start.shape[0]
-        )
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(
-        self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
-    ):
-        """
-        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
-        the initial x, x_0.
-
-        :param model: the model, which takes a signal and a batch of timesteps
-                      as input.
-        :param x: the [N x C x ...] tensor at time t.
-        :param t: a 1-D Tensor of timesteps.
-        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample. Applies before
-            clip_denoised.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict with the following keys:
-                 - 'mean': the model mean output.
-                 - 'variance': the model variance output.
-                 - 'log_variance': the log of 'variance'.
-                 - 'pred_xstart': the prediction for x_0.
-        """
-        if model_kwargs is None:
-            model_kwargs = {}
-
-        B, C = x.shape[:2]
-        assert t.shape == (B,)
-        model_output = model(x, self._scale_timesteps(t), **model_kwargs)
-
-        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
-            assert model_output.shape == (B, 2 * C, *x.shape[2:])
-            model_output, model_var_values = th.split(model_output, C, dim=1)
-            if self.model_var_type == ModelVarType.LEARNED:
-                model_log_variance = model_var_values
-                model_variance = th.exp(model_log_variance)
-            else:
-                min_log = _extract_into_tensor(
-                    self.posterior_log_variance_clipped, t, x.shape
-                )
-                max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
-                # The model_var_values is [-1, 1] for [min_var, max_var].
-                frac = (model_var_values + 1) / 2
-                model_log_variance = frac * max_log + (1 - frac) * min_log
-                model_variance = th.exp(model_log_variance)
-        else:
-            model_variance, model_log_variance = {
-                # for fixedlarge, we set the initial (log-)variance like so
-                # to get a better decoder log likelihood.
-                ModelVarType.FIXED_LARGE: (
-                    np.append(self.posterior_variance[1], self.betas[1:]),
-                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
-                ),
-                ModelVarType.FIXED_SMALL: (
-                    self.posterior_variance,
-                    self.posterior_log_variance_clipped,
-                ),
-            }[self.model_var_type]
-            model_variance = _extract_into_tensor(model_variance, t, x.shape)
-            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
-
-        def process_xstart(x):
-            if denoised_fn is not None:
-                x = denoised_fn(x)
-            if clip_denoised:
-                return x.clamp(-1, 1)
-            return x
-
-        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
-            pred_xstart = process_xstart(
-                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
-            )
-            model_mean = model_output
-        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
-            if self.model_mean_type == ModelMeanType.START_X:
-                pred_xstart = process_xstart(model_output)
-            else:
-                pred_xstart = process_xstart(
-                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
-                )
-            model_mean, _, _ = self.q_posterior_mean_variance(
-                x_start=pred_xstart, x_t=x, t=t
-            )
-        else:
-            raise NotImplementedError(self.model_mean_type)
-
-        assert (
-            model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
-        )
-        return {
-            "mean": model_mean,
-            "variance": model_variance,
-            "log_variance": model_log_variance,
-            "pred_xstart": pred_xstart,
-        }
-
-    def _predict_xstart_from_eps(self, x_t, t, eps):
-        assert x_t.shape == eps.shape
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
-        )
-
-    def _predict_xstart_from_xprev(self, x_t, t, xprev):
-        assert x_t.shape == xprev.shape
-        return (  # (xprev - coef2*x_t) / coef1
-            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
-            - _extract_into_tensor(
-                self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
-            )
-            * x_t
-        )
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - pred_xstart
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _scale_timesteps(self, t):
-        if self.rescale_timesteps:
-            return t.float() * (1000.0 / self.num_timesteps)
-        return t
-
-    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute the mean for the previous step, given a function cond_fn that
-        computes the gradient of a conditional log probability with respect to
-        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
-        condition on y.
-
-        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
-        """
-        gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
-        new_mean = (
-            p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
-        )
-        return new_mean
-
-    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute what the p_mean_variance output would have been, should the
-        model's score function be conditioned by cond_fn.
-
-        See condition_mean() for details on cond_fn.
-
-        Unlike condition_mean(), this instead uses the conditioning strategy
-        from Song et al (2020).
-        """
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-
-        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
-        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
-            x, self._scale_timesteps(t), **model_kwargs
-        )
-
-        out = p_mean_var.copy()
-        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
-        out["mean"], _, _ = self.q_posterior_mean_variance(
-            x_start=out["pred_xstart"], x_t=x, t=t
-        )
-        return out
-
-    def p_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        pre_seq=None,
-        transl_req=None,
-        model_kwargs=None,
-    ):
-        """
-        Sample x_{t-1} from the model at the given timestep.
-
-        :param model: the model to sample from.
-        :param x: the current tensor at x_{t-1}.
-        :param t: the value of t, starting at 0 for the first diffusion step.
-        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param cond_fn: if not None, this is a gradient function that acts
-                        similarly to the model.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict containing the following keys:
-                 - 'sample': a random sample from the model.
-                 - 'pred_xstart': a prediction of x_0.
-        """
-        # concat seq
-        if pre_seq is not None:
-            T = pre_seq.shape[2]
-            noise = th.randn_like(pre_seq)
-            x_t = self.q_sample(pre_seq, t, noise=noise)
-            x[:, :, :T] = x_t
-
-        if transl_req is not None:
-            for item in transl_req:
-                noise = th.randn(2).type_as(x)
-                transl = th.Tensor(item[1:]).type_as(x)
-                x_t = self.q_sample(transl, t, noise=noise)
-                x[:, :2, item[0]] = x_t
-
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        noise = th.randn_like(x)
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        if cond_fn is not None:
-            out["mean"] = self.condition_mean(
-                cond_fn, out, x, t, model_kwargs=model_kwargs
-            )
-        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-
-    def p_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        pre_seq=None,
-        transl_req=None,
-        progress=False,
-    ):
-        """
-        Generate samples from the model.
-
-        :param model: the model module.
-        :param shape: the shape of the samples, (N, C, H, W).
-        :param noise: if specified, the noise from the encoder to sample.
-                      Should be of the same shape as `shape`.
-        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param cond_fn: if not None, this is a gradient function that acts
-                        similarly to the model.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :param device: if specified, the device to create the samples on.
-                       If not specified, use a model parameter's device.
-        :param progress: if True, show a tqdm progress bar.
-        :return: a non-differentiable batch of samples.
-        """
-        final = None
-        for sample in self.p_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            pre_seq=pre_seq,
-            transl_req=transl_req,
-            progress=progress,
-        ):
-            final = sample
-        return final["sample"]
-
-    def p_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        pre_seq=None,
-        transl_req=None,
-        progress=False,
-    ):
-        """
-        Generate samples from the model and yield intermediate samples from
-        each timestep of diffusion.
-
-        Arguments are the same as p_sample_loop().
-        Returns a generator over dicts, where each dict is the return value of
-        p_sample().
-        """
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        indices = list(range(self.num_timesteps))[::-1]
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
-        for i in indices:
-            t = th.tensor([i] * shape[0], device=device)
-            with th.no_grad():
-                out = self.p_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                    pre_seq=pre_seq,
-                    transl_req=transl_req
-                )
-                yield out
-                img = out["sample"]
-
-    def ddim_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        eta=0.0,
-    ):
-        """
-        Sample x_{t-1} from the model using DDIM.
-
-        Same usage as p_sample().
-        """
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        if cond_fn is not None:
-            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
-
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
-
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
-        sigma = (
-            eta
-            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
-            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
-        )
-        # Equation 12.
-        noise = th.randn_like(x)
-        mean_pred = (
-            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
-            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
-        )
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        sample = mean_pred + nonzero_mask * sigma * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-
-    def ddim_reverse_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        model_kwargs=None,
-        eta=0.0,
-    ):
-        """
-        Sample x_{t+1} from the model using DDIM reverse ODE.
-        """
-        assert eta == 0.0, "Reverse ODE only for deterministic path"
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
-            - out["pred_xstart"]
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
-        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
-
-        # Equation 12. reversed
-        mean_pred = (
-            out["pred_xstart"] * th.sqrt(alpha_bar_next)
-            + th.sqrt(1 - alpha_bar_next) * eps
-        )
-
-        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
-
-    def ddim_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-    ):
-        """
-        Generate samples from the model using DDIM.
-
-        Same usage as p_sample_loop().
-        """
-        final = None
-        for sample in self.ddim_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            progress=progress,
-            eta=eta,
-        ):
-            final = sample
-        return final["sample"]
-
-    def ddim_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-    ):
-        """
-        Use DDIM to sample from the model and yield intermediate samples from
-        each timestep of DDIM.
-
-        Same usage as p_sample_loop_progressive().
-        """
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        indices = list(range(self.num_timesteps))[::-1]
-
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
-        for i in indices:
-            t = th.tensor([i] * shape[0], device=device)
-            with th.no_grad():
-                out = self.ddim_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                    eta=eta,
-                )
-                yield out
-                img = out["sample"]
-
-    def _vb_terms_bpd(
-        self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
-    ):
-        """
-        Get a term for the variational lower-bound.
-
-        The resulting units are bits (rather than nats, as one might expect).
-        This allows for comparison to other papers.
-
-        :return: a dict with the following keys:
-                 - 'output': a shape [N] tensor of NLLs or KLs.
-                 - 'pred_xstart': the x_0 predictions.
-        """
-        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
-            x_start=x_start, x_t=x_t, t=t
-        )
-        out = self.p_mean_variance(
-            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
-        )
-        kl = normal_kl(
-            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
-        )
-        kl = mean_flat(kl) / np.log(2.0)
-
-        decoder_nll = -discretized_gaussian_log_likelihood(
-            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
-        )
-        assert decoder_nll.shape == x_start.shape
-        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
-
-        # At the first timestep return the decoder NLL,
-        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
-        output = th.where((t == 0), decoder_nll, kl)
-        return {"output": output, "pred_xstart": out["pred_xstart"]}
-
-    def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
-        """
-        Compute training losses for a single timestep.
-
-        :param model: the model to evaluate loss on.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :param t: a batch of timestep indices.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :param noise: if specified, the specific Gaussian noise to try to remove.
-        :return: a dict with the key "loss" containing a tensor of shape [N].
-                 Some mean or variance settings may also have other keys.
-        """
-        if model_kwargs is None:
-            model_kwargs = {}
-        if noise is None:
-            noise = th.randn_like(x_start)
-        x_t = self.q_sample(x_start, t, noise=noise)
-
-        terms = {}
-
-        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
-            terms["loss"] = self._vb_terms_bpd(
-                model=model,
-                x_start=x_start,
-                x_t=x_t,
-                t=t,
-                clip_denoised=False,
-                model_kwargs=model_kwargs,
-            )["output"]
-            if self.loss_type == LossType.RESCALED_KL:
-                terms["loss"] *= self.num_timesteps
-        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
-            model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
-
-            if self.model_var_type in [
-                ModelVarType.LEARNED,
-                ModelVarType.LEARNED_RANGE,
-            ]:
-                B, C = x_t.shape[:2]
-                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
-                model_output, model_var_values = th.split(model_output, C, dim=1)
-                # Learn the variance using the variational bound, but don't let
-                # it affect our mean prediction.
-                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
-                terms["vb"] = self._vb_terms_bpd(
-                    model=lambda *args, r=frozen_out: r,
-                    x_start=x_start,
-                    x_t=x_t,
-                    t=t,
-                    clip_denoised=False,
-                )["output"]
-                if self.loss_type == LossType.RESCALED_MSE:
-                    # Divide by 1000 for equivalence with initial implementation.
-                    # Without a factor of 1/1000, the VB term hurts the MSE term.
-                    terms["vb"] *= self.num_timesteps / 1000.0
-
-            target = {
-                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
-                    x_start=x_start, x_t=x_t, t=t
-                )[0],
-                ModelMeanType.START_X: x_start,
-                ModelMeanType.EPSILON: noise,
-            }[self.model_mean_type]
-            assert model_output.shape == target.shape == x_start.shape
-            terms["mse"] = mean_flat((target - model_output) ** 2).view(-1, 1).mean(-1)
-            # if "vb" in terms:
-            #     terms["loss"] = terms["mse"] + terms["vb"]
-            # else:
-            #     terms["loss"] = terms["mse"]
-            terms["target"] = target
-            terms["pred"] = model_output
-        else:
-            raise NotImplementedError(self.loss_type)
-
-        return terms
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-
-        This term can't be optimized, as it only depends on the encoder.
-
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(
-            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
-        )
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
-        """
-        Compute the entire variational lower-bound, measured in bits-per-dim,
-        as well as other related quantities.
-
-        :param model: the model to evaluate loss on.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :param clip_denoised: if True, clip denoised samples.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-
-        :return: a dict containing the following keys:
-                 - total_bpd: the total variational lower-bound, per batch element.
-                 - prior_bpd: the prior term in the lower-bound.
-                 - vb: an [N x T] tensor of terms in the lower-bound.
-                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
-                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
-        """
-        device = x_start.device
-        batch_size = x_start.shape[0]
-
-        vb = []
-        xstart_mse = []
-        mse = []
-        for t in list(range(self.num_timesteps))[::-1]:
-            t_batch = th.tensor([t] * batch_size, device=device)
-            noise = th.randn_like(x_start)
-            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
-            # Calculate VLB term at the current timestep
-            with th.no_grad():
-                out = self._vb_terms_bpd(
-                    model,
-                    x_start=x_start,
-                    x_t=x_t,
-                    t=t_batch,
-                    clip_denoised=clip_denoised,
-                    model_kwargs=model_kwargs,
-                )
-            vb.append(out["output"])
-            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
-            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
-            mse.append(mean_flat((eps - noise) ** 2))
-
-        vb = th.stack(vb, dim=1)
-        xstart_mse = th.stack(xstart_mse, dim=1)
-        mse = th.stack(mse, dim=1)
-
-        prior_bpd = self._prior_bpd(x_start)
-        total_bpd = vb.sum(dim=1) + prior_bpd
-        return {
-            "total_bpd": total_bpd,
-            "prior_bpd": prior_bpd,
-            "vb": vb,
-            "xstart_mse": xstart_mse,
-            "mse": mse,
-        }
-
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    """
-    Extract values from a 1-D numpy array for a batch of indices.
-
-    :param arr: the 1-D numpy array.
-    :param timesteps: a tensor of indices into the array to extract.
-    :param broadcast_shape: a larger shape of K dimensions with the batch
-                            dimension equal to the length of timesteps.
-    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
-    """
-    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
-    while len(res.shape) < len(broadcast_shape):
-        res = res[..., None]
-    return res.expand(broadcast_shape)
-
diff --git a/spaces/mingyuan/ReMoDiffuse/README.md b/spaces/mingyuan/ReMoDiffuse/README.md
deleted file mode 100644
index 4eed18672676ec39a7be8a4f8331adab0d39be00..0000000000000000000000000000000000000000
--- a/spaces/mingyuan/ReMoDiffuse/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ReMoDiffuse
-emoji: 🏢
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mithril-security/blind_chat/.svelte-kit/types/src/routes/proxy+layout.server.ts b/spaces/mithril-security/blind_chat/.svelte-kit/types/src/routes/proxy+layout.server.ts
deleted file mode 100644
index 1a0c7f05de7a1cc1f5c5f8eacfbdcbd7688e907a..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/.svelte-kit/types/src/routes/proxy+layout.server.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-// @ts-nocheck
-import { redirect } from "@sveltejs/kit";
-import type { LayoutServerLoad } from "./$types";
-import { collections } from "$lib/server/database";
-import type { Conversation } from "$lib/types/Conversation";
-import { UrlDependency } from "$lib/types/UrlDependency";
-import { defaultModel, models, oldModels, validateModel } from "$lib/server/models";
-import { authCondition, requiresUser } from "$lib/server/auth";
-import { DEFAULT_SETTINGS } from "$lib/types/Settings";
-import { SERPAPI_KEY, SERPER_API_KEY, MESSAGES_BEFORE_LOGIN } from "$env/static/private";
-
-export const load = async ({ locals, depends, url }: Parameters<LayoutServerLoad>[0]) => {
-	const { conversations } = collections;
-	const urlModel = url.searchParams.get("model");
-
-	depends(UrlDependency.ConversationList);
-
-	if (urlModel) {
-		const isValidModel = validateModel(models).safeParse(urlModel).success;
-
-		if (isValidModel) {
-			await collections.settings.updateOne(
-				authCondition(locals),
-				{ $set: { activeModel: urlModel } },
-				{ upsert: true }
-			);
-		}
-
-		throw redirect(302, url.pathname);
-	}
-
-	return {
-		conversations: [],
-		settings: {
-			shareConversationsWithModelAuthors: DEFAULT_SETTINGS.shareConversationsWithModelAuthors,
-			ethicsModalAcceptedAt: null,
-			activeModel: DEFAULT_SETTINGS.activeModel,
-			searchEnabled: false,
-			customPrompts: {},
-		},
-		models: models.map((model) => ({
-			id: model.id,
-			name: model.name,
-			websiteUrl: model.websiteUrl,
-			modelUrl: model.modelUrl,
-			is_local: model.is_local,
-			is_phi: model.is_phi,
-			is_code: model.is_code,
-			type: model.type,
-			datasetName: model.datasetName,
-			datasetUrl: model.datasetUrl,
-			displayName: model.displayName,
-			description: model.description,
-			promptExamples: model.promptExamples,
-			parameters: model.parameters,
-			preprompt: model.preprompt,
-		})),
-		oldModels,
-		user: locals.user && {
-			username: locals.user.username,
-			avatarUrl: locals.user.avatarUrl,
-			email: locals.user.email,
-		},
-		requiresLogin: requiresUser,
-		messagesBeforeLogin: MESSAGES_BEFORE_LOGIN ? parseInt(MESSAGES_BEFORE_LOGIN) : 0,
-	};
-};
diff --git a/spaces/miyaaa666/bingo/src/components/ui/separator.tsx b/spaces/miyaaa666/bingo/src/components/ui/separator.tsx
deleted file mode 100644
index 6c55e0b2ca8e2436658a06748aadbff7cd700db0..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/components/ui/separator.tsx
+++ /dev/null
@@ -1,31 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SeparatorPrimitive from '@radix-ui/react-separator'
-
-import { cn } from '@/lib/utils'
-
-const Separator = React.forwardRef<
-  React.ElementRef<typeof SeparatorPrimitive.Root>,
-  React.ComponentPropsWithoutRef<typeof SeparatorPrimitive.Root>
->(
-  (
-    { className, orientation = 'horizontal', decorative = true, ...props },
-    ref
-  ) => (
-    <SeparatorPrimitive.Root
-      ref={ref}
-      decorative={decorative}
-      orientation={orientation}
-      className={cn(
-        'shrink-0 bg-border',
-        orientation === 'horizontal' ? 'h-[1px] w-full' : 'h-full w-[1px]',
-        className
-      )}
-      {...props}
-    />
-  )
-)
-Separator.displayName = SeparatorPrimitive.Root.displayName
-
-export { Separator }
diff --git a/spaces/mnauf/detect-bees/utils/segment/metrics.py b/spaces/mnauf/detect-bees/utils/segment/metrics.py
deleted file mode 100644
index b09ce23fb9e398ab654fce676d23f74d81cc5c57..0000000000000000000000000000000000000000
--- a/spaces/mnauf/detect-bees/utils/segment/metrics.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Model validation metrics
-"""
-
-import numpy as np
-
-from ..metrics import ap_per_class
-
-
-def fitness(x):
-    # Model fitness as a weighted combination of metrics
-    w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]
-    return (x[:, :8] * w).sum(1)
-
-
-def ap_per_class_box_and_mask(
-        tp_m,
-        tp_b,
-        conf,
-        pred_cls,
-        target_cls,
-        plot=False,
-        save_dir=".",
-        names=(),
-):
-    """
-    Args:
-        tp_b: tp of boxes.
-        tp_m: tp of masks.
-        other arguments see `func: ap_per_class`.
-    """
-    results_boxes = ap_per_class(tp_b,
-                                 conf,
-                                 pred_cls,
-                                 target_cls,
-                                 plot=plot,
-                                 save_dir=save_dir,
-                                 names=names,
-                                 prefix="Box")[2:]
-    results_masks = ap_per_class(tp_m,
-                                 conf,
-                                 pred_cls,
-                                 target_cls,
-                                 plot=plot,
-                                 save_dir=save_dir,
-                                 names=names,
-                                 prefix="Mask")[2:]
-
-    results = {
-        "boxes": {
-            "p": results_boxes[0],
-            "r": results_boxes[1],
-            "ap": results_boxes[3],
-            "f1": results_boxes[2],
-            "ap_class": results_boxes[4]},
-        "masks": {
-            "p": results_masks[0],
-            "r": results_masks[1],
-            "ap": results_masks[3],
-            "f1": results_masks[2],
-            "ap_class": results_masks[4]}}
-    return results
-
-
-class Metric:
-
-    def __init__(self) -> None:
-        self.p = []  # (nc, )
-        self.r = []  # (nc, )
-        self.f1 = []  # (nc, )
-        self.all_ap = []  # (nc, 10)
-        self.ap_class_index = []  # (nc, )
-
-    @property
-    def ap50(self):
-        """AP@0.5 of all classes.
-        Return:
-            (nc, ) or [].
-        """
-        return self.all_ap[:, 0] if len(self.all_ap) else []
-
-    @property
-    def ap(self):
-        """AP@0.5:0.95
-        Return:
-            (nc, ) or [].
-        """
-        return self.all_ap.mean(1) if len(self.all_ap) else []
-
-    @property
-    def mp(self):
-        """mean precision of all classes.
-        Return:
-            float.
-        """
-        return self.p.mean() if len(self.p) else 0.0
-
-    @property
-    def mr(self):
-        """mean recall of all classes.
-        Return:
-            float.
-        """
-        return self.r.mean() if len(self.r) else 0.0
-
-    @property
-    def map50(self):
-        """Mean AP@0.5 of all classes.
-        Return:
-            float.
-        """
-        return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
-
-    @property
-    def map(self):
-        """Mean AP@0.5:0.95 of all classes.
-        Return:
-            float.
-        """
-        return self.all_ap.mean() if len(self.all_ap) else 0.0
-
-    def mean_results(self):
-        """Mean of results, return mp, mr, map50, map"""
-        return (self.mp, self.mr, self.map50, self.map)
-
-    def class_result(self, i):
-        """class-aware result, return p[i], r[i], ap50[i], ap[i]"""
-        return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
-
-    def get_maps(self, nc):
-        maps = np.zeros(nc) + self.map
-        for i, c in enumerate(self.ap_class_index):
-            maps[c] = self.ap[i]
-        return maps
-
-    def update(self, results):
-        """
-        Args:
-            results: tuple(p, r, ap, f1, ap_class)
-        """
-        p, r, all_ap, f1, ap_class_index = results
-        self.p = p
-        self.r = r
-        self.all_ap = all_ap
-        self.f1 = f1
-        self.ap_class_index = ap_class_index
-
-
-class Metrics:
-    """Metric for boxes and masks."""
-
-    def __init__(self) -> None:
-        self.metric_box = Metric()
-        self.metric_mask = Metric()
-
-    def update(self, results):
-        """
-        Args:
-            results: Dict{'boxes': Dict{}, 'masks': Dict{}}
-        """
-        self.metric_box.update(list(results["boxes"].values()))
-        self.metric_mask.update(list(results["masks"].values()))
-
-    def mean_results(self):
-        return self.metric_box.mean_results() + self.metric_mask.mean_results()
-
-    def class_result(self, i):
-        return self.metric_box.class_result(i) + self.metric_mask.class_result(i)
-
-    def get_maps(self, nc):
-        return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
-
-    @property
-    def ap_class_index(self):
-        # boxes and masks have the same ap_class_index
-        return self.metric_box.ap_class_index
-
-
-KEYS = [
-    "train/box_loss",
-    "train/seg_loss",  # train loss
-    "train/obj_loss",
-    "train/cls_loss",
-    "metrics/precision(B)",
-    "metrics/recall(B)",
-    "metrics/mAP_0.5(B)",
-    "metrics/mAP_0.5:0.95(B)",  # metrics
-    "metrics/precision(M)",
-    "metrics/recall(M)",
-    "metrics/mAP_0.5(M)",
-    "metrics/mAP_0.5:0.95(M)",  # metrics
-    "val/box_loss",
-    "val/seg_loss",  # val loss
-    "val/obj_loss",
-    "val/cls_loss",
-    "x/lr0",
-    "x/lr1",
-    "x/lr2",]
-
-BEST_KEYS = [
-    "best/epoch",
-    "best/precision(B)",
-    "best/recall(B)",
-    "best/mAP_0.5(B)",
-    "best/mAP_0.5:0.95(B)",
-    "best/precision(M)",
-    "best/recall(M)",
-    "best/mAP_0.5(M)",
-    "best/mAP_0.5:0.95(M)",]
diff --git a/spaces/mohamedemam/bert_sentaces_similarty/bmtss.py b/spaces/mohamedemam/bert_sentaces_similarty/bmtss.py
deleted file mode 100644
index 01adf2b8310347750320e1f34668c91947fedcdf..0000000000000000000000000000000000000000
--- a/spaces/mohamedemam/bert_sentaces_similarty/bmtss.py
+++ /dev/null
@@ -1,189 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-get_ipython().system(' pip install transformers')
-
-
-# In[2]:
-
-
-import pandas as pd
-import streamlit as st
-import torch
-from torch.utils.data import DataLoader ,Dataset
-from transformers import AutoTokenizer,BertForQuestionAnswering,AutoModel
-
-
-# In[3]:
-
-
-from transformers import AutoTokenizer,BertForQuestionAnswering,AutoModel
-model_checkpoint = "bert-base-uncased"
-tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-
-
-# In[4]:
-
-
-from transformers import DataCollatorWithPadding
-
-
-# In[5]:
-
-
-torch.set_default_device('cpu')
-
-
-# In[6]:
-
-
-from transformers import BertTokenizer, BertModel
-
-
-# In[7]:
-
-
-class bert_compare(torch.nn.Module):
-    def __init__ (self):
-        super(bert_compare,self).__init__()
-        self.bert=BertModel.from_pretrained("bert-base-uncased")
-        
-        self.Linear=torch.nn.Linear(768,30 )
-        self.elu=torch.nn.ELU()
-        self.Linear2=torch.nn.Linear(280 ,1 )
-        self.cnn1=torch.nn.Conv1d(768,256,kernel_size=2)
-        self.cnn2=torch.nn.Conv1d(256,10,kernel_size=2)
-
-        self.relu=torch.nn.ReLU()
-    def forward(self,x):
-        x=self.bert(**x).last_hidden_state
-        x=x.permute(0,2,1)
-        x=self.cnn1(x)
-        x=self.relu(x)
-        x=self.cnn2(x)
-        x=torch.nn.Flatten()(x)
-        x=self.Linear2(x)
-        return x
-
-
-# In[8]:
-
-
-model=bert_compare()
-optim=torch.optim.AdamW(model.parameters(),lr=5e-5)
-loss=torch.nn.BCEWithLogitsLoss()
-
-
-# In[9]:
-
-
-def tok(x,y):
-        out=tokenizer(x,y, truncation=True, max_length=30,padding='max_length', return_tensors="pt")
-        out={key:value for key,value in out.items()}
-        return out
-h=tok('my name is mohamed','what is your name')    
-model(h)
-
-
-# In[10]:
-
-
-get_ipython().system(' pip install tqdm')
-
-
-# In[11]:
-
-
-from  tqdm import tqdm
-
-
-# In[12]:
-
-
-model.train()
-
-
-# In[13]:
-
-
-model.bert.train()
-
-
-# In[14]:
-
-
-model=torch.load('Downloads/model9.pth',map_location=torch.device('cpu'))
-
-
-# In[15]:
-
-
-word=['my name is mohamed ', "How do I read and find my YouTube comments?" ,"How can I see all my Youtube comments?","How can Internet speed be increased by hacking through DNS?","What is the step by step guide to invest in share market in india?","where is capital of egypt?",'when did you born ','what is your name',"what is capital of egypt",'how old are you']
-
-
-# In[16]:
-
-
-def tok(x,y):
-        out=tokenizer(x,y, truncation=True, max_length=30,padding='max_length', return_tensors="pt")
-        out={key:value for key,value in out.items()}
-        return out
-for i in range(9):
-        r=torch.randint(len(word),size=(1,))
-        r2=torch.randint(len(word),size=(1,))
-        h=tok(word[r],word[r2])    
-        e=model(h)
-        ans= 'the same' if  int(torch.sigmoid( e)>=.5) else 'not the same'
-        print (f'{word[r]} is {ans} {word[r2]}'  )
-
-
-# In[17]:
-
-
-
-        h=tok("what is capital of egypt","when is  capital of egypt")    
-        e=model(h)
-        ans= 'the same' if  int(torch.sigmoid( e)>=.5) else 'not the same'
-        print (f' {ans} '  )
-
-
-# In[19]:
-
-
-def are_sentences_same(sentence1, sentence2):
-    doc1=tok(sentence1,sentence2)   
-    out_model=model(doc2)
-    ans= 'the same' if  int(torch.sigmoid( out_model)>=.5) else 'not the same'
-
-    return torch.sigmoid( ans)
-
-def main():
-    st.title('Sentence Similarity Checker')
-    st.write('Enter two sentences to check if they are the same.')
-
-    # Input sentences
-    sentence1 = st.text_input('Enter the first sentence:')
-    sentence2 = st.text_input('Enter the second sentence:')
-
-    # Check if both sentences are provided
-    if sentence1 and sentence2:
-        similarity_score = are_sentences_same(sentence1, sentence2)
-        st.write(f'Similarity Score: {similarity_score:.2f}')
-
-        if similarity_score >= 0.5:
-            st.write('The sentences are very similar.')
-        else:
-            st.write('The sentences are different.')
-
-if __name__ == '__main__':
-    main()
-
-
-# In[ ]:
-
-
-
-
diff --git a/spaces/mohitmayank/law-finder-ipc/app.py b/spaces/mohitmayank/law-finder-ipc/app.py
deleted file mode 100644
index 4c16d88b9b39b1431d5299593cb244012d9893fb..0000000000000000000000000000000000000000
--- a/spaces/mohitmayank/law-finder-ipc/app.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# import
-import pandas as pd
-import streamlit as st
-from txtai.embeddings import Embeddings
-
-# set config
-st.set_page_config(layout="wide", page_title="⚖️ Law Finder - IPC")
-
-# load the summarization model (cache for faster loading)
-@st.cache(allow_output_mutation=True)
-def load_model_embeddings_data():
-    embeddings = Embeddings({"path": "sentence-transformers/nli-mpnet-base-v2"})
-    # embeddings.load("embedding")
-    df = pd.read_csv("devganscrap/sections_desc.csv")
-    embeddings.index([(uid, str(text), None) for uid, text in enumerate(df['description'].tolist())]) 
-    return embeddings, df
-
-# loading the model
-embeddings, df = load_model_embeddings_data()
-
-# APP
-# set title and subtitle
-st.title("⚖️ Law Finder - IPC")
-st.markdown("Search the [Indian Penal Code](https://en.wikipedia.org/wiki/Indian_Penal_Code) Sections with simple english.")
-st.markdown("The data scraping procedure is explained in detail on [my website](http://mohitmayank.com/a_lazy_data_science_guide/python/scraping_websites/)")
-st.markdown("The complete code is on [Github](https://github.com/imohitmayank/ipc_semantic_search)")
-
-# create the input text box
-query = st.text_area("Input your search phrase here!", "animal cruelty")
-button = st.button("Find sections...")
-
-# if button is clicked
-with st.spinner("Finding the most similar sections...."):
-    if button:
-        # find and display the sections
-        st.markdown("**Sections:**")
-        results = []
-        for id, score in embeddings.search(query, limit=5):
-            st.write({
-                'section': df.loc[id, 'section'],
-                'description': df.loc[id, 'description']
-            })
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/linformer/linformer_src/modules/__init__.py b/spaces/mshukor/UnIVAL/fairseq/examples/linformer/linformer_src/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/utils.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/utils.py
deleted file mode 100644
index 2ec6af3fcb09ccaf853be15a84ed8181f9e2f546..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/utils.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from operator import attrgetter
-
-import torch.distributed as dist
-import torch.nn as nn
-
-from ..pq.utils import attrsetter, get_layers
-from .modules import ActivationQuantizer, IntConv2d, IntEmbedding, IntLinear
-
-
-MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d}
-
-
-def quantize_model_(model, p=0.2, bits=8, update_step=3000, method="histogram", remove_weights=False):
-    """
-    Replaces all modules with their scalar quantized counterpart and
-    registers hooks to quantize the post-ativations of those modules.
-
-    Args:
-        - model: a nn.Module
-        - p: amount of noise (0 for no noise, 1 to quantize all the weights/activations)
-        - bits: number of bits
-        - update_step: update quantization parameters every update_step steps
-    """
-    # quantize all layers
-    # remove weights indicates whether the weights extension should be removed, in addition to
-    # weight_orig and weight extension on names
-    quantized_layers = get_layers(model, "(.*?)", remove_weights=remove_weights)
-
-    for layer in quantized_layers:
-
-        # book-keeping
-        is_master_process = (not dist.is_initialized()) or (
-            dist.is_initialized() and dist.get_rank() == 0
-        )
-
-        # recover module
-        module = attrgetter(layer)(model)
-        if is_master_process:
-            logging.info(
-                f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}"
-            )
-
-        # quantization params
-        q_params = {
-            "p": p,
-            "update_step": update_step,
-            "bits": bits,
-            "method": method,
-            "counter": 0,
-        }
-
-        # instantiate the quantized counterpart
-        if isinstance(module, tuple(MAPPING.keys())):
-            QuantizedModule = MAPPING[module.__class__]
-            quantized_module = QuantizedModule.__new__(QuantizedModule)
-            params = module.__dict__
-            params.update(q_params)
-            quantized_module.__dict__.update(params)
-
-        else:
-            if is_master_process:
-                logging.info(f"Module {module} not yet supported for quantization")
-            continue
-
-        # activation quantization
-        a_q = ActivationQuantizer(quantized_module, p=0, bits=bits, method=method)
-
-        # replace layer by its quantized counterpart
-        attrsetter(layer)(model, quantized_module)
-
-    # return name of quantized layers
-    return quantized_layers
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py
deleted file mode 100644
index 8cb20068606a4afd2983430b794fa24647de2e7b..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections.abc import Collection
-from dataclasses import dataclass, field
-from typing import List
-
-from omegaconf import II
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class StepLRScheduleConfig(FairseqDataclass):
-    warmup_updates: int = field(
-        default=0,
-        metadata={"help": "warmup the learning rate linearly for the first N updates"},
-    )
-    warmup_init_lr: float = field(
-        default=-1,
-        metadata={
-            "help": "initial learning rate during warmup phase; default is cfg.lr"
-        },
-    )
-    lr: List[float] = field(
-        default=II("optimization.lr"),
-        metadata={"help": "max learning rate, must be more than cfg.min_lr"},
-    )
-    min_lr: float = field(default=0.0, metadata={"help": "min learning rate"})
-    lr_deacy_period: int = field(default=25000, metadata={"help": "decay period"})
-    lr_decay: float = field(default=0.5, metadata={"help": "decay factor"})
-
-
-@register_lr_scheduler("step", dataclass=StepLRScheduleConfig)
-class StepLRSchedule(FairseqLRScheduler):
-    """Decay learning rate every k updates by a fixed factor
-    """
-
-    def __init__(self, cfg: StepLRScheduleConfig, fairseq_optimizer):
-        super().__init__(cfg, fairseq_optimizer)
-        self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr
-        self.min_lr = cfg.min_lr
-        self.lr_deacy_period = cfg.lr_deacy_period
-        self.lr_decay = cfg.lr_decay
-        self.warmup_updates = cfg.warmup_updates
-        self.warmup_init_lr = (
-            cfg.warmup_init_lr if cfg.warmup_init_lr >= 0 else self.min_lr
-        )
-
-        assert(self.lr_deacy_period > 0)
-        assert(self.lr_decay <= 1)
-        assert(self.min_lr >= 0)
-        assert(self.max_lr > self.min_lr)
-
-        if cfg.warmup_updates > 0:
-            # linearly warmup for the first cfg.warmup_updates
-            self.warmup_lr_step = (
-                (self.max_lr - self.warmup_init_lr) / self.warmup_updates
-            )
-        else:
-            self.warmup_lr_step = 1
-
-        # initial learning rate
-        self.lr = self.warmup_init_lr
-        self.optimizer.set_lr(self.lr)
-
-    def step(self, epoch, val_loss=None):
-        """Update the learning rate at the end of the given epoch."""
-        super().step(epoch, val_loss)
-        # we don't change the learning rate at epoch boundaries
-        return self.optimizer.get_lr()
-
-    def step_update(self, num_updates):
-        """Update the learning rate after each update."""
-        if num_updates < self.cfg.warmup_updates:
-            self.lr = self.warmup_init_lr + num_updates * self.warmup_lr_step
-        else:
-            curr_updates = num_updates - self.cfg.warmup_updates
-            lr_mult = self.lr_decay ** (curr_updates // self.lr_deacy_period)
-            self.lr = max(self.max_lr * lr_mult, self.min_lr)
-
-        self.optimizer.set_lr(self.lr)
-        return self.lr
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/text_to_speech.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/text_to_speech.py
deleted file mode 100644
index 5646e41d39f6e39d4b046ee34ff69b998dab160d..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/text_to_speech.py
+++ /dev/null
@@ -1,467 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import os.path as op
-
-import torch
-import torch.nn.functional as F
-import numpy as np
-
-from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDatasetCreator
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.speech_generator import (
-    AutoRegressiveSpeechGenerator, NonAutoregressiveSpeechGenerator,
-    TeacherForcingAutoRegressiveSpeechGenerator
-)
-
-logging.basicConfig(
-        format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO
-)
-logger = logging.getLogger(__name__)
-
-
-try:
-    from tensorboardX import SummaryWriter
-except ImportError:
-    logger.info("Please install tensorboardX: pip install tensorboardX")
-    SummaryWriter = None
-
-
-@register_task('text_to_speech')
-class TextToSpeechTask(SpeechToTextTask):
-    @staticmethod
-    def add_args(parser):
-        parser.add_argument('data', help='manifest root path')
-        parser.add_argument(
-            '--config-yaml', type=str, default='config.yaml',
-            help='Configuration YAML filename (under manifest root)'
-        )
-        parser.add_argument('--max-source-positions', default=1024, type=int,
-                            metavar='N',
-                            help='max number of tokens in the source sequence')
-        parser.add_argument('--max-target-positions', default=1200, type=int,
-                            metavar='N',
-                            help='max number of tokens in the target sequence')
-        parser.add_argument("--n-frames-per-step", type=int, default=1)
-        parser.add_argument("--eos-prob-threshold", type=float, default=0.5)
-        parser.add_argument("--eval-inference", action="store_true")
-        parser.add_argument("--eval-tb-nsample", type=int, default=8)
-        parser.add_argument("--vocoder", type=str, default="griffin_lim")
-        parser.add_argument("--spec-bwd-max-iter", type=int, default=8)
-
-    def __init__(self, args, src_dict):
-        super().__init__(args, src_dict)
-        self.src_dict = src_dict
-        self.sr = self.data_cfg.config.get("features").get("sample_rate")
-
-        self.tensorboard_writer = None
-        self.tensorboard_dir = ""
-        if args.tensorboard_logdir and SummaryWriter is not None:
-            self.tensorboard_dir = os.path.join(args.tensorboard_logdir,
-                                                "valid_extra")
-
-    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
-        is_train_split = split.startswith('train')
-        pre_tokenizer = self.build_tokenizer(self.args)
-        bpe_tokenizer = self.build_bpe(self.args)
-        self.datasets[split] = TextToSpeechDatasetCreator.from_tsv(
-            self.args.data, self.data_cfg, split, self.src_dict,
-            pre_tokenizer, bpe_tokenizer, is_train_split=is_train_split,
-            epoch=epoch, seed=self.args.seed,
-            n_frames_per_step=self.args.n_frames_per_step,
-            speaker_to_id=self.speaker_to_id
-        )
-
-    @property
-    def target_dictionary(self):
-        return None
-
-    @property
-    def source_dictionary(self):
-        return self.src_dict
-
-    def get_speaker_embeddings_path(self):
-        speaker_emb_path = None
-        if self.data_cfg.config.get("speaker_emb_filename") is not None:
-            speaker_emb_path = op.join(
-                self.args.data, self.data_cfg.config.get("speaker_emb_filename")
-            )
-        return speaker_emb_path
-
-    @classmethod
-    def get_speaker_embeddings(cls, args):
-        embed_speaker = None
-        if args.speaker_to_id is not None:
-            if args.speaker_emb_path is None:
-                embed_speaker = torch.nn.Embedding(
-                    len(args.speaker_to_id), args.speaker_embed_dim
-                )
-            else:
-                speaker_emb_mat = np.load(args.speaker_emb_path)
-                assert speaker_emb_mat.shape[1] == args.speaker_embed_dim
-                embed_speaker = torch.nn.Embedding.from_pretrained(
-                    torch.from_numpy(speaker_emb_mat), freeze=True,
-                )
-                logger.info(
-                    f"load speaker embeddings from {args.speaker_emb_path}. "
-                    f"train embedding? {embed_speaker.weight.requires_grad}\n"
-                    f"embeddings:\n{speaker_emb_mat}"
-                )
-        return embed_speaker
-
-    def build_model(self, cfg):
-        cfg.pitch_min = self.data_cfg.config["features"].get("pitch_min", None)
-        cfg.pitch_max = self.data_cfg.config["features"].get("pitch_max", None)
-        cfg.energy_min = self.data_cfg.config["features"].get("energy_min", None)
-        cfg.energy_max = self.data_cfg.config["features"].get("energy_max", None)
-        cfg.speaker_emb_path = self.get_speaker_embeddings_path()
-        model = super().build_model(cfg)
-        self.generator = None
-        if getattr(cfg, "eval_inference", False):
-            self.generator = self.build_generator([model], cfg)
-        return model
-
-    def build_generator(self, models, cfg, vocoder=None, **unused):
-        if vocoder is None:
-            vocoder = self.build_default_vocoder()
-        model = models[0]
-        if getattr(model, "NON_AUTOREGRESSIVE", False):
-            return NonAutoregressiveSpeechGenerator(
-                model, vocoder, self.data_cfg
-            )
-        else:
-            generator = AutoRegressiveSpeechGenerator
-            if getattr(cfg, "teacher_forcing", False):
-                generator = TeacherForcingAutoRegressiveSpeechGenerator
-                logger.info("Teacher forcing mode for generation")
-            return generator(
-                model, vocoder, self.data_cfg,
-                max_iter=self.args.max_target_positions,
-                eos_prob_threshold=self.args.eos_prob_threshold
-            )
-
-    def build_default_vocoder(self):
-        from fairseq.models.text_to_speech.vocoder import get_vocoder
-        vocoder = get_vocoder(self.args, self.data_cfg)
-        if torch.cuda.is_available() and not self.args.cpu:
-            vocoder = vocoder.cuda()
-        else:
-            vocoder = vocoder.cpu()
-        return vocoder
-
-    def valid_step(self, sample, model, criterion):
-        loss, sample_size, logging_output = super().valid_step(
-            sample, model, criterion
-        )
-
-        if getattr(self.args, "eval_inference", False):
-            hypos, inference_losses = self.valid_step_with_inference(
-                sample, model, self.generator
-            )
-            for k, v in inference_losses.items():
-                assert(k not in logging_output)
-                logging_output[k] = v
-
-            picked_id = 0
-            if self.tensorboard_dir and (sample["id"] == picked_id).any():
-                self.log_tensorboard(
-                    sample,
-                    hypos[:self.args.eval_tb_nsample],
-                    model._num_updates,
-                    is_na_model=getattr(model, "NON_AUTOREGRESSIVE", False)
-                )
-        return loss, sample_size, logging_output
-
-    def valid_step_with_inference(self, sample, model, generator):
-        hypos = generator.generate(model, sample, has_targ=True)
-
-        losses = {
-            "mcd_loss": 0.,
-            "targ_frames": 0.,
-            "pred_frames": 0.,
-            "nins": 0.,
-            "ndel": 0.,
-        }
-        rets = batch_mel_cepstral_distortion(
-            [hypo["targ_waveform"] for hypo in hypos],
-            [hypo["waveform"] for hypo in hypos],
-            self.sr,
-            normalize_type=None
-        )
-        for d, extra in rets:
-            pathmap = extra[-1]
-            losses["mcd_loss"] += d.item()
-            losses["targ_frames"] += pathmap.size(0)
-            losses["pred_frames"] += pathmap.size(1)
-            losses["nins"] += (pathmap.sum(dim=1) - 1).sum().item()
-            losses["ndel"] += (pathmap.sum(dim=0) - 1).sum().item()
-
-        return hypos, losses
-
-    def log_tensorboard(self, sample, hypos, num_updates, is_na_model=False):
-        if self.tensorboard_writer is None:
-            self.tensorboard_writer = SummaryWriter(self.tensorboard_dir)
-        tb_writer = self.tensorboard_writer
-        for b in range(len(hypos)):
-            idx = sample["id"][b]
-            text = sample["src_texts"][b]
-            targ = hypos[b]["targ_feature"]
-            pred = hypos[b]["feature"]
-            attn = hypos[b]["attn"]
-
-            if is_na_model:
-                data = plot_tts_output(
-                    [targ.transpose(0, 1), pred.transpose(0, 1)],
-                    [f"target (idx={idx})", "output"], attn,
-                    "alignment", ret_np=True, suptitle=text,
-                )
-            else:
-                eos_prob = hypos[b]["eos_prob"]
-                data = plot_tts_output(
-                    [targ.transpose(0, 1), pred.transpose(0, 1), attn],
-                    [f"target (idx={idx})", "output", "alignment"], eos_prob,
-                    "eos prob", ret_np=True, suptitle=text,
-                )
-
-            tb_writer.add_image(
-                f"inference_sample_{b}", data, num_updates,
-                dataformats="HWC"
-            )
-
-            if hypos[b]["waveform"] is not None:
-                targ_wave = hypos[b]["targ_waveform"].detach().cpu().float()
-                pred_wave = hypos[b]["waveform"].detach().cpu().float()
-                tb_writer.add_audio(
-                    f"inference_targ_{b}",
-                    targ_wave,
-                    num_updates,
-                    sample_rate=self.sr
-                )
-                tb_writer.add_audio(
-                    f"inference_pred_{b}",
-                    pred_wave,
-                    num_updates,
-                    sample_rate=self.sr
-                )
-
-
-def save_figure_to_numpy(fig):
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    return data
-
-
-DEFAULT_V_MIN = np.log(1e-5)
-
-
-def plot_tts_output(
-        data_2d, title_2d, data_1d, title_1d, figsize=(24, 4),
-        v_min=DEFAULT_V_MIN, v_max=3, ret_np=False, suptitle=""
-):
-    try:
-        import matplotlib.pyplot as plt
-        from mpl_toolkits.axes_grid1 import make_axes_locatable
-    except ImportError:
-        raise ImportError("Please install Matplotlib: pip install matplotlib")
-
-    data_2d = [
-        x.detach().cpu().float().numpy()
-        if isinstance(x, torch.Tensor) else x for x in data_2d
-    ]
-    fig, axes = plt.subplots(1, len(data_2d) + 1, figsize=figsize)
-    if suptitle:
-        fig.suptitle(suptitle[:400])  # capped at 400 chars
-    axes = [axes] if len(data_2d) == 0 else axes
-    for ax, x, name in zip(axes, data_2d, title_2d):
-        ax.set_title(name)
-        divider = make_axes_locatable(ax)
-        cax = divider.append_axes('right', size='5%', pad=0.05)
-        im = ax.imshow(
-            x, origin="lower", aspect="auto", vmin=max(x.min(), v_min),
-            vmax=min(x.max(), v_max)
-        )
-        fig.colorbar(im, cax=cax, orientation='vertical')
-
-    if isinstance(data_1d, torch.Tensor):
-        data_1d = data_1d.detach().cpu().numpy()
-    axes[-1].plot(data_1d)
-    axes[-1].set_title(title_1d)
-    plt.tight_layout()
-
-    if ret_np:
-        fig.canvas.draw()
-        data = save_figure_to_numpy(fig)
-        plt.close(fig)
-        return data
-
-
-def antidiag_indices(offset, min_i=0, max_i=None, min_j=0, max_j=None):
-    """
-    for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs
-
-    offset=2 (1, 1),
-    offset=3 (2, 1), (1, 2)
-    offset=4 (2, 2), (1, 3)
-    offset=5 (2, 3)
-
-    constraints:
-        i + j = offset
-        min_j <= j < max_j
-        min_i <= offset - j < max_i
-    """
-    if max_i is None:
-        max_i = offset + 1
-    if max_j is None:
-        max_j = offset + 1
-    min_j = max(min_j, offset - max_i + 1, 0)
-    max_j = min(max_j, offset - min_i + 1, offset + 1)
-    j = torch.arange(min_j, max_j)
-    i = offset - j
-    return torch.stack([i, j])
-
-
-def batch_dynamic_time_warping(distance, shapes=None):
-    """full batched DTW without any constraints
-
-    distance:  (batchsize, max_M, max_N) matrix
-    shapes: (batchsize,) vector specifying (M, N) for each entry
-    """
-    # ptr: 0=left, 1=up-left, 2=up
-    ptr2dij = {0: (0, -1), 1: (-1, -1), 2: (-1, 0)}
-
-    bsz, m, n = distance.size()
-    cumdist = torch.zeros_like(distance)
-    backptr = torch.zeros_like(distance).type(torch.int32) - 1
-
-    # initialize
-    cumdist[:, 0, :] = distance[:, 0, :].cumsum(dim=-1)
-    cumdist[:, :, 0] = distance[:, :, 0].cumsum(dim=-1)
-    backptr[:, 0, :] = 0
-    backptr[:, :, 0] = 2
-
-    # DP with optimized anti-diagonal parallelization, O(M+N) steps
-    for offset in range(2, m + n - 1):
-        ind = antidiag_indices(offset, 1, m, 1, n)
-        c = torch.stack(
-            [cumdist[:, ind[0], ind[1] - 1], cumdist[:, ind[0] - 1, ind[1] - 1],
-             cumdist[:, ind[0] - 1, ind[1]], ],
-            dim=2
-        )
-        v, b = c.min(axis=-1)
-        backptr[:, ind[0], ind[1]] = b.int()
-        cumdist[:, ind[0], ind[1]] = v + distance[:, ind[0], ind[1]]
-
-    # backtrace
-    pathmap = torch.zeros_like(backptr)
-    for b in range(bsz):
-        i = m - 1 if shapes is None else (shapes[b][0] - 1).item()
-        j = n - 1 if shapes is None else (shapes[b][1] - 1).item()
-        dtwpath = [(i, j)]
-        while (i != 0 or j != 0) and len(dtwpath) < 10000:
-            assert (i >= 0 and j >= 0)
-            di, dj = ptr2dij[backptr[b, i, j].item()]
-            i, j = i + di, j + dj
-            dtwpath.append((i, j))
-        dtwpath = dtwpath[::-1]
-        indices = torch.from_numpy(np.array(dtwpath))
-        pathmap[b, indices[:, 0], indices[:, 1]] = 1
-
-    return cumdist, backptr, pathmap
-
-
-def compute_l2_dist(x1, x2):
-    """compute an (m, n) L2 distance matrix from (m, d) and (n, d) matrices"""
-    return torch.cdist(x1.unsqueeze(0), x2.unsqueeze(0), p=2).squeeze(0).pow(2)
-
-
-def compute_rms_dist(x1, x2):
-    l2_dist = compute_l2_dist(x1, x2)
-    return (l2_dist / x1.size(1)).pow(0.5)
-
-
-def get_divisor(pathmap, normalize_type):
-    if normalize_type is None:
-        return 1
-    elif normalize_type == "len1":
-        return pathmap.size(0)
-    elif normalize_type == "len2":
-        return pathmap.size(1)
-    elif normalize_type == "path":
-        return pathmap.sum().item()
-    else:
-        raise ValueError(f"normalize_type {normalize_type} not supported")
-
-
-def batch_compute_distortion(y1, y2, sr, feat_fn, dist_fn, normalize_type):
-    d, s, x1, x2 = [], [], [], []
-    for cur_y1, cur_y2 in zip(y1, y2):
-        assert (cur_y1.ndim == 1 and cur_y2.ndim == 1)
-        cur_x1 = feat_fn(cur_y1)
-        cur_x2 = feat_fn(cur_y2)
-        x1.append(cur_x1)
-        x2.append(cur_x2)
-
-        cur_d = dist_fn(cur_x1, cur_x2)
-        d.append(cur_d)
-        s.append(d[-1].size())
-    max_m = max(ss[0] for ss in s)
-    max_n = max(ss[1] for ss in s)
-    d = torch.stack(
-        [F.pad(dd, (0, max_n - dd.size(1), 0, max_m - dd.size(0))) for dd in d]
-    )
-    s = torch.LongTensor(s).to(d.device)
-    cumdists, backptrs, pathmaps = batch_dynamic_time_warping(d, s)
-
-    rets = []
-    itr = zip(s, x1, x2, d, cumdists, backptrs, pathmaps)
-    for (m, n), cur_x1, cur_x2, dist, cumdist, backptr, pathmap in itr:
-        cumdist = cumdist[:m, :n]
-        backptr = backptr[:m, :n]
-        pathmap = pathmap[:m, :n]
-        divisor = get_divisor(pathmap, normalize_type)
-
-        distortion = cumdist[-1, -1] / divisor
-        ret = distortion, (cur_x1, cur_x2, dist, cumdist, backptr, pathmap)
-        rets.append(ret)
-    return rets
-
-
-def batch_mel_cepstral_distortion(
-        y1, y2, sr, normalize_type="path", mfcc_fn=None
-):
-    """
-    https://arxiv.org/pdf/2011.03568.pdf
-
-    The root mean squared error computed on 13-dimensional MFCC using DTW for
-    alignment. MFCC features are computed from an 80-channel log-mel
-    spectrogram using a 50ms Hann window and hop of 12.5ms.
-
-    y1: list of waveforms
-    y2: list of waveforms
-    sr: sampling rate
-    """
-
-    try:
-        import torchaudio
-    except ImportError:
-        raise ImportError("Please install torchaudio: pip install torchaudio")
-
-    if mfcc_fn is None or mfcc_fn.sample_rate != sr:
-        melkwargs = {
-            "n_fft": int(0.05 * sr), "win_length": int(0.05 * sr),
-            "hop_length": int(0.0125 * sr), "f_min": 20,
-            "n_mels": 80, "window_fn": torch.hann_window
-        }
-        mfcc_fn = torchaudio.transforms.MFCC(
-            sr, n_mfcc=13, log_mels=True, melkwargs=melkwargs
-        ).to(y1[0].device)
-    return batch_compute_distortion(
-        y1, y2, sr, lambda y: mfcc_fn(y).transpose(-1, -2), compute_rms_dist,
-        normalize_type
-    )
diff --git a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/branching/refcoco/ofa_ratarefcocoplus_pretrain_qa_ground_10epmore.sh b/spaces/mshukor/UnIVAL/slurm_adastra/averaging/branching/refcoco/ofa_ratarefcocoplus_pretrain_qa_ground_10epmore.sh
deleted file mode 100644
index ac5f6ff2108133e0517e81ecc5a5b713191786be..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/branching/refcoco/ofa_ratarefcocoplus_pretrain_qa_ground_10epmore.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-   
-#SBATCH --job-name=ofa_ratarefcocoplus_pretrain_qa_ground_10epmore
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --gpus=8
-#SBATCH --threads-per-core=2
-#SBATCH --gpu-bind=closest 
-#SBATCH -C MI250
-#SBATCH -A gda2204
-#SBATCH --time=24:00:00
-#SBATCH --mail-type=END,FAIL
-#SBATCH --output=/lus/home/NAT/gda2204/mshukor/logs/slurm/ofa_ratarefcocoplus_pretrain_qa_ground_10epmore.out
-#SBATCH --exclusive
-#SBATCH --mail-user=mustafa.shukor@isir.upmc.fr
-
-
-cd /lus/home/NAT/gda2204/mshukor/code/ofa_ours/run_scripts
-source /lus/home/NAT/gda2204/mshukor/.bashrc
-
-conda activate main
- 
-
-rm core-python3*
-
-
-srun -l -N 1 -n 1 -c 128 --gpus=8 bash averaging/branching/refcoco/ofa_ratarefcocoplus_pretrain_qa_ground_10epmore.sh
-
-
diff --git a/spaces/naotakigawa/qatool/log.py b/spaces/naotakigawa/qatool/log.py
deleted file mode 100644
index 5374e93bfb2ac7b6c629bf70b145e3e6d5c8858f..0000000000000000000000000000000000000000
--- a/spaces/naotakigawa/qatool/log.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import logging
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("__name__")
-
diff --git a/spaces/nateraw/yolov6/yolov6/layers/dbb_transforms.py b/spaces/nateraw/yolov6/yolov6/layers/dbb_transforms.py
deleted file mode 100644
index e60cbd4d475846a21922d4be7a8a9ee98b8eb473..0000000000000000000000000000000000000000
--- a/spaces/nateraw/yolov6/yolov6/layers/dbb_transforms.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-import numpy as np
-import torch.nn.functional as F
-
-
-def transI_fusebn(kernel, bn):
-    gamma = bn.weight
-    std = (bn.running_var + bn.eps).sqrt()
-    return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std
-
-
-def transII_addbranch(kernels, biases):
-    return sum(kernels), sum(biases)
-
-
-def transIII_1x1_kxk(k1, b1, k2, b2, groups):
-    if groups == 1:
-        k = F.conv2d(k2, k1.permute(1, 0, 2, 3))      #
-        b_hat = (k2 * b1.reshape(1, -1, 1, 1)).sum((1, 2, 3))
-    else:
-        k_slices = []
-        b_slices = []
-        k1_T = k1.permute(1, 0, 2, 3)
-        k1_group_width = k1.size(0) // groups
-        k2_group_width = k2.size(0) // groups
-        for g in range(groups):
-            k1_T_slice = k1_T[:, g*k1_group_width:(g+1)*k1_group_width, :, :]
-            k2_slice = k2[g*k2_group_width:(g+1)*k2_group_width, :, :, :]
-            k_slices.append(F.conv2d(k2_slice, k1_T_slice))
-            b_slices.append((k2_slice * b1[g*k1_group_width:(g+1)*k1_group_width].reshape(1, -1, 1, 1)).sum((1, 2, 3)))
-        k, b_hat = transIV_depthconcat(k_slices, b_slices)
-    return k, b_hat + b2
-
-
-def transIV_depthconcat(kernels, biases):
-    return torch.cat(kernels, dim=0), torch.cat(biases)
-
-
-def transV_avg(channels, kernel_size, groups):
-    input_dim = channels // groups
-    k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
-    k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
-    return k
-
-
-#   This has not been tested with non-square kernels (kernel.size(2) != kernel.size(3)) nor even-size kernels
-def transVI_multiscale(kernel, target_kernel_size):
-    H_pixels_to_pad = (target_kernel_size - kernel.size(2)) // 2
-    W_pixels_to_pad = (target_kernel_size - kernel.size(3)) // 2
-    return F.pad(kernel, [H_pixels_to_pad, H_pixels_to_pad, W_pixels_to_pad, W_pixels_to_pad])
diff --git a/spaces/naveed92/topic_segmentation/utils.py b/spaces/naveed92/topic_segmentation/utils.py
deleted file mode 100644
index 7809035b803df60df533385e48000577bd7e87c3..0000000000000000000000000000000000000000
--- a/spaces/naveed92/topic_segmentation/utils.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from itertools import islice
-import numpy as np
-
-# Sliding window function
-def window(seq, n=3):
-    """https://stackoverflow.com/questions/6822725/rolling-or-sliding-window-iterator
-    Returns a sliding window of width n over data from the iterable seq"""
-    
-    it = iter(seq)
-    result = tuple(islice(it, n))
-    if len(result) == n:
-        yield result
-    for elem in it:
-        result = result[1:] + (elem,)
-        yield result
-        
-# Compute depth scores
-def get_depths(scores):
-    """Given a sequence of coherence scores of length n, compute a sequence of depth scores of similar length"""
-    
-    def climb(seq, i, mode='left'):
-        """Given a sequence seq of values and index i, advance the index either to the right or left while the 
-        value keeps increasing, then return the value at new index
-        """
-        if mode == 'left':
-            while True:
-                curr = seq[i]
-                if i == 0:
-                    return curr
-                i = i-1
-                if not seq[i] > curr:
-                    return curr
-
-        if mode == 'right':
-            while True:
-                curr = seq[i]
-                if i == (len(seq)-1):
-                    return curr
-                i = i+1
-                if not seq[i] > curr:
-                    return curr
-    
-    depths = []
-    for i in range(len(scores)):
-        score = scores[i]
-        l_peak = climb(scores, i, mode='left')
-        r_peak = climb(scores, i, mode='right')
-        depth = 0.5 * (l_peak + r_peak - (2*score))
-        depths.append(depth)
-        
-    return np.array(depths)
-
-
-from scipy.signal import argrelmax
-
-# Filter out local maxima
-def get_local_maxima(depth_scores, order=1):
-    """Given a sequence of depth scores, return a filtered sequence where only local maxima 
-    selected based on the given order"""
-
-    maxima_ids = argrelmax(depth_scores, order=order)[0]
-    filtered_scores = np.zeros(len(depth_scores))
-    filtered_scores[maxima_ids] = depth_scores[maxima_ids]
-    return filtered_scores
-
-# Automatic threshold computation
-def compute_threshold(scores):
-    """From Texttiling: https://aclanthology.org/J97-1003.pdf
-    Automatically compute an appropriate threshold given a sequence of depth scores
-    """
-    
-    s = scores[np.nonzero(scores)]
-    threshold = np.mean(s) - (np.std(s) / 2)
-    # threshold = np.mean(s) - (np.std(s))
-    return threshold
-
-def get_threshold_segments(scores, threshold=0.1):
-    """Given a sequence of depth scores, return indexes where the value is greater than the threshold"""
-    segment_ids = np.where(scores >= threshold)[0]
-    return segment_ids
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Airbox Playout Software Crack Download 2021.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Airbox Playout Software Crack Download 2021.md
deleted file mode 100644
index 74ccd4a3e80a3c55eeff62007b414901e5ae8699..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Airbox Playout Software Crack Download 2021.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-
-<h1>Airbox Playout Software Crack Download: How to Get It for Free</h1>
-<p>Airbox playout software is a popular solution for automated content streaming and broadcast playout for TV channels, cable head-ends, satellite providers and corporate TV users. It offers a flexible and reliable way to manage and control multiple channels from a single interface, with features such as playlist scheduling, graphics overlay, live input, video trimming, time delay and more.</p>
-<h2>airbox playout software crack download</h2><br /><p><b><b>Download</b> >>> <a href="https://urlcod.com/2uIay6">https://urlcod.com/2uIay6</a></b></p><br /><br />
-<p>However, Airbox playout software is not cheap. The official website lists the prices for different modules and options, ranging from €2,500 to €15,000 per channel. For many users, especially small or independent broadcasters, this may be too expensive or unaffordable.</p>
-<p>That's why some people are looking for ways to get Airbox playout software crack download for free. A crack is a modified version of a software that bypasses the original protection or activation mechanism, allowing users to use it without paying or registering. A crack download is a file that contains the cracked software or the instructions on how to crack it.</p>
-<p>But is it possible to find a reliable and safe Airbox playout software crack download? And what are the risks and consequences of using it?</p>
-<p></p>
-<h2>The Challenges of Finding a Working Airbox Playout Software Crack Download</h2>
-<p>One of the main challenges of finding a working Airbox playout software crack download is that there are not many sources that offer it. A quick web search reveals only a few websites that claim to have the crack, such as [^1^], [^3^] and [^4^]. However, these websites are not trustworthy or reputable. They may contain malware, viruses, spyware or other harmful programs that can damage your computer or steal your personal information. They may also require you to complete surveys, download additional software or enter your credit card details before giving you access to the crack.</p>
-<p>Another challenge is that even if you manage to find a working Airbox playout software crack download, it may not be compatible with your system or hardware. Airbox playout software requires specific dongles or devices to run properly, such as WIBU-BOX[^5^] or PlayBox. If you don't have these dongles or devices, the crack may not work or cause errors. Moreover, the crack may be outdated or incompatible with the latest version of Airbox playout software or Windows operating system.</p>
-<h2>The Risks and Consequences of Using an Airbox Playout Software Crack Download</h2>
-<p>Even if you overcome the challenges of finding a working Airbox playout software crack download, you still face many risks and consequences of using it. Some of them are:</p>
-<ul>
-<li><b>Legal issues:</b> Using an Airbox playout software crack download is illegal and violates the intellectual property rights of the software developer. You may face legal actions, fines or penalties if you are caught using or distributing the crack.</li>
-<li><b>Ethical issues:</b> Using an Airbox playout software crack download is unethical and unfair to the software developer who invested time, money and effort to create and maintain the software. You are depriving them of their rightful income and recognition.</li>
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-<li><b>Security issues:</b> Using an Airbox playout software crack download may expose your computer and network to security threats. The crack may contain malicious code that can infect your system with malware, viruses, spyware or other harmful programs that can damage your data, files or hardware. The crack may also allow hackers or cybercriminals to access your system remotely and steal your personal information, passwords, bank accounts or other sensitive data.</li>
-<li><b>Support issues:</b> Using an Airbox playout software crack download means that you cannot access the official support and updates from the software developer. You cannot contact them for technical assistance, troubleshooting or feedback. You cannot benefit from their latest features, improvements or bug fixes. You cannot get any warranty or guarantee for their product</p> 81aa517590<br />
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\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solucionario Wayne Tomasi Sistema De Comunicaciones Electronicas.iso.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solucionario Wayne Tomasi Sistema De Comunicaciones Electronicas.iso.md
deleted file mode 100644
index 33ad9a3afe772ccab0d494cf3357c8c5f0ec543c..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solucionario Wayne Tomasi Sistema De Comunicaciones Electronicas.iso.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-
-<h1>Solucionario Wayne Tomasi Sistema De Comunicaciones Electronicas.iso</h1>
-<p>Si estás buscando el solucionario del libro de Wayne Tomasi sobre sistemas de comunicaciones electrónicas, aquí te presentamos algunas opciones que podrían serte útiles.</p>
-<ul>
-<li>La primera opción es descargar el archivo PDF del libro de Tomasi desde el siguiente enlace: <a href="https://drive.google.com/file/d/1-eLsSAC4Lv3wpFJY6CzLr0rgyYMhz_Ky/view">sistemas-de-comunicaciones-electronicas-tomasi-4ta-edicion.pdf</a> [^1^]. Este archivo contiene el texto completo del libro, incluyendo los ejercicios propuestos al final de cada capítulo. Sin embargo, no incluye las soluciones de los mismos.</li>
-<li>La segunda opción es visitar el sitio web de Kit, donde un usuario llamado unuwlimen ha compartido un enlace a un archivo que supuestamente contiene el solucionario del libro de Tomasi: <a href="https://kit.co/unuwlimen/solucionario-sistemas-de-comunicaciones-electronicas-4ta-edicion-wayne-tomasi-571/solucionario-sistema">Solucionario Sistemas De Comunicaciones Electronicas 4ta Edicion Wayne Tomasi.571</a> [^2^]. Sin embargo, no podemos garantizar la veracidad ni la seguridad de este archivo, ya que proviene de una fuente desconocida y podría contener virus o malware. Por lo tanto, te recomendamos que lo descargues bajo tu propia responsabilidad y con precaución.</li>
-<li>La tercera opción es acceder al sitio web de Microsoft Sway, donde otro usuario ha creado una presentación con el mismo título que el solucionario que buscas: <a href="https://sway.office.com/bbX2L8M0acuKGTwM">Solucionario Sistemas De Comunicaciones Electronicas 4ta Edicion Wayne Tomasi.571</a> [^3^]. Esta presentación contiene algunas imágenes y textos extraídos del libro de Tomasi, pero no parece contener las soluciones de los ejercicios. Además, la calidad y la legibilidad de las imágenes son muy bajas, por lo que no te resultará fácil seguir el contenido.</li>
-</ul>
-<p>Esperamos que alguna de estas opciones te sea de ayuda para resolver tus dudas sobre el libro de Wayne Tomasi. Sin embargo, te recordamos que la mejor forma de aprender sobre sistemas de comunicaciones electrónicas es leyendo el libro con atención y tratando de resolver los ejercicios por tu cuenta, sin recurrir a soluciones externas. Así podrás desarrollar tus habilidades y conocimientos en esta materia.</p>
-<h2>Solucionario Wayne Tomasi Sistema De Comunicaciones Electronicas.iso</h2><br /><p><b><b>Download Zip</b> &#127379; <a href="https://urlcod.com/2uIaxJ">https://urlcod.com/2uIaxJ</a></b></p><br /><br /> 7196e7f11a<br />
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diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/checkpoint/detection_checkpoint.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/checkpoint/detection_checkpoint.py
deleted file mode 100644
index cecb1fc2cfe46283b47096bcbcb2be3181431bf2..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/checkpoint/detection_checkpoint.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-import pickle
-from urllib.parse import parse_qs, urlparse
-import torch
-from fvcore.common.checkpoint import Checkpointer
-from torch.nn.parallel import DistributedDataParallel
-
-import detectron2.utils.comm as comm
-from detectron2.utils.file_io import PathManager
-
-from .c2_model_loading import align_and_update_state_dicts
-
-
-class DetectionCheckpointer(Checkpointer):
-    """
-    Same as :class:`Checkpointer`, but is able to:
-    1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models.
-    2. correctly load checkpoints that are only available on the master worker
-    """
-
-    def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
-        is_main_process = comm.is_main_process()
-        super().__init__(
-            model,
-            save_dir,
-            save_to_disk=is_main_process if save_to_disk is None else save_to_disk,
-            **checkpointables,
-        )
-        self.path_manager = PathManager
-        self._parsed_url_during_load = None
-
-    def load(self, path, *args, **kwargs):
-        assert self._parsed_url_during_load is None
-        need_sync = False
-        logger = logging.getLogger(__name__)
-        logger.info("[DetectionCheckpointer] Loading from {} ...".format(path))
-
-        if path and isinstance(self.model, DistributedDataParallel):
-            path = self.path_manager.get_local_path(path)
-            has_file = os.path.isfile(path)
-            all_has_file = comm.all_gather(has_file)
-            if not all_has_file[0]:
-                raise OSError(f"File {path} not found on main worker.")
-            if not all(all_has_file):
-                logger.warning(
-                    f"Not all workers can read checkpoint {path}. "
-                    "Training may fail to fully resume."
-                )
-                # TODO: broadcast the checkpoint file contents from main
-                # worker, and load from it instead.
-                need_sync = True
-            if not has_file:
-                path = None  # don't load if not readable
-
-        if path:
-            parsed_url = urlparse(path)
-            self._parsed_url_during_load = parsed_url
-            path = parsed_url._replace(query="").geturl()  # remove query from filename
-            path = self.path_manager.get_local_path(path)
-        ret = super().load(path, *args, **kwargs)
-
-        if need_sync:
-            logger.info("Broadcasting model states from main worker ...")
-            self.model._sync_params_and_buffers()
-        self._parsed_url_during_load = None  # reset to None
-        return ret
-
-    def _load_file(self, filename):
-        if filename.endswith(".pkl"):
-            with PathManager.open(filename, "rb") as f:
-                data = pickle.load(f, encoding="latin1")
-            if "model" in data and "__author__" in data:
-                # file is in Detectron2 model zoo format
-                self.logger.info("Reading a file from '{}'".format(data["__author__"]))
-                return data
-            else:
-                # assume file is from Caffe2 / Detectron1 model zoo
-                if "blobs" in data:
-                    # Detection models have "blobs", but ImageNet models don't
-                    data = data["blobs"]
-                data = {k: v for k, v in data.items() if not k.endswith("_momentum")}
-                return {"model": data, "__author__": "Caffe2", "matching_heuristics": True}
-        elif filename.endswith(".pyth"):
-            # assume file is from pycls; no one else seems to use the ".pyth" extension
-            with PathManager.open(filename, "rb") as f:
-                data = torch.load(f)
-            assert (
-                "model_state" in data
-            ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'."
-            model_state = {
-                k: v
-                for k, v in data["model_state"].items()
-                if not k.endswith("num_batches_tracked")
-            }
-            return {"model": model_state, "__author__": "pycls", "matching_heuristics": True}
-
-        loaded = self._torch_load(filename)
-        if "model" not in loaded:
-            loaded = {"model": loaded}
-        assert self._parsed_url_during_load is not None, "`_load_file` must be called inside `load`"
-        parsed_url = self._parsed_url_during_load
-        queries = parse_qs(parsed_url.query)
-        if queries.pop("matching_heuristics", "False") == ["True"]:
-            loaded["matching_heuristics"] = True
-        if len(queries) > 0:
-            raise ValueError(
-                f"Unsupported query remaining: f{queries}, orginal filename: {parsed_url.geturl()}"
-            )
-        return loaded
-
-    def _torch_load(self, f):
-        return super()._load_file(f)
-
-    def _load_model(self, checkpoint):
-        if checkpoint.get("matching_heuristics", False):
-            self._convert_ndarray_to_tensor(checkpoint["model"])
-            # convert weights by name-matching heuristics
-            checkpoint["model"] = align_and_update_state_dicts(
-                self.model.state_dict(),
-                checkpoint["model"],
-                c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
-            )
-        # for non-caffe2 models, use standard ways to load it
-        incompatible = super()._load_model(checkpoint)
-
-        model_buffers = dict(self.model.named_buffers(recurse=False))
-        for k in ["pixel_mean", "pixel_std"]:
-            # Ignore missing key message about pixel_mean/std.
-            # Though they may be missing in old checkpoints, they will be correctly
-            # initialized from config anyway.
-            if k in model_buffers:
-                try:
-                    incompatible.missing_keys.remove(k)
-                except ValueError:
-                    pass
-        for k in incompatible.unexpected_keys[:]:
-            # Ignore unexpected keys about cell anchors. They exist in old checkpoints
-            # but now they are non-persistent buffers and will not be in new checkpoints.
-            if "anchor_generator.cell_anchors" in k:
-                incompatible.unexpected_keys.remove(k)
-        return incompatible
diff --git a/spaces/nomic-ai/derek-thomas_ScienceQA/README.md b/spaces/nomic-ai/derek-thomas_ScienceQA/README.md
deleted file mode 100644
index ec0310a0bef0cbd79edf4f0f11ebeccecb886025..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/derek-thomas_ScienceQA/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: derek-thomas/ScienceQA
-emoji: 🗺️
-colorFrom: purple
-colorTo: red
-sdk: static
-pinned: false
-duplicated_from: nomic-ai/sciq
----
diff --git a/spaces/nr-rofiq/coba_chatbot/README.md b/spaces/nr-rofiq/coba_chatbot/README.md
deleted file mode 100644
index 7121f4c62c85e260fa8d96f02058b8ac4a08ae39..0000000000000000000000000000000000000000
--- a/spaces/nr-rofiq/coba_chatbot/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Coba Chatbot
-emoji: 📊
-colorFrom: pink
-colorTo: pink
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/omi0k/LoRA-DreamBooth-Training-UI/utils.py b/spaces/omi0k/LoRA-DreamBooth-Training-UI/utils.py
deleted file mode 100644
index 8fe82394db3a576d0b8bb94788cdc313a1b44392..0000000000000000000000000000000000000000
--- a/spaces/omi0k/LoRA-DreamBooth-Training-UI/utils.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from __future__ import annotations
-
-import pathlib
-
-
-def find_exp_dirs(ignore_repo: bool = False) -> list[str]:
-    repo_dir = pathlib.Path(__file__).parent
-    exp_root_dir = repo_dir / 'experiments'
-    if not exp_root_dir.exists():
-        return []
-    exp_dirs = sorted(exp_root_dir.glob('*'))
-    exp_dirs = [
-        exp_dir for exp_dir in exp_dirs
-        if (exp_dir / 'pytorch_lora_weights.bin').exists()
-    ]
-    if ignore_repo:
-        exp_dirs = [
-            exp_dir for exp_dir in exp_dirs if not (exp_dir / '.git').exists()
-        ]
-    return [path.relative_to(repo_dir).as_posix() for path in exp_dirs]
-
-
-def save_model_card(
-    save_dir: pathlib.Path,
-    base_model: str,
-    instance_prompt: str,
-    test_prompt: str = '',
-    test_image_dir: str = '',
-) -> None:
-    image_str = ''
-    if test_prompt and test_image_dir:
-        image_paths = sorted((save_dir / test_image_dir).glob('*'))
-        if image_paths:
-            image_str = f'Test prompt: {test_prompt}\n'
-            for image_path in image_paths:
-                rel_path = image_path.relative_to(save_dir)
-                image_str += f'![{image_path.stem}]({rel_path})\n'
-
-    model_card = f'''---
-license: creativeml-openrail-m
-base_model: {base_model}
-instance_prompt: {instance_prompt}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- lora
-inference: true
----
-# LoRA DreamBooth - {save_dir.name}
-
-These are LoRA adaption weights for [{base_model}](https://huggingface.co/{base_model}). The weights were trained on the instance prompt "{instance_prompt}" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
-
-{image_str}
-'''
-
-    with open(save_dir / 'README.md', 'w') as f:
-        f.write(model_card)
diff --git a/spaces/openMUSE/MUSE/README.md b/spaces/openMUSE/MUSE/README.md
deleted file mode 100644
index 6e8f8d0e7f93229ee5a732a693ac30f5b6a83190..0000000000000000000000000000000000000000
--- a/spaces/openMUSE/MUSE/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MUSE
-emoji: 📊
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.48.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/openai/openai-detector/detector/utils.py b/spaces/openai/openai-detector/detector/utils.py
deleted file mode 100644
index 7b636d6ee0a627a87fb6f8ceeb5cc37531d3f367..0000000000000000000000000000000000000000
--- a/spaces/openai/openai-detector/detector/utils.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import sys
-from functools import reduce
-
-from torch import nn
-import torch.distributed as dist
-
-
-def summary(model: nn.Module, file=sys.stdout):
-    def repr(model):
-        # We treat the extra repr like the sub-module, one item per line
-        extra_lines = []
-        extra_repr = model.extra_repr()
-        # empty string will be split into list ['']
-        if extra_repr:
-            extra_lines = extra_repr.split('\n')
-        child_lines = []
-        total_params = 0
-        for key, module in model._modules.items():
-            mod_str, num_params = repr(module)
-            mod_str = nn.modules.module._addindent(mod_str, 2)
-            child_lines.append('(' + key + '): ' + mod_str)
-            total_params += num_params
-        lines = extra_lines + child_lines
-
-        for name, p in model._parameters.items():
-            if hasattr(p, 'shape'):
-                total_params += reduce(lambda x, y: x * y, p.shape)
-
-        main_str = model._get_name() + '('
-        if lines:
-            # simple one-liner info, which most builtin Modules will use
-            if len(extra_lines) == 1 and not child_lines:
-                main_str += extra_lines[0]
-            else:
-                main_str += '\n  ' + '\n  '.join(lines) + '\n'
-
-        main_str += ')'
-        if file is sys.stdout:
-            main_str += ', \033[92m{:,}\033[0m params'.format(total_params)
-        else:
-            main_str += ', {:,} params'.format(total_params)
-        return main_str, total_params
-
-    string, count = repr(model)
-    if file is not None:
-        if isinstance(file, str):
-            file = open(file, 'w')
-        print(string, file=file)
-        file.flush()
-
-    return count
-
-
-def grad_norm(model: nn.Module):
-    total_norm = 0
-    for p in model.parameters():
-        param_norm = p.grad.data.norm(2)
-        total_norm += param_norm.item() ** 2
-    return total_norm ** 0.5
-
-def distributed():
-    return dist.is_available() and dist.is_initialized()
diff --git a/spaces/oshita-n/ControlNet/README.md b/spaces/oshita-n/ControlNet/README.md
deleted file mode 100644
index 22e8fde940cdd83b104629a8c7d87f8be10b6382..0000000000000000000000000000000000000000
--- a/spaces/oshita-n/ControlNet/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ControlNet
-emoji: 🐨
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/_functools.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/_functools.py
deleted file mode 100644
index e7053bac12fdb7b2cc50448f88318cd93f62cc0e..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/_functools.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import functools
-
-
-# from jaraco.functools 3.5
-def pass_none(func):
-    """
-    Wrap func so it's not called if its first param is None
-
-    >>> print_text = pass_none(print)
-    >>> print_text('text')
-    text
-    >>> print_text(None)
-    """
-
-    @functools.wraps(func)
-    def wrapper(param, *args, **kwargs):
-        if param is not None:
-            return func(param, *args, **kwargs)
-
-    return wrapper
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/core.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/core.py
deleted file mode 100644
index 05d2971994d36b29c6532e0c99115c1906b8e275..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/core.py
+++ /dev/null
@@ -1,291 +0,0 @@
-"""distutils.core
-
-The only module that needs to be imported to use the Distutils; provides
-the 'setup' function (which is to be called from the setup script).  Also
-indirectly provides the Distribution and Command classes, although they are
-really defined in distutils.dist and distutils.cmd.
-"""
-
-import os
-import sys
-import tokenize
-
-from .debug import DEBUG
-from .errors import (
-    DistutilsSetupError,
-    DistutilsError,
-    CCompilerError,
-    DistutilsArgError,
-)
-
-# Mainly import these so setup scripts can "from distutils.core import" them.
-from .dist import Distribution
-from .cmd import Command
-from .config import PyPIRCCommand
-from .extension import Extension
-
-
-__all__ = ['Distribution', 'Command', 'PyPIRCCommand', 'Extension', 'setup']
-
-# This is a barebones help message generated displayed when the user
-# runs the setup script with no arguments at all.  More useful help
-# is generated with various --help options: global help, list commands,
-# and per-command help.
-USAGE = """\
-usage: %(script)s [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
-   or: %(script)s --help [cmd1 cmd2 ...]
-   or: %(script)s --help-commands
-   or: %(script)s cmd --help
-"""
-
-
-def gen_usage(script_name):
-    script = os.path.basename(script_name)
-    return USAGE % locals()
-
-
-# Some mild magic to control the behaviour of 'setup()' from 'run_setup()'.
-_setup_stop_after = None
-_setup_distribution = None
-
-# Legal keyword arguments for the setup() function
-setup_keywords = (
-    'distclass',
-    'script_name',
-    'script_args',
-    'options',
-    'name',
-    'version',
-    'author',
-    'author_email',
-    'maintainer',
-    'maintainer_email',
-    'url',
-    'license',
-    'description',
-    'long_description',
-    'keywords',
-    'platforms',
-    'classifiers',
-    'download_url',
-    'requires',
-    'provides',
-    'obsoletes',
-)
-
-# Legal keyword arguments for the Extension constructor
-extension_keywords = (
-    'name',
-    'sources',
-    'include_dirs',
-    'define_macros',
-    'undef_macros',
-    'library_dirs',
-    'libraries',
-    'runtime_library_dirs',
-    'extra_objects',
-    'extra_compile_args',
-    'extra_link_args',
-    'swig_opts',
-    'export_symbols',
-    'depends',
-    'language',
-)
-
-
-def setup(**attrs):  # noqa: C901
-    """The gateway to the Distutils: do everything your setup script needs
-    to do, in a highly flexible and user-driven way.  Briefly: create a
-    Distribution instance; find and parse config files; parse the command
-    line; run each Distutils command found there, customized by the options
-    supplied to 'setup()' (as keyword arguments), in config files, and on
-    the command line.
-
-    The Distribution instance might be an instance of a class supplied via
-    the 'distclass' keyword argument to 'setup'; if no such class is
-    supplied, then the Distribution class (in dist.py) is instantiated.
-    All other arguments to 'setup' (except for 'cmdclass') are used to set
-    attributes of the Distribution instance.
-
-    The 'cmdclass' argument, if supplied, is a dictionary mapping command
-    names to command classes.  Each command encountered on the command line
-    will be turned into a command class, which is in turn instantiated; any
-    class found in 'cmdclass' is used in place of the default, which is
-    (for command 'foo_bar') class 'foo_bar' in module
-    'distutils.command.foo_bar'.  The command class must provide a
-    'user_options' attribute which is a list of option specifiers for
-    'distutils.fancy_getopt'.  Any command-line options between the current
-    and the next command are used to set attributes of the current command
-    object.
-
-    When the entire command-line has been successfully parsed, calls the
-    'run()' method on each command object in turn.  This method will be
-    driven entirely by the Distribution object (which each command object
-    has a reference to, thanks to its constructor), and the
-    command-specific options that became attributes of each command
-    object.
-    """
-
-    global _setup_stop_after, _setup_distribution
-
-    # Determine the distribution class -- either caller-supplied or
-    # our Distribution (see below).
-    klass = attrs.get('distclass')
-    if klass:
-        attrs.pop('distclass')
-    else:
-        klass = Distribution
-
-    if 'script_name' not in attrs:
-        attrs['script_name'] = os.path.basename(sys.argv[0])
-    if 'script_args' not in attrs:
-        attrs['script_args'] = sys.argv[1:]
-
-    # Create the Distribution instance, using the remaining arguments
-    # (ie. everything except distclass) to initialize it
-    try:
-        _setup_distribution = dist = klass(attrs)
-    except DistutilsSetupError as msg:
-        if 'name' not in attrs:
-            raise SystemExit("error in setup command: %s" % msg)
-        else:
-            raise SystemExit("error in {} setup command: {}".format(attrs['name'], msg))
-
-    if _setup_stop_after == "init":
-        return dist
-
-    # Find and parse the config file(s): they will override options from
-    # the setup script, but be overridden by the command line.
-    dist.parse_config_files()
-
-    if DEBUG:
-        print("options (after parsing config files):")
-        dist.dump_option_dicts()
-
-    if _setup_stop_after == "config":
-        return dist
-
-    # Parse the command line and override config files; any
-    # command-line errors are the end user's fault, so turn them into
-    # SystemExit to suppress tracebacks.
-    try:
-        ok = dist.parse_command_line()
-    except DistutilsArgError as msg:
-        raise SystemExit(gen_usage(dist.script_name) + "\nerror: %s" % msg)
-
-    if DEBUG:
-        print("options (after parsing command line):")
-        dist.dump_option_dicts()
-
-    if _setup_stop_after == "commandline":
-        return dist
-
-    # And finally, run all the commands found on the command line.
-    if ok:
-        return run_commands(dist)
-
-    return dist
-
-
-# setup ()
-
-
-def run_commands(dist):
-    """Given a Distribution object run all the commands,
-    raising ``SystemExit`` errors in the case of failure.
-
-    This function assumes that either ``sys.argv`` or ``dist.script_args``
-    is already set accordingly.
-    """
-    try:
-        dist.run_commands()
-    except KeyboardInterrupt:
-        raise SystemExit("interrupted")
-    except OSError as exc:
-        if DEBUG:
-            sys.stderr.write("error: {}\n".format(exc))
-            raise
-        else:
-            raise SystemExit("error: {}".format(exc))
-
-    except (DistutilsError, CCompilerError) as msg:
-        if DEBUG:
-            raise
-        else:
-            raise SystemExit("error: " + str(msg))
-
-    return dist
-
-
-def run_setup(script_name, script_args=None, stop_after="run"):
-    """Run a setup script in a somewhat controlled environment, and
-    return the Distribution instance that drives things.  This is useful
-    if you need to find out the distribution meta-data (passed as
-    keyword args from 'script' to 'setup()', or the contents of the
-    config files or command-line.
-
-    'script_name' is a file that will be read and run with 'exec()';
-    'sys.argv[0]' will be replaced with 'script' for the duration of the
-    call.  'script_args' is a list of strings; if supplied,
-    'sys.argv[1:]' will be replaced by 'script_args' for the duration of
-    the call.
-
-    'stop_after' tells 'setup()' when to stop processing; possible
-    values:
-      init
-        stop after the Distribution instance has been created and
-        populated with the keyword arguments to 'setup()'
-      config
-        stop after config files have been parsed (and their data
-        stored in the Distribution instance)
-      commandline
-        stop after the command-line ('sys.argv[1:]' or 'script_args')
-        have been parsed (and the data stored in the Distribution)
-      run [default]
-        stop after all commands have been run (the same as if 'setup()'
-        had been called in the usual way
-
-    Returns the Distribution instance, which provides all information
-    used to drive the Distutils.
-    """
-    if stop_after not in ('init', 'config', 'commandline', 'run'):
-        raise ValueError("invalid value for 'stop_after': {!r}".format(stop_after))
-
-    global _setup_stop_after, _setup_distribution
-    _setup_stop_after = stop_after
-
-    save_argv = sys.argv.copy()
-    g = {'__file__': script_name, '__name__': '__main__'}
-    try:
-        try:
-            sys.argv[0] = script_name
-            if script_args is not None:
-                sys.argv[1:] = script_args
-            # tokenize.open supports automatic encoding detection
-            with tokenize.open(script_name) as f:
-                code = f.read().replace(r'\r\n', r'\n')
-                exec(code, g)
-        finally:
-            sys.argv = save_argv
-            _setup_stop_after = None
-    except SystemExit:
-        # Hmm, should we do something if exiting with a non-zero code
-        # (ie. error)?
-        pass
-
-    if _setup_distribution is None:
-        raise RuntimeError(
-            (
-                "'distutils.core.setup()' was never called -- "
-                "perhaps '%s' is not a Distutils setup script?"
-            )
-            % script_name
-        )
-
-    # I wonder if the setup script's namespace -- g and l -- would be of
-    # any interest to callers?
-    # print "_setup_distribution:", _setup_distribution
-    return _setup_distribution
-
-
-# run_setup ()
diff --git a/spaces/plzdontcry/dakubettergpt/src/components/PopupModal/PopupModal.tsx b/spaces/plzdontcry/dakubettergpt/src/components/PopupModal/PopupModal.tsx
deleted file mode 100644
index dbe91a329d058546237950fc1af267bff211aa70..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/components/PopupModal/PopupModal.tsx
+++ /dev/null
@@ -1,104 +0,0 @@
-import React from 'react';
-import ReactDOM from 'react-dom';
-import { useTranslation } from 'react-i18next';
-
-import CrossIcon2 from '@icon/CrossIcon2';
-
-const PopupModal = ({
-  title = 'Information',
-  message,
-  setIsModalOpen,
-  handleConfirm,
-  handleClose,
-  handleClickBackdrop,
-  cancelButton = true,
-  children,
-}: {
-  title?: string;
-  message?: string;
-  setIsModalOpen: React.Dispatch<React.SetStateAction<boolean>>;
-  handleConfirm?: () => void;
-  handleClose?: () => void;
-  handleClickBackdrop?: () => void;
-  cancelButton?: boolean;
-  children?: React.ReactElement;
-}) => {
-  const modalRoot = document.getElementById('modal-root');
-  const { t } = useTranslation();
-
-  const _handleClose = () => {
-    handleClose && handleClose();
-    setIsModalOpen(false);
-  };
-
-  const _handleBackdropClose = () => {
-    if (handleClickBackdrop) handleClickBackdrop();
-    else _handleClose();
-  };
-
-  if (modalRoot) {
-    return ReactDOM.createPortal(
-      <div className='fixed top-0 left-0 z-[999] w-full p-4 overflow-x-hidden overflow-y-auto h-full flex justify-center items-center'>
-        <div className='relative z-2 max-w-2xl md:h-auto flex justify-center max-h-full'>
-          <div className='relative bg-gray-50 rounded-lg shadow dark:bg-gray-700 max-h-full overflow-y-auto hide-scroll-bar'>
-            <div className='flex items-center justify-between p-4 border-b rounded-t dark:border-gray-600'>
-              <h3 className='ml-2 text-lg font-semibold text-gray-900 dark:text-white'>
-                {title}
-              </h3>
-              <button
-                type='button'
-                className='text-gray-400 bg-transparent hover:bg-gray-200 hover:text-gray-900 rounded-lg text-sm p-1.5 ml-auto inline-flex items-center dark:hover:bg-gray-600 dark:hover:text-white'
-                onClick={_handleClose}
-                aria-label='close modal'
-              >
-                <CrossIcon2 />
-              </button>
-            </div>
-
-            {message && (
-              <div className='p-6 border-b border-gray-200 dark:border-gray-600'>
-                <div className='min-w-fit text-gray-900 dark:text-gray-300 text-sm mt-4'>
-                  {message}
-                </div>
-              </div>
-            )}
-
-            {children}
-
-            <div className='flex items-center justify-center p-6 gap-4'>
-              {handleConfirm && (
-                <button
-                  type='button'
-                  className='btn btn-primary'
-                  onClick={handleConfirm}
-                  aria-label='confirm'
-                >
-                  {t('confirm')}
-                </button>
-              )}
-              {cancelButton && (
-                <button
-                  type='button'
-                  className='btn btn-neutral'
-                  onClick={_handleClose}
-                  aria-label='cancel'
-                >
-                  {t('cancel')}
-                </button>
-              )}
-            </div>
-          </div>
-        </div>
-        <div
-          className='bg-gray-800/90 absolute top-0 left-0 h-full w-full z-[-1]'
-          onClick={_handleBackdropClose}
-        />
-      </div>,
-      modalRoot
-    );
-  } else {
-    return null;
-  }
-};
-
-export default PopupModal;
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/__init__.py
deleted file mode 100644
index ae532cd31b6eb54bdd5778c13989c1475b643db3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""fontTools.feaLib -- a package for dealing with OpenType feature files."""
-
-# The structure of OpenType feature files is defined here:
-# http://www.adobe.com/devnet/opentype/afdko/topic_feature_file_syntax.html
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/_commit_scheduler.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/_commit_scheduler.py
deleted file mode 100644
index 80d8dac7866a4eeef888d36cfbb974aa06a9930b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/_commit_scheduler.py
+++ /dev/null
@@ -1,327 +0,0 @@
-import atexit
-import logging
-import os
-import time
-from concurrent.futures import Future
-from dataclasses import dataclass
-from io import SEEK_END, SEEK_SET, BytesIO
-from pathlib import Path
-from threading import Lock, Thread
-from typing import Dict, List, Optional, Union
-
-from .hf_api import IGNORE_GIT_FOLDER_PATTERNS, CommitInfo, CommitOperationAdd, HfApi
-from .utils import filter_repo_objects
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(frozen=True)
-class _FileToUpload:
-    """Temporary dataclass to store info about files to upload. Not meant to be used directly."""
-
-    local_path: Path
-    path_in_repo: str
-    size_limit: int
-    last_modified: float
-
-
-class CommitScheduler:
-    """
-    Scheduler to upload a local folder to the Hub at regular intervals (e.g. push to hub every 5 minutes).
-
-    The scheduler is started when instantiated and run indefinitely. At the end of your script, a last commit is
-    triggered. Checkout the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#scheduled-uploads)
-    to learn more about how to use it.
-
-    Args:
-        repo_id (`str`):
-            The id of the repo to commit to.
-        folder_path (`str` or `Path`):
-            Path to the local folder to upload regularly.
-        every (`int` or `float`, *optional*):
-            The number of minutes between each commit. Defaults to 5 minutes.
-        path_in_repo (`str`, *optional*):
-            Relative path of the directory in the repo, for example: `"checkpoints/"`. Defaults to the root folder
-            of the repository.
-        repo_type (`str`, *optional*):
-            The type of the repo to commit to. Defaults to `model`.
-        revision (`str`, *optional*):
-            The revision of the repo to commit to. Defaults to `main`.
-        private (`bool`, *optional*):
-            Whether to make the repo private. Defaults to `False`. This value is ignored if the repo already exist.
-        token (`str`, *optional*):
-            The token to use to commit to the repo. Defaults to the token saved on the machine.
-        allow_patterns (`List[str]` or `str`, *optional*):
-            If provided, only files matching at least one pattern are uploaded.
-        ignore_patterns (`List[str]` or `str`, *optional*):
-            If provided, files matching any of the patterns are not uploaded.
-        squash_history (`bool`, *optional*):
-            Whether to squash the history of the repo after each commit. Defaults to `False`. Squashing commits is
-            useful to avoid degraded performances on the repo when it grows too large.
-        hf_api (`HfApi`, *optional*):
-            The [`HfApi`] client to use to commit to the Hub. Can be set with custom settings (user agent, token,...).
-
-    Example:
-    ```py
-    >>> from pathlib import Path
-    >>> from huggingface_hub import CommitScheduler
-
-    # Scheduler uploads every 10 minutes
-    >>> csv_path = Path("watched_folder/data.csv")
-    >>> CommitScheduler(repo_id="test_scheduler", repo_type="dataset", folder_path=csv_path.parent, every=10)
-
-    >>> with csv_path.open("a") as f:
-    ...     f.write("first line")
-
-    # Some time later (...)
-    >>> with csv_path.open("a") as f:
-    ...     f.write("second line")
-    ```
-    """
-
-    def __init__(
-        self,
-        *,
-        repo_id: str,
-        folder_path: Union[str, Path],
-        every: Union[int, float] = 5,
-        path_in_repo: Optional[str] = None,
-        repo_type: Optional[str] = None,
-        revision: Optional[str] = None,
-        private: bool = False,
-        token: Optional[str] = None,
-        allow_patterns: Optional[Union[List[str], str]] = None,
-        ignore_patterns: Optional[Union[List[str], str]] = None,
-        squash_history: bool = False,
-        hf_api: Optional["HfApi"] = None,
-    ) -> None:
-        self.api = hf_api or HfApi(token=token)
-
-        # Folder
-        self.folder_path = Path(folder_path).expanduser().resolve()
-        self.path_in_repo = path_in_repo or ""
-        self.allow_patterns = allow_patterns
-
-        if ignore_patterns is None:
-            ignore_patterns = []
-        elif isinstance(ignore_patterns, str):
-            ignore_patterns = [ignore_patterns]
-        self.ignore_patterns = ignore_patterns + IGNORE_GIT_FOLDER_PATTERNS
-
-        if self.folder_path.is_file():
-            raise ValueError(f"'folder_path' must be a directory, not a file: '{self.folder_path}'.")
-        self.folder_path.mkdir(parents=True, exist_ok=True)
-
-        # Repository
-        repo_url = self.api.create_repo(repo_id=repo_id, private=private, repo_type=repo_type, exist_ok=True)
-        self.repo_id = repo_url.repo_id
-        self.repo_type = repo_type
-        self.revision = revision
-        self.token = token
-
-        # Keep track of already uploaded files
-        self.last_uploaded: Dict[Path, float] = {}  # key is local path, value is timestamp
-
-        # Scheduler
-        if not every > 0:
-            raise ValueError(f"'every' must be a positive integer, not '{every}'.")
-        self.lock = Lock()
-        self.every = every
-        self.squash_history = squash_history
-
-        logger.info(f"Scheduled job to push '{self.folder_path}' to '{self.repo_id}' every {self.every} minutes.")
-        self._scheduler_thread = Thread(target=self._run_scheduler, daemon=True)
-        self._scheduler_thread.start()
-        atexit.register(self._push_to_hub)
-
-        self.__stopped = False
-
-    def stop(self) -> None:
-        """Stop the scheduler.
-
-        A stopped scheduler cannot be restarted. Mostly for tests purposes.
-        """
-        self.__stopped = True
-
-    def _run_scheduler(self) -> None:
-        """Dumb thread waiting between each scheduled push to Hub."""
-        while True:
-            self.last_future = self.trigger()
-            time.sleep(self.every * 60)
-            if self.__stopped:
-                break
-
-    def trigger(self) -> Future:
-        """Trigger a `push_to_hub` and return a future.
-
-        This method is automatically called every `every` minutes. You can also call it manually to trigger a commit
-        immediately, without waiting for the next scheduled commit.
-        """
-        return self.api.run_as_future(self._push_to_hub)
-
-    def _push_to_hub(self) -> Optional[CommitInfo]:
-        if self.__stopped:  # If stopped, already scheduled commits are ignored
-            return None
-
-        logger.info("(Background) scheduled commit triggered.")
-        try:
-            value = self.push_to_hub()
-            if self.squash_history:
-                logger.info("(Background) squashing repo history.")
-                self.api.super_squash_history(repo_id=self.repo_id, repo_type=self.repo_type, branch=self.revision)
-            return value
-        except Exception as e:
-            logger.error(f"Error while pushing to Hub: {e}")  # Depending on the setup, error might be silenced
-            raise
-
-    def push_to_hub(self) -> Optional[CommitInfo]:
-        """
-        Push folder to the Hub and return the commit info.
-
-        <Tip warning={true}>
-
-        This method is not meant to be called directly. It is run in the background by the scheduler, respecting a
-        queue mechanism to avoid concurrent commits. Making a direct call to the method might lead to concurrency
-        issues.
-
-        </Tip>
-
-        The default behavior of `push_to_hub` is to assume an append-only folder. It lists all files in the folder and
-        uploads only changed files. If no changes are found, the method returns without committing anything. If you want
-        to change this behavior, you can inherit from [`CommitScheduler`] and override this method. This can be useful
-        for example to compress data together in a single file before committing. For more details and examples, check
-        out our [integration guide](https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads).
-        """
-        # Check files to upload (with lock)
-        with self.lock:
-            logger.debug("Listing files to upload for scheduled commit.")
-
-            # List files from folder (taken from `_prepare_upload_folder_additions`)
-            relpath_to_abspath = {
-                path.relative_to(self.folder_path).as_posix(): path
-                for path in sorted(self.folder_path.glob("**/*"))  # sorted to be deterministic
-                if path.is_file()
-            }
-            prefix = f"{self.path_in_repo.strip('/')}/" if self.path_in_repo else ""
-
-            # Filter with pattern + filter out unchanged files + retrieve current file size
-            files_to_upload: List[_FileToUpload] = []
-            for relpath in filter_repo_objects(
-                relpath_to_abspath.keys(), allow_patterns=self.allow_patterns, ignore_patterns=self.ignore_patterns
-            ):
-                local_path = relpath_to_abspath[relpath]
-                stat = local_path.stat()
-                if self.last_uploaded.get(local_path) is None or self.last_uploaded[local_path] != stat.st_mtime:
-                    files_to_upload.append(
-                        _FileToUpload(
-                            local_path=local_path,
-                            path_in_repo=prefix + relpath,
-                            size_limit=stat.st_size,
-                            last_modified=stat.st_mtime,
-                        )
-                    )
-
-        # Return if nothing to upload
-        if len(files_to_upload) == 0:
-            logger.debug("Dropping schedule commit: no changed file to upload.")
-            return None
-
-        # Convert `_FileToUpload` as `CommitOperationAdd` (=> compute file shas + limit to file size)
-        logger.debug("Removing unchanged files since previous scheduled commit.")
-        add_operations = [
-            CommitOperationAdd(
-                # Cap the file to its current size, even if the user append data to it while a scheduled commit is happening
-                path_or_fileobj=PartialFileIO(file_to_upload.local_path, size_limit=file_to_upload.size_limit),
-                path_in_repo=file_to_upload.path_in_repo,
-            )
-            for file_to_upload in files_to_upload
-        ]
-
-        # Upload files (append mode expected - no need for lock)
-        logger.debug("Uploading files for scheduled commit.")
-        commit_info = self.api.create_commit(
-            repo_id=self.repo_id,
-            repo_type=self.repo_type,
-            operations=add_operations,
-            commit_message="Scheduled Commit",
-            revision=self.revision,
-        )
-
-        # Successful commit: keep track of the latest "last_modified" for each file
-        for file in files_to_upload:
-            self.last_uploaded[file.local_path] = file.last_modified
-        return commit_info
-
-
-class PartialFileIO(BytesIO):
-    """A file-like object that reads only the first part of a file.
-
-    Useful to upload a file to the Hub when the user might still be appending data to it. Only the first part of the
-    file is uploaded (i.e. the part that was available when the filesystem was first scanned).
-
-    In practice, only used internally by the CommitScheduler to regularly push a folder to the Hub with minimal
-    disturbance for the user. The object is passed to `CommitOperationAdd`.
-
-    Only supports `read`, `tell` and `seek` methods.
-
-    Args:
-        file_path (`str` or `Path`):
-            Path to the file to read.
-        size_limit (`int`):
-            The maximum number of bytes to read from the file. If the file is larger than this, only the first part
-            will be read (and uploaded).
-    """
-
-    def __init__(self, file_path: Union[str, Path], size_limit: int) -> None:
-        self._file_path = Path(file_path)
-        self._file = self._file_path.open("rb")
-        self._size_limit = min(size_limit, os.fstat(self._file.fileno()).st_size)
-
-    def __del__(self) -> None:
-        self._file.close()
-        return super().__del__()
-
-    def __repr__(self) -> str:
-        return f"<PartialFileIO file_path={self._file_path} size_limit={self._size_limit}>"
-
-    def __len__(self) -> int:
-        return self._size_limit
-
-    def __getattribute__(self, name: str):
-        if name.startswith("_") or name in ("read", "tell", "seek"):  # only 3 public methods supported
-            return super().__getattribute__(name)
-        raise NotImplementedError(f"PartialFileIO does not support '{name}'.")
-
-    def tell(self) -> int:
-        """Return the current file position."""
-        return self._file.tell()
-
-    def seek(self, __offset: int, __whence: int = SEEK_SET) -> int:
-        """Change the stream position to the given offset.
-
-        Behavior is the same as a regular file, except that the position is capped to the size limit.
-        """
-        if __whence == SEEK_END:
-            # SEEK_END => set from the truncated end
-            __offset = len(self) + __offset
-            __whence = SEEK_SET
-
-        pos = self._file.seek(__offset, __whence)
-        if pos > self._size_limit:
-            return self._file.seek(self._size_limit)
-        return pos
-
-    def read(self, __size: Optional[int] = -1) -> bytes:
-        """Read at most `__size` bytes from the file.
-
-        Behavior is the same as a regular file, except that it is capped to the size limit.
-        """
-        current = self._file.tell()
-        if __size is None or __size < 0:
-            # Read until file limit
-            truncated_size = self._size_limit - current
-        else:
-            # Read until file limit or __size
-            truncated_size = min(__size, self._size_limit - current)
-        return self._file.read(truncated_size)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_ps.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_ps.py
deleted file mode 100644
index 2f9faa0ef6da3ac905b252d719c3be5c29f137a7..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_ps.py
+++ /dev/null
@@ -1,1340 +0,0 @@
-"""
-A PostScript backend, which can produce both PostScript .ps and .eps.
-"""
-
-import codecs
-import datetime
-from enum import Enum
-import functools
-from io import StringIO
-import itertools
-import logging
-import os
-import pathlib
-import shutil
-from tempfile import TemporaryDirectory
-import time
-
-import numpy as np
-
-import matplotlib as mpl
-from matplotlib import _api, cbook, _path, _text_helpers
-from matplotlib._afm import AFM
-from matplotlib.backend_bases import (
-    _Backend, FigureCanvasBase, FigureManagerBase, RendererBase)
-from matplotlib.cbook import is_writable_file_like, file_requires_unicode
-from matplotlib.font_manager import get_font
-from matplotlib.ft2font import LOAD_NO_SCALE, FT2Font
-from matplotlib._ttconv import convert_ttf_to_ps
-from matplotlib._mathtext_data import uni2type1
-from matplotlib.path import Path
-from matplotlib.texmanager import TexManager
-from matplotlib.transforms import Affine2D
-from matplotlib.backends.backend_mixed import MixedModeRenderer
-from . import _backend_pdf_ps
-
-
-_log = logging.getLogger(__name__)
-debugPS = False
-
-
-@_api.deprecated("3.7")
-class PsBackendHelper:
-    def __init__(self):
-        self._cached = {}
-
-
-@_api.caching_module_getattr
-class __getattr__:
-    # module-level deprecations
-    ps_backend_helper = _api.deprecated("3.7", obj_type="")(
-        property(lambda self: PsBackendHelper()))
-    psDefs = _api.deprecated("3.8", obj_type="")(property(lambda self: _psDefs))
-
-
-papersize = {'letter': (8.5, 11),
-             'legal': (8.5, 14),
-             'ledger': (11, 17),
-             'a0': (33.11, 46.81),
-             'a1': (23.39, 33.11),
-             'a2': (16.54, 23.39),
-             'a3': (11.69, 16.54),
-             'a4': (8.27, 11.69),
-             'a5': (5.83, 8.27),
-             'a6': (4.13, 5.83),
-             'a7': (2.91, 4.13),
-             'a8': (2.05, 2.91),
-             'a9': (1.46, 2.05),
-             'a10': (1.02, 1.46),
-             'b0': (40.55, 57.32),
-             'b1': (28.66, 40.55),
-             'b2': (20.27, 28.66),
-             'b3': (14.33, 20.27),
-             'b4': (10.11, 14.33),
-             'b5': (7.16, 10.11),
-             'b6': (5.04, 7.16),
-             'b7': (3.58, 5.04),
-             'b8': (2.51, 3.58),
-             'b9': (1.76, 2.51),
-             'b10': (1.26, 1.76)}
-
-
-def _get_papertype(w, h):
-    for key, (pw, ph) in sorted(papersize.items(), reverse=True):
-        if key.startswith('l'):
-            continue
-        if w < pw and h < ph:
-            return key
-    return 'a0'
-
-
-def _nums_to_str(*args, sep=" "):
-    return sep.join(f"{arg:1.3f}".rstrip("0").rstrip(".") for arg in args)
-
-
-def _move_path_to_path_or_stream(src, dst):
-    """
-    Move the contents of file at *src* to path-or-filelike *dst*.
-
-    If *dst* is a path, the metadata of *src* are *not* copied.
-    """
-    if is_writable_file_like(dst):
-        fh = (open(src, encoding='latin-1')
-              if file_requires_unicode(dst)
-              else open(src, 'rb'))
-        with fh:
-            shutil.copyfileobj(fh, dst)
-    else:
-        shutil.move(src, dst, copy_function=shutil.copyfile)
-
-
-def _font_to_ps_type3(font_path, chars):
-    """
-    Subset *chars* from the font at *font_path* into a Type 3 font.
-
-    Parameters
-    ----------
-    font_path : path-like
-        Path to the font to be subsetted.
-    chars : str
-        The characters to include in the subsetted font.
-
-    Returns
-    -------
-    str
-        The string representation of a Type 3 font, which can be included
-        verbatim into a PostScript file.
-    """
-    font = get_font(font_path, hinting_factor=1)
-    glyph_ids = [font.get_char_index(c) for c in chars]
-
-    preamble = """\
-%!PS-Adobe-3.0 Resource-Font
-%%Creator: Converted from TrueType to Type 3 by Matplotlib.
-10 dict begin
-/FontName /{font_name} def
-/PaintType 0 def
-/FontMatrix [{inv_units_per_em} 0 0 {inv_units_per_em} 0 0] def
-/FontBBox [{bbox}] def
-/FontType 3 def
-/Encoding [{encoding}] def
-/CharStrings {num_glyphs} dict dup begin
-/.notdef 0 def
-""".format(font_name=font.postscript_name,
-           inv_units_per_em=1 / font.units_per_EM,
-           bbox=" ".join(map(str, font.bbox)),
-           encoding=" ".join(f"/{font.get_glyph_name(glyph_id)}"
-                             for glyph_id in glyph_ids),
-           num_glyphs=len(glyph_ids) + 1)
-    postamble = """
-end readonly def
-
-/BuildGlyph {
- exch begin
- CharStrings exch
- 2 copy known not {pop /.notdef} if
- true 3 1 roll get exec
- end
-} _d
-
-/BuildChar {
- 1 index /Encoding get exch get
- 1 index /BuildGlyph get exec
-} _d
-
-FontName currentdict end definefont pop
-"""
-
-    entries = []
-    for glyph_id in glyph_ids:
-        g = font.load_glyph(glyph_id, LOAD_NO_SCALE)
-        v, c = font.get_path()
-        entries.append(
-            "/%(name)s{%(bbox)s sc\n" % {
-                "name": font.get_glyph_name(glyph_id),
-                "bbox": " ".join(map(str, [g.horiAdvance, 0, *g.bbox])),
-            }
-            + _path.convert_to_string(
-                # Convert back to TrueType's internal units (1/64's).
-                # (Other dimensions are already in these units.)
-                Path(v * 64, c), None, None, False, None, 0,
-                # No code for quad Beziers triggers auto-conversion to cubics.
-                # Drop intermediate closepolys (relying on the outline
-                # decomposer always explicitly moving to the closing point
-                # first).
-                [b"m", b"l", b"", b"c", b""], True).decode("ascii")
-            + "ce} _d"
-        )
-
-    return preamble + "\n".join(entries) + postamble
-
-
-def _font_to_ps_type42(font_path, chars, fh):
-    """
-    Subset *chars* from the font at *font_path* into a Type 42 font at *fh*.
-
-    Parameters
-    ----------
-    font_path : path-like
-        Path to the font to be subsetted.
-    chars : str
-        The characters to include in the subsetted font.
-    fh : file-like
-        Where to write the font.
-    """
-    subset_str = ''.join(chr(c) for c in chars)
-    _log.debug("SUBSET %s characters: %s", font_path, subset_str)
-    try:
-        fontdata = _backend_pdf_ps.get_glyphs_subset(font_path, subset_str)
-        _log.debug("SUBSET %s %d -> %d", font_path, os.stat(font_path).st_size,
-                   fontdata.getbuffer().nbytes)
-
-        # Give ttconv a subsetted font along with updated glyph_ids.
-        font = FT2Font(fontdata)
-        glyph_ids = [font.get_char_index(c) for c in chars]
-        with TemporaryDirectory() as tmpdir:
-            tmpfile = os.path.join(tmpdir, "tmp.ttf")
-
-            with open(tmpfile, 'wb') as tmp:
-                tmp.write(fontdata.getvalue())
-
-            # TODO: allow convert_ttf_to_ps to input file objects (BytesIO)
-            convert_ttf_to_ps(os.fsencode(tmpfile), fh, 42, glyph_ids)
-    except RuntimeError:
-        _log.warning(
-            "The PostScript backend does not currently "
-            "support the selected font.")
-        raise
-
-
-def _log_if_debug_on(meth):
-    """
-    Wrap `RendererPS` method *meth* to emit a PS comment with the method name,
-    if the global flag `debugPS` is set.
-    """
-    @functools.wraps(meth)
-    def wrapper(self, *args, **kwargs):
-        if debugPS:
-            self._pswriter.write(f"% {meth.__name__}\n")
-        return meth(self, *args, **kwargs)
-
-    return wrapper
-
-
-class RendererPS(_backend_pdf_ps.RendererPDFPSBase):
-    """
-    The renderer handles all the drawing primitives using a graphics
-    context instance that controls the colors/styles.
-    """
-
-    _afm_font_dir = cbook._get_data_path("fonts/afm")
-    _use_afm_rc_name = "ps.useafm"
-
-    def __init__(self, width, height, pswriter, imagedpi=72):
-        # Although postscript itself is dpi independent, we need to inform the
-        # image code about a requested dpi to generate high resolution images
-        # and them scale them before embedding them.
-        super().__init__(width, height)
-        self._pswriter = pswriter
-        if mpl.rcParams['text.usetex']:
-            self.textcnt = 0
-            self.psfrag = []
-        self.imagedpi = imagedpi
-
-        # current renderer state (None=uninitialised)
-        self.color = None
-        self.linewidth = None
-        self.linejoin = None
-        self.linecap = None
-        self.linedash = None
-        self.fontname = None
-        self.fontsize = None
-        self._hatches = {}
-        self.image_magnification = imagedpi / 72
-        self._clip_paths = {}
-        self._path_collection_id = 0
-
-        self._character_tracker = _backend_pdf_ps.CharacterTracker()
-        self._logwarn_once = functools.cache(_log.warning)
-
-    def _is_transparent(self, rgb_or_rgba):
-        if rgb_or_rgba is None:
-            return True  # Consistent with rgbFace semantics.
-        elif len(rgb_or_rgba) == 4:
-            if rgb_or_rgba[3] == 0:
-                return True
-            if rgb_or_rgba[3] != 1:
-                self._logwarn_once(
-                    "The PostScript backend does not support transparency; "
-                    "partially transparent artists will be rendered opaque.")
-            return False
-        else:  # len() == 3.
-            return False
-
-    def set_color(self, r, g, b, store=True):
-        if (r, g, b) != self.color:
-            self._pswriter.write(f"{_nums_to_str(r)} setgray\n"
-                                 if r == g == b else
-                                 f"{_nums_to_str(r, g, b)} setrgbcolor\n")
-            if store:
-                self.color = (r, g, b)
-
-    def set_linewidth(self, linewidth, store=True):
-        linewidth = float(linewidth)
-        if linewidth != self.linewidth:
-            self._pswriter.write(f"{_nums_to_str(linewidth)} setlinewidth\n")
-            if store:
-                self.linewidth = linewidth
-
-    @staticmethod
-    def _linejoin_cmd(linejoin):
-        # Support for directly passing integer values is for backcompat.
-        linejoin = {'miter': 0, 'round': 1, 'bevel': 2, 0: 0, 1: 1, 2: 2}[
-            linejoin]
-        return f"{linejoin:d} setlinejoin\n"
-
-    def set_linejoin(self, linejoin, store=True):
-        if linejoin != self.linejoin:
-            self._pswriter.write(self._linejoin_cmd(linejoin))
-            if store:
-                self.linejoin = linejoin
-
-    @staticmethod
-    def _linecap_cmd(linecap):
-        # Support for directly passing integer values is for backcompat.
-        linecap = {'butt': 0, 'round': 1, 'projecting': 2, 0: 0, 1: 1, 2: 2}[
-            linecap]
-        return f"{linecap:d} setlinecap\n"
-
-    def set_linecap(self, linecap, store=True):
-        if linecap != self.linecap:
-            self._pswriter.write(self._linecap_cmd(linecap))
-            if store:
-                self.linecap = linecap
-
-    def set_linedash(self, offset, seq, store=True):
-        if self.linedash is not None:
-            oldo, oldseq = self.linedash
-            if np.array_equal(seq, oldseq) and oldo == offset:
-                return
-
-        self._pswriter.write(f"[{_nums_to_str(*seq)}] {_nums_to_str(offset)} setdash\n"
-                             if seq is not None and len(seq) else
-                             "[] 0 setdash\n")
-        if store:
-            self.linedash = (offset, seq)
-
-    def set_font(self, fontname, fontsize, store=True):
-        if (fontname, fontsize) != (self.fontname, self.fontsize):
-            self._pswriter.write(f"/{fontname} {fontsize:1.3f} selectfont\n")
-            if store:
-                self.fontname = fontname
-                self.fontsize = fontsize
-
-    def create_hatch(self, hatch):
-        sidelen = 72
-        if hatch in self._hatches:
-            return self._hatches[hatch]
-        name = 'H%d' % len(self._hatches)
-        linewidth = mpl.rcParams['hatch.linewidth']
-        pageheight = self.height * 72
-        self._pswriter.write(f"""\
-  << /PatternType 1
-     /PaintType 2
-     /TilingType 2
-     /BBox[0 0 {sidelen:d} {sidelen:d}]
-     /XStep {sidelen:d}
-     /YStep {sidelen:d}
-
-     /PaintProc {{
-        pop
-        {linewidth:g} setlinewidth
-{self._convert_path(
-    Path.hatch(hatch), Affine2D().scale(sidelen), simplify=False)}
-        gsave
-        fill
-        grestore
-        stroke
-     }} bind
-   >>
-   matrix
-   0 {pageheight:g} translate
-   makepattern
-   /{name} exch def
-""")
-        self._hatches[hatch] = name
-        return name
-
-    def get_image_magnification(self):
-        """
-        Get the factor by which to magnify images passed to draw_image.
-        Allows a backend to have images at a different resolution to other
-        artists.
-        """
-        return self.image_magnification
-
-    def _convert_path(self, path, transform, clip=False, simplify=None):
-        if clip:
-            clip = (0.0, 0.0, self.width * 72.0, self.height * 72.0)
-        else:
-            clip = None
-        return _path.convert_to_string(
-            path, transform, clip, simplify, None,
-            6, [b"m", b"l", b"", b"c", b"cl"], True).decode("ascii")
-
-    def _get_clip_cmd(self, gc):
-        clip = []
-        rect = gc.get_clip_rectangle()
-        if rect is not None:
-            clip.append(f"{_nums_to_str(*rect.p0, *rect.size)} rectclip\n")
-        path, trf = gc.get_clip_path()
-        if path is not None:
-            key = (path, id(trf))
-            custom_clip_cmd = self._clip_paths.get(key)
-            if custom_clip_cmd is None:
-                custom_clip_cmd = "c%d" % len(self._clip_paths)
-                self._pswriter.write(f"""\
-/{custom_clip_cmd} {{
-{self._convert_path(path, trf, simplify=False)}
-clip
-newpath
-}} bind def
-""")
-                self._clip_paths[key] = custom_clip_cmd
-            clip.append(f"{custom_clip_cmd}\n")
-        return "".join(clip)
-
-    @_log_if_debug_on
-    def draw_image(self, gc, x, y, im, transform=None):
-        # docstring inherited
-
-        h, w = im.shape[:2]
-        imagecmd = "false 3 colorimage"
-        data = im[::-1, :, :3]  # Vertically flipped rgb values.
-        hexdata = data.tobytes().hex("\n", -64)  # Linewrap to 128 chars.
-
-        if transform is None:
-            matrix = "1 0 0 1 0 0"
-            xscale = w / self.image_magnification
-            yscale = h / self.image_magnification
-        else:
-            matrix = " ".join(map(str, transform.frozen().to_values()))
-            xscale = 1.0
-            yscale = 1.0
-
-        self._pswriter.write(f"""\
-gsave
-{self._get_clip_cmd(gc)}
-{x:g} {y:g} translate
-[{matrix}] concat
-{xscale:g} {yscale:g} scale
-/DataString {w:d} string def
-{w:d} {h:d} 8 [ {w:d} 0 0 -{h:d} 0 {h:d} ]
-{{
-currentfile DataString readhexstring pop
-}} bind {imagecmd}
-{hexdata}
-grestore
-""")
-
-    @_log_if_debug_on
-    def draw_path(self, gc, path, transform, rgbFace=None):
-        # docstring inherited
-        clip = rgbFace is None and gc.get_hatch_path() is None
-        simplify = path.should_simplify and clip
-        ps = self._convert_path(path, transform, clip=clip, simplify=simplify)
-        self._draw_ps(ps, gc, rgbFace)
-
-    @_log_if_debug_on
-    def draw_markers(
-            self, gc, marker_path, marker_trans, path, trans, rgbFace=None):
-        # docstring inherited
-
-        ps_color = (
-            None
-            if self._is_transparent(rgbFace)
-            else f'{_nums_to_str(rgbFace[0])} setgray'
-            if rgbFace[0] == rgbFace[1] == rgbFace[2]
-            else f'{_nums_to_str(*rgbFace[:3])} setrgbcolor')
-
-        # construct the generic marker command:
-
-        # don't want the translate to be global
-        ps_cmd = ['/o {', 'gsave', 'newpath', 'translate']
-
-        lw = gc.get_linewidth()
-        alpha = (gc.get_alpha()
-                 if gc.get_forced_alpha() or len(gc.get_rgb()) == 3
-                 else gc.get_rgb()[3])
-        stroke = lw > 0 and alpha > 0
-        if stroke:
-            ps_cmd.append('%.1f setlinewidth' % lw)
-            ps_cmd.append(self._linejoin_cmd(gc.get_joinstyle()))
-            ps_cmd.append(self._linecap_cmd(gc.get_capstyle()))
-
-        ps_cmd.append(self._convert_path(marker_path, marker_trans,
-                                         simplify=False))
-
-        if rgbFace:
-            if stroke:
-                ps_cmd.append('gsave')
-            if ps_color:
-                ps_cmd.extend([ps_color, 'fill'])
-            if stroke:
-                ps_cmd.append('grestore')
-
-        if stroke:
-            ps_cmd.append('stroke')
-        ps_cmd.extend(['grestore', '} bind def'])
-
-        for vertices, code in path.iter_segments(
-                trans,
-                clip=(0, 0, self.width*72, self.height*72),
-                simplify=False):
-            if len(vertices):
-                x, y = vertices[-2:]
-                ps_cmd.append(f"{x:g} {y:g} o")
-
-        ps = '\n'.join(ps_cmd)
-        self._draw_ps(ps, gc, rgbFace, fill=False, stroke=False)
-
-    @_log_if_debug_on
-    def draw_path_collection(self, gc, master_transform, paths, all_transforms,
-                             offsets, offset_trans, facecolors, edgecolors,
-                             linewidths, linestyles, antialiaseds, urls,
-                             offset_position):
-        # Is the optimization worth it? Rough calculation:
-        # cost of emitting a path in-line is
-        #     (len_path + 2) * uses_per_path
-        # cost of definition+use is
-        #     (len_path + 3) + 3 * uses_per_path
-        len_path = len(paths[0].vertices) if len(paths) > 0 else 0
-        uses_per_path = self._iter_collection_uses_per_path(
-            paths, all_transforms, offsets, facecolors, edgecolors)
-        should_do_optimization = \
-            len_path + 3 * uses_per_path + 3 < (len_path + 2) * uses_per_path
-        if not should_do_optimization:
-            return RendererBase.draw_path_collection(
-                self, gc, master_transform, paths, all_transforms,
-                offsets, offset_trans, facecolors, edgecolors,
-                linewidths, linestyles, antialiaseds, urls,
-                offset_position)
-
-        path_codes = []
-        for i, (path, transform) in enumerate(self._iter_collection_raw_paths(
-                master_transform, paths, all_transforms)):
-            name = 'p%d_%d' % (self._path_collection_id, i)
-            path_bytes = self._convert_path(path, transform, simplify=False)
-            self._pswriter.write(f"""\
-/{name} {{
-newpath
-translate
-{path_bytes}
-}} bind def
-""")
-            path_codes.append(name)
-
-        for xo, yo, path_id, gc0, rgbFace in self._iter_collection(
-                gc, path_codes, offsets, offset_trans,
-                facecolors, edgecolors, linewidths, linestyles,
-                antialiaseds, urls, offset_position):
-            ps = f"{xo:g} {yo:g} {path_id}"
-            self._draw_ps(ps, gc0, rgbFace)
-
-        self._path_collection_id += 1
-
-    @_log_if_debug_on
-    def draw_tex(self, gc, x, y, s, prop, angle, *, mtext=None):
-        # docstring inherited
-        if self._is_transparent(gc.get_rgb()):
-            return  # Special handling for fully transparent.
-
-        if not hasattr(self, "psfrag"):
-            self._logwarn_once(
-                "The PS backend determines usetex status solely based on "
-                "rcParams['text.usetex'] and does not support having "
-                "usetex=True only for some elements; this element will thus "
-                "be rendered as if usetex=False.")
-            self.draw_text(gc, x, y, s, prop, angle, False, mtext)
-            return
-
-        w, h, bl = self.get_text_width_height_descent(s, prop, ismath="TeX")
-        fontsize = prop.get_size_in_points()
-        thetext = 'psmarker%d' % self.textcnt
-        color = _nums_to_str(*gc.get_rgb()[:3], sep=',')
-        fontcmd = {'sans-serif': r'{\sffamily %s}',
-                   'monospace': r'{\ttfamily %s}'}.get(
-                       mpl.rcParams['font.family'][0], r'{\rmfamily %s}')
-        s = fontcmd % s
-        tex = r'\color[rgb]{%s} %s' % (color, s)
-
-        # Stick to bottom-left alignment, so subtract descent from the text-normal
-        # direction since text is normally positioned by its baseline.
-        rangle = np.radians(angle + 90)
-        pos = _nums_to_str(x - bl * np.cos(rangle), y - bl * np.sin(rangle))
-        self.psfrag.append(
-            r'\psfrag{%s}[bl][bl][1][%f]{\fontsize{%f}{%f}%s}' % (
-                thetext, angle, fontsize, fontsize*1.25, tex))
-
-        self._pswriter.write(f"""\
-gsave
-{pos} moveto
-({thetext})
-show
-grestore
-""")
-        self.textcnt += 1
-
-    @_log_if_debug_on
-    def draw_text(self, gc, x, y, s, prop, angle, ismath=False, mtext=None):
-        # docstring inherited
-
-        if self._is_transparent(gc.get_rgb()):
-            return  # Special handling for fully transparent.
-
-        if ismath == 'TeX':
-            return self.draw_tex(gc, x, y, s, prop, angle)
-
-        if ismath:
-            return self.draw_mathtext(gc, x, y, s, prop, angle)
-
-        stream = []  # list of (ps_name, x, char_name)
-
-        if mpl.rcParams['ps.useafm']:
-            font = self._get_font_afm(prop)
-            ps_name = (font.postscript_name.encode("ascii", "replace")
-                        .decode("ascii"))
-            scale = 0.001 * prop.get_size_in_points()
-            thisx = 0
-            last_name = None  # kerns returns 0 for None.
-            for c in s:
-                name = uni2type1.get(ord(c), f"uni{ord(c):04X}")
-                try:
-                    width = font.get_width_from_char_name(name)
-                except KeyError:
-                    name = 'question'
-                    width = font.get_width_char('?')
-                kern = font.get_kern_dist_from_name(last_name, name)
-                last_name = name
-                thisx += kern * scale
-                stream.append((ps_name, thisx, name))
-                thisx += width * scale
-
-        else:
-            font = self._get_font_ttf(prop)
-            self._character_tracker.track(font, s)
-            for item in _text_helpers.layout(s, font):
-                ps_name = (item.ft_object.postscript_name
-                           .encode("ascii", "replace").decode("ascii"))
-                glyph_name = item.ft_object.get_glyph_name(item.glyph_idx)
-                stream.append((ps_name, item.x, glyph_name))
-        self.set_color(*gc.get_rgb())
-
-        for ps_name, group in itertools. \
-                groupby(stream, lambda entry: entry[0]):
-            self.set_font(ps_name, prop.get_size_in_points(), False)
-            thetext = "\n".join(f"{x:g} 0 m /{name:s} glyphshow"
-                                for _, x, name in group)
-            self._pswriter.write(f"""\
-gsave
-{self._get_clip_cmd(gc)}
-{x:g} {y:g} translate
-{angle:g} rotate
-{thetext}
-grestore
-""")
-
-    @_log_if_debug_on
-    def draw_mathtext(self, gc, x, y, s, prop, angle):
-        """Draw the math text using matplotlib.mathtext."""
-        width, height, descent, glyphs, rects = \
-            self._text2path.mathtext_parser.parse(s, 72, prop)
-        self.set_color(*gc.get_rgb())
-        self._pswriter.write(
-            f"gsave\n"
-            f"{x:g} {y:g} translate\n"
-            f"{angle:g} rotate\n")
-        lastfont = None
-        for font, fontsize, num, ox, oy in glyphs:
-            self._character_tracker.track_glyph(font, num)
-            if (font.postscript_name, fontsize) != lastfont:
-                lastfont = font.postscript_name, fontsize
-                self._pswriter.write(
-                    f"/{font.postscript_name} {fontsize} selectfont\n")
-            glyph_name = (
-                font.get_name_char(chr(num)) if isinstance(font, AFM) else
-                font.get_glyph_name(font.get_char_index(num)))
-            self._pswriter.write(
-                f"{ox:g} {oy:g} moveto\n"
-                f"/{glyph_name} glyphshow\n")
-        for ox, oy, w, h in rects:
-            self._pswriter.write(f"{ox} {oy} {w} {h} rectfill\n")
-        self._pswriter.write("grestore\n")
-
-    @_log_if_debug_on
-    def draw_gouraud_triangle(self, gc, points, colors, trans):
-        self.draw_gouraud_triangles(gc, points.reshape((1, 3, 2)),
-                                    colors.reshape((1, 3, 4)), trans)
-
-    @_log_if_debug_on
-    def draw_gouraud_triangles(self, gc, points, colors, trans):
-        assert len(points) == len(colors)
-        if len(points) == 0:
-            return
-        assert points.ndim == 3
-        assert points.shape[1] == 3
-        assert points.shape[2] == 2
-        assert colors.ndim == 3
-        assert colors.shape[1] == 3
-        assert colors.shape[2] == 4
-
-        shape = points.shape
-        flat_points = points.reshape((shape[0] * shape[1], 2))
-        flat_points = trans.transform(flat_points)
-        flat_colors = colors.reshape((shape[0] * shape[1], 4))
-        points_min = np.min(flat_points, axis=0) - (1 << 12)
-        points_max = np.max(flat_points, axis=0) + (1 << 12)
-        factor = np.ceil((2 ** 32 - 1) / (points_max - points_min))
-
-        xmin, ymin = points_min
-        xmax, ymax = points_max
-
-        data = np.empty(
-            shape[0] * shape[1],
-            dtype=[('flags', 'u1'), ('points', '2>u4'), ('colors', '3u1')])
-        data['flags'] = 0
-        data['points'] = (flat_points - points_min) * factor
-        data['colors'] = flat_colors[:, :3] * 255.0
-        hexdata = data.tobytes().hex("\n", -64)  # Linewrap to 128 chars.
-
-        self._pswriter.write(f"""\
-gsave
-<< /ShadingType 4
-   /ColorSpace [/DeviceRGB]
-   /BitsPerCoordinate 32
-   /BitsPerComponent 8
-   /BitsPerFlag 8
-   /AntiAlias true
-   /Decode [ {xmin:g} {xmax:g} {ymin:g} {ymax:g} 0 1 0 1 0 1 ]
-   /DataSource <
-{hexdata}
->
->>
-shfill
-grestore
-""")
-
-    def _draw_ps(self, ps, gc, rgbFace, *, fill=True, stroke=True):
-        """
-        Emit the PostScript snippet *ps* with all the attributes from *gc*
-        applied.  *ps* must consist of PostScript commands to construct a path.
-
-        The *fill* and/or *stroke* kwargs can be set to False if the *ps*
-        string already includes filling and/or stroking, in which case
-        `_draw_ps` is just supplying properties and clipping.
-        """
-        write = self._pswriter.write
-        mightstroke = (gc.get_linewidth() > 0
-                       and not self._is_transparent(gc.get_rgb()))
-        if not mightstroke:
-            stroke = False
-        if self._is_transparent(rgbFace):
-            fill = False
-        hatch = gc.get_hatch()
-
-        if mightstroke:
-            self.set_linewidth(gc.get_linewidth())
-            self.set_linejoin(gc.get_joinstyle())
-            self.set_linecap(gc.get_capstyle())
-            self.set_linedash(*gc.get_dashes())
-        if mightstroke or hatch:
-            self.set_color(*gc.get_rgb()[:3])
-        write('gsave\n')
-
-        write(self._get_clip_cmd(gc))
-
-        write(ps.strip())
-        write("\n")
-
-        if fill:
-            if stroke or hatch:
-                write("gsave\n")
-            self.set_color(*rgbFace[:3], store=False)
-            write("fill\n")
-            if stroke or hatch:
-                write("grestore\n")
-
-        if hatch:
-            hatch_name = self.create_hatch(hatch)
-            write("gsave\n")
-            write(_nums_to_str(*gc.get_hatch_color()[:3]))
-            write(f" {hatch_name} setpattern fill grestore\n")
-
-        if stroke:
-            write("stroke\n")
-
-        write("grestore\n")
-
-
-class _Orientation(Enum):
-    portrait, landscape = range(2)
-
-    def swap_if_landscape(self, shape):
-        return shape[::-1] if self.name == "landscape" else shape
-
-
-class FigureCanvasPS(FigureCanvasBase):
-    fixed_dpi = 72
-    filetypes = {'ps': 'Postscript',
-                 'eps': 'Encapsulated Postscript'}
-
-    def get_default_filetype(self):
-        return 'ps'
-
-    def _print_ps(
-            self, fmt, outfile, *,
-            metadata=None, papertype=None, orientation='portrait',
-            bbox_inches_restore=None, **kwargs):
-
-        dpi = self.figure.dpi
-        self.figure.dpi = 72  # Override the dpi kwarg
-
-        dsc_comments = {}
-        if isinstance(outfile, (str, os.PathLike)):
-            filename = pathlib.Path(outfile).name
-            dsc_comments["Title"] = \
-                filename.encode("ascii", "replace").decode("ascii")
-        dsc_comments["Creator"] = (metadata or {}).get(
-            "Creator",
-            f"Matplotlib v{mpl.__version__}, https://matplotlib.org/")
-        # See https://reproducible-builds.org/specs/source-date-epoch/
-        source_date_epoch = os.getenv("SOURCE_DATE_EPOCH")
-        dsc_comments["CreationDate"] = (
-            datetime.datetime.fromtimestamp(
-                int(source_date_epoch),
-                datetime.timezone.utc).strftime("%a %b %d %H:%M:%S %Y")
-            if source_date_epoch
-            else time.ctime())
-        dsc_comments = "\n".join(
-            f"%%{k}: {v}" for k, v in dsc_comments.items())
-
-        if papertype is None:
-            papertype = mpl.rcParams['ps.papersize']
-        papertype = papertype.lower()
-        _api.check_in_list(['figure', 'auto', *papersize], papertype=papertype)
-
-        orientation = _api.check_getitem(
-            _Orientation, orientation=orientation.lower())
-
-        printer = (self._print_figure_tex
-                   if mpl.rcParams['text.usetex'] else
-                   self._print_figure)
-        printer(fmt, outfile, dpi=dpi, dsc_comments=dsc_comments,
-                orientation=orientation, papertype=papertype,
-                bbox_inches_restore=bbox_inches_restore, **kwargs)
-
-    def _print_figure(
-            self, fmt, outfile, *,
-            dpi, dsc_comments, orientation, papertype,
-            bbox_inches_restore=None):
-        """
-        Render the figure to a filesystem path or a file-like object.
-
-        Parameters are as for `.print_figure`, except that *dsc_comments* is a
-        string containing Document Structuring Convention comments,
-        generated from the *metadata* parameter to `.print_figure`.
-        """
-        is_eps = fmt == 'eps'
-        if not (isinstance(outfile, (str, os.PathLike))
-                or is_writable_file_like(outfile)):
-            raise ValueError("outfile must be a path or a file-like object")
-
-        # find the appropriate papertype
-        width, height = self.figure.get_size_inches()
-        if papertype == 'auto':
-            papertype = _get_papertype(*orientation.swap_if_landscape((width, height)))
-
-        if is_eps or papertype == 'figure':
-            paper_width, paper_height = width, height
-        else:
-            paper_width, paper_height = orientation.swap_if_landscape(
-                papersize[papertype])
-
-        # center the figure on the paper
-        xo = 72 * 0.5 * (paper_width - width)
-        yo = 72 * 0.5 * (paper_height - height)
-
-        llx = xo
-        lly = yo
-        urx = llx + self.figure.bbox.width
-        ury = lly + self.figure.bbox.height
-        rotation = 0
-        if orientation is _Orientation.landscape:
-            llx, lly, urx, ury = lly, llx, ury, urx
-            xo, yo = 72 * paper_height - yo, xo
-            rotation = 90
-        bbox = (llx, lly, urx, ury)
-
-        self._pswriter = StringIO()
-
-        # mixed mode rendering
-        ps_renderer = RendererPS(width, height, self._pswriter, imagedpi=dpi)
-        renderer = MixedModeRenderer(
-            self.figure, width, height, dpi, ps_renderer,
-            bbox_inches_restore=bbox_inches_restore)
-
-        self.figure.draw(renderer)
-
-        def print_figure_impl(fh):
-            # write the PostScript headers
-            if is_eps:
-                print("%!PS-Adobe-3.0 EPSF-3.0", file=fh)
-            else:
-                print("%!PS-Adobe-3.0", file=fh)
-                if papertype != 'figure':
-                    print(f"%%DocumentPaperSizes: {papertype}", file=fh)
-                print("%%Pages: 1", file=fh)
-            print(f"%%LanguageLevel: 3\n"
-                  f"{dsc_comments}\n"
-                  f"%%Orientation: {orientation.name}\n"
-                  f"{get_bbox_header(bbox)[0]}\n"
-                  f"%%EndComments\n",
-                  end="", file=fh)
-
-            Ndict = len(_psDefs)
-            print("%%BeginProlog", file=fh)
-            if not mpl.rcParams['ps.useafm']:
-                Ndict += len(ps_renderer._character_tracker.used)
-            print("/mpldict %d dict def" % Ndict, file=fh)
-            print("mpldict begin", file=fh)
-            print("\n".join(_psDefs), file=fh)
-            if not mpl.rcParams['ps.useafm']:
-                for font_path, chars \
-                        in ps_renderer._character_tracker.used.items():
-                    if not chars:
-                        continue
-                    fonttype = mpl.rcParams['ps.fonttype']
-                    # Can't use more than 255 chars from a single Type 3 font.
-                    if len(chars) > 255:
-                        fonttype = 42
-                    fh.flush()
-                    if fonttype == 3:
-                        fh.write(_font_to_ps_type3(font_path, chars))
-                    else:  # Type 42 only.
-                        _font_to_ps_type42(font_path, chars, fh)
-            print("end", file=fh)
-            print("%%EndProlog", file=fh)
-
-            if not is_eps:
-                print("%%Page: 1 1", file=fh)
-            print("mpldict begin", file=fh)
-
-            print("%s translate" % _nums_to_str(xo, yo), file=fh)
-            if rotation:
-                print("%d rotate" % rotation, file=fh)
-            print(f"0 0 {_nums_to_str(width*72, height*72)} rectclip", file=fh)
-
-            # write the figure
-            print(self._pswriter.getvalue(), file=fh)
-
-            # write the trailer
-            print("end", file=fh)
-            print("showpage", file=fh)
-            if not is_eps:
-                print("%%EOF", file=fh)
-            fh.flush()
-
-        if mpl.rcParams['ps.usedistiller']:
-            # We are going to use an external program to process the output.
-            # Write to a temporary file.
-            with TemporaryDirectory() as tmpdir:
-                tmpfile = os.path.join(tmpdir, "tmp.ps")
-                with open(tmpfile, 'w', encoding='latin-1') as fh:
-                    print_figure_impl(fh)
-                if mpl.rcParams['ps.usedistiller'] == 'ghostscript':
-                    _try_distill(gs_distill,
-                                 tmpfile, is_eps, ptype=papertype, bbox=bbox)
-                elif mpl.rcParams['ps.usedistiller'] == 'xpdf':
-                    _try_distill(xpdf_distill,
-                                 tmpfile, is_eps, ptype=papertype, bbox=bbox)
-                _move_path_to_path_or_stream(tmpfile, outfile)
-
-        else:  # Write directly to outfile.
-            with cbook.open_file_cm(outfile, "w", encoding="latin-1") as file:
-                if not file_requires_unicode(file):
-                    file = codecs.getwriter("latin-1")(file)
-                print_figure_impl(file)
-
-    def _print_figure_tex(
-            self, fmt, outfile, *,
-            dpi, dsc_comments, orientation, papertype,
-            bbox_inches_restore=None):
-        """
-        If :rc:`text.usetex` is True, a temporary pair of tex/eps files
-        are created to allow tex to manage the text layout via the PSFrags
-        package. These files are processed to yield the final ps or eps file.
-
-        The rest of the behavior is as for `._print_figure`.
-        """
-        is_eps = fmt == 'eps'
-
-        width, height = self.figure.get_size_inches()
-        xo = 0
-        yo = 0
-
-        llx = xo
-        lly = yo
-        urx = llx + self.figure.bbox.width
-        ury = lly + self.figure.bbox.height
-        bbox = (llx, lly, urx, ury)
-
-        self._pswriter = StringIO()
-
-        # mixed mode rendering
-        ps_renderer = RendererPS(width, height, self._pswriter, imagedpi=dpi)
-        renderer = MixedModeRenderer(self.figure,
-                                     width, height, dpi, ps_renderer,
-                                     bbox_inches_restore=bbox_inches_restore)
-
-        self.figure.draw(renderer)
-
-        # write to a temp file, we'll move it to outfile when done
-        with TemporaryDirectory() as tmpdir:
-            tmppath = pathlib.Path(tmpdir, "tmp.ps")
-            tmppath.write_text(
-                f"""\
-%!PS-Adobe-3.0 EPSF-3.0
-%%LanguageLevel: 3
-{dsc_comments}
-{get_bbox_header(bbox)[0]}
-%%EndComments
-%%BeginProlog
-/mpldict {len(_psDefs)} dict def
-mpldict begin
-{"".join(_psDefs)}
-end
-%%EndProlog
-mpldict begin
-{_nums_to_str(xo, yo)} translate
-0 0 {_nums_to_str(width*72, height*72)} rectclip
-{self._pswriter.getvalue()}
-end
-showpage
-""",
-                encoding="latin-1")
-
-            if orientation is _Orientation.landscape:  # now, ready to rotate
-                width, height = height, width
-                bbox = (lly, llx, ury, urx)
-
-            # set the paper size to the figure size if is_eps. The
-            # resulting ps file has the given size with correct bounding
-            # box so that there is no need to call 'pstoeps'
-            if is_eps or papertype == 'figure':
-                paper_width, paper_height = orientation.swap_if_landscape(
-                    self.figure.get_size_inches())
-            else:
-                if papertype == 'auto':
-                    papertype = _get_papertype(width, height)
-                paper_width, paper_height = papersize[papertype]
-
-            psfrag_rotated = _convert_psfrags(
-                tmppath, ps_renderer.psfrag, paper_width, paper_height,
-                orientation.name)
-
-            if (mpl.rcParams['ps.usedistiller'] == 'ghostscript'
-                    or mpl.rcParams['text.usetex']):
-                _try_distill(gs_distill,
-                             tmppath, is_eps, ptype=papertype, bbox=bbox,
-                             rotated=psfrag_rotated)
-            elif mpl.rcParams['ps.usedistiller'] == 'xpdf':
-                _try_distill(xpdf_distill,
-                             tmppath, is_eps, ptype=papertype, bbox=bbox,
-                             rotated=psfrag_rotated)
-
-            _move_path_to_path_or_stream(tmppath, outfile)
-
-    print_ps = functools.partialmethod(_print_ps, "ps")
-    print_eps = functools.partialmethod(_print_ps, "eps")
-
-    def draw(self):
-        self.figure.draw_without_rendering()
-        return super().draw()
-
-
-def _convert_psfrags(tmppath, psfrags, paper_width, paper_height, orientation):
-    """
-    When we want to use the LaTeX backend with postscript, we write PSFrag tags
-    to a temporary postscript file, each one marking a position for LaTeX to
-    render some text. convert_psfrags generates a LaTeX document containing the
-    commands to convert those tags to text. LaTeX/dvips produces the postscript
-    file that includes the actual text.
-    """
-    with mpl.rc_context({
-            "text.latex.preamble":
-            mpl.rcParams["text.latex.preamble"] +
-            mpl.texmanager._usepackage_if_not_loaded("color") +
-            mpl.texmanager._usepackage_if_not_loaded("graphicx") +
-            mpl.texmanager._usepackage_if_not_loaded("psfrag") +
-            r"\geometry{papersize={%(width)sin,%(height)sin},margin=0in}"
-            % {"width": paper_width, "height": paper_height}
-    }):
-        dvifile = TexManager().make_dvi(
-            "\n"
-            r"\begin{figure}""\n"
-            r"  \centering\leavevmode""\n"
-            r"  %(psfrags)s""\n"
-            r"  \includegraphics*[angle=%(angle)s]{%(epsfile)s}""\n"
-            r"\end{figure}"
-            % {
-                "psfrags": "\n".join(psfrags),
-                "angle": 90 if orientation == 'landscape' else 0,
-                "epsfile": tmppath.resolve().as_posix(),
-            },
-            fontsize=10)  # tex's default fontsize.
-
-    with TemporaryDirectory() as tmpdir:
-        psfile = os.path.join(tmpdir, "tmp.ps")
-        cbook._check_and_log_subprocess(
-            ['dvips', '-q', '-R0', '-o', psfile, dvifile], _log)
-        shutil.move(psfile, tmppath)
-
-    # check if the dvips created a ps in landscape paper.  Somehow,
-    # above latex+dvips results in a ps file in a landscape mode for a
-    # certain figure sizes (e.g., 8.3in, 5.8in which is a5). And the
-    # bounding box of the final output got messed up. We check see if
-    # the generated ps file is in landscape and return this
-    # information. The return value is used in pstoeps step to recover
-    # the correct bounding box. 2010-06-05 JJL
-    with open(tmppath) as fh:
-        psfrag_rotated = "Landscape" in fh.read(1000)
-    return psfrag_rotated
-
-
-def _try_distill(func, tmppath, *args, **kwargs):
-    try:
-        func(str(tmppath), *args, **kwargs)
-    except mpl.ExecutableNotFoundError as exc:
-        _log.warning("%s.  Distillation step skipped.", exc)
-
-
-def gs_distill(tmpfile, eps=False, ptype='letter', bbox=None, rotated=False):
-    """
-    Use ghostscript's pswrite or epswrite device to distill a file.
-    This yields smaller files without illegal encapsulated postscript
-    operators. The output is low-level, converting text to outlines.
-    """
-
-    if eps:
-        paper_option = ["-dEPSCrop"]
-    elif ptype == "figure":
-        # The bbox will have its lower-left corner at (0, 0), so upper-right
-        # corner corresponds with paper size.
-        paper_option = [f"-dDEVICEWIDTHPOINTS={bbox[2]}",
-                        f"-dDEVICEHEIGHTPOINTS={bbox[3]}"]
-    else:
-        paper_option = [f"-sPAPERSIZE={ptype}"]
-
-    psfile = tmpfile + '.ps'
-    dpi = mpl.rcParams['ps.distiller.res']
-
-    cbook._check_and_log_subprocess(
-        [mpl._get_executable_info("gs").executable,
-         "-dBATCH", "-dNOPAUSE", "-r%d" % dpi, "-sDEVICE=ps2write",
-         *paper_option, f"-sOutputFile={psfile}", tmpfile],
-        _log)
-
-    os.remove(tmpfile)
-    shutil.move(psfile, tmpfile)
-
-    # While it is best if above steps preserve the original bounding
-    # box, there seem to be cases when it is not. For those cases,
-    # the original bbox can be restored during the pstoeps step.
-
-    if eps:
-        # For some versions of gs, above steps result in a ps file where the
-        # original bbox is no more correct. Do not adjust bbox for now.
-        pstoeps(tmpfile, bbox, rotated=rotated)
-
-
-def xpdf_distill(tmpfile, eps=False, ptype='letter', bbox=None, rotated=False):
-    """
-    Use ghostscript's ps2pdf and xpdf's/poppler's pdftops to distill a file.
-    This yields smaller files without illegal encapsulated postscript
-    operators. This distiller is preferred, generating high-level postscript
-    output that treats text as text.
-    """
-    mpl._get_executable_info("gs")  # Effectively checks for ps2pdf.
-    mpl._get_executable_info("pdftops")
-
-    if eps:
-        paper_option = ["-dEPSCrop"]
-    elif ptype == "figure":
-        # The bbox will have its lower-left corner at (0, 0), so upper-right
-        # corner corresponds with paper size.
-        paper_option = [f"-dDEVICEWIDTHPOINTS#{bbox[2]}",
-                        f"-dDEVICEHEIGHTPOINTS#{bbox[3]}"]
-    else:
-        paper_option = [f"-sPAPERSIZE#{ptype}"]
-
-    with TemporaryDirectory() as tmpdir:
-        tmppdf = pathlib.Path(tmpdir, "tmp.pdf")
-        tmpps = pathlib.Path(tmpdir, "tmp.ps")
-        # Pass options as `-foo#bar` instead of `-foo=bar` to keep Windows
-        # happy (https://ghostscript.com/doc/9.56.1/Use.htm#MS_Windows).
-        cbook._check_and_log_subprocess(
-            ["ps2pdf",
-             "-dAutoFilterColorImages#false",
-             "-dAutoFilterGrayImages#false",
-             "-sAutoRotatePages#None",
-             "-sGrayImageFilter#FlateEncode",
-             "-sColorImageFilter#FlateEncode",
-             *paper_option,
-             tmpfile, tmppdf], _log)
-        cbook._check_and_log_subprocess(
-            ["pdftops", "-paper", "match", "-level3", tmppdf, tmpps], _log)
-        shutil.move(tmpps, tmpfile)
-    if eps:
-        pstoeps(tmpfile)
-
-
-def get_bbox_header(lbrt, rotated=False):
-    """
-    Return a postscript header string for the given bbox lbrt=(l, b, r, t).
-    Optionally, return rotate command.
-    """
-
-    l, b, r, t = lbrt
-    if rotated:
-        rotate = f"{l+r:.2f} {0:.2f} translate\n90 rotate"
-    else:
-        rotate = ""
-    bbox_info = '%%%%BoundingBox: %d %d %d %d' % (l, b, np.ceil(r), np.ceil(t))
-    hires_bbox_info = f'%%HiResBoundingBox: {l:.6f} {b:.6f} {r:.6f} {t:.6f}'
-
-    return '\n'.join([bbox_info, hires_bbox_info]), rotate
-
-
-def pstoeps(tmpfile, bbox=None, rotated=False):
-    """
-    Convert the postscript to encapsulated postscript.  The bbox of
-    the eps file will be replaced with the given *bbox* argument. If
-    None, original bbox will be used.
-    """
-
-    # if rotated==True, the output eps file need to be rotated
-    if bbox:
-        bbox_info, rotate = get_bbox_header(bbox, rotated=rotated)
-    else:
-        bbox_info, rotate = None, None
-
-    epsfile = tmpfile + '.eps'
-    with open(epsfile, 'wb') as epsh, open(tmpfile, 'rb') as tmph:
-        write = epsh.write
-        # Modify the header:
-        for line in tmph:
-            if line.startswith(b'%!PS'):
-                write(b"%!PS-Adobe-3.0 EPSF-3.0\n")
-                if bbox:
-                    write(bbox_info.encode('ascii') + b'\n')
-            elif line.startswith(b'%%EndComments'):
-                write(line)
-                write(b'%%BeginProlog\n'
-                      b'save\n'
-                      b'countdictstack\n'
-                      b'mark\n'
-                      b'newpath\n'
-                      b'/showpage {} def\n'
-                      b'/setpagedevice {pop} def\n'
-                      b'%%EndProlog\n'
-                      b'%%Page 1 1\n')
-                if rotate:
-                    write(rotate.encode('ascii') + b'\n')
-                break
-            elif bbox and line.startswith((b'%%Bound', b'%%HiResBound',
-                                           b'%%DocumentMedia', b'%%Pages')):
-                pass
-            else:
-                write(line)
-        # Now rewrite the rest of the file, and modify the trailer.
-        # This is done in a second loop such that the header of the embedded
-        # eps file is not modified.
-        for line in tmph:
-            if line.startswith(b'%%EOF'):
-                write(b'cleartomark\n'
-                      b'countdictstack\n'
-                      b'exch sub { end } repeat\n'
-                      b'restore\n'
-                      b'showpage\n'
-                      b'%%EOF\n')
-            elif line.startswith(b'%%PageBoundingBox'):
-                pass
-            else:
-                write(line)
-
-    os.remove(tmpfile)
-    shutil.move(epsfile, tmpfile)
-
-
-FigureManagerPS = FigureManagerBase
-
-
-# The following Python dictionary psDefs contains the entries for the
-# PostScript dictionary mpldict.  This dictionary implements most of
-# the matplotlib primitives and some abbreviations.
-#
-# References:
-# https://www.adobe.com/content/dam/acom/en/devnet/actionscript/articles/PLRM.pdf
-# http://preserve.mactech.com/articles/mactech/Vol.09/09.04/PostscriptTutorial
-# http://www.math.ubc.ca/people/faculty/cass/graphics/text/www/
-#
-
-# The usage comments use the notation of the operator summary
-# in the PostScript Language reference manual.
-_psDefs = [
-    # name proc  *_d*  -
-    # Note that this cannot be bound to /d, because when embedding a Type3 font
-    # we may want to define a "d" glyph using "/d{...} d" which would locally
-    # overwrite the definition.
-    "/_d { bind def } bind def",
-    # x y  *m*  -
-    "/m { moveto } _d",
-    # x y  *l*  -
-    "/l { lineto } _d",
-    # x y  *r*  -
-    "/r { rlineto } _d",
-    # x1 y1 x2 y2 x y *c*  -
-    "/c { curveto } _d",
-    # *cl*  -
-    "/cl { closepath } _d",
-    # *ce*  -
-    "/ce { closepath eofill } _d",
-    # wx wy llx lly urx ury  *setcachedevice*  -
-    "/sc { setcachedevice } _d",
-]
-
-
-@_Backend.export
-class _BackendPS(_Backend):
-    backend_version = 'Level II'
-    FigureCanvas = FigureCanvasPS
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_limited_api.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_limited_api.py
deleted file mode 100644
index 725de19bdaed5b46218dd7d2c882728bfc3c11be..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_limited_api.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os
-import shutil
-import subprocess
-import sys
-import sysconfig
-import pytest
-
-from numpy.testing import IS_WASM
-
-
-@pytest.mark.skipif(IS_WASM, reason="Can't start subprocess")
-@pytest.mark.xfail(
-    sysconfig.get_config_var("Py_DEBUG"),
-    reason=(
-        "Py_LIMITED_API is incompatible with Py_DEBUG, Py_TRACE_REFS, "
-        "and Py_REF_DEBUG"
-    ),
-)
-def test_limited_api(tmp_path):
-    """Test building a third-party C extension with the limited API."""
-    # Based in part on test_cython from random.tests.test_extending
-
-    here = os.path.dirname(__file__)
-    ext_dir = os.path.join(here, "examples", "limited_api")
-
-    cytest = str(tmp_path / "limited_api")
-
-    shutil.copytree(ext_dir, cytest)
-    # build the examples and "install" them into a temporary directory
-
-    install_log = str(tmp_path / "tmp_install_log.txt")
-    subprocess.check_output(
-        [
-            sys.executable,
-            "setup.py",
-            "build",
-            "install",
-            "--prefix", str(tmp_path / "installdir"),
-            "--single-version-externally-managed",
-            "--record",
-            install_log,
-        ],
-        cwd=cytest,
-    )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/interval/test_pickle.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/interval/test_pickle.py
deleted file mode 100644
index 308a90e72eab5db55f300341212d2c04e82c6900..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/interval/test_pickle.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import pytest
-
-from pandas import IntervalIndex
-import pandas._testing as tm
-
-
-class TestPickle:
-    @pytest.mark.parametrize("closed", ["left", "right", "both"])
-    def test_pickle_round_trip_closed(self, closed):
-        # https://github.com/pandas-dev/pandas/issues/35658
-        idx = IntervalIndex.from_tuples([(1, 2), (2, 3)], closed=closed)
-        result = tm.round_trip_pickle(idx)
-        tm.assert_index_equal(result, idx)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/sas/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/sas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/moments/test_moments_consistency_rolling.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/moments/test_moments_consistency_rolling.py
deleted file mode 100644
index 62bfc66b124f3d0cca707a794a4c9e5d8453e1d0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/moments/test_moments_consistency_rolling.py
+++ /dev/null
@@ -1,244 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import Series
-import pandas._testing as tm
-
-
-def no_nans(x):
-    return x.notna().all().all()
-
-
-def all_na(x):
-    return x.isnull().all().all()
-
-
-@pytest.fixture(params=[(1, 0), (5, 1)])
-def rolling_consistency_cases(request):
-    """window, min_periods"""
-    return request.param
-
-
-@pytest.mark.parametrize("f", [lambda v: Series(v).sum(), np.nansum, np.sum])
-def test_rolling_apply_consistency_sum(
-    request, all_data, rolling_consistency_cases, center, f
-):
-    window, min_periods = rolling_consistency_cases
-
-    if f is np.sum:
-        if not no_nans(all_data) and not (
-            all_na(all_data) and not all_data.empty and min_periods > 0
-        ):
-            request.node.add_marker(
-                pytest.mark.xfail(reason="np.sum has different behavior with NaNs")
-            )
-    rolling_f_result = all_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).sum()
-    rolling_apply_f_result = all_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).apply(func=f, raw=True)
-    tm.assert_equal(rolling_f_result, rolling_apply_f_result)
-
-
-@pytest.mark.parametrize("ddof", [0, 1])
-def test_moments_consistency_var(all_data, rolling_consistency_cases, center, ddof):
-    window, min_periods = rolling_consistency_cases
-
-    var_x = all_data.rolling(window=window, min_periods=min_periods, center=center).var(
-        ddof=ddof
-    )
-    assert not (var_x < 0).any().any()
-
-    if ddof == 0:
-        # check that biased var(x) == mean(x^2) - mean(x)^2
-        mean_x = all_data.rolling(
-            window=window, min_periods=min_periods, center=center
-        ).mean()
-        mean_x2 = (
-            (all_data * all_data)
-            .rolling(window=window, min_periods=min_periods, center=center)
-            .mean()
-        )
-        tm.assert_equal(var_x, mean_x2 - (mean_x * mean_x))
-
-
-@pytest.mark.parametrize("ddof", [0, 1])
-def test_moments_consistency_var_constant(
-    consistent_data, rolling_consistency_cases, center, ddof
-):
-    window, min_periods = rolling_consistency_cases
-
-    count_x = consistent_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).count()
-    var_x = consistent_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).var(ddof=ddof)
-
-    # check that variance of constant series is identically 0
-    assert not (var_x > 0).any().any()
-    expected = consistent_data * np.nan
-    expected[count_x >= max(min_periods, 1)] = 0.0
-    if ddof == 1:
-        expected[count_x < 2] = np.nan
-    tm.assert_equal(var_x, expected)
-
-
-@pytest.mark.parametrize("ddof", [0, 1])
-def test_rolling_consistency_var_std_cov(
-    all_data, rolling_consistency_cases, center, ddof
-):
-    window, min_periods = rolling_consistency_cases
-
-    var_x = all_data.rolling(window=window, min_periods=min_periods, center=center).var(
-        ddof=ddof
-    )
-    assert not (var_x < 0).any().any()
-
-    std_x = all_data.rolling(window=window, min_periods=min_periods, center=center).std(
-        ddof=ddof
-    )
-    assert not (std_x < 0).any().any()
-
-    # check that var(x) == std(x)^2
-    tm.assert_equal(var_x, std_x * std_x)
-
-    cov_x_x = all_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).cov(all_data, ddof=ddof)
-    assert not (cov_x_x < 0).any().any()
-
-    # check that var(x) == cov(x, x)
-    tm.assert_equal(var_x, cov_x_x)
-
-
-@pytest.mark.parametrize("ddof", [0, 1])
-def test_rolling_consistency_series_cov_corr(
-    series_data, rolling_consistency_cases, center, ddof
-):
-    window, min_periods = rolling_consistency_cases
-
-    var_x_plus_y = (
-        (series_data + series_data)
-        .rolling(window=window, min_periods=min_periods, center=center)
-        .var(ddof=ddof)
-    )
-    var_x = series_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).var(ddof=ddof)
-    var_y = series_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).var(ddof=ddof)
-    cov_x_y = series_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).cov(series_data, ddof=ddof)
-    # check that cov(x, y) == (var(x+y) - var(x) -
-    # var(y)) / 2
-    tm.assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y))
-
-    # check that corr(x, y) == cov(x, y) / (std(x) *
-    # std(y))
-    corr_x_y = series_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).corr(series_data)
-    std_x = series_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).std(ddof=ddof)
-    std_y = series_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).std(ddof=ddof)
-    tm.assert_equal(corr_x_y, cov_x_y / (std_x * std_y))
-
-    if ddof == 0:
-        # check that biased cov(x, y) == mean(x*y) -
-        # mean(x)*mean(y)
-        mean_x = series_data.rolling(
-            window=window, min_periods=min_periods, center=center
-        ).mean()
-        mean_y = series_data.rolling(
-            window=window, min_periods=min_periods, center=center
-        ).mean()
-        mean_x_times_y = (
-            (series_data * series_data)
-            .rolling(window=window, min_periods=min_periods, center=center)
-            .mean()
-        )
-        tm.assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y))
-
-
-def test_rolling_consistency_mean(all_data, rolling_consistency_cases, center):
-    window, min_periods = rolling_consistency_cases
-
-    result = all_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).mean()
-    expected = (
-        all_data.rolling(window=window, min_periods=min_periods, center=center)
-        .sum()
-        .divide(
-            all_data.rolling(
-                window=window, min_periods=min_periods, center=center
-            ).count()
-        )
-    )
-    tm.assert_equal(result, expected.astype("float64"))
-
-
-def test_rolling_consistency_constant(
-    consistent_data, rolling_consistency_cases, center
-):
-    window, min_periods = rolling_consistency_cases
-
-    count_x = consistent_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).count()
-    mean_x = consistent_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).mean()
-    # check that correlation of a series with itself is either 1 or NaN
-    corr_x_x = consistent_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).corr(consistent_data)
-
-    exp = (
-        consistent_data.max()
-        if isinstance(consistent_data, Series)
-        else consistent_data.max().max()
-    )
-
-    # check mean of constant series
-    expected = consistent_data * np.nan
-    expected[count_x >= max(min_periods, 1)] = exp
-    tm.assert_equal(mean_x, expected)
-
-    # check correlation of constant series with itself is NaN
-    expected[:] = np.nan
-    tm.assert_equal(corr_x_x, expected)
-
-
-def test_rolling_consistency_var_debiasing_factors(
-    all_data, rolling_consistency_cases, center
-):
-    window, min_periods = rolling_consistency_cases
-
-    # check variance debiasing factors
-    var_unbiased_x = all_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).var()
-    var_biased_x = all_data.rolling(
-        window=window, min_periods=min_periods, center=center
-    ).var(ddof=0)
-    var_debiasing_factors_x = (
-        all_data.rolling(window=window, min_periods=min_periods, center=center)
-        .count()
-        .divide(
-            (
-                all_data.rolling(
-                    window=window, min_periods=min_periods, center=center
-                ).count()
-                - 1.0
-            ).replace(0.0, np.nan)
-        )
-    )
-    tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_timeseries_window.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_timeseries_window.py
deleted file mode 100644
index caea3e98f262ff952fdc79826eb8368fd9e17230..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_timeseries_window.py
+++ /dev/null
@@ -1,692 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    DataFrame,
-    Index,
-    MultiIndex,
-    NaT,
-    Series,
-    Timestamp,
-    date_range,
-)
-import pandas._testing as tm
-
-from pandas.tseries import offsets
-
-
-@pytest.fixture
-def regular():
-    return DataFrame(
-        {"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
-    ).set_index("A")
-
-
-@pytest.fixture
-def ragged():
-    df = DataFrame({"B": range(5)})
-    df.index = [
-        Timestamp("20130101 09:00:00"),
-        Timestamp("20130101 09:00:02"),
-        Timestamp("20130101 09:00:03"),
-        Timestamp("20130101 09:00:05"),
-        Timestamp("20130101 09:00:06"),
-    ]
-    return df
-
-
-class TestRollingTS:
-    # rolling time-series friendly
-    # xref GH13327
-
-    def test_doc_string(self):
-        df = DataFrame(
-            {"B": [0, 1, 2, np.nan, 4]},
-            index=[
-                Timestamp("20130101 09:00:00"),
-                Timestamp("20130101 09:00:02"),
-                Timestamp("20130101 09:00:03"),
-                Timestamp("20130101 09:00:05"),
-                Timestamp("20130101 09:00:06"),
-            ],
-        )
-        df
-        df.rolling("2s").sum()
-
-    def test_invalid_window_non_int(self, regular):
-        # not a valid freq
-        msg = "passed window foobar is not compatible with a datetimelike index"
-        with pytest.raises(ValueError, match=msg):
-            regular.rolling(window="foobar")
-        # not a datetimelike index
-        msg = "window must be an integer"
-        with pytest.raises(ValueError, match=msg):
-            regular.reset_index().rolling(window="foobar")
-
-    @pytest.mark.parametrize("freq", ["2MS", offsets.MonthBegin(2)])
-    def test_invalid_window_nonfixed(self, freq, regular):
-        # non-fixed freqs
-        msg = "\\<2 \\* MonthBegins\\> is a non-fixed frequency"
-        with pytest.raises(ValueError, match=msg):
-            regular.rolling(window=freq)
-
-    @pytest.mark.parametrize("freq", ["1D", offsets.Day(2), "2ms"])
-    def test_valid_window(self, freq, regular):
-        regular.rolling(window=freq)
-
-    @pytest.mark.parametrize("minp", [1.0, "foo", np.array([1, 2, 3])])
-    def test_invalid_minp(self, minp, regular):
-        # non-integer min_periods
-        msg = (
-            r"local variable 'minp' referenced before assignment|"
-            "min_periods must be an integer"
-        )
-        with pytest.raises(ValueError, match=msg):
-            regular.rolling(window="1D", min_periods=minp)
-
-    def test_on(self, regular):
-        df = regular
-
-        # not a valid column
-        msg = (
-            r"invalid on specified as foobar, must be a column "
-            "\\(of DataFrame\\), an Index or None"
-        )
-        with pytest.raises(ValueError, match=msg):
-            df.rolling(window="2s", on="foobar")
-
-        # column is valid
-        df = df.copy()
-        df["C"] = date_range("20130101", periods=len(df))
-        df.rolling(window="2d", on="C").sum()
-
-        # invalid columns
-        msg = "window must be an integer"
-        with pytest.raises(ValueError, match=msg):
-            df.rolling(window="2d", on="B")
-
-        # ok even though on non-selected
-        df.rolling(window="2d", on="C").B.sum()
-
-    def test_monotonic_on(self):
-        # on/index must be monotonic
-        df = DataFrame(
-            {"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
-        )
-
-        assert df.A.is_monotonic_increasing
-        df.rolling("2s", on="A").sum()
-
-        df = df.set_index("A")
-        assert df.index.is_monotonic_increasing
-        df.rolling("2s").sum()
-
-    def test_non_monotonic_on(self):
-        # GH 19248
-        df = DataFrame(
-            {"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
-        )
-        df = df.set_index("A")
-        non_monotonic_index = df.index.to_list()
-        non_monotonic_index[0] = non_monotonic_index[3]
-        df.index = non_monotonic_index
-
-        assert not df.index.is_monotonic_increasing
-
-        msg = "index values must be monotonic"
-        with pytest.raises(ValueError, match=msg):
-            df.rolling("2s").sum()
-
-        df = df.reset_index()
-
-        msg = (
-            r"invalid on specified as A, must be a column "
-            "\\(of DataFrame\\), an Index or None"
-        )
-        with pytest.raises(ValueError, match=msg):
-            df.rolling("2s", on="A").sum()
-
-    def test_frame_on(self):
-        df = DataFrame(
-            {"B": range(5), "C": date_range("20130101 09:00:00", periods=5, freq="3s")}
-        )
-
-        df["A"] = [
-            Timestamp("20130101 09:00:00"),
-            Timestamp("20130101 09:00:02"),
-            Timestamp("20130101 09:00:03"),
-            Timestamp("20130101 09:00:05"),
-            Timestamp("20130101 09:00:06"),
-        ]
-
-        # we are doing simulating using 'on'
-        expected = df.set_index("A").rolling("2s").B.sum().reset_index(drop=True)
-
-        result = df.rolling("2s", on="A").B.sum()
-        tm.assert_series_equal(result, expected)
-
-        # test as a frame
-        # we should be ignoring the 'on' as an aggregation column
-        # note that the expected is setting, computing, and resetting
-        # so the columns need to be switched compared
-        # to the actual result where they are ordered as in the
-        # original
-        expected = (
-            df.set_index("A").rolling("2s")[["B"]].sum().reset_index()[["B", "A"]]
-        )
-
-        result = df.rolling("2s", on="A")[["B"]].sum()
-        tm.assert_frame_equal(result, expected)
-
-    def test_frame_on2(self):
-        # using multiple aggregation columns
-        df = DataFrame(
-            {
-                "A": [0, 1, 2, 3, 4],
-                "B": [0, 1, 2, np.nan, 4],
-                "C": Index(
-                    [
-                        Timestamp("20130101 09:00:00"),
-                        Timestamp("20130101 09:00:02"),
-                        Timestamp("20130101 09:00:03"),
-                        Timestamp("20130101 09:00:05"),
-                        Timestamp("20130101 09:00:06"),
-                    ]
-                ),
-            },
-            columns=["A", "C", "B"],
-        )
-
-        expected1 = DataFrame(
-            {"A": [0.0, 1, 3, 3, 7], "B": [0, 1, 3, np.nan, 4], "C": df["C"]},
-            columns=["A", "C", "B"],
-        )
-
-        result = df.rolling("2s", on="C").sum()
-        expected = expected1
-        tm.assert_frame_equal(result, expected)
-
-        expected = Series([0, 1, 3, np.nan, 4], name="B")
-        result = df.rolling("2s", on="C").B.sum()
-        tm.assert_series_equal(result, expected)
-
-        expected = expected1[["A", "B", "C"]]
-        result = df.rolling("2s", on="C")[["A", "B", "C"]].sum()
-        tm.assert_frame_equal(result, expected)
-
-    def test_basic_regular(self, regular):
-        df = regular.copy()
-
-        df.index = date_range("20130101", periods=5, freq="D")
-        expected = df.rolling(window=1, min_periods=1).sum()
-        result = df.rolling(window="1D").sum()
-        tm.assert_frame_equal(result, expected)
-
-        df.index = date_range("20130101", periods=5, freq="2D")
-        expected = df.rolling(window=1, min_periods=1).sum()
-        result = df.rolling(window="2D", min_periods=1).sum()
-        tm.assert_frame_equal(result, expected)
-
-        expected = df.rolling(window=1, min_periods=1).sum()
-        result = df.rolling(window="2D", min_periods=1).sum()
-        tm.assert_frame_equal(result, expected)
-
-        expected = df.rolling(window=1).sum()
-        result = df.rolling(window="2D").sum()
-        tm.assert_frame_equal(result, expected)
-
-    def test_min_periods(self, regular):
-        # compare for min_periods
-        df = regular
-
-        # these slightly different
-        expected = df.rolling(2, min_periods=1).sum()
-        result = df.rolling("2s").sum()
-        tm.assert_frame_equal(result, expected)
-
-        expected = df.rolling(2, min_periods=1).sum()
-        result = df.rolling("2s", min_periods=1).sum()
-        tm.assert_frame_equal(result, expected)
-
-    def test_closed(self, regular):
-        # xref GH13965
-
-        df = DataFrame(
-            {"A": [1] * 5},
-            index=[
-                Timestamp("20130101 09:00:01"),
-                Timestamp("20130101 09:00:02"),
-                Timestamp("20130101 09:00:03"),
-                Timestamp("20130101 09:00:04"),
-                Timestamp("20130101 09:00:06"),
-            ],
-        )
-
-        # closed must be 'right', 'left', 'both', 'neither'
-        msg = "closed must be 'right', 'left', 'both' or 'neither'"
-        with pytest.raises(ValueError, match=msg):
-            regular.rolling(window="2s", closed="blabla")
-
-        expected = df.copy()
-        expected["A"] = [1.0, 2, 2, 2, 1]
-        result = df.rolling("2s", closed="right").sum()
-        tm.assert_frame_equal(result, expected)
-
-        # default should be 'right'
-        result = df.rolling("2s").sum()
-        tm.assert_frame_equal(result, expected)
-
-        expected = df.copy()
-        expected["A"] = [1.0, 2, 3, 3, 2]
-        result = df.rolling("2s", closed="both").sum()
-        tm.assert_frame_equal(result, expected)
-
-        expected = df.copy()
-        expected["A"] = [np.nan, 1.0, 2, 2, 1]
-        result = df.rolling("2s", closed="left").sum()
-        tm.assert_frame_equal(result, expected)
-
-        expected = df.copy()
-        expected["A"] = [np.nan, 1.0, 1, 1, np.nan]
-        result = df.rolling("2s", closed="neither").sum()
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_sum(self, ragged):
-        df = ragged
-        result = df.rolling(window="1s", min_periods=1).sum()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=1).sum()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 3, 3, 7]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=2).sum()
-        expected = df.copy()
-        expected["B"] = [np.nan, np.nan, 3, np.nan, 7]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="3s", min_periods=1).sum()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 3, 5, 7]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="3s").sum()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 3, 5, 7]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="4s", min_periods=1).sum()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 3, 6, 9]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="4s", min_periods=3).sum()
-        expected = df.copy()
-        expected["B"] = [np.nan, np.nan, 3, 6, 9]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="5s", min_periods=1).sum()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 3, 6, 10]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_mean(self, ragged):
-        df = ragged
-        result = df.rolling(window="1s", min_periods=1).mean()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=1).mean()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 1.5, 3.0, 3.5]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_median(self, ragged):
-        df = ragged
-        result = df.rolling(window="1s", min_periods=1).median()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=1).median()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 1.5, 3.0, 3.5]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_quantile(self, ragged):
-        df = ragged
-        result = df.rolling(window="1s", min_periods=1).quantile(0.5)
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=1).quantile(0.5)
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 1.5, 3.0, 3.5]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_std(self, ragged):
-        df = ragged
-        result = df.rolling(window="1s", min_periods=1).std(ddof=0)
-        expected = df.copy()
-        expected["B"] = [0.0] * 5
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="1s", min_periods=1).std(ddof=1)
-        expected = df.copy()
-        expected["B"] = [np.nan] * 5
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="3s", min_periods=1).std(ddof=0)
-        expected = df.copy()
-        expected["B"] = [0.0] + [0.5] * 4
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="5s", min_periods=1).std(ddof=1)
-        expected = df.copy()
-        expected["B"] = [np.nan, 0.707107, 1.0, 1.0, 1.290994]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_var(self, ragged):
-        df = ragged
-        result = df.rolling(window="1s", min_periods=1).var(ddof=0)
-        expected = df.copy()
-        expected["B"] = [0.0] * 5
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="1s", min_periods=1).var(ddof=1)
-        expected = df.copy()
-        expected["B"] = [np.nan] * 5
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="3s", min_periods=1).var(ddof=0)
-        expected = df.copy()
-        expected["B"] = [0.0] + [0.25] * 4
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="5s", min_periods=1).var(ddof=1)
-        expected = df.copy()
-        expected["B"] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.0]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_skew(self, ragged):
-        df = ragged
-        result = df.rolling(window="3s", min_periods=1).skew()
-        expected = df.copy()
-        expected["B"] = [np.nan] * 5
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="5s", min_periods=1).skew()
-        expected = df.copy()
-        expected["B"] = [np.nan] * 2 + [0.0, 0.0, 0.0]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_kurt(self, ragged):
-        df = ragged
-        result = df.rolling(window="3s", min_periods=1).kurt()
-        expected = df.copy()
-        expected["B"] = [np.nan] * 5
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="5s", min_periods=1).kurt()
-        expected = df.copy()
-        expected["B"] = [np.nan] * 4 + [-1.2]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_count(self, ragged):
-        df = ragged
-        result = df.rolling(window="1s", min_periods=1).count()
-        expected = df.copy()
-        expected["B"] = [1.0, 1, 1, 1, 1]
-        tm.assert_frame_equal(result, expected)
-
-        df = ragged
-        result = df.rolling(window="1s").count()
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=1).count()
-        expected = df.copy()
-        expected["B"] = [1.0, 1, 2, 1, 2]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=2).count()
-        expected = df.copy()
-        expected["B"] = [np.nan, np.nan, 2, np.nan, 2]
-        tm.assert_frame_equal(result, expected)
-
-    def test_regular_min(self):
-        df = DataFrame(
-            {"A": date_range("20130101", periods=5, freq="s"), "B": [0.0, 1, 2, 3, 4]}
-        ).set_index("A")
-        result = df.rolling("1s").min()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        df = DataFrame(
-            {"A": date_range("20130101", periods=5, freq="s"), "B": [5, 4, 3, 4, 5]}
-        ).set_index("A")
-
-        tm.assert_frame_equal(result, expected)
-        result = df.rolling("2s").min()
-        expected = df.copy()
-        expected["B"] = [5.0, 4, 3, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling("5s").min()
-        expected = df.copy()
-        expected["B"] = [5.0, 4, 3, 3, 3]
-        tm.assert_frame_equal(result, expected)
-
-    def test_ragged_min(self, ragged):
-        df = ragged
-
-        result = df.rolling(window="1s", min_periods=1).min()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=1).min()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 1, 3, 3]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="5s", min_periods=1).min()
-        expected = df.copy()
-        expected["B"] = [0.0, 0, 0, 1, 1]
-        tm.assert_frame_equal(result, expected)
-
-    def test_perf_min(self):
-        N = 10000
-
-        dfp = DataFrame(
-            {"B": np.random.default_rng(2).standard_normal(N)},
-            index=date_range("20130101", periods=N, freq="s"),
-        )
-        expected = dfp.rolling(2, min_periods=1).min()
-        result = dfp.rolling("2s").min()
-        assert ((result - expected) < 0.01).all().all()
-
-        expected = dfp.rolling(200, min_periods=1).min()
-        result = dfp.rolling("200s").min()
-        assert ((result - expected) < 0.01).all().all()
-
-    def test_ragged_max(self, ragged):
-        df = ragged
-
-        result = df.rolling(window="1s", min_periods=1).max()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="2s", min_periods=1).max()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-        result = df.rolling(window="5s", min_periods=1).max()
-        expected = df.copy()
-        expected["B"] = [0.0, 1, 2, 3, 4]
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "freq, op, result_data",
-        [
-            ("ms", "min", [0.0] * 10),
-            ("ms", "mean", [0.0] * 9 + [2.0 / 9]),
-            ("ms", "max", [0.0] * 9 + [2.0]),
-            ("s", "min", [0.0] * 10),
-            ("s", "mean", [0.0] * 9 + [2.0 / 9]),
-            ("s", "max", [0.0] * 9 + [2.0]),
-            ("min", "min", [0.0] * 10),
-            ("min", "mean", [0.0] * 9 + [2.0 / 9]),
-            ("min", "max", [0.0] * 9 + [2.0]),
-            ("h", "min", [0.0] * 10),
-            ("h", "mean", [0.0] * 9 + [2.0 / 9]),
-            ("h", "max", [0.0] * 9 + [2.0]),
-            ("D", "min", [0.0] * 10),
-            ("D", "mean", [0.0] * 9 + [2.0 / 9]),
-            ("D", "max", [0.0] * 9 + [2.0]),
-        ],
-    )
-    def test_freqs_ops(self, freq, op, result_data):
-        # GH 21096
-        index = date_range(start="2018-1-1 01:00:00", freq=f"1{freq}", periods=10)
-        # Explicit cast to float to avoid implicit cast when setting nan
-        s = Series(data=0, index=index, dtype="float")
-        s.iloc[1] = np.nan
-        s.iloc[-1] = 2
-        result = getattr(s.rolling(window=f"10{freq}"), op)()
-        expected = Series(data=result_data, index=index)
-
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "f",
-        [
-            "sum",
-            "mean",
-            "count",
-            "median",
-            "std",
-            "var",
-            "kurt",
-            "skew",
-            "min",
-            "max",
-        ],
-    )
-    def test_all(self, f, regular):
-        # simple comparison of integer vs time-based windowing
-        df = regular * 2
-        er = df.rolling(window=1)
-        r = df.rolling(window="1s")
-
-        result = getattr(r, f)()
-        expected = getattr(er, f)()
-        tm.assert_frame_equal(result, expected)
-
-        result = r.quantile(0.5)
-        expected = er.quantile(0.5)
-        tm.assert_frame_equal(result, expected)
-
-    def test_all2(self, arithmetic_win_operators):
-        f = arithmetic_win_operators
-        # more sophisticated comparison of integer vs.
-        # time-based windowing
-        df = DataFrame(
-            {"B": np.arange(50)}, index=date_range("20130101", periods=50, freq="H")
-        )
-        # in-range data
-        dft = df.between_time("09:00", "16:00")
-
-        r = dft.rolling(window="5H")
-
-        result = getattr(r, f)()
-
-        # we need to roll the days separately
-        # to compare with a time-based roll
-        # finally groupby-apply will return a multi-index
-        # so we need to drop the day
-        def agg_by_day(x):
-            x = x.between_time("09:00", "16:00")
-            return getattr(x.rolling(5, min_periods=1), f)()
-
-        expected = (
-            df.groupby(df.index.day).apply(agg_by_day).reset_index(level=0, drop=True)
-        )
-
-        tm.assert_frame_equal(result, expected)
-
-    def test_rolling_cov_offset(self):
-        # GH16058
-
-        idx = date_range("2017-01-01", periods=24, freq="1h")
-        ss = Series(np.arange(len(idx)), index=idx)
-
-        result = ss.rolling("2h").cov()
-        expected = Series([np.nan] + [0.5] * (len(idx) - 1), index=idx)
-        tm.assert_series_equal(result, expected)
-
-        expected2 = ss.rolling(2, min_periods=1).cov()
-        tm.assert_series_equal(result, expected2)
-
-        result = ss.rolling("3h").cov()
-        expected = Series([np.nan, 0.5] + [1.0] * (len(idx) - 2), index=idx)
-        tm.assert_series_equal(result, expected)
-
-        expected2 = ss.rolling(3, min_periods=1).cov()
-        tm.assert_series_equal(result, expected2)
-
-    def test_rolling_on_decreasing_index(self):
-        # GH-19248, GH-32385
-        index = [
-            Timestamp("20190101 09:00:30"),
-            Timestamp("20190101 09:00:27"),
-            Timestamp("20190101 09:00:20"),
-            Timestamp("20190101 09:00:18"),
-            Timestamp("20190101 09:00:10"),
-        ]
-
-        df = DataFrame({"column": [3, 4, 4, 5, 6]}, index=index)
-        result = df.rolling("5s").min()
-        expected = DataFrame({"column": [3.0, 3.0, 4.0, 4.0, 6.0]}, index=index)
-        tm.assert_frame_equal(result, expected)
-
-    def test_rolling_on_empty(self):
-        # GH-32385
-        df = DataFrame({"column": []}, index=[])
-        result = df.rolling("5s").min()
-        expected = DataFrame({"column": []}, index=[])
-        tm.assert_frame_equal(result, expected)
-
-    def test_rolling_on_multi_index_level(self):
-        # GH-15584
-        df = DataFrame(
-            {"column": range(6)},
-            index=MultiIndex.from_product(
-                [date_range("20190101", periods=3), range(2)], names=["date", "seq"]
-            ),
-        )
-        result = df.rolling("10d", on=df.index.get_level_values("date")).sum()
-        expected = DataFrame(
-            {"column": [0.0, 1.0, 3.0, 6.0, 10.0, 15.0]}, index=df.index
-        )
-        tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("msg, axis", [["column", 1], ["index", 0]])
-def test_nat_axis_error(msg, axis):
-    idx = [Timestamp("2020"), NaT]
-    kwargs = {"columns" if axis == 1 else "index": idx}
-    df = DataFrame(np.eye(2), **kwargs)
-    warn_msg = "The 'axis' keyword in DataFrame.rolling is deprecated"
-    if axis == 1:
-        warn_msg = "Support for axis=1 in DataFrame.rolling is deprecated"
-    with pytest.raises(ValueError, match=f"{msg} values must not have NaT"):
-        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
-            df.rolling("D", axis=axis).mean()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/regexopt.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/regexopt.py
deleted file mode 100644
index cb2c8e21a9e3ab9b6b58df67e9cfd08f4977ef30..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/regexopt.py
+++ /dev/null
@@ -1,91 +0,0 @@
-"""
-    pygments.regexopt
-    ~~~~~~~~~~~~~~~~~
-
-    An algorithm that generates optimized regexes for matching long lists of
-    literal strings.
-
-    :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-from re import escape
-from os.path import commonprefix
-from itertools import groupby
-from operator import itemgetter
-
-CS_ESCAPE = re.compile(r'[\[\^\\\-\]]')
-FIRST_ELEMENT = itemgetter(0)
-
-
-def make_charset(letters):
-    return '[' + CS_ESCAPE.sub(lambda m: '\\' + m.group(), ''.join(letters)) + ']'
-
-
-def regex_opt_inner(strings, open_paren):
-    """Return a regex that matches any string in the sorted list of strings."""
-    close_paren = open_paren and ')' or ''
-    # print strings, repr(open_paren)
-    if not strings:
-        # print '-> nothing left'
-        return ''
-    first = strings[0]
-    if len(strings) == 1:
-        # print '-> only 1 string'
-        return open_paren + escape(first) + close_paren
-    if not first:
-        # print '-> first string empty'
-        return open_paren + regex_opt_inner(strings[1:], '(?:') \
-            + '?' + close_paren
-    if len(first) == 1:
-        # multiple one-char strings? make a charset
-        oneletter = []
-        rest = []
-        for s in strings:
-            if len(s) == 1:
-                oneletter.append(s)
-            else:
-                rest.append(s)
-        if len(oneletter) > 1:  # do we have more than one oneletter string?
-            if rest:
-                # print '-> 1-character + rest'
-                return open_paren + regex_opt_inner(rest, '') + '|' \
-                    + make_charset(oneletter) + close_paren
-            # print '-> only 1-character'
-            return open_paren + make_charset(oneletter) + close_paren
-    prefix = commonprefix(strings)
-    if prefix:
-        plen = len(prefix)
-        # we have a prefix for all strings
-        # print '-> prefix:', prefix
-        return open_paren + escape(prefix) \
-            + regex_opt_inner([s[plen:] for s in strings], '(?:') \
-            + close_paren
-    # is there a suffix?
-    strings_rev = [s[::-1] for s in strings]
-    suffix = commonprefix(strings_rev)
-    if suffix:
-        slen = len(suffix)
-        # print '-> suffix:', suffix[::-1]
-        return open_paren \
-            + regex_opt_inner(sorted(s[:-slen] for s in strings), '(?:') \
-            + escape(suffix[::-1]) + close_paren
-    # recurse on common 1-string prefixes
-    # print '-> last resort'
-    return open_paren + \
-        '|'.join(regex_opt_inner(list(group[1]), '')
-                 for group in groupby(strings, lambda s: s[0] == first[0])) \
-        + close_paren
-
-
-def regex_opt(strings, prefix='', suffix=''):
-    """Return a compiled regex that matches any string in the given list.
-
-    The strings to match must be literal strings, not regexes.  They will be
-    regex-escaped.
-
-    *prefix* and *suffix* are pre- and appended to the final regex.
-    """
-    strings = sorted(strings)
-    return prefix + regex_opt_inner(strings, '(') + suffix
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/asm.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/asm.py
deleted file mode 100644
index 0035c723f094771057a20308f2cb1d9129079e50..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/asm.py
+++ /dev/null
@@ -1,1037 +0,0 @@
-"""
-    pygments.lexers.asm
-    ~~~~~~~~~~~~~~~~~~~
-
-    Lexers for assembly languages.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.lexer import RegexLexer, include, bygroups, using, words, \
-    DelegatingLexer, default
-from pygments.lexers.c_cpp import CppLexer, CLexer
-from pygments.lexers.d import DLexer
-from pygments.token import Text, Name, Number, String, Comment, Punctuation, \
-    Other, Keyword, Operator, Whitespace
-
-__all__ = ['GasLexer', 'ObjdumpLexer', 'DObjdumpLexer', 'CppObjdumpLexer',
-           'CObjdumpLexer', 'HsailLexer', 'LlvmLexer', 'LlvmMirBodyLexer',
-           'LlvmMirLexer', 'NasmLexer', 'NasmObjdumpLexer', 'TasmLexer',
-           'Ca65Lexer', 'Dasm16Lexer']
-
-
-class GasLexer(RegexLexer):
-    """
-    For Gas (AT&T) assembly code.
-    """
-    name = 'GAS'
-    aliases = ['gas', 'asm']
-    filenames = ['*.s', '*.S']
-    mimetypes = ['text/x-gas']
-
-    #: optional Comment or Whitespace
-    string = r'"(\\"|[^"])*"'
-    char = r'[\w$.@-]'
-    identifier = r'(?:[a-zA-Z$_]' + char + r'*|\.' + char + '+)'
-    number = r'(?:0[xX][a-fA-F0-9]+|#?-?\d+)'
-    register = '%' + identifier + r'\b'
-
-    tokens = {
-        'root': [
-            include('whitespace'),
-            (identifier + ':', Name.Label),
-            (r'\.' + identifier, Name.Attribute, 'directive-args'),
-            (r'lock|rep(n?z)?|data\d+', Name.Attribute),
-            (identifier, Name.Function, 'instruction-args'),
-            (r'[\r\n]+', Text)
-        ],
-        'directive-args': [
-            (identifier, Name.Constant),
-            (string, String),
-            ('@' + identifier, Name.Attribute),
-            (number, Number.Integer),
-            (register, Name.Variable),
-            (r'[\r\n]+', Whitespace, '#pop'),
-            (r'([;#]|//).*?\n', Comment.Single, '#pop'),
-            (r'/[*].*?[*]/', Comment.Multiline),
-            (r'/[*].*?\n[\w\W]*?[*]/', Comment.Multiline, '#pop'),
-
-            include('punctuation'),
-            include('whitespace')
-        ],
-        'instruction-args': [
-            # For objdump-disassembled code, shouldn't occur in
-            # actual assembler input
-            ('([a-z0-9]+)( )(<)('+identifier+')(>)',
-                bygroups(Number.Hex, Text, Punctuation, Name.Constant,
-                         Punctuation)),
-            ('([a-z0-9]+)( )(<)('+identifier+')([-+])('+number+')(>)',
-                bygroups(Number.Hex, Text, Punctuation, Name.Constant,
-                         Punctuation, Number.Integer, Punctuation)),
-
-            # Address constants
-            (identifier, Name.Constant),
-            (number, Number.Integer),
-            # Registers
-            (register, Name.Variable),
-            # Numeric constants
-            ('$'+number, Number.Integer),
-            (r"$'(.|\\')'", String.Char),
-            (r'[\r\n]+', Whitespace, '#pop'),
-            (r'([;#]|//).*?\n', Comment.Single, '#pop'),
-            (r'/[*].*?[*]/', Comment.Multiline),
-            (r'/[*].*?\n[\w\W]*?[*]/', Comment.Multiline, '#pop'),
-
-            include('punctuation'),
-            include('whitespace')
-        ],
-        'whitespace': [
-            (r'\n', Whitespace),
-            (r'\s+', Whitespace),
-            (r'([;#]|//).*?\n', Comment.Single),
-            (r'/[*][\w\W]*?[*]/', Comment.Multiline)
-        ],
-        'punctuation': [
-            (r'[-*,.()\[\]!:{}]+', Punctuation)
-        ]
-    }
-
-    def analyse_text(text):
-        if re.search(r'^\.(text|data|section)', text, re.M):
-            return True
-        elif re.search(r'^\.\w+', text, re.M):
-            return 0.1
-
-
-def _objdump_lexer_tokens(asm_lexer):
-    """
-    Common objdump lexer tokens to wrap an ASM lexer.
-    """
-    hex_re = r'[0-9A-Za-z]'
-    return {
-        'root': [
-            # File name & format:
-            ('(.*?)(:)( +file format )(.*?)$',
-                bygroups(Name.Label, Punctuation, Text, String)),
-            # Section header
-            ('(Disassembly of section )(.*?)(:)$',
-                bygroups(Text, Name.Label, Punctuation)),
-            # Function labels
-            # (With offset)
-            ('('+hex_re+'+)( )(<)(.*?)([-+])(0[xX][A-Za-z0-9]+)(>:)$',
-                bygroups(Number.Hex, Whitespace, Punctuation, Name.Function,
-                         Punctuation, Number.Hex, Punctuation)),
-            # (Without offset)
-            ('('+hex_re+'+)( )(<)(.*?)(>:)$',
-                bygroups(Number.Hex, Whitespace, Punctuation, Name.Function,
-                         Punctuation)),
-            # Code line with disassembled instructions
-            ('( *)('+hex_re+r'+:)(\t)((?:'+hex_re+hex_re+' )+)( *\t)([a-zA-Z].*?)$',
-                bygroups(Whitespace, Name.Label, Whitespace, Number.Hex, Whitespace,
-                         using(asm_lexer))),
-            # Code line without raw instructions (objdump --no-show-raw-insn)
-            ('( *)('+hex_re+r'+:)( *\t)([a-zA-Z].*?)$',
-                bygroups(Whitespace, Name.Label, Whitespace,
-                         using(asm_lexer))),
-            # Code line with ascii
-            ('( *)('+hex_re+r'+:)(\t)((?:'+hex_re+hex_re+' )+)( *)(.*?)$',
-                bygroups(Whitespace, Name.Label, Whitespace, Number.Hex, Whitespace, String)),
-            # Continued code line, only raw opcodes without disassembled
-            # instruction
-            ('( *)('+hex_re+r'+:)(\t)((?:'+hex_re+hex_re+' )+)$',
-                bygroups(Whitespace, Name.Label, Whitespace, Number.Hex)),
-            # Skipped a few bytes
-            (r'\t\.\.\.$', Text),
-            # Relocation line
-            # (With offset)
-            (r'(\t\t\t)('+hex_re+r'+:)( )([^\t]+)(\t)(.*?)([-+])(0x'+hex_re+'+)$',
-                bygroups(Whitespace, Name.Label, Whitespace, Name.Property, Whitespace,
-                         Name.Constant, Punctuation, Number.Hex)),
-            # (Without offset)
-            (r'(\t\t\t)('+hex_re+r'+:)( )([^\t]+)(\t)(.*?)$',
-                bygroups(Whitespace, Name.Label, Whitespace, Name.Property, Whitespace,
-                         Name.Constant)),
-            (r'[^\n]+\n', Other)
-        ]
-    }
-
-
-class ObjdumpLexer(RegexLexer):
-    """
-    For the output of ``objdump -dr``.
-    """
-    name = 'objdump'
-    aliases = ['objdump']
-    filenames = ['*.objdump']
-    mimetypes = ['text/x-objdump']
-
-    tokens = _objdump_lexer_tokens(GasLexer)
-
-
-class DObjdumpLexer(DelegatingLexer):
-    """
-    For the output of ``objdump -Sr`` on compiled D files.
-    """
-    name = 'd-objdump'
-    aliases = ['d-objdump']
-    filenames = ['*.d-objdump']
-    mimetypes = ['text/x-d-objdump']
-
-    def __init__(self, **options):
-        super().__init__(DLexer, ObjdumpLexer, **options)
-
-
-class CppObjdumpLexer(DelegatingLexer):
-    """
-    For the output of ``objdump -Sr`` on compiled C++ files.
-    """
-    name = 'cpp-objdump'
-    aliases = ['cpp-objdump', 'c++-objdumb', 'cxx-objdump']
-    filenames = ['*.cpp-objdump', '*.c++-objdump', '*.cxx-objdump']
-    mimetypes = ['text/x-cpp-objdump']
-
-    def __init__(self, **options):
-        super().__init__(CppLexer, ObjdumpLexer, **options)
-
-
-class CObjdumpLexer(DelegatingLexer):
-    """
-    For the output of ``objdump -Sr`` on compiled C files.
-    """
-    name = 'c-objdump'
-    aliases = ['c-objdump']
-    filenames = ['*.c-objdump']
-    mimetypes = ['text/x-c-objdump']
-
-    def __init__(self, **options):
-        super().__init__(CLexer, ObjdumpLexer, **options)
-
-
-class HsailLexer(RegexLexer):
-    """
-    For HSAIL assembly code.
-
-    .. versionadded:: 2.2
-    """
-    name = 'HSAIL'
-    aliases = ['hsail', 'hsa']
-    filenames = ['*.hsail']
-    mimetypes = ['text/x-hsail']
-
-    string = r'"[^"]*?"'
-    identifier = r'[a-zA-Z_][\w.]*'
-    # Registers
-    register_number = r'[0-9]+'
-    register = r'(\$(c|s|d|q)' + register_number + r')\b'
-    # Qualifiers
-    alignQual = r'(align\(\d+\))'
-    widthQual = r'(width\((\d+|all)\))'
-    allocQual = r'(alloc\(agent\))'
-    # Instruction Modifiers
-    roundingMod = (r'((_ftz)?(_up|_down|_zero|_near))')
-    datatypeMod = (r'_('
-                   # packedTypes
-                   r'u8x4|s8x4|u16x2|s16x2|u8x8|s8x8|u16x4|s16x4|u32x2|s32x2|'
-                   r'u8x16|s8x16|u16x8|s16x8|u32x4|s32x4|u64x2|s64x2|'
-                   r'f16x2|f16x4|f16x8|f32x2|f32x4|f64x2|'
-                   # baseTypes
-                   r'u8|s8|u16|s16|u32|s32|u64|s64|'
-                   r'b128|b8|b16|b32|b64|b1|'
-                   r'f16|f32|f64|'
-                   # opaqueType
-                   r'roimg|woimg|rwimg|samp|sig32|sig64)')
-
-    # Numeric Constant
-    float = r'((\d+\.)|(\d*\.\d+))[eE][+-]?\d+'
-    hexfloat = r'0[xX](([0-9a-fA-F]+\.[0-9a-fA-F]*)|([0-9a-fA-F]*\.[0-9a-fA-F]+))[pP][+-]?\d+'
-    ieeefloat = r'0((h|H)[0-9a-fA-F]{4}|(f|F)[0-9a-fA-F]{8}|(d|D)[0-9a-fA-F]{16})'
-
-    tokens = {
-        'root': [
-            include('whitespace'),
-            include('comments'),
-
-            (string, String),
-
-            (r'@' + identifier + ':?', Name.Label),
-
-            (register, Name.Variable.Anonymous),
-
-            include('keyword'),
-
-            (r'&' + identifier, Name.Variable.Global),
-            (r'%' + identifier, Name.Variable),
-
-            (hexfloat, Number.Hex),
-            (r'0[xX][a-fA-F0-9]+', Number.Hex),
-            (ieeefloat, Number.Float),
-            (float, Number.Float),
-            (r'\d+', Number.Integer),
-
-            (r'[=<>{}\[\]()*.,:;!]|x\b', Punctuation)
-        ],
-        'whitespace': [
-            (r'(\n|\s)+', Whitespace),
-        ],
-        'comments': [
-            (r'/\*.*?\*/', Comment.Multiline),
-            (r'//.*?\n', Comment.Single),
-        ],
-        'keyword': [
-            # Types
-            (r'kernarg' + datatypeMod, Keyword.Type),
-
-            # Regular keywords
-            (r'\$(full|base|small|large|default|zero|near)', Keyword),
-            (words((
-                'module', 'extension', 'pragma', 'prog', 'indirect', 'signature',
-                'decl', 'kernel', 'function', 'enablebreakexceptions',
-                'enabledetectexceptions', 'maxdynamicgroupsize', 'maxflatgridsize',
-                'maxflatworkgroupsize', 'requireddim', 'requiredgridsize',
-                'requiredworkgroupsize', 'requirenopartialworkgroups'),
-                suffix=r'\b'), Keyword),
-
-            # instructions
-            (roundingMod, Keyword),
-            (datatypeMod, Keyword),
-            (r'_(' + alignQual + '|' + widthQual + ')', Keyword),
-            (r'_kernarg', Keyword),
-            (r'(nop|imagefence)\b', Keyword),
-            (words((
-                'cleardetectexcept', 'clock', 'cuid', 'debugtrap', 'dim',
-                'getdetectexcept', 'groupbaseptr', 'kernargbaseptr', 'laneid',
-                'maxcuid', 'maxwaveid', 'packetid', 'setdetectexcept', 'waveid',
-                'workitemflatabsid', 'workitemflatid', 'nullptr', 'abs', 'bitrev',
-                'currentworkgroupsize', 'currentworkitemflatid', 'fract', 'ncos',
-                'neg', 'nexp2', 'nlog2', 'nrcp', 'nrsqrt', 'nsin', 'nsqrt',
-                'gridgroups', 'gridsize', 'not', 'sqrt', 'workgroupid',
-                'workgroupsize', 'workitemabsid', 'workitemid', 'ceil', 'floor',
-                'rint', 'trunc', 'add', 'bitmask', 'borrow', 'carry', 'copysign',
-                'div', 'rem', 'sub', 'shl', 'shr', 'and', 'or', 'xor', 'unpackhi',
-                'unpacklo', 'max', 'min', 'fma', 'mad', 'bitextract', 'bitselect',
-                'shuffle', 'cmov', 'bitalign', 'bytealign', 'lerp', 'nfma', 'mul',
-                'mulhi', 'mul24hi', 'mul24', 'mad24', 'mad24hi', 'bitinsert',
-                'combine', 'expand', 'lda', 'mov', 'pack', 'unpack', 'packcvt',
-                'unpackcvt', 'sad', 'sementp', 'ftos', 'stof', 'cmp', 'ld', 'st',
-                '_eq', '_ne', '_lt', '_le', '_gt', '_ge', '_equ', '_neu', '_ltu',
-                '_leu', '_gtu', '_geu', '_num', '_nan', '_seq', '_sne', '_slt',
-                '_sle', '_sgt', '_sge', '_snum', '_snan', '_sequ', '_sneu', '_sltu',
-                '_sleu', '_sgtu', '_sgeu', 'atomic', '_ld', '_st', '_cas', '_add',
-                '_and', '_exch', '_max', '_min', '_or', '_sub', '_wrapdec',
-                '_wrapinc', '_xor', 'ret', 'cvt', '_readonly', '_kernarg', '_global',
-                'br', 'cbr', 'sbr', '_scacq', '_screl', '_scar', '_rlx', '_wave',
-                '_wg', '_agent', '_system', 'ldimage', 'stimage', '_v2', '_v3', '_v4',
-                '_1d', '_2d', '_3d', '_1da', '_2da', '_1db', '_2ddepth', '_2dadepth',
-                '_width', '_height', '_depth', '_array', '_channelorder',
-                '_channeltype', 'querysampler', '_coord', '_filter', '_addressing',
-                'barrier', 'wavebarrier', 'initfbar', 'joinfbar', 'waitfbar',
-                'arrivefbar', 'leavefbar', 'releasefbar', 'ldf', 'activelaneid',
-                'activelanecount', 'activelanemask', 'activelanepermute', 'call',
-                'scall', 'icall', 'alloca', 'packetcompletionsig',
-                'addqueuewriteindex', 'casqueuewriteindex', 'ldqueuereadindex',
-                'stqueuereadindex', 'readonly', 'global', 'private', 'group',
-                'spill', 'arg', '_upi', '_downi', '_zeroi', '_neari', '_upi_sat',
-                '_downi_sat', '_zeroi_sat', '_neari_sat', '_supi', '_sdowni',
-                '_szeroi', '_sneari', '_supi_sat', '_sdowni_sat', '_szeroi_sat',
-                '_sneari_sat', '_pp', '_ps', '_sp', '_ss', '_s', '_p', '_pp_sat',
-                '_ps_sat', '_sp_sat', '_ss_sat', '_s_sat', '_p_sat')), Keyword),
-
-            # Integer types
-            (r'i[1-9]\d*', Keyword)
-        ]
-    }
-
-
-class LlvmLexer(RegexLexer):
-    """
-    For LLVM assembly code.
-    """
-    name = 'LLVM'
-    url = 'https://llvm.org/docs/LangRef.html'
-    aliases = ['llvm']
-    filenames = ['*.ll']
-    mimetypes = ['text/x-llvm']
-
-    #: optional Comment or Whitespace
-    string = r'"[^"]*?"'
-    identifier = r'([-a-zA-Z$._][\w\-$.]*|' + string + ')'
-    block_label = r'(' + identifier + r'|(\d+))'
-
-    tokens = {
-        'root': [
-            include('whitespace'),
-
-            # Before keywords, because keywords are valid label names :(...
-            (block_label + r'\s*:', Name.Label),
-
-            include('keyword'),
-
-            (r'%' + identifier, Name.Variable),
-            (r'@' + identifier, Name.Variable.Global),
-            (r'%\d+', Name.Variable.Anonymous),
-            (r'@\d+', Name.Variable.Global),
-            (r'#\d+', Name.Variable.Global),
-            (r'!' + identifier, Name.Variable),
-            (r'!\d+', Name.Variable.Anonymous),
-            (r'c?' + string, String),
-
-            (r'0[xX][a-fA-F0-9]+', Number),
-            (r'-?\d+(?:[.]\d+)?(?:[eE][-+]?\d+(?:[.]\d+)?)?', Number),
-
-            (r'[=<>{}\[\]()*.,!]|x\b', Punctuation)
-        ],
-        'whitespace': [
-            (r'(\n|\s+)+', Whitespace),
-            (r';.*?\n', Comment)
-        ],
-        'keyword': [
-            # Regular keywords
-            (words((
-                'aarch64_sve_vector_pcs', 'aarch64_vector_pcs', 'acq_rel',
-                'acquire', 'add', 'addrspace', 'addrspacecast', 'afn', 'alias',
-                'aliasee', 'align', 'alignLog2', 'alignstack', 'alloca',
-                'allocsize', 'allOnes', 'alwaysinline', 'alwaysInline',
-                'amdgpu_cs', 'amdgpu_es', 'amdgpu_gfx', 'amdgpu_gs',
-                'amdgpu_hs', 'amdgpu_kernel', 'amdgpu_ls', 'amdgpu_ps',
-                'amdgpu_vs', 'and', 'any', 'anyregcc', 'appending', 'arcp',
-                'argmemonly', 'args', 'arm_aapcs_vfpcc', 'arm_aapcscc',
-                'arm_apcscc', 'ashr', 'asm', 'atomic', 'atomicrmw',
-                'attributes', 'available_externally', 'avr_intrcc',
-                'avr_signalcc', 'bit', 'bitcast', 'bitMask', 'blockaddress',
-                'blockcount', 'br', 'branchFunnel', 'builtin', 'byArg',
-                'byref', 'byte', 'byteArray', 'byval', 'c', 'call', 'callbr',
-                'callee', 'caller', 'calls', 'canAutoHide', 'catch',
-                'catchpad', 'catchret', 'catchswitch', 'cc', 'ccc',
-                'cfguard_checkcc', 'cleanup', 'cleanuppad', 'cleanupret',
-                'cmpxchg', 'cold', 'coldcc', 'comdat', 'common', 'constant',
-                'contract', 'convergent', 'critical', 'cxx_fast_tlscc',
-                'datalayout', 'declare', 'default', 'define', 'deplibs',
-                'dereferenceable', 'dereferenceable_or_null', 'distinct',
-                'dllexport', 'dllimport', 'dso_local', 'dso_local_equivalent',
-                'dso_preemptable', 'dsoLocal', 'eq', 'exact', 'exactmatch',
-                'extern_weak', 'external', 'externally_initialized',
-                'extractelement', 'extractvalue', 'fadd', 'false', 'fast',
-                'fastcc', 'fcmp', 'fdiv', 'fence', 'filter', 'flags', 'fmul',
-                'fneg', 'fpext', 'fptosi', 'fptoui', 'fptrunc', 'freeze',
-                'frem', 'from', 'fsub', 'funcFlags', 'function', 'gc',
-                'getelementptr', 'ghccc', 'global', 'guid', 'gv', 'hash',
-                'hhvm_ccc', 'hhvmcc', 'hidden', 'hot', 'hotness', 'icmp',
-                'ifunc', 'inaccessiblemem_or_argmemonly',
-                'inaccessiblememonly', 'inalloca', 'inbounds', 'indir',
-                'indirectbr', 'info', 'initialexec', 'inline', 'inlineBits',
-                'inlinehint', 'inrange', 'inreg', 'insertelement',
-                'insertvalue', 'insts', 'intel_ocl_bicc', 'inteldialect',
-                'internal', 'inttoptr', 'invoke', 'jumptable', 'kind',
-                'landingpad', 'largest', 'linkage', 'linkonce', 'linkonce_odr',
-                'live', 'load', 'local_unnamed_addr', 'localdynamic',
-                'localexec', 'lshr', 'max', 'metadata', 'min', 'minsize',
-                'module', 'monotonic', 'msp430_intrcc', 'mul', 'mustprogress',
-                'musttail', 'naked', 'name', 'nand', 'ne', 'nest', 'ninf',
-                'nnan', 'noalias', 'nobuiltin', 'nocallback', 'nocapture',
-                'nocf_check', 'noduplicate', 'noduplicates', 'nofree',
-                'noimplicitfloat', 'noinline', 'noInline', 'nomerge', 'none',
-                'nonlazybind', 'nonnull', 'noprofile', 'norecurse',
-                'noRecurse', 'noredzone', 'noreturn', 'nosync', 'notail',
-                'notEligibleToImport', 'noundef', 'nounwind', 'nsw',
-                'nsz', 'null', 'null_pointer_is_valid', 'nuw', 'oeq', 'offset',
-                'oge', 'ogt', 'ole', 'olt', 'one', 'opaque', 'optforfuzzing',
-                'optnone', 'optsize', 'or', 'ord', 'param', 'params',
-                'partition', 'path', 'personality', 'phi', 'poison',
-                'preallocated', 'prefix', 'preserve_allcc', 'preserve_mostcc',
-                'private', 'prologue', 'protected', 'ptrtoint', 'ptx_device',
-                'ptx_kernel', 'readnone', 'readNone', 'readonly', 'readOnly',
-                'reassoc', 'refs', 'relbf', 'release', 'resByArg', 'resume',
-                'ret', 'returnDoesNotAlias', 'returned', 'returns_twice',
-                'safestack', 'samesize', 'sanitize_address',
-                'sanitize_hwaddress', 'sanitize_memory', 'sanitize_memtag',
-                'sanitize_thread', 'sdiv', 'section', 'select', 'seq_cst',
-                'sext', 'sge', 'sgt', 'shadowcallstack', 'shl',
-                'shufflevector', 'sideeffect', 'signext', 'single',
-                'singleImpl', 'singleImplName', 'sitofp', 'sizeM1',
-                'sizeM1BitWidth', 'sle', 'slt', 'source_filename',
-                'speculatable', 'speculative_load_hardening', 'spir_func',
-                'spir_kernel', 'srem', 'sret', 'ssp', 'sspreq', 'sspstrong',
-                'store', 'strictfp', 'sub', 'summaries', 'summary', 'swiftcc',
-                'swifterror', 'swiftself', 'switch', 'syncscope', 'tail',
-                'tailcc', 'target', 'thread_local', 'to', 'token', 'triple',
-                'true', 'trunc', 'type', 'typeCheckedLoadConstVCalls',
-                'typeCheckedLoadVCalls', 'typeid', 'typeidCompatibleVTable',
-                'typeIdInfo', 'typeTestAssumeConstVCalls',
-                'typeTestAssumeVCalls', 'typeTestRes', 'typeTests', 'udiv',
-                'ueq', 'uge', 'ugt', 'uitofp', 'ule', 'ult', 'umax', 'umin',
-                'undef', 'une', 'uniformRetVal', 'uniqueRetVal', 'unknown',
-                'unnamed_addr', 'uno', 'unordered', 'unreachable', 'unsat',
-                'unwind', 'urem', 'uselistorder', 'uselistorder_bb', 'uwtable',
-                'va_arg', 'varFlags', 'variable', 'vcall_visibility',
-                'vFuncId', 'virtFunc', 'virtualConstProp', 'void', 'volatile',
-                'vscale', 'vTableFuncs', 'weak', 'weak_odr', 'webkit_jscc',
-                'win64cc', 'within', 'wpdRes', 'wpdResolutions', 'writeonly',
-                'x', 'x86_64_sysvcc', 'x86_fastcallcc', 'x86_intrcc',
-                'x86_mmx', 'x86_regcallcc', 'x86_stdcallcc', 'x86_thiscallcc',
-                'x86_vectorcallcc', 'xchg', 'xor', 'zeroext',
-                'zeroinitializer', 'zext', 'immarg', 'willreturn'),
-                suffix=r'\b'), Keyword),
-
-            # Types
-            (words(('void', 'half', 'bfloat', 'float', 'double', 'fp128',
-                    'x86_fp80', 'ppc_fp128', 'label', 'metadata', 'x86_mmx',
-                    'x86_amx', 'token', 'ptr')),
-                   Keyword.Type),
-
-            # Integer types
-            (r'i[1-9]\d*', Keyword.Type)
-        ]
-    }
-
-
-class LlvmMirBodyLexer(RegexLexer):
-    """
-    For LLVM MIR examples without the YAML wrapper.
-
-    .. versionadded:: 2.6
-    """
-    name = 'LLVM-MIR Body'
-    url = 'https://llvm.org/docs/MIRLangRef.html'
-    aliases = ['llvm-mir-body']
-    filenames = []
-    mimetypes = []
-
-    tokens = {
-        'root': [
-            # Attributes on basic blocks
-            (words(('liveins', 'successors'), suffix=':'), Keyword),
-            # Basic Block Labels
-            (r'bb\.[0-9]+(\.[a-zA-Z0-9_.-]+)?( \(address-taken\))?:', Name.Label),
-            (r'bb\.[0-9]+ \(%[a-zA-Z0-9_.-]+\)( \(address-taken\))?:', Name.Label),
-            (r'%bb\.[0-9]+(\.\w+)?', Name.Label),
-            # Stack references
-            (r'%stack\.[0-9]+(\.\w+\.addr)?', Name),
-            # Subreg indices
-            (r'%subreg\.\w+', Name),
-            # Virtual registers
-            (r'%[a-zA-Z0-9_]+ *', Name.Variable, 'vreg'),
-            # Reference to LLVM-IR global
-            include('global'),
-            # Reference to Intrinsic
-            (r'intrinsic\(\@[a-zA-Z0-9_.]+\)', Name.Variable.Global),
-            # Comparison predicates
-            (words(('eq', 'ne', 'sgt', 'sge', 'slt', 'sle', 'ugt', 'uge', 'ult',
-                    'ule'), prefix=r'intpred\(', suffix=r'\)'), Name.Builtin),
-            (words(('oeq', 'one', 'ogt', 'oge', 'olt', 'ole', 'ugt', 'uge',
-                    'ult', 'ule'), prefix=r'floatpred\(', suffix=r'\)'),
-             Name.Builtin),
-            # Physical registers
-            (r'\$\w+', String.Single),
-            # Assignment operator
-            (r'=', Operator),
-            # gMIR Opcodes
-            (r'(G_ANYEXT|G_[SZ]EXT|G_SEXT_INREG|G_TRUNC|G_IMPLICIT_DEF|G_PHI|'
-             r'G_FRAME_INDEX|G_GLOBAL_VALUE|G_INTTOPTR|G_PTRTOINT|G_BITCAST|'
-             r'G_CONSTANT|G_FCONSTANT|G_VASTART|G_VAARG|G_CTLZ|G_CTLZ_ZERO_UNDEF|'
-             r'G_CTTZ|G_CTTZ_ZERO_UNDEF|G_CTPOP|G_BSWAP|G_BITREVERSE|'
-             r'G_ADDRSPACE_CAST|G_BLOCK_ADDR|G_JUMP_TABLE|G_DYN_STACKALLOC|'
-             r'G_ADD|G_SUB|G_MUL|G_[SU]DIV|G_[SU]REM|G_AND|G_OR|G_XOR|G_SHL|'
-             r'G_[LA]SHR|G_[IF]CMP|G_SELECT|G_GEP|G_PTR_MASK|G_SMIN|G_SMAX|'
-             r'G_UMIN|G_UMAX|G_[US]ADDO|G_[US]ADDE|G_[US]SUBO|G_[US]SUBE|'
-             r'G_[US]MULO|G_[US]MULH|G_FNEG|G_FPEXT|G_FPTRUNC|G_FPTO[US]I|'
-             r'G_[US]ITOFP|G_FABS|G_FCOPYSIGN|G_FCANONICALIZE|G_FMINNUM|'
-             r'G_FMAXNUM|G_FMINNUM_IEEE|G_FMAXNUM_IEEE|G_FMINIMUM|G_FMAXIMUM|'
-             r'G_FADD|G_FSUB|G_FMUL|G_FMA|G_FMAD|G_FDIV|G_FREM|G_FPOW|G_FEXP|'
-             r'G_FEXP2|G_FLOG|G_FLOG2|G_FLOG10|G_FCEIL|G_FCOS|G_FSIN|G_FSQRT|'
-             r'G_FFLOOR|G_FRINT|G_FNEARBYINT|G_INTRINSIC_TRUNC|'
-             r'G_INTRINSIC_ROUND|G_LOAD|G_[ZS]EXTLOAD|G_INDEXED_LOAD|'
-             r'G_INDEXED_[ZS]EXTLOAD|G_STORE|G_INDEXED_STORE|'
-             r'G_ATOMIC_CMPXCHG_WITH_SUCCESS|G_ATOMIC_CMPXCHG|'
-             r'G_ATOMICRMW_(XCHG|ADD|SUB|AND|NAND|OR|XOR|MAX|MIN|UMAX|UMIN|FADD|'
-                           r'FSUB)'
-             r'|G_FENCE|G_EXTRACT|G_UNMERGE_VALUES|G_INSERT|G_MERGE_VALUES|'
-             r'G_BUILD_VECTOR|G_BUILD_VECTOR_TRUNC|G_CONCAT_VECTORS|'
-             r'G_INTRINSIC|G_INTRINSIC_W_SIDE_EFFECTS|G_BR|G_BRCOND|'
-             r'G_BRINDIRECT|G_BRJT|G_INSERT_VECTOR_ELT|G_EXTRACT_VECTOR_ELT|'
-             r'G_SHUFFLE_VECTOR)\b',
-             Name.Builtin),
-            # Target independent opcodes
-            (r'(COPY|PHI|INSERT_SUBREG|EXTRACT_SUBREG|REG_SEQUENCE)\b',
-             Name.Builtin),
-            # Flags
-            (words(('killed', 'implicit')), Keyword),
-            # ConstantInt values
-            (r'(i[0-9]+)( +)', bygroups(Keyword.Type, Whitespace), 'constantint'),
-            # ConstantFloat values
-            (r'(half|float|double) +', Keyword.Type, 'constantfloat'),
-            # Bare immediates
-            include('integer'),
-            # MMO's
-            (r'(::)( *)', bygroups(Operator, Whitespace), 'mmo'),
-            # MIR Comments
-            (r';.*', Comment),
-            # If we get here, assume it's a target instruction
-            (r'[a-zA-Z0-9_]+', Name),
-            # Everything else that isn't highlighted
-            (r'[(), \n]+', Text),
-        ],
-        # The integer constant from a ConstantInt value
-        'constantint': [
-            include('integer'),
-            (r'(?=.)', Text, '#pop'),
-        ],
-        # The floating point constant from a ConstantFloat value
-        'constantfloat': [
-            include('float'),
-            (r'(?=.)', Text, '#pop'),
-        ],
-        'vreg': [
-            # The bank or class if there is one
-            (r'( *)(:(?!:))', bygroups(Whitespace, Keyword), ('#pop', 'vreg_bank_or_class')),
-            # The LLT if there is one
-            (r'( *)(\()', bygroups(Whitespace, Text), 'vreg_type'),
-            (r'(?=.)', Text, '#pop'),
-        ],
-        'vreg_bank_or_class': [
-            # The unassigned bank/class
-            (r'( *)(_)', bygroups(Whitespace, Name.Variable.Magic)),
-            (r'( *)([a-zA-Z0-9_]+)', bygroups(Whitespace, Name.Variable)),
-            # The LLT if there is one
-            (r'( *)(\()', bygroups(Whitespace, Text), 'vreg_type'),
-            (r'(?=.)', Text, '#pop'),
-        ],
-        'vreg_type': [
-            # Scalar and pointer types
-            (r'( *)([sp][0-9]+)', bygroups(Whitespace, Keyword.Type)),
-            (r'( *)(<[0-9]+ *x *[sp][0-9]+>)', bygroups(Whitespace, Keyword.Type)),
-            (r'\)', Text, '#pop'),
-            (r'(?=.)', Text, '#pop'),
-        ],
-        'mmo': [
-            (r'\(', Text),
-            (r' +', Whitespace),
-            (words(('load', 'store', 'on', 'into', 'from', 'align', 'monotonic',
-                    'acquire', 'release', 'acq_rel', 'seq_cst')),
-             Keyword),
-            # IR references
-            (r'%ir\.[a-zA-Z0-9_.-]+', Name),
-            (r'%ir-block\.[a-zA-Z0-9_.-]+', Name),
-            (r'[-+]', Operator),
-            include('integer'),
-            include('global'),
-            (r',', Punctuation),
-            (r'\), \(', Text),
-            (r'\)', Text, '#pop'),
-        ],
-        'integer': [(r'-?[0-9]+', Number.Integer),],
-        'float': [(r'-?[0-9]+\.[0-9]+(e[+-][0-9]+)?', Number.Float)],
-        'global': [(r'\@[a-zA-Z0-9_.]+', Name.Variable.Global)],
-    }
-
-
-class LlvmMirLexer(RegexLexer):
-    """
-    Lexer for the overall LLVM MIR document format.
-
-    MIR is a human readable serialization format that's used to represent LLVM's
-    machine specific intermediate representation. It allows LLVM's developers to
-    see the state of the compilation process at various points, as well as test
-    individual pieces of the compiler.
-
-    .. versionadded:: 2.6
-    """
-    name = 'LLVM-MIR'
-    url = 'https://llvm.org/docs/MIRLangRef.html'
-    aliases = ['llvm-mir']
-    filenames = ['*.mir']
-
-    tokens = {
-        'root': [
-            # Comments are hashes at the YAML level
-            (r'#.*', Comment),
-            # Documents starting with | are LLVM-IR
-            (r'--- \|$', Keyword, 'llvm_ir'),
-            # Other documents are MIR
-            (r'---', Keyword, 'llvm_mir'),
-            # Consume everything else in one token for efficiency
-            (r'[^-#]+|.', Text),
-        ],
-        'llvm_ir': [
-            # Documents end with '...' or '---'
-            (r'(\.\.\.|(?=---))', Keyword, '#pop'),
-            # Delegate to the LlvmLexer
-            (r'((?:.|\n)+?)(?=(\.\.\.|---))', bygroups(using(LlvmLexer))),
-        ],
-        'llvm_mir': [
-            # Comments are hashes at the YAML level
-            (r'#.*', Comment),
-            # Documents end with '...' or '---'
-            (r'(\.\.\.|(?=---))', Keyword, '#pop'),
-            # Handle the simple attributes
-            (r'name:', Keyword, 'name'),
-            (words(('alignment', ),
-                   suffix=':'), Keyword, 'number'),
-            (words(('legalized', 'regBankSelected', 'tracksRegLiveness',
-                    'selected', 'exposesReturnsTwice'),
-                   suffix=':'), Keyword, 'boolean'),
-            # Handle the attributes don't highlight inside
-            (words(('registers', 'stack', 'fixedStack', 'liveins', 'frameInfo',
-                    'machineFunctionInfo'),
-                   suffix=':'), Keyword),
-            # Delegate the body block to the LlvmMirBodyLexer
-            (r'body: *\|', Keyword, 'llvm_mir_body'),
-            # Consume everything else
-            (r'.+', Text),
-            (r'\n', Whitespace),
-        ],
-        'name': [
-            (r'[^\n]+', Name),
-            default('#pop'),
-        ],
-        'boolean': [
-            (r' *(true|false)', Name.Builtin),
-            default('#pop'),
-        ],
-        'number': [
-            (r' *[0-9]+', Number),
-            default('#pop'),
-        ],
-        'llvm_mir_body': [
-            # Documents end with '...' or '---'.
-            # We have to pop llvm_mir_body and llvm_mir
-            (r'(\.\.\.|(?=---))', Keyword, '#pop:2'),
-            # Delegate the body block to the LlvmMirBodyLexer
-            (r'((?:.|\n)+?)(?=\.\.\.|---)', bygroups(using(LlvmMirBodyLexer))),
-            # The '...' is optional. If we didn't already find it then it isn't
-            # there. There might be a '---' instead though.
-            (r'(?!\.\.\.|---)((?:.|\n)+)', bygroups(using(LlvmMirBodyLexer))),
-        ],
-    }
-
-
-class NasmLexer(RegexLexer):
-    """
-    For Nasm (Intel) assembly code.
-    """
-    name = 'NASM'
-    aliases = ['nasm']
-    filenames = ['*.asm', '*.ASM', '*.nasm']
-    mimetypes = ['text/x-nasm']
-
-    # Tasm uses the same file endings, but TASM is not as common as NASM, so
-    # we prioritize NASM higher by default
-    priority = 1.0
-
-    identifier = r'[a-z$._?][\w$.?#@~]*'
-    hexn = r'(?:0x[0-9a-f]+|$0[0-9a-f]*|[0-9]+[0-9a-f]*h)'
-    octn = r'[0-7]+q'
-    binn = r'[01]+b'
-    decn = r'[0-9]+'
-    floatn = decn + r'\.e?' + decn
-    string = r'"(\\"|[^"\n])*"|' + r"'(\\'|[^'\n])*'|" + r"`(\\`|[^`\n])*`"
-    declkw = r'(?:res|d)[bwdqt]|times'
-    register = (r'(r[0-9][0-5]?[bwd]?|'
-                r'[a-d][lh]|[er]?[a-d]x|[er]?[sb]p|[er]?[sd]i|[c-gs]s|st[0-7]|'
-                r'mm[0-7]|cr[0-4]|dr[0-367]|tr[3-7]|k[0-7]|'
-                r'[xyz]mm(?:[12][0-9]?|3[01]?|[04-9]))\b')
-    wordop = r'seg|wrt|strict|rel|abs'
-    type = r'byte|[dq]?word'
-    # Directives must be followed by whitespace, otherwise CPU will match
-    # cpuid for instance.
-    directives = (r'(?:BITS|USE16|USE32|SECTION|SEGMENT|ABSOLUTE|EXTERN|GLOBAL|'
-                  r'ORG|ALIGN|STRUC|ENDSTRUC|COMMON|CPU|GROUP|UPPERCASE|IMPORT|'
-                  r'EXPORT|LIBRARY|MODULE)(?=\s)')
-
-    flags = re.IGNORECASE | re.MULTILINE
-    tokens = {
-        'root': [
-            (r'^\s*%', Comment.Preproc, 'preproc'),
-            include('whitespace'),
-            (identifier + ':', Name.Label),
-            (r'(%s)(\s+)(equ)' % identifier,
-                bygroups(Name.Constant, Whitespace, Keyword.Declaration),
-                'instruction-args'),
-            (directives, Keyword, 'instruction-args'),
-            (declkw, Keyword.Declaration, 'instruction-args'),
-            (identifier, Name.Function, 'instruction-args'),
-            (r'[\r\n]+', Whitespace)
-        ],
-        'instruction-args': [
-            (string, String),
-            (hexn, Number.Hex),
-            (octn, Number.Oct),
-            (binn, Number.Bin),
-            (floatn, Number.Float),
-            (decn, Number.Integer),
-            include('punctuation'),
-            (register, Name.Builtin),
-            (identifier, Name.Variable),
-            (r'[\r\n]+', Whitespace, '#pop'),
-            include('whitespace')
-        ],
-        'preproc': [
-            (r'[^;\n]+', Comment.Preproc),
-            (r';.*?\n', Comment.Single, '#pop'),
-            (r'\n', Comment.Preproc, '#pop'),
-        ],
-        'whitespace': [
-            (r'\n', Whitespace),
-            (r'[ \t]+', Whitespace),
-            (r';.*', Comment.Single),
-            (r'#.*', Comment.Single)
-        ],
-        'punctuation': [
-            (r'[,{}():\[\]]+', Punctuation),
-            (r'[&|^<>+*/%~-]+', Operator),
-            (r'[$]+', Keyword.Constant),
-            (wordop, Operator.Word),
-            (type, Keyword.Type)
-        ],
-    }
-
-    def analyse_text(text):
-        # Probably TASM
-        if re.match(r'PROC', text, re.IGNORECASE):
-            return False
-
-
-class NasmObjdumpLexer(ObjdumpLexer):
-    """
-    For the output of ``objdump -d -M intel``.
-
-    .. versionadded:: 2.0
-    """
-    name = 'objdump-nasm'
-    aliases = ['objdump-nasm']
-    filenames = ['*.objdump-intel']
-    mimetypes = ['text/x-nasm-objdump']
-
-    tokens = _objdump_lexer_tokens(NasmLexer)
-
-
-class TasmLexer(RegexLexer):
-    """
-    For Tasm (Turbo Assembler) assembly code.
-    """
-    name = 'TASM'
-    aliases = ['tasm']
-    filenames = ['*.asm', '*.ASM', '*.tasm']
-    mimetypes = ['text/x-tasm']
-
-    identifier = r'[@a-z$._?][\w$.?#@~]*'
-    hexn = r'(?:0x[0-9a-f]+|$0[0-9a-f]*|[0-9]+[0-9a-f]*h)'
-    octn = r'[0-7]+q'
-    binn = r'[01]+b'
-    decn = r'[0-9]+'
-    floatn = decn + r'\.e?' + decn
-    string = r'"(\\"|[^"\n])*"|' + r"'(\\'|[^'\n])*'|" + r"`(\\`|[^`\n])*`"
-    declkw = r'(?:res|d)[bwdqt]|times'
-    register = (r'(r[0-9][0-5]?[bwd]|'
-                r'[a-d][lh]|[er]?[a-d]x|[er]?[sb]p|[er]?[sd]i|[c-gs]s|st[0-7]|'
-                r'mm[0-7]|cr[0-4]|dr[0-367]|tr[3-7])\b')
-    wordop = r'seg|wrt|strict'
-    type = r'byte|[dq]?word'
-    directives = (r'BITS|USE16|USE32|SECTION|SEGMENT|ABSOLUTE|EXTERN|GLOBAL|'
-                  r'ORG|ALIGN|STRUC|ENDSTRUC|ENDS|COMMON|CPU|GROUP|UPPERCASE|INCLUDE|'
-                  r'EXPORT|LIBRARY|MODULE|PROC|ENDP|USES|ARG|DATASEG|UDATASEG|END|IDEAL|'
-                  r'P386|MODEL|ASSUME|CODESEG|SIZE')
-    # T[A-Z][a-z] is more of a convention. Lexer should filter out STRUC definitions
-    # and then 'add' them to datatype somehow.
-    datatype = (r'db|dd|dw|T[A-Z][a-z]+')
-
-    flags = re.IGNORECASE | re.MULTILINE
-    tokens = {
-        'root': [
-            (r'^\s*%', Comment.Preproc, 'preproc'),
-            include('whitespace'),
-            (identifier + ':', Name.Label),
-            (directives, Keyword, 'instruction-args'),
-            (r'(%s)(\s+)(%s)' % (identifier, datatype),
-                bygroups(Name.Constant, Whitespace, Keyword.Declaration),
-                'instruction-args'),
-            (declkw, Keyword.Declaration, 'instruction-args'),
-            (identifier, Name.Function, 'instruction-args'),
-            (r'[\r\n]+', Whitespace)
-        ],
-        'instruction-args': [
-            (string, String),
-            (hexn, Number.Hex),
-            (octn, Number.Oct),
-            (binn, Number.Bin),
-            (floatn, Number.Float),
-            (decn, Number.Integer),
-            include('punctuation'),
-            (register, Name.Builtin),
-            (identifier, Name.Variable),
-            # Do not match newline when it's preceded by a backslash
-            (r'(\\)(\s*)(;.*)([\r\n])',
-             bygroups(Text, Whitespace, Comment.Single, Whitespace)),
-            (r'[\r\n]+', Whitespace, '#pop'),
-            include('whitespace')
-        ],
-        'preproc': [
-            (r'[^;\n]+', Comment.Preproc),
-            (r';.*?\n', Comment.Single, '#pop'),
-            (r'\n', Comment.Preproc, '#pop'),
-        ],
-        'whitespace': [
-            (r'[\n\r]', Whitespace),
-            (r'(\\)([\n\r])', bygroups(Text, Whitespace)),
-            (r'[ \t]+', Whitespace),
-            (r';.*', Comment.Single)
-        ],
-        'punctuation': [
-            (r'[,():\[\]]+', Punctuation),
-            (r'[&|^<>+*=/%~-]+', Operator),
-            (r'[$]+', Keyword.Constant),
-            (wordop, Operator.Word),
-            (type, Keyword.Type)
-        ],
-    }
-
-    def analyse_text(text):
-        # See above
-        if re.match(r'PROC', text, re.I):
-            return True
-
-
-class Ca65Lexer(RegexLexer):
-    """
-    For ca65 assembler sources.
-
-    .. versionadded:: 1.6
-    """
-    name = 'ca65 assembler'
-    aliases = ['ca65']
-    filenames = ['*.s']
-
-    flags = re.IGNORECASE
-
-    tokens = {
-        'root': [
-            (r';.*', Comment.Single),
-            (r'\s+', Whitespace),
-            (r'[a-z_.@$][\w.@$]*:', Name.Label),
-            (r'((ld|st)[axy]|(in|de)[cxy]|asl|lsr|ro[lr]|adc|sbc|cmp|cp[xy]'
-             r'|cl[cvdi]|se[cdi]|jmp|jsr|bne|beq|bpl|bmi|bvc|bvs|bcc|bcs'
-             r'|p[lh][ap]|rt[is]|brk|nop|ta[xy]|t[xy]a|txs|tsx|and|ora|eor'
-             r'|bit)\b', Keyword),
-            (r'\.\w+', Keyword.Pseudo),
-            (r'[-+~*/^&|!<>=]', Operator),
-            (r'"[^"\n]*.', String),
-            (r"'[^'\n]*.", String.Char),
-            (r'\$[0-9a-f]+|[0-9a-f]+h\b', Number.Hex),
-            (r'\d+', Number.Integer),
-            (r'%[01]+', Number.Bin),
-            (r'[#,.:()=\[\]]', Punctuation),
-            (r'[a-z_.@$][\w.@$]*', Name),
-        ]
-    }
-
-    def analyse_text(self, text):
-        # comments in GAS start with "#"
-        if re.search(r'^\s*;', text, re.MULTILINE):
-            return 0.9
-
-
-class Dasm16Lexer(RegexLexer):
-    """
-    For DCPU-16 Assembly.
-
-    .. versionadded:: 2.4
-    """
-    name = 'DASM16'
-    url = 'http://0x10c.com/doc/dcpu-16.txt'
-    aliases = ['dasm16']
-    filenames = ['*.dasm16', '*.dasm']
-    mimetypes = ['text/x-dasm16']
-
-    INSTRUCTIONS = [
-        'SET',
-        'ADD', 'SUB',
-        'MUL', 'MLI',
-        'DIV', 'DVI',
-        'MOD', 'MDI',
-        'AND', 'BOR', 'XOR',
-        'SHR', 'ASR', 'SHL',
-        'IFB', 'IFC', 'IFE', 'IFN', 'IFG', 'IFA', 'IFL', 'IFU',
-        'ADX', 'SBX',
-        'STI', 'STD',
-        'JSR',
-        'INT', 'IAG', 'IAS', 'RFI', 'IAQ', 'HWN', 'HWQ', 'HWI',
-    ]
-
-    REGISTERS = [
-        'A', 'B', 'C',
-        'X', 'Y', 'Z',
-        'I', 'J',
-        'SP', 'PC', 'EX',
-        'POP', 'PEEK', 'PUSH'
-    ]
-
-    # Regexes yo
-    char = r'[a-zA-Z0-9_$@.]'
-    identifier = r'(?:[a-zA-Z$_]' + char + r'*|\.' + char + '+)'
-    number = r'[+-]?(?:0[xX][a-zA-Z0-9]+|\d+)'
-    binary_number = r'0b[01_]+'
-    instruction = r'(?i)(' + '|'.join(INSTRUCTIONS) + ')'
-    single_char = r"'\\?" + char + "'"
-    string = r'"(\\"|[^"])*"'
-
-    def guess_identifier(lexer, match):
-        ident = match.group(0)
-        klass = Name.Variable if ident.upper() in lexer.REGISTERS else Name.Label
-        yield match.start(), klass, ident
-
-    tokens = {
-        'root': [
-            include('whitespace'),
-            (':' + identifier, Name.Label),
-            (identifier + ':', Name.Label),
-            (instruction, Name.Function, 'instruction-args'),
-            (r'\.' + identifier, Name.Function, 'data-args'),
-            (r'[\r\n]+', Whitespace)
-        ],
-
-        'numeric' : [
-            (binary_number, Number.Integer),
-            (number, Number.Integer),
-            (single_char, String),
-        ],
-
-        'arg' : [
-            (identifier, guess_identifier),
-            include('numeric')
-        ],
-
-        'deref' : [
-            (r'\+', Punctuation),
-            (r'\]', Punctuation, '#pop'),
-            include('arg'),
-            include('whitespace')
-        ],
-
-        'instruction-line' : [
-            (r'[\r\n]+', Whitespace, '#pop'),
-            (r';.*?$', Comment, '#pop'),
-            include('whitespace')
-        ],
-
-        'instruction-args': [
-            (r',', Punctuation),
-            (r'\[', Punctuation, 'deref'),
-            include('arg'),
-            include('instruction-line')
-        ],
-
-        'data-args' : [
-            (r',', Punctuation),
-            include('numeric'),
-            (string, String),
-            include('instruction-line')
-        ],
-
-        'whitespace': [
-            (r'\n', Whitespace),
-            (r'\s+', Whitespace),
-            (r';.*?\n', Comment)
-        ],
-    }
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/solidity.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/solidity.py
deleted file mode 100644
index f1654e46fad0d391c24cab6dc921bbd912551f57..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/solidity.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""
-    pygments.lexers.solidity
-    ~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for Solidity.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import RegexLexer, bygroups, include, words
-from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
-    Number, Punctuation, Whitespace
-
-__all__ = ['SolidityLexer']
-
-
-class SolidityLexer(RegexLexer):
-    """
-    For Solidity source code.
-
-    .. versionadded:: 2.5
-    """
-
-    name = 'Solidity'
-    aliases = ['solidity']
-    filenames = ['*.sol']
-    mimetypes = []
-
-    datatype = (
-        r'\b(address|bool|(?:(?:bytes|hash|int|string|uint)(?:8|16|24|32|40|48|56|64'
-        r'|72|80|88|96|104|112|120|128|136|144|152|160|168|176|184|192|200|208'
-        r'|216|224|232|240|248|256)?))\b'
-    )
-
-    tokens = {
-        'root': [
-            include('whitespace'),
-            include('comments'),
-            (r'\bpragma\s+solidity\b', Keyword, 'pragma'),
-            (r'\b(contract)(\s+)([a-zA-Z_]\w*)',
-             bygroups(Keyword, Whitespace, Name.Entity)),
-            (datatype + r'(\s+)((?:external|public|internal|private)\s+)?' +
-             r'([a-zA-Z_]\w*)',
-             bygroups(Keyword.Type, Whitespace, Keyword, Name.Variable)),
-            (r'\b(enum|event|function|struct)(\s+)([a-zA-Z_]\w*)',
-             bygroups(Keyword.Type, Whitespace, Name.Variable)),
-            (r'\b(msg|block|tx)\.([A-Za-z_][a-zA-Z0-9_]*)\b', Keyword),
-            (words((
-                'block', 'break', 'constant', 'constructor', 'continue',
-                'contract', 'do', 'else', 'external', 'false', 'for',
-                'function', 'if', 'import', 'inherited', 'internal', 'is',
-                'library', 'mapping', 'memory', 'modifier', 'msg', 'new',
-                'payable', 'private', 'public', 'require', 'return',
-                'returns', 'struct', 'suicide', 'throw', 'this', 'true',
-                'tx', 'var', 'while'), prefix=r'\b', suffix=r'\b'),
-             Keyword.Type),
-            (words(('keccak256',), prefix=r'\b', suffix=r'\b'), Name.Builtin),
-            (datatype, Keyword.Type),
-            include('constants'),
-            (r'[a-zA-Z_]\w*', Text),
-            (r'[~!%^&*+=|?:<>/-]', Operator),
-            (r'[.;{}(),\[\]]', Punctuation)
-        ],
-        'comments': [
-            (r'//(\n|[\w\W]*?[^\\]\n)', Comment.Single),
-            (r'/(\\\n)?[*][\w\W]*?[*](\\\n)?/', Comment.Multiline),
-            (r'/(\\\n)?[*][\w\W]*', Comment.Multiline)
-        ],
-        'constants': [
-            (r'("(\\"|.)*?")', String.Double),
-            (r"('(\\'|.)*?')", String.Single),
-            (r'\b0[xX][0-9a-fA-F]+\b', Number.Hex),
-            (r'\b\d+\b', Number.Decimal),
-        ],
-        'pragma': [
-            include('whitespace'),
-            include('comments'),
-            (r'(\^|>=|<)(\s*)(\d+\.\d+\.\d+)',
-             bygroups(Operator, Whitespace, Keyword)),
-            (r';', Punctuation, '#pop')
-        ],
-        'whitespace': [
-            (r'\s+', Whitespace),
-            (r'\n', Whitespace)
-        ]
-    }
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/url.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/url.py
deleted file mode 100644
index d53ea932a0309181a4e07596c773f3765eb36977..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/url.py
+++ /dev/null
@@ -1,471 +0,0 @@
-from __future__ import annotations
-
-import re
-import typing
-
-from ..exceptions import LocationParseError
-from .util import to_str
-
-# We only want to normalize urls with an HTTP(S) scheme.
-# urllib3 infers URLs without a scheme (None) to be http.
-_NORMALIZABLE_SCHEMES = ("http", "https", None)
-
-# Almost all of these patterns were derived from the
-# 'rfc3986' module: https://github.com/python-hyper/rfc3986
-_PERCENT_RE = re.compile(r"%[a-fA-F0-9]{2}")
-_SCHEME_RE = re.compile(r"^(?:[a-zA-Z][a-zA-Z0-9+-]*:|/)")
-_URI_RE = re.compile(
-    r"^(?:([a-zA-Z][a-zA-Z0-9+.-]*):)?"
-    r"(?://([^\\/?#]*))?"
-    r"([^?#]*)"
-    r"(?:\?([^#]*))?"
-    r"(?:#(.*))?$",
-    re.UNICODE | re.DOTALL,
-)
-
-_IPV4_PAT = r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}"
-_HEX_PAT = "[0-9A-Fa-f]{1,4}"
-_LS32_PAT = "(?:{hex}:{hex}|{ipv4})".format(hex=_HEX_PAT, ipv4=_IPV4_PAT)
-_subs = {"hex": _HEX_PAT, "ls32": _LS32_PAT}
-_variations = [
-    #                            6( h16 ":" ) ls32
-    "(?:%(hex)s:){6}%(ls32)s",
-    #                       "::" 5( h16 ":" ) ls32
-    "::(?:%(hex)s:){5}%(ls32)s",
-    # [               h16 ] "::" 4( h16 ":" ) ls32
-    "(?:%(hex)s)?::(?:%(hex)s:){4}%(ls32)s",
-    # [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32
-    "(?:(?:%(hex)s:)?%(hex)s)?::(?:%(hex)s:){3}%(ls32)s",
-    # [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32
-    "(?:(?:%(hex)s:){0,2}%(hex)s)?::(?:%(hex)s:){2}%(ls32)s",
-    # [ *3( h16 ":" ) h16 ] "::"    h16 ":"   ls32
-    "(?:(?:%(hex)s:){0,3}%(hex)s)?::%(hex)s:%(ls32)s",
-    # [ *4( h16 ":" ) h16 ] "::"              ls32
-    "(?:(?:%(hex)s:){0,4}%(hex)s)?::%(ls32)s",
-    # [ *5( h16 ":" ) h16 ] "::"              h16
-    "(?:(?:%(hex)s:){0,5}%(hex)s)?::%(hex)s",
-    # [ *6( h16 ":" ) h16 ] "::"
-    "(?:(?:%(hex)s:){0,6}%(hex)s)?::",
-]
-
-_UNRESERVED_PAT = r"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._\-~"
-_IPV6_PAT = "(?:" + "|".join([x % _subs for x in _variations]) + ")"
-_ZONE_ID_PAT = "(?:%25|%)(?:[" + _UNRESERVED_PAT + "]|%[a-fA-F0-9]{2})+"
-_IPV6_ADDRZ_PAT = r"\[" + _IPV6_PAT + r"(?:" + _ZONE_ID_PAT + r")?\]"
-_REG_NAME_PAT = r"(?:[^\[\]%:/?#]|%[a-fA-F0-9]{2})*"
-_TARGET_RE = re.compile(r"^(/[^?#]*)(?:\?([^#]*))?(?:#.*)?$")
-
-_IPV4_RE = re.compile("^" + _IPV4_PAT + "$")
-_IPV6_RE = re.compile("^" + _IPV6_PAT + "$")
-_IPV6_ADDRZ_RE = re.compile("^" + _IPV6_ADDRZ_PAT + "$")
-_BRACELESS_IPV6_ADDRZ_RE = re.compile("^" + _IPV6_ADDRZ_PAT[2:-2] + "$")
-_ZONE_ID_RE = re.compile("(" + _ZONE_ID_PAT + r")\]$")
-
-_HOST_PORT_PAT = ("^(%s|%s|%s)(?::0*?(|0|[1-9][0-9]{0,4}))?$") % (
-    _REG_NAME_PAT,
-    _IPV4_PAT,
-    _IPV6_ADDRZ_PAT,
-)
-_HOST_PORT_RE = re.compile(_HOST_PORT_PAT, re.UNICODE | re.DOTALL)
-
-_UNRESERVED_CHARS = set(
-    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._-~"
-)
-_SUB_DELIM_CHARS = set("!$&'()*+,;=")
-_USERINFO_CHARS = _UNRESERVED_CHARS | _SUB_DELIM_CHARS | {":"}
-_PATH_CHARS = _USERINFO_CHARS | {"@", "/"}
-_QUERY_CHARS = _FRAGMENT_CHARS = _PATH_CHARS | {"?"}
-
-
-class Url(
-    typing.NamedTuple(
-        "Url",
-        [
-            ("scheme", typing.Optional[str]),
-            ("auth", typing.Optional[str]),
-            ("host", typing.Optional[str]),
-            ("port", typing.Optional[int]),
-            ("path", typing.Optional[str]),
-            ("query", typing.Optional[str]),
-            ("fragment", typing.Optional[str]),
-        ],
-    )
-):
-    """
-    Data structure for representing an HTTP URL. Used as a return value for
-    :func:`parse_url`. Both the scheme and host are normalized as they are
-    both case-insensitive according to RFC 3986.
-    """
-
-    def __new__(  # type: ignore[no-untyped-def]
-        cls,
-        scheme: str | None = None,
-        auth: str | None = None,
-        host: str | None = None,
-        port: int | None = None,
-        path: str | None = None,
-        query: str | None = None,
-        fragment: str | None = None,
-    ):
-        if path and not path.startswith("/"):
-            path = "/" + path
-        if scheme is not None:
-            scheme = scheme.lower()
-        return super().__new__(cls, scheme, auth, host, port, path, query, fragment)
-
-    @property
-    def hostname(self) -> str | None:
-        """For backwards-compatibility with urlparse. We're nice like that."""
-        return self.host
-
-    @property
-    def request_uri(self) -> str:
-        """Absolute path including the query string."""
-        uri = self.path or "/"
-
-        if self.query is not None:
-            uri += "?" + self.query
-
-        return uri
-
-    @property
-    def authority(self) -> str | None:
-        """
-        Authority component as defined in RFC 3986 3.2.
-        This includes userinfo (auth), host and port.
-
-        i.e.
-            userinfo@host:port
-        """
-        userinfo = self.auth
-        netloc = self.netloc
-        if netloc is None or userinfo is None:
-            return netloc
-        else:
-            return f"{userinfo}@{netloc}"
-
-    @property
-    def netloc(self) -> str | None:
-        """
-        Network location including host and port.
-
-        If you need the equivalent of urllib.parse's ``netloc``,
-        use the ``authority`` property instead.
-        """
-        if self.host is None:
-            return None
-        if self.port:
-            return f"{self.host}:{self.port}"
-        return self.host
-
-    @property
-    def url(self) -> str:
-        """
-        Convert self into a url
-
-        This function should more or less round-trip with :func:`.parse_url`. The
-        returned url may not be exactly the same as the url inputted to
-        :func:`.parse_url`, but it should be equivalent by the RFC (e.g., urls
-        with a blank port will have : removed).
-
-        Example:
-
-        .. code-block:: python
-
-            import urllib3
-
-            U = urllib3.util.parse_url("https://google.com/mail/")
-
-            print(U.url)
-            # "https://google.com/mail/"
-
-            print( urllib3.util.Url("https", "username:password",
-                                    "host.com", 80, "/path", "query", "fragment"
-                                    ).url
-                )
-            # "https://username:password@host.com:80/path?query#fragment"
-        """
-        scheme, auth, host, port, path, query, fragment = self
-        url = ""
-
-        # We use "is not None" we want things to happen with empty strings (or 0 port)
-        if scheme is not None:
-            url += scheme + "://"
-        if auth is not None:
-            url += auth + "@"
-        if host is not None:
-            url += host
-        if port is not None:
-            url += ":" + str(port)
-        if path is not None:
-            url += path
-        if query is not None:
-            url += "?" + query
-        if fragment is not None:
-            url += "#" + fragment
-
-        return url
-
-    def __str__(self) -> str:
-        return self.url
-
-
-@typing.overload
-def _encode_invalid_chars(
-    component: str, allowed_chars: typing.Container[str]
-) -> str:  # Abstract
-    ...
-
-
-@typing.overload
-def _encode_invalid_chars(
-    component: None, allowed_chars: typing.Container[str]
-) -> None:  # Abstract
-    ...
-
-
-def _encode_invalid_chars(
-    component: str | None, allowed_chars: typing.Container[str]
-) -> str | None:
-    """Percent-encodes a URI component without reapplying
-    onto an already percent-encoded component.
-    """
-    if component is None:
-        return component
-
-    component = to_str(component)
-
-    # Normalize existing percent-encoded bytes.
-    # Try to see if the component we're encoding is already percent-encoded
-    # so we can skip all '%' characters but still encode all others.
-    component, percent_encodings = _PERCENT_RE.subn(
-        lambda match: match.group(0).upper(), component
-    )
-
-    uri_bytes = component.encode("utf-8", "surrogatepass")
-    is_percent_encoded = percent_encodings == uri_bytes.count(b"%")
-    encoded_component = bytearray()
-
-    for i in range(0, len(uri_bytes)):
-        # Will return a single character bytestring
-        byte = uri_bytes[i : i + 1]
-        byte_ord = ord(byte)
-        if (is_percent_encoded and byte == b"%") or (
-            byte_ord < 128 and byte.decode() in allowed_chars
-        ):
-            encoded_component += byte
-            continue
-        encoded_component.extend(b"%" + (hex(byte_ord)[2:].encode().zfill(2).upper()))
-
-    return encoded_component.decode()
-
-
-def _remove_path_dot_segments(path: str) -> str:
-    # See http://tools.ietf.org/html/rfc3986#section-5.2.4 for pseudo-code
-    segments = path.split("/")  # Turn the path into a list of segments
-    output = []  # Initialize the variable to use to store output
-
-    for segment in segments:
-        # '.' is the current directory, so ignore it, it is superfluous
-        if segment == ".":
-            continue
-        # Anything other than '..', should be appended to the output
-        if segment != "..":
-            output.append(segment)
-        # In this case segment == '..', if we can, we should pop the last
-        # element
-        elif output:
-            output.pop()
-
-    # If the path starts with '/' and the output is empty or the first string
-    # is non-empty
-    if path.startswith("/") and (not output or output[0]):
-        output.insert(0, "")
-
-    # If the path starts with '/.' or '/..' ensure we add one more empty
-    # string to add a trailing '/'
-    if path.endswith(("/.", "/..")):
-        output.append("")
-
-    return "/".join(output)
-
-
-@typing.overload
-def _normalize_host(host: None, scheme: str | None) -> None:
-    ...
-
-
-@typing.overload
-def _normalize_host(host: str, scheme: str | None) -> str:
-    ...
-
-
-def _normalize_host(host: str | None, scheme: str | None) -> str | None:
-    if host:
-        if scheme in _NORMALIZABLE_SCHEMES:
-            is_ipv6 = _IPV6_ADDRZ_RE.match(host)
-            if is_ipv6:
-                # IPv6 hosts of the form 'a::b%zone' are encoded in a URL as
-                # such per RFC 6874: 'a::b%25zone'. Unquote the ZoneID
-                # separator as necessary to return a valid RFC 4007 scoped IP.
-                match = _ZONE_ID_RE.search(host)
-                if match:
-                    start, end = match.span(1)
-                    zone_id = host[start:end]
-
-                    if zone_id.startswith("%25") and zone_id != "%25":
-                        zone_id = zone_id[3:]
-                    else:
-                        zone_id = zone_id[1:]
-                    zone_id = _encode_invalid_chars(zone_id, _UNRESERVED_CHARS)
-                    return f"{host[:start].lower()}%{zone_id}{host[end:]}"
-                else:
-                    return host.lower()
-            elif not _IPV4_RE.match(host):
-                return to_str(
-                    b".".join([_idna_encode(label) for label in host.split(".")]),
-                    "ascii",
-                )
-    return host
-
-
-def _idna_encode(name: str) -> bytes:
-    if not name.isascii():
-        try:
-            import idna
-        except ImportError:
-            raise LocationParseError(
-                "Unable to parse URL without the 'idna' module"
-            ) from None
-
-        try:
-            return idna.encode(name.lower(), strict=True, std3_rules=True)
-        except idna.IDNAError:
-            raise LocationParseError(
-                f"Name '{name}' is not a valid IDNA label"
-            ) from None
-
-    return name.lower().encode("ascii")
-
-
-def _encode_target(target: str) -> str:
-    """Percent-encodes a request target so that there are no invalid characters
-
-    Pre-condition for this function is that 'target' must start with '/'.
-    If that is the case then _TARGET_RE will always produce a match.
-    """
-    match = _TARGET_RE.match(target)
-    if not match:  # Defensive:
-        raise LocationParseError(f"{target!r} is not a valid request URI")
-
-    path, query = match.groups()
-    encoded_target = _encode_invalid_chars(path, _PATH_CHARS)
-    if query is not None:
-        query = _encode_invalid_chars(query, _QUERY_CHARS)
-        encoded_target += "?" + query
-    return encoded_target
-
-
-def parse_url(url: str) -> Url:
-    """
-    Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
-    performed to parse incomplete urls. Fields not provided will be None.
-    This parser is RFC 3986 and RFC 6874 compliant.
-
-    The parser logic and helper functions are based heavily on
-    work done in the ``rfc3986`` module.
-
-    :param str url: URL to parse into a :class:`.Url` namedtuple.
-
-    Partly backwards-compatible with :mod:`urllib.parse`.
-
-    Example:
-
-    .. code-block:: python
-
-        import urllib3
-
-        print( urllib3.util.parse_url('http://google.com/mail/'))
-        # Url(scheme='http', host='google.com', port=None, path='/mail/', ...)
-
-        print( urllib3.util.parse_url('google.com:80'))
-        # Url(scheme=None, host='google.com', port=80, path=None, ...)
-
-        print( urllib3.util.parse_url('/foo?bar'))
-        # Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)
-    """
-    if not url:
-        # Empty
-        return Url()
-
-    source_url = url
-    if not _SCHEME_RE.search(url):
-        url = "//" + url
-
-    scheme: str | None
-    authority: str | None
-    auth: str | None
-    host: str | None
-    port: str | None
-    port_int: int | None
-    path: str | None
-    query: str | None
-    fragment: str | None
-
-    try:
-        scheme, authority, path, query, fragment = _URI_RE.match(url).groups()  # type: ignore[union-attr]
-        normalize_uri = scheme is None or scheme.lower() in _NORMALIZABLE_SCHEMES
-
-        if scheme:
-            scheme = scheme.lower()
-
-        if authority:
-            auth, _, host_port = authority.rpartition("@")
-            auth = auth or None
-            host, port = _HOST_PORT_RE.match(host_port).groups()  # type: ignore[union-attr]
-            if auth and normalize_uri:
-                auth = _encode_invalid_chars(auth, _USERINFO_CHARS)
-            if port == "":
-                port = None
-        else:
-            auth, host, port = None, None, None
-
-        if port is not None:
-            port_int = int(port)
-            if not (0 <= port_int <= 65535):
-                raise LocationParseError(url)
-        else:
-            port_int = None
-
-        host = _normalize_host(host, scheme)
-
-        if normalize_uri and path:
-            path = _remove_path_dot_segments(path)
-            path = _encode_invalid_chars(path, _PATH_CHARS)
-        if normalize_uri and query:
-            query = _encode_invalid_chars(query, _QUERY_CHARS)
-        if normalize_uri and fragment:
-            fragment = _encode_invalid_chars(fragment, _FRAGMENT_CHARS)
-
-    except (ValueError, AttributeError) as e:
-        raise LocationParseError(source_url) from e
-
-    # For the sake of backwards compatibility we put empty
-    # string values for path if there are any defined values
-    # beyond the path in the URL.
-    # TODO: Remove this when we break backwards compatibility.
-    if not path:
-        if query is not None or fragment is not None:
-            path = ""
-        else:
-            path = None
-
-    return Url(
-        scheme=scheme,
-        auth=auth,
-        host=host,
-        port=port_int,
-        path=path,
-        query=query,
-        fragment=fragment,
-    )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/uvicorn/loops/auto.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/uvicorn/loops/auto.py
deleted file mode 100644
index 2285457bf181d31d36b74ba6c368eac604178645..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/uvicorn/loops/auto.py
+++ /dev/null
@@ -1,11 +0,0 @@
-def auto_loop_setup(use_subprocess: bool = False) -> None:
-    try:
-        import uvloop  # noqa
-    except ImportError:  # pragma: no cover
-        from uvicorn.loops.asyncio import asyncio_setup as loop_setup
-
-        loop_setup(use_subprocess=use_subprocess)
-    else:  # pragma: no cover
-        from uvicorn.loops.uvloop import uvloop_setup
-
-        uvloop_setup(use_subprocess=use_subprocess)
diff --git a/spaces/pseudolab/Rice_Disease_Classifier/README.md b/spaces/pseudolab/Rice_Disease_Classifier/README.md
deleted file mode 100644
index 2ab4aebe218676d37a6fb194259d911ab2f49b79..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/Rice_Disease_Classifier/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AutoTrain Advanced
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pyodide-demo/self-hosted/asciitree.js b/spaces/pyodide-demo/self-hosted/asciitree.js
deleted file mode 100644
index f82e962b2b117bd48c1954e4593abf891340011c..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/asciitree.js
+++ /dev/null
@@ -1 +0,0 @@
-var Module=typeof globalThis.__pyodide_module!=="undefined"?globalThis.__pyodide_module:{};if(!Module.expectedDataFileDownloads){Module.expectedDataFileDownloads=0}Module.expectedDataFileDownloads++;(function(){var loadPackage=function(metadata){var PACKAGE_PATH="";if(typeof window==="object"){PACKAGE_PATH=window["encodeURIComponent"](window.location.pathname.toString().substring(0,window.location.pathname.toString().lastIndexOf("/"))+"/")}else if(typeof process==="undefined"&&typeof location!=="undefined"){PACKAGE_PATH=encodeURIComponent(location.pathname.toString().substring(0,location.pathname.toString().lastIndexOf("/"))+"/")}var PACKAGE_NAME="asciitree.data";var REMOTE_PACKAGE_BASE="asciitree.data";if(typeof Module["locateFilePackage"]==="function"&&!Module["locateFile"]){Module["locateFile"]=Module["locateFilePackage"];err("warning: you defined Module.locateFilePackage, that has been renamed to Module.locateFile (using your locateFilePackage for now)")}var REMOTE_PACKAGE_NAME=Module["locateFile"]?Module["locateFile"](REMOTE_PACKAGE_BASE,""):REMOTE_PACKAGE_BASE;var REMOTE_PACKAGE_SIZE=metadata["remote_package_size"];var PACKAGE_UUID=metadata["package_uuid"];function fetchRemotePackage(packageName,packageSize,callback,errback){if(typeof process==="object"){require("fs").readFile(packageName,(function(err,contents){if(err){errback(err)}else{callback(contents.buffer)}}));return}var xhr=new XMLHttpRequest;xhr.open("GET",packageName,true);xhr.responseType="arraybuffer";xhr.onprogress=function(event){var url=packageName;var size=packageSize;if(event.total)size=event.total;if(event.loaded){if(!xhr.addedTotal){xhr.addedTotal=true;if(!Module.dataFileDownloads)Module.dataFileDownloads={};Module.dataFileDownloads[url]={loaded:event.loaded,total:size}}else{Module.dataFileDownloads[url].loaded=event.loaded}var total=0;var loaded=0;var num=0;for(var download in Module.dataFileDownloads){var data=Module.dataFileDownloads[download];total+=data.total;loaded+=data.loaded;num++}total=Math.ceil(total*Module.expectedDataFileDownloads/num);if(Module["setStatus"])Module["setStatus"]("Downloading data... ("+loaded+"/"+total+")")}else if(!Module.dataFileDownloads){if(Module["setStatus"])Module["setStatus"]("Downloading data...")}};xhr.onerror=function(event){throw new Error("NetworkError for: "+packageName)};xhr.onload=function(event){if(xhr.status==200||xhr.status==304||xhr.status==206||xhr.status==0&&xhr.response){var packageData=xhr.response;callback(packageData)}else{throw new Error(xhr.statusText+" : "+xhr.responseURL)}};xhr.send(null)}function handleError(error){console.error("package error:",error)}var fetchedCallback=null;var fetched=Module["getPreloadedPackage"]?Module["getPreloadedPackage"](REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE):null;if(!fetched)fetchRemotePackage(REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE,(function(data){if(fetchedCallback){fetchedCallback(data);fetchedCallback=null}else{fetched=data}}),handleError);function runWithFS(){function assert(check,msg){if(!check)throw msg+(new Error).stack}Module["FS_createPath"]("/","lib",true,true);Module["FS_createPath"]("/lib","python3.9",true,true);Module["FS_createPath"]("/lib/python3.9","site-packages",true,true);Module["FS_createPath"]("/lib/python3.9/site-packages","asciitree",true,true);Module["FS_createPath"]("/lib/python3.9/site-packages","asciitree-0.3.3-py3.9.egg-info",true,true);function processPackageData(arrayBuffer){assert(arrayBuffer,"Loading data file failed.");assert(arrayBuffer instanceof ArrayBuffer,"bad input to processPackageData");var byteArray=new Uint8Array(arrayBuffer);var curr;var compressedData={data:null,cachedOffset:4362,cachedIndexes:[-1,-1],cachedChunks:[null,null],offsets:[0,1027,1949,3163],sizes:[1027,922,1214,1199],successes:[1,1,1,1]};compressedData["data"]=byteArray;assert(typeof Module.LZ4==="object","LZ4 not present - was your app build with  -s LZ4=1  ?");Module.LZ4.loadPackage({metadata:metadata,compressedData:compressedData},true);Module["removeRunDependency"]("datafile_asciitree.data")}Module["addRunDependency"]("datafile_asciitree.data");if(!Module.preloadResults)Module.preloadResults={};Module.preloadResults[PACKAGE_NAME]={fromCache:false};if(fetched){processPackageData(fetched);fetched=null}else{fetchedCallback=processPackageData}}if(Module["calledRun"]){runWithFS()}else{if(!Module["preRun"])Module["preRun"]=[];Module["preRun"].push(runWithFS)}};loadPackage({files:[{filename:"/lib/python3.9/site-packages/asciitree/util.py",start:0,end:136,audio:0},{filename:"/lib/python3.9/site-packages/asciitree/traversal.py",start:136,end:1278,audio:0},{filename:"/lib/python3.9/site-packages/asciitree/drawing.py",start:1278,end:4187,audio:0},{filename:"/lib/python3.9/site-packages/asciitree/__init__.py",start:4187,end:6686,audio:0},{filename:"/lib/python3.9/site-packages/asciitree-0.3.3-py3.9.egg-info/top_level.txt",start:6686,end:6696,audio:0},{filename:"/lib/python3.9/site-packages/asciitree-0.3.3-py3.9.egg-info/dependency_links.txt",start:6696,end:6697,audio:0},{filename:"/lib/python3.9/site-packages/asciitree-0.3.3-py3.9.egg-info/SOURCES.txt",start:6697,end:6962,audio:0},{filename:"/lib/python3.9/site-packages/asciitree-0.3.3-py3.9.egg-info/PKG-INFO",start:6962,end:7979,audio:0}],remote_package_size:8458,package_uuid:"a1e1580a-7465-46ee-aa07-d5ebe3e18b77"})})();
\ No newline at end of file
diff --git a/spaces/pytorch/RoBERTa/app.py b/spaces/pytorch/RoBERTa/app.py
deleted file mode 100644
index c18e978b6f567acfc411dc7e61b940e2cea56af7..0000000000000000000000000000000000000000
--- a/spaces/pytorch/RoBERTa/app.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import gradio as gr
-
-description = "Gradio demo for Roberta, A Robustly Optimized BERT Pretraining Approach. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.11692'>RoBERTa: A Robustly Optimized BERT Pretraining Approach</a> | <a href='https://github.com/pytorch/fairseq/'>Github Repo</a></p>"
-
-
-
-gr.Interface.load("huggingface/roberta-large", inputs=gr.inputs.Textbox(lines=5,label="Input Text"), description=description, article=article,examples=[
-  ["Life is like a box of <mask>"],
-  ["The capital of France is <mask>"]
-]).launch()
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Manthena Satyanarayana Raju Books Pdf Free Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Manthena Satyanarayana Raju Books Pdf Free Download.md
deleted file mode 100644
index 06698ec3337829a771ea696cd2388c59224274bf..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Manthena Satyanarayana Raju Books Pdf Free Download.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-<h1>Manthena Satyanarayana Raju Books Pdf Free Download: A Source of Natural Healing</h1>
-
-<p>If you are looking for a way to improve your health and well-being without relying on drugs or surgery, you may want to check out the books by Dr. Manthena Satyanarayana Raju, a renowned naturopath from India. Dr. Raju has written several books on naturopathy, which is a system of healing that uses natural methods such as diet, exercise, water therapy, and meditation. In this article, we will introduce you to some of his books that you can download for free in pdf format.</p>
-
-<h2>Neeru Meeru: The Importance of Water</h2>
-
-<p>One of the books by Dr. Raju that you can download for free is Neeru Meeru, which means Water - You in Telugu. In this book, Dr. Raju explains how water is essential for our health and how drinking enough water can prevent and cure many diseases. He also gives practical tips on how to drink water properly, when to drink water, and how much water to drink according to your body type and condition. You can download Neeru Meeru from <a href="https://archive.org/details/neeru-meeru">this link</a>.</p>
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-<h2>Edhi Apoha Edhi Nijam: Myths and Truths about Naturopathy</h2>
-
-<p>Another book by Dr. Raju that you can download for free is Edhi Apoha Edhi Nijam, which means What is False - What is True in Telugu. In this book, Dr. Raju debunks some of the common myths that people have about naturopathy and reveals the scientific basis behind its principles and practices. He also answers some of the frequently asked questions about naturopathy and gives examples of real-life cases where naturopathy has helped people recover from various ailments. You can download Edhi Apoha Edhi Nijam from <a href="https://archive.org/details/EdhiApohaEdhiNijam">this link</a>.</p>
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-<h2>Food and Thought: The Connection between Diet and Mind</h2>
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-<p>The third book by Dr. Raju that you can download for free is Food and Thought, which explores the relationship between what we eat and how we think. Dr. Raju explains how different foods affect our physical, mental, and emotional health and how we can choose the right foods for our needs and goals. He also gives advice on how to plan a balanced diet, how to eat mindfully, and how to avoid overeating and cravings. You can download Food and Thought from <a href="https://archive.org/details/41-food-and-thought">this link</a>.</p>
-
-<h3>Conclusion</h3>
-
-<p>These are just some of the books by Dr. Manthena Satyanarayana Raju that you can download for free in pdf format. There are many more books by him that cover various topics related to naturopathy and health. If you want to learn more about his work and philosophy, you can visit his website <a href="http://www.manthenasatyanarayanaraju.com/">here</a>. You can also watch his videos on YouTube <a href="https://www.youtube.com/channel/UC4E9Uz7-9-5yUicufyZouSQ">here</a>. We hope that you find his books useful and inspiring for your journey towards natural healing.</p>
-<p></p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Micromax G3100 Gameplayer Games Free !FREE! Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Micromax G3100 Gameplayer Games Free !FREE! Download.md
deleted file mode 100644
index 768751875fdb6648f94cbac867665722f554fb74..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Micromax G3100 Gameplayer Games Free !FREE! Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Micromax G3100 Gameplayer Games Free Download</h2><br /><p><b><b>Download Zip</b> &#187; <a href="https://geags.com/2uCsVE">https://geags.com/2uCsVE</a></b></p><br /><br />
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diff --git a/spaces/radames/PIFu-Clothed-Human-Digitization/README.md b/spaces/radames/PIFu-Clothed-Human-Digitization/README.md
deleted file mode 100644
index 374b8ce74028785586e1479cb5a9be5b8cb6a236..0000000000000000000000000000000000000000
--- a/spaces/radames/PIFu-Clothed-Human-Digitization/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PIFu Clothed Human Digitization
-emoji: 🧍🏽‍♀️🧍🏻🧍🏽‍♂️   
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.2
-app_file: ./PIFu/spaces.py
-pinned: false
-python_version: 3.7.13
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/.github/ISSUE_TEMPLATE/feature_request.md b/spaces/raedeXanto/academic-chatgpt-beta/.github/ISSUE_TEMPLATE/feature_request.md
deleted file mode 100644
index e46a4c01e804aa4b649bd40af6c13d5981c873d4..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/.github/ISSUE_TEMPLATE/feature_request.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: ''
-assignees: ''
-
----
-
-
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Alcpt Form 80 Test [FULL Version] Download.210.md b/spaces/raedeXanto/academic-chatgpt-beta/Alcpt Form 80 Test [FULL Version] Download.210.md
deleted file mode 100644
index 12d842a2034cb9fb55da89a5ec2c49c22721dbf6..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Alcpt Form 80 Test [FULL Version] Download.210.md	
+++ /dev/null
@@ -1,85 +0,0 @@
-
-<h1><b>ALCPT Form 80 Test: What You Need to Know</b></h1>
-<p>If you are a foreign military or US government-sponsored English language learner, you may have heard of the American Language Course Placement Test (ALCPT). This test is designed to measure your English proficiency through listening and reading. But did you know that there are different forms of ALCPT, each with its own characteristics and difficulty level? In this article, we will focus on one of the most recent and challenging forms of ALCPT: form 80. We will explain what it is, how it differs from other forms, and how you can prepare for it effectively.</p>
-<h2>alcpt form 80 test [FULL Version] download.210</h2><br /><p><b><b>Download File</b> &#10037;&#10037;&#10037; <a href="https://tinourl.com/2uL4tz">https://tinourl.com/2uL4tz</a></b></p><br /><br />
- <h2><b>What is ALCPT and why it is important</b></h2>
-<p>ALCPT is an English language test developed by the Defense Language Institute English Language Center (DLIELC) for foreign military and US government-sponsored English language training programs around the world. It measures your English ability levels through listening and reading. The test consists of 100 multiple-choice items, divided into two sections: listening (50 items) and reading (50 items). The test takes about an hour to complete.</p>
-<p>ALCPT is important because it helps you determine your current level of English proficiency, which can affect your eligibility for certain training courses or assignments. It also helps you monitor your progress and identify your strengths and weaknesses in English. Moreover, ALCPT can help you prepare for other standardized English tests, such as TOEFL or IELTS, by familiarizing you with the format and content of these tests.</p>
- <h2><b>What is ALCPT form 80 and how it differs from other forms</b></h2>
-<p>ALCPT form 80 is one of the latest and most difficult forms of ALCPT. It was released in February 2021 and has been used by many testing centers since then. Unlike other forms of ALCPT, which range from form 1 to form 127, form 80 has a higher level of difficulty and complexity. It requires you to have a more advanced vocabulary, grammar, and comprehension skills. It also covers a wider range of topics, such as science, technology, culture, politics, and military affairs.</p>
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-<p>Another difference between ALCPT form 80 and other forms is that it has a different scoring system. While other forms use a scale from 10 to 100, form 80 uses a scale from 20 to 120. This means that you need to score at least 20 points on each section (listening and reading) to pass the test. The highest possible score on form 80 is 120 points.</p>
- <h2><b>How to prepare for ALCPT form 80 test</b></h2>
-<p>Preparing for ALCPT form 80 test can be challenging, but not impossible. Here are some tips that can help you ace this test:</p>
-<ul>
-<li><b>Improve your listening and reading skills.</b> The best way to improve your listening and reading skills is to expose yourself to authentic English materials as much as possible. You can listen to podcasts, radio shows, audiobooks, or videos on topics that interest you or relate to your field of study or work. You can also read articles, books, magazines, or blogs on various subjects. Try to understand the main idea, the details, and the tone of each material. Take notes, summarize, or paraphrase what you hear or read.</li>
-<li><b>Avoid common mistakes.</b> Some common mistakes that can lower your score on ALCPT form 80 are: guessing randomly, skipping questions, not following instructions, not checking your answers, or running out of time. To avoid these mistakes, you should practice with sample tests or previous forms of ALCPT. You should also read each question carefully, eliminate wrong answers, mark difficult questions for later review, and manage your time wisely.</li>
-<li><b>Find more resources and practice materials.</b> There are many online resources and practice materials that can help you prepare for ALCPT form 80 test. For example, you can visit the official website of DLIELC (https://www.dlielc.edu/testing/alcpt_test.php) where you can find more information about the test format, scoring system, administration procedures, and sample questions. You can also download ALCPT form 80 [FULL Version] from this link: https://www.alcptenglish.com/2021/02/alcpt-forms.html. This website also offers other forms of ALCPT that you can use for practice.</li>
-</ul>
- <h2><b>Conclusion</b></h2>
-<p>In conclusion, ALCPT form 80 test is a challenging but rewarding test that can help you assess your English proficiency level and prepare for other standardized tests. To succeed on this test, you need to have a good command of English vocabulary, grammar, and comprehension skills. You also need to avoid common mistakes and practice with authentic materials and resources. By following these tips, you can achieve your desired score on ALCPT form 80 test.</p>
- <h3><b>Frequently Asked Questions</b></h3>
-<ol>
-<li><b>What is the passing score for ALCPT form 80?</b></li>
-<p>The passing score for ALCPT form 80 is at least 20 points on each section (listening and reading). The highest possible score on this test is 120 points.</p>
-<li><b>How long does it take to complete ALCPT form 80?</b></li>
-<p>ALCPT form 80 takes about an hour to complete. The listening section takes about half an hour and the reading section takes about another half an hour.</p>
-<li><b>How often can I take ALCPT form 80?</b></li>
-<p>You can take ALCPT form 80 once every six months. However, if you fail the test twice in a row, you have to wait for one year before taking it again.</p>
-<li><b>Where can I take ALCPT form 80?</b></li>
-<p>You can take ALCPT form 80 at any authorized testing center around the world. You can find a list of testing centers here: https://www.dlielc.edu/testing/alcpt_test.php#testcenters</p>
-<li><b>How can I get my score report for ALCPT form 80?</b></li>
-<p>You can get your score report for ALCPT form 80 from your testing center within two weeks after taking the test. You can also request a copy of your score report from DLIELC by filling out this form: https://www.dlielc.edu/testing/alcpt_test.php#score_report_request</p>
-</ol>
- </p> 0a6ba089eb<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download Dedh Ishqiya Full Movie Kickass Torrent The Critically Acclaimed Sequel that Surpassed the Original.md b/spaces/raedeXanto/academic-chatgpt-beta/Download Dedh Ishqiya Full Movie Kickass Torrent The Critically Acclaimed Sequel that Surpassed the Original.md
deleted file mode 100644
index a3e4873336ccfccddb3eb3e34edaa3d7b0b35468..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download Dedh Ishqiya Full Movie Kickass Torrent The Critically Acclaimed Sequel that Surpassed the Original.md	
+++ /dev/null
@@ -1,152 +0,0 @@
-<br />
-<h1>Download Dedh Ishqiya Full Movie Kickass Torrent</h1>
-<p>If you are looking for a comedy thriller movie that will keep you entertained and amused, then you should definitely watch Dedh Ishqiya. This movie is a sequel to the 2010 hit Ishqiya, and it stars Naseeruddin Shah, Madhuri Dixit, Arshad Warsi and Huma Qureshi in the lead roles. In this article, we will tell you what Dedh Ishqiya is about, why you should watch it, and how you can download it using a kickass torrent.</p>
- <h2>What is Dedh Ishqiya?</h2>
-<p>Dedh Ishqiya is a 2014 Indian black comedy film directed by Abhishek Chaubey and produced by Raman Maroo and Vishal Bhardwaj. The movie is a sequel to Ishqiya, which was also directed by Chaubey and starred Shah and Warsi. The movie follows the adventures of two conmen, Khalujaan (Shah) and Babban (Warsi), who pose as a nawab and his attendant to woo a widowed begum (Dixit) and her maid (Qureshi). However, they soon find themselves in a web of lies, deceit and danger as they compete with a local gangster (Raaz) for the begum's hand.</p>
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- <h3>A brief summary of the plot</h3>
-<p>The movie begins with Khalujaan and Babban stealing a necklace made of royal jewels from a jewellery shop. They are chased by the police and get separated. Babban escapes, but Khalujaan is injured and loses the necklace. Babban goes to meet his boss Mushtaq Bhai (Shahid), who does not believe him and orders him to dig his own grave. Babban manages to escape and vows to find Khalujaan and the necklace.</p>
-<p>Months later, Babban finds Khalujaan posing as a nawab at a poetry contest organised by Begum Para (Dixit), the widowed begum of Majidabad. The contest is held in accordance with the wishes of her late husband, who wanted her to marry a poet after his death. The winner of the contest will become the new nawab of Majidabad and marry the begum. Babban confronts Khalujaan and tries to get him to return with him, but Khalujaan refuses. He has fallen in love with the begum and wants to win her heart. Babban also falls in love with Muniya (Qureshi), the begum's maid and confidante.</p>
-<p>One of the other contestants is Jaan Mohammed (Raaz), a local politician and gangster who has kidnapped a famous poet Nawab Italwi (Pahwa) and forced him to write poetry for him. Jaan Mohammed is also in love with the begum and wants to marry her by any means. He has also hired a hitman named Liyaqat (Gosain) to kill Khalujaan.</p>
-<p>The movie follows the twists and turns that happen as Khalujaan and Babban try to outsmart Jaan Mohammed and his men, while also wooing the begum and Muniya. They discover that the begum has a dark secret that could change everything. They also find out that Muniya is not who she seems to be. They also have to deal with Mushtaq Bhai, who is still after them for the necklace.</p>
- <h3>The cast and crew of the movie</h3>
-<p>The movie features an ensemble cast of talented actors who deliver memorable performances. Here are some of them:</p>
-<ul>
-<li>Naseeruddin Shah as Khalujaan/Iftekhar: He plays an aging conman who pretends to be a nawab and falls in love with Begum Para. He recites poetry with flair and charm, but also has a cunning side.</li>
-<li>Madhuri Dixit as Begum Para: She plays the widowed begum of Majidabad who organises a poetry contest to find a new husband. She is elegant, graceful and mysterious, but also has a hidden agenda.</li>
-<li>Arshad Warsi as Babban Hussain: He plays Khalujaan's nephew and partner in crime who poses as his attendant. He is witty, sarcastic and street-smart, but also loyal and romantic.</li>
-<li>Huma Qureshi as Muniya/Munira: She plays Begum Para's maid and friend who helps her with her plans. She is beautiful, smart and feisty, but also has a secret identity.</li>
-<li>Vijay Raaz as Jaan Mohammed/Jaanu Bhai: He plays a local politician and gangster who wants to marry Begum Para by force. He is ruthless, ambitious and violent, but also has a soft spot for poetry.</li>
-<li>Manoj Pahwa as Nawab Italwi: He plays a famous poet who is kidnapped by Jaan Mohammed and forced to write poetry for him. He is witty, humorous and clever, but also helpless.</li>
-<li>Salman Shahid as Mushtaq Bhai: He plays Khalujaan's boss who wants him dead for stealing his necklace. He is menacing, cruel and vengeful.</li>
-</ul>
-<p>The movie is directed by Abhishek Chaubey, who also co-wrote the screenplay with Vishal Bhardwaj. Bhardwaj also composed the music for the movie, which features songs written by Gulzar. The cinematography is done by Satyajit Pande (Setu) and the editing by A. Sreekar Prasad.</p>
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- <h3>The awards and nominations received by the movie</h3>
-<p>The movie received critical acclaim for its direction, screenplay, performances, music and cinematography. It was also a moderate commercial success at the box office. It received several awards and nominations at various film festivals and ceremonies. Here are some of them:</p>
-<table>
-<tr><th>Award</th><th>Category</th><th>Nominee(s)</th><th>Result</th></tr>
-<tr><td>Filmfare Awards</td><td>Best Actress</td><td>Madhuri Dixit</td><td>Nominated</td></tr>
-<tr><td>Filmfare Awards</td><td>Best Female Playback Singer</td><td>Rekha Bhardwaj for "Hamari Atariya Pe"</td><td>Nominated</td></tr>
-<tr><td>Filmfare Awards</td><td>Best Dialogue</td><td>Vishal Bhardwaj</td><td>Nominated</td></tr>
-<tr><td>Filmfare Awards</td><td>Best Cinematography</td><td>Satyajit Pande (Setu)</td><td>Nominated</td></tr>
-<tr><td>Filmfare Awards</td><td>Best Choreography</td><td>Remo D'Souza for "Hamari Atariya Pe"</td><td>Nominated</td></tr>
-<tr><td>IIFA Awards</td><td>Best Actress in a Supporting Role</td><td>Huma Qureshi</td><td>Nominated</td></tr>
-<tr><td>IIFA Awards</td><td>Best Story</td><td>Darab Farooqui</td><td>Nominated</td></tr>
-<tr><td>IIFA Awards</td><td>Best Dialogue</td><td>Vishal Bhardwaj</td><td>Nominated</td></tr>
-<tr><td>IIFA Awards</td><td>Best Cinematography</td><td>Satyajit Pande (Setu)</td><td>Nominated</td></tr>
-<h2>Why should you watch Dedh Ishqiya?</h2>
-<p>Dedh Ishqiya is not just a comedy thriller movie, but also a movie that explores the themes of love, betrayal, friendship and poetry. The movie has many reasons to watch it, such as:</p>
- <h3>The reasons to watch the movie</h3>
- <h4>The brilliant performances by the actors</h4>
-<p>The movie boasts of some of the finest actors in the industry, who deliver outstanding performances in their respective roles. Naseeruddin Shah and Arshad Warsi have a great chemistry as the conmen duo, who are both funny and charming. Madhuri Dixit makes a comeback with a stunning performance as the begum, who is graceful and mysterious. Huma Qureshi impresses with her role as the maid, who is smart and feisty. Vijay Raaz is hilarious as the gangster, who is ruthless and romantic. The supporting cast also does a great job in adding to the humor and drama of the movie.</p>
- <h4>The witty and engaging dialogues</h4>
-<p>The movie has some of the most witty and engaging dialogues that will make you laugh and think at the same time. The dialogues are written by Vishal Bhardwaj, who is known for his mastery over language and poetry. The dialogues are full of puns, metaphors, references and innuendos that add to the flavor of the movie. The dialogues also reflect the personalities and emotions of the characters, and create a contrast between their words and actions.</p>
- <h4>The beautiful cinematography and music</h4>
-<p>The movie has a beautiful cinematography that captures the essence of the setting and the mood of the movie. The movie is shot in various locations in Uttar Pradesh, such as Lucknow, Mahoba and Sitapur. The movie showcases the rich culture and heritage of the region, such as the architecture, the costumes, the food and the poetry. The movie also has a soothing and melodious music that complements the story and the characters. The music is composed by Vishal Bhardwaj, who also sings some of the songs himself. The songs are written by Gulzar, who is a legendary poet and lyricist. The songs are sung by various singers, such as Rekha Bhardwaj, Rahat Fateh Ali Khan, Jazim Sharma and Master Saleem.</p>
- <h3>The reviews and ratings of the movie</h3>
-<p>The movie received positive reviews from critics and audiences alike for its direction, screenplay, performances, music and cinematography. The movie has a rating of 7/10 on IMDb , 3.5/5 on Times of India , 4/5 on Hindustan Times  and 3/5 on Rediff . Here are some of the reviews:</p>
-<blockquote>
-<p>"Dedh Ishqiya is a deliciously dark comedy that sparkles with wit and charm." - Rajeev Masand </p>
-</blockquote>
-<blockquote>
-<p>"Dedh Ishqiya entertains you with its smart plot that involves love-lust-dhokha peppered with Urdu shayari." - Meena Iyer </p>
-</blockquote>
-<blockquote>
-<p>"Dedh Ishqiya is a rare treat where you savour every character's lines." - Anupama Chopra </p>
-</blockquote>
-<blockquote>
-<p>"Dedh Ishqiya is an intelligent film that stays true to its genre." - Sukanya Verma </p>
-</blockquote>
- <h2>How to download Dedh Ishqiya full movie kickass torrent?</h2>
-<p>If you want to watch Dedh Ishqiya at your own convenience and comfort, then you can download it using a kickass torrent. A torrent is a file that contains information about other files that are shared by users over a peer-to-peer network. A kickass torrent is a popular website that hosts torrents for various movies, shows, games and software. However, downloading torrents can be risky and illegal in some countries, so you need to be careful and take some precautions while doing so.</p>
- <h3>The steps to download the movie using a torrent client</h3>
-<p>Here are the steps to download Dedh Ishqiya full movie kickass torrent:</p>
-<ol>
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-<li>Go to the kickass torrent website  and search for Dedh Ishqiya full movie in the search bar. You will see a list of results with different qualities and sizes. Choose one that suits your needs and click on it.</li>
-<li>You will be redirected to another page where you will see more details about the torrent, such as its name, size, seeds, peers, comments and ratings. Seeds are users who have downloaded the file completely and are sharing it with others. Peers are users who are downloading or uploading parts of the file. The more seeds and peers a torrent has, the faster it will download.</li>
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-<p>If you do not want to download torrents or if you cannot access them due to any reason, you can also watch Dedh Ishqiya online using some legal and safe alternatives. Here are some of them:</p>
-<ul>
-<li>Streaming platforms: You can watch Dedh Ishqiya online on various streaming platforms that offer movies and shows on demand. Some of the popular streaming platforms are Netflix , Amazon Prime Video , Disney+ Hotstar  and Zee5 . You can choose any one of them according to your subscription and availability.</li>
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- <h2>Conclusion</h2>
-<p>Dedh Ishqiya is a comedy thriller movie that will keep you entertained and amused with its smart plot, witty dialogues, brilliant performances, beautiful cinematography and music. The movie is a sequel to Ishqiya, but it can also be enjoyed as a standalone movie. The movie is available for download using a kickass torrent, but you need to take some precautions while doing so. You can also watch the movie online using some legal and safe alternatives. We hope this article has helped you in finding out how to download Dedh Ishqiya full movie kickass torrent.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Dedh Ishqiya:</p>
-<ol>
-<li>Is Dedh Ishqiya based on a true story?</li>
-<p>No, Dedh Ishqiya is not based on a true story. It is a fictional story written by Darab Farooqui.</p>
-<li>Is Dedh Ishqiya a remake of another movie?</li>
-<p>No, Dedh Ishqiya is not a remake of another movie. It is an original movie directed by Abhishek Chaubey.</p>
-<li>Is Dedh Ishqiya suitable for children?</li>
-<p>No, Dedh Ishqiya is not suitable for children. It has some scenes and dialogues that may be inappropriate for young viewers. It has been rated Not Rated by IMDb , which means it has not been submitted for rating by any official body.</p>
-<li>Is Dedh Ishqiya available in other languages?</li>
-<p>Yes, Dedh Ishqiya is available in other languages besides Hindi. It has been dubbed in Tamil , Telugu  and Malayalam . It also has subtitles in English , Arabic , French  and Spanish .</p>
-<li>Is there a sequel to Dedh Ishqiya?</li>
-<p>No, there is no sequel to Dedh Ishqiya as of now. However, there have been rumors that the makers are planning to make a third part in the series called Ishqiya 3 . However, nothing has been confirmed yet.</p>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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-
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-<ul>
-<li>What is The Amazing Spider-Man movie and why should you download it in Mp4 format?</li>
-<li>How to download it legally and safely from reputable and authorized platforms?</li>
-<li>What are the features of The Amazing Spider-Man movie that make it stand out from other adaptations of the comic book character?</li>
-<li>What are the benefits of downloading The Amazing Spider-Man movie in Mp4 format for your convenience and enjoyment?</li>
-</ul>
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-<p>If you are interested in downloading The Amazing Spider-Man movie in Mp4 format, we recommend you to visit one of the following platforms:</p>
-<ul>
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-<li><a href="https://www.apple.com/itunes/">iTunes</a></li>
-<li><a href="https://play.google.com/store/movies/details/The_Amazing_Spider_Man?id=5yZzrQY6j7w">Google Play Movies & TV</a></li>
-</ul>
-<p>These platforms offer high quality and secure downloads of The Amazing Spider-Man movie in Mp4 format at reasonable prices. You can also watch the movie online or offline on any device that supports Mp4 playback.</p>
- <p>Don't miss this opportunity to watch one of the best superhero movies of all time. Download The Amazing Spider-Man movie in Mp4 format today and enjoy the amazing adventure of Spider-Man!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about The Amazing Spider-Man movie and downloading it in Mp4 format:</p>
- <h3>Q: When was The Amazing Spider-Man movie released?</h3>
-<p>A: The Amazing Spider-Man movie was released on June 28, 2012 in Tokyo, Japan, and on July 3, 2012 in the United States.</p>
- <h3>Q: How much did The Amazing Spider-Man movie cost to make and how much did it earn?</h3>
-<p>A: The Amazing Spider-Man movie had a production budget of $230 million and a marketing budget of $75 million. It earned $757.9 million worldwide, making it the seventh-highest-grossing film of 2012.</p>
- <h3>Q: Who composed the music for The Amazing Spider-Man movie?</h3>
-<p>A: The music for The Amazing Spider-Man movie was composed by James Horner, who also composed the music for Titanic, Avatar, Braveheart, and many other films.</p>
- <h3>Q: Is The Amazing Spider-Man movie based on a specific comic book storyline?</h3>
-<p>A: The Amazing Spider-Man movie is not based on a specific comic book storyline, but it draws inspiration from various sources, such as the Ultimate Spider-Man comics, the original Stan Lee and Steve Ditko comics, and the Sam Raimi films.</p>
- <h3>Q: Is there a sequel to The Amazing Spider-Man movie?</h3>
-<p>Emma Stone as Gwen Stacy, Jamie Foxx as Max Dillon/Electro, Dane DeHaan as Harry Osborn/Green Goblin, Paul Giamatti as Aleksei Sytsevich/Rhino, and Sally Field as Aunt May. It is the second and final installment of the planned trilogy, as the third film was cancelled due to Sony's deal with Marvel Studios to share the rights of Spider-Man and include him in the Marvel Cinematic Universe.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/ESET Internet Security 11.0.149.0 X32 Crack By Zuket Creation !FREE!.md b/spaces/raedeXanto/academic-chatgpt-beta/ESET Internet Security 11.0.149.0 X32 Crack By Zuket Creation !FREE!.md
deleted file mode 100644
index f235d23cb31d05bf9838f01d6502ac33d3ada282..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/ESET Internet Security 11.0.149.0 X32 Crack By Zuket Creation !FREE!.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>ESET Internet Security 11.0.149.0 x32 Crack By Zuket Creation: How to Download and Install It</h1>
-<p>If you are looking for a reliable and effective antivirus software, you might want to consider ESET Internet Security 11.0.149.0 x32 Crack By Zuket Creation. This is a cracked version of the popular ESET Internet Security software, which offers comprehensive protection against various types of online threats, such as malware, ransomware, phishing, spyware, and more.</p>
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-<p>In this article, we will show you how to download and install ESET Internet Security 11.0.149.0 x32 Crack By Zuket Creation on your Windows PC. Follow these simple steps and enjoy the benefits of this powerful security solution.</p>
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\ No newline at end of file
diff --git a/spaces/ramiin2/AutoGPT/tests/local_cache_test.py b/spaces/ramiin2/AutoGPT/tests/local_cache_test.py
deleted file mode 100644
index bb10862656bb500f319ac231ff5bd5438d6fe7e2..0000000000000000000000000000000000000000
--- a/spaces/ramiin2/AutoGPT/tests/local_cache_test.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# sourcery skip: snake-case-functions
-"""Tests for LocalCache class"""
-import os
-import sys
-import unittest
-
-import pytest
-
-from autogpt.memory.local import LocalCache
-
-
-def mock_config() -> dict:
-    """Mock the Config class"""
-    return type(
-        "MockConfig",
-        (object,),
-        {
-            "debug_mode": False,
-            "continuous_mode": False,
-            "speak_mode": False,
-            "memory_index": "auto-gpt",
-        },
-    )
-
-
-@pytest.mark.integration_test
-class TestLocalCache(unittest.TestCase):
-    """Tests for LocalCache class"""
-
-    def setUp(self) -> None:
-        """Set up the test environment"""
-        self.cfg = mock_config()
-        self.cache = LocalCache(self.cfg)
-
-    def test_add(self) -> None:
-        """Test adding a text to the cache"""
-        text = "Sample text"
-        self.cache.add(text)
-        self.assertIn(text, self.cache.data.texts)
-
-    def test_clear(self) -> None:
-        """Test clearing the cache"""
-        self.cache.clear()
-        self.assertEqual(self.cache.data.texts, [])
-
-    def test_get(self) -> None:
-        """Test getting a text from the cache"""
-        text = "Sample text"
-        self.cache.add(text)
-        result = self.cache.get(text)
-        self.assertEqual(result, [text])
-
-    def test_get_relevant(self) -> None:
-        """Test getting relevant texts from the cache"""
-        text1 = "Sample text 1"
-        text2 = "Sample text 2"
-        self.cache.add(text1)
-        self.cache.add(text2)
-        result = self.cache.get_relevant(text1, 1)
-        self.assertEqual(result, [text1])
-
-    def test_get_stats(self) -> None:
-        """Test getting the cache stats"""
-        text = "Sample text"
-        self.cache.add(text)
-        stats = self.cache.get_stats()
-        self.assertEqual(stats, (4, self.cache.data.embeddings.shape))
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/binary-extensions/index.js b/spaces/rayan-saleh/whisper2notion/server/node_modules/binary-extensions/index.js
deleted file mode 100644
index d46e46886711415a0a1064443c20e8a3704714c3..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/binary-extensions/index.js
+++ /dev/null
@@ -1 +0,0 @@
-module.exports = require('./binary-extensions.json');
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/cookie-signature/Readme.md b/spaces/rayan-saleh/whisper2notion/server/node_modules/cookie-signature/Readme.md
deleted file mode 100644
index 2559e841b02edfdc128176bfbdc0b938209a99ea..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/cookie-signature/Readme.md
+++ /dev/null
@@ -1,42 +0,0 @@
-
-# cookie-signature
-
-  Sign and unsign cookies.
-
-## Example
-
-```js
-var cookie = require('cookie-signature');
-
-var val = cookie.sign('hello', 'tobiiscool');
-val.should.equal('hello.DGDUkGlIkCzPz+C0B064FNgHdEjox7ch8tOBGslZ5QI');
-
-var val = cookie.sign('hello', 'tobiiscool');
-cookie.unsign(val, 'tobiiscool').should.equal('hello');
-cookie.unsign(val, 'luna').should.be.false;
-```
-
-## License 
-
-(The MIT License)
-
-Copyright (c) 2012 LearnBoost &lt;tj@learnboost.com&gt;
-
-Permission is hereby granted, free of charge, to any person obtaining
-a copy of this software and associated documentation files (the
-'Software'), to deal in the Software without restriction, including
-without limitation the rights to use, copy, modify, merge, publish,
-distribute, sublicense, and/or sell copies of the Software, and to
-permit persons to whom the Software is furnished to do so, subject to
-the following conditions:
-
-The above copyright notice and this permission notice shall be
-included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
-TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/3d Sexvilla 2 Model Download ((FREE)).md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/3d Sexvilla 2 Model Download ((FREE)).md
deleted file mode 100644
index d6cf8b3401cff241df53addde1bb7722e5b255a4..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/3d Sexvilla 2 Model Download ((FREE)).md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aventura De Verano 5 Y 6 -incesto- (comic Espanol) _VERIFIED_.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aventura De Verano 5 Y 6 -incesto- (comic Espanol) _VERIFIED_.md
deleted file mode 100644
index bba57e3a83867b48eaa9f9250ef2936fd467b831..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aventura De Verano 5 Y 6 -incesto- (comic Espanol) _VERIFIED_.md	
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deleted file mode 100644
index e96dfbdf5ff989bc1fcd259ba959e4e2324428ad..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Dark Parables The Little Mermaid And The Purple Tide Collectors Edition Torrent Full __EXCLUSIVE__.md	
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diff --git a/spaces/reha/Stick_Tech/hubert/hubert_model.py b/spaces/reha/Stick_Tech/hubert/hubert_model.py
deleted file mode 100644
index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000
--- a/spaces/reha/Stick_Tech/hubert/hubert_model.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/replit/README/README.md b/spaces/replit/README/README.md
deleted file mode 100644
index 0c10a98700301f80a5c4d2e23f7630d9a070a865..0000000000000000000000000000000000000000
--- a/spaces/replit/README/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: README
-emoji: 🧑‍💻
-colorFrom: gray
-colorTo: orange
-sdk: static
-pinned: true
----
-
-![Replit logo](replit-logo.png)
-
-
-With [Replit](https://replit.com), you can build software collaboratively with the power of AI, on any device, without spending a second on setup.
\ No newline at end of file
diff --git a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/lib/mesh_util.py b/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/lib/mesh_util.py
deleted file mode 100644
index 39934219011401e194c61cc00034b12dad4072d3..0000000000000000000000000000000000000000
--- a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/lib/mesh_util.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from skimage import measure
-import numpy as np
-import torch
-from .sdf import create_grid, eval_grid_octree, eval_grid
-from skimage import measure
-
-
-def reconstruction(net, cuda, calib_tensor,
-                   resolution, b_min, b_max,
-                   use_octree=False, num_samples=10000, transform=None):
-    '''
-    Reconstruct meshes from sdf predicted by the network.
-    :param net: a BasePixImpNet object. call image filter beforehead.
-    :param cuda: cuda device
-    :param calib_tensor: calibration tensor
-    :param resolution: resolution of the grid cell
-    :param b_min: bounding box corner [x_min, y_min, z_min]
-    :param b_max: bounding box corner [x_max, y_max, z_max]
-    :param use_octree: whether to use octree acceleration
-    :param num_samples: how many points to query each gpu iteration
-    :return: marching cubes results.
-    '''
-    # First we create a grid by resolution
-    # and transforming matrix for grid coordinates to real world xyz
-    coords, mat = create_grid(resolution, resolution, resolution,
-                              b_min, b_max, transform=transform)
-
-    # Then we define the lambda function for cell evaluation
-    def eval_func(points):
-        points = np.expand_dims(points, axis=0)
-        points = np.repeat(points, net.num_views, axis=0)
-        samples = torch.from_numpy(points).to(device=cuda).float()
-        net.query(samples, calib_tensor)
-        pred = net.get_preds()[0][0]
-        return pred.detach().cpu().numpy()
-
-    # Then we evaluate the grid
-    if use_octree:
-        sdf = eval_grid_octree(coords, eval_func, num_samples=num_samples)
-    else:
-        sdf = eval_grid(coords, eval_func, num_samples=num_samples)
-
-    # Finally we do marching cubes
-    try:
-        verts, faces, normals, values = measure.marching_cubes_lewiner(sdf, 0.5)
-        # transform verts into world coordinate system
-        verts = np.matmul(mat[:3, :3], verts.T) + mat[:3, 3:4]
-        verts = verts.T
-        return verts, faces, normals, values
-    except:
-        print('error cannot marching cubes')
-        return -1
-
-
-def save_obj_mesh(mesh_path, verts, faces):
-    file = open(mesh_path, 'w')
-
-    for v in verts:
-        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
-    file.close()
-
-
-def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
-    file = open(mesh_path, 'w')
-
-    for idx, v in enumerate(verts):
-        c = colors[idx]
-        file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' % (v[0], v[1], v[2], c[0], c[1], c[2]))
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
-    file.close()
-
-
-def save_obj_mesh_with_uv(mesh_path, verts, faces, uvs):
-    file = open(mesh_path, 'w')
-
-    for idx, v in enumerate(verts):
-        vt = uvs[idx]
-        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
-        file.write('vt %.4f %.4f\n' % (vt[0], vt[1]))
-
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d/%d %d/%d %d/%d\n' % (f_plus[0], f_plus[0],
-                                              f_plus[2], f_plus[2],
-                                              f_plus[1], f_plus[1]))
-    file.close()
diff --git a/spaces/rmazarei/mann-e-mann-e_4_rev-1-3/README.md b/spaces/rmazarei/mann-e-mann-e_4_rev-1-3/README.md
deleted file mode 100644
index 9af1b2a7724aa8cc0bed3b28fa0a32c52a3978b8..0000000000000000000000000000000000000000
--- a/spaces/rmazarei/mann-e-mann-e_4_rev-1-3/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mann-e-mann-e 4 Rev-1-3
-emoji: 📚
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/coder/__init__.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/coder/__init__.py
deleted file mode 100644
index e12fd64e12b5e76a014da9bd724f1b6f50b488c4..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/coder/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base_bbox_coder import BaseBBoxCoder
-from .bucketing_bbox_coder import BucketingBBoxCoder
-from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
-from .distance_point_bbox_coder import DistancePointBBoxCoder
-from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder
-from .pseudo_bbox_coder import PseudoBBoxCoder
-from .tblr_bbox_coder import TBLRBBoxCoder
-from .yolo_bbox_coder import YOLOBBoxCoder
-
-__all__ = [
-    'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder',
-    'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder',
-    'BucketingBBoxCoder', 'DistancePointBBoxCoder'
-]
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/faster_rcnn.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/faster_rcnn.py
deleted file mode 100644
index 70fb662f1705997be8d899f4760ab9a3aafec18d..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/faster_rcnn.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class FasterRCNN(TwoStageDetector):
-    """Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
-
-    def __init__(self,
-                 backbone,
-                 rpn_head,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None,
-                 init_cfg=None):
-        super(FasterRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained,
-            init_cfg=init_cfg)
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Cappuccetto Rosso Cartone Ita Torrent.md b/spaces/rorallitri/biomedical-language-models/logs/Cappuccetto Rosso Cartone Ita Torrent.md
deleted file mode 100644
index e8bd2c18ce2949ab6b7b04cbc7417c31b47644b7..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Cappuccetto Rosso Cartone Ita Torrent.md	
+++ /dev/null
@@ -1,91 +0,0 @@
-<br />
-<h1>Cappuccetto Rosso Cartone Ita Torrent: come scaricare e guardare il film d'animazione</h1>
-
-<p>Cappuccetto Rosso è una delle fiabe più amate dai bambini di tutto il mondo. La storia della ragazzina che deve portare una torta alla nonna malata e si imbatte nel lupo cattivo ha affascinato generazioni di lettori e spettatori. Ma sai che esiste anche un film d'animazione dedicato a Cappuccetto Rosso? Si tratta di un capolavoro prodotto dalla Jetlag Productions nel 1995 e distribuito in Italia dalla Stardust. In questo articolo ti spiegheremo come scaricare e guardare Cappuccetto Rosso cartone ita torrent in modo semplice e sicuro.</p>
-<h2>Cappuccetto Rosso Cartone Ita Torrent</h2><br /><p><b><b>Download File</b> &middot;&middot;&middot;&middot;&middot; <a href="https://tinurll.com/2uzmQy">https://tinurll.com/2uzmQy</a></b></p><br /><br />
-
-<h2>Cos'è Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Cappuccetto Rosso cartone ita torrent è il file che ti permette di scaricare il film d'animazione di Cappuccetto Rosso in italiano tramite il protocollo BitTorrent. BitTorrent è un sistema peer-to-peer che consente di condividere file tra utenti senza passare per un server centrale. In questo modo puoi scaricare il film velocemente e gratuitamente, a patto di avere un programma apposito e una buona connessione internet.</p>
-
-<h2>Come scaricare Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Per scaricare Cappuccetto Rosso cartone ita torrent devi seguire questi passi:</p>
-<ul>
-<li>Scarica e installa un programma per BitTorrent, come uTorrent, qBittorrent o BitComet.</li>
-<li>Cerca il file torrent di Cappuccetto Rosso cartone ita su un motore di ricerca dedicato, come Torrentz2, 1337x o The Pirate Bay.</li>
-<li>Scegli il file torrent con più seeders (utenti che condividono il file) e meno leechers (utenti che scaricano il file) per avere una maggiore velocità di download.</li>
-<li>Apri il file torrent con il programma per BitTorrent e avvia il download.</li>
-<li>Aspetta che il download sia completato e apri il file video con un lettore multimediale, come VLC o Windows Media Player.</li>
-</ul>
-
-<h2>Come guardare Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Una volta scaricato Cappuccetto Rosso cartone ita torrent puoi guardare il film d'animazione quando vuoi e quante volte vuoi. Il film dura circa 45 minuti e racconta la fiaba di Cappuccetto Rosso in modo fedele e divertente. Nel film troverai anche dei simpatici personaggi animali che aiuteranno Cappuccetto Rosso a sfuggire al lupo malvagio e a salvare la nonna. Il film è adatto a tutta la famiglia e ti farà rivivere la magia della fiaba classica.</p>
-
-<h2>Perché scegliere Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Ci sono molti motivi per scegliere Cappuccetto Rosso cartone ita torrent come opzione per vedere il film d'animazione. Ecco alcuni dei vantaggi:</p>
-<ul>
-<li>Puoi scaricare il film gratuitamente e legalmente, senza pagare abbonamenti o noleggi.</li>
-<li>Puoi guardare il film in alta qualità e in italiano, senza sottotitoli o doppiaggi.</li>
-<li>Puoi conservare il film sul tuo computer o su una chiavetta USB e portarlo sempre con te.</li>
-<li>Puoi condividere il film con i tuoi amici e parenti, basta che abbiano un programma per BitTorrent.</li>
-</ul>
-
-<p>Cosa aspetti? Scarica subito Cappuccetto Rosso cartone ita torrent e goditi il film d'animazione!</p>
-<p></p>
-<h2>Quali sono le caratteristiche di Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Cappuccetto Rosso cartone ita torrent è un file di dimensioni ridotte, circa 700 MB, che contiene il film d'animazione di Cappuccetto Rosso in formato ISO. Questo significa che puoi masterizzare il file su un CD o un DVD e guardarlo sul tuo lettore domestico. Oppure puoi montare il file su una unità virtuale e guardarlo sul tuo computer. Il film ha una risoluzione di 720x480 pixel e un audio stereo in italiano. Il film è diviso in 12 capitoli che corrispondono alle scene principali della fiaba.</p>
-
-<h2>Quali sono i vantaggi di Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Cappuccetto Rosso cartone ita torrent ha molti vantaggi rispetto ad altre opzioni per vedere il film d'animazione. Ecco alcuni dei benefici:</p>
-<ul>
-<li>Puoi scaricare il film in pochi minuti grazie alla rete BitTorrent che garantisce una elevata velocità di trasferimento.</li>
-<li>Puoi guardare il film in alta qualità e senza interruzioni o pubblicità.</li>
-<li>Puoi goderti il film con la tua famiglia o i tuoi amici, senza limiti di tempo o di visione.</li>
-<li>Puoi apprezzare il film nella sua versione originale, senza modifiche o tagli.</li>
-</ul>
-
-<h2>Quali sono i rischi di Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Cappuccetto Rosso cartone ita torrent è un file sicuro e legale, ma ci sono alcuni rischi che devi tenere in considerazione prima di scaricarlo e guardarlo. Ecco alcuni dei pericoli:</p>
-<ul>
-<li>Potresti incorrere in virus o malware che possono danneggiare il tuo computer o rubare i tuoi dati personali.</li>
-<li>Potresti violare i diritti d'autore dei produttori del film se lo condividi o lo distribuisci senza autorizzazione.</li>
-<li>Potresti ricevere sanzioni o multe da parte delle autorità competenti se non rispetti le norme sul download e la condivisione dei file protetti.</li>
-</ul>
-
-<p>Per evitare questi rischi ti consigliamo di usare un antivirus aggiornato, una VPN per proteggere la tua privacy e il tuo indirizzo IP, e di scaricare solo file da fonti affidabili e verificate.</p>
-<h2>Come scegliere il miglior programma per BitTorrent?</h2>
-
-<p>Per scaricare Cappuccetto Rosso cartone ita torrent hai bisogno di un programma per BitTorrent che sia affidabile, veloce e facile da usare. Ci sono molti programmi disponibili sul mercato, ma non tutti offrono le stesse prestazioni e funzionalità. Ecco alcuni dei criteri che devi considerare per scegliere il miglior programma per BitTorrent:</p>
-<ul>
-<li>La compatibilità con il tuo sistema operativo, che sia Windows, Mac o Linux.</li>
-<li>La sicurezza e la privacy, che ti proteggano da virus, malware e attacchi hacker.</li>
-<li>La velocità e la stabilità, che ti garantiscano un download rapido e senza interruzioni.</li>
-<li>La semplicità e l'usabilità, che ti permettano di gestire facilmente i file torrent e le impostazioni del programma.</li>
-<li>Le funzioni extra, come il controllo remoto, il motore di ricerca integrato, il supporto ai magnet link e ai file RSS.</li>
-</ul>
-
-<p>Tra i programmi più popolari e consigliati per BitTorrent ci sono uTorrent, qBittorrent e BitComet. Puoi scaricarli gratuitamente dai loro siti ufficiali o da altre fonti sicure.</p>
-
-<h2>Come evitare i problemi legali con Cappuccetto Rosso cartone ita torrent?</h2>
-
-<p>Cappuccetto Rosso cartone ita torrent è un file legale che puoi scaricare e guardare senza problemi. Tuttavia, devi essere consapevole che il download e la condivisione di file protetti da diritti d'autore possono comportare delle conseguenze legali se non rispetti le norme vigenti. Per evitare di incorrere in sanzioni o multe da parte delle autorità competenti devi seguire queste regole:</p>
-<ul>
-<li>Scarica e condividi solo file di cui hai il permesso o l'autorizzazione dei proprietari o dei detentori dei diritti.</li>
-<li>Non usare i file scaricati o condivisi per scopi commerciali o di lucro.</li>
-<li>Non modificare o alterare i file scaricati o condivisi senza il consenso dei proprietari o dei detentori dei diritti.</li>
-<li>Rispetta le leggi e le normative del tuo paese e di quello in cui scarichi o condividi i file.</li>
-</ul>
-
-<p>Se hai dei dubbi o delle domande sulle questioni legali legate a Cappuccetto Rosso cartone ita torrent puoi consultare un avvocato specializzato in materia di diritto d'autore e di internet.</p>
-
-<h2>Conclusion</h2>
-
-<p>Cappuccetto Rosso cartone ita torrent è un'ottima opzione per vedere il film d'animazione di Cappuccetto Rosso in italiano. Puoi scaricare il film gratuitamente e legalmente tramite il protocollo BitTorrent e guardarlo in alta qualità sul tuo computer o sul tuo lettore domestico. Il film è fedele alla fiaba originale e ti farà divertire con le avventure di Cappuccetto Rosso e dei suoi amici animali. Per scaricare e guardare Cappuccetto Rosso cartone ita torrent devi solo seguire i passi che ti abbiamo spiegato in questo articolo e tenere in considerazione i vantaggi, i rischi e le regole legate a questo tipo di file. Buona visione!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download Google Now Launcher APK (Official) and Transform Your Android Device into a Google-Powered Machine.md b/spaces/rorallitri/biomedical-language-models/logs/Download Google Now Launcher APK (Official) and Transform Your Android Device into a Google-Powered Machine.md
deleted file mode 100644
index ca2d830443dbc7d7f1c79326c00cb4bd999965b9..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download Google Now Launcher APK (Official) and Transform Your Android Device into a Google-Powered Machine.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-<br />
-<p>The launcher is officially exclusive to Nexus and Google Play Edition devices at this time, but you can, of course, download the APK (a link to which has been provided at the end) to install it on other devices.</p>
-<h2>Download Google Now Launcher APK (Official)</h2><br /><p><b><b>DOWNLOAD</b> &#8250; <a href="https://tinurll.com/2uzmbS">https://tinurll.com/2uzmbS</a></b></p><br /><br />
-<p>Below is a direct link to download to your Android phone or PC or any local storage<br />[CMDM-download id=83140]How To Install Google Now Launcher APK on AndroidTo Install the Google Now launcher Android Application You have to follow few Steps-</p>
-<p>If you love to install launcher APK on your ANdroid to customize, then we have a lot of Android launcher options to download alongside this Google Now Launcher APK. Have a look on the alternate Android Launcher APKs:</p>
-<p>Google Now Launcher's most recent update features Material Design cues and can be downloaded right now from the Google Play Store. After installing, make sure to make it the default launcher. You'll also want to make sure you're on the most recent version of the Google app itself.</p>
-<p></p>
-<p>Are you looking for a best launcher for your android smartphone? We have a really big collection of customizable launchers for you. You can choose any launcher for your mobile phone to customize your home screen as you want. But google launcher apk is the most known and high rating launcher in android market.</p>
-<p>Paid and free versions (having ads) of all of these and a lot of other launchers are found in the Android Marketplace providing by different websites and google play store. There is no need to hack anything or to root your mobile phone to install this nova google companion apk, all a user needs to do is to download the launcher from the website and install it in your mobile phone.</p>
-<p>If you need to change your launcher you need to press the home button the Android system will ask the user to choose what launcher they want to use, and you can use default launcher and google now launcher apk 2021, it depends on your own choice. Users should try several and go with the one that fits their needs the best.</p>
-<p>No doubt ok google now apk download will change around everything in your mobile phone but some specific parts of your mobile phone that will be untouched by google now launcher mod apk. Do you want to know what those things that are untouched by any launcher? Those things are notification bar, certain app icons and lock screen. You need to customize your Rom file if you want to customize them.</p>
-<p>Just because google will discontinue the launcher means you can not use it. Fortunately, there are websites which store APK files, so it is easy to get the APK of Google now launcher even after it is erased from the Play Store. Below are the following sites you can visit to download the Google Now launcher and install on your device.</p>
-<p>This launcher for android free download app is quite simple on its look, but do not let it trick you! This Google launcher application has always be powerful when it comes to search something, just like how Google always do in the other platform. This app is said to be the most important application of the entire years since the first android iOS was launched and keep upgraded while the android series is getting tougher by the days running forward.</p>
-<p>Well, dont worry. No matter if your is not a flagship phone, you can still taste the Marshmallow right away on your phone. How? By downloading the new Google Now Launcher. It is really a light weight launcher that makes your phone not just faster and smoother than before but also brings Marshmallow and some of its features to the device.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download Sqldbx Professional Edition Access Multiple Database Servers with One Tool.md b/spaces/rorallitri/biomedical-language-models/logs/Download Sqldbx Professional Edition Access Multiple Database Servers with One Tool.md
deleted file mode 100644
index aeb1b59f13be6f7cb7a2b3f7695aefa0fd25b8ed..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download Sqldbx Professional Edition Access Multiple Database Servers with One Tool.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Download Sqldbx Professional Edition</h2><br /><p><b><b>Download Zip</b> ::: <a href="https://tinurll.com/2uzlDT">https://tinurll.com/2uzlDT</a></b></p><br /><br />
-
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Electrical Wiring Book In Urdu Download.md b/spaces/rorallitri/biomedical-language-models/logs/Electrical Wiring Book In Urdu Download.md
deleted file mode 100644
index 83377fb392f908a2891ee8b9ca26cdefa96a4dc7..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Electrical Wiring Book In Urdu Download.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-
-<p><strong>Electrical Wiring By David Herres :: About The Book:</strong> This practical book covers basic electronics and explains how to provide electrical service in your home. It describes how to install and test electrical systems, lighting, and repair of devices and televisions, and upgrade to the latest innovations such as home networks, home automation, and alternative energy systems. You will learn the procedures used by professional electricians to create this kind of good work that will pass the examination and add value to your home.</p>
-<h2>electrical wiring book in urdu download</h2><br /><p><b><b>Download Zip</b> >>>>> <a href="https://tinurll.com/2uznpc">https://tinurll.com/2uznpc</a></b></p><br /><br />
-<p>Here we mentioned the brief details and topics available in the programmable logic controller book.Topics<ul><li>PLC History<li>Ladder Logic and Relays<li>PLC Programming<li>PLC Operation<li>PLC hardware configurations<li>Input and outputs PLC types<li>Electrical wiring for inputs and outputs<li>Relays<li>Electrical Ladder Diagrams and JIC wiring symbols<li>Sensor wiring; switches, TTL, sourcing, sinking<li>Proximity detection; contact switches, photo-optics, capacitive, inductive, and ultrasonic<li>Solenoids, valves, and cylinders<li>Hydraulics and pneumatics<li>The computer structure of a PLC<li>The sanity check, input, output, and logic scans<li>Status and memory types<li>Latches, timers, counters, and MCRs<li>Design examples<li>Internal memory locations are available and act like outputs<li>ControlLogix memory types; program and data<li>Data types; output, input, status, bit, timer, counter, integer, floating-point, etc.<li>Memory addresses; words, bits, data files, expressions, literal values, and indirect.if(typeof ez_ad_units!='undefined')ez_ad_units.push([[300,250],'instrumentationtools_com-box-4','ezslot_21',260,'0','0']);__ez_fad_position('div-gpt-ad-instrumentationtools_com-box-4-0');<li>Shift registers, stacks, and sequencers<li>Program control; branching, looping, subroutines, temporary ends, and one-shots<li>Interrupts; timed, fault, and input-driven<li>Immediate inputs and outputs<li>Conversion of State diagrams using program subroutines<li>Instruction list (IL) opcodes and operations<li>Converting from ladder logic to IL<li>The Allen Bradley version of IL<li>Describing process control SFCs<li>Conversion of SFCs to ladder logic<li>The basic construction of FBDs<li>The relationship between ST and FBDs<li>Constructing function blocks with structured text<li>Analog inputs and outputsif(typeof ez_ad_units!='undefined')ez_ad_units.push([[336,280],'instrumentationtools_com-banner-1','ezslot_22',360,'0','0']);__ez_fad_position('div-gpt-ad-instrumentationtools_com-banner-1-0');<li>Analog I/O with a PLC<li>Feedback control of continuous systems<li>Control of systems with logical actuators<li>PID control with continuous actuators<li>Analysis of PID controlled systems<li>PID control with a PLC<li>Serial communication and RS-232c<li>ASCII ladder logic functions<li>Electrical wiring issues; cabinet wiring and layout, grounding, enclosures, shielding, and inductive loads.</ul>Objectives<ul><li>Know general PLC issues<li>To be able to write simple ladder logic programs<li>Understand the operation of a PLCif(typeof ez_ad_units!='undefined')ez_ad_units.push([[300,250],'instrumentationtools_com-large-leaderboard-2','ezslot_23',550,'0','0']);__ez_fad_position('div-gpt-ad-instrumentationtools_com-large-leaderboard-2-0');<li>Be able to understand and design basic input and output wiring.<li>Be able to produce industrial wiring diagrams.<li>Understand the different types of sensor outputs.<li>Know the basic sensor types and understand application issues.<li>Be aware of various actuators available.<li>Understand the operation of a PLC.<li>Understand latches, timers, counters, and MCRs.<li>To be able to select simple internal memory bits.<li>To know the basic memory types available<li>To be able to use addresses for locations in memory<li>To understand shift registers, stacks, and sequencers.<li>To understand program control statements.<li>To understand the use of interrupts.<li>To understand the operation of immediate input and output instructions.<li>To be prepared to use the block transfer instruction later.<li>Be able to apply the advanced function in ladder logic design.<li>To learn the fundamentals of IL programming.<li>To understand the relationship between ladder logic and IL programs<li>To be able to write functions in Structured Text programs<li>To understand the parallels between Ladder Logic and Structured Text<li>To understand the differences between Allen Bradley and the standard<li>Learn to recognize parallel control problems.<li>Be able to develop SFCs for a process.<li>Be able to convert SFCs to ladder logic.<li>To be able to write simple FBD programs<li>To understand the basics of conversion to and from analog values.<li>Be able to use analog I/O on a PLC.<li>To understand the concepts behind continuous control<li>Be able to control a system with logical actuators<li>Be able to analyze and control system with a PID controller<li>To understand serial communications with RS-232<li>Be able to use serial communications with a PLC<li>To learn the major issues in designing controllers including; electrical schematics, panel layout, grounding, shielding, enclosures.<li>Be able to select a hardware and software vendor.<li>Be able to size a PLC to an application<li>Be able to select needed hardware and software.</ul></p>
-<p>Chapter 1315 of the Minnesota State Building Code adopts a national standard for the installation of electrical wiring, apparatus and equipment for electric light, heat, power, technology circuits and systems, and alarm and communication systems. Minnesota's electrical code consists of the National Electrical Code (NEC) as published by the National Fire Protection Association (NFPA). View electrical laws and rules.</p>
-<p>Specialties : Cell/Communication Towers; Equipment Installation; Vinyl/Wood Siding; Fabrication of Above-Ground Storage Tanks/Vessels; Glass Installation; etc. Note: Specialty license classifications are for unique types of contracting where there is not a standard classification provided in the Rules. The Board specifies the exact type of work you perform, such as "S-Installation of Paint Finishing Equipment", "S-Solar Panel (must have a CE electrical license to perform electrical wiring or hire an electrical subcontractor); S-Meter Installation (must have a CE electrical license to perform electrical hook-ups).</p>
-<p></p>
-<p>1 An electrical slab inspection is required when any wiring is installed below a concrete slab.<br>2 A trench inspection is required when wiring is installed below the surface of the ground.<br>3 Pool potting, bonding and underground inspections are very important to prevent electrical shock.<br>4 A change of service will not be needed if the service equipment is not modified.</p> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Joan Manen Fantasia Sonata Pdf Download A Challenge and a Reward for Guitar Players.md b/spaces/rorallitri/biomedical-language-models/logs/Joan Manen Fantasia Sonata Pdf Download A Challenge and a Reward for Guitar Players.md
deleted file mode 100644
index cf3d732837a8687259be104e9b2ad06ed7466823..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Joan Manen Fantasia Sonata Pdf Download A Challenge and a Reward for Guitar Players.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Joan Manen Fantasia Sonata Pdf Download</h2><br /><p><b><b>Download</b> &#8230; <a href="https://tinurll.com/2uznYP">https://tinurll.com/2uznYP</a></b></p><br /><br />
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diff --git a/spaces/roxas010394/parts-of-cars/README.md b/spaces/roxas010394/parts-of-cars/README.md
deleted file mode 100644
index 111086b4028ef09c79d06a8cfab08565765a01c4..0000000000000000000000000000000000000000
--- a/spaces/roxas010394/parts-of-cars/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Parts Of Cars
-emoji: 👁
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/rushic24/Priyanka-Chopra-TTS/README.md b/spaces/rushic24/Priyanka-Chopra-TTS/README.md
deleted file mode 100644
index 7f63c48ca971b3a3b94d57a9c6fa7466a09902c5..0000000000000000000000000000000000000000
--- a/spaces/rushic24/Priyanka-Chopra-TTS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Priyanka Chopra TTS
-emoji: 🌍
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Text-to-Speech (TTS) model for Priyanka Chopra's voice
diff --git a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/stable_diffusion/__init__.py b/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/stable_diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/sanjay7178/FAS-demo/test.py b/spaces/sanjay7178/FAS-demo/test.py
deleted file mode 100644
index e0917f66bb6d33c66c0464cfef6f0ca8a3b5bb1a..0000000000000000000000000000000000000000
--- a/spaces/sanjay7178/FAS-demo/test.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import aiohttp
-import json 
-import base64
-
-async def spoof_trigger(b64):
-    url = "https://spoofapi1.azurewebsites.net/api/spoofvisualize"
-    payload = {"img": b64}
-    headers = {
-    'x-functions-key': 'wGw3zXXPlLCez-VrcSs9RTahE4gLC674pf7Fp6Au2kUHAzFuNnZZMw==',
-    'Content-Type': 'text/plain'
-    }
-
-    async with aiohttp.ClientSession() as session:
-        async with session.post(url, json=payload, headers=headers) as response:
-            response_text = await response.text()
-            spoof_res = json.loads(response_text)
-    return spoof_res
-
-a = input('input:')
-x = spoof_trigger(a)
-print(x)
\ No newline at end of file
diff --git a/spaces/sayakpaul/convert-kerascv-sd-diffusers/conversion_utils/unet.py b/spaces/sayakpaul/convert-kerascv-sd-diffusers/conversion_utils/unet.py
deleted file mode 100644
index aff7deeddb2c53ae9879b731127398a52d8fecfd..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/convert-kerascv-sd-diffusers/conversion_utils/unet.py
+++ /dev/null
@@ -1,497 +0,0 @@
-from itertools import product
-from typing import Dict
-
-import tensorflow as tf
-import torch
-from keras_cv.models import stable_diffusion
-
-
-def port_transformer_block(
-    transformer_block: tf.keras.Model, up_down: int, block_id: int, attention_id: int
-) -> Dict[str, torch.Tensor]:
-    """Populates a Transformer block."""
-    transformer_dict = dict()
-    if block_id is not None:
-        prefix = f"{up_down}_blocks.{block_id}"
-    else:
-        prefix = "mid_block"
-
-    # Norms.
-    for i in range(1, 4):
-        if i == 1:
-            norm = transformer_block.norm1
-        elif i == 2:
-            norm = transformer_block.norm2
-        elif i == 3:
-            norm = transformer_block.norm3
-        transformer_dict[
-            f"{prefix}.attentions.{attention_id}.transformer_blocks.0.norm{i}.weight"
-        ] = torch.from_numpy(norm.get_weights()[0])
-        transformer_dict[
-            f"{prefix}.attentions.{attention_id}.transformer_blocks.0.norm{i}.bias"
-        ] = torch.from_numpy(norm.get_weights()[1])
-
-    # Attentions.
-    for i in range(1, 3):
-        if i == 1:
-            attn = transformer_block.attn1
-        else:
-            attn = transformer_block.attn2
-        transformer_dict[
-            f"{prefix}.attentions.{attention_id}.transformer_blocks.0.attn{i}.to_q.weight"
-        ] = torch.from_numpy(attn.to_q.get_weights()[0].transpose())
-        transformer_dict[
-            f"{prefix}.attentions.{attention_id}.transformer_blocks.0.attn{i}.to_k.weight"
-        ] = torch.from_numpy(attn.to_k.get_weights()[0].transpose())
-        transformer_dict[
-            f"{prefix}.attentions.{attention_id}.transformer_blocks.0.attn{i}.to_v.weight"
-        ] = torch.from_numpy(attn.to_v.get_weights()[0].transpose())
-        transformer_dict[
-            f"{prefix}.attentions.{attention_id}.transformer_blocks.0.attn{i}.to_out.0.weight"
-        ] = torch.from_numpy(attn.out_proj.get_weights()[0].transpose())
-        transformer_dict[
-            f"{prefix}.attentions.{attention_id}.transformer_blocks.0.attn{i}.to_out.0.bias"
-        ] = torch.from_numpy(attn.out_proj.get_weights()[1])
-
-    # Dense.
-    for i in range(0, 3, 2):
-        if i == 0:
-            layer = transformer_block.geglu.dense
-            transformer_dict[
-                f"{prefix}.attentions.{attention_id}.transformer_blocks.0.ff.net.{i}.proj.weight"
-            ] = torch.from_numpy(layer.get_weights()[0].transpose())
-            transformer_dict[
-                f"{prefix}.attentions.{attention_id}.transformer_blocks.0.ff.net.{i}.proj.bias"
-            ] = torch.from_numpy(layer.get_weights()[1])
-        else:
-            layer = transformer_block.dense
-            transformer_dict[
-                f"{prefix}.attentions.{attention_id}.transformer_blocks.0.ff.net.{i}.weight"
-            ] = torch.from_numpy(layer.get_weights()[0].transpose())
-            transformer_dict[
-                f"{prefix}.attentions.{attention_id}.transformer_blocks.0.ff.net.{i}.bias"
-            ] = torch.from_numpy(layer.get_weights()[1])
-
-    return transformer_dict
-
-
-def populate_unet(tf_unet: tf.keras.Model) -> Dict[str, torch.Tensor]:
-    """Populates the state dict from the provided TensorFlow model
-    (applicable only for the UNet)."""
-    unet_state_dict = dict()
-
-    timstep_emb = 1
-    padded_conv = 1
-    up_block = 0
-
-    up_res_blocks = list(product([0, 1, 2, 3], [0, 1, 2]))
-    up_res_block_flag = 0
-
-    up_spatial_transformer_blocks = list(product([1, 2, 3], [0, 1, 2]))
-    up_spatial_transformer_flag = 0
-
-    for layer in tf_unet.layers:
-        # Timstep embedding.
-        if isinstance(layer, tf.keras.layers.Dense):
-            unet_state_dict[
-                f"time_embedding.linear_{timstep_emb}.weight"
-            ] = torch.from_numpy(layer.get_weights()[0].transpose())
-            unet_state_dict[
-                f"time_embedding.linear_{timstep_emb}.bias"
-            ] = torch.from_numpy(layer.get_weights()[1])
-            timstep_emb += 1
-
-        # Padded convs (downsamplers).
-        elif isinstance(
-            layer, stable_diffusion.__internal__.layers.padded_conv2d.PaddedConv2D
-        ):
-            if padded_conv == 1:
-                # Transposition axes taken from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_pytorch_utils.py#L104
-                unet_state_dict["conv_in.weight"] = torch.from_numpy(
-                    layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict["conv_in.bias"] = torch.from_numpy(
-                    layer.get_weights()[1]
-                )
-            elif padded_conv in [2, 3, 4]:
-                unet_state_dict[
-                    f"down_blocks.{padded_conv-2}.downsamplers.0.conv.weight"
-                ] = torch.from_numpy(layer.get_weights()[0].transpose(3, 2, 0, 1))
-                unet_state_dict[
-                    f"down_blocks.{padded_conv-2}.downsamplers.0.conv.bias"
-                ] = torch.from_numpy(layer.get_weights()[1])
-            elif padded_conv == 5:
-                unet_state_dict["conv_out.weight"] = torch.from_numpy(
-                    layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict["conv_out.bias"] = torch.from_numpy(
-                    layer.get_weights()[1]
-                )
-
-            padded_conv += 1
-
-        # Upsamplers.
-        elif isinstance(layer, stable_diffusion.diffusion_model.Upsample):
-            conv = layer.conv
-            unet_state_dict[
-                f"up_blocks.{up_block}.upsamplers.0.conv.weight"
-            ] = torch.from_numpy(conv.get_weights()[0].transpose(3, 2, 0, 1))
-            unet_state_dict[
-                f"up_blocks.{up_block}.upsamplers.0.conv.bias"
-            ] = torch.from_numpy(conv.get_weights()[1])
-            up_block += 1
-
-        # Output norms.
-        elif isinstance(
-            layer,
-            stable_diffusion.__internal__.layers.group_normalization.GroupNormalization,
-        ):
-            unet_state_dict["conv_norm_out.weight"] = torch.from_numpy(
-                layer.get_weights()[0]
-            )
-            unet_state_dict["conv_norm_out.bias"] = torch.from_numpy(
-                layer.get_weights()[1]
-            )
-
-        # All ResBlocks.
-        elif isinstance(layer, stable_diffusion.diffusion_model.ResBlock):
-            layer_name = layer.name
-            parts = layer_name.split("_")
-
-            # Down.
-            if len(parts) == 2 or int(parts[-1]) < 8:
-                entry_flow = layer.entry_flow
-                embedding_flow = layer.embedding_flow
-                exit_flow = layer.exit_flow
-
-                down_block_id = 0 if len(parts) == 2 else int(parts[-1]) // 2
-                down_resnet_id = 0 if len(parts) == 2 else int(parts[-1]) % 2
-
-                # Conv blocks.
-                first_conv_layer = entry_flow[-1]
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.conv1.weight"
-                ] = torch.from_numpy(
-                    first_conv_layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.conv1.bias"
-                ] = torch.from_numpy(first_conv_layer.get_weights()[1])
-                second_conv_layer = exit_flow[-1]
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.conv2.weight"
-                ] = torch.from_numpy(
-                    second_conv_layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.conv2.bias"
-                ] = torch.from_numpy(second_conv_layer.get_weights()[1])
-
-                # Residual blocks.
-                if hasattr(layer, "residual_projection"):
-                    if isinstance(
-                        layer.residual_projection,
-                        stable_diffusion.__internal__.layers.padded_conv2d.PaddedConv2D,
-                    ):
-                        residual = layer.residual_projection
-                        unet_state_dict[
-                            f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.conv_shortcut.weight"
-                        ] = torch.from_numpy(
-                            residual.get_weights()[0].transpose(3, 2, 0, 1)
-                        )
-                        unet_state_dict[
-                            f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.conv_shortcut.bias"
-                        ] = torch.from_numpy(residual.get_weights()[1])
-
-                # Timestep embedding.
-                embedding_proj = embedding_flow[-1]
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.time_emb_proj.weight"
-                ] = torch.from_numpy(embedding_proj.get_weights()[0].transpose())
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.time_emb_proj.bias"
-                ] = torch.from_numpy(embedding_proj.get_weights()[1])
-
-                # Norms.
-                first_group_norm = entry_flow[0]
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.norm1.weight"
-                ] = torch.from_numpy(first_group_norm.get_weights()[0])
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.norm1.bias"
-                ] = torch.from_numpy(first_group_norm.get_weights()[1])
-                second_group_norm = exit_flow[0]
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.norm2.weight"
-                ] = torch.from_numpy(second_group_norm.get_weights()[0])
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.resnets.{down_resnet_id}.norm2.bias"
-                ] = torch.from_numpy(second_group_norm.get_weights()[1])
-
-            # Middle.
-            elif int(parts[-1]) == 8 or int(parts[-1]) == 9:
-                entry_flow = layer.entry_flow
-                embedding_flow = layer.embedding_flow
-                exit_flow = layer.exit_flow
-
-                mid_resnet_id = int(parts[-1]) % 2
-
-                # Conv blocks.
-                first_conv_layer = entry_flow[-1]
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.conv1.weight"
-                ] = torch.from_numpy(
-                    first_conv_layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.conv1.bias"
-                ] = torch.from_numpy(first_conv_layer.get_weights()[1])
-                second_conv_layer = exit_flow[-1]
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.conv2.weight"
-                ] = torch.from_numpy(
-                    second_conv_layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.conv2.bias"
-                ] = torch.from_numpy(second_conv_layer.get_weights()[1])
-
-                # Residual blocks.
-                if hasattr(layer, "residual_projection"):
-                    if isinstance(
-                        layer.residual_projection,
-                        stable_diffusion.__internal__.layers.padded_conv2d.PaddedConv2D,
-                    ):
-                        residual = layer.residual_projection
-                        unet_state_dict[
-                            f"mid_block.resnets.{mid_resnet_id}.conv_shortcut.weight"
-                        ] = torch.from_numpy(
-                            residual.get_weights()[0].transpose(3, 2, 0, 1)
-                        )
-                        unet_state_dict[
-                            f"mid_block.resnets.{mid_resnet_id}.conv_shortcut.bias"
-                        ] = torch.from_numpy(residual.get_weights()[1])
-
-                # Timestep embedding.
-                embedding_proj = embedding_flow[-1]
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.time_emb_proj.weight"
-                ] = torch.from_numpy(embedding_proj.get_weights()[0].transpose())
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.time_emb_proj.bias"
-                ] = torch.from_numpy(embedding_proj.get_weights()[1])
-
-                # Norms.
-                first_group_norm = entry_flow[0]
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.norm1.weight"
-                ] = torch.from_numpy(first_group_norm.get_weights()[0])
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.norm1.bias"
-                ] = torch.from_numpy(first_group_norm.get_weights()[1])
-                second_group_norm = exit_flow[0]
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.norm2.weight"
-                ] = torch.from_numpy(second_group_norm.get_weights()[0])
-                unet_state_dict[
-                    f"mid_block.resnets.{mid_resnet_id}.norm2.bias"
-                ] = torch.from_numpy(second_group_norm.get_weights()[1])
-
-            # Up.
-            elif int(parts[-1]) > 9 and up_res_block_flag < len(up_res_blocks):
-                entry_flow = layer.entry_flow
-                embedding_flow = layer.embedding_flow
-                exit_flow = layer.exit_flow
-
-                up_res_block = up_res_blocks[up_res_block_flag]
-                up_block_id = up_res_block[0]
-                up_resnet_id = up_res_block[1]
-
-                # Conv blocks.
-                first_conv_layer = entry_flow[-1]
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.conv1.weight"
-                ] = torch.from_numpy(
-                    first_conv_layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.conv1.bias"
-                ] = torch.from_numpy(first_conv_layer.get_weights()[1])
-                second_conv_layer = exit_flow[-1]
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.conv2.weight"
-                ] = torch.from_numpy(
-                    second_conv_layer.get_weights()[0].transpose(3, 2, 0, 1)
-                )
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.conv2.bias"
-                ] = torch.from_numpy(second_conv_layer.get_weights()[1])
-
-                # Residual blocks.
-                if hasattr(layer, "residual_projection"):
-                    if isinstance(
-                        layer.residual_projection,
-                        stable_diffusion.__internal__.layers.padded_conv2d.PaddedConv2D,
-                    ):
-                        residual = layer.residual_projection
-                        unet_state_dict[
-                            f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.conv_shortcut.weight"
-                        ] = torch.from_numpy(
-                            residual.get_weights()[0].transpose(3, 2, 0, 1)
-                        )
-                        unet_state_dict[
-                            f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.conv_shortcut.bias"
-                        ] = torch.from_numpy(residual.get_weights()[1])
-
-                # Timestep embedding.
-                embedding_proj = embedding_flow[-1]
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.time_emb_proj.weight"
-                ] = torch.from_numpy(embedding_proj.get_weights()[0].transpose())
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.time_emb_proj.bias"
-                ] = torch.from_numpy(embedding_proj.get_weights()[1])
-
-                # Norms.
-                first_group_norm = entry_flow[0]
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.norm1.weight"
-                ] = torch.from_numpy(first_group_norm.get_weights()[0])
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.norm1.bias"
-                ] = torch.from_numpy(first_group_norm.get_weights()[1])
-                second_group_norm = exit_flow[0]
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.norm2.weight"
-                ] = torch.from_numpy(second_group_norm.get_weights()[0])
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.resnets.{up_resnet_id}.norm2.bias"
-                ] = torch.from_numpy(second_group_norm.get_weights()[1])
-
-                up_res_block_flag += 1
-
-        # All SpatialTransformer blocks.
-        elif isinstance(layer, stable_diffusion.diffusion_model.SpatialTransformer):
-            layer_name = layer.name
-            parts = layer_name.split("_")
-
-            # Down.
-            if len(parts) == 2 or int(parts[-1]) < 6:
-                down_block_id = 0 if len(parts) == 2 else int(parts[-1]) // 2
-                down_attention_id = 0 if len(parts) == 2 else int(parts[-1]) % 2
-
-                # Convs.
-                proj1 = layer.proj1
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.attentions.{down_attention_id}.proj_in.weight"
-                ] = torch.from_numpy(proj1.get_weights()[0].transpose(3, 2, 0, 1))
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.attentions.{down_attention_id}.proj_in.bias"
-                ] = torch.from_numpy(proj1.get_weights()[1])
-                proj2 = layer.proj2
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.attentions.{down_attention_id}.proj_out.weight"
-                ] = torch.from_numpy(proj2.get_weights()[0].transpose(3, 2, 0, 1))
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.attentions.{down_attention_id}.proj_out.bias"
-                ] = torch.from_numpy(proj2.get_weights()[1])
-
-                # Transformer blocks.
-                transformer_block = layer.transformer_block
-                unet_state_dict.update(
-                    port_transformer_block(
-                        transformer_block, "down", down_block_id, down_attention_id
-                    )
-                )
-
-                # Norms.
-                norm = layer.norm
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.attentions.{down_attention_id}.norm.weight"
-                ] = torch.from_numpy(norm.get_weights()[0])
-                unet_state_dict[
-                    f"down_blocks.{down_block_id}.attentions.{down_attention_id}.norm.bias"
-                ] = torch.from_numpy(norm.get_weights()[1])
-
-            # Middle.
-            elif int(parts[-1]) == 6:
-                mid_attention_id = int(parts[-1]) % 2
-                # Convs.
-                proj1 = layer.proj1
-                unet_state_dict[
-                    f"mid_block.attentions.{mid_attention_id}.proj_in.weight"
-                ] = torch.from_numpy(proj1.get_weights()[0].transpose(3, 2, 0, 1))
-                unet_state_dict[
-                    f"mid_block.attentions.{mid_attention_id}.proj_in.bias"
-                ] = torch.from_numpy(proj1.get_weights()[1])
-                proj2 = layer.proj2
-                unet_state_dict[
-                    f"mid_block.attentions.{mid_resnet_id}.proj_out.weight"
-                ] = torch.from_numpy(proj2.get_weights()[0].transpose(3, 2, 0, 1))
-                unet_state_dict[
-                    f"mid_block.attentions.{mid_attention_id}.proj_out.bias"
-                ] = torch.from_numpy(proj2.get_weights()[1])
-
-                # Transformer blocks.
-                transformer_block = layer.transformer_block
-                unet_state_dict.update(
-                    port_transformer_block(
-                        transformer_block, "mid", None, mid_attention_id
-                    )
-                )
-
-                # Norms.
-                norm = layer.norm
-                unet_state_dict[
-                    f"mid_block.attentions.{mid_attention_id}.norm.weight"
-                ] = torch.from_numpy(norm.get_weights()[0])
-                unet_state_dict[
-                    f"mid_block.attentions.{mid_attention_id}.norm.bias"
-                ] = torch.from_numpy(norm.get_weights()[1])
-
-            # Up.
-            elif int(parts[-1]) > 6 and up_spatial_transformer_flag < len(
-                up_spatial_transformer_blocks
-            ):
-                up_spatial_transformer_block = up_spatial_transformer_blocks[
-                    up_spatial_transformer_flag
-                ]
-                up_block_id = up_spatial_transformer_block[0]
-                up_attention_id = up_spatial_transformer_block[1]
-
-                # Convs.
-                proj1 = layer.proj1
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.attentions.{up_attention_id}.proj_in.weight"
-                ] = torch.from_numpy(proj1.get_weights()[0].transpose(3, 2, 0, 1))
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.attentions.{up_attention_id}.proj_in.bias"
-                ] = torch.from_numpy(proj1.get_weights()[1])
-                proj2 = layer.proj2
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.attentions.{up_attention_id}.proj_out.weight"
-                ] = torch.from_numpy(proj2.get_weights()[0].transpose(3, 2, 0, 1))
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.attentions.{up_attention_id}.proj_out.bias"
-                ] = torch.from_numpy(proj2.get_weights()[1])
-
-                # Transformer blocks.
-                transformer_block = layer.transformer_block
-                unet_state_dict.update(
-                    port_transformer_block(
-                        transformer_block, "up", up_block_id, up_attention_id
-                    )
-                )
-
-                # Norms.
-                norm = layer.norm
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.attentions.{up_attention_id}.norm.weight"
-                ] = torch.from_numpy(norm.get_weights()[0])
-                unet_state_dict[
-                    f"up_blocks.{up_block_id}.attentions.{up_attention_id}.norm.bias"
-                ] = torch.from_numpy(norm.get_weights()[1])
-
-                up_spatial_transformer_flag += 1
-
-    return unet_state_dict
diff --git a/spaces/sayakpaul/sots-outdoor-dehazing-maxim/README.md b/spaces/sayakpaul/sots-outdoor-dehazing-maxim/README.md
deleted file mode 100644
index 8ee3ef059e3151ce7d4f9cf5a2a412831c195d3e..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/sots-outdoor-dehazing-maxim/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sots Outdoor Dehazing MAXIM
-emoji: 🥑
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.5
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/scedlatioru/img-to-music/example/Anvil Studio Print Sheet Full Version HOT.md b/spaces/scedlatioru/img-to-music/example/Anvil Studio Print Sheet Full Version HOT.md
deleted file mode 100644
index 8f28f39ee7eccd676c21a95ccc31de13f551d3e5..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Anvil Studio Print Sheet Full Version HOT.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>anvil studio print sheet full version</h2><br /><p><b><b>Download File</b> &#8250;&#8250;&#8250; <a href="https://gohhs.com/2uEzDh">https://gohhs.com/2uEzDh</a></b></p><br /><br />
-<br />
-Anvil Studio for Mac OS X, download best alternative solutions carefully ... access to everything you need to compose, arrange, play, and print music. ... LilyPond is a music engraving program, devoted to producing the highest-quality sheet ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Bruce Springsteen - Greatest Hits 1995.zip !!INSTALL!!.md b/spaces/scedlatioru/img-to-music/example/Bruce Springsteen - Greatest Hits 1995.zip !!INSTALL!!.md
deleted file mode 100644
index 7815ff4a3e198c8e2665e1878478b415450ffafb..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Bruce Springsteen - Greatest Hits 1995.zip !!INSTALL!!.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<h2>Bruce Springsteen - Greatest Hits 1995.zip</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://gohhs.com/2uEzqJ">https://gohhs.com/2uEzqJ</a></b></p><br /><br />
-<br />
-7 Download Link Songs: Greatest Hits 1995.mp3 (750 KB), Greatest Hits 1995.wma (1.31 MB), Greatest Hits 1995.mp3 (222 KB), Greatest Hits 1995.mp3 (166 KB). Has many songs including Tracks 1-4, The Wild and the Innocent, Human Touch, Dancing in the Dark, The Man In the Box, Badlands, Glory Days, and Walk Like a Man. The track lengths are between 5 and 6 minutes. This song is in a flac (lossless) encoding. This means it is able to give you the best sound quality.Paragon Software
-
-Paragon Software is a privately owned software company based in Manchester, United Kingdom. It develops and manufactures software for the National Health Service (NHS) including the Clinical Information Systems for England (CiSE), Wales (CiW) and Northern Ireland (CiNI).
-
-History 
-
-Paragon Software was formed in 2003 as a commercial subsidiary of Medical Solutions Limited (MSL). MSL was a subsidiary of Intralinks, a global software company that was sold to Gartner for £450m in 2012. MSL was a provider of software applications and services to the NHS and other healthcare organisations.
-
-With the new owners of Intralinks, healthcare software expert, Huw Thomas, and Chris Willett, formerly of npower, formed a new management team to establish Paragon.
-
-Development 
-
-In 2013 Paragon Software won a contract to provide the Clinical Information Systems for England (CiSE) and Wales (CiW) applications. Paragon is also working with the NHS on the Clinical Information Systems for Northern Ireland (CiNI) which is due to be delivered in 2018.
-
-Paragon Software’s responsibilities include the development, maintenance and support of the software applications.
-
-Development tools 
-
-Paragon Software’s software is developed using the Infor Architect ODM (Object Data Model) for mainframe systems and Microsoft.NET and SQL Server for Windows and Linux systems.  
-
-Paragon Software also provides development tools including a testing toolkit, Infor COM Objects (Icos) (2013) and in 2016 it launched a series of interactive graphical user interfaces (GUIs) to make the software more user friendly.
-
-In July 2013 Paragon Software launched a new plug-in technology called Infor Data Hub to provide a range of data services that have been made available to non- 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Dragon Ball Z Kakarot DLC Unlocker-CODEX UPD.md b/spaces/scedlatioru/img-to-music/example/Dragon Ball Z Kakarot DLC Unlocker-CODEX UPD.md
deleted file mode 100644
index 1108d6e8d400080e7276cfb1e7eb3f87895844a1..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Dragon Ball Z Kakarot DLC Unlocker-CODEX UPD.md	
+++ /dev/null
@@ -1,148 +0,0 @@
-<br />
-<h1>How to Unlock All DLCs in Dragon Ball Z Kakarot with CODEX Crack</h1>
-
-<p>Dragon Ball Z Kakarot is an action RPG game that lets you relive the story of Goku and other Z Fighters in the Dragon Ball Z universe. You can experience epic battles, explore new areas, and form bonds with other characters as you fight, fish, eat, and train with them.</p>
-
-<p>However, if you want to enjoy the full content of the game, you will need to unlock all the DLCs that add more story episodes, characters, transformations, and items. The game has 8 DLCs so far:</p>
-<h2>Dragon Ball Z Kakarot DLC Unlocker-CODEX</h2><br /><p><b><b>DOWNLOAD</b> &#10003; <a href="https://gohhs.com/2uEzZ0">https://gohhs.com/2uEzZ0</a></b></p><br /><br />
-
-<ul>
-<li>DRAGON BALL Z: KAKAROT Pre-Order DLC Pack</li>
-<li>DRAGON BALL Z: KAKAROT Aged Wild Steak</li>
-<li>DRAGON BALL Z: KAKAROT Dragon Palace Bowl</li>
-<li>DRAGON BALL Z: KAKAROT Steaming-Hot Grilled Fish</li>
-<li>DRAGON BALL Z: KAKAROT A NEW POWER AWAKENS Part 1</li>
-<li>DRAGON BALL Z: KAKAROT A NEW POWER AWAKENS Part 2</li>
-<li>DRAGON BALL Z: KAKAROT TRUNKS THE WARRIOR OF HOPE</li>
-<li>DRAGON BALL Z: KAKAROT MUSIC COMPILATION PACK</li>
-</ul>
-
-<p>If you don't want to buy these DLCs separately or as part of the Season Pass, you can use a simple trick to unlock them for free. All you need is a CODEX crack that will bypass the DRM protection and allow you to access all the DLCs without paying anything.</p>
-
-<h2>How to Use Dragon Ball Z Kakarot DLC Unlocker-CODEX</h2>
-
-<p>The process of using Dragon Ball Z Kakarot DLC Unlocker-CODEX is very easy and straightforward. Just follow these steps:</p>
-
-<ol>
-<li>Download Dragon Ball Z Kakarot DLC Unlocker-CODEX from a reliable source. You can find it on various torrent sites or file hosting services.</li>
-<li>Extract the file using WinRAR or 7-Zip. You will get a folder named CODEX with two files inside: codex64.dll and steam_emu.ini.</li>
-<li>Copy these two files and paste them into your Dragon Ball Z Kakarot installation folder. This is usually located in C:\Program Files (x86)\Steam\steamapps\common\DRAGON BALL Z KAKAROT.</li>
-<li>Replace the existing files when prompted.</li>
-<li>Launch the game from Steam or from the desktop shortcut. You will see that all the DLCs are unlocked and available in the game menu.</li>
-<li>Enjoy!</li>
-</ol>
-
-<h3>Tips and Warnings</h3>
-
-<p>Before using Dragon Ball Z Kakarot DLC Unlocker-CODEX, make sure you have the following requirements:</p>
-
-<ul>
-<li>A copy of Dragon Ball Z Kakarot installed on your PC. You can buy it from Steam or use a CODEX release.</li>
-<li>The latest version of the game (v1.60) with all updates applied.</li>
-<li>A backup of your original codex64.dll and steam_emu.ini files in case something goes wrong.</li>
-<li>A good antivirus software that will not interfere with the crack or delete it as a false positive.</li>
-</ul>
-
-<p>Some things to keep in mind when using Dragon Ball Z Kakarot DLC Unlocker-CODEX are:</p>
-
-<ul>
-<li>This crack is only compatible with CODEX releases of the game. It may not work with other versions or repacks.</li>
-<li>This crack may not work with future updates or DLCs of the game. You may need to wait for a new crack or buy them legitimately.</li>
-<li>This crack may cause some issues or bugs with the game performance or functionality. Use it at your own risk.</li>
-<li>This crack may violate the terms of service or end-user license agreement of the game. Use it at your own discretion.</li>
-</ul>
-
-<h4>Conclusion</h4>
-
-<p>Dragon Ball Z Kakarot DLC Unlocker-CODEX is a handy tool that will let you enjoy all the extra content of the game without spending any money. It is easy to use and works well with the latest version of the game. However, it is not a legal or official way to unlock the DLCs, so you should be careful and responsible when using it.</p>
-
-<p>If you liked this article, please share it with your friends who are also fans of Dragon Ball Z Kakarot. And if you have any questions or feedback, feel free to leave a comment below. Thank you for reading!</p>
-<p></p>
-<h3>What are the Benefits of Using Dragon Ball Z Kakarot DLC Unlocker-CODEX?</h3>
-
-<p>By using Dragon Ball Z Kakarot DLC Unlocker-CODEX, you can enjoy many benefits that will enhance your gaming experience. Some of these benefits are:</p>
-
-<ul>
-<li>You can access all the DLCs for free, saving you money and time.</li>
-<li>You can play new story episodes that feature characters like Beerus, Whis, Trunks, and Zamasu.</li>
-<li>You can unlock new transformations and skills for Goku and Vegeta, such as Super Saiyan God and Super Saiyan Blue.</li>
-<li>You can get exclusive items and recipes that will boost your stats and abilities.</li>
-<li>You can listen to original soundtracks from the anime series and movies.</li>
-</ul>
-
-<p>With Dragon Ball Z Kakarot DLC Unlocker-CODEX, you can experience the full potential of the game and immerse yourself in the Dragon Ball Z world like never before.</p>
-
-<h4>How to Troubleshoot Dragon Ball Z Kakarot DLC Unlocker-CODEX?</h4>
-
-<p>While Dragon Ball Z Kakarot DLC Unlocker-CODEX is a simple and effective tool, it may not work perfectly for everyone. If you encounter any problems or errors while using it, you can try some of these solutions:</p>
-
-<ol>
-<li>Make sure you have the latest version of the game (v1.60) with all updates applied.</li>
-<li>Make sure you have the latest version of the crack (CODEX) with all files intact.</li>
-<li>Make sure you have disabled your antivirus software or added an exception for the crack files.</li>
-<li>Make sure you have run the game as administrator.</li>
-<li>Make sure you have installed the game in a folder without special characters or spaces.</li>
-<li>Make sure you have enough free space on your hard drive.</li>
-</ol>
-
-<p>If none of these solutions work, you can contact the crack developer (CODEX) or visit online forums for more help and support.</p>
-<h3>What are the Features of Dragon Ball Z Kakarot DLC Unlocker-CODEX?</h3>
-
-<p>Dragon Ball Z Kakarot DLC Unlocker-CODEX is a simple but powerful tool that will enhance your gaming experience by unlocking all the DLCs in the game. Some of the features of this tool are:</p>
-
-<ul>
-<li>It is easy to use and install. You just need to download, extract, and copy the files to your game folder.</li>
-<li>It is compatible with the latest version of the game (v1.60) and works with both Steam and CODEX releases.</li>
-<li>It is safe and secure. It does not contain any viruses or malware and does not modify any game files.</li>
-<li>It is lightweight and fast. It does not affect your game performance or loading time.</li>
-<li>It is updated regularly. It will support future updates and DLCs of the game as soon as they are released.</li>
-</ul>
-
-<p>With Dragon Ball Z Kakarot DLC Unlocker-CODEX, you can enjoy all the features and content of the game without any limitations or restrictions.</p>
-
-<h4>How to Uninstall Dragon Ball Z Kakarot DLC Unlocker-CODEX?</h4>
-
-<p>If you want to uninstall Dragon Ball Z Kakarot DLC Unlocker-CODEX for any reason, you can do it easily and quickly. Just follow these steps:</p>
-
-<ol>
-<li>Delete the codex64.dll and steam_emu.ini files from your game folder.</li>
-<li>Restore your original codex64.dll and steam_emu.ini files from your backup folder.</li>
-<li>Delete the Dragon Ball Z Kakarot DLC Unlocker-CODEX folder from your download location.</li>
-<li>That's it. You have successfully uninstalled Dragon Ball Z Kakarot DLC Unlocker-CODEX from your PC.</li>
-</ol>
-
-<p>If you want to reinstall it later, you can follow the same steps as before.</p>
-<h3>What are the Reviews of Dragon Ball Z Kakarot DLC Unlocker-CODEX?</h3>
-
-<p>Dragon Ball Z Kakarot DLC Unlocker-CODEX has received positive reviews from many users who have tried it. Here are some of the comments they have left on various online platforms:</p>
-
-<blockquote>
-<p>"This is awesome! I can play all the DLCs without paying anything. Thank you CODEX!"</p>
-<cite>- Reddit user</cite>
-</blockquote>
-
-<blockquote>
-<p>"I love this game and this crack makes it even better. The DLCs are amazing and add so much to the story and gameplay. Great job CODEX!"</p>
-<cite>- Steam user</cite>
-</blockquote>
-
-<blockquote>
-<p>"This is the best thing ever! I can finally play as Super Saiyan God and Super Saiyan Blue. The DLCs are so fun and challenging. CODEX rocks!"</p>
-<cite>- YouTube user</cite>
-</blockquote>
-
-<p>As you can see, Dragon Ball Z Kakarot DLC Unlocker-CODEX has satisfied many fans of the game who wanted to experience all the extra content without spending any money.</p>
-
-<h4>How to Download Dragon Ball Z Kakarot DLC Unlocker-CODEX?</h4>
-
-<p>If you want to download Dragon Ball Z Kakarot DLC Unlocker-CODEX, you can do it easily and quickly from various sources. Here are some of the options you have:</p>
-
-<ul>
-<li>You can download it from torrent sites like 1337x, RARBG, or The Pirate Bay. You will need a torrent client like uTorrent or BitTorrent to download the file.</li>
-<li>You can download it from file hosting services like 1fichier, Mega, or Google Drive. You will need a browser or a download manager like JDownloader to download the file.</li>
-<li>You can download it from direct links like FileCrypt, PixelDrain, or LetsUpload. You will need a browser or a download manager like IDM to download the file.</li>
-</ul>
-
-<p>Whichever option you choose, make sure you download from a reliable and trusted source that does not contain any viruses or malware. Also, make sure you have enough free space on your hard drive to store the file.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/inference/memory_manager.py b/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/inference/memory_manager.py
deleted file mode 100644
index 53995a0f3c0998191858b955415a549dfcef248e..0000000000000000000000000000000000000000
--- a/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/inference/memory_manager.py
+++ /dev/null
@@ -1,378 +0,0 @@
-import logging
-from omegaconf import DictConfig
-from typing import List, Dict
-import torch
-
-from tracker.inference.object_manager import ObjectManager
-from tracker.inference.kv_memory_store import KeyValueMemoryStore
-from tracker.model.cutie import CUTIE
-from tracker.model.utils.memory_utils import *
-
-log = logging.getLogger()
-
-
-class MemoryManager:
-    """
-    Manages all three memory stores and the transition between working/long-term memory
-    """
-    def __init__(self, cfg: DictConfig, object_manager: ObjectManager):
-        self.object_manager = object_manager
-        self.sensory_dim = cfg.model.sensory_dim
-        self.top_k = cfg.top_k
-        self.chunk_size = cfg.chunk_size
-
-        self.save_aux = cfg.save_aux
-
-        self.use_long_term = cfg.use_long_term
-        self.count_long_term_usage = cfg.long_term.count_usage
-        # subtract 1 because the first-frame is now counted as "permanent memory"
-        # and is not counted towards max_mem_frames
-        # but we want to keep the hyperparameters consistent as before for the same behavior
-        if self.use_long_term:
-            self.max_mem_frames = cfg.long_term.max_mem_frames - 1
-            self.min_mem_frames = cfg.long_term.min_mem_frames - 1
-            self.num_prototypes = cfg.long_term.num_prototypes
-            self.max_long_tokens = cfg.long_term.max_num_tokens
-            self.buffer_tokens = cfg.long_term.buffer_tokens
-        else:
-            self.max_mem_frames = cfg.max_mem_frames - 1
-
-        # dimensions will be inferred from input later
-        self.CK = self.CV = None
-        self.H = self.W = None
-
-        # The sensory memory is stored as a dictionary indexed by object ids
-        # each of shape bs * C^h * H * W
-        self.sensory = {}
-
-        # a dictionary indexed by object ids, each of shape bs * T * Q * C
-        self.obj_v = {}
-
-        self.work_mem = KeyValueMemoryStore(save_selection=self.use_long_term,
-                                            save_usage=self.use_long_term)
-        if self.use_long_term:
-            self.long_mem = KeyValueMemoryStore(save_usage=self.count_long_term_usage)
-
-        self.config_stale = True
-        self.engaged = False
-
-    def update_config(self, cfg: DictConfig) -> None:
-        self.config_stale = True
-        self.top_k = cfg['top_k']
-
-        assert self.use_long_term == cfg.use_long_term, 'cannot update this'
-        assert self.count_long_term_usage == cfg.long_term.count_usage, 'cannot update this'
-
-        self.use_long_term = cfg.use_long_term
-        self.count_long_term_usage = cfg.long_term.count_usage
-        if self.use_long_term:
-            self.max_mem_frames = cfg.long_term.max_mem_frames - 1
-            self.min_mem_frames = cfg.long_term.min_mem_frames - 1
-            self.num_prototypes = cfg.long_term.num_prototypes
-            self.max_long_tokens = cfg.long_term.max_num_tokens
-            self.buffer_tokens = cfg.long_term.buffer_tokens
-        else:
-            self.max_mem_frames = cfg.max_mem_frames - 1
-
-    def _readout(self, affinity, v) -> torch.Tensor:
-        # affinity: bs*N*HW
-        # v: bs*C*N or bs*num_objects*C*N
-        # returns bs*C*HW or bs*num_objects*C*HW
-        if len(v.shape) == 3:
-            # single object
-            return v @ affinity
-        else:
-            bs, num_objects, C, N = v.shape
-            v = v.view(bs, num_objects * C, N)
-            out = v @ affinity
-            return out.view(bs, num_objects, C, -1)
-
-    def _get_mask_by_ids(self, mask: torch.Tensor, obj_ids: List[int]) -> torch.Tensor:
-        # -1 because the mask does not contain the background channel
-        return mask[:, [self.object_manager.find_tmp_by_id(obj) - 1 for obj in obj_ids]]
-
-    def _get_sensory_by_ids(self, obj_ids: List[int]) -> torch.Tensor:
-        return torch.stack([self.sensory[obj] for obj in obj_ids], dim=1)
-
-    def _get_object_mem_by_ids(self, obj_ids: List[int]) -> torch.Tensor:
-        return torch.stack([self.obj_v[obj] for obj in obj_ids], dim=1)
-
-    def _get_visual_values_by_ids(self, obj_ids: List[int]) -> torch.Tensor:
-        # All the values that the object ids refer to should have the same shape
-        value = torch.stack([self.work_mem.value[obj] for obj in obj_ids], dim=1)
-        if self.use_long_term and obj_ids[0] in self.long_mem.value:
-            lt_value = torch.stack([self.long_mem.value[obj] for obj in obj_ids], dim=1)
-            value = torch.cat([lt_value, value], dim=-1)
-
-        return value
-
-    def read(self, pix_feat: torch.Tensor, query_key: torch.Tensor, selection: torch.Tensor,
-             last_mask: torch.Tensor, network: CUTIE) -> Dict[int, torch.Tensor]:
-        """
-        Read from all memory stores and returns a single memory readout tensor for each object
-
-        pix_feat: (1/2) x C x H x W
-        query_key: (1/2) x C^k x H x W
-        selection:  (1/2) x C^k x H x W
-        last_mask: (1/2) x num_objects x H x W (at stride 16)
-        return a dict of memory readouts, indexed by object indices. Each readout is C*H*W
-        """
-        h, w = pix_feat.shape[-2:]
-        bs = pix_feat.shape[0]
-        assert query_key.shape[0] == bs
-        assert selection.shape[0] == bs
-        assert last_mask.shape[0] == bs
-
-        query_key = query_key.flatten(start_dim=2)  # bs*C^k*HW
-        selection = selection.flatten(start_dim=2)  # bs*C^k*HW
-        """
-        Compute affinity and perform readout
-        """
-        all_readout_mem = {}
-        buckets = self.work_mem.buckets
-        for bucket_id, bucket in buckets.items():
-            if self.use_long_term and self.long_mem.engaged(bucket_id):
-                # Use long-term memory
-                long_mem_size = self.long_mem.size(bucket_id)
-                memory_key = torch.cat([self.long_mem.key[bucket_id], self.work_mem.key[bucket_id]],
-                                       -1)
-                shrinkage = torch.cat(
-                    [self.long_mem.shrinkage[bucket_id], self.work_mem.shrinkage[bucket_id]], -1)
-
-                similarity = get_similarity(memory_key, shrinkage, query_key, selection)
-                affinity, usage = do_softmax(similarity,
-                                             top_k=self.top_k,
-                                             inplace=True,
-                                             return_usage=True)
-                """
-                Record memory usage for working and long-term memory
-                """
-                # ignore the index return for long-term memory
-                work_usage = usage[:, long_mem_size:]
-                self.work_mem.update_bucket_usage(bucket_id, work_usage)
-
-                if self.count_long_term_usage:
-                    # ignore the index return for working memory
-                    long_usage = usage[:, :long_mem_size]
-                    self.long_mem.update_bucket_usage(bucket_id, long_usage)
-            else:
-                # no long-term memory
-                memory_key = self.work_mem.key[bucket_id]
-                shrinkage = self.work_mem.shrinkage[bucket_id]
-                similarity = get_similarity(memory_key, shrinkage, query_key, selection)
-
-                if self.use_long_term:
-                    affinity, usage = do_softmax(similarity,
-                                                 top_k=self.top_k,
-                                                 inplace=True,
-                                                 return_usage=True)
-                    self.work_mem.update_bucket_usage(bucket_id, usage)
-                else:
-                    affinity = do_softmax(similarity, top_k=self.top_k, inplace=True)
-
-            if self.chunk_size < 1:
-                object_chunks = [bucket]
-            else:
-                object_chunks = [
-                    bucket[i:i + self.chunk_size] for i in range(0, len(bucket), self.chunk_size)
-                ]
-
-            for objects in object_chunks:
-                this_sensory = self._get_sensory_by_ids(objects)
-                this_last_mask = self._get_mask_by_ids(last_mask, objects)
-                this_msk_value = self._get_visual_values_by_ids(objects)  # (1/2)*num_objects*C*N
-                visual_readout = self._readout(affinity,
-                                               this_msk_value).view(bs, len(objects), self.CV, h, w)
-                pixel_readout = network.pixel_fusion(pix_feat, visual_readout, this_sensory,
-                                                     this_last_mask)
-                this_obj_mem = self._get_object_mem_by_ids(objects).unsqueeze(2)
-                readout_memory, aux_features = network.readout_query(pixel_readout, this_obj_mem)
-                for i, obj in enumerate(objects):
-                    all_readout_mem[obj] = readout_memory[:, i]
-
-                if self.save_aux:
-                    aux_output = {
-                        'sensory': this_sensory,
-                        'pixel_readout': pixel_readout,
-                        'q_logits': aux_features['logits'] if aux_features else None,
-                        'q_weights': aux_features['q_weights'] if aux_features else None,
-                        'p_weights': aux_features['p_weights'] if aux_features else None,
-                        'attn_mask': aux_features['attn_mask'].float() if aux_features else None,
-                    }
-                    self.aux = aux_output
-
-        return all_readout_mem
-
-    def add_memory(self,
-                   key: torch.Tensor,
-                   shrinkage: torch.Tensor,
-                   msk_value: torch.Tensor,
-                   obj_value: torch.Tensor,
-                   objects: List[int],
-                   selection: torch.Tensor = None,
-                   *,
-                   as_permanent: bool = False) -> None:
-        # key: (1/2)*C*H*W
-        # msk_value: (1/2)*num_objects*C*H*W
-        # obj_value: (1/2)*num_objects*Q*C
-        # objects contains a list of object ids corresponding to the objects in msk_value/obj_value
-        bs = key.shape[0]
-        assert shrinkage.shape[0] == bs
-        assert msk_value.shape[0] == bs
-        assert obj_value.shape[0] == bs
-
-        self.engaged = True
-        if self.H is None or self.config_stale:
-            self.config_stale = False
-            self.H, self.W = msk_value.shape[-2:]
-            self.HW = self.H * self.W
-            # convert from num. frames to num. tokens
-            self.max_work_tokens = self.max_mem_frames * self.HW
-            if self.use_long_term:
-                self.min_work_tokens = self.min_mem_frames * self.HW
-
-        # key:   bs*C*N
-        # value: bs*num_objects*C*N
-        key = key.flatten(start_dim=2)
-        shrinkage = shrinkage.flatten(start_dim=2)
-        self.CK = key.shape[1]
-
-        msk_value = msk_value.flatten(start_dim=3)
-        self.CV = msk_value.shape[2]
-
-        if selection is not None:
-            # not used in non-long-term mode
-            selection = selection.flatten(start_dim=2)
-
-        # insert object values into object memory
-        for obj_id, obj in enumerate(objects):
-            if obj in self.obj_v:
-                """streaming average
-                each self.obj_v[obj] is (1/2)*num_summaries*(embed_dim+1)
-                first embed_dim keeps track of the sum of embeddings
-                the last dim keeps the total count
-                averaging in done inside the object transformer
-
-                incoming obj_value is (1/2)*num_objects*num_summaries*(embed_dim+1)
-                self.obj_v[obj] = torch.cat([self.obj_v[obj], obj_value[:, obj_id]], dim=0)
-                """
-                last_acc = self.obj_v[obj][:, :, -1]
-                new_acc = last_acc + obj_value[:, obj_id, :, -1]
-
-                self.obj_v[obj][:, :, :-1] = (self.obj_v[obj][:, :, :-1] +
-                                              obj_value[:, obj_id, :, :-1])
-                self.obj_v[obj][:, :, -1] = new_acc
-            else:
-                self.obj_v[obj] = obj_value[:, obj_id]
-
-        # convert mask value tensor into a dict for insertion
-        msk_values = {obj: msk_value[:, obj_id] for obj_id, obj in enumerate(objects)}
-        self.work_mem.add(key,
-                          msk_values,
-                          shrinkage,
-                          selection=selection,
-                          as_permanent=as_permanent)
-
-        for bucket_id in self.work_mem.buckets.keys():
-            # long-term memory cleanup
-            if self.use_long_term:
-                # Do memory compressed if needed
-                if self.work_mem.non_perm_size(bucket_id) >= self.max_work_tokens:
-                    # Remove obsolete features if needed
-                    if self.long_mem.non_perm_size(bucket_id) >= (self.max_long_tokens -
-                                                         self.num_prototypes):
-                        self.long_mem.remove_obsolete_features(
-                            bucket_id,
-                            self.max_long_tokens - self.num_prototypes - self.buffer_tokens)
-
-                    self.compress_features(bucket_id)
-            else:
-                # FIFO
-                self.work_mem.remove_old_memory(bucket_id, self.max_work_tokens)
-
-    def purge_except(self, obj_keep_idx: List[int]) -> None:
-        # purge certain objects from the memory except the one listed
-        self.work_mem.purge_except(obj_keep_idx)
-        if self.use_long_term and self.long_mem.engaged():
-            self.long_mem.purge_except(obj_keep_idx)
-        self.sensory = {k: v for k, v in self.sensory.items() if k in obj_keep_idx}
-
-        if not self.work_mem.engaged():
-            # everything is removed!
-            self.engaged = False
-
-    def compress_features(self, bucket_id: int) -> None:
-        HW = self.HW
-
-        # perform memory consolidation
-        prototype_key, prototype_value, prototype_shrinkage = self.consolidation(
-            *self.work_mem.get_all_sliced(bucket_id, 0, -self.min_work_tokens))
-
-        # remove consolidated working memory
-        self.work_mem.sieve_by_range(bucket_id,
-                                     0,
-                                     -self.min_work_tokens,
-                                     min_size=self.min_work_tokens)
-
-        # add to long-term memory
-        self.long_mem.add(prototype_key,
-                          prototype_value,
-                          prototype_shrinkage,
-                          selection=None,
-                          supposed_bucket_id=bucket_id)
-
-    def consolidation(self, candidate_key: torch.Tensor, candidate_shrinkage: torch.Tensor,
-                      candidate_selection: torch.Tensor, candidate_value: Dict[int, torch.Tensor],
-                      usage: torch.Tensor) -> (torch.Tensor, Dict[int, torch.Tensor], torch.Tensor):
-        # find the indices with max usage
-        bs = candidate_key.shape[0]
-        assert bs in [1, 2]
-
-        prototype_key = []
-        prototype_selection = []
-        for bi in range(bs):
-            _, max_usage_indices = torch.topk(usage[bi], k=self.num_prototypes, dim=-1, sorted=True)
-            prototype_indices = max_usage_indices.flatten()
-            prototype_key.append(candidate_key[bi, :, prototype_indices])
-            prototype_selection.append(candidate_selection[bi, :, prototype_indices])
-        prototype_key = torch.stack(prototype_key, dim=0)
-        prototype_selection = torch.stack(prototype_selection, dim=0)
-        """
-        Potentiation step
-        """
-        similarity = get_similarity(candidate_key, candidate_shrinkage, prototype_key,
-                                    prototype_selection)
-        affinity = do_softmax(similarity)
-
-        # readout the values
-        prototype_value = {k: self._readout(affinity, v) for k, v in candidate_value.items()}
-
-        # readout the shrinkage term
-        prototype_shrinkage = self._readout(affinity, candidate_shrinkage)
-
-        return prototype_key, prototype_value, prototype_shrinkage
-
-    def initialize_sensory_if_needed(self, sample_key: torch.Tensor, ids: List[int]):
-        for obj in ids:
-            if obj not in self.sensory:
-                # also initializes the sensory memory
-                bs, _, h, w = sample_key.shape
-                self.sensory[obj] = torch.zeros((bs, self.sensory_dim, h, w),
-                                                device=sample_key.device)
-
-    def update_sensory(self, sensory: torch.Tensor, ids: List[int]):
-        # sensory: 1*num_objects*C*H*W
-        for obj_id, obj in enumerate(ids):
-            self.sensory[obj] = sensory[:, obj_id]
-
-    def get_sensory(self, ids: List[int]):
-        # returns (1/2)*num_objects*C*H*W
-        return self._get_sensory_by_ids(ids)
-    
-    def clear_non_permanent_memory(self):
-        self.work_mem.clear_non_permanent_memory()
-        if self.use_long_term:
-            self.long_mem.clear_non_permanent_memory()
-
-    def clear_sensory_memory(self):
-        self.sensory = {}
diff --git a/spaces/seduerr/text_analytics/text_analytics/pipes/asks_tagger.py b/spaces/seduerr/text_analytics/text_analytics/pipes/asks_tagger.py
deleted file mode 100644
index 26a84afe4b1813465d430c3d6410187c3a59ed86..0000000000000000000000000000000000000000
--- a/spaces/seduerr/text_analytics/text_analytics/pipes/asks_tagger.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from spacy.matcher import PhraseMatcher
-from spacy.tokens import Doc
-from spacy.tokens import Span
-from spacy.util import filter_spans
-from spacy.language import Language
-
-from text_analytics.constants import ACCEPTED_LANGUAGES
-
-asks_getter = lambda doc: [doc[span['start']:span['end']] for span in doc._.asks_span_indices]
-
-Doc.set_extension('asks_span_indices', force=False, default=[])
-Doc.set_extension('asks', force=False, getter=asks_getter)
-
-@Language.factory('asks tagger')
-class AsksTagger:
-    def __init__(self, name, nlp, language: str='en') -> None:
-        if not language in ACCEPTED_LANGUAGES:
-            raise ValueError(f'Language {language} is not supported yet')
-
-        self._language = language
-        self._matcher = PhraseMatcher(nlp.vocab, attr='LOWER')
-        self._connectives = []
-        if language == 'en': # question words and questionmark
-            self._connectives = ['?', 'what', 'how', 'who', 'when']
-        else: # Support for future languages
-            pass
-
-        for con in self._connectives:
-            self._matcher.add(con, None, nlp(con))
-        
-
-    def __call__(self, doc: Doc) -> Doc:
-        matches = self._matcher(doc)
-        asks_spans = [doc[start:end] for _, start, end in matches]
-        doc._.asks_span_indices = [{'start': span.start, 'end': span.end, 'label': span.label} for span in filter_spans(asks_spans)] # Save the asks connectives found
-        
-        return doc
\ No newline at end of file
diff --git a/spaces/segments-tobias/conex/espnet2/schedulers/__init__.py b/spaces/segments-tobias/conex/espnet2/schedulers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/shgao/EditAnything/ldm/modules/image_degradation/bsrgan.py b/spaces/shgao/EditAnything/ldm/modules/image_degradation/bsrgan.py
deleted file mode 100644
index 32ef56169978e550090261cddbcf5eb611a6173b..0000000000000000000000000000000000000000
--- a/spaces/shgao/EditAnything/ldm/modules/image_degradation/bsrgan.py
+++ /dev/null
@@ -1,730 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(30, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        elif i == 1:
-            image = add_blur(image, sf=sf)
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image":image}
-    return example
-
-
-# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
-    """
-    This is an extended degradation model by combining
-    the degradation models of BSRGAN and Real-ESRGAN
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    use_shuffle: the degradation shuffle
-    use_sharp: sharpening the img
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    if use_sharp:
-        img = add_sharpening(img)
-    hq = img.copy()
-
-    if random.random() < shuffle_prob:
-        shuffle_order = random.sample(range(13), 13)
-    else:
-        shuffle_order = list(range(13))
-        # local shuffle for noise, JPEG is always the last one
-        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
-        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
-
-    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
-
-    for i in shuffle_order:
-        if i == 0:
-            img = add_blur(img, sf=sf)
-        elif i == 1:
-            img = add_resize(img, sf=sf)
-        elif i == 2:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 3:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 4:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 5:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        elif i == 6:
-            img = add_JPEG_noise(img)
-        elif i == 7:
-            img = add_blur(img, sf=sf)
-        elif i == 8:
-            img = add_resize(img, sf=sf)
-        elif i == 9:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 10:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 11:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 12:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        else:
-            print('check the shuffle!')
-
-    # resize to desired size
-    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
-                     interpolation=random.choice([1, 2, 3]))
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf, lq_patchsize)
-
-    return img, hq
-
-
-if __name__ == '__main__':
-	print("hey")
-	img = util.imread_uint('utils/test.png', 3)
-	print(img)
-	img = util.uint2single(img)
-	print(img)
-	img = img[:448, :448]
-	h = img.shape[0] // 4
-	print("resizing to", h)
-	sf = 4
-	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-	for i in range(20):
-		print(i)
-		img_lq = deg_fn(img)
-		print(img_lq)
-		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
-		print(img_lq.shape)
-		print("bicubic", img_lq_bicubic.shape)
-		print(img_hq.shape)
-		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-		util.imsave(img_concat, str(i) + '.png')
-
-
diff --git a/spaces/siddh4rth/narrify/app.py b/spaces/siddh4rth/narrify/app.py
deleted file mode 100644
index 2527fce0419a4150d97cce99098ed1b75cfe5548..0000000000000000000000000000000000000000
--- a/spaces/siddh4rth/narrify/app.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import gradio as gr
-import os
-import openai
-import pinecone
-
-openai.api_key = os.environ["OPENAI-API-KEY"]
-
-pinecone.init(
-    api_key=os.environ["PINECONE-API-KEY-2"],
-    environment="us-east1-gcp",
-)
-
-limit = 5000
-# 3750
-
-embed_model = "text-embedding-ada-002"
-
-index_name = 'extractive-qa'
-index = pinecone.Index(index_name)
-
-# retrieve relevant answers
-def retrieve(query):
-    res = openai.Embedding.create(
-        input=[query],
-        engine=embed_model,
-    )
-
-    # retrieve from Pinecone
-    xq = res['data'][0]['embedding']
-
-    # get relevant contexts
-    res = index.query(xq, top_k=3, include_metadata=True)
-    contexts = [
-        x['metadata']['text'] for x in res['matches']
-    ]
-
-    # build our prompt with the retrieved contexts included
-    prompt_start = (
-        "Answer the question based on the context below.\n\n"+
-        "Context:\n"
-    )
-    prompt_end = (
-        f"\n\nQuestion: {query}\nAnswer:"
-    )
-
-    # append contexts until hitting limit
-    for i in range(1, len(contexts)):
-        if len("\n\n---\n\n".join(contexts[:i])) >= limit:
-            prompt = (
-                prompt_start +
-                "\n\n---\n\n".join(contexts[:i-1]) +
-                prompt_end
-            )
-            break
-        elif i == len(contexts)-1:
-            prompt = (
-                prompt_start +
-                "\n\n---\n\n".join(contexts) +
-                prompt_end
-            )
-    return prompt
-
-# then we complete the context-infused query
-def complete(prompt):
-    # query text-davinci-003
-    res = openai.Completion.create(
-        engine='text-davinci-003',
-        prompt=prompt,
-        temperature=0,
-        max_tokens=600,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0,
-        stop=None
-    )
-    return res['choices'][0]['text'].strip()
-
-def greet(query):
-    # first we retrieve relevant items from Pinecone
-    query_with_contexts = retrieve(query)
-    # return only the main answer
-    result = complete(query_with_contexts)
-    return result
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-
diff --git a/spaces/simonduerr/ProteinMPNNESM/ProteinMPNN/vanilla_proteinmpnn/helper_scripts/assign_fixed_chains.py b/spaces/simonduerr/ProteinMPNNESM/ProteinMPNN/vanilla_proteinmpnn/helper_scripts/assign_fixed_chains.py
deleted file mode 100644
index 0dcf7b688d177d6c83129d4e1e44c75cd254f44a..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNNESM/ProteinMPNN/vanilla_proteinmpnn/helper_scripts/assign_fixed_chains.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import argparse
-
-def main(args):
-    import json
-
-    with open(args.input_path, 'r') as json_file:
-        json_list = list(json_file)
-    
-    global_designed_chain_list = []
-    if args.chain_list != '':
-        global_designed_chain_list = [str(item) for item in args.chain_list.split()]
-    my_dict = {}
-    for json_str in json_list:
-        result = json.loads(json_str)
-        all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] #['A','B', 'C',...]
-        if len(global_designed_chain_list) > 0:
-            designed_chain_list = global_designed_chain_list
-        else:
-            #manually specify, e.g.
-            designed_chain_list = ["A"]
-        fixed_chain_list = [letter for letter in all_chain_list if letter not in designed_chain_list] #fix/do not redesign these chains 
-        my_dict[result['name']]= (designed_chain_list, fixed_chain_list)
-    
-    with open(args.output_path, 'w') as f:
-        f.write(json.dumps(my_dict) + '\n')
-
-
-if __name__ == "__main__":
-    argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    argparser.add_argument("--input_path", type=str, help="Path to the parsed PDBs")
-    argparser.add_argument("--output_path", type=str, help="Path to the output dictionary")
-    argparser.add_argument("--chain_list", type=str, default='', help="List of the chains that need to be designed")    
-
-    args = argparser.parse_args()
-    main(args) 
-
-# Output looks like this:
-# {"5TTA": [["A"], ["B"]], "3LIS": [["A"], ["B"]]}
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Call of Duty Mobile - Garena PC Version Everything You Need to Know.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Call of Duty Mobile - Garena PC Version Everything You Need to Know.md
deleted file mode 100644
index a36c0d9e58ada519b013b536ad8566497cb66185..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Call of Duty Mobile - Garena PC Version Everything You Need to Know.md	
+++ /dev/null
@@ -1,194 +0,0 @@
-
-<h1>How to Download and Play Call of Duty Mobile PC Garena</h1>
-<p>If you are a fan of first-person shooter games, you might have heard of <strong>Call of Duty Mobile</strong>, one of the most popular and successful mobile games in the world. But did you know that you can also play this game on your PC with better graphics, performance, and controls? In this article, we will show you how to download and play <strong>Call of Duty Mobile PC Garena</strong>, a special version of the game designed for Southeast Asian players. We will also share some tips and tricks to help you improve your skills and win more matches.</p>
- <h2>What is Call of Duty Mobile PC Garena?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Call of Duty Mobile is a free-to-play online multiplayer shooter game that brings the best elements of the Call of Duty franchise to your mobile device. You can choose from a variety of game modes, such as Team Deathmatch, Domination, Search and Destroy, Battle Royale, Zombies, and more. You can also customize your loadout, weapons, perks, skins, and operators to suit your playstyle and preferences.</p>
-<h2>download call of duty mobile pc garena</h2><br /><p><b><b>DOWNLOAD</b> >> <a href="https://ssurll.com/2uNYpL">https://ssurll.com/2uNYpL</a></b></p><br /><br />
-<p>Call of Duty Mobile PC Garena is a version of the game that is exclusive to players in Southeast Asia, including Indonesia, Malaysia, Philippines, Singapore, Thailand, Taiwan, Hong Kong, Macau, Cambodia, Laos, Myanmar, Brunei, Timor-Leste, Maldives, India, Pakistan, Bangladesh, Sri Lanka, Nepal, Bhutan, Afghanistan. It has some unique features that are not available in other versions, such as:</p>
-<ul>
-<li>A dedicated server for faster matchmaking and smoother gameplay</li>
-<li>A localized interface and voice-over in English or Bahasa Indonesia</li>
-<li>A regional leaderboard and ranking system</li>
-<li>Exclusive events, rewards, promotions, and tournaments</li>
-<li>A community platform where you can interact with other players and get support from the developers</li>
-</ul>
- <h3>The difference between Call of Duty Mobile PC Garena and other versions</h3>
-<p>Aside from the features mentioned above, there are some other differences between Call of Duty Mobile PC Garena and other versions that you should be aware of:</p>
-<ul>
-<li>Call of Duty Mobile PC Garena is only compatible with Gameloop emulator (more on that later), while other versions can be played on various emulators such as BlueStacks or LDPlayer <li>Call of Duty Mobile PC Garena has a different account system and currency than other versions. You need to create a Garena account and use Garena Shells to purchase in-game items and services</li>
-<li>Call of Duty Mobile PC Garena has a different update schedule and content than other versions. You may not be able to access the latest features, maps, modes, or weapons that are available in other regions</li>
-</ul>
-<p>Despite these differences, Call of Duty Mobile PC Garena still offers a fun and immersive gaming experience that is comparable to other versions. You can enjoy the same high-quality graphics, sound effects, and gameplay mechanics that make Call of Duty Mobile one of the best mobile games ever.</p>
- <h2>What are the system requirements for Call of Duty Mobile PC Garena?</h2>
-<h3>The minimum and recommended specifications for PC</h3>
-<p>To play Call of Duty Mobile PC Garena, you need a PC that meets the following system requirements:</p>
-<table>
-<tr>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>CPU: Dual-core 1.8 GHz or higher</td>
-<td>CPU: Quad-core 2.0 GHz or higher</td>
-</tr>
-<tr>
-<td>RAM: 3 GB or higher</td>
-<td>RAM: 4 GB or higher</td>
-</tr>
-<tr>
-<td>GPU: Intel HD Graphics 4000 or higher</td>
-<td>GPU: NVIDIA GeForce GTX 660 or higher</td>
-</tr>
-<tr>
-<td>OS: Windows 7 or higher (64-bit)</td>
-<td>OS: Windows 10 (64-bit)</td>
-</tr>
-<tr>
-<td>Storage: 4 GB or higher</td>
-<td>Storage: 8 GB or higher</td>
-</tr>
-<tr>
-<td>Internet: Broadband connection with low latency</td>
-<td>Internet: Broadband connection with low latency</td>
-</tr>
-</table>
- <h3>How to check your PC compatibility and performance</h3>
-<p>To check if your PC is compatible with Call of Duty Mobile PC Garena, you can use the Gameloop emulator's built-in diagnostic tool. This tool will scan your PC and give you a score based on your hardware and software configuration. The higher the score, the better your PC performance will be.</p>
-<p>To use the diagnostic tool, follow these steps:</p>
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-<ol>
-<li>Download and install Gameloop emulator from the official website (more on that later)</li>
-<li>Launch Gameloop and click on the "Menu" icon on the top right corner</li>
-<li>Select "Diagnostics" from the drop-down menu</li>
-<li>Wait for the scan to complete and check your score and details</li>
-<li>If your score is below 1000, you may need to upgrade your PC or lower your game settings to play smoothly</li>
-<li>If your score is above 1000, you can enjoy playing Call of Duty Mobile PC Garena with optimal settings and performance</li>
-</ol>
- <h2>How to download and install Call of Duty Mobile PC Garena?</h2> <h3>The official emulator for Call of Duty Mobile PC Garena: Gameloop</h3>
-<p>To play Call of Duty Mobile PC Garena, you need to use the official emulator provided by the developers: Gameloop. Gameloop is a free and powerful Android emulator that allows you to play mobile games on your PC with ease. It has many advantages over other emulators, such as:</p>
-<ul>
-<li>It is optimized for Call of Duty Mobile PC Garena and other popular games, ensuring smooth and stable gameplay</li>
-<li>It supports high-definition graphics, sound effects, and FPS settings, enhancing your visual and audio experience</li>
-<li>It offers flexible and customizable controls, allowing you to use your keyboard and mouse or a controller to play the game</li>
-<li>It has a built-in anti-cheat system, ensuring fair and secure gaming environment</li>
-<li>It has a user-friendly interface and a large game library, making it easy to find and download your favorite games</li>
-</ul>
-<p>To download Gameloop emulator, you can visit the official website or click on this link. The file size is about 10 MB and it will take a few minutes to install on your PC.</p>
- <h3>The steps to download and install Gameloop and Call of Duty Mobile PC Garena</h3>
-<p>After you have downloaded and installed Gameloop emulator, you can follow these steps to download and install Call of Duty Mobile PC Garena:</p>
-<ol>
-<li>Launch Gameloop and click on the "Game Center" tab on the left side</li>
-<li>Search for "Call of Duty Mobile PC Garena" in the search bar or find it in the featured games section</li>
-<li>Click on the "Download" button and wait for the game to be downloaded and installed on your PC. The file size is about 2 GB and it will take some time depending on your internet speed</li>
-<li>Once the game is installed, click on the "Play" button and enjoy playing Call of Duty Mobile PC Garena on your PC</li>
-</ol> <h2>How to play Call of Duty Mobile PC Garena?</h2>
-<h3>How to adjust the settings and controls for optimal gameplay</h3>
-<p>Before you start playing Call of Duty Mobile PC Garena, you may want to adjust the settings and controls to suit your preferences and PC specifications. You can access the settings menu by clicking on the "Gear" icon on the top right corner of the game screen. Here are some of the settings and controls that you can customize:</p>
-<ul>
-<li>Graphics: You can change the graphic quality, frame rate, brightness, and resolution of the game. You can also enable or disable anti-aliasing, depth of field, bloom, and ragdoll effects. For better performance, you may want to lower the graphic quality and frame rate if your PC is not powerful enough</li>
-<li>Audio: You can adjust the volume, sound effects, music, and voice chat settings of the game. You can also enable or disable 3D sound and voice chat push-to-talk. For better communication, you may want to use a headset and microphone and enable voice chat</li>
-<li>Controls: You can choose between simple mode or advanced mode for your shooting controls. You can also customize your keyboard and mouse or controller settings for your movement, aiming, shooting, reloading, jumping, crouching, prone, and other actions. For better accuracy, you may want to use a mouse with high DPI and sensitivity settings</li>
-<li>Gameplay: You can change the camera field of view, crosshair color and style, aim assist, quick run, quick throw grenade, slide while running, and other gameplay options. You can also enable or disable auto-open doors, auto-loot items, auto-pickup weapons, and other convenience features</li>
-<li>Language: You can change the language of the game interface and voice-over to English or Bahasa Indonesia</li>
-</ul>
-<p>You can experiment with different settings and controls until you find the ones that work best for you. You can also reset the settings and controls to default if you want to start over.</p>
- <h3>How to access the different game modes and features</h3>
-<p>Call of Duty Mobile PC Garena offers a variety of game modes and features that you can access from the main menu. Here are some of the game modes and features that you can enjoy:</p>
-<ul>
-<li>Multiplayer: This is where you can play online matches with other players in different modes such as Team Deathmatch, Domination, Search and Destroy, Hardpoint, Frontline, Free for All, Kill Confirmed, Capture the Flag, Gunfight, Rapid Fire, Cranked, Attack of the Undead, Prop Hunt, Sticks and Stones, One Shot One Kill, Sniper Only, Gun Game Team Fight, 10v10 Team Deathmatch, 10v10 Domination, 10v10 Hardpoint. You can also play ranked matches to earn points and rewards</li>
-<li>Battle Royale: This is where you can play online matches with up to 100 players in a large map with different terrains and locations. You can choose between solo mode or squad mode with up to four players in your team. You can also choose between first-person perspective or third-person perspective for your camera view. You can loot weapons, items, vehicles, and airdrops to survive and eliminate other players. The last player or team standing wins the match</li>
-<li>Zombies: This is where you can play online matches with up to four players in your team against waves of zombies in different maps such as Nacht der Untoten (Night of the Undead), Shi No Numa (Swamp of Death), Raid (Outbreak), Survival (Isolated). You can also play solo mode if you prefer. You can loot weapons, items, perks, mystery boxes, traps, turrets, and power-ups to fight off the zombies. You can also complete objectives and challenges to earn rewards</li>
-<li>Events: This is where you can participate in various events that are held regularly by the developers. These events may include seasonal themes (such as Halloween or Christmas), limited-time modes (such as Alcatraz or Warfare), special missions (such as Anniversary Celebration or Going Dark), exclusive rewards (such as skins or weapons), tournaments (such as World Championship or Regional Championship), and more</li>
-<li>Store: This is where you can purchase in-game items and services using Garena Shells (the currency for Call of Duty Mobile PC Garena). These items and services may include crates (such as Premium Pass Crate or Lucky Draw Crate), bundles (such as Battle Pass Bundle or Weapon Bundle), skins (such as characters or weapons), weapons (such as legendary or epic), perks (such as double XP or weapon XP), CP (the currency for Call of Duty Mobile global version), and more</li>
-<li>Loadout: This is where you can customize your loadout, weapons, perks, skins, and operators for multiplayer and battle royale modes. You can also upgrade your weapons using weapon XP cards and unlock attachments and camos. You can also create custom loadouts for different situations and preferences</li>
-<li>Profile: This is where you can view your profile, stats, achievements, medals, leaderboards, friends, clans, and settings. You can also edit your profile picture, name, frame, tagline, and privacy options. You can also check your inventory, mail, notifications, and rewards</li>
-</ul>
-<p>You can explore the different game modes and features and find the ones that you like the most. You can also switch between the game modes and features anytime by clicking on the icons on the bottom of the screen.</p>
- <h2>How to improve your skills and win in Call of Duty Mobile PC Garena?</h2>
-<h3>Some tips and tricks for multiplayer and battle royale modes</h3>
-<p>Playing Call of Duty Mobile PC Garena can be challenging and competitive, especially if you are new to the game or playing against experienced players. Here are some tips and tricks that can help you improve your skills and win more matches in multiplayer and battle royale modes:</p>
-<ul>
-<li>Practice: The best way to improve your skills is to practice regularly and learn from your mistakes. You can play solo or with your friends in different modes and maps to familiarize yourself with the game mechanics, controls, weapons, items, locations, strategies, and tactics. You can also watch replays of your matches or other players' matches to analyze your performance and identify your strengths and weaknesses</li>
-<li>Choose the right loadout: Depending on the mode, map, team composition, enemy composition, and personal preference, you may want to choose a different loadout for each match. You should consider the range, damage, accuracy, fire rate, mobility, recoil, magazine size, reload speed, and attachments of each weapon. You should also consider the benefits and drawbacks of each perk, skin, and operator. You should experiment with different combinations and find the ones that suit your playstyle and situation</li>
-<li>Use the environment: The environment can be your friend or foe in Call of Duty Mobile PC Garena. You should use the cover, elevation, angles, corners, windows, doors, walls, vehicles, traps, turrets, airdrops, and other environmental elements to your advantage. You should also be aware of the potential threats and opportunities that the environment can pose to you and your enemies</li>
-<li>Communicate with your team: Communication is key to teamwork and coordination in Call of Duty Mobile PC Garena. You should use voice chat or text chat to communicate with your team members effectively. You should share information such as enemy location, health status, weapon type, item availability, strategy plan, backup request, danger alert, etc. You should also listen to your team members' calls and follow their instructions or suggestions</li>
-<li>Be flexible and adaptable: Call of Duty Mobile PC Garena is a dynamic and unpredictable game that requires you to be flexible and adaptable to changing situations. You should be ready to switch your loadout, weapon mode (single fire or auto fire), camera view (first-person or third-person), movement mode (walk or run), action mode (shoot or melee), etc. depending on the circumstances. You should also be ready to change your strategy or tactic if your current one is not working or if the enemy has countered it</li>
-</ul>
- <h3>Some resources and guides for more advanced strategies and techniques</h3>
-<p>If you want to learn more advanced strategies and techniques for Call of Duty Mobile PC Garena <p>If you want to learn more advanced strategies and techniques for Call of Duty Mobile PC Garena, you can check out some of the resources and guides that are available online. These resources and guides can provide you with valuable insights, tips, tricks, tutorials, reviews, news, updates, and more. Here are some of the resources and guides that we recommend:</p>
-<ul>
-<li>[Download Call of Duty®: Mobile - Garena android on PC - GameLoop](^1^): This is the official website of Gameloop emulator, where you can find more information about the emulator and how to play Call of Duty Mobile PC Garena with it. You can also download the latest version of Gameloop and Call of Duty Mobile PC Garena from here</li>
-<li>[How to Play Call of Duty: Mobile on PC | Digital Trends](^2^): This is a comprehensive guide that covers everything you need to know about playing Call of Duty Mobile on PC, including how to download and install Gameloop and Call of Duty Mobile PC Garena, how to adjust the settings and controls, how to access the different game modes and features, and how to improve your skills and win</li>
-<li>[Call of Duty: Mobile - Garena](^3^): This is the official website of Call of Duty Mobile PC Garena, where you can find more information about the game and its features. You can also participate in various events, rewards, promotions, and tournaments that are exclusive to Southeast Asian players</li>
-<li>[Play Call of Duty Mobile on PC: The Best Settings for CoD - BlueStacks](^4^): This is a guide that shows you how to play Call of Duty Mobile on PC using another popular Android emulator: BlueStacks. You can learn how to download and install BlueStacks and Call of Duty Mobile, how to optimize the graphics, gameplay, and controls settings, and how to use the advanced features of BlueStacks such as key mapping, macros, multi-instance, etc.</li>
-</ul>
-<p>These are just some of the resources and guides that you can use to enhance your gaming experience with Call of Duty Mobile PC Garena. You can also search for more resources and guides online or join the online community platforms where you can interact with other players and get support from the developers.</p>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and benefits of playing Call of Duty Mobile PC Garena</h3>
-<p>In conclusion, Call of Duty Mobile PC Garena is a special version of Call of Duty Mobile that is designed for Southeast Asian players. It offers a fun and immersive gaming experience that is comparable to other versions. You can play this game on your PC with better graphics, performance, and controls using the official emulator: Gameloop. You can also enjoy some unique features that are not available in other regions, such as a dedicated server, a localized interface and voice-over, a regional leaderboard and ranking system, exclusive events, rewards, promotions, and tournaments, and a community platform.</p>
-<p>Playing Call of Duty Mobile PC Garena can be challenging and competitive, but also rewarding and satisfying. You can choose from a variety of game modes, such as multiplayer, battle royale, zombies, events, etc. You can also customize your loadout, weapons, perks, skins, and operators to suit your playstyle and preferences. You can also improve your skills and win more matches by practicing regularly, choosing the right loadout, using the environment, communicating with your team, being flexible and adaptable, and using some resources and guides for more advanced strategies and techniques. <h3>A call to action to download and play the game now</h3>
-<p>If you are interested in playing Call of Duty Mobile PC Garena, you should not hesitate to download and play the game now. You can download Gameloop emulator and Call of Duty Mobile PC Garena from the links provided in this article or from the official websites. You can also join the online community platforms and follow the social media accounts of Call of Duty Mobile PC Garena to stay updated with the latest news, updates, events, rewards, promotions, and tournaments. You can also invite your friends to play with you and have more fun and excitement.</p>
-<p>Call of Duty Mobile PC Garena is a game that you will not regret playing. It is a game that will challenge your skills, test your strategies, reward your efforts, and satisfy your cravings for action and adventure. It is a game that will make you feel like a real soldier in a virtual battlefield. It is a game that will give you hours of entertainment and enjoyment.</p>
-<p>So what are you waiting for? Download and play Call of Duty Mobile PC Garena now and experience the thrill and excitement of one of the best mobile games ever on your PC.</p>
- <h2>FAQs</h2>
-<h3>Q1. Is Call of Duty Mobile PC Garena free to play?</h3>
-<p>A1. Yes, Call of Duty Mobile PC Garena is free to play. You do not need to pay anything to download and play the game. However, you can purchase in-game items and services using Garena Shells if you want to enhance your gaming experience.</p>
- <h3>Q2. Can I play Call of Duty Mobile PC Garena with a controller or keyboard and mouse?</h3>
-<p>A2. Yes, you can play Call of Duty Mobile PC Garena with a controller or keyboard and mouse. You can use Gameloop emulator's settings to customize your controls for different devices. You can also use other devices such as joysticks, steering wheels, or flight sticks if they are compatible with Gameloop emulator.</p>
- <h3>Q3. Can I play Call of Duty Mobile PC Garena with my friends on mobile devices?</h3>
-<p>A3. Yes, you can play Call of Duty Mobile PC Garena with your friends on mobile devices. You can add your friends as contacts in the game and invite them to join your matches or squads. You can also chat with them using voice chat or text chat in the game.</p>
- <h3>Q4. What are the rewards and incentives for playing Call of Duty Mobile PC Garena?</h3>
-<p>A4. There are many rewards and incentives for playing Call of Duty Mobile PC Garena. You can earn XP, CP, credits, crates, skins, weapons, perks, operators, medals, achievements, etc. by playing the game and completing missions and challenges. You can also participate in various events, rewards, promotions, and tournaments that are exclusive to Southeast Asian players and win more prizes and rewards.</p>
- <h3>Q5. Where can I get more information and support for Call of Duty Mobile PC Garena?</h3>
-<p>A5. You can get more information and support for Call of Duty Mobile PC Garena by visiting the official website or the online community platforms. You can also contact the customer service team by submitting a ticket or sending an email. You can also follow the social media accounts of Call of Duty Mobile PC Garena on Facebook, Instagram, YouTube, Twitter, etc.</p> 197e85843d<br />
-<br />
-<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enchantment Fix for Shadow Fight 2 Mod APK Tips and Tricks to Master the Game.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enchantment Fix for Shadow Fight 2 Mod APK Tips and Tricks to Master the Game.md
deleted file mode 100644
index 780ff0c4ef3ac780f096ed66a67d2397db370255..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enchantment Fix for Shadow Fight 2 Mod APK Tips and Tricks to Master the Game.md	
+++ /dev/null
@@ -1,79 +0,0 @@
-
-<h1>Shadow Fight 2 Mod Apk Enchantment Fix: How to Solve the Problem of Missing Enchant Button</h1>
-<p>If you are a fan of Shadow Fight 2, you know how important enchantments are for your weapons and armor. They can give you an edge over your enemies and make your battles more exciting. But what if you encounter a problem where the enchant button does not appear on your screen, even if you have enough gems and shadow orbs to use it? How can you fix this annoying issue and enjoy the game to the fullest? In this article, we will explain what causes this problem and how you can solve it easily.</p>
-<h2>What is Shadow Fight 2 and why do you need enchantments?</h2>
-<h3>Shadow Fight 2 is a popular fighting game with RPG elements</h3>
-<p>Shadow Fight 2 is a sequel to the original Shadow Fight, which was released in 2011. It is a 2D fighting game that combines martial arts, weapons, magic, and RPG elements. You play as a shadow warrior who travels across six different worlds, facing various enemies and bosses. You can customize your character with different weapons, armor, skills, and perks. You can also participate in tournaments, survival mode, raids, and special events.</p>
-<h2>shadow fight 2 mod apk enchantment fix</h2><br /><p><b><b>DOWNLOAD</b> &#8230; <a href="https://ssurll.com/2uNZFq">https://ssurll.com/2uNZFq</a></b></p><br /><br />
-<h3>Enchantments are special effects that enhance your weapons and armor</h3>
-<p>Enchantments are one of the features that make Shadow Fight 2 more fun and challenging. They are special effects that you can apply to your weapons and armor using gems and shadow orbs. Gems are the premium currency of the game, while shadow orbs are earned by defeating enemies and bosses. There are four types of enchantments: simple, medium, mythical, and legendary. Each type has different effects and costs. For example, simple enchantments can increase your damage, critical chance, or bleed effect, while legendary enchantments can give you unique abilities like teleportation, regeneration, or shadow form.</p>
-<h2>What is the problem of missing enchant button and how does it affect your gameplay?</h2>
-<h3>The enchant button is supposed to appear when you have enough gems and shadow orbs to enchant your equipment</h3>
-<p>The enchant button is located at the bottom right corner of your screen when you are in the inventory menu. It shows how many gems and shadow orbs you need to enchant your selected weapon or armor. When you have enough resources, you can tap on the button to open a window where you can choose which type of enchantment you want to apply. You can also see the possible effects and probabilities of each enchantment.</p>
-<h3>The problem occurs when the enchant button does not show up even if you have enough resources</h3>
-<p>Some players have reported that they encounter a problem where the enchant button does not appear even if they have enough resources. This means that they cannot enchant their equipment and make it more powerful. This can affect their gameplay negatively, as they may face difficulties in defeating stronger enemies and bosses. They may also miss out on the fun and satisfaction of using enchantments.</p>
-<h2>What are the possible causes of the missing enchant button problem?</h2>
-<h3>The problem may be caused by a bug in the game or a corrupted game file</h3>
-<p>One of the possible causes of the missing enchant button problem is a bug in the game. A bug is an error or flaw in the game's code that causes it to malfunction or behave unexpectedly. Sometimes, bugs can be fixed by updating the game to the latest version, which may contain patches or fixes for the known issues. Another possible cause is a corrupted game file. A corrupted game file is a damaged or incomplete file that prevents the game from running properly. Sometimes, corrupted game files can be fixed by clearing the cache and data of the game, which may remove any unwanted or temporary files that interfere with the game's performance.</p>
-<h3>The problem may also be caused by using a modded version of the game that has some glitches or errors</h3>
-<p>Another possible cause of the missing enchant button problem is using a modded version of the game. A modded version of the game is a modified or hacked version that has some changes or additions to the original game. For example, some modded versions of Shadow Fight 2 may have unlimited gems and shadow orbs, unlocked weapons and armor, or other features that are not available in the official version. However, using a modded version of the game may also have some risks and drawbacks. For instance, some modded versions of the game may have some glitches or errors that affect the game's functionality or stability. One of these glitches or errors may be the missing enchant button problem.</p>
-<h3>The problem may also be caused by an incompatible device or operating system</h3>
-<p>A third possible cause of the missing enchant button problem is an incompatible device or operating system. An incompatible device or operating system is one that does not meet the minimum requirements or specifications to run the game smoothly and correctly. For example, some devices or operating systems may have low memory, storage, processor, or graphics capabilities that prevent them from supporting the game's features or graphics. One of these features may be the enchantment system, which may not work properly on some devices or operating systems.</p>
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-<h2>How can you fix the missing enchant button problem?</h2>
-<h3>There are several methods that you can try to fix the problem, depending on the cause</h3>
-<p>If you are facing the missing enchant button problem, do not worry. There are several methods that you can try to fix it, depending on what is causing it. Here are some of them:</p>
-<h4>Method 1: Update the game to the latest version</h4>
-<p>One of the easiest and simplest methods to fix the missing enchant button problem is to update the game to the latest version. Updating the game may solve the problem if it is caused by a bug in the game that has been fixed by the developer. To update the game, you can go to your device's app store and check if there is any available update for Shadow Fight 2. If there is, you can download and install it and see if it fixes the problem.</p>
-<h4>Method 2: Clear the cache and data of the game</h4>
-<p>Another method to fix the missing enchant button problem is to clear the cache and data of the game. Clearing the cache and data of the game may solve the problem if it is caused by a corrupted game file that is interfering with the game's performance. To clear the cache and data of the game, you can go to your device's settings and find the app manager or application list. Then, you can select Shadow Fight 2 and tap on clear cache and clear data. This will delete any temporary or unwanted files that are stored by the game. However, this will also delete your game progress and settings, so make sure you have a backup before doing this.</p>
-<h4>Method 3: Reinstall the game or use the original version instead of the modded one</h4>
-<p>A third method to fix the missing enchant button problem is to reinstall the game or use the original version instead of the modded one. Reinstalling the game or using the original version may solve the problem if it is caused by using a modded version of the game that has some glitches or errors. To reinstall the game, you can uninstall it from your device and then download and install it again from your device's app store. To use the original version, you can uninstall the modded version and then download and install the official version from your device's app store. However, this may also delete your game progress and settings, so make sure you have a backup before doing this.</p>
-<h4>Method 4: Contact the game developer or customer support for help</h4>
-<p>A fourth method to fix the missing enchant button problem is to contact the game developer or customer support for help. Contacting the game developer or customer support may solve the problem if it is caused by an incompatible device or operating system that cannot be fixed by yourself. To contact the game developer, you can visit their website or social media pages and send them a message or feedback about your issue. To contact customer support, you can visit their help center or support page and submit a ticket or request for assistance. You may need to provide some information about your device, operating system, game version, and problem details.</p>
-<h2>Conclusion and FAQs</h2>
-<p>In conclusion, Shadow Fight 2 is a fun and challenging fighting game that lets you customize your character with different weapons, armor, skills, and enchantments. However, some players may encounter a problem where the enchant button does not appear on their screen, even if they have enough gems and shadow orbs to use it. This problem can be caused by various factors, such as a bug in the game, a corrupted game file, a modded version of the game, or an incompatible device or operating system. Fortunately, there are several methods that you can try to fix this problem, such as updating the game, clearing the cache and data of the game, reinstalling the game or using the original version, or contacting the game developer or customer support. We hope that this article has helped you solve your problem and enjoy Shadow Fight 2 to the fullest.</p>
-<p>Here are some FAQs that you may have about Shadow Fight 2:</p>
-<ul>
-<li>Q: How can I get more gems and shadow orbs in Shadow Fight 2?</li>
-<li>A: You can get more gems and shadow orbs by completing quests, achievements, tournaments, survival mode, raids, special events, daily rewards, watching ads, or buying them with real money.</li>
-<li>Q: How can I unlock more weapons and armor in Shadow Fight 2?</li>
-<li>A: You can unlock more weapons and armor by progressing through the story mode, defeating bosses, opening chests, buying them with coins or gems, or crafting them with recipes.</li>
-<li>Q: How can I level up my character in Shadow Fight 2?</li>
-<li>A: You can level up your character by earning experience points (XP) from fighting enemies and bosses. You can also use XP boosters to increase your XP gain.</li>
-<li>Q: How can I change my character's appearance in Shadow Fight 2?</li>
-<li>A: You can change your character's appearance by equipping different weapons, armor, helmets, ranged weapons, magic spells, perks, and enchantments.</li>
-<li>Q: How can I play Shadow Fight 2 with my friends?</li>
-<li>A: You can play Shadow Fight 2 with your friends by connecting your Facebook account to the game and inviting them to join your clan or challenge them to a duel.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sklearn-docs/Segmenting_greek_coins_using_Segmental_Clustering/app.py b/spaces/sklearn-docs/Segmenting_greek_coins_using_Segmental_Clustering/app.py
deleted file mode 100644
index 81ac57feabe613eb1395beefd62ab6489c232825..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/Segmenting_greek_coins_using_Segmental_Clustering/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import gradio as gr
-import time
-import numpy as np
-from scipy.ndimage import gaussian_filter
-import matplotlib.pyplot as plt
-from skimage.data import coins
-from skimage.transform import rescale
-from sklearn.feature_extraction import image
-from sklearn.cluster import spectral_clustering
-import gradio as gr
-
-
-# function for making the clustering plot.
-# input: one of the following algorithms: "kmeans", "discretize", "cluster_qr"
-def getClusteringPlot(algorithm):
-    # load the coins as a numpy array
-    orig_coins = coins()
-
-    # Pre-processing the image
-    smoothened_coins = gaussian_filter(orig_coins, sigma=2)
-    rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect", anti_aliasing=False)
-
-    # Convert the image into a graph 
-    graph = image.img_to_graph(rescaled_coins)
-
-    beta = 10
-    eps = 1e-6
-    graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
-
-    # The number of segmented regions to display needs to be chosen manually
-    n_regions = 26
-
-    # The spectral clustering quality may also benetif from requesting
-    # extra regions for segmentation.
-    n_regions_plus = 3
-    
-    t0 = time.time()
-    labels = spectral_clustering(
-        graph,
-        n_clusters=(n_regions + n_regions_plus),
-        eigen_tol=1e-7,
-        assign_labels=algorithm,
-        random_state=42,
-    )
-
-    t1 = time.time()
-    labels = labels.reshape(rescaled_coins.shape)
-    plt.figure(figsize=(5, 5))
-    plt.imshow(rescaled_coins, cmap=plt.cm.gray)
-
-    plt.xticks(())
-    plt.yticks(())
-    title = "Spectral clustering: %s, %.2fs" % (algorithm, (t1 - t0))
-    plt.title(title)
-    for l in range(n_regions):
-        colors = [plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))]
-        plt.contour(labels == l, colors=colors)
-        # To view individual segments as appear comment in plt.pause(0.5)
-    return (plt, "%.3fs" % (t1 - t0))
-
-
-# building the gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("## Segmenting the picture of Greek coins in regions 🪙")
-    gr.Markdown("This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py).")
-    gr.Markdown("In this demo, we compare three strategies for performing segmentation-clustering and breaking the below image of Greek coins into multiple partly-homogeneous regions.")
-    
-    inp = gr.Radio(["kmeans", "discretize", "cluster_qr"], label="Solver", info="Choose a clustering algorithm", value="kmeans")
-    with gr.Row():
-        plot = gr.Plot(label="Plot")
-        num = gr.Textbox(label="Running Time")
-    inp.change(getClusteringPlot, inputs=[inp], outputs=[plot, num])
-    demo.load(getClusteringPlot, inputs=[inp], outputs=[plot, num])
-
-    gr.HTML("<hr>")
-    gr.Image(coins(), label="An image of 24 Greek coins")
-    gr.Markdown("The image is retrieved from scikit-image's data [gallery](https://scikit-image.org/docs/stable/auto_examples/).")
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/sklearn-docs/ridge-coefficients-vs-L2/README.md b/spaces/sklearn-docs/ridge-coefficients-vs-L2/README.md
deleted file mode 100644
index d3696635223411de221536be2df65b92e8870839..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/ridge-coefficients-vs-L2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ridge Coefficients Vs L2
-emoji: 🏢
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/smjain/smjainvoice/starganv2vc_paddle/transforms.py b/spaces/smjain/smjainvoice/starganv2vc_paddle/transforms.py
deleted file mode 100644
index e8fad6006dda710c670902d32e64076db562db5e..0000000000000000000000000000000000000000
--- a/spaces/smjain/smjainvoice/starganv2vc_paddle/transforms.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import numpy as np
-import paddle
-from paddle import nn
-import paddle.nn.functional as F
-import paddleaudio
-import paddleaudio.functional as audio_F
-import random
-
-## 1. RandomTimeStrech
-
-class TimeStrech(nn.Layer):
-    def __init__(self, scale):
-        super(TimeStrech, self).__init__()
-        self.scale = scale
-
-    def forward(self, x):
-        mel_size = x.shape[-1]
-        
-        x = F.interpolate(x, scale_factor=(1, self.scale), align_corners=False,
-                          mode='bilinear').squeeze()
-        
-        if x.shape[-1] < mel_size:
-            noise_length = (mel_size - x.shape[-1])
-            random_pos = random.randint(0, x.shape[-1]) - noise_length
-            if random_pos < 0:
-                random_pos = 0
-            noise = x[..., random_pos:random_pos + noise_length]
-            x = paddle.concat([x, noise], axis=-1)
-        else:
-            x = x[..., :mel_size]
-        
-        return x.unsqueeze(1)
-
-## 2. PitchShift
-class PitchShift(nn.Layer):
-    def __init__(self, shift):
-        super(PitchShift, self).__init__()
-        self.shift = shift
-
-    def forward(self, x):
-        if len(x.shape) == 2:
-            x = x.unsqueeze(0)
-        x = x.squeeze()
-        mel_size = x.shape[1]
-        shift_scale = (mel_size + self.shift) / mel_size
-        x = F.interpolate(x.unsqueeze(1), scale_factor=(shift_scale, 1.), align_corners=False,
-                          mode='bilinear').squeeze(1)
-
-        x = x[:, :mel_size]
-        if x.shape[1] < mel_size:
-            pad_size = mel_size - x.shape[1]
-            x = paddle.cat([x, paddle.zeros(x.shape[0], pad_size, x.shape[2])], axis=1)
-        x = x.squeeze()
-        return x.unsqueeze(1)
-
-## 3. ShiftBias
-class ShiftBias(nn.Layer):
-    def __init__(self, bias):
-        super(ShiftBias, self).__init__()
-        self.bias = bias
-
-    def forward(self, x):
-        return x + self.bias
-
-## 4. Scaling
-class SpectScaling(nn.Layer):
-    def __init__(self, scale):
-        super(SpectScaling, self).__init__()
-        self.scale = scale
-
-    def forward(self, x):
-        return x * self.scale
-
-## 5. Time Flip
-class TimeFlip(nn.Layer):
-    def __init__(self, length):
-        super(TimeFlip, self).__init__()
-        self.length = round(length)
-
-    def forward(self, x):
-        if self.length > 1:
-          start = np.random.randint(0, x.shape[-1] - self.length)
-          x_ret = x.clone()
-          x_ret[..., start:start + self.length] = paddle.flip(x[..., start:start + self.length], axis=[-1])
-          x = x_ret
-        return x
-
-class PhaseShuffle2D(nn.Layer):
-    def __init__(self, n=2):
-        super(PhaseShuffle2D, self).__init__()
-        self.n = n
-        self.random = random.Random(1)
-
-    def forward(self, x, move=None):
-        # x.size = (B, C, M, L)
-        if move is None:
-            move = self.random.randint(-self.n, self.n)
-
-        if move == 0:
-            return x
-        else:
-            left = x[:, :, :, :move]
-            right = x[:, :, :, move:]
-            shuffled = paddle.concat([right, left], axis=3)
-            
-        return shuffled
-
-def build_transforms():
-    transforms = [
-        lambda M: TimeStrech(1+ (np.random.random()-0.5)*M*0.2),
-        lambda M: SpectScaling(1 + (np.random.random()-1)*M*0.1),
-        lambda M: PhaseShuffle2D(192),
-    ]
-    N, M = len(transforms), np.random.random()
-    composed = nn.Sequential(
-        *[trans(M) for trans in np.random.choice(transforms, N)]
-    )
-    return composed
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/noisychannel/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/noisychannel/__init__.py
deleted file mode 100644
index 89f1aef4f6328d25425e0bcabb42dfffd2ed35f0..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/noisychannel/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .rerank_options import *  # noqa
diff --git a/spaces/srush/minichain/stats.py b/spaces/srush/minichain/stats.py
deleted file mode 100644
index 589a2860f8d501b9778a2a20d489722805d3425e..0000000000000000000000000000000000000000
--- a/spaces/srush/minichain/stats.py
+++ /dev/null
@@ -1,95 +0,0 @@
-
-desc = """
-### Typed Extraction
-
-Information extraction that is automatically generated from a typed specification. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/pal.ipynb)
-
-(Novel to MiniChain)
-"""
-
-# $
-
-from minichain import prompt, show, OpenAI, transform
-from dataclasses import dataclass, is_dataclass, fields
-from typing import List, Type, Dict, Any, get_origin, get_args
-from enum import Enum
-from jinja2 import select_autoescape, FileSystemLoader, Environment
-import json
-
-def enum(x: Type[Enum]) -> Dict[str, int]:
-    d = {e.name: e.value for e in x}
-    return d
-
-
-def walk(x: Any) -> Any:
-    if issubclass(x if get_origin(x) is None else get_origin(x), List):
-        return {"_t_": "list", "t": walk(get_args(x)[0])}
-    if issubclass(x, Enum):
-        return enum(x)
-
-    if is_dataclass(x):
-        return {y.name: walk(y.type) for y in fields(x)}
-    return x.__name__
-
-
-def type_to_prompt(out: type) -> str:
-    tmp = env.get_template("type_prompt.pmpt.tpl")
-    d = walk(out)
-    return tmp.render({"typ": d})
-
-env = Environment(
-    loader=FileSystemLoader("."),
-    autoescape=select_autoescape(),
-    extensions=["jinja2_highlight.HighlightExtension"],
-)
-
-
-
-# Data specification
-
-# +
-class StatType(Enum):
-    POINTS = 1
-    REBOUNDS = 2
-    ASSISTS = 3
-
-@dataclass
-class Stat:
-    value: int
-    stat: StatType
-
-@dataclass
-class Player:
-    player: str
-    stats: List[Stat]
-# -
-
-
-@prompt(OpenAI(), template_file="stats.pmpt.tpl")
-def stats(model, passage):
-    return model.stream(dict(passage=passage, typ=type_to_prompt(Player)))
-
-@transform()
-def to_data(s:str):
-    return [Player(**j) for j in json.loads(s)]
-
-# $
-
-article = open("sixers.txt").read()
-gradio = show(lambda passage: to_data(stats(passage)),
-              examples=[article],
-              subprompts=[stats],
-              out_type="json",
-              description=desc,
-              code=open("stats.py", "r").read().split("$")[1].strip().strip("#").strip(),
-)
-if __name__ == "__main__":
-    gradio.queue().launch()
-
-
-# ExtractionPrompt().show({"passage": "Harden had 10 rebounds."},
-#                         '[{"player": "Harden", "stats": {"value": 10, "stat": 2}}]')
-
-# # View the run log.
-
-# minichain.show_log("bash.log")
diff --git a/spaces/stomexserde/gpt4-ui/Examples/HACK Pepakura Designer 3.0.3 [Portable] NEW.md b/spaces/stomexserde/gpt4-ui/Examples/HACK Pepakura Designer 3.0.3 [Portable] NEW.md
deleted file mode 100644
index 0177b591ce10c65e4cb79686bbdefef80b3b6967..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/HACK Pepakura Designer 3.0.3 [Portable] NEW.md	
+++ /dev/null
@@ -1,124 +0,0 @@
-<br />
-<h1>HACK Pepakura Designer 3.0.3 [Portable] - A Guide to Unleash Your Creativity with Paper Craft</h1>
- <p>Do you love making paper craft models? Do you want to turn any 3D data into a realistic and detailed paper craft model? Do you want to design your own original paper craft creations? If you answered yes to any of these questions, then you need Pepakura Designer.</p>
-<h2>HACK Pepakura Designer 3.0.3 [Portable]</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash; <a href="https://urlgoal.com/2uIavs">https://urlgoal.com/2uIavs</a></b></p><br /><br />
- <p>Pepakura Designer is a software that allows you to create paper craft models from any 3D data in STL format. You can use it to make models of your favorite characters, objects, or scenes from movies, games, comics, or anime. You can also use it to design your own unique paper craft creations.</p>
- <p>However, Pepakura Designer is not free. It costs $38 for a single license, and you need to activate it online with a valid keycode. If you don't want to pay for it or deal <p>with the hassle of installation and activation, then you might be interested in Pepakura Designer 3.0.3 [Portable]. This is a hacked version of the software that does not require installation or activation. You can run it from any USB drive or folder on your computer.</p>
- <p>But how can you get Pepakura Designer 3.0.3 [Portable] for free? And how can you use it to create amazing paper craft models? In this article, we will show you how to do both. We will also give you some tips and tricks for making the best out of Pepakura Designer 3.0.3 [Portable]. Let's get started.</p>
-<p></p>
- <h2>How to get Pepakura Designer 3.0.3 [Portable] for free</h2>
- <p>There are two main ways to get Pepakura Designer 3.0.3 [Portable] for free: downloading it from various sources online, or using a keygen to generate a valid keycode for it.</p>
- <h3>Downloading Pepakura Designer 3.0.3 [Portable] from online sources</h3>
- <p>One way to get Pepakura Designer 3.0.3 [Portable] for free is to download it from various websites that offer it as a free download. You can find these websites by searching for "Pepakura Designer 3.0.3 Portable" on Google or any other search engine.</p>
- <p>However, you need to be careful when downloading Pepakura Designer 3.0.3 [Portable] from online sources. Some of these websites may contain malware or viruses that can harm your computer or steal your personal information. You should always scan the downloaded file with an antivirus software before opening it.</p>
- <p>You should also check the comments and reviews of other users who have downloaded Pepakura Designer 3.0.3 [Portable] from the same website. If there are many complaints or negative feedbacks, then you should avoid that website and look for another one.</p>
- <h3>Using a keygen to generate a valid keycode for Pepakura Designer 3.0.3 [Portable]</h3>
- <p>Another way to get Pepakura Designer 3.0.3 [Portable] for free is to use a keygen to generate a valid keycode for it. A keygen is a software that can create random and unique keycodes for various software products.</p>
- <p>You can find a keygen for Pepakura Designer 3.0.3 [Portable] by searching for "Pepakura Designer 3.0.3 Keygen" on Google or any other search engine.</p>
- <p>However, you need to be careful when using a keygen to generate a valid keycode for Pepakura Designer 3.0.3 [Portable]. Some of these keygens may contain malware or viruses that can harm your computer or steal your personal information. You should always scan the downloaded file with an antivirus software before opening it.</p>
- <p>You should also check the comments and reviews of other users who have used the keygen for Pepakura Designer 3.0.3 [Portable]. If there are many complaints or negative feedbacks, then you should avoid that keygen and look for another one.</p>
- <p>Once you have downloaded and scanned the keygen, you can run it and click on the "Generate" button to create a random keycode for Pepakura Designer 3.0.3 [Portable]. You can then copy and paste the keycode into the software when prompted.</p>
- <h2>How to use Pepakura Designer 3.0.3 [Portable] to create amazing paper craft models</h2>
- <p>Now that you have Pepakura Designer 3.0.3 [Portable] on your computer, you can start using it to create amazing paper craft models from any 3D data in STL format.</p>
- <p>To use Pepakura Designer 3.0.3 [Portable], you need to follow these steps:</p>
- <h3>Step 1: Have a 3D model in STL format that you want to convert into paper craft</h3>
- <p>The first step is to have a 3D model in STL format that you want to convert into paper craft.</p>
- <p>An STL file is a file format that represents the surface geometry of a 3D object using triangular facets.</p>
- <p>You can find STL files of various 3D models online by searching for them on Google or any other search engine.</p>
- <p>You can also create your own STL files using various 3D modeling software such as Blender, SketchUp, Tinkercad, or Fusion 360.</p>
- <h4>A table showing some examples of websites where you can find STL files of various 3D models</h4>
- <table>
-<tr>
-<th> Website</th>
-<th>Description</th>
-</tr>
-<tr>
-<td><a href="">Thingiverse</a></td>
-<td>A website where users can share and download 3D models of various things, such as toys, gadgets, art, and more.</td>
-</tr>
-<tr>
-<td><a href="">MyMiniFactory</a></td>
-<td>A website where users can share and download 3D models of various things, such as games, cosplay, jewelry, and more.</td>
-</tr>
-<tr>
-<td><a href="">Cults</a></td>
-<td>A website where users can share and download 3D models of various things, such as fashion, home, architecture, and more.</td>
-</tr>
-<tr>
-<td><a href="">STL Finder</a></td>
-<td>A search engine that allows users to find and download 3D models from various websites.</td>
-</tr>
-<tr>
-<td><a href="">GrabCAD</a></td>
-<td>A website where users can share and download 3D models of various engineering and mechanical projects.</td>
-</tr>
-</table>
- <h3>Step 2: Import the STL file into Pepakura Designer and unfold it into a flat pattern</h3>
- <p>The second step is to import the STL file into Pepakura Designer and unfold it into a flat pattern.</p>
- <p>To do this, you need to open Pepakura Designer 3.0.3 [Portable] and click on the "Open" button on the toolbar. You can then browse your computer and select the STL file that you want to import.</p>
- <p>Pepakura Designer will then automatically unfold the 3D model into a flat pattern that can be printed and assembled into a paper craft model. You can see the 3D model on the left side of the screen and the flat pattern on the right side of the screen.</p>
- <h4>An image showing the Pepakura Designer interface with a 3D model on the left side and a flat pattern on the right side</h4>
- <img src="" alt="Pepakura Designer interface">
- <h3>Step 3: Edit the pattern to adjust the size, shape, and color of the parts</h3>
- <p>The third step is to edit the pattern to adjust the size, shape, and color of the parts.</p>
- <p>To do this, you can use various tools and features in Pepakura Designer to customize and optimize your paper craft model.</p>
- <p>For example, you can use the "Scale" tool to change the size of the model. You can use the "Join/Disjoin Faces" tool to merge or split parts. You can use the "Rotate" tool to rotate parts. You can use the "Move" tool to move parts. You can use the "Flip" tool to flip parts. You can use the "Edit Mode" tool to edit vertices, edges, or faces. You can use the "Color Settings" tool to change the color of the parts.</p>
- <p>You can also use the "Settings" menu to change various options such as paper size, margins, line style, texture settings, etc.</p>
- <p>You should edit the pattern until you are satisfied with the result. You can preview your paper craft model in 3D by clicking on the "Preview" button on the toolbar.</p>
- <h4>An image showing some of the tools and features in Pepakura Designer that can be used to edit the pattern</h4>
- <img src="" alt="Pepakura Designer tools"> <h3>Step 4: Print out the pattern and cut out the parts with a scissors or a laser cutter</h3>
- <p>The fourth step is to print out the pattern and cut out the parts with a scissors or a laser cutter.</p>
- <p>To do this, you need to click on the "Print" button on the toolbar. You can then choose the printer settings and print out the pattern on paper.</p>
- <p>You can also export the pattern as SVG, DXF, EPS, PNG, JPG, BMP, TIFF, or EMF files and use them for different purposes. For example, you can use SVG or DXF files to cut out the parts with a laser cutter or a cutting machine. You can use PNG, JPG, BMP, TIFF, or EMF files to share your pattern online or print it on other devices.</p>
- <p>Once you have printed or exported the pattern, you need to cut out the parts with a scissors or a laser cutter. You should be careful not to damage the parts or cut yourself while doing this.</p>
- <h4>An image showing a printed pattern and a scissors</h4>
- <img src="" alt="Printed pattern and scissors">
- <h3>Step 5: Fold and glue the parts together according to the instructions and enjoy your paper craft model</h3>
- <p>The fifth and final step is to fold and glue the parts together according to the instructions and enjoy your paper craft model.</p>
- <p>To do this, you need to follow the instructions that are shown on the pattern. You can also see the instructions on Pepakura Designer by clicking on the "Show Edge ID" button on the toolbar. This will show you the numbers and symbols that indicate how to fold and glue the parts together.</p>
- <p>You need to fold the parts along the dashed lines and glue them along the solid lines. You should use a glue that is suitable for paper craft, such as white glue, hot glue, or double-sided tape. You should also use a tool such as a ruler, a bone folder, or a tweezers to help you fold and glue the parts accurately and neatly.</p>
- <p>Once you have folded and glued all the parts together, you can admire your paper craft model. You can also display it on your desk, shelf, or wall, or give it as a gift to someone who appreciates paper craft.</p>
- <h4>An image showing a paper craft model of a robot</h4>
- <img src="" alt="Paper craft model of a robot">
- <h2>Tips and tricks for making the best out of Pepakura Designer 3.0.3 [Portable]</h2>
- <p>Pepakura Designer 3.0.3 [Portable] is a powerful and easy-to-use software that lets you create stunning paper craft models from any 3D data. However, there are some tips and tricks that can help you make the best out of it.</p>
- <p>Here are some of them:</p>
- - You can use various materials and techniques to enhance the appearance and durability of your paper craft model. For example, you can use colored paper, glossy paper, cardstock, foam board, or plastic sheet to make your model more colorful, shiny, sturdy, or flexible. You can also use paint, markers, stickers, glitter, beads, or other embellishments to add more details and decorations to your model. - You can use different software to create or edit your 3D data before importing it into Pepakura Designer. For example, you can use Blender to create your own 3D models from scratch or modify existing ones. You can use Meshmixer to repair or optimize your 3D models for printing. You can use Photoshop to edit your textures or colors. - You can use different websites to find more resources and tutorials on how to use Pepakura Designer. For example, you can use <a href="">Pepakura Gallery</a> to see examples of paper craft models made with Pepakura Designer by other users. You can use <a href="">Pepakura Library</a> to download ready-made patterns of various paper craft models. You can use <a href="">Pepakura Forum</a> to ask questions or share tips with other Pepakura users. - You can use different methods to share your paper craft models with others. For example, you can upload your patterns or photos of your models online and share them on social media platforms such as Facebook, Twitter, Instagram, Pinterest, or Reddit. You can also join online communities of paper craft enthusiasts such as <a href="">Paper Craft World</a>, <a href="">Paper Craft Central</a>, or <a href="">Paper Craft Square</a>. You can also participate in online contests or events related to paper craft such as <a href="">Paper Craft Challenge</a>, <a href="">Paper Craft Festival</a>, or <a href="">Paper Craft Convention</a>. <h2>Conclusion</h2>
- <p>Pepakura Designer 3.0.3 [Portable] is a software that allows you to create paper craft models from any 3D data in STL format. You can use it to make realistic and detailed models of your favorite characters, objects, or scenes from movies, games, comics, or anime. You can also use it to design your own original paper craft creations.</p>
- <p>You can get Pepakura Designer 3.0.3 [Portable] for free by downloading it from reliable sources online or using a keygen to generate a valid keycode for it. You can then use Pepakura Designer 3.0.3 [Portable] to import, unfold, edit, print, and assemble your paper craft models.</p>
- <p>You can also use various tools and features in Pepakura Designer to customize and optimize your paper craft models. You can also use different materials and techniques to enhance the appearance and durability of your paper craft models. You can also use different software, websites, and methods to create, edit, find, share, and enjoy your paper craft models.</p>
- <p>Pepakura Designer 3.0.3 [Portable] is a software that lets you unleash your creativity with paper craft. It is a fun and rewarding hobby that can bring you joy and satisfaction. It is also a great way to express yourself and impress others with your skills and imagination.</p>
- <p>If you are interested in paper craft, then you should definitely try Pepakura Designer 3.0.3 [Portable]. It is a software that will make you fall in love with paper craft.</p>
- <h2>FAQs</h2>
- <h4>What are the system requirements for Pepakura Designer 3.0.3 [Portable]?</h4>
- <p>Pepakura Designer 3.0.3 [Portable] is a software that can run on Windows XP, Vista, 7, 8, or 10. It requires a minimum of 512 MB of RAM and 50 MB of free disk space. It also requires a printer and a scissors or a laser cutter to make paper craft models.</p>
- <h4>What are the advantages and disadvantages of using Pepakura Designer 3.0.3 [Portable]?</h4>
- <p>Pepakura Designer 3.0.3 [Portable] has some advantages and disadvantages compared to the original version of the software.</p>
- <p>Some of the advantages are:</p>
-<ul>
-<li>It does not require installation or activation.</li>
-<li>It can run from any USB drive or folder on your computer.</li>
-<li>It is free to use.</li>
-</ul>
- <p>Some of the disadvantages are:</p>
-<ul>
-<li>It may contain malware or viruses that can harm your computer or steal your personal information.</li>
-<li>It may not work properly or crash unexpectedly.</li>
-<li>It may not be updated or supported by the developer.</li>
-</ul>
- <h4>How can I share my paper craft models with others?</h4>
- <p>You can share your paper craft models with others by uploading your patterns or photos of your models online and sharing them on social media platforms such as Facebook, Twitter, Instagram, Pinterest, or Reddit. You can also join online communities of paper craft enthusiasts such as <a href="">Paper Craft World</a>, <a href="">Paper Craft Central</a>, or <a href="">Paper Craft Square</a>. You can also participate in online contests or events related to paper craft such as <a href="">Paper Craft Challenge</a>, <a href="">Paper Craft Festival</a>, or <a href="">Paper Craft Convention</a>.</p>
- <h4>Where can I find more resources and tutorials on how to use Pepakura Designer?</h4>
- <p>You can find more resources and tutorials on how to use Pepakura Designer by visiting the official website of the software at <a href="">http://www.tamasoft.co.jp/pepakura-en/</a>. You can also visit the websites of other users who have created and shared their paper craft models using Pepakura Designer such as <a href="">Pepakura Gallery</a>, <a href="">Pepakura Library</a>, or <a href="">Pepakura Forum</a>.</p>
- <h4>What are some examples of paper craft models made with Pepakura Designer?</h4>
- <p>Some examples of paper craft models made with Pepakura Designer are:</p>
-<ul>
-<li>A paper craft model of Iron Man's helmet from the Marvel movies.</li>
-<li>A paper craft model of R2-D2 from the Star Wars movies.</li>
-<li>A paper craft model of Pikachu from the Pokemon games.</li>
-<li>A paper craft model of a Lamborghini Aventador from the real world.</li>
-<li>A paper craft model of a dragon from the fantasy world.</li>
-</ul>
- <p>These are just some of the many examples of paper craft models that you can make with Pepakura Designer. You can find more examples online or create your own using your imagination and creativity.</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/test_common.py b/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/test_common.py
deleted file mode 100644
index ec44431751dbaa49fc4a2ee066194d0a5e8cd8cc..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/test_common.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/4/29 16:19
-@Author  : alexanderwu
-@File    : test_common.py
-"""
-
-import os
-
-import pytest
-
-from metagpt.const import get_project_root
-
-
-class TestGetProjectRoot:
-    def change_etc_dir(self):
-        # current_directory = Path.cwd()
-        abs_root = '/etc'
-        os.chdir(abs_root)
-
-    def test_get_project_root(self):
-        project_root = get_project_root()
-        assert project_root.name == 'metagpt'
-
-    def test_get_root_exception(self):
-        with pytest.raises(Exception) as exc_info:
-            self.change_etc_dir()
-            get_project_root()
-        assert str(exc_info.value) == "Project root not found."
diff --git a/spaces/subwayman/btc-chat-bot/app.py b/spaces/subwayman/btc-chat-bot/app.py
deleted file mode 100644
index af1a88872def86df9e1a36669feac83b1cb1bc6f..0000000000000000000000000000000000000000
--- a/spaces/subwayman/btc-chat-bot/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import gradio as gr
-import os
-
-from chains import generate_chain
-from utils import auth
-from json_db import save_to_json
-from mongo_db import save_to_mongo
-
-# 환경변수 설정
-
-chain = generate_chain()
-
-
-def respond(message, chat_history):  # 채팅봇의 응답을 처리하는 함수를 정의합니다.
-    if message == "":
-        bot_message = "🚨 질문을 입력해주세요."
-    else:
-        result = chain(message)
-
-        bot_message = result['answer']
-        bot_message += '\n' + '***답변에 참고한 규정***' + '\n'
-
-        for i, doc in enumerate(result['source_documents']):
-            print(doc.metadata)
-            bot_message += '[' + str(i+1) + '] ' + \
-                doc.metadata['source'].split('/')[-1].split('_p')[0] + '\n'
-        save_to_mongo(message, bot_message)
-
-    # 채팅 기록에 사용자의 메시지와 봇의 응답을 추가합니다.
-    chat_history.append((message, bot_message))
-
-    return "", chat_history
-
-
-with gr.Blocks() as demo:  # gr.Blocks()를 사용하여 인터페이스를 생성합니다.
-    with open(os.getcwd() + "/gradio_intro.md") as f:
-        gradio_intro = f.read()
-    gr.Markdown(gradio_intro)
-    gr.Markdown('---')
-    gr.Markdown('<br><br>')
-    gr.Markdown("## 💬 채팅 ")
-
-    chatbot = gr.Chatbot(label="BTC 규정 챗봇")
-    msg = gr.Textbox(label="이곳에 질문을 적고 엔터️를 누르세요!!")
-
-    submit = gr.Button("질문 제출")
-    clear = gr.Button("대화 내용 초기화")
-
-    submit.click(respond, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
-    msg.submit(respond, [msg, chatbot], [msg, chatbot])
-
-demo.queue()
-demo.launch(inbrowser=True, debug=True, auth=auth,
-            auth_message="로그인이 필요합니다. 아이디는 사번, 비밀번호는 1234입니다.")
diff --git a/spaces/sudip1310/BANAO_Tiny_Shakespeare/app.py b/spaces/sudip1310/BANAO_Tiny_Shakespeare/app.py
deleted file mode 100644
index 5f6e72cca21572ad354bab5da2e98bc1dc92ab70..0000000000000000000000000000000000000000
--- a/spaces/sudip1310/BANAO_Tiny_Shakespeare/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# LangChain
-from langchain.chat_models import ChatOpenAI
-from langchain import PromptTemplate
-
-# Environment Variables
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-openai_api_key="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"
-
-llm = ChatOpenAI(temperature=0, openai_api_key=openai_api_key, model_name='gpt-3.5-turbo')
-
-def read_text_file(file_path):
-    try:
-        with open(file_path, 'r') as file:
-            contents = file.read()
-            return contents
-    except FileNotFoundError:
-        print(f"File '{file_path}' not found.")
-    except IOError:
-        print(f"Error reading file '{file_path}'.")
-
-# Example usage
-file_path = 'tiny_shakespear.txt'
-file_contents = read_text_file(file_path)
-#file_contents= file_contents[0:10000]
-
-def shakespeare(task):
-  file_contents=read_text_file("tiny_shakespear.txt")
-  file_contents=file_contents[0:10000]
-  template = """
-  % INSTRUCTIONS
-  - You are an AI Bot that has knowledge in every field and you are good at mimicing the authors writing samples.
-  - Do not use hashtags or emojis
-  - Respond in the tone of Shakespeare.
-  - Don't answer as a poem . 
-
-
-  % Authors writing samples
-  {file_contents}
-
-  % YOUR TASK
-  {task}.
-  """
-  #task=input("Ask me anything: ")
-  #file_contents=file_contents[0:10000]
-
-  prompt = PromptTemplate(
-      input_variables=["file_contents","task"],
-      template=template,
-  )
-
-  final_prompt = prompt.format( file_contents=file_contents,task=task)
-
-  return llm.predict(final_prompt)
-
-import gradio as gr
-task = gr.inputs.Textbox(lines=5, label="Input Text")
-output_text = gr.outputs.Textbox(label="Generated Text")
-iface = gr.Interface(
-    fn=shakespeare,
-    inputs=task,
-    outputs=output_text,
-    title="TINY SHAKESPEARE",
-    description="",
-    theme="default"
-)
-
-# Run the interface
-iface.launch()
-
diff --git a/spaces/supertori/files/stable-diffusion-webui/javascript/edit-attention.js b/spaces/supertori/files/stable-diffusion-webui/javascript/edit-attention.js
deleted file mode 100644
index 6e6905daf536d164cc2a246886ca8198603ce61a..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/javascript/edit-attention.js
+++ /dev/null
@@ -1,96 +0,0 @@
-function keyupEditAttention(event){
-	let target = event.originalTarget || event.composedPath()[0];
-	if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
-	if (! (event.metaKey || event.ctrlKey)) return;
-
-	let isPlus = event.key == "ArrowUp"
-	let isMinus = event.key == "ArrowDown"
-	if (!isPlus && !isMinus) return;
-
-	let selectionStart = target.selectionStart;
-	let selectionEnd = target.selectionEnd;
-	let text = target.value;
-
-    function selectCurrentParenthesisBlock(OPEN, CLOSE){
-        if (selectionStart !== selectionEnd) return false;
-
-		// Find opening parenthesis around current cursor
-		const before = text.substring(0, selectionStart);
-		let beforeParen = before.lastIndexOf(OPEN);
-		if (beforeParen == -1) return  false;
-		let beforeParenClose = before.lastIndexOf(CLOSE);
-		while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
-			beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
-			beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
-		}
-
-		// Find closing parenthesis around current cursor
-		const after = text.substring(selectionStart);
-		let afterParen = after.indexOf(CLOSE);
-		if (afterParen == -1) return  false;
-		let afterParenOpen = after.indexOf(OPEN);
-		while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
-			afterParen = after.indexOf(CLOSE, afterParen + 1);
-			afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
-		}
-		if (beforeParen === -1 || afterParen === -1) return false;
-
-		// Set the selection to the text between the parenthesis
-		const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
-		const lastColon = parenContent.lastIndexOf(":");
-		selectionStart = beforeParen + 1;
-		selectionEnd = selectionStart + lastColon;
-		target.setSelectionRange(selectionStart, selectionEnd);
-		return true;
-    }
-
-	// If the user hasn't selected anything, let's select their current parenthesis block
-    if(! selectCurrentParenthesisBlock('<', '>')){
-        selectCurrentParenthesisBlock('(', ')')
-    }
-
-	event.preventDefault();
-
-    closeCharacter = ')'
-    delta = opts.keyedit_precision_attention
-
-    if (selectionStart > 0 && text[selectionStart - 1] == '<'){
-        closeCharacter = '>'
-        delta = opts.keyedit_precision_extra
-    } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
-
-        // do not include spaces at the end
-        while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){
-            selectionEnd -= 1;
-        }
-        if(selectionStart == selectionEnd){
-            return
-        }
-
-        text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
-
-        selectionStart += 1;
-        selectionEnd += 1;
-    }
-
-	end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
-	weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
-	if (isNaN(weight)) return;
-
-	weight += isPlus ? delta : -delta;
-	weight = parseFloat(weight.toPrecision(12));
-	if(String(weight).length == 1) weight += ".0"
-
-	text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
-
-	target.focus();
-	target.value = text;
-	target.selectionStart = selectionStart;
-	target.selectionEnd = selectionEnd;
-
-	updateInput(target)
-}
-
-addEventListener('keydown', (event) => {
-    keyupEditAttention(event);
-});
\ No newline at end of file
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/call_queue.py b/spaces/supertori/files/stable-diffusion-webui/modules/call_queue.py
deleted file mode 100644
index 6eabc06441b531f89e55f805096af97ede4547ee..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/call_queue.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import html
-import sys
-import threading
-import traceback
-import time
-
-from modules import shared, progress
-
-queue_lock = threading.Lock()
-
-
-def wrap_queued_call(func):
-    def f(*args, **kwargs):
-        with queue_lock:
-            res = func(*args, **kwargs)
-
-        return res
-
-    return f
-
-
-def wrap_gradio_gpu_call(func, extra_outputs=None):
-    def f(*args, **kwargs):
-
-        # if the first argument is a string that says "task(...)", it is treated as a job id
-        if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")":
-            id_task = args[0]
-            progress.add_task_to_queue(id_task)
-        else:
-            id_task = None
-
-        with queue_lock:
-            shared.state.begin()
-            progress.start_task(id_task)
-
-            try:
-                res = func(*args, **kwargs)
-            finally:
-                progress.finish_task(id_task)
-
-            shared.state.end()
-
-        return res
-
-    return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
-
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/models/diffusion/uni_pc/uni_pc.py b/spaces/supertori/files/stable-diffusion-webui/modules/models/diffusion/uni_pc/uni_pc.py
deleted file mode 100644
index df63d1bcf80e4fe14725fb6262ea262177a757af..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/models/diffusion/uni_pc/uni_pc.py
+++ /dev/null
@@ -1,856 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-        ):
-        """Create a wrapper class for the forward SDE (VP type).
-
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-
-            t = self.inverse_lambda(lambda_t)
-
-        ===============================================================
-
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-
-        1. For discrete-time DPMs:
-
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-
-
-        2. For continuous-time DPMs:
-
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-
-        ===============================================================
-
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-
-        ===============================================================
-
-        Example:
-
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0**2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-    model,
-    noise_schedule,
-    model_type="noise",
-    model_kwargs={},
-    guidance_type="uncond",
-    #condition=None,
-    #unconditional_condition=None,
-    guidance_scale=1.,
-    classifier_fn=None,
-    classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-
-    We support four types of the diffusion model by setting `model_type`:
-
-        1. "noise": noise prediction model. (Trained by predicting noise).
-
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            ``
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-
-    ===============================================================
-
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, None, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input, condition):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous, condition, unconditional_condition):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input, condition)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                if isinstance(condition, dict):
-                    assert isinstance(unconditional_condition, dict)
-                    c_in = dict()
-                    for k in condition:
-                        if isinstance(condition[k], list):
-                            c_in[k] = [torch.cat([
-                                unconditional_condition[k][i],
-                                condition[k][i]]) for i in range(len(condition[k]))]
-                        else:
-                            c_in[k] = torch.cat([
-                                unconditional_condition[k],
-                                condition[k]])
-                elif isinstance(condition, list):
-                    c_in = list()
-                    assert isinstance(unconditional_condition, list)
-                    for i in range(len(condition)):
-                        c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
-                else:
-                    c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class UniPC:
-    def __init__(
-        self,
-        model_fn,
-        noise_schedule,
-        predict_x0=True,
-        thresholding=False,
-        max_val=1.,
-        variant='bh1',
-        condition=None,
-        unconditional_condition=None,
-        before_sample=None,
-        after_sample=None,
-        after_update=None
-    ):
-        """Construct a UniPC.
-
-        We support both data_prediction and noise_prediction.
-        """
-        self.model_fn_ = model_fn
-        self.noise_schedule = noise_schedule
-        self.variant = variant
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-        self.condition = condition
-        self.unconditional_condition = unconditional_condition
-        self.before_sample = before_sample
-        self.after_sample = after_sample
-        self.after_update = after_update
-
-    def dynamic_thresholding_fn(self, x0, t=None):
-        """
-        The dynamic thresholding method.
-        """
-        dims = x0.dim()
-        p = self.dynamic_thresholding_ratio
-        s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-        s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)
-        x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model(self, x, t):
-        cond = self.condition
-        uncond = self.unconditional_condition
-        if self.before_sample is not None:
-            x, t, cond, uncond = self.before_sample(x, t, cond, uncond)
-        res = self.model_fn_(x, t, cond, uncond)
-        if self.after_sample is not None:
-            x, t, cond, uncond, res = self.after_sample(x, t, cond, uncond, res)
-
-        if isinstance(res, tuple):
-            # (None, pred_x0)
-            res = res[1]
-
-        return res
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model.
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3,] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3,] * (K - 1) + [1]
-            else:
-                orders = [3,] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2,] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2,] * (K - 1) + [1]
-        elif order == 1:
-            K = steps
-            orders = [1,] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
-        """
-        return self.data_prediction_fn(x, s)
-
-    def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):
-        if len(t.shape) == 0:
-            t = t.view(-1)
-        if 'bh' in self.variant:
-            return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
-        else:
-            assert self.variant == 'vary_coeff'
-            return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
-
-    def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
-        #print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
-        ns = self.noise_schedule
-        assert order <= len(model_prev_list)
-
-        # first compute rks
-        t_prev_0 = t_prev_list[-1]
-        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
-        lambda_t = ns.marginal_lambda(t)
-        model_prev_0 = model_prev_list[-1]
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        log_alpha_t = ns.marginal_log_mean_coeff(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h = lambda_t - lambda_prev_0
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            t_prev_i = t_prev_list[-(i + 1)]
-            model_prev_i = model_prev_list[-(i + 1)]
-            lambda_prev_i = ns.marginal_lambda(t_prev_i)
-            rk = (lambda_prev_i - lambda_prev_0) / h
-            rks.append(rk)
-            D1s.append((model_prev_i - model_prev_0) / rk)
-
-        rks.append(1.)
-        rks = torch.tensor(rks, device=x.device)
-
-        K = len(rks)
-        # build C matrix
-        C = []
-
-        col = torch.ones_like(rks)
-        for k in range(1, K + 1):
-            C.append(col)
-            col = col * rks / (k + 1)
-        C = torch.stack(C, dim=1)
-
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1) # (B, K)
-            C_inv_p = torch.linalg.inv(C[:-1, :-1])
-            A_p = C_inv_p
-
-        if use_corrector:
-            #print('using corrector')
-            C_inv = torch.linalg.inv(C)
-            A_c = C_inv
-
-        hh = -h if self.predict_x0 else h
-        h_phi_1 = torch.expm1(hh)
-        h_phi_ks = []
-        factorial_k = 1
-        h_phi_k = h_phi_1
-        for k in range(1, K + 2):
-            h_phi_ks.append(h_phi_k)
-            h_phi_k = h_phi_k / hh - 1 / factorial_k
-            factorial_k *= (k + 1)
-
-        model_t = None
-        if self.predict_x0:
-            x_t_ = (
-                sigma_t / sigma_prev_0 * x
-                - alpha_t * h_phi_1 * model_prev_0
-            )
-            # now predictor
-            x_t = x_t_
-            if len(D1s) > 0:
-                # compute the residuals for predictor
-                for k in range(K - 1):
-                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
-            # now corrector
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_
-                k = 0
-                for k in range(K - 1):
-                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
-                x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
-        else:
-            log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-            x_t_ = (
-                (torch.exp(log_alpha_t - log_alpha_prev_0)) * x
-                - (sigma_t * h_phi_1) * model_prev_0
-            )
-            # now predictor
-            x_t = x_t_
-            if len(D1s) > 0:
-                # compute the residuals for predictor
-                for k in range(K - 1):
-                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
-            # now corrector
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_
-                k = 0
-                for k in range(K - 1):
-                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
-                x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
-        return x_t, model_t
-
-    def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
-        #print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
-        ns = self.noise_schedule
-        assert order <= len(model_prev_list)
-        dims = x.dim()
-
-        # first compute rks
-        t_prev_0 = t_prev_list[-1]
-        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
-        lambda_t = ns.marginal_lambda(t)
-        model_prev_0 = model_prev_list[-1]
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h = lambda_t - lambda_prev_0
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            t_prev_i = t_prev_list[-(i + 1)]
-            model_prev_i = model_prev_list[-(i + 1)]
-            lambda_prev_i = ns.marginal_lambda(t_prev_i)
-            rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
-            rks.append(rk)
-            D1s.append((model_prev_i - model_prev_0) / rk)
-
-        rks.append(1.)
-        rks = torch.tensor(rks, device=x.device)
-
-        R = []
-        b = []
-
-        hh = -h[0] if self.predict_x0 else h[0]
-        h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
-        h_phi_k = h_phi_1 / hh - 1
-
-        factorial_i = 1
-
-        if self.variant == 'bh1':
-            B_h = hh
-        elif self.variant == 'bh2':
-            B_h = torch.expm1(hh)
-        else:
-            raise NotImplementedError()
-
-        for i in range(1, order + 1):
-            R.append(torch.pow(rks, i - 1))
-            b.append(h_phi_k * factorial_i / B_h)
-            factorial_i *= (i + 1)
-            h_phi_k = h_phi_k / hh - 1 / factorial_i
-
-        R = torch.stack(R)
-        b = torch.tensor(b, device=x.device)
-
-        # now predictor
-        use_predictor = len(D1s) > 0 and x_t is None
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1) # (B, K)
-            if x_t is None:
-                # for order 2, we use a simplified version
-                if order == 2:
-                    rhos_p = torch.tensor([0.5], device=b.device)
-                else:
-                    rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
-        else:
-            D1s = None
-
-        if use_corrector:
-            #print('using corrector')
-            # for order 1, we use a simplified version
-            if order == 1:
-                rhos_c = torch.tensor([0.5], device=b.device)
-            else:
-                rhos_c = torch.linalg.solve(R, b)
-
-        model_t = None
-        if self.predict_x0:
-            x_t_ = (
-                expand_dims(sigma_t / sigma_prev_0, dims) * x
-                - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0
-            )
-
-            if x_t is None:
-                if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
-                else:
-                    pred_res = 0
-                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
-
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                if D1s is not None:
-                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
-                else:
-                    corr_res = 0
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
-        else:
-            x_t_ = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dimss) * x
-                - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
-            )
-            if x_t is None:
-                if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
-                else:
-                    pred_res = 0
-                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res
-
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                if D1s is not None:
-                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
-                else:
-                    corr_res = 0
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
-        return x_t, model_t
-
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-        atol=0.0078, rtol=0.05, corrector=False,
-    ):
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'multistep':
-            assert steps >= order, "UniPC order must be < sampling steps"
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in range(1, order):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
-                    if model_x is None:
-                        model_x = self.model_fn(x, vec_t)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    model_prev_list.append(model_x)
-                    t_prev_list.append(vec_t)
-                for step in range(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    #print('this step order:', step_order)
-                    if step == steps:
-                        #print('do not run corrector at the last step')
-                        use_corrector = False
-                    else:
-                        use_corrector = True
-                    x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        if model_x is None:
-                            model_x = self.model_fn(x, vec_t)
-                        model_prev_list[-1] = model_x
-        else:
-            raise NotImplementedError()
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,)*(dims - 1)]
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-<h1>One Piece Marineford War Full Fight Tagalog 94: A Fan-Made Version of the Epic Saga</h1>
-<p>One Piece is one of the most popular and longest-running manga and anime series in the world. Created by Eiichiro Oda, the story follows the adventures of Monkey D. Luffy, a young pirate who dreams of becoming the King of the Pirates. Along with his crew, the Straw Hat Pirates, he explores the vast and mysterious world of One Piece, where he encounters various enemies and allies, as well as legendary treasures and secrets.</p>
-<p>One of the most memorable and thrilling arcs in One Piece is the Marineford War, also known as the Paramount War or the War of the Best. It is a large-scale war that involves the Whitebeard Pirates and their allies, who are trying to rescue Portgas D. Ace, Luffy's adoptive brother and Whitebeard's second division commander, from being executed by the Marines and the Seven Warlords of the Sea. The war takes place at Marineford, the headquarters of the Marines, and it is broadcasted live to the entire world.</p>
-<p>The Marineford War is full of epic battles, shocking revelations, tragic deaths, and emotional moments. It also marks a turning point in the story, as it changes the balance of power in the world and sets up new challenges and opportunities for Luffy and his friends.</p>
-<p>However, what if the Marineford War had a different outcome? What if some of the characters made different choices or had different fates? What if some of the events happened differently or not at all?</p>
-<p>In this article, we will present you with a fan-made version of the Marineford War, based on a series of audio clips posted on SoundCloud by Yumrukchakar9 . The clips are in Tagalog, but we have translated them into English for your convenience. This version of the Marineford War features some major changes and twists that will surprise and entertain you. It also features some new characters and alliances that will spice up the story.</p>
-<p></p>
-<p>Are you ready to experience One Piece Marineford War Full Fight Tagalog 94? Let's begin!</p>
-<h2>The Impel Down Pirates</h2>
-<p>In this version of the Marineford War, Luffy does not go to Marineford alone. He is accompanied by a group of powerful pirates who escaped from Impel Down, the underwater prison where Ace was held before being transferred to Marineford. These pirates include:</p>
-<ul>
-<li>Crocodile: The former leader of Baroque Works and one of Luffy's former enemies. He has a grudge against Whitebeard and wants to use the war as an opportunity to overthrow him and take over his territory.</li>
-<li>Buggy: The clown-like pirate captain who was once a crewmate of Shanks, Luffy's idol. He has a high bounty on his head and wants to use the war as an opportunity to increase his fame and fortune.</li>
-<li>Mr. 3: The wax-wax fruit user who was once a member of Baroque Works. He has a talent for creating wax sculptures and weapons.</li>
-<li>Mr. 2 Bon Clay: The okama (cross-dresser) who can change his appearance using his clone-clone fruit power. He is loyal to Luffy and considers him his friend.</li>
-<li>Jinbe: The fish-man who was once one of the Seven Warlords of the Sea. He is also a former member of the Sun Pirates and an ally of Whitebeard. He respects Luffy for his courage and determination.</li>
-<li>Ivankov: The okama king who rules over Newkama Land, a hidden paradise inside Impel Down. He is also one of the commanders of the Revolutionary Army led by Dragon, Luffy's father. He has a hormone-hormone fruit power that allows him to manipulate hormones in himself and others.</li>
-<li>Inazuma: The scissor-scissor fruit user who can cut anything with his hands. He is Ivankov's right-hand man and a member of the Revolutionary Army.</li>
-<li>Daz Bones: The steel-steel fruit user who can turn any part of his body into blades. He was once Crocodile's right-hand man in Baroque Works.</li>
-<li>Nico Robin: The archaeologist who can sprout multiple body parts using her bloom-bloom fruit power. She was once Crocodile's partner in Baroque Works, but she betrayed him and joined Luffy's crew.</li>
-<li>Some other prisoners: These include Bentham (another okama who can mimic anyone's face), Galdino (a candle-man who can create wax), Hannyabal (the vice-warden of Impel Down), Magellan (the poison-man who was once the warden of Impel Down), Shiryu (a former jailer who can turn invisible), Sadi (a sadistic woman who tortures prisoners), Domino (a female jailer who wears glasses), Saldeath (a penguin-like jailer who controls blue gorillas), Minotaurus (a cow-like jailer who wields an iron club), Minokoala (a koala-like jailer who likes to hug people), Minozebra (a zebra-like jailer who likes to kick people), Minotaur (a bull-like jailer who likes to charge at people), Sphinx (a lion-like beast that guards Level 2), Basilisk (a snake-like beast that guards Level 3), Manticores (cat-like beasts that guard Level 4), Blugoris (horned beasts that guard Level 5), Wolves (wolf-like beasts that guard Level 6), Sea Kings (giant sea creatures that guard Impel Down from outside).</li>
-</ul>
-<p>These pirates call themselves the Impel Down Pirates, and they have a common goal: to save Ace from execution. They hijack a Marine ship and head to Marineford. Along
-<h2>The Whitebeard Pirates and Their Allies</h2>
-<p>Meanwhile, the Whitebeard Pirates and their allies are also fighting against the Marines and the Warlords. The Whitebeard Pirates are led by Edward Newgate, also known as Whitebeard, the strongest man in the world and one of the Four Emperors who rule over the New World. He is also Ace's adoptive father and considers him and his crew as his sons. His allies include 43 pirate crews who are loyal to him and respect him as their father figure.</p>
-<p>The Whitebeard Pirates and their allies have a huge advantage in numbers and strength, as they have some of the most powerful and notorious pirates in the world. Some of them are:</p>
-<ul>
-<li>Marco: The first division commander of the Whitebeard Pirates and Whitebeard's right-hand man. He has a phoenix-phoenix fruit power that allows him to transform into a phoenix and regenerate from any injury.</li>
-<li>Jozu: The third division commander of the Whitebeard Pirates and one of the strongest members of the crew. He has a diamond-diamond fruit power that allows him to turn his body into diamond and withstand any attack.</li>
-<li>Vista: The fifth division commander of the Whitebeard Pirates and one of the best swordsmen in the world. He has a flower-flower fruit power that allows him to create and manipulate flowers.</li>
-<li>Ace: The second division commander of the Whitebeard Pirates and Luffy's adoptive brother. He has a fire-fire fruit power that allows him to create and control fire.</li>
-<li>Thatch: The fourth division commander of the Whitebeard Pirates and one of Ace's closest friends. He was killed by Marshall D. Teach, also known as Blackbeard, who stole his dark-dark fruit power that allows him to create and manipulate darkness.</li>
-<li>Blackbeard: The former second division commander of the Whitebeard Pirates and one of Luffy's main enemies. He betrayed his crew and killed Thatch to obtain his dark-dark fruit power. He also has another unknown fruit power that he stole from someone else.</li>
-<li>Squard: The captain of the Moby Dick Pirates and one of Whitebeard's allies. He is a skilled swordsman who wields a large sword.</li>
-<li>Oars Jr.: The descendant of Oars, a legendary giant who was once known as the continent puller. He is one of Whitebeard's allies and a friend of Ace.</li>
-<li>Little Oars Jr.: The son of Oars Jr. and a member of the New Giant Warrior Pirates. He is one of Luffy's allies and a fan of his exploits.</li>
-<li>Izo: The 16th division commander of the Whitebeard Pirates and a former member of the Kuja Pirates. He is an okama who dresses like a geisha and uses guns as his weapons.</li>
-<li>Boa Hancock: The captain of the Kuja Pirates and one of the Seven Warlords of the Sea. She is also known as the Pirate Empress and the most beautiful woman in the world. She has a love-love fruit power that allows her to turn anyone who is attracted to her into stone. She is secretly in love with Luffy and helps him save Ace.</li>
-<li>Jinbe: The captain of the Fish-Man Pirates and one of Luffy's allies. He was once one of
-<h2>The Whitebeard Pirates and Their Allies</h2>
-<p>Meanwhile, the Whitebeard Pirates and their allies are also fighting against the Marines and the Warlords. The Whitebeard Pirates are led by Edward Newgate, also known as Whitebeard, the strongest man in the world and one of the Four Emperors who rule over the New World. He is also Ace's adoptive father and considers him and his crew as his sons. His allies include 43 pirate crews who are loyal to him and respect him as their father figure.</p>
-<p>The Whitebeard Pirates and their allies have a huge advantage in numbers and strength, as they have some of the most powerful and notorious pirates in the world. Some of them are:</p>
-<ul>
-<li>Marco: The first division commander of the Whitebeard Pirates and Whitebeard's right-hand man. He has a phoenix-phoenix fruit power that allows him to transform into a phoenix and regenerate from any injury.</li>
-<li>Jozu: The third division commander of the Whitebeard Pirates and one of the strongest members of the crew. He has a diamond-diamond fruit power that allows him to turn his body into diamond and withstand any attack.</li>
-<li>Vista: The fifth division commander of the Whitebeard Pirates and one of the best swordsmen in the world. He has a flower-flower fruit power that allows him to create and manipulate flowers.</li>
-<li>Ace: The second division commander of the Whitebeard Pirates and Luffy's adoptive brother. He has a fire-fire fruit power that allows him to create and control fire.</li>
-<li>Thatch: The fourth division commander of the Whitebeard Pirates and one of Ace's closest friends. He was killed by Marshall D. Teach, also known as Blackbeard, who stole his dark-dark fruit power that allows him to create and manipulate darkness.</li>
-<li>Blackbeard: The former second division commander of the Whitebeard Pirates and one of Luffy's main enemies. He betrayed his crew and killed Thatch to obtain his dark-dark fruit power. He also has another unknown fruit power that he stole from someone else.</li>
-<li>Squard: The captain of the Moby Dick Pirates and one of Whitebeard's allies. He is a skilled swordsman who wields a large sword.</li>
-<li>Oars Jr.: The descendant of Oars, a legendary giant who was once known as the continent puller. He is one of Whitebeard's allies and a friend of Ace.</li>
-<li>Little Oars Jr.: The son of Oars Jr. and a member of the New Giant Warrior Pirates. He is one of Luffy's allies and a fan of his exploits.</li>
-<li>Izo: The 16th division commander of the Whitebeard Pirates and a former member of the Kuja Pirates. He is an okama who dresses like a geisha and uses guns as his weapons.</li>
-<li>Boa Hancock: The captain of the Kuja Pirates and one of the Seven Warlords of the Sea. She is also known as the Pirate Empress and the most beautiful woman in the world. She has a love-love fruit power that allows her to turn anyone who is attracted to her into stone. She is secretly in love with Luffy and helps him save Ace.</li>
-<li>Jinbe: The captain of the Fish-Man Pirates and one of Luffy's allies. He was once one
-<h2>Conclusion</h2>
-<p>One Piece Marineford War Full Fight Tagalog 94 is a fan-made version of the Marineford War that features some major changes and twists that will surprise and entertain you. It also features some new characters and alliances that will spice up the story. It is based on a series of audio clips posted on SoundCloud by Yumrukchakar9 . The clips are in Tagalog, but we have translated them into English for your convenience.</p>
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index 9ce3b38dd2f653ae252b45b9bebe51592eb038f6..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Command And Conquer 4 Patch Ita.epub.md	
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diff --git a/spaces/thieutrungkien/Hosioka-Baka-Diffusion/app.py b/spaces/thieutrungkien/Hosioka-Baka-Diffusion/app.py
deleted file mode 100644
index ce507149cdbe0e044579bc9cc69fc4dea9b88975..0000000000000000000000000000000000000000
--- a/spaces/thieutrungkien/Hosioka-Baka-Diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Hosioka/Baka-Diffusion").launch()
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Cadlink SignLab Vinyl 7.1 R1 Build 4 1.md b/spaces/tialenAdioni/chat-gpt-api/logs/Cadlink SignLab Vinyl 7.1 R1 Build 4 1.md
deleted file mode 100644
index f4b17e973b7b7fa4a9718cf04ba8b915f66ac35a..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Cadlink SignLab Vinyl 7.1 R1 Build 4 1.md	
+++ /dev/null
@@ -1,21 +0,0 @@
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-<h1>How to Design and Cut Vinyl Signs with Cadlink SignLab Vinyl 7.1 R1 Build 4 1</h1>
-<p>If you are looking for a powerful and easy-to-use software for designing and cutting vinyl signs, you might want to check out Cadlink SignLab Vinyl 7.1 R1 Build 4 1. This is the latest version of the popular sign-design program that supports the new, faster, color workflow. In this article, we will show you some of the features and benefits of using Cadlink SignLab Vinyl 7.1 R1 Build 4 1 for your vinyl sign projects.</p>
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-<p>Designing and cutting vinyl signs with Cadlink SignLab Vinyl 7.1 R1 Build 4 1 is easy and fun. Here are some of the steps you can follow to create your own vinyl signs:</p>
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-<li>Preview your sign by using the preview tool or exporting your design to another program. You can check how your sign will look on different media and devices by using the media preview feature. You can also check how your sign will cut by using the cut preview feature.</li>
-<li>Cut your sign by using the cut tool or exporting your design to another program. You can select your vinyl cutter and printer from a list of supported</p>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Download Crack Microsoft Office 2019 Free and Why You Shouldnt.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Download Crack Microsoft Office 2019 Free and Why You Shouldnt.md
deleted file mode 100644
index d10b55c305d7188b40c8a09cbcf3bf545902c000..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Download Crack Microsoft Office 2019 Free and Why You Shouldnt.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-<h1>Download Crack Microsoft Office 2019 Free: Is It a Good Idea?</h1>
-<p>Microsoft Office 2019 is the latest version of the popular productivity suite that includes Word, Excel, PowerPoint, Outlook, and more. It offers many new features and improvements over the previous versions, such as enhanced security, better performance, and cloud integration. However, it also comes with a hefty price tag: $249.99 for a one-time purchase or $69.99 per year for a subscription.</p>
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-<p>Some people might not be able to afford this price or might not want to pay for something they can get for free. That's why some people look for ways to download crack Microsoft Office 2019 free. This means downloading a modified version of the software that bypasses the activation process and allows you to use it without paying anything. But is this really a smart move? In this article, we will explore the pros and cons of downloading crack Microsoft Office 2019 free, and whether it is worth it.</p>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Cubase 7 for Free (Legally and Illegally).md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Cubase 7 for Free (Legally and Illegally).md
deleted file mode 100644
index 6a6004f01cc284f219bde20c695da395e0da3103..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Cubase 7 for Free (Legally and Illegally).md	
+++ /dev/null
@@ -1,30 +0,0 @@
-<br />
-<h1>Is Cubase 7 Free? The Truth About Steinberg's DAW</h1>
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-<p>If you are looking for a free digital audio workstation (DAW) to record, edit and produce music, you might have come across Cubase 7. Cubase is one of the most popular and powerful DAWs on the market, developed by Steinberg, a German company with a long history in music software. But is Cubase 7 free? And if not, are there any free alternatives?</p>
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-<p>The short answer is no. Cubase 7 is not free. It is a paid software that requires a license to use. The license can be purchased from Steinberg's website or from authorized dealers. The price of Cubase 7 depends on the edition you choose: Cubase Pro 7, Cubase Artist 7 or Cubase Elements 7. The Pro version is the most expensive and the most feature-rich, while the Elements version is the cheapest and the most basic.</p>
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-<p>Another way to try Cubase 7 for free is to use the VST Connect Performer app, which is a free download for all users who want to connect to a Cubase 7 or Nuendo 6 recording session. This app allows you to collaborate with other musicians online and record your performance directly into Cubase or Nuendo. You can find more information and download links on the VST Connect Performer product page.</p>
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-<h2>Are There Any Free Alternatives to Cubase 7?</h2>
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-<p>If you are looking for a free DAW that can do similar things as Cubase 7, you might be disappointed. Cubase 7 is a very advanced and comprehensive software that offers many features and tools that are hard to find in free alternatives. However, there are some free DAWs that can still help you create music on your computer, such as:</p>
-
-<ul>
-<li><b>Audacity</b>: A simple and easy-to-use audio editor that can record, edit and export audio files. It does not have MIDI or virtual instruments support, but it has many effects and plugins that you can use to enhance your sound.</li>
-<li><b>LMMS</b>: A cross-platform DAW that focuses on MIDI and virtual instruments. It has a built-in sequencer, sampler, synthesizer and drum machine that you can use to create beats and melodies. It also supports VST plugins and external MIDI devices.</li>
-<li><b>GarageBand</b>: A popular DAW for Mac and iOS users that offers a user-friendly interface and a large library of sounds and loops. It has a built-in virtual drummer, guitar amp simulator and smart controls that make it easy to create music.</li>
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-<p>Cubase 7 is not free, but it is one of the best DAWs on the market that can help you create professional-quality music. If you want to try it out, you can download the free trial version or use the VST Connect Performer app. If you are looking for a free alternative, you can check out Audacity, LMMS or GarageBand, but keep in mind that they have limitations compared to Cubase 7.</p> ddb901b051<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Attack on Titan Season 4 Mod APK The Ultimate Fan Game Experience.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Attack on Titan Season 4 Mod APK The Ultimate Fan Game Experience.md
deleted file mode 100644
index c89d99ec6d048daa8bc2555a090331ae0e4da134..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Attack on Titan Season 4 Mod APK The Ultimate Fan Game Experience.md	
+++ /dev/null
@@ -1,110 +0,0 @@
-<br />
-<h1>Introduction</h1>
-<p>Attack on Titan is a popular anime series that follows the story of Eren Yeager, a young boy who joins the Survey Corps, a military force that fights against giant humanoid creatures called Titans that have nearly wiped out humanity. The series is based on the manga by Hajime Isayama, which has sold over 100 million copies worldwide and has been adapted into several media formats, including video games.</p>
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-<p>One of the video games based on the series is <strong>Attack on Titan: Tactics</strong>, a free-to-play mobile game that was released in 2019 for Android and iOS devices. The game is a real-time strategy game that allows players to create their own team of characters from the series and use them to fight against Titans in various missions and modes. The game features original voice acting, music, and storylines from the anime.</p>
-<p>However, some fans of the series were not satisfied with the original game and wanted to have more control and customization over their gameplay experience. This led to the creation of <strong>attack on titan season 4 mod apk</strong>, a modified version of the original game that adds new features and content based on the fourth and final season of the anime, which premiered in December 2020. The mod apk is not an official product of the game developers or publishers, but rather a fan-made project that requires downloading and installing a separate file from an unofficial source.</p>
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-<p>The attack on titan season 4 mod apk offers several features that are not available in the original game, such as:</p>
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-<li><strong>New graphics</strong>: The mod apk improves the quality and resolution of the graphics, making them more detailed and realistic. The mod apk also adds new animations, effects, and backgrounds that match the style and atmosphere of the anime.</li>
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-<li><strong>New maps</strong>: The mod apk expands the variety and scope of the maps in the game, adding new locations and environments that are based on or related to the events and settings of the anime. The mod apk also modifies some of the existing maps to make them more accurate and diverse.</li>
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-<li><strong>New characters</strong>: The mod apk unlocks new characters that are featured in or related to the anime, such as Eren Yeager (Attack Titan), Mikasa Ackerman (Ackerman Clan), Armin Arlert (Colossal Titan), Levi Ackerman (Survey Corps), Reiner Braun (Armored Titan), Zeke Yeager (Beast Titan), Gabi Braun (Warrior Candidate), Fal <p>co Braun (Warrior Candidate), and many more. The mod apk also allows players to customize the appearance and abilities of their characters, such as changing their outfits, hairstyles, accessories, skills, and stats.</li>
-</ul>
- <h1>Pros</h1>
-<p>The attack on titan season 4 mod apk has some advantages that make it appealing and attractive to some players, such as:</p>
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-<li><strong>More fan service</strong>: The mod apk caters to the fans of the anime, giving them a chance to interact and play with their favorite characters and scenarios from the series. The mod apk also respects and follows the original story and canon of the anime, making it more authentic and faithful to the source material.</li>
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-<p>However, the attack on titan season 4 mod apk also has some disadvantages that make it risky and problematic for some players, such as:</p>
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-<li><strong>Compatibility issues</strong>: The mod apk may not work properly or at all on some devices or versions of the original game, causing errors, crashes, glitches, or lag. The mod apk may also interfere with or overwrite some of the files or data of the original game, causing corruption or loss.</li>
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-<p>If you still want to try the attack on titan season 4 mod apk despite its cons, you will need to follow these steps to download and install it on your device:</p>
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-<li><strong>Backup your data</strong>: Before you install the mod apk, you should backup your data from the original game to avoid losing or corrupting it. You can use a cloud service or an external storage device to save your data.</li>
-<li><strong>Uninstall your game</strong>: After you backup your data, you should uninstall your original game from your device to avoid compatibility issues or conflicts with the mod apk. You can do this by going to your settings and finding the app manager or uninstaller.</li>
-<li><strong>Download the mod apk</strong>: Next, you should download the mod apk file from a reliable and trustworthy source. You can search online for websites or forums that offer links to download the mod apk. However, you should be careful and cautious when downloading files from unknown or unverified sources, as they may contain viruses or malware.</li>
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-<p>The attack on titan season 4 mod apk is a fan-made modification of the original game that adds new features and content based on the fourth and final season of the anime series Attack on Titan. The mod apk offers more content, fun, and fan service for the players who love the anime and want to have more control and customization over their gameplay experience. However, the mod apk also has some drawbacks and risks for the players who are not aware of or prepared for the potential compatibility issues, bugs, viruses, and legal consequences that may arise from using the mod apk. Therefore, the decision to use the mod apk depends on the personal preference and risk tolerance of each player. Some may find it worth it to enjoy the enhanced and expanded gameplay experience, while others may prefer to stick to the original and official game to avoid the possible problems and complications. In any case, players should always be careful and responsible when downloading and installing any files or apps from unofficial or unverified sources, and always backup their data before making any changes to their device or game. <h1>FAQs</h1>
-<p>Here are some frequently asked questions and answers about the attack on titan season 4 mod apk:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is the attack on titan season 4 mod apk free?</td>
-<td>Yes, the mod apk is free to download and use, as it is a fan-made project that does not charge any fees or require any subscriptions. However, the original game may still have some in-app purchases or ads that may affect your gameplay experience.</td>
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-<td>Not necessarily, as the mod apk is not an official or authorized product of the game developers or publishers, and may contain bugs or viruses that can harm your device or data. The mod apk may also violate some terms of service or policies of the original game or the anime creators, resulting in legal actions or penalties against you. Therefore, you should always be cautious and vigilant when downloading and installing any files or apps from unofficial or unverified sources, and always backup your data before making any changes to your device or game.</td>
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-<td>Is the attack on titan season 4 mod apk compatible with my device?</td>
-<td>It depends on your device model, version, and specifications, as the mod apk may not work properly or at all on some devices or versions of the original game. The mod apk may also interfere with or overwrite some of the files or data of the original game, causing errors, crashes, glitches, or lag. Therefore, you should always check the compatibility and requirements of the mod apk before downloading and installing it on your device, and always backup your data before making any changes to your device or game.</td>
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-<td>How can I update the attack on titan season 4 mod apk?</td>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download APK Real Piano and Discover the Joy of Music with this Amazing App.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download APK Real Piano and Discover the Joy of Music with this Amazing App.md
deleted file mode 100644
index ca4a532f0853f4fae27558fd45718bc69c1623ad..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download APK Real Piano and Discover the Joy of Music with this Amazing App.md	
+++ /dev/null
@@ -1,104 +0,0 @@
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-<p>Do you love playing piano but don't have access to a real one? Do you want to learn how to play piano without spending a lot of money on lessons and equipment? Do you want to have fun and express yourself through music? If you answered yes to any of these questions, then you should download APK Real Piano, a free app that lets you play piano on your Android device.</p>
-<h2>What is APK Real Piano?</h2>
-<p>APK Real Piano is an app that simulates a real piano keyboard on your Android device. You can play different musical instruments, such as piano, flute, organ, and guitar, with realistic sounds and effects. You can also record and share your music with your friends and family.</p>
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-<h3>Features of APK Real Piano</h3>
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-<li>Multi-touch keyboard with 88 keys</li>
-<li>Adjustable keyboard size and layout</li>
-<li>Various musical instruments to choose from</li>
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-<li>Supports MIDI and MP3 files</li>
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-<h2>How to Download and Install APK Real Piano</h2>
-<p>If you want to download APK Real Piano, you need to follow these simple steps:</p>
-<h3>Step 1: Find a Reliable Source</h3>
-<p>The first thing you need to do is find a reliable source where you can download the APK file of the app. APK stands for Android Package Kit, which is a file format that contains all the components of an app. You can find many websites that offer APK files for free, but be careful because some of them may contain viruses or malware. One of the best sources where you can download APK Real Piano is [APKCombo](^1^), which is a safe and trusted website that provides thousands of Android games and apps.</p>
-<h3>Step 2: Download the APK File</h3>
-<p>The next thing you need to do is download the APK file of the app from the website. You can either use your browser or a download manager to do this. The file size of the app is about 20 MB, so it shouldn't take too long to download. Once the download is complete, you can find the file in your device's download folder or in the notification bar.</p>
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-<h3>Step 3: Enable Unknown Sources</h3>
-<p>The third thing you need to do is enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. To do this, go to your device's settings, then security, then unknown sources, and toggle it on. You may see a warning message that says installing apps from unknown sources may harm your device, but don't worry because APKCombo is a safe website.</p>
-<h3>Step 4: Install the APK File</h3>
-<p>The last thing you need to do is install the APK file of the app on your device. To do this, go to your device's file manager, then locate the file you downloaded, then tap on it. You may see a pop-up window that asks you to confirm the installation, just tap on install and wait for the process to finish. Congratulations, you have successfully installed APK Real Piano on your device!</p>
-<h2>How to Use APK Real Piano</h2>
-<p>Now that you have installed APK Real Piano, you can start using it to play piano on your device. Here are some tips on how to use the app:</p>
-<h3>Choose Your Instrument</h3>
-<p>The first thing you need to do is choose your instrument. You can do this by tapping on the instrument icon on the top left corner of the screen. You will see a list of different musical instruments, such as piano, flute, organ, and guitar. Tap on the one you want to play and it will appear on the keyboard.</p>
-<h3>Adjust the Settings</h3>
-<p>The next thing you need to do is adjust the settings. You can do this by tapping on the settings icon on the top right corner of the screen. You will see a menu of different options, such as keyboard size, keyboard layout, sound volume, metronome, tempo, and record. You can customize these options according to your preference and needs.</p>
-<h3>Play and Record Your Music</h3>
-<p>The last thing you need to do is play and record your music. You can do this by tapping on the keys on the keyboard. You will hear the sound of the instrument you chose. You can also use the pedals at the bottom of the screen to control the sustain and expression of the sound. If you want to record your music, you can tap on the record button on the top center of the screen. You will see a red dot indicating that the recording has started. To stop the recording, tap on the record button again. You will see a pop-up window that asks you to name and save your file. You can also play back your recording by tapping on the play button next to the record button.</p>
-<h2>Conclusion</h2>
-<p>APK Real Piano is a great app that lets you play piano on your Android device. It has many features and benefits that make it fun and easy to use. It also allows you to record and share your music with others. If you want to download APK Real Piano, you can follow the steps above and enjoy playing piano anytime and anywhere.</p>
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-<li>Q: Is APK Real Piano safe to download and install?</li>
-<li>A: Yes, APK Real Piano is safe to download and install from [APKCombo], which is a reliable and trusted website that provides virus-free APK files.</li>
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-<li>Q: How can I share my music with others?</li>
-<li>A: You can share your music with others by tapping on the share button on the top right corner of the screen after recording your music. You will see a list of different apps and platforms where you can share your music, such as email, WhatsApp, Facebook, etc.</li>
-<li>Q: How can I play MIDI and MP3 files with APK Real Piano?</li>
-<li>A: You can play MIDI and MP3 files with APK Real Piano by tapping on the folder icon on the top left corner of the screen. You will see a list of different folders where you can find your MIDI and MP3 files. Tap on the file you want to play and it will load on the keyboard.</li>
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-<li>A: You can contact the developer of APK Real Piano by tapping on the info icon on the top right corner of the screen. You will see a page with information about the app and the developer. You can also send an email to [apkpiano@gmail.com] or visit their website [www.apkpiano.com].</li>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/ Petralex (iPhone Android).md b/spaces/tioseFevbu/cartoon-converter/scripts/ Petralex (iPhone Android).md
deleted file mode 100644
index 9c52c78e8eed4a6904f9a3df7f5f22ad464dd885..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/ Petralex (iPhone Android).md	
+++ /dev/null
@@ -1,74 +0,0 @@
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-<ol>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Award Flash Utility Dos Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Award Flash Utility Dos Download.md
deleted file mode 100644
index ddaf927147e24ce748f83e75ddad1eb4f9d9cc2e..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Award Flash Utility Dos Download.md	
+++ /dev/null
@@ -1,41 +0,0 @@
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Bhaangarh Full Movie Download Hd 720p.md b/spaces/tioseFevbu/cartoon-converter/scripts/Bhaangarh Full Movie Download Hd 720p.md
deleted file mode 100644
index 0471df16569439ef6e3689dfc9feef4528e6c91c..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Bhaangarh Full Movie Download Hd 720p.md	
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Dream Plan Home Design Software Crack 48 VERIFIED.md b/spaces/tioseFevbu/cartoon-converter/scripts/Dream Plan Home Design Software Crack 48 VERIFIED.md
deleted file mode 100644
index 64c8e82e8f880cebb787020ce9f2e70741db9fd1..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Dream Plan Home Design Software Crack 48 VERIFIED.md	
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@@ -1,26 +0,0 @@
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/FBX 2017 Et Covadis 2017.zip.md b/spaces/tioseFevbu/cartoon-converter/scripts/FBX 2017 Et Covadis 2017.zip.md
deleted file mode 100644
index e80ca00530819a195108b0a7f24569b488e3439e..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/FBX 2017 Et Covadis 2017.zip.md	
+++ /dev/null
@@ -1,35 +0,0 @@
-
-Hello, this is Bing. I can help you write a title and an article with HTML formatting for the keyword "FBX 2017 Et Covadis 2017.zip". Here is a possible example:
-
-<h1>How to Install FBX 2017 and Covadis 2017 on Your Computer</h1>
-<p>FBX 2017 and Covadis 2017 are two software applications that can help you create and edit 3D models, animations, and scenes. FBX 2017 is a file format that can store data from various 3D software, such as Autodesk 3ds Max, Maya, and Revit. Covadis 2017 is a civil engineering software that can perform calculations, design, and drafting for roads, bridges, tunnels, and other infrastructure projects.</p>
-<p>If you want to install FBX 2017 and Covadis 2017 on your computer, you will need to follow these steps:</p>
-<h2>FBX 2017 Et Covadis 2017.zip</h2><br /><p><b><b>Download Zip</b> &#128504;&#128504;&#128504; <a href="https://urlcod.com/2uHw5c">https://urlcod.com/2uHw5c</a></b></p><br /><br />
-<ol>
-<li>Download the ZIP file that contains the installation files for both software from the official website or a trusted source. The file name should be FBX 2017 Et Covadis 2017.zip[^2^].</li>
-<li>Extract the ZIP file to a folder on your computer. You should see two folders named FBX 2017 and Covadis 2017.</li>
-<li>Open the FBX 2017 folder and run the setup.exe file. Follow the instructions on the screen to install FBX 2017 on your computer. You may need to enter a serial number or an activation code that you received when you purchased or downloaded the software.</li>
-<li>Open the Covadis 2017 folder and run the setup.exe file. Follow the instructions on the screen to install Covadis 2017 on your computer. You may need to enter a serial number or an activation code that you received when you purchased or downloaded the software.</li>
-<li>Restart your computer after the installation is complete.</li>
-<li>Launch FBX 2017 and Covadis 2017 from your desktop or start menu. You can now use them to create and edit your 3D projects.</li>
-</ol>
-<p>If you encounter any problems during the installation or usage of FBX 2017 and Covadis 2017, you can refer to the user manuals or online help resources that are available on their websites or in their software menus.</p>To continue the article, you may want to add some information about the features and benefits of FBX 2017 and Covadis 2017, as well as some tips and examples on how to use them effectively. Here is a possible example:
-
-<h2>Features and Benefits of FBX 2017 and Covadis 2017</h2>
-<p>FBX 2017 and Covadis 2017 are powerful tools that can help you create and edit 3D models, animations, and scenes for various purposes, such as games, movies, architecture, engineering, and more. Here are some of the features and benefits of using these software:</p>
-<ul>
-<li>FBX 2017 is a file format that can store data from various 3D software, such as Autodesk 3ds Max, Maya, and Revit. It can also store data from other formats, such as OBJ, DXF, DAE, and more. This means that you can easily transfer your 3D data between different applications and platforms without losing quality or compatibility.</li>
-<li>FBX 2017 also supports advanced features, such as animation curves, blend shapes, skinning, cameras, lights, materials, textures, and more. You can use these features to create realistic and expressive 3D characters and environments.</li>
-<li>Covadis 2017 is a civil engineering software that can perform calculations, design, and drafting for roads, bridges, tunnels, and other infrastructure projects. It can also integrate with other Autodesk software, such as AutoCAD Civil 3D, InfraWorks, and Revit.</li>
-<li>Covadis 2017 also supports advanced features, such as terrain modeling, georeferencing, topographic surveying, earthworks estimation, drainage design, traffic simulation, and more. You can use these features to create accurate and efficient civil engineering projects.</li>
-</ul>
-<h2>Tips and Examples on How to Use FBX 2017 and Covadis 2017</h2>
-<p>If you want to learn how to use FBX 2017 and Covadis 2017 effectively, you can follow these tips and examples:</p>
-<ul>
-<li>To import or export FBX files in your 3D software, you can use the FBX plug-ins that are available for download from the Autodesk website[^2^]. You can also use the FBX Converter tool that can convert between different FBX versions and other formats.</li>
-<li>To view or edit FBX files without opening your 3D software, you can use the FBX Review tool that can display your 3D data in a simple and interactive way. You can also use the FBX Explorer tool that can show you the structure and properties of your FBX file.</li>
-<li>To create or edit civil engineering projects in Covadis 2017, you can use the various modules that are available for different tasks. For example, you can use the Road module to design roads with horizontal and vertical alignments. You can also use the Bridge module to design bridges with piers and abutments.</li>
-<li>To visualize or analyze your civil engineering projects in Covadis 2017, you can use the various tools that are available for different purposes. For example, you can use the Profile tool to view the cross-sections of your roads or bridges. You can also use the Simulation tool to simulate the traffic flow on your roads.</li>
-</ul></p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Macro Scheduler Automation Tool Crackl.md b/spaces/tioseFevbu/cartoon-converter/scripts/Macro Scheduler Automation Tool Crackl.md
deleted file mode 100644
index 970e57646dabe560b1b0918e1df59355f6a716af..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Macro Scheduler Automation Tool Crackl.md	
+++ /dev/null
@@ -1,123 +0,0 @@
-<br />
-<h1>Macro Scheduler Automation Tool Crack: What You Need to Know</h1>
- <p>If you are looking for a way to automate your Windows tasks, you might have heard of Macro Scheduler Automation Tool. This is a powerful and easy-to-use software that can help you create macros, scripts, and workflows to control any Windows application or process. But what if you don't want to pay for the full version of Macro Scheduler? You might be tempted to look for a crack that can unlock all the features and functions of Macro Scheduler for free. But is this a good idea? What are the risks and consequences of using a Macro Scheduler Automation Tool crack? In this article, we will answer these questions and more. We will explain what Macro Scheduler Automation Tool is, what a crack is, why you should avoid using a crack, and what are the alternatives and solutions to get the most out of Macro Scheduler.</p>
-<h2>Macro Scheduler Automation Tool Crackl</h2><br /><p><b><b>Download File</b> &#127383; <a href="https://urlcod.com/2uHxo1">https://urlcod.com/2uHxo1</a></b></p><br /><br />
- <h2>What is Macro Scheduler Automation Tool?</h2>
- <p>Before we talk about what a crack is, let's first understand what Macro Scheduler Automation Tool is. Macro Scheduler is a software that allows you to automate anything in Windows. You can use it to record macros, write scripts, build workflows, schedule tasks, create shortcuts, and more. You can automate any Windows application or process, such as web automation, desktop automation, file management, data extraction, report generation, software testing, etc. You can also use Macro Scheduler to integrate different applications and systems using various methods such as DDE, COM, DLLs, etc.</p>
- <h3>A brief introduction to Macro Scheduler</h3>
- <p>Macro Scheduler was developed by MJT Net Ltd., a UK-based company that specializes in automation software. It was first released in 1997 and has since been updated regularly with new features and improvements. It is used by thousands of customers worldwide, including Fortune 100 companies, government agencies, educational institutions, small businesses, and individual users.</p>
- <p>Macro Scheduler has two versions: Standard and Pro. The Standard version has all the basic features you need to create simple macros and scripts. The Pro version has more advanced features such as screen image recognition, screen text capture, web recorder, code wizards, UI automation functions, exe compiler, etc. The Pro version also allows you to distribute your macros as standalone executable files that can run on any Windows PC without requiring Macro Scheduler.</p>
- <h3>The features and benefits of Macro Scheduler</h3>
- <p>Macro Scheduler has many features that make it a powerful and versatile automation tool for Windows. Some of these features are:</p>
- <ul>
-<li><b>Macro Recorder:</b> You can use the macro recorder to capture your mouse clicks and keyboard strokes and turn them into a macro. You can then edit the macro to add logic, variables, loops, conditions, etc.</li>
-<li><b>Script Editor:</b> You can use the script editor to write your own scripts using Macro Scheduler's scripting language. The scripting language is easy to learn and has over 300 commands and functions. You can also use code snippets, code folding, syntax highlighting, auto-complete, etc. to make your scripting easier and faster.</li>
-<li><b>Workflow Designer:</b> You can use the workflow designer to create graphical workflows that represent your automation logic. You can drag and drop actions, conditions, loops, branches, etc. to build your workflow. You can also use variables, expressions, functions, etc. to manipulate data and control the flow of your workflow.</li>
-<li><b>Task Scheduler:</b> You can use the task scheduler to run your macros and scripts at specific times or events. You can set up triggers such as date and time, system startup or shutdown, file or folder changes, window events, etc. You can also use advanced options such as recurrence patterns, exceptions, delays, priorities, etc. to fine-tune your scheduling.</li>
-<li><b>Macro Player:</b> You can use the macro player to run your macros and scripts on demand or in the background. You can also use hotkeys, shortcuts, menus, toolbars, etc. to launch your macros and scripts quickly and easily.</li>
-</ul>
- <p>Some of the benefits of using Macro Scheduler are:</p>
- <ul>
-<li><b>Save time and money:</b> You can automate repetitive and tedious tasks that would otherwise take you hours or days to complete manually. You can also reduce errors and mistakes that could cost you money and reputation.</li>
-<li><b>Increase productivity and efficiency:</b> You can perform complex and challenging tasks that would otherwise require specialized skills or knowledge. You can also improve the quality and consistency of your work output.</li>
-<li><b>Enhance creativity and innovation:</b> You can explore new possibilities and opportunities that would otherwise be impossible or impractical. You can also experiment with different scenarios and solutions to find the best one for your needs.</li>
-</ul>
- <h3>The pricing and licensing options of Macro Scheduler</h3>
- <p>Macro Scheduler has different pricing and licensing options depending on your needs and preferences. You can choose from the following options:</p>
- <table>
-<tr>
-<th>Option</th>
-<th>Description</th>
-<th>Price</th>
-</tr>
-<tr>
-<td>Standard Edition</td>
-<td>A single-user license for the Standard version of Macro Scheduler.</td>
-<td>$95</td>
-</tr>
-<tr>
-<td>Pro Edition</td>
-<td>A single-user license for the Pro version of Macro Scheduler.</td>
-<td>$195</td>
-</tr>
-<tr>
-<td>Site License</td>
-<td>A multi-user license for the Pro version of Macro Scheduler that allows you to install it on unlimited PCs within a single site or location.</td>
-<td>$995</td>
-</tr>
-<tr>
-<td>Enterprise License</td>
-<td>A multi-user license for the Pro version of Macro Scheduler that allows you to install it on unlimited PCs within multiple sites or locations.</td>
-<td>$1995</td>
-</tr>
-<tr>
-<td>Maintenance Plan</td>
-<td>An optional plan that gives you free upgrades and priority support for one year.</td>
-<td>$49 (Standard) / $99 (Pro)</td>
-</tr>
-<tr>
-<td>Trial Version</td>
-<td>A free version of Macro Scheduler that you can download and use for 30 days with no limitations.</td>
-<td>$0</td>
-</tr> </table>
- <p>You can purchase Macro Scheduler from the official website or from authorized resellers. You can also download the trial version from the website and try it for free for 30 days. You can activate the full version by entering a license key that you will receive after your purchase.</p>
-<p></p>
- <h2>What is Macro Scheduler Automation Tool Crack?</h2>
- <p>Now that you know what Macro Scheduler Automation Tool is, let's talk about what a crack is. A crack is a modified version of a software that bypasses or removes its protection mechanisms, such as serial numbers, activation codes, license keys, etc. The purpose of a crack is to enable the user to use the software without paying for it or following its terms and conditions.</p>
- <h3>The definition and purpose of cracking software</h3>
- <p>Cracking software is the process of modifying or breaking the software's code to alter its behavior or functionality. Cracking software is usually done by hackers or crackers who have advanced knowledge and skills in programming, reverse engineering, cryptography, etc. Cracking software is often motivated by various reasons, such as:</p>
- <ul>
-<li><b>Piracy:</b> Some crackers crack software to distribute it for free or for profit to other users who do not want to pay for it or cannot afford it.</li>
-<li><b>Curiosity:</b> Some crackers crack software to challenge themselves and test their abilities. They also crack software to learn how it works and how to improve it.</li>
-<li><b>Hacking:</b> Some crackers crack software to exploit its vulnerabilities and gain unauthorized access to its data or resources. They also crack software to cause damage or disruption to its users or owners.</li>
-</ul>
- <h3>The methods and sources of cracking Macro Scheduler</h3>
- <p>There are different methods and sources of cracking Macro Scheduler. Some of the common ones are:</p>
- <ul>
-<li><b>Keygen:</b> A keygen is a program that generates valid license keys for Macro Scheduler. The user can enter the license key in Macro Scheduler to activate the full version.</li>
-<li><b>Patch:</b> A patch is a program that modifies the executable file of Macro Scheduler. The patch can remove the license check, change the trial period, unlock the Pro features, etc.</li>
-<li><b>Cracked version:</b> A cracked version is a pre-modified version of Macro Scheduler that has already been patched or keygened. The user can download and install the cracked version without needing any license key or activation code.</li>
-</ul>
- <p>The sources of cracking Macro Scheduler are usually websites, forums, blogs, torrents, etc. that offer free downloads of cracks, keygens, patches, or cracked versions of Macro Scheduler. These sources are often hosted on shady or illegal domains that may contain malware, viruses, spyware, etc.</p>
- <h3>The risks and consequences of cracking Macro Scheduler</h3>
- <p>While cracking Macro Scheduler may seem like a tempting option to get the full version for free, it is not worth it. Cracking Macro Scheduler has many risks and consequences that can harm you and your computer. Some of these are:</p>
- <ul>
-<li><b>Malware infection:</b> The cracks, keygens, patches, or cracked versions of Macro Scheduler may contain malicious code that can infect your computer with malware. Malware can steal your personal information, damage your files, slow down your system, display unwanted ads, etc.</li>
-<li><b>Lack of updates and support:</b> The cracked version of Macro Scheduler may not be compatible with the latest updates and patches from MJT Net Ltd. You may miss out on new features, bug fixes, security improvements, etc. You may also not be able to get any technical support or customer service from MJT Net Ltd.</li>
-<li><b>Lack of quality and reliability:</b> The cracked version of Macro Scheduler may not work properly or as expected. It may have errors, glitches, crashes, etc. that can affect your automation tasks and results. It may also have missing or corrupted files that can cause problems with your system.</li>
-<li><b>Lack of legality and morality:</b> Cracking Macro Scheduler is illegal and immoral. It violates the intellectual property rights and terms and conditions of MJT Net Ltd. It also deprives them of their rightful revenue and recognition for their hard work and innovation. It also shows disrespect and dishonesty towards the developers and creators of Macro Scheduler.</li>
-</ul>
- <h2>Why You Should Avoid Macro Scheduler Automation Tool Crack?</h2>
- <p>As you can see, cracking Macro Scheduler is not a good idea at all. It has more disadvantages than advantages. It can cause more harm than good. It can cost you more than you save. Therefore, you should avoid using a crack for Macro Scheduler Automation Tool at all costs. Here are some reasons why you should avoid cracking Macro Scheduler:</p <h3>The ethical and legal issues of cracking software</h3>
- <p>One of the main reasons why you should avoid cracking Macro Scheduler is because it is unethical and illegal. Cracking software is a form of software piracy, which is the unauthorized copying, distribution, or use of software. Software piracy is a serious crime that can have severe penalties, such as fines, lawsuits, or even jail time. According to the Business Software Alliance (BSA), software piracy causes losses of billions of dollars every year to the software industry and the economy. It also harms the innovation and development of new software products and services.</p>
- <p>By cracking Macro Scheduler, you are not only breaking the law, but also violating the moral principles of fairness, honesty, and respect. You are stealing the intellectual property and hard work of MJT Net Ltd. and other software developers. You are also cheating yourself and other legitimate users of Macro Scheduler who pay for the software and follow its terms and conditions. You are also contributing to the spread of malware and cybercrime that can affect millions of people and businesses.</p>
- <h3>The security and performance issues of cracking software</h3>
- <p>Another reason why you should avoid cracking Macro Scheduler is because it can compromise your security and performance. Cracking software can expose your computer to malware and viruses that can infect your system and damage your files. Malware can also steal your personal information, such as passwords, credit card numbers, bank accounts, etc. Malware can also hijack your browser, display unwanted ads, redirect your searches, etc. Malware can also slow down your computer, cause crashes, freezes, errors, etc.</p>
- <p>Cracking software can also affect the quality and reliability of Macro Scheduler. The cracked version of Macro Scheduler may not work properly or as expected. It may have bugs, glitches, crashes, etc. that can interfere with your automation tasks and results. It may also have missing or corrupted files that can cause problems with your system. The cracked version of Macro Scheduler may also not be compatible with the latest updates and patches from MJT Net Ltd. You may miss out on new features, bug fixes, security improvements, etc.</p>
- <h3>The alternatives and solutions to cracking software</h3>
- <p>The final reason why you should avoid cracking Macro Scheduler is because there are better alternatives and solutions to get the full version of Macro Scheduler without breaking the law or risking your security and performance. Some of these alternatives and solutions are:</p>
- <ul>
-<li><b>Buy the full version:</b> The best and most obvious solution is to buy the full version of Macro Scheduler from the official website or from authorized resellers. You can choose from different pricing and licensing options depending on your needs and preferences. You can also get a maintenance plan that gives you free upgrades and priority support for one year. By buying the full version of Macro Scheduler, you will get all the features and functions of Macro Scheduler without any limitations or risks. You will also support MJT Net Ltd. and other software developers who create useful and innovative software products and services.</li>
-<li><b>Use the trial version:</b> Another solution is to use the trial version of Macro Scheduler that you can download for free from the official website. The trial version allows you to use Macro Scheduler for 30 days with no limitations or restrictions. You can test all the features and functions of Macro Scheduler and see if it meets your needs and expectations. You can also get technical support and customer service from MJT Net Ltd. during the trial period. If you like Macro Scheduler after the trial period, you can buy the full version to continue using it.</li>
-<li><b>Use alternative software:</b> A third solution is to use alternative software that can perform similar or comparable tasks as Macro Scheduler. There are many other automation tools for Windows that you can choose from, such as AutoHotkey, AutoIt, WinAutomation, UiPath, etc. Some of these tools are free or open source, while others are paid or commercial. You can compare their features, benefits, prices, reviews, etc. to find the best one for your needs.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Macro Scheduler Automation Tool Crack is not a good option to get the full version of Macro Scheduler for free. It has many risks and consequences that can harm you and your computer. It is also unethical and illegal to crack software. Therefore, you should avoid using a crack for Macro Scheduler Automation Tool at all costs.</p>
- <p>Instead, you should use one of the alternatives or solutions that we have discussed in this article. You can buy the full version of Macro Scheduler from the official website or from authorized resellers. You can use the trial version of Macro Scheduler for 30 days with no limitations or restrictions. You can use alternative software that can perform similar or comparable tasks as Macro Scheduler.</p>
- <p>By doing so, you will get the most out of Macro Scheduler without breaking the law or risking your security and performance. You will also support the software industry and the economy. You will also enhance your productivity and efficiency. You will also enjoy the creativity and innovation that Macro Scheduler can offer.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Macro Scheduler Automation Tool Crack:</p>
- <ol>
-<li><b>What is Macro Scheduler Automation Tool?</b>
-<p>Macro Scheduler Automation Tool is a software that allows you to automate anything in Windows. You can use it to create macros, scripts, workflows, schedules, shortcuts, etc. to control any Windows application or process.</p></li>
-<li><b>What is Macro Scheduler Automation Tool Crack?</b>
-<p>Macro Scheduler Automation Tool Crack is a modified version of Macro Scheduler that bypasses or removes its protection mechanisms, such as serial numbers, activation codes, license keys, etc. The purpose of a crack is to enable the user to use Macro Scheduler without paying for it or following its terms and conditions.</p></li>
-<li><b>Why should I avoid Macro Scheduler Automation Tool Crack?</b>
-<p>You should avoid Macro Scheduler Automation Tool Crack because it is unethical and illegal to crack software. It also has many risks and consequences that can harm you and your computer. It can expose you to malware and viruses, lack of updates and support, lack of quality and reliability, lack of legality and morality, etc.</p></li>
-<li><b>How can I get the full version of Macro Scheduler without cracking it?</b>
-<p>You can get the full version of Macro Scheduler without cracking it by buying it from the official website or from authorized resellers. You can also use the trial version of Macro Scheduler for 30 days with no limitations or restrictions. You can also use alternative software that can perform similar or comparable tasks as Macro Scheduler.</p></li>
-<li><b>Where can I learn more about Macro Scheduler and its features?</b>
-<p>You can learn more about Macro Scheduler and its features by visiting the official website of MJT Net Ltd. at https://www.mjtnet.com/. You can also find tutorials, videos, forums, blogs, etc. that can help you learn how to use Macro Scheduler effectively and efficiently.</p></li>
-</ol></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/certifi/core.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/certifi/core.py
deleted file mode 100644
index f34045b527010945ade1a345e574117f5222f105..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/certifi/core.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""
-certifi.py
-~~~~~~~~~~
-
-This module returns the installation location of cacert.pem or its contents.
-"""
-import os
-import types
-from typing import Union
-
-
-class _PipPatchedCertificate(Exception):
-    pass
-
-
-try:
-    # Return a certificate file on disk for a standalone pip zipapp running in
-    # an isolated build environment to use. Passing --cert to the standalone
-    # pip does not work since requests calls where() unconditionally on import.
-    _PIP_STANDALONE_CERT = os.environ.get("_PIP_STANDALONE_CERT")
-    if _PIP_STANDALONE_CERT:
-        def where():
-            return _PIP_STANDALONE_CERT
-        raise _PipPatchedCertificate()
-
-    from importlib.resources import path as get_path, read_text
-
-    _CACERT_CTX = None
-    _CACERT_PATH = None
-
-    def where() -> str:
-        # This is slightly terrible, but we want to delay extracting the file
-        # in cases where we're inside of a zipimport situation until someone
-        # actually calls where(), but we don't want to re-extract the file
-        # on every call of where(), so we'll do it once then store it in a
-        # global variable.
-        global _CACERT_CTX
-        global _CACERT_PATH
-        if _CACERT_PATH is None:
-            # This is slightly janky, the importlib.resources API wants you to
-            # manage the cleanup of this file, so it doesn't actually return a
-            # path, it returns a context manager that will give you the path
-            # when you enter it and will do any cleanup when you leave it. In
-            # the common case of not needing a temporary file, it will just
-            # return the file system location and the __exit__() is a no-op.
-            #
-            # We also have to hold onto the actual context manager, because
-            # it will do the cleanup whenever it gets garbage collected, so
-            # we will also store that at the global level as well.
-            _CACERT_CTX = get_path("pip._vendor.certifi", "cacert.pem")
-            _CACERT_PATH = str(_CACERT_CTX.__enter__())
-
-        return _CACERT_PATH
-
-except _PipPatchedCertificate:
-    pass
-
-except ImportError:
-    Package = Union[types.ModuleType, str]
-    Resource = Union[str, "os.PathLike"]
-
-    # This fallback will work for Python versions prior to 3.7 that lack the
-    # importlib.resources module but relies on the existing `where` function
-    # so won't address issues with environments like PyOxidizer that don't set
-    # __file__ on modules.
-    def read_text(
-        package: Package,
-        resource: Resource,
-        encoding: str = 'utf-8',
-        errors: str = 'strict'
-    ) -> str:
-        with open(where(), encoding=encoding) as data:
-            return data.read()
-
-    # If we don't have importlib.resources, then we will just do the old logic
-    # of assuming we're on the filesystem and munge the path directly.
-    def where() -> str:
-        f = os.path.dirname(__file__)
-
-        return os.path.join(f, "cacert.pem")
-
-
-def contents() -> str:
-    return read_text("certifi", "cacert.pem", encoding="ascii")
diff --git a/spaces/tmaham/DS-Fusion-Express/app.py b/spaces/tmaham/DS-Fusion-Express/app.py
deleted file mode 100644
index 9aaa1819286308500584a7b5e02dccd863f8c453..0000000000000000000000000000000000000000
--- a/spaces/tmaham/DS-Fusion-Express/app.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import gradio as gr
-import os 
-# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-from categories import categories, font_list
-import pdb 
-from PIL import Image
-import random
-
-try:
-    import pygsheets
-except Exception as e:
-    print("pygsheets not found", e)
-
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
-
-
-global_index = 0
-mode_global = "DS-Fusion-Express"
-prompt_global = ""
-
-def log_data_to_sheet(pre_style, style_custom, glyph, attribute):
-    try:
-        #authorization
-        gc = pygsheets.authorize(service_file='./huggingface-connector-02adcb4cdf00.json')
-
-        #open the google spreadsheet (where 'PY to Gsheet Test' is the name of my sheet)
-        sh = gc.open('HuggingFace Logs')
-
-        #select the first sheet 
-        wks = sh[0]
-
-        # Send fields list
-        wks.append_table(values=[pre_style, style_custom, glyph, attribute])
-    except:
-        pass
-
-def change_font(evt: gr.SelectData):
-    global global_index
-    global_index = evt.index
-
-def my_main(pre_style, style_custom, glyph, attribute):
-
-    log_data_to_sheet(pre_style, style_custom, glyph, attribute)
-
-    global prompt_global
-    glyph = glyph[0]
-    
-    command = "rm -r out_cur/*"
-    os.system(command)
-    for i in range(1,5):
-        command = "cp initial_show/" + str(i) +".png out_cur/"+str(i)+".png"
-        os.system(command)
-
-    style = pre_style
-    
-    command = "rm -r data_style/"
-    os.system(command)
-
-    if style_custom != "":
-        style = style_custom
-
-    if len(glyph) != 1:
-        prompt_global = f" {style}"
-    else:
-        prompt_global = f" {style} {glyph}"
-
-    li = "ckpt/all2.ckpt"
-    output_path = f"out/express"
-    if attribute == "":
-        prompt = f" '{style} {glyph}'"
-    else:
-        prompt = f" '{attribute} {style} {glyph}'"
-
-    command = "rm -r out/"
-    os.system(command)
-    
-    print(prompt)
-    
-    command = "python txt2img.py --ddim_eta 1.0 \
-                        --n_samples 4 \
-                        --n_iter 1\
-                        --ddim_steps 50 \
-                        --scale 5.0\
-                        --H 256\
-                        --W 256\
-                        --outdir " + output_path + " --ckpt " +li +" --prompt " + prompt
-                        
-    os.system(command)
-
-    command = "rm -r out_cur/*"
-    os.system(command)
-    path = []
-    final_imgs = os.listdir(output_path+"/samples")
-    for i in range(4):
-        path.append(os.path.join(output_path+"/samples", final_imgs[i]))
-        path_in = os.path.join(output_path+"/samples", final_imgs[i])
-        command = "cp " + path_in + " " + "out_cur/"+final_imgs[i]
-        os.system(command)
-
-    return gr.update(value=path)
-
-
-
-def rem_bg():
-    command= "rm -r out_bg/*"
-    os.system(command)
-    files = os.listdir("out_cur")
-    if len(files)>0:
-        command_3 = f"python script_step3.py --input_dir out_cur --method 'rembg'"
-        os.system(command_3)
-
-    for file in files:
-        command = "cp out_bg/"+file +" out_cur/"
-        os.system(command)
-    
-    path = []
-    for file in files:
-        file_path = os.path.join("out_cur", file)
-        image = Image.open(file_path)
-        new_image = Image.new("RGBA", image.size, "WHITE") 
-        new_image.paste(image, (0, 0), image)            
-        new_image.save(file_path, "PNG")  
-        path.append(file_path)
-
-    return gr.update(value = path)
-
-font_list_express = [
-    'Caladea-Regular', #works good
-    # 'Garuda-Bold', #works poorlyß
-    'FreeSansOblique', #works average
-    "Purisa", #works good
-    "Uroob" #worksaverage
-    ]
-
-path_fonts_express = []
-for font in font_list_express:
-    path_in = "font_list/fonts/"+font+".png"
-    path_fonts_express.append(path_in)
-    
-def make_upper(value):
-    if value == "":
-        return ""
-    return value[0].upper()
-
-def get_out_cur():
-    path = []
-    pth = "log_view"
-    for file in os.listdir(pth):
-        file_final = os.path.join(pth, file)
-        path.append(file_final)
-    return gr.update(value=path) 
-
-
-def update_time_mode(value):
-    if value == 'DS-Fusion':
-        return gr.update(value="Generation Time: ~5 mins")
-    else:
-        return gr.update(value="Generation Time: ~30 seconds")
-
-def update_time_cb(value):
-    if value:
-        if mode_global == "DS-Fusion":
-            return gr.update(value="Generation Time: ~8 mins")
-        return gr.update(value="Generation Time: ~30 seconds")
-    else:
-        if mode_global == "DS-Fusion":
-            return gr.update(value="Generation Time: ~5 mins")
-        return gr.update(value="Generation Time: ~30 seconds")
-    
-            
-
-def load_img():
-
-    path = []
-    dir = "out_cur"
-    for file in os.listdir(dir):
-        file_full = os.path.join(dir, file)
-        path.append(file_full)
-    return path
-
-css = '''
-<!-- this makes the items inside the gallery to shrink -->
-div#gallery_64 div.grid {
-    height: 64px;
-    width: 180px;
-}
-
-<!-- this makes the gallery's height to shrink -->
-div#gallery_64 > div:nth-child(3) {
-    min-height: 172px !important;
-}
-
-<!-- this makes the gallery's height to shrink when you click one image to view it bigger -->
-div#gallery_64 > div:nth-child(4) {
-    min-height: 172px !important;
-}
-'''
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(): 
-        with gr.Row():
-            with gr.Column(): 
-                with gr.Row():
-                    in4 = gr.Text(label="Character (A-Z, 0-9) to Stylize", info = "Only works with capitals. Will pick first letter if more than one", value = "R", interactive = True)
-                    in2 = gr.Dropdown(categories, label="Pre-Defined Style Categories", info = "Categories used to train Express", value = "DRAGON", interactive = True)
-                
-                with gr.Row():
-                    in3 = gr.Text(label="Override Style Category ", info="This will replace the pre-defined style value", value = "", interactive = True)
-                    in5 = gr.Text(label="Additional Style Attribute ",info= "e.g. pixel, grayscale, etc", value = "", interactive = True)
-
-                # with gr.Row():
-                #     with gr.Column():
-                #         in8 = gr.Checkbox(label="MULTI FONT INPUT - font selection below is over-ridden", info="Select for more abstract results", value = False, interactive = True).style(container=True)
-                #         gallery = gr.Gallery([], label="Select Font", show_label=True, elem_id="gallery_64").style(grid=[2,6],  preview=True, height="auto")
-                        
-                with gr.Row():
-                    btn = gr.Button("Let's Stylize It - Generation Time: ~60 seconds", interactive = True)
-                    # btn_bg = gr.Button("Remove Background", interactive = True)
-
-            with gr.Column():
-                gallery_out = gr.Gallery(label="Generated images", elem_id="gallery_out").style(grid=[2,2], height="full")
-        
-    
-    
-    outputs = [gallery_out]    
-    # gallery.select(change_font, None, None)
-        
-
-    inputs = [in2,in3,in4,in5]
-    
-    btn.click(my_main, inputs, outputs)
-    # btn_bg.click(rem_bg, None, outputs)
-
-
-
-
-if __name__ == "__main__":
-    command = "rm -r out_cur/*"
-    os.system(command)
-    for i in range(1,5):
-        command = "cp initial_show/" + str(i) +".png out_cur/"+str(i)+".png"
-        os.system(command)
-    demo.queue()
-    demo.launch(share=False)
diff --git a/spaces/tokudai/GODEL-Demo/app.py b/spaces/tokudai/GODEL-Demo/app.py
deleted file mode 100644
index f2c18772437610a313eb12e1f16e7471b2bccd8e..0000000000000000000000000000000000000000
--- a/spaces/tokudai/GODEL-Demo/app.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import gradio as gr
-
-from transformers import (
-    AutoTokenizer,
-    AutoModel,
-    AutoModelForSeq2SeqLM,
-    AutoModelForCausalLM
-)
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
-model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
-
-preset_examples = [
-    ('Instruction: given a dialog context, you need to response empathically.',
-     '', 'Does money buy happiness?', 'Chitchat'),
-    ('Instruction: given a dialog context, you need to response empathically.',
-     '', 'What is the goal of life?', 'Chitchat'),
-    ('Instruction: given a dialog context, you need to response empathically.',
-     '', 'What is the most interesing thing about our universe?', 'Chitchat'),
-     ('Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.', 
-     '''Scooby-Doo is the eponymous character and protagonist of the animated television franchise of the same name, created in 1969 by the American animation company Hanna-Barbera.[1] He is a male Great Dane and lifelong companion of amateur detective Shaggy Rogers, with whom he shares many personality traits. He features a mix of both canine and human behaviors (reminiscent of other talking animals in Hanna-Barbera's series), and is treated by his friends more or less as an equal. Scooby often speaks in a rhotacized way, substituting the first letters of many words with the letter 'r'. His catchphrase is "Scooby-Dooby-Doo!"
-     ''',
-     'What kind of animal is scooby from scooby doo?', 'Conversational Question Answering'
-     ),
-     ('Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.', 
-     '''Subject: faa demos 
-    Dan: PM Team, Attached are some general ideas and issues around developing new demos for our new target markets. Please review and provide feedback. Also, please provide links where we can learn more about various FAA applications. Thanx, Dan. 
-    Alex: Dan, Thanks for putting the high level descriptions together. My questions are: *Is it practical to do an EAI demo given the inherent complexity of application integration? ... * Should we delay looking at Outlook for now?... *What do you think that timelines are developing these demos? ... Alex 
-    Dan: Alex, Thanks for the feedback, please see my comments below:
-     ''',
-     'what does Dan ask PM team to do?', 'Conversational Question Answering'
-     ),
-     ('Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.', 
-     '''Carlos Alcaraz, at just 19, completed an improbable journey on Sunday in Flushing Meadows as he defeated No. 5 Casper Ruud to win the 2022 US Open. Alcaraz came away with a 6-4, 2-6, 7-6, 6-2 win over Ruud to win his first career Grand Slam title.
-     
-     In doing so, Alcaraz became the second-youngest player to win a men's US Open title at 19 years, 129 days old, only trailing Pete Sampras. In addition, Alcaraz is the seventh youngest male or female to ever win a Grand Slam tournament. With the Grand Slam victory, Alcaraz becomes the No. 1 ranked player in the world. Additionally, the 19-year-old budding star is also the youngest player to ever be ranked as the world's No. 1 player.
-     ''',
-     'who won the 2022 US Open? EOS Carlos Alcaraz EOS how old?', 'Conversational Question Answering'
-     ),
-     (
-        'Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge.',
-        '''Over-the-counter medications such as ibuprofen (Advil, Motrin IB, others), acetaminophen (Tylenol, others) and aspirin.
-        ''',
-        'I have a headache, what should I do?', "Grounded Response Generation"
-     ),
-     (
-        'Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge.',
-        '''The best Stardew Valley mods PCGamesN_0 / About SMAPI
-        ''',
-        'My favorite game is stardew valley. stardew valley is very fun.', "Grounded Response Generation"
-     ),
-     (
-        'Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge.',
-        '''Wong Kar-wai BBS (born 17 July 1958) is a Hong Kong film director, screenwriter, and producer. His films are characterised by nonlinear narratives, atmospheric music, and vivid cinematography involving bold, saturated colours. A pivotal figure of Hong Kong cinema, Wong is considered a contemporary auteur, and ranks third on Sight & Sound's 2002 poll of the greatest filmmakers of modern times.[note 1] His films frequently appear on best-of lists domestically and internationally.
-        ''',
-        'My favorite director is wrong kar wai. i think in modern cinema there is no other director is is making the medium as cool', "Grounded Response Generation"
-     )
-]
-
-
-def generate(instruction, knowledge, dialog, top_p, min_length, max_length):
-    if knowledge != '':
-        knowledge = '[KNOWLEDGE] ' + knowledge
-    dialog = ' EOS '.join(dialog)
-    query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
-
-    input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
-    outputs = model.generate(input_ids, min_length=int(
-        min_length), max_length=int(max_length), top_p=top_p, do_sample=True)
-    output = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    print(query)
-    print(output)
-    return output
-
-
-def api_call_generation(instruction, knowledge, query, top_p, min_length, max_length):
-
-    dialog = [
-        query
-    ]
-    response = generate(instruction, knowledge, dialog,
-                        top_p, min_length, max_length)
-
-    return response
-
-
-# def change_example(choice):
-#     choice_idx = int(choice.split()[-1]) - 1
-#     instruction, knowledge, query, instruction_type = preset_examples[choice_idx]
-#     return [gr.update(lines=1, visible=True, value=instruction), gr.update(visible=True, value=knowledge), gr.update(lines=1, visible=True, value=query), gr.update(visible=True, value=instruction_type)]
-
-def change_textbox(choice):
-    if choice == "Chitchat":
-        return gr.update(lines=1, visible=True, value="Instruction: given a dialog context, you need to response empathically.")
-    elif choice == "Grounded Response Generation":
-        return gr.update(lines=1, visible=True, value="Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge.")
-    else:
-        return gr.update(lines=1, visible=True, value="Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.")
-
-
-with gr.Blocks() as demo:
-#     gr.Markdown("# GODEL: Large-Scale Pre-Training for Goal-Directed Dialog")
-#     gr.Markdown('''GODEL is a large open-source pre-trained language model for dialog. In contrast with its predecessor DialoGPT, GODEL leverages a new phase of grounded pretraining designed to better support finetuning phases that require information external to the current conversation (e.g., a database or document) to produce good responses. More information about this work can be found in the paper [GODEL: Large-Scale Pre-training for Goal-Directed Dialog.](https://www.microsoft.com/en-us/research/project/godel/)
-
-# >Looking for a large open-source pre-trained language model for dialog? Look no further than GODEL! GODEL leverages a new phase of grounded pretraining designed to better support finetuning phases that require information external to the current conversation (e.g., a database or document) to produce good responses. So if you're looking for a language model that can help you produce better responses in a variety of situations, GODEL is the right choice for you!<p style="text-align:right"> ------ a copy from GPT-3</p>''')
-
-    # dropdown = gr.Dropdown(
-    #     [f"Example {i+1}" for i in range(9)], label='Examples')
-
-    radio = gr.Radio(
-        ["Conversational Question Answering", "Chitchat", "Grounded Response Generation"], label="Instruction Type", value='Conversational Question Answering'
-    )
-    # radio = gr.Radio(
-    #     ["Conversational Question Answering"], label="Instruction Type", value='Conversational Question Answering'
-    # )
-    instruction = gr.Textbox(lines=1, interactive=True, label="Instruction",
-                             value="Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.")
-    # instruction = "Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge."
-    radio.change(fn=change_textbox, inputs=radio, outputs=instruction)
-    knowledge = gr.Textbox(lines=6, label="Knowledge")
-    query = gr.Textbox(lines=1, label="User Query")
-
-    # dropdown.change(change_example, dropdown, [instruction, knowledge, query, radio])
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            response = gr.Textbox(label="Response", lines=2)
-
-        with gr.Column(scale=1):
-            top_p = gr.Slider(0, 1, value=0.9, label='top_p')
-            min_length = gr.Number(8, label='min_length')
-            max_length = gr.Number(
-                64, label='max_length (should be larger than min_length)')
-
-    greet_btn = gr.Button("Generate")
-    greet_btn.click(fn=api_call_generation, inputs=[
-                    instruction, knowledge, query, top_p, min_length, max_length], outputs=response)
-
-demo.launch()
diff --git a/spaces/tomofi/ABINet-OCR/modules/__init__.py b/spaces/tomofi/ABINet-OCR/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/tomofi/MMOCR/configs/textdet/psenet/README.md b/spaces/tomofi/MMOCR/configs/textdet/psenet/README.md
deleted file mode 100644
index c0053c6b9bb920a0243aa41f3f30ed5afc8cdf4b..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/textdet/psenet/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# PSENet
-
->[Shape robust text detection with progressive scale expansion network](https://arxiv.org/abs/1903.12473)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future.
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/22607038/142795864-9b455b10-8a19-45bb-aeaf-4b733f341afc.png"/>
-</div>
-
-
-## Results and models
-
-### CTW1500
-
-|                               Method                                | Backbone | Extra Data | Training set  |   Test set   | #epochs | Test size |    Recall     |   Precision   |     Hmean     |                                                                                                Download                                                                                                |
-| :-----------------------------------------------------------------: | :------: | :--------: | :-----------: | :----------: | :-----: | :-------: | :-----------: | :-----------: | :-----------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [PSENet-4s](configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py) | ResNet50 |     -      | CTW1500 Train | CTW1500 Test |   600   |   1280    | 0.728 (0.717) | 0.849 (0.852) | 0.784 (0.779) | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_ctw1500_20210401-216fed50.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/20210401_215421.log.json) |
-
-### ICDAR2015
-
-|                                Method                                 | Backbone |                                                                Extra Data                                                                 | Training set | Test set  | #epochs | Test size |    Recall     |   Precision   |     Hmean     |                                                                                            Download                                                                                             |
-| :-------------------------------------------------------------------: | :------: | :---------------------------------------------------------------------------------------------------------------------------------------: | :----------: | :-------: | :-----: | :-------: | :-----------: | :-----------: | :-----------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [PSENet-4s](configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 |                                                                     -                                                                     |  IC15 Train  | IC15 Test |   600   |   2240    | 0.784 (0.753) | 0.831 (0.867) | 0.807 (0.806) | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015-c6131f0d.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/20210331_214145.log.json) |
-| [PSENet-4s](configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 | pretrain on IC17 MLT [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2017_as_pretrain-3bd6056c.pth) |  IC15 Train  | IC15 Test |   600   |   2240    |     0.834     |     0.861     |     0.847     |                                  [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth) \| [log]()                                   |
-
-:::{note}
-We've upgraded our IoU backend from `Polygon3` to `shapely`. There are some performance differences for some models due to the backends' different logics to handle invalid polygons (more info [here](https://github.com/open-mmlab/mmocr/issues/465)). **New evaluation result is presented in brackets** and new logs will be uploaded soon.
-:::
-
-
-## Citation
-
-```bibtex
-@inproceedings{wang2019shape,
-  title={Shape robust text detection with progressive scale expansion network},
-  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={9336--9345},
-  year={2019}
-}
-```
diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/roi_heads/__init__.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/roi_heads/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/tovaru/vits-for-ba/mel_processing.py b/spaces/tovaru/vits-for-ba/mel_processing.py
deleted file mode 100644
index 9da68a75b8538040fefd94d03aaddc78b2c87721..0000000000000000000000000000000000000000
--- a/spaces/tovaru/vits-for-ba/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/trysem/image-matting-app/ppmatting/transforms/transforms.py b/spaces/trysem/image-matting-app/ppmatting/transforms/transforms.py
deleted file mode 100644
index afd28b4917a890890820e56785b81c841b2d387a..0000000000000000000000000000000000000000
--- a/spaces/trysem/image-matting-app/ppmatting/transforms/transforms.py
+++ /dev/null
@@ -1,791 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import random
-import string
-
-import cv2
-import numpy as np
-from paddleseg.transforms import functional
-from paddleseg.cvlibs import manager
-from paddleseg.utils import seg_env
-from PIL import Image
-
-
-@manager.TRANSFORMS.add_component
-class Compose:
-    """
-    Do transformation on input data with corresponding pre-processing and augmentation operations.
-    The shape of input data to all operations is [height, width, channels].
-    """
-
-    def __init__(self, transforms, to_rgb=True):
-        if not isinstance(transforms, list):
-            raise TypeError('The transforms must be a list!')
-        self.transforms = transforms
-        self.to_rgb = to_rgb
-
-    def __call__(self, data):
-        """
-        Args:
-            data (dict): The data to transform.
-
-        Returns:
-            dict: Data after transformation
-        """
-        if 'trans_info' not in data:
-            data['trans_info'] = []
-        for op in self.transforms:
-            data = op(data)
-            if data is None:
-                return None
-
-        data['img'] = np.transpose(data['img'], (2, 0, 1))
-        for key in data.get('gt_fields', []):
-            if len(data[key].shape) == 2:
-                continue
-            data[key] = np.transpose(data[key], (2, 0, 1))
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class LoadImages:
-    def __init__(self, to_rgb=False):
-        self.to_rgb = to_rgb
-
-    def __call__(self, data):
-        if isinstance(data['img'], str):
-            data['img'] = cv2.imread(data['img'])
-        for key in data.get('gt_fields', []):
-            if isinstance(data[key], str):
-                data[key] = cv2.imread(data[key], cv2.IMREAD_UNCHANGED)
-            # if alpha and trimap has 3 channels, extract one.
-            if key in ['alpha', 'trimap']:
-                if len(data[key].shape) > 2:
-                    data[key] = data[key][:, :, 0]
-
-        if self.to_rgb:
-            data['img'] = cv2.cvtColor(data['img'], cv2.COLOR_BGR2RGB)
-            for key in data.get('gt_fields', []):
-                if len(data[key].shape) == 2:
-                    continue
-                data[key] = cv2.cvtColor(data[key], cv2.COLOR_BGR2RGB)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class Resize:
-    def __init__(self, target_size=(512, 512), random_interp=False):
-        if isinstance(target_size, list) or isinstance(target_size, tuple):
-            if len(target_size) != 2:
-                raise ValueError(
-                    '`target_size` should include 2 elements, but it is {}'.
-                    format(target_size))
-        else:
-            raise TypeError(
-                "Type of `target_size` is invalid. It should be list or tuple, but it is {}"
-                .format(type(target_size)))
-
-        self.target_size = target_size
-        self.random_interp = random_interp
-        self.interps = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC]
-
-    def __call__(self, data):
-        if self.random_interp:
-            interp = np.random.choice(self.interps)
-        else:
-            interp = cv2.INTER_LINEAR
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        data['img'] = functional.resize(data['img'], self.target_size, interp)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize(data[key], self.target_size,
-                                              cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize(data[key], self.target_size,
-                                              interp)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomResize:
-    """
-    Resize image to a size determinned by `scale` and `size`.
-
-    Args:
-        size(tuple|list): The reference size to resize. A tuple or list with length 2.
-        scale(tupel|list, optional): A range of scale base on `size`. A tuple or list with length 2. Default: None.
-    """
-
-    def __init__(self, size=None, scale=None):
-        if isinstance(size, list) or isinstance(size, tuple):
-            if len(size) != 2:
-                raise ValueError(
-                    '`size` should include 2 elements, but it is {}'.format(
-                        size))
-        elif size is not None:
-            raise TypeError(
-                "Type of `size` is invalid. It should be list or tuple, but it is {}"
-                .format(type(size)))
-
-        if scale is not None:
-            if isinstance(scale, list) or isinstance(scale, tuple):
-                if len(scale) != 2:
-                    raise ValueError(
-                        '`scale` should include 2 elements, but it is {}'.
-                        format(scale))
-            else:
-                raise TypeError(
-                    "Type of `scale` is invalid. It should be list or tuple, but it is {}"
-                    .format(type(scale)))
-        self.size = size
-        self.scale = scale
-
-    def __call__(self, data):
-        h, w = data['img'].shape[:2]
-        if self.scale is not None:
-            scale = np.random.uniform(self.scale[0], self.scale[1])
-        else:
-            scale = 1.
-        if self.size is not None:
-            scale_factor = max(self.size[0] / w, self.size[1] / h)
-        else:
-            scale_factor = 1
-        scale = scale * scale_factor
-
-        w = int(round(w * scale))
-        h = int(round(h * scale))
-        data['img'] = functional.resize(data['img'], (w, h))
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize(data[key], (w, h),
-                                              cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize(data[key], (w, h))
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class ResizeByLong:
-    """
-    Resize the long side of an image to given size, and then scale the other side proportionally.
-
-    Args:
-        long_size (int): The target size of long side.
-    """
-
-    def __init__(self, long_size):
-        self.long_size = long_size
-
-    def __call__(self, data):
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        data['img'] = functional.resize_long(data['img'], self.long_size)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize_long(data[key], self.long_size,
-                                                   cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize_long(data[key], self.long_size)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class ResizeByShort:
-    """
-    Resize the short side of an image to given size, and then scale the other side proportionally.
-
-    Args:
-        short_size (int): The target size of short side.
-    """
-
-    def __init__(self, short_size):
-        self.short_size = short_size
-
-    def __call__(self, data):
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        data['img'] = functional.resize_short(data['img'], self.short_size)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize_short(data[key], self.short_size,
-                                                    cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize_short(data[key], self.short_size)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class ResizeToIntMult:
-    """
-    Resize to some int muitple, d.g. 32.
-    """
-
-    def __init__(self, mult_int=32):
-        self.mult_int = mult_int
-
-    def __call__(self, data):
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-
-        h, w = data['img'].shape[0:2]
-        rw = w - w % self.mult_int
-        rh = h - h % self.mult_int
-        data['img'] = functional.resize(data['img'], (rw, rh))
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize(data[key], (rw, rh),
-                                              cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize(data[key], (rw, rh))
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class Normalize:
-    """
-    Normalize an image.
-
-    Args:
-        mean (list, optional): The mean value of a data set. Default: [0.5, 0.5, 0.5].
-        std (list, optional): The standard deviation of a data set. Default: [0.5, 0.5, 0.5].
-
-    Raises:
-        ValueError: When mean/std is not list or any value in std is 0.
-    """
-
-    def __init__(self, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
-        self.mean = mean
-        self.std = std
-        if not (isinstance(self.mean,
-                           (list, tuple)) and isinstance(self.std,
-                                                         (list, tuple))):
-            raise ValueError(
-                "{}: input type is invalid. It should be list or tuple".format(
-                    self))
-        from functools import reduce
-        if reduce(lambda x, y: x * y, self.std) == 0:
-            raise ValueError('{}: std is invalid!'.format(self))
-
-    def __call__(self, data):
-        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-        std = np.array(self.std)[np.newaxis, np.newaxis, :]
-        data['img'] = functional.normalize(data['img'], mean, std)
-        if 'fg' in data.get('gt_fields', []):
-            data['fg'] = functional.normalize(data['fg'], mean, std)
-        if 'bg' in data.get('gt_fields', []):
-            data['bg'] = functional.normalize(data['bg'], mean, std)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomCropByAlpha:
-    """
-    Randomly crop while centered on uncertain area by a certain probability.
-
-    Args:
-        crop_size (tuple|list): The size you want to crop from image.
-        p (float): The probability centered on uncertain area.
-
-    """
-
-    def __init__(self, crop_size=((320, 320), (480, 480), (640, 640)),
-                 prob=0.5):
-        self.crop_size = crop_size
-        self.prob = prob
-
-    def __call__(self, data):
-        idex = np.random.randint(low=0, high=len(self.crop_size))
-        crop_w, crop_h = self.crop_size[idex]
-
-        img_h = data['img'].shape[0]
-        img_w = data['img'].shape[1]
-        if np.random.rand() < self.prob:
-            crop_center = np.where((data['alpha'] > 0) & (data['alpha'] < 255))
-            center_h_array, center_w_array = crop_center
-            if len(center_h_array) == 0:
-                return data
-            rand_ind = np.random.randint(len(center_h_array))
-            center_h = center_h_array[rand_ind]
-            center_w = center_w_array[rand_ind]
-            delta_h = crop_h // 2
-            delta_w = crop_w // 2
-            start_h = max(0, center_h - delta_h)
-            start_w = max(0, center_w - delta_w)
-        else:
-            start_h = 0
-            start_w = 0
-            if img_h > crop_h:
-                start_h = np.random.randint(img_h - crop_h + 1)
-            if img_w > crop_w:
-                start_w = np.random.randint(img_w - crop_w + 1)
-
-        end_h = min(img_h, start_h + crop_h)
-        end_w = min(img_w, start_w + crop_w)
-
-        data['img'] = data['img'][start_h:end_h, start_w:end_w]
-        for key in data.get('gt_fields', []):
-            data[key] = data[key][start_h:end_h, start_w:end_w]
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomCrop:
-    """
-    Randomly crop
-
-    Args:
-    crop_size (tuple|list): The size you want to crop from image.
-    """
-
-    def __init__(self, crop_size=((320, 320), (480, 480), (640, 640))):
-        if not isinstance(crop_size[0], (list, tuple)):
-            crop_size = [crop_size]
-        self.crop_size = crop_size
-
-    def __call__(self, data):
-        idex = np.random.randint(low=0, high=len(self.crop_size))
-        crop_w, crop_h = self.crop_size[idex]
-        img_h, img_w = data['img'].shape[0:2]
-
-        start_h = 0
-        start_w = 0
-        if img_h > crop_h:
-            start_h = np.random.randint(img_h - crop_h + 1)
-        if img_w > crop_w:
-            start_w = np.random.randint(img_w - crop_w + 1)
-
-        end_h = min(img_h, start_h + crop_h)
-        end_w = min(img_w, start_w + crop_w)
-
-        data['img'] = data['img'][start_h:end_h, start_w:end_w]
-        for key in data.get('gt_fields', []):
-            data[key] = data[key][start_h:end_h, start_w:end_w]
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class LimitLong:
-    """
-    Limit the long edge of image.
-
-    If the long edge is larger than max_long, resize the long edge
-    to max_long, while scale the short edge proportionally.
-
-    If the long edge is smaller than min_long, resize the long edge
-    to min_long, while scale the short edge proportionally.
-
-    Args:
-        max_long (int, optional): If the long edge of image is larger than max_long,
-            it will be resize to max_long. Default: None.
-        min_long (int, optional): If the long edge of image is smaller than min_long,
-            it will be resize to min_long. Default: None.
-    """
-
-    def __init__(self, max_long=None, min_long=None):
-        if max_long is not None:
-            if not isinstance(max_long, int):
-                raise TypeError(
-                    "Type of `max_long` is invalid. It should be int, but it is {}"
-                    .format(type(max_long)))
-        if min_long is not None:
-            if not isinstance(min_long, int):
-                raise TypeError(
-                    "Type of `min_long` is invalid. It should be int, but it is {}"
-                    .format(type(min_long)))
-        if (max_long is not None) and (min_long is not None):
-            if min_long > max_long:
-                raise ValueError(
-                    '`max_long should not smaller than min_long, but they are {} and {}'
-                    .format(max_long, min_long))
-        self.max_long = max_long
-        self.min_long = min_long
-
-    def __call__(self, data):
-        h, w = data['img'].shape[:2]
-        long_edge = max(h, w)
-        target = long_edge
-        if (self.max_long is not None) and (long_edge > self.max_long):
-            target = self.max_long
-        elif (self.min_long is not None) and (long_edge < self.min_long):
-            target = self.min_long
-
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        if target != long_edge:
-            data['img'] = functional.resize_long(data['img'], target)
-            for key in data.get('gt_fields', []):
-                if key == 'trimap':
-                    data[key] = functional.resize_long(data[key], target,
-                                                       cv2.INTER_NEAREST)
-                else:
-                    data[key] = functional.resize_long(data[key], target)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class LimitShort:
-    """
-    Limit the short edge of image.
-
-    If the short edge is larger than max_short, resize the short edge
-    to max_short, while scale the long edge proportionally.
-
-    If the short edge is smaller than min_short, resize the short edge
-    to min_short, while scale the long edge proportionally.
-
-    Args:
-        max_short (int, optional): If the short edge of image is larger than max_short,
-            it will be resize to max_short. Default: None.
-        min_short (int, optional): If the short edge of image is smaller than min_short,
-            it will be resize to min_short. Default: None.
-    """
-
-    def __init__(self, max_short=None, min_short=None):
-        if max_short is not None:
-            if not isinstance(max_short, int):
-                raise TypeError(
-                    "Type of `max_short` is invalid. It should be int, but it is {}"
-                    .format(type(max_short)))
-        if min_short is not None:
-            if not isinstance(min_short, int):
-                raise TypeError(
-                    "Type of `min_short` is invalid. It should be int, but it is {}"
-                    .format(type(min_short)))
-        if (max_short is not None) and (min_short is not None):
-            if min_short > max_short:
-                raise ValueError(
-                    '`max_short should not smaller than min_short, but they are {} and {}'
-                    .format(max_short, min_short))
-        self.max_short = max_short
-        self.min_short = min_short
-
-    def __call__(self, data):
-        h, w = data['img'].shape[:2]
-        short_edge = min(h, w)
-        target = short_edge
-        if (self.max_short is not None) and (short_edge > self.max_short):
-            target = self.max_short
-        elif (self.min_short is not None) and (short_edge < self.min_short):
-            target = self.min_short
-
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        if target != short_edge:
-            data['img'] = functional.resize_short(data['img'], target)
-            for key in data.get('gt_fields', []):
-                if key == 'trimap':
-                    data[key] = functional.resize_short(data[key], target,
-                                                        cv2.INTER_NEAREST)
-                else:
-                    data[key] = functional.resize_short(data[key], target)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomHorizontalFlip:
-    """
-    Flip an image horizontally with a certain probability.
-
-    Args:
-        prob (float, optional): A probability of horizontally flipping. Default: 0.5.
-    """
-
-    def __init__(self, prob=0.5):
-        self.prob = prob
-
-    def __call__(self, data):
-        if random.random() < self.prob:
-            data['img'] = functional.horizontal_flip(data['img'])
-            for key in data.get('gt_fields', []):
-                data[key] = functional.horizontal_flip(data[key])
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomBlur:
-    """
-    Blurring an image by a Gaussian function with a certain probability.
-
-    Args:
-        prob (float, optional): A probability of blurring an image. Default: 0.1.
-    """
-
-    def __init__(self, prob=0.1):
-        self.prob = prob
-
-    def __call__(self, data):
-        if self.prob <= 0:
-            n = 0
-        elif self.prob >= 1:
-            n = 1
-        else:
-            n = int(1.0 / self.prob)
-        if n > 0:
-            if np.random.randint(0, n) == 0:
-                radius = np.random.randint(3, 10)
-                if radius % 2 != 1:
-                    radius = radius + 1
-                if radius > 9:
-                    radius = 9
-                data['img'] = cv2.GaussianBlur(data['img'], (radius, radius), 0,
-                                               0)
-                for key in data.get('gt_fields', []):
-                    if key == 'trimap':
-                        continue
-                    data[key] = cv2.GaussianBlur(data[key], (radius, radius), 0,
-                                                 0)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomDistort:
-    """
-    Distort an image with random configurations.
-
-    Args:
-        brightness_range (float, optional): A range of brightness. Default: 0.5.
-        brightness_prob (float, optional): A probability of adjusting brightness. Default: 0.5.
-        contrast_range (float, optional): A range of contrast. Default: 0.5.
-        contrast_prob (float, optional): A probability of adjusting contrast. Default: 0.5.
-        saturation_range (float, optional): A range of saturation. Default: 0.5.
-        saturation_prob (float, optional): A probability of adjusting saturation. Default: 0.5.
-        hue_range (int, optional): A range of hue. Default: 18.
-        hue_prob (float, optional): A probability of adjusting hue. Default: 0.5.
-    """
-
-    def __init__(self,
-                 brightness_range=0.5,
-                 brightness_prob=0.5,
-                 contrast_range=0.5,
-                 contrast_prob=0.5,
-                 saturation_range=0.5,
-                 saturation_prob=0.5,
-                 hue_range=18,
-                 hue_prob=0.5):
-        self.brightness_range = brightness_range
-        self.brightness_prob = brightness_prob
-        self.contrast_range = contrast_range
-        self.contrast_prob = contrast_prob
-        self.saturation_range = saturation_range
-        self.saturation_prob = saturation_prob
-        self.hue_range = hue_range
-        self.hue_prob = hue_prob
-
-    def __call__(self, data):
-        brightness_lower = 1 - self.brightness_range
-        brightness_upper = 1 + self.brightness_range
-        contrast_lower = 1 - self.contrast_range
-        contrast_upper = 1 + self.contrast_range
-        saturation_lower = 1 - self.saturation_range
-        saturation_upper = 1 + self.saturation_range
-        hue_lower = -self.hue_range
-        hue_upper = self.hue_range
-        ops = [
-            functional.brightness, functional.contrast, functional.saturation,
-            functional.hue
-        ]
-        random.shuffle(ops)
-        params_dict = {
-            'brightness': {
-                'brightness_lower': brightness_lower,
-                'brightness_upper': brightness_upper
-            },
-            'contrast': {
-                'contrast_lower': contrast_lower,
-                'contrast_upper': contrast_upper
-            },
-            'saturation': {
-                'saturation_lower': saturation_lower,
-                'saturation_upper': saturation_upper
-            },
-            'hue': {
-                'hue_lower': hue_lower,
-                'hue_upper': hue_upper
-            }
-        }
-        prob_dict = {
-            'brightness': self.brightness_prob,
-            'contrast': self.contrast_prob,
-            'saturation': self.saturation_prob,
-            'hue': self.hue_prob
-        }
-
-        im = data['img'].astype('uint8')
-        im = Image.fromarray(im)
-        for id in range(len(ops)):
-            params = params_dict[ops[id].__name__]
-            params['im'] = im
-            prob = prob_dict[ops[id].__name__]
-            if np.random.uniform(0, 1) < prob:
-                im = ops[id](**params)
-        data['img'] = np.asarray(im)
-
-        for key in data.get('gt_fields', []):
-            if key in ['alpha', 'trimap']:
-                continue
-            else:
-                im = data[key].astype('uint8')
-                im = Image.fromarray(im)
-                for id in range(len(ops)):
-                    params = params_dict[ops[id].__name__]
-                    params['im'] = im
-                    prob = prob_dict[ops[id].__name__]
-                    if np.random.uniform(0, 1) < prob:
-                        im = ops[id](**params)
-                data[key] = np.asarray(im)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class Padding:
-    """
-    Add bottom-right padding to a raw image or annotation image.
-
-    Args:
-        target_size (list|tuple): The target size after padding.
-        im_padding_value (list, optional): The padding value of raw image.
-            Default: [127.5, 127.5, 127.5].
-        label_padding_value (int, optional): The padding value of annotation image. Default: 255.
-
-    Raises:
-        TypeError: When target_size is neither list nor tuple.
-        ValueError: When the length of target_size is not 2.
-    """
-
-    def __init__(self, target_size, im_padding_value=(127.5, 127.5, 127.5)):
-        if isinstance(target_size, list) or isinstance(target_size, tuple):
-            if len(target_size) != 2:
-                raise ValueError(
-                    '`target_size` should include 2 elements, but it is {}'.
-                    format(target_size))
-        else:
-            raise TypeError(
-                "Type of target_size is invalid. It should be list or tuple, now is {}"
-                .format(type(target_size)))
-
-        self.target_size = target_size
-        self.im_padding_value = im_padding_value
-
-    def __call__(self, data):
-        im_height, im_width = data['img'].shape[0], data['img'].shape[1]
-        target_height = self.target_size[1]
-        target_width = self.target_size[0]
-        pad_height = max(0, target_height - im_height)
-        pad_width = max(0, target_width - im_width)
-        data['trans_info'].append(('padding', data['img'].shape[0:2]))
-        if (pad_height == 0) and (pad_width == 0):
-            return data
-        else:
-            data['img'] = cv2.copyMakeBorder(
-                data['img'],
-                0,
-                pad_height,
-                0,
-                pad_width,
-                cv2.BORDER_CONSTANT,
-                value=self.im_padding_value)
-            for key in data.get('gt_fields', []):
-                if key in ['trimap', 'alpha']:
-                    value = 0
-                else:
-                    value = self.im_padding_value
-                data[key] = cv2.copyMakeBorder(
-                    data[key],
-                    0,
-                    pad_height,
-                    0,
-                    pad_width,
-                    cv2.BORDER_CONSTANT,
-                    value=value)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomSharpen:
-    def __init__(self, prob=0.1):
-        if prob < 0:
-            self.prob = 0
-        elif prob > 1:
-            self.prob = 1
-        else:
-            self.prob = prob
-
-    def __call__(self, data):
-        if np.random.rand() > self.prob:
-            return data
-
-        radius = np.random.choice([0, 3, 5, 7, 9])
-        w = np.random.uniform(0.1, 0.5)
-        blur_img = cv2.GaussianBlur(data['img'], (radius, radius), 5)
-        data['img'] = cv2.addWeighted(data['img'], 1 + w, blur_img, -w, 0)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap' or key == 'alpha':
-                continue
-            blur_img = cv2.GaussianBlur(data[key], (0, 0), 5)
-            data[key] = cv2.addWeighted(data[key], 1.5, blur_img, -0.5, 0)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomNoise:
-    def __init__(self, prob=0.1):
-        if prob < 0:
-            self.prob = 0
-        elif prob > 1:
-            self.prob = 1
-        else:
-            self.prob = prob
-
-    def __call__(self, data):
-        if np.random.rand() > self.prob:
-            return data
-        mean = np.random.uniform(0, 0.04)
-        var = np.random.uniform(0, 0.001)
-        noise = np.random.normal(mean, var**0.5, data['img'].shape) * 255
-        data['img'] = data['img'] + noise
-        data['img'] = np.clip(data['img'], 0, 255)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomReJpeg:
-    def __init__(self, prob=0.1):
-        if prob < 0:
-            self.prob = 0
-        elif prob > 1:
-            self.prob = 1
-        else:
-            self.prob = prob
-
-    def __call__(self, data):
-        if np.random.rand() > self.prob:
-            return data
-        q = np.random.randint(70, 95)
-        img = data['img'].astype('uint8')
-
-        # Ensure no conflicts between processes
-        tmp_name = str(os.getpid()) + '.jpg'
-        tmp_name = os.path.join(seg_env.TMP_HOME, tmp_name)
-        cv2.imwrite(tmp_name, img, [int(cv2.IMWRITE_JPEG_QUALITY), q])
-        data['img'] = cv2.imread(tmp_name)
-
-        return data
diff --git a/spaces/tsi-org/LLaVA/llava/train/train.py b/spaces/tsi-org/LLaVA/llava/train/train.py
deleted file mode 100644
index 0d198f7e379df541e2b9a6fbd61310ea30f16f82..0000000000000000000000000000000000000000
--- a/spaces/tsi-org/LLaVA/llava/train/train.py
+++ /dev/null
@@ -1,950 +0,0 @@
-# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
-# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
-#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-import os
-import copy
-from dataclasses import dataclass, field
-import json
-import logging
-import pathlib
-from typing import Dict, Optional, Sequence, List
-
-import torch
-
-import transformers
-
-from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
-from torch.utils.data import Dataset
-from llava.train.llava_trainer import LLaVATrainer
-
-from llava import conversation as conversation_lib
-from llava.model import *
-from llava.mm_utils import tokenizer_image_token
-
-from PIL import Image
-
-
-local_rank = None
-
-
-def rank0_print(*args):
-    if local_rank == 0:
-        print(*args)
-
-
-@dataclass
-class ModelArguments:
-    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
-    version: Optional[str] = field(default="v0")
-    freeze_backbone: bool = field(default=False)
-    tune_mm_mlp_adapter: bool = field(default=False)
-    vision_tower: Optional[str] = field(default=None)
-    mm_vision_select_layer: Optional[int] = field(default=-1)   # default to the last layer
-    pretrain_mm_mlp_adapter: Optional[str] = field(default=None)
-    mm_projector_type: Optional[str] = field(default='linear')
-    mm_use_im_start_end: bool = field(default=False)
-    mm_use_im_patch_token: bool = field(default=True)
-    mm_vision_select_feature: Optional[str] = field(default="patch")
-
-
-@dataclass
-class DataArguments:
-    data_path: str = field(default=None,
-                           metadata={"help": "Path to the training data."})
-    lazy_preprocess: bool = False
-    is_multimodal: bool = False
-    image_folder: Optional[str] = field(default=None)
-    image_aspect_ratio: str = 'square'
-    image_grid_pinpoints: Optional[str] = field(default=None)
-
-
-@dataclass
-class TrainingArguments(transformers.TrainingArguments):
-    cache_dir: Optional[str] = field(default=None)
-    optim: str = field(default="adamw_torch")
-    remove_unused_columns: bool = field(default=False)
-    freeze_mm_mlp_adapter: bool = field(default=False)
-    mpt_attn_impl: Optional[str] = field(default="triton")
-    model_max_length: int = field(
-        default=512,
-        metadata={
-            "help":
-            "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
-        },
-    )
-    double_quant: bool = field(
-        default=True,
-        metadata={"help": "Compress the quantization statistics through double quantization."}
-    )
-    quant_type: str = field(
-        default="nf4",
-        metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
-    )
-    bits: int = field(
-        default=16,
-        metadata={"help": "How many bits to use."}
-    )
-    lora_enable: bool = False
-    lora_r: int = 64
-    lora_alpha: int = 16
-    lora_dropout: float = 0.05
-    lora_weight_path: str = ""
-    lora_bias: str = "none"
-    group_by_modality_length: bool = field(default=False)
-
-
-def maybe_zero_3(param, ignore_status=False, name=None):
-    from deepspeed import zero
-    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
-    if hasattr(param, "ds_id"):
-        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
-            if not ignore_status:
-                logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
-        with zero.GatheredParameters([param]):
-            param = param.data.detach().cpu().clone()
-    else:
-        param = param.detach().cpu().clone()
-    return param
-
-
-# Borrowed from peft.utils.get_peft_model_state_dict
-def get_peft_state_maybe_zero_3(named_params, bias):
-    if bias == "none":
-        to_return = {k: t for k, t in named_params if "lora_" in k}
-    elif bias == "all":
-        to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
-    elif bias == "lora_only":
-        to_return = {}
-        maybe_lora_bias = {}
-        lora_bias_names = set()
-        for k, t in named_params:
-            if "lora_" in k:
-                to_return[k] = t
-                bias_name = k.split("lora_")[0] + "bias"
-                lora_bias_names.add(bias_name)
-            elif "bias" in k:
-                maybe_lora_bias[k] = t
-        for k, t in maybe_lora_bias:
-            if bias_name in lora_bias_names:
-                to_return[bias_name] = t
-    else:
-        raise NotImplementedError
-    to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
-    return to_return
-
-
-def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
-    to_return = {k: t for k, t in named_params if "lora_" not in k}
-    if require_grad_only:
-        to_return = {k: t for k, t in to_return.items() if t.requires_grad}
-    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
-    return to_return
-
-
-def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
-    to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
-    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
-    return to_return
-
-
-def find_all_linear_names(model):
-    cls = torch.nn.Linear
-    lora_module_names = set()
-    for name, module in model.named_modules():
-        if isinstance(module, cls):
-            names = name.split('.')
-            lora_module_names.add(names[0] if len(names) == 1 else names[-1])
-
-
-    if 'lm_head' in lora_module_names: # needed for 16-bit
-        lora_module_names.remove('lm_head')
-    return list(lora_module_names)
-
-
-def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
-                                   output_dir: str):
-    """Collects the state dict and dump to disk."""
-
-    if getattr(trainer.args, "tune_mm_mlp_adapter", False):
-        # Only save Adapter
-        keys_to_match = ['mm_projector']
-        if getattr(trainer.args, "use_im_start_end", False):
-            keys_to_match.extend(['embed_tokens', 'embed_in'])
-
-        weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match)
-        trainer.model.config.save_pretrained(output_dir)
-
-        current_folder = output_dir.split('/')[-1]
-        parent_folder = os.path.dirname(output_dir)
-        if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
-            if current_folder.startswith('checkpoint-'):
-                mm_projector_folder = os.path.join(parent_folder, "mm_projector")
-                os.makedirs(mm_projector_folder, exist_ok=True)
-                torch.save(weight_to_save, os.path.join(mm_projector_folder, f'{current_folder}.bin'))
-            else:
-                torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
-        return
-
-    if trainer.deepspeed:
-        torch.cuda.synchronize()
-        trainer.save_model(output_dir)
-        return
-
-    state_dict = trainer.model.state_dict()
-    if trainer.args.should_save:
-        cpu_state_dict = {
-            key: value.cpu()
-            for key, value in state_dict.items()
-        }
-        del state_dict
-        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa
-
-
-def smart_tokenizer_and_embedding_resize(
-    special_tokens_dict: Dict,
-    tokenizer: transformers.PreTrainedTokenizer,
-    model: transformers.PreTrainedModel,
-):
-    """Resize tokenizer and embedding.
-
-    Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
-    """
-    num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
-    model.resize_token_embeddings(len(tokenizer))
-
-    if num_new_tokens > 0:
-        input_embeddings = model.get_input_embeddings().weight.data
-        output_embeddings = model.get_output_embeddings().weight.data
-
-        input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
-            dim=0, keepdim=True)
-        output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
-            dim=0, keepdim=True)
-
-        input_embeddings[-num_new_tokens:] = input_embeddings_avg
-        output_embeddings[-num_new_tokens:] = output_embeddings_avg
-
-
-def _tokenize_fn(strings: Sequence[str],
-                 tokenizer: transformers.PreTrainedTokenizer) -> Dict:
-    """Tokenize a list of strings."""
-    tokenized_list = [
-        tokenizer(
-            text,
-            return_tensors="pt",
-            padding="longest",
-            max_length=tokenizer.model_max_length,
-            truncation=True,
-        ) for text in strings
-    ]
-    input_ids = labels = [
-        tokenized.input_ids[0] for tokenized in tokenized_list
-    ]
-    input_ids_lens = labels_lens = [
-        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
-        for tokenized in tokenized_list
-    ]
-    return dict(
-        input_ids=input_ids,
-        labels=labels,
-        input_ids_lens=input_ids_lens,
-        labels_lens=labels_lens,
-    )
-
-
-def _mask_targets(target, tokenized_lens, speakers):
-    # cur_idx = 0
-    cur_idx = tokenized_lens[0]
-    tokenized_lens = tokenized_lens[1:]
-    target[:cur_idx] = IGNORE_INDEX
-    for tokenized_len, speaker in zip(tokenized_lens, speakers):
-        if speaker == "human":
-            target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
-        cur_idx += tokenized_len
-
-
-def _add_speaker_and_signal(header, source, get_conversation=True):
-    """Add speaker and start/end signal on each round."""
-    BEGIN_SIGNAL = "### "
-    END_SIGNAL = "\n"
-    conversation = header
-    for sentence in source:
-        from_str = sentence["from"]
-        if from_str.lower() == "human":
-            from_str = conversation_lib.default_conversation.roles[0]
-        elif from_str.lower() == "gpt":
-            from_str = conversation_lib.default_conversation.roles[1]
-        else:
-            from_str = 'unknown'
-        sentence["value"] = (BEGIN_SIGNAL + from_str + ": " +
-                             sentence["value"] + END_SIGNAL)
-        if get_conversation:
-            conversation += sentence["value"]
-    conversation += BEGIN_SIGNAL
-    return conversation
-
-
-def preprocess_multimodal(
-    sources: Sequence[str],
-    data_args: DataArguments
-) -> Dict:
-    is_multimodal = data_args.is_multimodal
-    if not is_multimodal:
-        return sources
-
-    for source in sources:
-        for sentence in source:
-            if DEFAULT_IMAGE_TOKEN in sentence['value']:
-                sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
-                sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value']
-                sentence['value'] = sentence['value'].strip()
-                if "mmtag" in conversation_lib.default_conversation.version:
-                    sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '<Image>' + DEFAULT_IMAGE_TOKEN + '</Image>')
-            replace_token = DEFAULT_IMAGE_TOKEN
-            if data_args.mm_use_im_start_end:
-                replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
-            sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
-
-    return sources
-
-
-def preprocess_llama_2(
-    sources,
-    tokenizer: transformers.PreTrainedTokenizer,
-    has_image: bool = False
-) -> Dict:
-    conv = conversation_lib.default_conversation.copy()
-    roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
-
-    # Apply prompt templates
-    conversations = []
-    for i, source in enumerate(sources):
-        if roles[source[0]["from"]] != conv.roles[0]:
-            # Skip the first one if it is not from human
-            source = source[1:]
-
-        conv.messages = []
-        for j, sentence in enumerate(source):
-            role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], f"{i}"
-            conv.append_message(role, sentence["value"])
-        conversations.append(conv.get_prompt())
-
-    # Tokenize conversations
-
-    if has_image:
-        input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
-    else:
-        input_ids = tokenizer(
-            conversations,
-            return_tensors="pt",
-            padding="longest",
-            max_length=tokenizer.model_max_length,
-            truncation=True,
-        ).input_ids
-
-    targets = input_ids.clone()
-
-    assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2
-
-    # Mask targets
-    sep = "[/INST] "
-    for conversation, target in zip(conversations, targets):
-        total_len = int(target.ne(tokenizer.pad_token_id).sum())
-
-        rounds = conversation.split(conv.sep2)
-        cur_len = 1
-        target[:cur_len] = IGNORE_INDEX
-        for i, rou in enumerate(rounds):
-            if rou == "":
-                break
-
-            parts = rou.split(sep)
-            if len(parts) != 2:
-                break
-            parts[0] += sep
-
-            if has_image:
-                round_len = len(tokenizer_image_token(rou, tokenizer))
-                instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
-            else:
-                round_len = len(tokenizer(rou).input_ids)
-                instruction_len = len(tokenizer(parts[0]).input_ids) - 2
-
-            target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
-
-            cur_len += round_len
-        target[cur_len:] = IGNORE_INDEX
-
-        if cur_len < tokenizer.model_max_length:
-            if cur_len != total_len:
-                target[:] = IGNORE_INDEX
-                print(
-                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
-                    f" (ignored)"
-                )
-
-    return dict(
-        input_ids=input_ids,
-        labels=targets,
-    )
-
-
-def preprocess_v1(
-    sources,
-    tokenizer: transformers.PreTrainedTokenizer,
-    has_image: bool = False
-) -> Dict:
-    conv = conversation_lib.default_conversation.copy()
-    roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
-
-    # Apply prompt templates
-    conversations = []
-    for i, source in enumerate(sources):
-        if roles[source[0]["from"]] != conv.roles[0]:
-            # Skip the first one if it is not from human
-            source = source[1:]
-
-        conv.messages = []
-        for j, sentence in enumerate(source):
-            role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], f"{i}"
-            conv.append_message(role, sentence["value"])
-        conversations.append(conv.get_prompt())
-
-    # Tokenize conversations
-
-    if has_image:
-        input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
-    else:
-        input_ids = tokenizer(
-            conversations,
-            return_tensors="pt",
-            padding="longest",
-            max_length=tokenizer.model_max_length,
-            truncation=True,
-        ).input_ids
-
-    targets = input_ids.clone()
-
-    assert conv.sep_style == conversation_lib.SeparatorStyle.TWO
-
-    # Mask targets
-    sep = conv.sep + conv.roles[1] + ": "
-    for conversation, target in zip(conversations, targets):
-        total_len = int(target.ne(tokenizer.pad_token_id).sum())
-
-        rounds = conversation.split(conv.sep2)
-        cur_len = 1
-        target[:cur_len] = IGNORE_INDEX
-        for i, rou in enumerate(rounds):
-            if rou == "":
-                break
-
-            parts = rou.split(sep)
-            if len(parts) != 2:
-                break
-            parts[0] += sep
-
-            if has_image:
-                round_len = len(tokenizer_image_token(rou, tokenizer))
-                instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
-            else:
-                round_len = len(tokenizer(rou).input_ids)
-                instruction_len = len(tokenizer(parts[0]).input_ids) - 2
-
-            target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
-
-            cur_len += round_len
-        target[cur_len:] = IGNORE_INDEX
-
-        if cur_len < tokenizer.model_max_length:
-            if cur_len != total_len:
-                target[:] = IGNORE_INDEX
-                print(
-                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
-                    f" (ignored)"
-                )
-
-    return dict(
-        input_ids=input_ids,
-        labels=targets,
-    )
-
-
-def preprocess_mpt(
-    sources,
-    tokenizer: transformers.PreTrainedTokenizer,
-) -> Dict:
-    conv = conversation_lib.default_conversation.copy()
-    roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
-
-    # Apply prompt templates
-    conversations = []
-    for i, source in enumerate(sources):
-        if roles[source[0]["from"]] != conv.roles[0]:
-            # Skip the first one if it is not from human
-            source = source[1:]
-
-        conv.messages = []
-        for j, sentence in enumerate(source):
-            role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], f"{i}"
-            conv.append_message(role, sentence["value"])
-        conversations.append(conv.get_prompt())
-
-    # Tokenize conversations
-    input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
-    targets = input_ids.clone()
-    assert conv.sep_style == conversation_lib.SeparatorStyle.MPT
-
-    # Mask targets
-    sep = conv.sep + conv.roles[1]
-    for conversation, target in zip(conversations, targets):
-        total_len = int(target.ne(tokenizer.pad_token_id).sum())
-
-        rounds = conversation.split(conv.sep)
-        re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt
-        for conv_idx in range(3, len(rounds), 2):
-            re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx+2]))    # user + gpt
-        cur_len = 0
-        target[:cur_len] = IGNORE_INDEX
-        for i, rou in enumerate(re_rounds):
-            if rou == "":
-                break
-
-            parts = rou.split(sep)
-            if len(parts) != 2:
-                break
-            parts[0] += sep
-            round_len = len(tokenizer_image_token(rou, tokenizer)) + len(tokenizer_image_token(conv.sep, tokenizer))
-            instruction_len = len(tokenizer_image_token(parts[0], tokenizer))
-            target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
-
-            cur_len += round_len
-        target[cur_len:] = IGNORE_INDEX
-
-        if cur_len < tokenizer.model_max_length:
-            if cur_len != total_len:
-                target[:] = IGNORE_INDEX
-                print(
-                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
-                    f" (ignored)"
-                )
-
-    return dict(
-        input_ids=input_ids,
-        labels=targets,
-    )
-
-
-def preprocess_plain(
-    sources: Sequence[str],
-    tokenizer: transformers.PreTrainedTokenizer,
-) -> Dict:
-    # add end signal and concatenate together
-    conversations = []
-    for source in sources:
-        assert len(source) == 2
-        assert DEFAULT_IMAGE_TOKEN in source[0]['value']
-        source[0]['value'] = DEFAULT_IMAGE_TOKEN
-        conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep
-        conversations.append(conversation)
-    # tokenize conversations
-    input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
-    targets = copy.deepcopy(input_ids)
-    for target, source in zip(targets, sources):
-        tokenized_len = len(tokenizer_image_token(source[0]['value'], tokenizer))
-        target[:tokenized_len] = IGNORE_INDEX
-
-    return dict(input_ids=input_ids, labels=targets)
-
-
-def preprocess(
-    sources: Sequence[str],
-    tokenizer: transformers.PreTrainedTokenizer,
-    has_image: bool = False
-) -> Dict:
-    """
-    Given a list of sources, each is a conversation list. This transform:
-    1. Add signal '### ' at the beginning each sentence, with end signal '\n';
-    2. Concatenate conversations together;
-    3. Tokenize the concatenated conversation;
-    4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
-    """
-    if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN:
-        return preprocess_plain(sources, tokenizer)
-    if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_2:
-        return preprocess_llama_2(sources, tokenizer, has_image=has_image)
-    if conversation_lib.default_conversation.version.startswith("v1"):
-        return preprocess_v1(sources, tokenizer, has_image=has_image)
-    if conversation_lib.default_conversation.version == "mpt":
-        return preprocess_mpt(sources, tokenizer)
-    # add end signal and concatenate together
-    conversations = []
-    for source in sources:
-        header = f"{conversation_lib.default_conversation.system}\n\n"
-        conversation = _add_speaker_and_signal(header, source)
-        conversations.append(conversation)
-    # tokenize conversations
-    def get_tokenize_len(prompts):
-        return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts]
-
-    if has_image:
-        input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
-    else:
-        conversations_tokenized = _tokenize_fn(conversations, tokenizer)
-        input_ids = conversations_tokenized["input_ids"]
-
-    targets = copy.deepcopy(input_ids)
-    for target, source in zip(targets, sources):
-        if has_image:
-            tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source])
-        else:
-            tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"]
-        speakers = [sentence["from"] for sentence in source]
-        _mask_targets(target, tokenized_lens, speakers)
-
-    return dict(input_ids=input_ids, labels=targets)
-
-
-class LazySupervisedDataset(Dataset):
-    """Dataset for supervised fine-tuning."""
-
-    def __init__(self, data_path: str,
-                 tokenizer: transformers.PreTrainedTokenizer,
-                 data_args: DataArguments):
-        super(LazySupervisedDataset, self).__init__()
-        list_data_dict = json.load(open(data_path, "r"))
-
-        rank0_print("Formatting inputs...Skip in lazy mode")
-        self.tokenizer = tokenizer
-        self.list_data_dict = list_data_dict
-        self.data_args = data_args
-
-    def __len__(self):
-        return len(self.list_data_dict)
-
-    @property
-    def lengths(self):
-        length_list = []
-        for sample in self.list_data_dict:
-            img_tokens = 128 if 'image' in sample else 0
-            length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens)
-        return length_list
-
-    @property
-    def modality_lengths(self):
-        length_list = []
-        for sample in self.list_data_dict:
-            cur_len = sum(len(conv['value'].split()) for conv in sample['conversations'])
-            cur_len = cur_len if 'image' in sample else -cur_len
-            length_list.append(cur_len)
-        return length_list
-
-    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
-        sources = self.list_data_dict[i]
-        if isinstance(i, int):
-            sources = [sources]
-        assert len(sources) == 1, "Don't know why it is wrapped to a list"  # FIXME
-        if 'image' in sources[0]:
-            image_file = self.list_data_dict[i]['image']
-            image_folder = self.data_args.image_folder
-            processor = self.data_args.image_processor
-            image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
-            if self.data_args.image_aspect_ratio == 'pad':
-                def expand2square(pil_img, background_color):
-                    width, height = pil_img.size
-                    if width == height:
-                        return pil_img
-                    elif width > height:
-                        result = Image.new(pil_img.mode, (width, width), background_color)
-                        result.paste(pil_img, (0, (width - height) // 2))
-                        return result
-                    else:
-                        result = Image.new(pil_img.mode, (height, height), background_color)
-                        result.paste(pil_img, ((height - width) // 2, 0))
-                        return result
-                image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
-                image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-            else:
-                image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-            sources = preprocess_multimodal(
-                copy.deepcopy([e["conversations"] for e in sources]),
-                self.data_args)
-        else:
-            sources = copy.deepcopy([e["conversations"] for e in sources])
-        data_dict = preprocess(
-            sources,
-            self.tokenizer,
-            has_image=('image' in self.list_data_dict[i]))
-        if isinstance(i, int):
-            data_dict = dict(input_ids=data_dict["input_ids"][0],
-                             labels=data_dict["labels"][0])
-
-        # image exist in the data
-        if 'image' in self.list_data_dict[i]:
-            data_dict['image'] = image
-        elif self.data_args.is_multimodal:
-            # image does not exist in the data, but the model is multimodal
-            crop_size = self.data_args.image_processor.crop_size
-            data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
-        return data_dict
-
-
-@dataclass
-class DataCollatorForSupervisedDataset(object):
-    """Collate examples for supervised fine-tuning."""
-
-    tokenizer: transformers.PreTrainedTokenizer
-
-    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
-        input_ids, labels = tuple([instance[key] for instance in instances]
-                                  for key in ("input_ids", "labels"))
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            input_ids,
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id)
-        labels = torch.nn.utils.rnn.pad_sequence(labels,
-                                                 batch_first=True,
-                                                 padding_value=IGNORE_INDEX)
-        input_ids = input_ids[:, :self.tokenizer.model_max_length]
-        labels = labels[:, :self.tokenizer.model_max_length]
-        batch = dict(
-            input_ids=input_ids,
-            labels=labels,
-            attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
-        )
-
-        if 'image' in instances[0]:
-            images = [instance['image'] for instance in instances]
-            if all(x is not None and x.shape == images[0].shape for x in images):
-                batch['images'] = torch.stack(images)
-            else:
-                batch['images'] = images
-
-        return batch
-
-
-def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
-                                data_args) -> Dict:
-    """Make dataset and collator for supervised fine-tuning."""
-    train_dataset = LazySupervisedDataset(tokenizer=tokenizer,
-                                data_path=data_args.data_path,
-                                data_args=data_args)
-    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
-    return dict(train_dataset=train_dataset,
-                eval_dataset=None,
-                data_collator=data_collator)
-
-
-def train():
-    global local_rank
-
-    parser = transformers.HfArgumentParser(
-        (ModelArguments, DataArguments, TrainingArguments))
-    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-    local_rank = training_args.local_rank
-    compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
-
-    bnb_model_from_pretrained_args = {}
-    if training_args.bits in [4, 8]:
-        from transformers import BitsAndBytesConfig
-        bnb_model_from_pretrained_args.update(dict(
-            device_map={"": training_args.device},
-            load_in_4bit=training_args.bits == 4,
-            load_in_8bit=training_args.bits == 8,
-            quantization_config=BitsAndBytesConfig(
-                load_in_4bit=training_args.bits == 4,
-                load_in_8bit=training_args.bits == 8,
-                llm_int8_threshold=6.0,
-                llm_int8_has_fp16_weight=False,
-                bnb_4bit_compute_dtype=compute_dtype,
-                bnb_4bit_use_double_quant=training_args.double_quant,
-                bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
-            )
-        ))
-
-    if model_args.vision_tower is not None:
-        if 'mpt' in model_args.model_name_or_path:
-            config = transformers.AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
-            config.attn_config['attn_impl'] = training_args.mpt_attn_impl
-            model = LlavaMPTForCausalLM.from_pretrained(
-                model_args.model_name_or_path,
-                config=config,
-                cache_dir=training_args.cache_dir,
-                **bnb_model_from_pretrained_args
-            )
-        else:
-            model = LlavaLlamaForCausalLM.from_pretrained(
-                model_args.model_name_or_path,
-                cache_dir=training_args.cache_dir,
-                **bnb_model_from_pretrained_args
-            )
-    else:
-        model = transformers.LlamaForCausalLM.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=training_args.cache_dir,
-            **bnb_model_from_pretrained_args
-        )
-    model.config.use_cache = False
-
-    if model_args.freeze_backbone:
-        model.model.requires_grad_(False)
-
-    if training_args.bits in [4, 8]:
-        from peft import prepare_model_for_kbit_training
-        model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
-        model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
-
-    if training_args.gradient_checkpointing:
-        if hasattr(model, "enable_input_require_grads"):
-            model.enable_input_require_grads()
-        else:
-            def make_inputs_require_grad(module, input, output):
-                output.requires_grad_(True)
-            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
-
-    if training_args.lora_enable:
-        from peft import LoraConfig, get_peft_model
-        lora_config = LoraConfig(
-            r=training_args.lora_r,
-            lora_alpha=training_args.lora_alpha,
-            target_modules=find_all_linear_names(model),
-            lora_dropout=training_args.lora_dropout,
-            bias=training_args.lora_bias,
-            task_type="CAUSAL_LM",
-        )
-        if training_args.bits == 16:
-            if training_args.bf16:
-                model.to(torch.bfloat16)
-            if training_args.fp16:
-                model.to(torch.float16)
-        rank0_print("Adding LoRA adapters...")
-        model = get_peft_model(model, lora_config)
-
-    if 'mpt' in model_args.model_name_or_path:
-        tokenizer = transformers.AutoTokenizer.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=training_args.cache_dir,
-            model_max_length=training_args.model_max_length,
-            padding_side="right"
-        )
-    else:
-        tokenizer = transformers.AutoTokenizer.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=training_args.cache_dir,
-            model_max_length=training_args.model_max_length,
-            padding_side="right",
-            use_fast=False,
-        )
-
-    if model_args.version == "v0":
-        if tokenizer.pad_token is None:
-            smart_tokenizer_and_embedding_resize(
-                special_tokens_dict=dict(pad_token="[PAD]"),
-                tokenizer=tokenizer,
-                model=model,
-            )
-    elif model_args.version == "v0.5":
-        tokenizer.pad_token = tokenizer.unk_token
-    else:
-        tokenizer.pad_token = tokenizer.unk_token
-        if model_args.version in conversation_lib.conv_templates:
-            conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version]
-        else:
-            conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"]
-
-    if model_args.vision_tower is not None:
-        model.get_model().initialize_vision_modules(
-            model_args=model_args,
-            fsdp=training_args.fsdp
-        )
-        
-        vision_tower = model.get_vision_tower()
-        vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
-
-        data_args.image_processor = vision_tower.image_processor
-        data_args.is_multimodal = True
-
-        model.config.image_aspect_ratio = data_args.image_aspect_ratio
-        model.config.image_grid_pinpoints = data_args.image_grid_pinpoints
-
-        model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
-        if model_args.tune_mm_mlp_adapter:
-            model.requires_grad_(False)
-            for p in model.get_model().mm_projector.parameters():
-                p.requires_grad = True
-
-        model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter
-        if training_args.freeze_mm_mlp_adapter:
-            for p in model.get_model().mm_projector.parameters():
-                p.requires_grad = False
-
-        if training_args.bits in [4, 8]:
-            model.get_model().mm_projector.to(dtype=compute_dtype, device=training_args.device)
-
-        model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = model_args.mm_use_im_start_end
-        training_args.use_im_start_end = model_args.mm_use_im_start_end
-        model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token
-        model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer)
-
-    if training_args.bits in [4, 8]:
-        from peft.tuners.lora import LoraLayer
-        for name, module in model.named_modules():
-            if isinstance(module, LoraLayer):
-                if training_args.bf16:
-                    module = module.to(torch.bfloat16)
-            if 'norm' in name:
-                module = module.to(torch.float32)
-            if 'lm_head' in name or 'embed_tokens' in name:
-                if hasattr(module, 'weight'):
-                    if training_args.bf16 and module.weight.dtype == torch.float32:
-                        module = module.to(torch.bfloat16)
-
-    data_module = make_supervised_data_module(tokenizer=tokenizer,
-                                              data_args=data_args)
-    trainer = LLaVATrainer(model=model,
-                    tokenizer=tokenizer,
-                    args=training_args,
-                    **data_module)
-
-    if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
-        trainer.train(resume_from_checkpoint=True)
-    else:
-        trainer.train()
-    trainer.save_state()
-
-    model.config.use_cache = True
-
-    if training_args.lora_enable:
-        state_dict = get_peft_state_maybe_zero_3(
-            model.named_parameters(), training_args.lora_bias
-        )
-        non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3(
-            model.named_parameters()
-        )
-        if training_args.local_rank == 0 or training_args.local_rank == -1:
-            model.config.save_pretrained(training_args.output_dir)
-            model.save_pretrained(training_args.output_dir, state_dict=state_dict)
-            torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
-    else:
-        safe_save_model_for_hf_trainer(trainer=trainer,
-                                       output_dir=training_args.output_dir)
-
-
-if __name__ == "__main__":
-    train()
diff --git a/spaces/ttt246/brain/Brain/src/router/api.py b/spaces/ttt246/brain/Brain/src/router/api.py
deleted file mode 100644
index ca729397359c9d3163bda3dbc32d14496959d73f..0000000000000000000000000000000000000000
--- a/spaces/ttt246/brain/Brain/src/router/api.py
+++ /dev/null
@@ -1,556 +0,0 @@
-import json
-
-from Brain.src.common.assembler import Assembler
-from Brain.src.common.brain_exception import BrainException
-from Brain.src.common.utils import ProgramType, DEFAULT_GPT_MODEL
-from Brain.src.firebase.firebase import (
-    firebase_admin_with_setting,
-    delete_data_from_realtime,
-)
-from Brain.src.model.image_model import ImageModel
-from Brain.src.model.requests.request_model import (
-    Notification,
-    UploadImage,
-    ImageRelatedness,
-    Feedback,
-    ChatRising,
-    SendSMS,
-    TrainContacts,
-    BasicReq,
-    ClientInfo,
-    get_client_info,
-    AutoTaskDelete,
-    GetContactsByIds,
-)
-from Brain.src.rising_plugin.risingplugin import (
-    getCompletion,
-    getTextFromImage,
-    query_image_ask,
-)
-from Brain.src.firebase.cloudmessage import CloudMessage
-from Brain.src.rising_plugin.image_embedding import embed_image_text, query_image_text
-
-from Brain.src.logs import logger
-from Brain.src.model.basic_model import BasicModel
-from Brain.src.model.feedback_model import FeedbackModel
-from Brain.src.service.BabyAGIService import BabyAGIService
-from Brain.src.service.auto_task_service import AutoTaskService
-from Brain.src.service.command_service import CommandService
-from Brain.src.service.contact_service import ContactsService
-from Brain.src.service.feedback_service import FeedbackService
-from Brain.src.service.llm.chat_service import ChatService
-from Brain.src.service.twilio_service import TwilioService
-
-from fastapi import APIRouter, Depends
-
-router = APIRouter()
-
-
-def construct_blueprint_api() -> APIRouter:
-    # Assembler
-    assembler = Assembler()
-
-    # Service
-
-    command_service = CommandService()
-
-    """@generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )
-    @generator.request_body(
-        {"message": "this is test message", "token": "test_token", "uuid": "test_uuid"}
-    )"""
-
-    @router.post("/sendNotification")
-    def send_notification(
-        data: Notification, client_info: ClientInfo = Depends(get_client_info)
-    ):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return ex.get_response_exp()
-
-        # cloud message
-        cloud_message = CloudMessage(firebase_app=firebase_app)
-
-        # parsing params
-        query = data.message
-        token = setting.token
-        uuid = setting.uuid
-        # check browser endpoint
-        # if client_info.is_browser():
-        # query = f"{query} in a web browser"
-        is_browser = client_info.is_browser()
-
-        result = getCompletion(
-            query=query,
-            setting=setting,
-            firebase_app=firebase_app,
-            is_browser=is_browser,
-        )
-
-        # check contact querying
-        try:
-            contacts_service = ContactsService(
-                firebase_app=firebase_app, setting=setting
-            )
-            if result["program"] == ProgramType.AUTO_TASK:
-                auto_task_service = AutoTaskService()
-                result["content"] = auto_task_service.ask_task_with_llm(
-                    query=query, firebase_app=firebase_app, setting=setting
-                )
-                return assembler.to_response(200, "", result)
-
-            if result["program"] == ProgramType.CONTACT:
-                # querying contacts to getting its expected results
-                contacts_results = contacts_service.query_contacts(
-                    uuid=uuid, search=result["content"]
-                )
-                result["content"] = str(contacts_results)
-            value = ""
-            if not client_info.is_browser():
-                notification = {"title": "alert", "content": json.dumps(result)}
-                state, value = cloud_message.send_message(notification, [token])
-
-            return assembler.to_response(200, value, result)
-        except Exception as e:
-            logger.error(
-                title="sendNotification", message="json parsing or get completion error"
-            )
-            if isinstance(e, BrainException):
-                return e.get_response_exp()
-
-    """@generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )
-    @generator.request_body(
-        {
-            "image_name": "this is test image path",
-            "token": "test_token",
-            "uuid": "test_uuid",
-            "status": "created | updated | deleted",
-        }
-    )"""
-
-    @router.post("/uploadImage")
-    def upload_image(data: UploadImage):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return ex.get_response_exp()
-        # cloud message
-        try:
-            cloud_message = CloudMessage(firebase_app=firebase_app)
-
-            image_model = ImageModel()
-            token = setting.token
-
-            image_model.image_name = data.image_name
-            image_model.uuid = setting.uuid
-            image_model.status = data.status
-
-            image_model.image_text = getTextFromImage(
-                filename=image_model.image_name, firebase_app=firebase_app
-            )
-
-            embed_result = embed_image_text(image=image_model, setting=setting)
-
-            notification = {"title": "alert", "content": embed_result}
-
-            state, value = cloud_message.send_message(notification, [token])
-            return assembler.to_response(200, value, image_model.to_json())
-        except BrainException as ex:
-            return ex.get_response_exp()
-
-    """@generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )
-    @generator.request_body(
-        {
-            "image_name": "this is test image path",
-            "message": "this is a test message",
-            "token": "test_token",
-            "uuid": "test_uuid",
-        }
-    )"""
-
-    @router.post("/image_relatedness")
-    def image_relatedness(data: ImageRelatedness):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return ex.get_response_exp()
-
-        # cloud message
-        cloud_message = CloudMessage(firebase_app=firebase_app)
-        # parsing params
-        image_name = data.image_name
-        message = data.message
-        token = setting.token
-        uuid = setting.uuid
-
-        image_content = getTextFromImage(filename=image_name, firebase_app=firebase_app)
-        # check message type
-        image_response = {}
-        try:
-            # check about asking image description with trained data
-            if query_image_ask(
-                image_content=image_content, message=message, setting=setting
-            ):
-                image_response["image_desc"] = image_content
-            else:
-                relatedness_data = query_image_text(image_content, message, setting)
-
-                image_response["image_name"] = relatedness_data
-        except ValueError as e:
-            print("image_relatedness parsing error for message chain data")
-            if isinstance(e, BrainException):
-                return e.get_response_exp()
-
-        notification = {"title": "alert", "content": json.dumps(image_response)}
-        state, value = cloud_message.send_message(notification, [token])
-
-        return assembler.to_response(
-            code=200,
-            message=value,
-            result={"program": "image", "content": image_response},
-        )
-
-    """@generator.request_body(
-        {
-            "token": "test_token",
-            "uuid": "test_uuid",
-            "prompt": {"image_name": "test_image", "message": "test_message"},
-            "completion": {"image_name": "test_image", "message": "test_message"},
-            "rating": 1,
-        }
-    )
-    @generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )"""
-
-    @router.post("/feedback")
-    def add_feedback(data: Feedback):
-        try:
-            # firebase admin init
-            setting, firebase_app = firebase_admin_with_setting(data)
-
-            # cloud message
-            cloud_message = CloudMessage(firebase_app=firebase_app)
-            feedback_service = FeedbackService(firebase_app=firebase_app)
-            token = setting.token
-            uuid = setting.uuid
-
-            # parsing feedback payload
-            prompt = BasicModel(
-                image_name=data.prompt.image_name, message=data.prompt.message
-            )
-            completion = BasicModel(
-                image_name=data.completion.image_name, message=data.completion.message
-            )
-            rating = data.rating
-            feedback = FeedbackModel(uuid, prompt, completion, rating)
-
-            # add the feedback
-            feedback_service.add(feedback)
-        except Exception as e:
-            if isinstance(e, BrainException):
-                return e.get_response_exp()
-            return assembler.to_response(400, "failed to add", "")
-        return assembler.to_response(200, "added successfully", "")
-
-    """@generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )"""
-
-    @router.post("/feedback/{search}/{rating}")
-    def get_feedback(search: str, rating: int, data: BasicReq):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return ex.get_response_exp()
-
-        # cloud message
-        cloud_message = CloudMessage(firebase_app=firebase_app)
-        feedback_service = FeedbackService(firebase_app=firebase_app)
-        result = feedback_service.get(search, rating)
-        return assembler.to_response(200, "added successfully", result)
-
-    """@generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )"""
-
-    @router.post("/commands")
-    def get_commands(data: BasicReq):
-        result = command_service.get()
-        return assembler.to_response(
-            200, "success", {"program": "help_command", "content": result}
-        )
-
-    """@generator.request_body(
-        {
-            "token": "test_token",
-            "uuid": "test_uuid",
-            "history": [{"role": "user", "content": "test_message"}],
-            "user_input": "user_message",
-            "model": "gpt-3.5-turbo",
-        }
-    )
-    @generator.response(
-        status_code=200,
-        schema={
-            "message": "message",
-            "result": {
-                "program": "agent",
-                "message": {"role": "assistant", "content": "content"},
-            },
-        },
-    )"""
-
-    @router.post("/chat_rising")
-    def message_agent(data: ChatRising):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return ex.get_response_exp()
-
-        # cloud message
-        cloud_message = CloudMessage(firebase_app=firebase_app)
-        try:
-            token = setting.token
-            uuid = setting.uuid
-            histories = assembler.to_array_message_model(data.history)  # json array
-            user_input = data.user_input
-            """init chat service with model"""
-            chat_service = ChatService(ai_name=uuid, llm_model=DEFAULT_GPT_MODEL)
-            # getting chat response from rising ai
-            assistant_reply = chat_service.chat_with_ai(
-                prompt="",
-                user_input=user_input,
-                full_message_history=histories,
-                permanent_memory=None,
-                setting=setting,
-            )
-        except Exception as e:
-            return assembler.to_response(
-                400, json.dumps(e.__getattribute__("error")), ""
-            )
-        return assembler.to_response(
-            200,
-            "added successfully",
-            {
-                "program": "agent",
-                "message": assistant_reply.get_one_message_item().to_json(),
-            },
-        )
-
-    """@generator.request_body(
-        {
-            "token": "test_token",
-            "uuid": "test_uuid",
-            "data": {
-                "_from": "+15005550006",
-                "to": "+12173748105",
-                "body": "All in the game, yo",
-            },
-        }
-    )
-    @generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )"""
-
-    @router.post("/send_sms")
-    def send_sms(data: SendSMS):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return assembler.to_response(ex.code, ex.message, "")
-        try:
-            token = setting.token
-            uuid = setting.uuid
-
-            # parsing feedback payload
-            sms_model = assembler.to_sms_model(data.data)
-            # send sms via twilio
-            twilio_service = TwilioService()
-            twilio_resp = twilio_service.send_sms(sms_model)
-        except Exception as e:
-            return assembler.to_response(400, "Failed to send sms", "")
-        return assembler.to_response(200, "Sent a sms successfully", twilio_resp.sid)
-
-    """@generator.request_body(
-        {
-            "token": "String",
-            "uuid": "String",
-            "contacts": [
-                {
-                    "contactId": "String",
-                    "displayName": "String",
-                    "phoneNumbers": ["String"],
-                    "status": "created | updated | deleted",
-                }
-            ],
-        }
-    )
-    @generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )"""
-
-    @router.post("/train/contacts")
-    def train_contacts(data: TrainContacts):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return assembler.to_response(ex.code, ex.message, "")
-        try:
-            token = setting.token
-            uuid = setting.uuid
-
-            # parsing contacts
-            contacts = []
-            for contact in data.contacts:
-                contacts.append(assembler.to_contact_model(contact))
-            # train contact
-            contacts_service = ContactsService(
-                firebase_app=firebase_app, setting=setting
-            )
-            contacts_service.train(uuid, contacts)
-        except Exception as e:
-            if isinstance(e, BrainException):
-                return e.get_response_exp()
-            return assembler.to_response(400, "Failed to train contacts", "")
-        return assembler.to_response(200, "Trained successfully", "")
-
-    """@generator.request_body(
-        {
-            "token": "String",
-            "uuid": "String",
-        }
-    )
-    @generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )"""
-
-    @router.post("/train/contacts/delete")
-    def delete_all_contacts(data: BasicReq):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return assembler.to_response(ex.code, ex.message, "")
-        try:
-            token = setting.token
-            uuid = setting.uuid
-
-            # parsing contacts
-            # train contact
-            contacts_service = ContactsService(
-                firebase_app=firebase_app, setting=setting
-            )
-            contacts_service.delete_all(uuid)
-        except Exception as e:
-            if isinstance(e, BrainException):
-                return e.get_response_exp()
-            return assembler.to_response(400, "Failed to delete contacts", "")
-        return assembler.to_response(
-            200, "Deleted all contacts from pinecone successfully", ""
-        )
-
-    """@generator.request_body(
-        {
-            "token": "String",
-            "uuid": "String",
-            "data": {
-                "reference_link": "test link",
-            },
-        }
-    )
-
-    @generator.response(
-        status_code=200, schema={"message": "message", "result": "test_result"}
-    )
-
-    """
-
-    @router.post("/auto_task/delete")
-    def delete_data(data: AutoTaskDelete):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return assembler.to_response(ex.code, ex.message, "")
-        try:
-            token = setting.token
-            uuid = setting.uuid
-
-            # parsing contacts
-            # train contact
-            delete_data_from_realtime(data.data.reference_link, firebase_app)
-        except Exception as e:
-            if isinstance(e, BrainException):
-                return e.get_response_exp()
-            return assembler.to_response(400, "Failed to delete data", "")
-        return assembler.to_response(
-            200, "Deleted data from real-time database of firebase", ""
-        )
-
-    @router.post("/auto_task/babyagi")
-    def autotask_babyagi(
-        data: Notification, client_info: ClientInfo = Depends(get_client_info)
-    ):
-        # firebase admin init
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return assembler.to_response(ex.code, ex.message, "")
-        try:
-            babyagi_service = BabyAGIService()
-            reference_link = babyagi_service.ask_task_with_llm(
-                query=data.message, firebase_app=firebase_app, setting=setting
-            )
-            return assembler.to_response(200, "", reference_link)
-        except Exception as e:
-            if isinstance(e, BrainException):
-                return e.get_response_exp()
-            return assembler.to_response(400, "Failed to handle it with BabyAGI", "")
-
-    """@generator.request_body(
-            {
-                "token": "String",
-                "uuid": "String",
-                "contactIds": [
-                    "String"
-                ]
-            }
-        )
-
-        @generator.response(
-            status_code=200, schema={"message": "message", "result": "test_result"}
-        )
-
-    """
-
-    @router.post("/contacts/get_by_ids")
-    def get_contacts_by_ids(data: GetContactsByIds):
-        try:
-            setting, firebase_app = firebase_admin_with_setting(data)
-        except BrainException as ex:
-            return ex.get_response_exp()
-
-        token: str = setting.token
-        uuid: str = setting.uuid
-
-        result = ContactsService(
-            firebase_app=firebase_app, setting=setting
-        ).get_contacts_by_ids(uuid=uuid, contactIds=data.contactIds)
-
-        return assembler.to_response(200, "Success to get contacts by uuid", result)
-
-    return router
diff --git a/spaces/uSerNameDDHL/bingo/next.config.js b/spaces/uSerNameDDHL/bingo/next.config.js
deleted file mode 100644
index 0e6ccd7fbc91d0459eaaff3e968ce0556789c605..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/next.config.js
+++ /dev/null
@@ -1,38 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  // output: 'export',
-  // assetPrefix: '.',
-  webpack: (config, { isServer }) => {
-    if (!isServer) {
-      config.resolve = {
-        ...config.resolve,
-        fallback: {
-          'bufferutil': false,
-          'utf-8-validate': false,
-          http: false,
-          https: false,
-          stream: false,
-          // fixes proxy-agent dependencies
-          net: false,
-          dns: false,
-          tls: false,
-          assert: false,
-          // fixes next-i18next dependencies
-          path: false,
-          fs: false,
-          // fixes mapbox dependencies
-          events: false,
-          // fixes sentry dependencies
-          process: false
-        }
-      };
-    }
-    config.module.exprContextCritical = false;
-
-    return config;
-  },
-}
-
-module.exports = (...args) => {
-  return nextConfig
-}
diff --git a/spaces/usbethFlerru/sovits-modelsV2/Cccam-Converter-V11-24.md b/spaces/usbethFlerru/sovits-modelsV2/Cccam-Converter-V11-24.md
deleted file mode 100644
index e3604b4de19bfd26f7cac980c4841ec437568ccf..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/Cccam-Converter-V11-24.md
+++ /dev/null
@@ -1,84 +0,0 @@
-## Cccam Converter V1.1 24
-
-
-
- 
-
- ![Cccam Converter V1.1 24](https://linuxsat-support.com/images/imported/2011/12/51.png)
-
- 
-
- 
-
-**Click Here >>> [https://lomasmavi.blogspot.com/?c=2txSZH](https://lomasmavi.blogspot.com/?c=2txSZH)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- Here is a possible title and article with html formatting for the keyword "Cccam Converter V1.1 24":  
-
-# How to Convert CCcam to Oscam with Cccam Converter V1.1 24
-
- 
-
-If you are looking for a way to convert your CCcam lines to Oscam format, you might be interested in Cccam Converter V1.1 24, a small tool that can do the job for you. Cccam Converter V1.1 24 is an online converter that can upload your CCcam.cfg file or paste your clines into a box and generate an oscam.server file that you can download or copy and paste into your oscam configuration.
-
- 
-
-Cccam Converter V1.1 24 is easy to use and fast. It can convert multiple lines at once and supports different formats such as \*.cfg, \*.txt, \*.list, \*.conf and \*.server. It can also convert Newcamd and CCcam in the same time. Cccam Converter V1.1 24 is compatible with all versions of Oscam and works with any device that supports Oscam.
-
- 
-
-To use Cccam Converter V1.1 24, you just need to follow these simple steps:
-
- 
-
-1. Go to the website of Cccam Converter V1.1 24[^1^].
-
-2. Upload your CCcam.cfg file or paste your clines into the box.
-
-3. Click the convert button to start the conversion process.
-
-4. Download the oscam.server file or copy the config and paste it in your oscam.server file.
-
-5. Restart your Oscam and enjoy your converted lines.
-
-
-
-Cccam Converter V1.1 24 is a handy tool for anyone who wants to switch from CCcam to Oscam without losing their lines or spending too much time on manual conversion. It is also a safe and secure tool that does not share, save or use your lines in any way[^2^]. You can try it for free and see how it works for you.
-
-Here is a possible continuation of the article:  
-
-If you are wondering why you should convert your CCcam lines to Oscam format, here are some of the benefits of using Oscam over CCcam:
-
-
-
-- Oscam is more stable and reliable than CCcam. It can handle more connections and avoid freezing or glitching issues.
-
-- Oscam is more secure and encrypted than CCcam. It can protect your lines from being hacked or stolen by others.
-
-- Oscam is more flexible and customizable than CCcam. It can support different protocols and features such as cache exchange, load balancing, anti-cascading, EMM handling and more.
-
-- Oscam is more compatible and updated than CCcam. It can work with newer cards and systems and receive regular updates from the developers.
-
-
-
-As you can see, Oscam has many advantages over CCcam and can improve your viewing experience. However, if you have already invested in CCcam lines and don't want to lose them, you can use Cccam Converter V1.1 24 to convert them to Oscam format and enjoy the best of both worlds.
-
-Here is a possible conclusion for the article:  
-
-In conclusion, Cccam Converter V1.1 24 is a useful and convenient tool that can help you convert your CCcam lines to Oscam format in a matter of seconds. It is easy to use, fast and secure. It can also convert multiple lines and formats at once and support all versions of Oscam and devices. If you want to switch from CCcam to Oscam without losing your lines or spending too much time on manual conversion, you should give Cccam Converter V1.1 24 a try and see how it works for you. You can visit their website and start your conversion today.
-
- dfd1c89656
-
- 
-
- 
-
diff --git a/spaces/usbethFlerru/sovits-modelsV2/Eng_docs.md b/spaces/usbethFlerru/sovits-modelsV2/Eng_docs.md
deleted file mode 100644
index 10770870d46914dbdacd59de71fab421f7663104..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/Eng_docs.md
+++ /dev/null
@@ -1,109 +0,0 @@
-# SoftVC VITS Singing Voice Conversion
-
-## Updates
-> According to incomplete statistics, it seems that training with multiple speakers may lead to **worsened leaking of voice timbre**. It is not recommended to train models with more than 5 speakers. The current suggestion is to try to train models with only a single speaker if you want to achieve a voice timbre that is more similar to the target.
-> Fixed the issue with unwanted staccato, improving audio quality by a decent amount.\
-> The 2.0 version has been moved to the 2.0 branch.\
-> Version 3.0 uses the code structure of FreeVC, which isn't compatible with older versions.\
-> Compared to [DiffSVC](https://github.com/prophesier/diff-svc) , diffsvc performs much better when the training data is of extremely high quality, but this repository may perform better on datasets with lower quality. Additionally, this repository is much faster in terms of inference speed compared to diffsvc.
-
-## Model Overview
-A singing voice coversion (SVC) model, using the SoftVC encoder to extract features from the input audio, sent into VITS along with the F0 to replace the original input to acheive a voice conversion effect. Additionally, changing the vocoder to [NSF HiFiGAN](https://github.com/openvpi/DiffSinger/tree/refactor/modules/nsf_hifigan) to fix the issue with unwanted staccato.
-
-## Notice
-+ The current branch is the 32kHz version, which requires less vram during inferencing, as well as faster inferencing speeds, and datasets for said branch take up less disk space. Thus the 32 kHz branch is recommended for use.
-+ If you want to train 48 kHz variant models, switch to the [main branch](https://github.com/innnky/so-vits-svc/tree/main).
-
-
-## Required models
-+ soft vc hubert：[hubert-soft-0d54a1f4.pt](https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt)
-  + Place under `hubert`.
-+ Pretrained models [G_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth) and [D_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth)
-  + Place under `logs/32k`.
-  + Pretrained models are required, because from experiments, training from scratch can be rather unpredictable to say the least, and training with a pretrained model can greatly improve training speeds.
-  + The pretrained model includes云灏, 即霜, 辉宇·星AI, 派蒙, and 绫地宁宁, covering the common ranges of both male and female voices, and so it can be seen as a rather universal pretrained model.
-  + The pretrained model exludes the `optimizer speaker_embedding` section, rendering it only usable for pretraining and incapable of inferencing with.
-```shell
-# For simple downloading.
-# hubert
-wget -P hubert/ https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt
-# G&D pretrained models
-wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth
-wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth
-
-```
-
-## Colab notebook script for dataset creation and training.
-[colab training notebook](https://colab.research.google.com/drive/1rCUOOVG7-XQlVZuWRAj5IpGrMM8t07pE?usp=sharing)
-
-## Dataset preparation
-All that is required is that the data be put under the `dataset_raw` folder in the structure format provided below.
-```shell
-dataset_raw
-├───speaker0
-│   ├───xxx1-xxx1.wav
-│   ├───...
-│   └───Lxx-0xx8.wav
-└───speaker1
-    ├───xx2-0xxx2.wav
-    ├───...
-    └───xxx7-xxx007.wav
-```
-
-## Data pre-processing.
-1. Resample to 32khz
-
-```shell
-python resample.py
- ```
-2. Automatically sort out training set, validation set, test set, and automatically generate configuration files.
-```shell
-python preprocess_flist_config.py
-# Notice.
-# The n_speakers value in the config will be set automatically according to the amount of speakers in the dataset.
-# To reserve space for additionally added speakers in the dataset, the n_speakers value will be be set to twice the actual amount.
-# If you want even more space for adding more data, you can edit the n_speakers value in the config after runing this step.
-# This can not be changed after training starts.
-```
-3. Generate hubert and F0 features/
-```shell
-python preprocess_hubert_f0.py
-```
-After running the step above, the `dataset` folder will contain all the pre-processed data, you can delete the `dataset_raw` folder after that.
-
-## Training.
-```shell
-python train.py -c configs/config.json -m 32k
-```
-
-## Inferencing.
-
-Use [inference_main.py](inference_main.py)
-+ Edit `model_path` to your newest checkpoint.
-+ Place the input audio under the `raw` folder.
-+ Change `clean_names` to the output file name.
-+ Use `trans` to edit the pitch shifting amount (semitones). 
-+ Change `spk_list` to the speaker name.
-
-## Onnx Exporting.
-### **When exporting Onnx, please make sure you re-clone the whole repository!!!**
-Use [onnx_export.py](onnx_export.py)
-+ Create a new folder called `checkpoints`.
-+ Create a project folder in `checkpoints` folder with the desired name for your project, let's use `myproject` as example. Folder structure looks like `./checkpoints/myproject`.
-+ Rename your model to `model.pth`, rename your config file to `config.json` then move them into `myproject` folder.
-+ Modify [onnx_export.py](onnx_export.py) where `path = "NyaruTaffy"`, change `NyaruTaffy` to your project name, here it will be `path = "myproject"`.
-+ Run [onnx_export.py](onnx_export.py)
-+ Once it finished, a `model.onnx` will be generated in `myproject` folder, that's the model you just exported.
-+ Notice: if you want to export a 48K model, please follow the instruction below or use `model_onnx_48k.py` directly.
-    + Open [model_onnx.py](model_onnx.py) and change `hps={"sampling_rate": 32000...}` to `hps={"sampling_rate": 48000}` in class `SynthesizerTrn`.
-    + Open [nvSTFT](/vdecoder/hifigan/nvSTFT.py) and replace all `32000` with `48000`
-    ### Onnx Model UI Support
-    + [MoeSS](https://github.com/NaruseMioShirakana/MoeSS)
-+ All training function and transformation are removed, only if they are all removed you are actually using Onnx.
-
-## Gradio (WebUI)
-Use [sovits_gradio.py](sovits_gradio.py) to run Gradio WebUI
-+ Create a new folder called `checkpoints`.
-+ Create a project folder in `checkpoints` folder with the desired name for your project, let's use `myproject` as example. Folder structure looks like `./checkpoints/myproject`.
-+ Rename your model to `model.pth`, rename your config file to `config.json` then move them into `myproject` folder.
-+ Run [sovits_gradio.py](sovits_gradio.py)
\ No newline at end of file
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py b/spaces/user238921933/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py
deleted file mode 100644
index 4ce82b1d4349b24192b1915d022ed4fda9f31e5c..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import os
-from modules import paths
-
-
-def preload(parser):
-    parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/upscaling.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/upscaling.py
deleted file mode 100644
index b5b841722b936135bf1f65e86e0c26d7084053b2..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/upscaling.py
+++ /dev/null
@@ -1,264 +0,0 @@
-import os
-import numpy as np
-import cv2
-from pathlib import Path
-from tqdm import tqdm
-from PIL import Image
-from modules.scripts_postprocessing import PostprocessedImage
-from modules import devices
-import shutil
-from queue import Queue, Empty
-import modules.scripts as scr
-from .frame_interpolation import clean_folder_name
-from .general_utils import duplicate_pngs_from_folder, checksum
-# TODO: move some funcs to this file?
-from .video_audio_utilities import get_quick_vid_info, vid2frames, ffmpeg_stitch_video, extract_number, media_file_has_audio
-from basicsr.utils.download_util import load_file_from_url
-from .rich import console
-import time
-import subprocess
-
-def process_upscale_vid_upload_logic(file, selected_tab, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, vid_file_name, keep_imgs, f_location, f_crf, f_preset):
-    print("Got a request to *upscale* an existing video.")
-
-    in_vid_fps, _, _ = get_quick_vid_info(file.name)
-    folder_name = clean_folder_name(Path(vid_file_name).stem)
-    outdir_no_tmp = os.path.join(os.getcwd(), 'outputs', 'frame-upscaling', folder_name)
-    i = 1
-    while os.path.exists(outdir_no_tmp):
-        outdir_no_tmp = os.path.join(os.getcwd(), 'outputs', 'frame-upscaling', folder_name + '_' + str(i))
-        i += 1
-
-    outdir = os.path.join(outdir_no_tmp, 'tmp_input_frames')
-    os.makedirs(outdir, exist_ok=True)
-    
-    vid2frames(video_path=file.name, video_in_frame_path=outdir, overwrite=True, extract_from_frame=0, extract_to_frame=-1, numeric_files_output=True, out_img_format='png')
-    
-    process_video_upscaling(selected_tab, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, orig_vid_fps=in_vid_fps, real_audio_track=file.name, raw_output_imgs_path=outdir, img_batch_id=None, ffmpeg_location=f_location, ffmpeg_crf=f_crf, ffmpeg_preset=f_preset, keep_upscale_imgs=keep_imgs, orig_vid_name=folder_name)
-
-def process_video_upscaling(resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, orig_vid_fps, real_audio_track, raw_output_imgs_path, img_batch_id, ffmpeg_location, ffmpeg_crf, ffmpeg_preset, keep_upscale_imgs, orig_vid_name):
-    devices.torch_gc()
-
-    print("Upscaling progress (it's OK if it finishes before 100%):")
-
-    upscaled_path = os.path.join(raw_output_imgs_path, 'upscaled_frames')
-    if orig_vid_name is not None: # upscaling a video (deforum or unrelated)
-        custom_upscale_path = "{}_{}".format(upscaled_path, orig_vid_name)
-    else: # upscaling after a deforum run:
-        custom_upscale_path = "{}_{}".format(upscaled_path, img_batch_id)
-    
-    temp_convert_raw_png_path = os.path.join(raw_output_imgs_path, "tmp_upscale_folder")
-    duplicate_pngs_from_folder(raw_output_imgs_path, temp_convert_raw_png_path, img_batch_id, orig_vid_name)
-
-    videogen = []
-    for f in os.listdir(temp_convert_raw_png_path):
-        # double check for old _depth_ files, not really needed probably but keeping it for now
-        if '_depth_' not in f:
-            videogen.append(f)
-            
-    videogen.sort(key= lambda x:int(x.split('.')[0]))
-    vid_out = None
-
-    if not os.path.exists(custom_upscale_path):
-        os.mkdir(custom_upscale_path)
-
-    # Upscaling is a slow and demanding operation, so we don't need as much parallelization here
-    for i in tqdm(range(len(videogen)), desc="Upscaling"):
-        lastframe = videogen[i]
-        img_path = os.path.join(temp_convert_raw_png_path, lastframe)
-        image = process_frame(resize_mode, Image.open(img_path).convert("RGB"), upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility)
-        filename = '{}/{:0>7d}.png'.format(custom_upscale_path, i)
-        image.save(filename)
-
-    shutil.rmtree(temp_convert_raw_png_path)
-    # stitch video from upscaled frames, and add audio if needed
-    try:
-        print (f"*Passing upsc frames to ffmpeg...*")
-        vid_out_path = stitch_video(img_batch_id, orig_vid_fps, custom_upscale_path, real_audio_track, ffmpeg_location, resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, ffmpeg_crf, ffmpeg_preset, keep_upscale_imgs, orig_vid_name)
-        # remove folder with raw (non-upscaled) vid input frames in case of input VID and not PNGs
-        if orig_vid_name is not None:
-            shutil.rmtree(raw_output_imgs_path)
-    except Exception as e:
-        print(f'Video stitching gone wrong. *Upscaled frames were saved to HD as backup!*. Actual error: {e}')
-
-    devices.torch_gc()
-
-def process_frame(resize_mode, image, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
-    pp = PostprocessedImage(image)
-    postproc = scr.scripts_postproc
-    upscaler_script = next(s for s in postproc.scripts if s.name == "Upscale")
-    upscaler_script.process(pp, resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility)
-    return pp.image
-
-def stitch_video(img_batch_id, fps, img_folder_path, audio_path, ffmpeg_location, resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, f_crf, f_preset, keep_imgs, orig_vid_name):        
-    parent_folder = os.path.dirname(img_folder_path)
-    grandparent_folder = os.path.dirname(parent_folder)
-    if orig_vid_name is not None:
-        mp4_path = os.path.join(grandparent_folder, str(orig_vid_name) +'_upscaled_' + (('by_' + str(upscaling_resize).replace('.', '-')) if resize_mode == 0 else f"to_{upscaling_resize_w}_{upscaling_resize_h}")) + f"_with_{extras_upscaler_1}" + (f"_then_{extras_upscaler_2}" if extras_upscaler_2_visibility > 0 else "")
-    else:
-        mp4_path = os.path.join(parent_folder, str(img_batch_id) +'_upscaled_' + (('by_' + str(upscaling_resize).replace('.', '-')) if resize_mode == 0 else f"to_{upscaling_resize_w}_{upscaling_resize_h}")) + f"_with_{extras_upscaler_1}_then_{extras_upscaler_2}"
-    
-    mp4_path = mp4_path + '.mp4'
-
-    t = os.path.join(img_folder_path, "%07d.png")
-    add_soundtrack = 'None'
-    if not audio_path is None:
-        add_soundtrack = 'File'
-        
-    exception_raised = False
-    try:
-        ffmpeg_stitch_video(ffmpeg_location=ffmpeg_location, fps=fps, outmp4_path=mp4_path, stitch_from_frame=0, stitch_to_frame=1000000, imgs_path=t, add_soundtrack=add_soundtrack, audio_path=audio_path, crf=f_crf, preset=f_preset)
-    except Exception as e:
-        exception_raised = True
-        print(f"An error occurred while stitching the video: {e}")
-
-    if not exception_raised and not keep_imgs:
-        shutil.rmtree(img_folder_path)
-
-    if (keep_imgs and orig_vid_name is not None) or (orig_vid_name is not None and exception_raised is True):
-        shutil.move(img_folder_path, grandparent_folder)
-
-    return mp4_path
-
-# NCNN Upscale section START
-def process_ncnn_upscale_vid_upload_logic(vid_path, in_vid_fps, in_vid_res, out_vid_res, models_path, upscale_model, upscale_factor, keep_imgs, f_location, f_crf, f_preset, current_user_os):
-    print(f"Got a request to *upscale* a video using {upscale_model} at {upscale_factor}")
-
-    folder_name = clean_folder_name(Path(vid_path.orig_name).stem)
-    outdir_no_tmp = os.path.join(os.getcwd(), 'outputs', 'frame-upscaling', folder_name)
-    i = 1
-    while os.path.exists(outdir_no_tmp):
-        outdir_no_tmp = os.path.join(os.getcwd(), 'outputs', 'frame-upscaling', folder_name + '_' + str(i))
-        i += 1
-
-    outdir = os.path.join(outdir_no_tmp, 'tmp_input_frames')
-    os.makedirs(outdir, exist_ok=True)
-    
-    vid2frames(video_path=vid_path.name, video_in_frame_path=outdir, overwrite=True, extract_from_frame=0, extract_to_frame=-1, numeric_files_output=True, out_img_format='png')
-    
-    process_ncnn_video_upscaling(vid_path, outdir, in_vid_fps, in_vid_res, out_vid_res, models_path, upscale_model, upscale_factor, keep_imgs, f_location, f_crf, f_preset, current_user_os)
-    
-def process_ncnn_video_upscaling(vid_path, outdir, in_vid_fps, in_vid_res, out_vid_res, models_path, upscale_model, upscale_factor, keep_imgs, f_location, f_crf, f_preset, current_user_os):
-    # get clean number from 'x2, x3' etc
-    clean_num_r_up_factor = extract_number(upscale_factor)
-    # set paths
-    realesrgan_ncnn_location = os.path.join(models_path, 'realesrgan_ncnn', 'realesrgan-ncnn-vulkan' + ('.exe' if current_user_os == 'Windows' else ''))
-    upscaled_folder_path = os.path.join(os.path.dirname(outdir), 'Upscaled_frames')
-    # create folder for upscaled imgs to live in. this folder will stay alive if keep_imgs=True, otherwise get deleted at the end
-    os.makedirs(upscaled_folder_path, exist_ok=True)
-    out_upscaled_mp4_path = os.path.join(os.path.dirname(outdir), f"{vid_path.orig_name}_Upscaled_{upscale_factor}.mp4")
-    # download upscaling model if needed
-    check_and_download_realesrgan_ncnn(models_path, current_user_os)
-    # set cmd command
-    cmd = [realesrgan_ncnn_location, '-i', outdir, '-o', upscaled_folder_path, '-s', str(clean_num_r_up_factor), '-n', upscale_model]
-    # msg to print - need it to hide that text later on (!)
-    msg_to_print = f"Upscaling raw PNGs using {upscale_model} at {upscale_factor}..."
-    # blink the msg in the cli until action is done
-    console.print(msg_to_print, style="blink yellow", end="") 
-    start_time = time.time()
-    # make call to ncnn upscaling executble
-    process = subprocess.run(cmd, capture_output=True, check=True, text=True)
-    print("\r" + " " * len(msg_to_print), end="", flush=True)
-    print(f"\r{msg_to_print}", flush=True)
-    print(f"\rUpscaling \033[0;32mdone\033[0m in {time.time() - start_time:.2f} seconds!", flush=True)
-    # set custom path for ffmpeg func below
-    upscaled_imgs_path_for_ffmpeg = os.path.join(upscaled_folder_path, "%05d.png")
-    add_soundtrack = 'None'
-    # don't pass add_soundtrack to ffmpeg if orig video doesn't contain any audio, so we won't get a message saying audio couldn't be added :)
-    if media_file_has_audio(vid_path.name, f_location):
-        add_soundtrack = 'File'
-    # stitch video from upscaled pngs 
-    ffmpeg_stitch_video(ffmpeg_location=f_location, fps=in_vid_fps, outmp4_path=out_upscaled_mp4_path, stitch_from_frame=0, stitch_to_frame=-1, imgs_path=upscaled_imgs_path_for_ffmpeg, add_soundtrack=add_soundtrack, audio_path=vid_path.name, crf=f_crf, preset=f_preset)
-    # delete the raw video pngs
-    shutil.rmtree(outdir)
-    # delete upscaled imgs if user requested
-    if not keep_imgs:
-        shutil.rmtree(upscaled_folder_path)
-        
-def check_and_download_realesrgan_ncnn(models_folder, current_user_os):
-    import zipfile
-    if current_user_os == 'Windows':
-        zip_file_name = 'realesrgan-ncnn-windows.zip'
-        executble_name = 'realesrgan-ncnn-vulkan.exe'
-        zip_checksum_value = '1d073f520a4a3f6438a500fea88407964da6d4a87489719bedfa7445b76c019fdd95a5c39576ca190d7ac22c906b33d5250a6f48cb7eda2b6af3e86ec5f09dfc'
-        download_url = 'https://github.com/hithereai/Real-ESRGAN/releases/download/real-esrgan-ncnn-windows/realesrgan-ncnn-windows.zip'
-    elif current_user_os == 'Linux':
-        zip_file_name = 'realesrgan-ncnn-linux.zip'
-        executble_name = 'realesrgan-ncnn-vulkan'
-        zip_checksum_value = 'df44c4e9a1ff66331079795f018a67fbad8ce37c4472929a56b5a38440cf96982d6e164a086b438c3d26d269025290dd6498bd50846bda8691521ecf8f0fafdf'
-        download_url = 'https://github.com/hithereai/Real-ESRGAN/releases/download/real-esrgan-ncnn-linux/realesrgan-ncnn-linux.zip'
-    elif current_user_os == 'Mac':
-        zip_file_name = 'realesrgan-ncnn-mac.zip'
-        executble_name = 'realesrgan-ncnn-vulkan'
-        zip_checksum_value = '65f09472025b55b18cf6ba64149ede8cded90c20e18d35a9edb1ab60715b383a6ffbf1be90d973fc2075cf99d4cc1411fbdc459411af5c904f544b8656111469'
-        download_url = 'https://github.com/hithereai/Real-ESRGAN/releases/download/real-esrgan-ncnn-mac/realesrgan-ncnn-mac.zip'
-    else: # who are you then?
-        raise Exception(f"No support for OS type: {current_user_os}")
-
-    # set paths
-    realesrgan_ncnn_folder = os.path.join(models_folder, 'realesrgan_ncnn')
-    realesrgan_exec_path = os.path.join(realesrgan_ncnn_folder, executble_name)
-    realesrgan_zip_path = os.path.join(realesrgan_ncnn_folder, zip_file_name)
-    # return if exec file already exist
-    if os.path.exists(realesrgan_exec_path):
-        return
-    try:
-        os.makedirs(realesrgan_ncnn_folder, exist_ok=True)
-        # download exec and model files from url
-        load_file_from_url(download_url, realesrgan_ncnn_folder)
-        # check downloaded zip's hash
-        with open(realesrgan_zip_path, 'rb') as f:
-            file_hash = checksum(realesrgan_zip_path)
-        # wrong hash, file is probably broken/ download interrupted 
-        if file_hash != zip_checksum_value:
-            raise Exception(f"Error while downloading {realesrgan_zip_path}. Please download from: {download_url}, and extract its contents into: {models_folder}/realesrgan_ncnn")
-        # hash ok, extract zip contents into our folder
-        with zipfile.ZipFile(realesrgan_zip_path, 'r') as zip_ref:
-            zip_ref.extractall(realesrgan_ncnn_folder)
-        # delete the zip file
-        os.remove(realesrgan_zip_path)
-        # chmod 755 the exec if we're in a linux machine, otherwise we'd get permission errors
-        if current_user_os in ('Linux', 'Mac'):
-            os.chmod(realesrgan_exec_path, 0o755)
-            # enable running the exec for mac users
-            if current_user_os == 'Mac':
-                os.system(f'xattr -d com.apple.quarantine "{realesrgan_exec_path}"')
-
-    except Exception as e:
-        raise Exception(f"Error while downloading {realesrgan_zip_path}. Please download from: {download_url}, and extract its contents into: {models_folder}/realesrgan_ncnn")
-
-def make_upscale_v2(upscale_factor, upscale_model, keep_imgs, imgs_raw_path, imgs_batch_id, deforum_models_path, current_user_os, ffmpeg_location, ffmpeg_crf, ffmpeg_preset, fps, stitch_from_frame, stitch_to_frame, audio_path, add_soundtrack):
-    # get clean number from 'x2, x3' etc
-    clean_num_r_up_factor = extract_number(upscale_factor)
-    # set paths
-    realesrgan_ncnn_location = os.path.join(deforum_models_path, 'realesrgan_ncnn', 'realesrgan-ncnn-vulkan' + ('.exe' if current_user_os == 'Windows' else ''))
-    upscaled_folder_path = os.path.join(imgs_raw_path, f"{imgs_batch_id}_upscaled")
-    temp_folder_to_keep_raw_ims = os.path.join(upscaled_folder_path, 'temp_raw_imgs_to_upscale')
-    out_upscaled_mp4_path = os.path.join(imgs_raw_path, f"{imgs_batch_id}_Upscaled_{upscale_factor}.mp4")
-    # download upscaling model if needed
-    check_and_download_realesrgan_ncnn(deforum_models_path, current_user_os)
-    # make a folder with only the imgs we need to duplicate so we can call the ncnn with the folder syntax (quicker!)
-    duplicate_pngs_from_folder(from_folder=imgs_raw_path, to_folder=temp_folder_to_keep_raw_ims, img_batch_id=imgs_batch_id, orig_vid_name='Dummy')
-    # set dynamic cmd command
-    cmd = [realesrgan_ncnn_location, '-i', temp_folder_to_keep_raw_ims, '-o', upscaled_folder_path, '-s', str(clean_num_r_up_factor), '-n', upscale_model]
-    # msg to print - need it to hide that text later on (!)
-    msg_to_print = f"Upscaling raw output PNGs using {upscale_model} at {upscale_factor}..."
-    # blink the msg in the cli until action is done
-    console.print(msg_to_print, style="blink yellow", end="") 
-    start_time = time.time()
-    # make call to ncnn upscaling executble
-    process = subprocess.run(cmd, capture_output=True, check=True, text=True, cwd=(os.path.join(deforum_models_path, 'realesrgan_ncnn') if current_user_os == 'Mac' else None))
-    print("\r" + " " * len(msg_to_print), end="", flush=True)
-    print(f"\r{msg_to_print}", flush=True)
-    print(f"\rUpscaling \033[0;32mdone\033[0m in {time.time() - start_time:.2f} seconds!", flush=True)
-    # set custom path for ffmpeg func below
-    upscaled_imgs_path_for_ffmpeg = os.path.join(upscaled_folder_path, f"{imgs_batch_id}_%05d.png")
-    # stitch video from upscaled pngs 
-    ffmpeg_stitch_video(ffmpeg_location=ffmpeg_location, fps=fps, outmp4_path=out_upscaled_mp4_path, stitch_from_frame=stitch_from_frame, stitch_to_frame=stitch_to_frame, imgs_path=upscaled_imgs_path_for_ffmpeg, add_soundtrack=add_soundtrack, audio_path=audio_path, crf=ffmpeg_crf, preset=ffmpeg_preset)
-
-    # delete the duplicated raw imgs
-    shutil.rmtree(temp_folder_to_keep_raw_ims)
-
-    if not keep_imgs:
-        shutil.rmtree(upscaled_folder_path)
-# NCNN Upscale section END
\ No newline at end of file
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/sd_samplers.py b/spaces/user238921933/stable-diffusion-webui/modules/sd_samplers.py
deleted file mode 100644
index 981702b85cbf5734bc42a3cbfeeedfc2db57b647..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/sd_samplers.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
-
-# imports for functions that previously were here and are used by other modules
-from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
-
-all_samplers = [
-    *sd_samplers_kdiffusion.samplers_data_k_diffusion,
-    *sd_samplers_compvis.samplers_data_compvis,
-]
-all_samplers_map = {x.name: x for x in all_samplers}
-
-samplers = []
-samplers_for_img2img = []
-samplers_map = {}
-
-
-def create_sampler(name, model):
-    if name is not None:
-        config = all_samplers_map.get(name, None)
-    else:
-        config = all_samplers[0]
-
-    assert config is not None, f'bad sampler name: {name}'
-
-    sampler = config.constructor(model)
-    sampler.config = config
-
-    return sampler
-
-
-def set_samplers():
-    global samplers, samplers_for_img2img
-
-    hidden = set(shared.opts.hide_samplers)
-    hidden_img2img = set(shared.opts.hide_samplers + ['PLMS'])
-
-    samplers = [x for x in all_samplers if x.name not in hidden]
-    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
-
-    samplers_map.clear()
-    for sampler in all_samplers:
-        samplers_map[sampler.name.lower()] = sampler.name
-        for alias in sampler.aliases:
-            samplers_map[alias.lower()] = sampler.name
-
-
-set_samplers()
diff --git a/spaces/videfikri/aicover/uvr5_pack/lib_v5/nets_123821KB.py b/spaces/videfikri/aicover/uvr5_pack/lib_v5/nets_123821KB.py
deleted file mode 100644
index ea6c45c968d66c75e577e8a0fcca9bf800eb4ed6..0000000000000000000000000000000000000000
--- a/spaces/videfikri/aicover/uvr5_pack/lib_v5/nets_123821KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from uvr5_pack.lib_v5 import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/vivek-a666/Health_Forecast/app.py b/spaces/vivek-a666/Health_Forecast/app.py
deleted file mode 100644
index c2e68fee0f9f7f9882776b738c595d690610ddb8..0000000000000000000000000000000000000000
--- a/spaces/vivek-a666/Health_Forecast/app.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import torch
-import torch.nn.functional as F
-import streamlit as st
-import pandas as pd
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import PorterStemmer
-import re
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import numpy as np
-import pickle
-import torch.nn as nn
-import openai
-import os
-
-openai.api_key = os.environ.get("openai_key")
-le = pickle.load(open(r"encoder (1).pkl", 'rb'))
-tokenizer = pickle.load(open(r"tokenizer (1).pkl", 'rb'))
-
-def clean_text(x):
-    pattern = r'[^a-zA-z0-9\s]'
-    text = re.sub(pattern, '', x)
-    return x
-
-def clean_numbers(x):
-    if bool(re.search(r'\d', x)):
-        x = re.sub('[0-9]{5,}', '#####', x)
-        x = re.sub('[0-9]{4}', '####', x)
-        x = re.sub('[0-9]{3}', '###', x)
-        x = re.sub('[0-9]{2}', '##', x)
-    return x
-
-contraction_dict = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not",  "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is",  "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will",  "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have","it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not","mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are",  "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would","y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have"}
-def _get_contractions(contraction_dict):
-    contraction_re = re.compile('(%s)' % '|'.join(contraction_dict.keys()))
-    return contraction_dict, contraction_re
-contractions, contractions_re = _get_contractions(contraction_dict)
-def replace_contractions(text):
-    def replace(match):
-        return contractions[match.group(0)]
-    return contractions_re.sub(replace, text)
-
-def condition_parser(x):
-    if x in target_conditions:
-        return x
-    else:
-        return "OTHER"
-
-maxlen = 750
-embed_size = 300
-max_features = 120000
-
-class CNN_Text(nn.Module):
-    
-    def __init__(self):
-        super(CNN_Text, self).__init__()
-        filter_sizes = [1,2,3,5]
-        num_filters = 36
-        n_classes = len(le.classes_)
-        self.embedding = nn.Embedding(max_features, embed_size)
-        self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
-        self.embedding.weight.requires_grad = False
-        self.convs1 = nn.ModuleList([nn.Conv2d(1, num_filters, (K, embed_size)) for K in filter_sizes])
-        self.dropout = nn.Dropout(0.1)
-        self.fc1 = nn.Linear(len(filter_sizes)*num_filters, n_classes)
-
-
-    def forward(self, x):
-        x = self.embedding(x)  
-        x = x.unsqueeze(1)  
-        x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1] 
-        x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]  
-        x = torch.cat(x, 1)
-        x = self.dropout(x)  
-        logit = self.fc1(x) 
-        return logit
-
-model = torch.load(r"textcnn_model (1)", map_location=torch.device('cpu'))
-        
-def predict_single(x):    
-    # lower the text
-    x = x.lower()
-    # Clean the text
-    x =  clean_text(x)
-    # Clean numbers
-    x =  clean_numbers(x)
-    # Clean Contractions
-    x = replace_contractions(x)
-    # tokenize
-    x = tokenizer.texts_to_sequences([x])
-    # pad
-    x = pad_sequences(x, maxlen=maxlen)
-    # create 
-    x = torch.tensor(x, dtype=torch.long).cpu()
-
-    pred = model(x).detach()
-    pred = F.softmax(pred).cpu().numpy()
-
-    pred = pred.argmax(axis=1)
-
-    pred = le.classes_[pred]
-    return pred[0]
-
-def generate_disease_description(disease):
-    # Create a conversation for OpenAI API
-    conversation = [
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": f"Tell me about {disease} in 4-5 lines."}
-    ]
-
-    # Use OpenAI API to generate a description for the disease
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=conversation,
-    )
-
-    # Extract the assistant's response
-    disease_description = response['choices'][0]['message']['content']
-    return disease_description
-
-def app():
-    st.title('Health Forecast')
-    st.write('Enter your symptoms below and the app will predict the possible medical condition:')
-    
-    # Get user input
-    symptoms = st.text_input('Symptoms')
-    
-    # Predict
-    if st.button('Predict'):
-        if not symptoms:
-            st.write('Please enter your symptoms')
-        else:
-            condition = predict_single(symptoms)
-            st.write('The predicted medical condition is:', condition)
-            
-            # Generate description for the predicted disease
-            disease_description = generate_disease_description(condition)
-            st.write('Description of the disease:', disease_description)
-
-if __name__ == '__main__':
-    app()
\ No newline at end of file
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/utils.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/utils.py
deleted file mode 100644
index c5befb8e56ece50b5fecfd007b26f8a29124c0bd..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/utils.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import random
-import sys
-import time
-import warnings
-from getpass import getuser
-from socket import gethostname
-
-import numpy as np
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-
-
-def get_host_info():
-    """Get hostname and username.
-
-    Return empty string if exception raised, e.g. ``getpass.getuser()`` will
-    lead to error in docker container
-    """
-    host = ''
-    try:
-        host = f'{getuser()}@{gethostname()}'
-    except Exception as e:
-        warnings.warn(f'Host or user not found: {str(e)}')
-    finally:
-        return host
-
-
-def get_time_str():
-    return time.strftime('%Y%m%d_%H%M%S', time.localtime())
-
-
-def obj_from_dict(info, parent=None, default_args=None):
-    """Initialize an object from dict.
-
-    The dict must contain the key "type", which indicates the object type, it
-    can be either a string or type, such as "list" or ``list``. Remaining
-    fields are treated as the arguments for constructing the object.
-
-    Args:
-        info (dict): Object types and arguments.
-        parent (:class:`module`): Module which may containing expected object
-            classes.
-        default_args (dict, optional): Default arguments for initializing the
-            object.
-
-    Returns:
-        any type: Object built from the dict.
-    """
-    assert isinstance(info, dict) and 'type' in info
-    assert isinstance(default_args, dict) or default_args is None
-    args = info.copy()
-    obj_type = args.pop('type')
-    if mmcv.is_str(obj_type):
-        if parent is not None:
-            obj_type = getattr(parent, obj_type)
-        else:
-            obj_type = sys.modules[obj_type]
-    elif not isinstance(obj_type, type):
-        raise TypeError('type must be a str or valid type, but '
-                        f'got {type(obj_type)}')
-    if default_args is not None:
-        for name, value in default_args.items():
-            args.setdefault(name, value)
-    return obj_type(**args)
-
-
-def set_random_seed(seed, deterministic=False, use_rank_shift=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-        rank_shift (bool): Whether to add rank number to the random seed to
-            have different random seed in different threads. Default: False.
-    """
-    if use_rank_shift:
-        rank, _ = mmcv.runner.get_dist_info()
-        seed += rank
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
diff --git a/spaces/welp234rt/rabiawerqayyum-autotrain-mental-health-analysis-752423172/README.md b/spaces/welp234rt/rabiawerqayyum-autotrain-mental-health-analysis-752423172/README.md
deleted file mode 100644
index 9c7213a7ba4514bdb12fcd5adc1309caf8467234..0000000000000000000000000000000000000000
--- a/spaces/welp234rt/rabiawerqayyum-autotrain-mental-health-analysis-752423172/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Rabiawerqayyum Autotrain Mental Health Analysis 752423172
-emoji: 👁
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/wenpeng/Sod_Inpaint/sod/Res.py b/spaces/wenpeng/Sod_Inpaint/sod/Res.py
deleted file mode 100644
index 9e8c9e4754ea75fa27d7344656ce52153d9dedc5..0000000000000000000000000000000000000000
--- a/spaces/wenpeng/Sod_Inpaint/sod/Res.py
+++ /dev/null
@@ -1,363 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
-           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
-           'wide_resnet50_2', 'wide_resnet101_2']
-
-
-model_urls = {
-    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
-    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
-    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
-    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
-    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
-    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
-    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
-    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
-    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=dilation, groups=groups, bias=False, dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
-                 base_width=64, dilation=1, norm_layer=None):
-        super(BasicBlock, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = norm_layer(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-    def initialize(self):
-        weight_init(self)
-
-
-class Bottleneck(nn.Module):
-    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
-    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
-    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
-    # This variant is also known as ResNet V1.5 and improves accuracy according to
-    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
-                 base_width=64, dilation=1, norm_layer=None):
-        super(Bottleneck, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        width = int(planes * (base_width / 64.)) * groups
-        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv1x1(inplanes, width)
-        self.bn1 = norm_layer(width)
-        self.conv2 = conv3x3(width, width, stride, groups, dilation)
-        self.bn2 = norm_layer(width)
-        self.conv3 = conv1x1(width, planes * self.expansion)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-    def initialize(self):
-        weight_init(self)
-
-def weight_init(module):
-    for n, m in module.named_children():
-      #  print('initialize: '+n)
-        if isinstance(m, nn.Conv2d):
-            nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
-            if m.bias is not None:
-                nn.init.zeros_(m.bias)
-        elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d)):
-            nn.init.ones_(m.weight)
-            if m.bias is not None:
-                nn.init.zeros_(m.bias)
-        elif isinstance(m, nn.Linear):
-            nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
-            if m.bias is not None:
-                nn.init.zeros_(m.bias)
-        elif isinstance(m, nn.Sequential):
-            weight_init(m)
-        elif isinstance(m, (nn.ReLU,nn.AdaptiveAvgPool2d,nn.Softmax,nn.MaxPool2d)):
-            pass
-        else:
-            m.initialize()
-
-
-class ResNet(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
-                 norm_layer=None):
-        super(ResNet, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        self._norm_layer = norm_layer
-
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            # each element in the tuple indicates if we should replace
-            # the 2x2 stride with a dilated convolution instead
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = norm_layer(self.inplanes)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        # Zero-initialize the last BN in each residual branch,
-        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
-        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, Bottleneck):
-                    nn.init.constant_(m.bn3.weight, 0)
-                elif isinstance(m, BasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        norm_layer = self._norm_layer
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                norm_layer(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
-                            self.base_width, previous_dilation, norm_layer))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=self.groups,
-                                base_width=self.base_width, dilation=self.dilation,
-                                norm_layer=norm_layer))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        out1 = F.relu(self.bn1(self.conv1(x)),inplace=True)
-        out1 = self.maxpool(out1)
-        out2 = self.layer1(out1)
-        out3 = self.layer2(out2)
-        out4 = self.layer3(out3)
-        out5 = self.layer4(out4)
-        return out2, out3, out4, out5
-    def initialize(self):
-        weight_init(self)
-
-
-def _resnet(arch, block, layers, pretrained, progress, **kwargs):
-    model = ResNet(block, layers, **kwargs)
-
-    return model
-
-
-def resnet18(pretrained=False, progress=True, **kwargs):
-    r"""ResNet-18 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
-                   **kwargs)
-
-
-def resnet34(pretrained=False, progress=True, **kwargs):
-    r"""ResNet-34 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet50(pretrained=False, progress=True, **kwargs):
-    r"""ResNet-50 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet101(pretrained=False, progress=True, **kwargs):
-    r"""ResNet-101 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet152(pretrained=False, progress=True, **kwargs):
-    r"""ResNet-152 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
-    r"""ResNeXt-50 32x4d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['groups'] = 32
-    kwargs['width_per_group'] = 4
-    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
-                   pretrained, progress, **kwargs)
-
-
-def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
-    r"""ResNeXt-101 32x8d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['groups'] = 32
-    kwargs['width_per_group'] = 8
-    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
-                   pretrained, progress, **kwargs)
-
-
-def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
-    r"""Wide ResNet-50-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
-
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
-    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['width_per_group'] = 64 * 2
-    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
-                   pretrained, progress, **kwargs)
-
-
-def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
-    r"""Wide ResNet-101-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
-
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
-    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['width_per_group'] = 64 * 2
-    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
-                   pretrained, progress, **kwargs)
diff --git a/spaces/wietsedv/xpos/app.py b/spaces/wietsedv/xpos/app.py
deleted file mode 100644
index f9514897468a58a9b354f5a1f2df77890011f2bd..0000000000000000000000000000000000000000
--- a/spaces/wietsedv/xpos/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import gradio as gr
-import gradio.inputs
-import gradio.outputs
-from transformers.pipelines import pipeline
-
-
-lang_names = ['Afrikaans', 'Ancient Greek', 'Arabic', 'Armenian', 'Basque', 'Belarusian', 'Bulgarian', 'Catalan', 'Chinese', 'Classical Chinese', 'Croatian', 'Czech', 'Danish', 'Dutch', 'English', 'Estonian', 'Faroese', 'Finnish', 'French', 'Galician', 'German', 'Gothic', 'Greek', 'Hebrew', 'Hindi', 'Hungarian', 'Icelandic', 'Indonesian', 'Irish', 'Italian', 'Japanese', 'Korean', 'Latin', 'Latvian', 'Lithuanian', 'Maltese', 'Marathi', 'Naija', 'North Sami', 'Norwegian', 'Old Church Slavonic', 'Old East Slavic', 'Old French', 'Persian', 'Polish', 'Portuguese', 'Romanian', 'Russian', 'Sanskrit', 'Scottish Gaelic', 'Serbian', 'Slovak', 'Slovenian', 'Spanish', 'Swedish', 'Tamil', 'Telugu', 'Turkish', 'Ukrainian', 'Urdu', 'Uyghur', 'Vietnamese', 'Welsh', 'Western Armenian', 'Wolof']
-
-lang_codes = ['af', 'grc', 'ar', 'hy', 'eu', 'be', 'bg', 'ca', 'zh', 'lzh', 'hr', 'cs', 'da', 'nl', 'en', 'et', 'fo', 'fi', 'fr', 'gl', 'de', 'got', 'el', 'he', 'hi', 'hu', 'is', 'id', 'ga', 'it', 'ja', 'ko', 'la', 'lv', 'lt', 'mt', 'mr', 'pcm', 'sme', 'no', 'cu', 'orv', 'fro', 'fa', 'pl', 'pt', 'ro', 'ru', 'sa', 'gd', 'sr', 'sk', 'sl', 'es', 'sv', 'ta', 'te', 'tr', 'uk', 'ur', 'ug', 'vi', 'cy', 'hyw', 'wo']
-
-model_ids = [
-    f"wietsedv/xlm-roberta-base-ft-udpos28-{code}" for code in lang_codes
-]
-
-def model_link(model_id):
-    return f"<a href='https://huggingface.co/{model_id}' target='_blank'>🤗 {model_id}</a>"
-
-article = "<table style='width:auto'>"
-article += "<thead><th>Source language</th><th>Model</th></thead><tbody>"
-article += "\n".join([f"<tr><td>{l}</td><td>{model_link(m)}</td></tr>" for l, m in zip(lang_names, model_ids)])
-article += "</tbody></table>"
-
-loaded_model_id = None
-pipe = None
-
-def tag(text, lang_index):
-    global loaded_model_id, pipe
-
-    model_id = model_ids[lang_index]
-    if pipe is None or model_id != loaded_model_id:
-        loaded_model_id = model_id
-        pipe = pipeline("token-classification", model_id, aggregation_strategy="first")
-
-    # Aggregate words:
-    # split on whitespace and PUNCT, but merge other subtokens (keep first tag)
-    out = []
-    for g in pipe(text):
-        if g["word"][0] == "▁" or g["entity"] == "PUNCT":
-            out.append((g["word"].lstrip("▁"), g["entity"]))
-        else:
-            out[-1] = (out[-1][0] + g["word"], out[-1][1])
-
-    return out, model_link(model_id)
-
-
-iface = gr.Interface(
-    fn=tag,
-    inputs=[
-        gradio.inputs.Textbox(label="Text", lines=3, placeholder="Enter a sentence here..."),
-        gradio.inputs.Dropdown(label="Source language", choices=lang_names, type="index"),
-    ],
-    outputs=[
-        gradio.outputs.HighlightedText(label="Output"),
-        gradio.outputs.HTML(label="Model"),
-    ],
-    title="Cross-lingual part-of-speech tagging",
-    description="Enter some text in any language and choose any of 65 source languages. The source language is the language for which XLM-RoBERTa is fine-tuned on Universal Dependencies v2.8 universal part-of-speech tagging data. This space is meant to demonstrate cross-lingual transfer, so the language of your sentence and the selected language do not have to match. You may find fewer mistakes if the selected language is similar to the actual language of your text.",
-    allow_screenshot=False,
-    allow_flagging="never",
-    article=article,
-    theme="huggingface",
-    # examples=[["Dit is een test.", "English"]]
-)
-iface.launch()
diff --git a/spaces/wilsonbritten/inference-client-test/app.py b/spaces/wilsonbritten/inference-client-test/app.py
deleted file mode 100644
index 60e8171914b949c86e9151ef8137b905cc1937ba..0000000000000000000000000000000000000000
--- a/spaces/wilsonbritten/inference-client-test/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-import tempfile
-import random
-import math
-from PIL import Image, ImageOps
-import logging
-
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-
-
-client = InferenceClient(model="timbrooks/instruct-pix2pix")
-# client = InferenceClient()
-def model_call(prompt, input_image):
-    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=True) as temp_file:
-        seed = random.randint(0, 100000)
-        text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2)
-        image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2)
-
-        width, height = input_image.size
-        factor = 512 / max(width, height)
-        factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
-        width = int((width * factor) // 64) * 64
-        height = int((height * factor) // 64) * 64
-        image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
-        image.save(temp_file.name)
-        print('test log here')
-        logger.info('logger test')
-        return client.image_to_image(temp_file.name, prompt=prompt)#, guidance_scale =7.5, num_inference_steps=50)
-
-iface = gr.Interface(
-    fn=model_call,
-     inputs=[gr.inputs.Textbox(label="How to transform this image?"), gr.inputs.Image(type="pil")],
-      outputs=gr.outputs.Image(type="pil")
-).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/wouaf/WOUAF-Text-to-Image/torch_utils/ops/bias_act.py b/spaces/wouaf/WOUAF-Text-to-Image/torch_utils/ops/bias_act.py
deleted file mode 100644
index 4bcb409a89ccf6c6f6ecfca5962683df2d280b1f..0000000000000000000000000000000000000000
--- a/spaces/wouaf/WOUAF-Text-to-Image/torch_utils/ops/bias_act.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom PyTorch ops for efficient bias and activation."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import dnnlib
-import traceback
-
-from .. import custom_ops
-from .. import misc
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
-    'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
-    'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
-    'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
-    'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
-    'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
-    'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
-    'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
-    'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
-    'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
-}
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-_null_tensor = torch.empty([0])
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        _inited = True
-        sources = ['bias_act.cpp', 'bias_act.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-#----------------------------------------------------------------------------
-
-def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
-    r"""Fused bias and activation function.
-
-    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
-    and scales the result by `gain`. Each of the steps is optional. In most cases,
-    the fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports first and second order gradients,
-    but not third order gradients.
-
-    Args:
-        x:      Input activation tensor. Can be of any shape.
-        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
-                as `x`. The shape must be known, and it must match the dimension of `x`
-                corresponding to `dim`.
-        dim:    The dimension in `x` corresponding to the elements of `b`.
-                The value of `dim` is ignored if `b` is not specified.
-        act:    Name of the activation function to evaluate, or `"linear"` to disable.
-                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
-                See `activation_funcs` for a full list. `None` is not allowed.
-        alpha:  Shape parameter for the activation function, or `None` to use the default.
-        gain:   Scaling factor for the output tensor, or `None` to use default.
-                See `activation_funcs` for the default scaling of each activation function.
-                If unsure, consider specifying 1.
-        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
-                the clamping (default).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
-    return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Add bias.
-    if b is not None:
-        assert isinstance(b, torch.Tensor) and b.ndim == 1
-        assert 0 <= dim < x.ndim
-        assert b.shape[0] == x.shape[dim]
-        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
-
-    # Evaluate activation function.
-    alpha = float(alpha)
-    x = spec.func(x, alpha=alpha)
-
-    # Scale by gain.
-    gain = float(gain)
-    if gain != 1:
-        x = x * gain
-
-    # Clamp.
-    if clamp >= 0:
-        x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
-    return x
-
-#----------------------------------------------------------------------------
-
-_bias_act_cuda_cache = dict()
-
-def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Fast CUDA implementation of `bias_act()` using custom ops.
-    """
-    # Parse arguments.
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Lookup from cache.
-    key = (dim, act, alpha, gain, clamp)
-    if key in _bias_act_cuda_cache:
-        return _bias_act_cuda_cache[key]
-
-    # Forward op.
-    class BiasActCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, b): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format
-            x = x.contiguous(memory_format=ctx.memory_format)
-            b = b.contiguous() if b is not None else _null_tensor
-            y = x
-            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
-                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                y if 'y' in spec.ref else _null_tensor)
-            return y
-
-        @staticmethod
-        def backward(ctx, dy): # pylint: disable=arguments-differ
-            dy = dy.contiguous(memory_format=ctx.memory_format)
-            x, b, y = ctx.saved_tensors
-            dx = None
-            db = None
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-                dx = dy
-                if act != 'linear' or gain != 1 or clamp >= 0:
-                    dx = BiasActCudaGrad.apply(dy, x, b, y)
-
-            if ctx.needs_input_grad[1]:
-                db = dx.sum([i for i in range(dx.ndim) if i != dim])
-
-            return dx, db
-
-    # Backward op.
-    class BiasActCudaGrad(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format
-            dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                dy if spec.has_2nd_grad else _null_tensor,
-                x, b, y)
-            return dx
-
-        @staticmethod
-        def backward(ctx, d_dx): # pylint: disable=arguments-differ
-            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
-            dy, x, b, y = ctx.saved_tensors
-            d_dy = None
-            d_x = None
-            d_b = None
-            d_y = None
-
-            if ctx.needs_input_grad[0]:
-                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
-
-            if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
-                d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
-
-            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
-                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
-
-            return d_dy, d_x, d_b, d_y
-
-    # Add to cache.
-    _bias_act_cuda_cache[key] = BiasActCuda
-    return BiasActCuda
-
-#----------------------------------------------------------------------------
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/utils/evaluation.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/utils/evaluation.py
deleted file mode 100644
index 100179407181933d59809b25400d115cfa789867..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/utils/evaluation.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import numpy as np
-import copy
-import motmetrics as mm
-mm.lap.default_solver = 'lap'
-from utils.io import read_results, unzip_objs
-
-
-class Evaluator(object):
-
-    def __init__(self, data_root, seq_name, data_type):
-        self.data_root = data_root
-        self.seq_name = seq_name
-        self.data_type = data_type
-
-        self.load_annotations()
-        self.reset_accumulator()
-
-    def load_annotations(self):
-        assert self.data_type == 'mot'
-
-        gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
-        self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
-        self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
-
-    def reset_accumulator(self):
-        self.acc = mm.MOTAccumulator(auto_id=True)
-
-    def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
-        # results
-        trk_tlwhs = np.copy(trk_tlwhs)
-        trk_ids = np.copy(trk_ids)
-
-        # gts
-        gt_objs = self.gt_frame_dict.get(frame_id, [])
-        gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
-
-        # ignore boxes
-        ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
-        ignore_tlwhs = unzip_objs(ignore_objs)[0]
-
-
-        # remove ignored results
-        keep = np.ones(len(trk_tlwhs), dtype=bool)
-        iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
-        if len(iou_distance) > 0:
-            match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
-            match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
-            match_ious = iou_distance[match_is, match_js]
-
-            match_js = np.asarray(match_js, dtype=int)
-            match_js = match_js[np.logical_not(np.isnan(match_ious))]
-            keep[match_js] = False
-            trk_tlwhs = trk_tlwhs[keep]
-            trk_ids = trk_ids[keep]
-
-        # get distance matrix
-        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
-
-        # acc
-        self.acc.update(gt_ids, trk_ids, iou_distance)
-
-        if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
-            events = self.acc.last_mot_events  # only supported by https://github.com/longcw/py-motmetrics
-        else:
-            events = None
-        return events
-
-    def eval_file(self, filename):
-        self.reset_accumulator()
-
-        result_frame_dict = read_results(filename, self.data_type, is_gt=False)
-        frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
-        for frame_id in frames:
-            trk_objs = result_frame_dict.get(frame_id, [])
-            trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
-            self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
-
-        return self.acc
-
-    @staticmethod
-    def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
-        names = copy.deepcopy(names)
-        if metrics is None:
-            metrics = mm.metrics.motchallenge_metrics
-        metrics = copy.deepcopy(metrics)
-
-        mh = mm.metrics.create()
-        summary = mh.compute_many(
-            accs,
-            metrics=metrics,
-            names=names,
-            generate_overall=True
-        )
-
-        return summary
-
-    @staticmethod
-    def save_summary(summary, filename):
-        import pandas as pd
-        writer = pd.ExcelWriter(filename)
-        summary.to_excel(writer)
-        writer.save()
diff --git a/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/tests/test_numeric_batchnorm.py b/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/tests/test_numeric_batchnorm.py
deleted file mode 100644
index 63661389782806ea2182c049448df5d05fc6d2f1..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/tests/test_numeric_batchnorm.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_numeric_batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-
-import unittest
-
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-from sync_batchnorm.unittest import TorchTestCase
-
-
-def handy_var(a, unbias=True):
-    n = a.size(0)
-    asum = a.sum(dim=0)
-    as_sum = (a ** 2).sum(dim=0)  # a square sum
-    sumvar = as_sum - asum * asum / n
-    if unbias:
-        return sumvar / (n - 1)
-    else:
-        return sumvar / n
-
-
-class NumericTestCase(TorchTestCase):
-    def testNumericBatchNorm(self):
-        a = torch.rand(16, 10)
-        bn = nn.BatchNorm1d(10, momentum=1, eps=1e-5, affine=False)
-        bn.train()
-
-        a_var1 = Variable(a, requires_grad=True)
-        b_var1 = bn(a_var1)
-        loss1 = b_var1.sum()
-        loss1.backward()
-
-        a_var2 = Variable(a, requires_grad=True)
-        a_mean2 = a_var2.mean(dim=0, keepdim=True)
-        a_std2 = torch.sqrt(handy_var(a_var2, unbias=False).clamp(min=1e-5))
-        # a_std2 = torch.sqrt(a_var2.var(dim=0, keepdim=True, unbiased=False) + 1e-5)
-        b_var2 = (a_var2 - a_mean2) / a_std2
-        loss2 = b_var2.sum()
-        loss2.backward()
-
-        self.assertTensorClose(bn.running_mean, a.mean(dim=0))
-        self.assertTensorClose(bn.running_var, handy_var(a))
-        self.assertTensorClose(a_var1.data, a_var2.data)
-        self.assertTensorClose(b_var1.data, b_var2.data)
-        self.assertTensorClose(a_var1.grad, a_var2.grad)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/yiwangshangxian/anime-remove-background/app.py b/spaces/yiwangshangxian/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/yiwangshangxian/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encodec/configuration_encodec.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encodec/configuration_encodec.py
deleted file mode 100644
index e75711d9264e00430f9a5da78a1fa9e77bd4c250..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encodec/configuration_encodec.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Meta Platforms, Inc. and affiliates, and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" EnCodec model configuration"""
-
-
-import math
-from typing import Optional
-
-import numpy as np
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
-    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
-}
-
-
-class EncodecConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of an [`EncodecModel`]. It is used to instantiate a
-    Encodec model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the
-    [facebook/encodec_24khz](https://huggingface.co/facebook/encodec_24khz) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        target_bandwidths (`List[float]`, *optional*, defaults to `[1.5, 3.0, 6.0, 12.0, 24.0]`):
-            The range of diffent bandwiths the model can encode audio with.
-        sampling_rate (`int`, *optional*, defaults to 24000):
-            The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
-        audio_channels (`int`, *optional*, defaults to 1):
-            Number of channels in the audio data. Either 1 for mono or 2 for stereo.
-        normalize (`bool`, *optional*, defaults to `False`):
-            Whether the audio shall be normalized when passed.
-        chunk_length_s (`float`, *optional*):
-            If defined the audio is pre-processed into chunks of lengths `chunk_length_s` and then encoded.
-        overlap (`float`, *optional*):
-            Defines the overlap between each chunk. It is used to compute the `chunk_stride` using the following
-            formulae : `int((1.0 - self.overlap) * self.chunk_length)`.
-        hidden_size (`int`, *optional*, defaults to 128):
-            Intermediate representation dimension.
-        num_filters (`int`, *optional*, defaults to 32):
-            Number of convolution kernels of first `EncodecConv1d` down sampling layer.
-        num_residual_layers (`int`,  *optional*, defaults to 1):
-            Number of residual layers.
-        upsampling_ratios (`Sequence[int]` , *optional*, defaults to `[8, 5, 4, 2]`):
-            Kernel size and stride ratios. The encoder uses downsampling ratios instead of upsampling ratios, hence it
-            will use the ratios in the reverse order to the ones specified here that must match the decoder order.
-        norm_type (`str`, *optional*, defaults to `"weight_norm"`):
-            Normalization method. Should be in `["weight_norm", "time_group_norm"]`
-        kernel_size (`int`, *optional*, defaults to 7):
-            Kernel size for the initial convolution.
-        last_kernel_size (`int`, *optional*, defaults to 7):
-            Kernel size for the last convolution layer.
-        residual_kernel_size (`int`, *optional*, defaults to 3):
-            Kernel size for the residual layers.
-        dilation_growth_rate (`int`, *optional*, defaults to 2):
-            How much to increase the dilation with each layer.
-        use_causal_conv (`bool`, *optional*, defaults to `True`):
-            Whether to use fully causal convolution.
-        pad_mode (`str`, *optional*, defaults to `"reflect"`):
-            Padding mode for the convolutions.
-        compress (`int`, *optional*, defaults to 2):
-            Reduced dimensionality in residual branches (from Demucs v3).
-        num_lstm_layers (`int`, *optional*, defaults to 2):
-            Number of LSTM layers at the end of the encoder.
-        trim_right_ratio (`float`, *optional*, defaults to 1.0):
-            Ratio for trimming at the right of the transposed convolution under the `use_causal_conv = True` setup. If
-            equal to 1.0, it means that all the trimming is done at the right.
-        codebook_size (`int`, *optional*, defaults to 1024):
-            Number of discret codes that make up VQVAE.
-        codebook_dim (`int`, *optional*):
-            Dimension of the codebook vectors. If not defined, uses `hidden_size`.
-        use_conv_shortcut (`bool`, *optional*, defaults to `True`):
-            Whether to use a convolutional layer as the 'skip' connection in the `EncodecResnetBlock` block. If False,
-            an identity function will be used, giving a generic residual connection.
-
-    Example:
-
-    ```python
-    >>> from transformers import EncodecModel, EncodecConfig
-
-    >>> # Initializing a "facebook/encodec_24khz" style configuration
-    >>> configuration = EncodecConfig()
-
-    >>> # Initializing a model (with random weights) from the "facebook/encodec_24khz" style configuration
-    >>> model = EncodecModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "encodec"
-
-    def __init__(
-        self,
-        target_bandwidths=[1.5, 3.0, 6.0, 12.0, 24.0],
-        sampling_rate=24_000,
-        audio_channels=1,
-        normalize=False,
-        chunk_length_s=None,
-        overlap=None,
-        hidden_size=128,
-        num_filters=32,
-        num_residual_layers=1,
-        upsampling_ratios=[8, 5, 4, 2],
-        norm_type="weight_norm",
-        kernel_size=7,
-        last_kernel_size=7,
-        residual_kernel_size=3,
-        dilation_growth_rate=2,
-        use_causal_conv=True,
-        pad_mode="reflect",
-        compress=2,
-        num_lstm_layers=2,
-        trim_right_ratio=1.0,
-        codebook_size=1024,
-        codebook_dim=None,
-        use_conv_shortcut=True,
-        **kwargs,
-    ):
-        self.target_bandwidths = target_bandwidths
-        self.sampling_rate = sampling_rate
-        self.audio_channels = audio_channels
-        self.normalize = normalize
-        self.chunk_length_s = chunk_length_s
-        self.overlap = overlap
-        self.hidden_size = hidden_size
-        self.num_filters = num_filters
-        self.num_residual_layers = num_residual_layers
-        self.upsampling_ratios = upsampling_ratios
-        self.norm_type = norm_type
-        self.kernel_size = kernel_size
-        self.last_kernel_size = last_kernel_size
-        self.residual_kernel_size = residual_kernel_size
-        self.dilation_growth_rate = dilation_growth_rate
-        self.use_causal_conv = use_causal_conv
-        self.pad_mode = pad_mode
-        self.compress = compress
-        self.num_lstm_layers = num_lstm_layers
-        self.trim_right_ratio = trim_right_ratio
-        self.codebook_size = codebook_size
-        self.codebook_dim = codebook_dim if codebook_dim is not None else hidden_size
-        self.use_conv_shortcut = use_conv_shortcut
-
-        if self.norm_type not in ["weight_norm", "time_group_norm"]:
-            raise ValueError(
-                f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'
-            )
-
-        super().__init__(**kwargs)
-
-    # This is a property because you might want to change the chunk_length_s on the fly
-    @property
-    def chunk_length(self) -> Optional[int]:
-        if self.chunk_length_s is None:
-            return None
-        else:
-            return int(self.chunk_length_s * self.sampling_rate)
-
-    # This is a property because you might want to change the chunk_length_s on the fly
-    @property
-    def chunk_stride(self) -> Optional[int]:
-        if self.chunk_length_s is None or self.overlap is None:
-            return None
-        else:
-            return max(1, int((1.0 - self.overlap) * self.chunk_length))
-
-    @property
-    def frame_rate(self) -> int:
-        hop_length = np.prod(self.upsampling_ratios)
-        return math.ceil(self.sampling_rate / hop_length)
-
-    @property
-    def num_quantizers(self) -> int:
-        return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encoder_decoder/modeling_tf_encoder_decoder.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encoder_decoder/modeling_tf_encoder_decoder.py
deleted file mode 100644
index 19fc47546b0f758bb21e57166a5b9399a7da6215..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encoder_decoder/modeling_tf_encoder_decoder.py
+++ /dev/null
@@ -1,663 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Classes to support TF Encoder-Decoder architectures"""
-
-
-from __future__ import annotations
-
-import inspect
-import re
-import warnings
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import tensorflow as tf
-
-from ...configuration_utils import PretrainedConfig
-from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput
-from ...modeling_tf_utils import (
-    TFCausalLanguageModelingLoss,
-    TFModelInputType,
-    TFPreTrainedModel,
-    get_initializer,
-    unpack_inputs,
-)
-from ...tf_utils import shape_list
-from ...utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from ..auto.configuration_auto import AutoConfig
-from ..auto.modeling_tf_auto import TFAutoModel, TFAutoModelForCausalLM
-from .configuration_encoder_decoder import EncoderDecoderConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "EncoderDecoderConfig"
-
-DEPRECATION_WARNING = (
-    "Version v4.17.0 introduces a better way to train encoder-decoder models by computing the loss inside the"
-    " encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if"
-    " fine-tuning a model trained with versions anterior to 4.17.0. The decoder_input_ids are now created based on the"
-    " labels, no need to pass them yourself anymore."
-)
-
-ENCODER_DECODER_START_DOCSTRING = r"""
-    This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the
-    encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via
-    [`~TFAutoModel.from_pretrained`] function and the decoder is loaded via [`~TFAutoModelForCausalLM.from_pretrained`]
-    function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
-    generative task, like summarization.
-
-    The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
-    tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
-    Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
-    Zhou, Wei Li, Peter J. Liu.
-
-    After such an Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models
-    (see the examples for more information).
-
-    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
-    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`EncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-ENCODER_DECODER_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        decoder_input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Indices of decoder input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-
-            Provide for sequence to sequence training to the decoder. Indices can be obtained using
-            [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
-            details.
-        decoder_attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
-            be used by default.
-        encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*):
-            This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` (`tf.Tensor` of shape `({0}, hidden_size)`) is a tensor of hidden-states at the output
-            of the last layer of the encoder. Used in the cross-attention of the decoder.
-        past_key_values (`tuple(tuple(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `({0})`.
-        inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        decoder_inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
-            representation. This is useful if you want more control over how to convert `decoder_input_ids` indices
-            into associated vectors than the model's internal embedding lookup matrix.
-        labels (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
-            Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0,
-            ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
-        training (`bool`, *optional*, defaults to `False`):
-            Whether or not to use the model in training mode (some modules like dropout modules have different
-            behaviors between training and evaluation).
-        kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
-
-            - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
-            - With a *decoder_* prefix which will be input as `**decoder_kwargs`` for the decoder forward function.
-"""
-
-
-def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
-    if pad_token_id is None:
-        raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
-    pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
-
-    if decoder_start_token_id is None:
-        raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
-    decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
-
-    start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id)
-    shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
-    # replace possible -100 values in labels by `pad_token_id`
-    shifted_input_ids = tf.where(
-        shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids
-    )
-
-    # "Verify that `labels` has only positive values and -100"
-    assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
-
-    # Make sure the assertion op is called by wrapping the result in an identity no-op
-    with tf.control_dependencies([assert_gte0]):
-        shifted_input_ids = tf.identity(shifted_input_ids)
-
-    return shifted_input_ids
-
-
-@add_start_docstrings(ENCODER_DECODER_START_DOCSTRING)
-class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
-    r"""
-    [`TFEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with one
-    of the base model classes of the library as encoder and another one as decoder when created with the
-    [`~TFAutoModel.from_pretrained`] class method for the encoder and [`~TFAutoModelForCausalLM.from_pretrained`] class
-    method for the decoder.
-    """
-    config_class = EncoderDecoderConfig
-    base_model_prefix = "encoder_decoder"
-    load_weight_prefix = "tf_encoder_decoder_model"
-
-    def __init__(
-        self,
-        config: Optional[PretrainedConfig] = None,
-        encoder: Optional[TFPreTrainedModel] = None,
-        decoder: Optional[TFPreTrainedModel] = None,
-    ):
-        if config is None and (encoder is None or decoder is None):
-            raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
-        if config is None:
-            config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
-        else:
-            if not isinstance(config, self.config_class):
-                raise ValueError(f"config: {config} has to be of type {self.config_class}")
-
-        if config.decoder.cross_attention_hidden_size is not None:
-            if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
-                raise ValueError(
-                    "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
-                    f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
-                    f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
-                    " `config.encoder.hidden_size`."
-                )
-
-        # initialize with config
-        super().__init__(config)
-
-        if encoder is None:
-            encoder = TFAutoModel.from_config(config.encoder, name="encoder")
-
-        if decoder is None:
-            decoder = TFAutoModelForCausalLM.from_config(config.decoder, name="decoder")
-
-        self.encoder = encoder
-        self.decoder = decoder
-
-        if self.encoder.config.to_dict() != self.config.encoder.to_dict():
-            logger.warning(
-                f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:"
-                f" {self.config.encoder}"
-            )
-        if self.decoder.config.to_dict() != self.config.decoder.to_dict():
-            logger.warning(
-                f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
-                f" {self.config.decoder}"
-            )
-
-        # make sure that the individual model's config refers to the shared config
-        # so that the updates to the config will be synced
-        self.encoder.config = self.config.encoder
-        self.decoder.config = self.config.decoder
-
-        # encoder outputs might need to be projected to different dimension for decoder
-        if (
-            self.encoder.config.hidden_size != self.decoder.config.hidden_size
-            and self.decoder.config.cross_attention_hidden_size is None
-        ):
-            self.enc_to_dec_proj = tf.keras.layers.Dense(
-                units=self.decoder.config.hidden_size,
-                kernel_initializer=get_initializer(config.encoder.initializer_range),
-                name="enc_to_dec_proj",
-            )
-
-        if self.encoder.get_output_embeddings() is not None:
-            raise ValueError(
-                f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
-            )
-
-        decoder_signature = set(inspect.signature(self.decoder.call).parameters.keys())
-        if "encoder_hidden_states" not in decoder_signature:
-            raise ValueError(
-                "The selected decoder is not prepared for the encoder hidden states to be passed. Please see the "
-                "following discussion on GitHub: https://github.com/huggingface/transformers/issues/23350"
-            )
-
-    def get_encoder(self):
-        return self.encoder
-
-    def get_decoder(self):
-        return self.decoder
-
-    def get_input_embeddings(self):
-        return self.encoder.get_input_embeddings()
-
-    def get_output_embeddings(self):
-        return self.decoder.get_output_embeddings()
-
-    def set_output_embeddings(self, new_embeddings):
-        return self.decoder.set_output_embeddings(new_embeddings)
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        r"""
-        Example:
-
-        ```python
-        >>> from transformers import TFEncoderDecoderModel
-
-        >>> model = TFEncoderDecoderModel.from_pretrained("ydshieh/bert2bert-cnn_dailymail-fp16")
-        ```"""
-        # Matt: The TF and PT weights don't align because our TF base classes have an extra layer compared to PT models
-        # (the main model stem is in the MainLayer class). If we remove that layer, then weight names sync up as normal.
-        # However, the name of that extra layer is the name of the MainLayer in the base model. We make the assumption
-        # here that the config model_type is the same as the name of the MainLayer. I don't know of anywhere that's
-        # not the case, and I wasn't sure how else to go from the config to the correct MainLayer name!
-
-        if kwargs.get("from_pt", False):
-            config = AutoConfig.from_pretrained(pretrained_model_name_or_path)
-            encoder_model_type = config.encoder.model_type
-
-            def tf_to_pt_weight_rename(tf_weight):
-                if "encoder" in tf_weight and "decoder" not in tf_weight:
-                    return re.sub(rf"encoder\.{encoder_model_type}\.", "encoder.", tf_weight)
-                else:
-                    return tf_weight
-
-            kwargs["tf_to_pt_weight_rename"] = tf_to_pt_weight_rename
-        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-
-    @classmethod
-    def from_encoder_decoder_pretrained(
-        cls,
-        encoder_pretrained_model_name_or_path: str = None,
-        decoder_pretrained_model_name_or_path: str = None,
-        *model_args,
-        **kwargs,
-    ) -> TFPreTrainedModel:
-        r"""
-        Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
-        checkpoints.
-
-
-        Params:
-            encoder_pretrained_model_name_or_path (`str`, *optional*):
-                Information necessary to initiate the encoder. Can be either:
-
-                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
-                      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
-                      user or organization name, like `dbmdz/bert-base-german-cased`.
-                    - A path to a *directory* containing model weights saved using
-                      [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
-                    - A path or url to a *pytorch index checkpoint file* (e.g, `./pt_model/`). In this case,
-                      `encoder_from_pt` should be set to `True`.
-
-            decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
-                Information necessary to initiate the decoder. Can be either:
-
-                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
-                      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
-                      user or organization name, like `dbmdz/bert-base-german-cased`.
-                    - A path to a *directory* containing model weights saved using
-                      [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
-                    - A path or url to a *pytorch checkpoint file* (e.g, `./pt_model/`). In this case,
-                      `decoder_from_pt` should be set to `True`.
-
-            model_args (remaining positional arguments, *optional*):
-                All remaning positional arguments will be passed to the underlying model's `__init__` method.
-
-            kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
-                `output_attentions=True`).
-
-                - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
-                - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
-                - To update the parent model configuration, do not use a prefix for each configuration parameter.
-
-                Behaves differently depending on whether a `config` is provided or automatically loaded.
-
-        Example:
-
-        ```python
-        >>> from transformers import TFEncoderDecoderModel
-
-        >>> # initialize a bert2gpt2 from two pretrained BERT models. Note that the cross-attention layers will be randomly initialized
-        >>> model = TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "gpt2")
-        >>> # saving model after fine-tuning
-        >>> model.save_pretrained("./bert2gpt2")
-        >>> # load fine-tuned model
-        >>> model = TFEncoderDecoderModel.from_pretrained("./bert2gpt2")
-        ```"""
-
-        kwargs_encoder = {
-            argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
-        }
-
-        kwargs_decoder = {
-            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
-        }
-
-        # remove encoder, decoder kwargs from kwargs
-        for key in kwargs_encoder.keys():
-            del kwargs["encoder_" + key]
-        for key in kwargs_decoder.keys():
-            del kwargs["decoder_" + key]
-
-        # Load and initialize the encoder and decoder
-        # The distinction between encoder and decoder at the model level is made
-        # by the value of the flag `is_decoder` that we need to set correctly.
-        encoder = kwargs_encoder.pop("model", None)
-        if encoder is None:
-            if encoder_pretrained_model_name_or_path is None:
-                raise ValueError(
-                    "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
-                    "to be defined."
-                )
-
-            if "config" not in kwargs_encoder:
-                encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path)
-                if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
-                    logger.info(
-                        f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
-                        "from a decoder model. Cross-attention and casual mask are disabled."
-                    )
-                    encoder_config.is_decoder = False
-                    encoder_config.add_cross_attention = False
-
-                kwargs_encoder["config"] = encoder_config
-
-            kwargs_encoder["name"] = "encoder"
-            kwargs_encoder["load_weight_prefix"] = cls.load_weight_prefix
-            encoder = TFAutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
-
-        decoder = kwargs_decoder.pop("model", None)
-        if decoder is None:
-            if decoder_pretrained_model_name_or_path is None:
-                raise ValueError(
-                    "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
-                    "to be defined."
-                )
-
-            if "config" not in kwargs_decoder:
-                decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path)
-                if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
-                    logger.info(
-                        f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
-                        f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
-                        f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
-                    )
-                    decoder_config.is_decoder = True
-                    decoder_config.add_cross_attention = True
-
-                kwargs_decoder["config"] = decoder_config
-
-            if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
-                logger.warning(
-                    f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
-                    f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
-                    "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
-                    "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
-                    "`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
-                )
-
-            kwargs_decoder["name"] = "decoder"
-            kwargs_decoder["load_weight_prefix"] = cls.load_weight_prefix
-            decoder = TFAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
-
-        # Make sure these 2 `tf.keras.Model` have fixed names so `from_pretrained` could load model weights correctly.
-        if encoder.name != "encoder":
-            raise ValueError("encoder model must be created with the name `encoder`.")
-        if decoder.name != "decoder":
-            raise ValueError("decoder model must be created with the name `decoder`.")
-
-        # instantiate config with corresponding kwargs
-        config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
-        return cls(encoder=encoder, decoder=decoder, config=config)
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ENCODER_DECODER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        decoder_input_ids: np.ndarray | tf.Tensor | None = None,
-        decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
-        encoder_outputs: np.ndarray | tf.Tensor | None = None,
-        past_key_values: Tuple[Tuple[tf.Tensor]] | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        labels: np.ndarray | tf.Tensor | None = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        training: bool = False,
-        **kwargs,
-    ) -> Union[TFSeq2SeqLMOutput, Tuple[tf.Tensor]]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import TFEncoderDecoderModel, BertTokenizer
-
-        >>> # initialize a bert2gpt2 from a pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized
-        >>> model = TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-cased", "gpt2")
-
-        >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-
-        >>> # forward
-        >>> input_ids = tokenizer.encode(
-        ...     "Hello, my dog is cute", add_special_tokens=True, return_tensors="tf"
-        ... )  # Batch size 1
-        >>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
-
-        >>> # training
-        >>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=input_ids)
-        >>> loss, logits = outputs.loss, outputs.logits
-
-        >>> # save and load from pretrained
-        >>> model.save_pretrained("bert2gpt2")
-        >>> model = TFEncoderDecoderModel.from_pretrained("bert2gpt2")
-
-        >>> # generation
-        >>> generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.bos_token_id)
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
-
-        kwargs_decoder = {
-            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
-        }
-
-        # Let the user be responsible for the expected format.
-        if encoder_outputs is not None:
-            if return_dict and not isinstance(encoder_outputs, ModelOutput):
-                raise ValueError(
-                    "If `return_dict=True` and `encoder_outputs` is provided, it should be an instance of "
-                    f"`ModelOutput`. Got an instance {type(encoder_outputs)} for `encoder_outputs`."
-                )
-
-        if encoder_outputs is None:
-            encoder_inputs = {
-                "input_ids": input_ids,
-                "attention_mask": attention_mask,
-                "inputs_embeds": inputs_embeds,
-                "output_attentions": output_attentions,
-                "output_hidden_states": output_hidden_states,
-                "return_dict": return_dict,
-                "training": training,
-            }
-
-            # Add arguments to encoder from `kwargs_encoder`
-            encoder_inputs.update(kwargs_encoder)
-
-            # Handle the case where the inputs are passed as a single dict which contains `labels`.
-            # The `labels` shouldn't be passed to `self.encoder` below, because it is a based model without this
-            # parameter (otherwise, an error occurs when `input_processing` is called inside `self.encoder.call()`).
-            if "labels" in encoder_inputs:
-                labels = encoder_inputs.pop("labels")
-
-            # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`.
-            if "decoder_input_ids" in encoder_inputs:
-                decoder_input_ids = encoder_inputs.pop("decoder_input_ids")
-            # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`.
-            if "decoder_attention_mask" in encoder_inputs:
-                decoder_attention_mask = encoder_inputs.pop("decoder_attention_mask")
-
-            encoder_outputs = self.encoder(**encoder_inputs)
-
-        encoder_hidden_states = encoder_outputs[0]
-
-        # optionally project encoder_hidden_states
-        if (
-            self.encoder.config.hidden_size != self.decoder.config.hidden_size
-            and self.decoder.config.cross_attention_hidden_size is None
-        ):
-            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
-
-        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
-            decoder_input_ids = shift_tokens_right(
-                labels, self.config.pad_token_id, self.config.decoder_start_token_id
-            )
-
-        decoder_inputs = {
-            "input_ids": decoder_input_ids,
-            "attention_mask": decoder_attention_mask,
-            "encoder_hidden_states": encoder_hidden_states,
-            "encoder_attention_mask": attention_mask,
-            "inputs_embeds": decoder_inputs_embeds,
-            "output_attentions": output_attentions,
-            "output_hidden_states": output_hidden_states,
-            "use_cache": use_cache,
-            "past_key_values": past_key_values,
-            "return_dict": return_dict,
-            "training": training,
-        }
-
-        # Add arguments to decoder from `kwargs_decoder`
-        decoder_inputs.update(kwargs_decoder)
-
-        decoder_outputs = self.decoder(**decoder_inputs)
-
-        logits = decoder_outputs[0]
-
-        # Compute loss independent from decoder (as some shift the logits inside them)
-        loss = None
-        if labels is not None:
-            warnings.warn(DEPRECATION_WARNING, FutureWarning)
-            loss = self.hf_compute_loss(labels, logits)
-
-        if not return_dict:
-            past_key_values = None
-            if use_cache:
-                past_key_values = decoder_outputs[1]
-            # The starting index of the remaining elements in `decoder_outputs`
-            start_index = sum([1 if x is not None else 0 for x in (loss, logits, past_key_values)])
-
-            if not isinstance(encoder_outputs, tuple):
-                encoder_outputs = encoder_outputs.to_tuple()
-            output = (loss, logits, past_key_values) + decoder_outputs[start_index:] + encoder_outputs
-            output = tuple([x for x in output if x is not None])
-            return output
-
-        return TFSeq2SeqLMOutput(
-            loss=loss,
-            logits=decoder_outputs.logits,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
-    ):
-        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
-        decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
-        past_key_values = decoder_inputs.get("past_key_values")
-        if past_key_values is None:
-            past_key_values = decoder_inputs.get("past")  # e.g. on TF GPT2
-        input_dict = {
-            "input_ids": None,  # needs to be passed to make Keras.layer.__call__ happy
-            "attention_mask": attention_mask,
-            "decoder_attention_mask": decoder_attention_mask,
-            "decoder_input_ids": decoder_inputs["input_ids"],
-            # TODO (joao): the `TFBaseModelOutput` wrapper should not be needed after the generate refactor is complete
-            "encoder_outputs": TFBaseModelOutput(last_hidden_state=encoder_outputs[0]),
-            "past_key_values": past_key_values,
-            "use_cache": use_cache,
-        }
-        return input_dict
-
-    def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
-        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
-
-    def resize_token_embeddings(self, *args, **kwargs):
-        raise NotImplementedError(
-            "Resizing the embedding layers via the TFEncoderDecoderModel directly is not supported.Please use the"
-            " respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or"
-            " model.decoder.resize_token_embeddings(...))"
-        )
-
-    def _reorder_cache(self, past, beam_idx):
-        # apply decoder cache reordering here
-        return self.decoder._reorder_cache(past, beam_idx)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/herbert/tokenization_herbert_fast.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/herbert/tokenization_herbert_fast.py
deleted file mode 100644
index 67e38c1c5ee7bd9d0cfbff7750ae592555c94335..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/herbert/tokenization_herbert_fast.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List, Optional, Tuple
-
-from ...tokenization_utils_fast import PreTrainedTokenizerFast
-from ...utils import logging
-from .tokenization_herbert import HerbertTokenizer
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
-    },
-    "merges_file": {
-        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"allegro/herbert-base-cased": 514}
-PRETRAINED_INIT_CONFIGURATION = {}
-
-
-class HerbertTokenizerFast(PreTrainedTokenizerFast):
-    """
-    Construct a "Fast" BPE tokenizer for HerBERT (backed by HuggingFace's *tokenizers* library).
-
-    Peculiarities:
-
-    - uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
-      a punctuation character will be treated separately.
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the methods. Users should refer to the
-    superclass for more information regarding methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    slow_tokenizer_class = HerbertTokenizer
-
-    def __init__(
-        self,
-        vocab_file=None,
-        merges_file=None,
-        tokenizer_file=None,
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        sep_token="</s>",
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_file,
-            merges_file,
-            tokenizer_file=tokenizer_file,
-            cls_token=cls_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            sep_token=sep_token,
-            **kwargs,
-        )
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. An HerBERT, like BERT sequence has the following format:
-
-        - single sequence: `<s> X </s>`
-        - pair of sequences: `<s> A </s> B </s>`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-
-        cls = [self.cls_token_id]
-        sep = [self.sep_token_id]
-        if token_ids_1 is None:
-            return cls + token_ids_0 + sep
-
-        return cls + token_ids_0 + sep + token_ids_1 + sep
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. HerBERT, like
-        BERT sequence pair mask has the following format:
-
-        ```
-        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
-        | first sequence    | second sequence |
-        ```
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
-        """
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
-        return tuple(files)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mgp_str/tokenization_mgp_str.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mgp_str/tokenization_mgp_str.py
deleted file mode 100644
index 7fe11061154093407ea1c7fdb17f66638a68f3f1..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mgp_str/tokenization_mgp_str.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes for MGT-STR CHAR."""
-
-import json
-import os
-from typing import Optional, Tuple
-
-from ...tokenization_utils import PreTrainedTokenizer
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "vocab.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
-    }
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"mgp-str": 27}
-
-
-class MgpstrTokenizer(PreTrainedTokenizer):
-    """
-    Construct a MGP-STR char tokenizer.
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        unk_token (`str`, *optional*, defaults to `"[GO]"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str`, *optional*, defaults to `"[GO]"`):
-            The beginning of sequence token.
-        eos_token (`str`, *optional*, defaults to `"[s]"`):
-            The end of sequence token.
-        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"[GO]"`):
-            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
-            attention mechanisms or loss computation.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-
-    def __init__(self, vocab_file, unk_token="[GO]", bos_token="[GO]", eos_token="[s]", pad_token="[GO]", **kwargs):
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.vocab = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.vocab.items()}
-        super().__init__(
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            **kwargs,
-        )
-
-    @property
-    def vocab_size(self):
-        return len(self.vocab)
-
-    def get_vocab(self):
-        vocab = dict(self.vocab).copy()
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def _tokenize(self, text):
-        """Tokenize a string."""
-        char_tokens = []
-        for s in text:
-            char_tokens.extend(s)
-        return char_tokens
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.vocab.get(token, self.vocab.get(self.unk_token))
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
-            return
-        vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        return (vocab_file,)
diff --git a/spaces/ykilcher/apes/torch_utils/ops/__init__.py b/spaces/ykilcher/apes/torch_utils/ops/__init__.py
deleted file mode 100644
index ece0ea08fe2e939cc260a1dafc0ab5b391b773d9..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/torch_utils/ops/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty
diff --git a/spaces/yongjae/whisper-webui/src/vad.py b/spaces/yongjae/whisper-webui/src/vad.py
deleted file mode 100644
index 4d11c28eb32953f3829ff4f8a4e4030e22a22140..0000000000000000000000000000000000000000
--- a/spaces/yongjae/whisper-webui/src/vad.py
+++ /dev/null
@@ -1,477 +0,0 @@
-from abc import ABC, abstractmethod
-from collections import Counter, deque
-
-from typing import Any, Deque, Iterator, List, Dict
-
-from pprint import pprint
-
-from src.segments import merge_timestamps
-
-# Workaround for https://github.com/tensorflow/tensorflow/issues/48797
-try:
-    import tensorflow as tf
-except ModuleNotFoundError:
-    # Error handling
-    pass
-
-import torch
-
-import ffmpeg
-import numpy as np
-
-from src.utils import format_timestamp
-from enum import Enum
-
-class NonSpeechStrategy(Enum):
-    """
-    Ignore non-speech frames segments.
-    """
-    SKIP = 1
-    """
-    Just treat non-speech segments as speech.
-    """
-    CREATE_SEGMENT = 2
-    """
-    Expand speech segments into subsequent non-speech segments.
-    """
-    EXPAND_SEGMENT = 3
-
-# Defaults for Silero
-SPEECH_TRESHOLD = 0.3
-
-# Minimum size of segments to process
-MIN_SEGMENT_DURATION = 1
-
-# The maximum time for texts from old segments to be used in the next segment 
-MAX_PROMPT_WINDOW = 0 # seconds (0 = disabled)
-PROMPT_NO_SPEECH_PROB = 0.1 # Do not pass the text from segments with a no speech probability higher than this
-
-VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio
-
-class TranscriptionConfig(ABC):
-    def __init__(self, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
-                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
-                       max_merge_size: float = None, max_prompt_window: float = None):
-        self.non_speech_strategy = non_speech_strategy
-        self.segment_padding_left = segment_padding_left
-        self.segment_padding_right = segment_padding_right
-        self.max_silent_period = max_silent_period
-        self.max_merge_size = max_merge_size
-        self.max_prompt_window = max_prompt_window
-
-class PeriodicTranscriptionConfig(TranscriptionConfig):
-    def __init__(self, periodic_duration: float, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
-                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
-                       max_merge_size: float = None, max_prompt_window: float = None):
-        super().__init__(non_speech_strategy, segment_padding_left, segment_padding_right, max_silent_period, max_merge_size, max_prompt_window)
-        self.periodic_duration = periodic_duration
-
-class AbstractTranscription(ABC):
-    def __init__(self, sampling_rate: int = 16000):
-        self.sampling_rate = sampling_rate
-
-    def get_audio_segment(self, str, start_time: str = None, duration: str = None):
-        return load_audio(str, self.sampling_rate, start_time, duration)
-
-    @abstractmethod
-    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig):
-        """
-        Get the start and end timestamps of the sections that should be transcribed by this VAD method.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file.
-        config: TranscriptionConfig
-            The transcription configuration.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-        return 
-
-    def transcribe(self, audio: str, whisperCallable, config: TranscriptionConfig):
-        """
-        Transcribe the given audo file.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file.
-
-        whisperCallable: Callable[[Union[str, np.ndarray, torch.Tensor], int, str, str], dict[str, Union[dict, Any]]]
-            The callback that is used to invoke Whisper on an audio file/buffer. The first parameter is the audio file/buffer, 
-            the second parameter is an optional text prompt, and the last is the current detected language. The return value is the result of the Whisper call.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-
-        # get speech timestamps from full audio file
-        seconds_timestamps = self.get_transcribe_timestamps(audio, config)
-
-        #for seconds_timestamp in seconds_timestamps:
-        #    print("VAD timestamp ", format_timestamp(seconds_timestamp['start']), " to ", format_timestamp(seconds_timestamp['end']))
-
-        merged = merge_timestamps(seconds_timestamps, config.max_silent_period, config.max_merge_size, config.segment_padding_left, config.segment_padding_right)
-
-        # A deque of transcribed segments that is passed to the next segment as a prompt
-        prompt_window = deque()
-
-        print("Timestamps:")
-        pprint(merged)
-
-        if config.non_speech_strategy != NonSpeechStrategy.SKIP:
-            max_audio_duration = get_audio_duration(audio)
-
-            # Expand segments to include the gaps between them
-            if (config.non_speech_strategy == NonSpeechStrategy.CREATE_SEGMENT):
-                # When we have a prompt window, we create speech segments betwen each segment if we exceed the merge size
-                merged = self.fill_gaps(merged, total_duration=max_audio_duration, max_expand_size=config.max_merge_size)
-            elif config.non_speech_strategy == NonSpeechStrategy.EXPAND_SEGMENT: 
-                # With no prompt window, it is better to just expand the segments (this effectively passes the prompt to the next segment)
-                merged = self.expand_gaps(merged, total_duration=max_audio_duration)
-            else:
-                raise Exception("Unknown non-speech strategy: " + str(config.non_speech_strategy))
-
-            print("Transcribing non-speech:")
-            pprint(merged)
-
-        result = {
-            'text': "",
-            'segments': [],
-            'language': ""
-        }
-        languageCounter = Counter()
-        detected_language = None
-
-        segment_index = -1
-
-        # For each time segment, run whisper
-        for segment in merged:
-            segment_index += 1
-            segment_start = segment['start']
-            segment_end = segment['end']
-            segment_expand_amount = segment.get('expand_amount', 0)
-            segment_gap = segment.get('gap', False)
-
-            segment_duration = segment_end - segment_start
-
-            if segment_duration < MIN_SEGMENT_DURATION:
-                continue;
-
-            # Audio to run on Whisper
-            segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration))
-            # Previous segments to use as a prompt
-            segment_prompt = ' '.join([segment['text'] for segment in prompt_window]) if len(prompt_window) > 0 else None
-    
-            # Detected language
-            detected_language = languageCounter.most_common(1)[0][0] if len(languageCounter) > 0 else None
-
-            print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", 
-                  segment_duration, "expanded: ", segment_expand_amount, "prompt: ", segment_prompt, "language: ", detected_language)
-            segment_result = whisperCallable(segment_audio, segment_index, segment_prompt, detected_language)
-
-            adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration)
-
-            # Propagate expand amount to the segments
-            if (segment_expand_amount > 0):
-                segment_without_expansion = segment_duration - segment_expand_amount
-
-                for adjusted_segment in adjusted_segments:
-                    adjusted_segment_end = adjusted_segment['end']
-
-                    # Add expand amount if the segment got expanded
-                    if (adjusted_segment_end > segment_without_expansion):
-                        adjusted_segment["expand_amount"] = adjusted_segment_end - segment_without_expansion
-
-            # Append to output
-            result['text'] += segment_result['text']
-            result['segments'].extend(adjusted_segments)
-
-            # Increment detected language
-            if not segment_gap:
-                languageCounter[segment_result['language']] += 1
-
-            # Update prompt window
-            self.__update_prompt_window(prompt_window, adjusted_segments, segment_end, segment_gap, config)
-            
-        if detected_language is not None:
-            result['language'] = detected_language
-
-        return result
-            
-    def __update_prompt_window(self, prompt_window: Deque, adjusted_segments: List, segment_end: float, segment_gap: bool, config: TranscriptionConfig):
-        if (config.max_prompt_window is not None and config.max_prompt_window > 0):
-            # Add segments to the current prompt window (unless it is a speech gap)
-            if not segment_gap:
-                for segment in adjusted_segments:
-                    if segment.get('no_speech_prob', 0) <= PROMPT_NO_SPEECH_PROB:
-                        prompt_window.append(segment)
-
-            while (len(prompt_window) > 0):
-                first_end_time = prompt_window[0].get('end', 0)
-                # Time expanded in the segments should be discounted from the prompt window
-                first_expand_time = prompt_window[0].get('expand_amount', 0)
-
-                if (first_end_time - first_expand_time < segment_end - config.max_prompt_window):
-                    prompt_window.popleft()
-                else:
-                    break
-
-    def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float):
-        result = []
-        last_end_time = 0
-
-        for segment in segments:
-            segment_start = float(segment['start'])
-            segment_end = float(segment['end'])
-
-            if (last_end_time != segment_start):
-                delta = segment_start - last_end_time
-
-                if (min_gap_length is None or delta >= min_gap_length):
-                    result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } )
-            
-            last_end_time = segment_end
-            result.append(segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None and last_end_time < total_duration):
-            delta = total_duration - segment_start
-
-            if (min_gap_length is None or delta >= min_gap_length):
-                result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } )
-
-        return result
-
-    # Expand the end time of each segment to the start of the next segment
-    def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float):
-        result = []
-
-        if len(segments) == 0:
-            return result
-
-        # Add gap at the beginning if needed
-        if (segments[0]['start'] > 0):
-            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
-        for i in range(len(segments) - 1):
-            current_segment = segments[i]
-            next_segment = segments[i + 1]
-
-            delta = next_segment['start'] - current_segment['end']
-
-            # Expand if the gap actually exists
-            if (delta >= 0):
-                current_segment = current_segment.copy()
-                current_segment['expand_amount'] = delta
-                current_segment['end'] = next_segment['start']
-            
-            result.append(current_segment)
-
-        # Add last segment
-        last_segment = segments[-1]
-        result.append(last_segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None):
-            last_segment = result[-1]
-
-            if (last_segment['end'] < total_duration):
-                last_segment = last_segment.copy()
-                last_segment['end'] = total_duration
-                result[-1] = last_segment
-
-        return result
-
-    def fill_gaps(self, segments: List[Dict[str, Any]], total_duration: float, max_expand_size: float = None):
-        result = []
-
-        if len(segments) == 0:
-            return result
-
-        # Add gap at the beginning if needed
-        if (segments[0]['start'] > 0):
-            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
-        for i in range(len(segments) - 1):
-            expanded = False
-            current_segment = segments[i]
-            next_segment = segments[i + 1]
-
-            delta = next_segment['start'] - current_segment['end']
-
-            if (max_expand_size is not None and delta <= max_expand_size):
-                # Just expand the current segment
-                current_segment = current_segment.copy()
-                current_segment['expand_amount'] = delta
-                current_segment['end'] = next_segment['start']
-                expanded = True
-
-            result.append(current_segment)
-
-            # Add a gap to the next segment if needed
-            if (delta >= 0 and not expanded):
-                result.append({ 'start': current_segment['end'], 'end': next_segment['start'], 'gap': True } )
-            
-        # Add last segment
-        last_segment = segments[-1]
-        result.append(last_segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None):
-            last_segment = result[-1]
-
-            delta = total_duration - last_segment['end']
-
-            if (delta > 0):
-                if (max_expand_size is not None and delta <= max_expand_size):
-                    # Expand the last segment
-                    last_segment = last_segment.copy()
-                    last_segment['expand_amount'] = delta
-                    last_segment['end'] = total_duration
-                    result[-1] = last_segment
-                else:
-                    result.append({ 'start': last_segment['end'], 'end': total_duration, 'gap': True } )
-
-        return result
-
-    def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None):
-        result = []
-
-        for segment in segments:
-            segment_start = float(segment['start'])
-            segment_end = float(segment['end'])
-
-            # Filter segments?
-            if (max_source_time is not None):
-                if (segment_start > max_source_time):
-                    continue
-                segment_end = min(max_source_time, segment_end)
-
-                new_segment = segment.copy()
-
-            # Add to start and end
-            new_segment['start'] = segment_start + adjust_seconds
-            new_segment['end'] = segment_end + adjust_seconds
-            result.append(new_segment)
-        return result
-
-    def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float):
-        result = []
-
-        for entry in timestamps:
-            start = entry['start']
-            end = entry['end']
-
-            result.append({
-                'start': start * factor,
-                'end': end * factor
-            })
-        return result
-
-class VadSileroTranscription(AbstractTranscription):
-    def __init__(self, sampling_rate: int = 16000):
-        super().__init__(sampling_rate=sampling_rate)
-
-        self.model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
-        (self.get_speech_timestamps, _, _, _, _) = utils
-
-
-    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig):
-        audio_duration = get_audio_duration(audio)
-        result = []
-
-        # Divide procesisng of audio into chunks
-        chunk_start = 0.0
-
-        while (chunk_start < audio_duration):
-            chunk_duration = min(audio_duration - chunk_start, VAD_MAX_PROCESSING_CHUNK)
-
-            print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration)))
-            wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration))
-
-            sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD)
-            seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate) 
-            adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration)
-
-            #pprint(adjusted)
-
-            result.extend(adjusted)
-            chunk_start += chunk_duration
-
-        return result
-
-# A very simple VAD that just marks every N seconds as speech
-class VadPeriodicTranscription(AbstractTranscription):
-    def __init__(self, sampling_rate: int = 16000):
-        super().__init__(sampling_rate=sampling_rate)
-
-    def get_transcribe_timestamps(self, audio: str, config: PeriodicTranscriptionConfig):
-        # Get duration in seconds
-        audio_duration = get_audio_duration(audio)
-        result = []
-
-        # Generate a timestamp every N seconds
-        start_timestamp = 0
-
-        while (start_timestamp < audio_duration):
-            end_timestamp = min(start_timestamp + config.periodic_duration, audio_duration)
-            segment_duration = end_timestamp - start_timestamp
-
-            # Minimum duration is 1 second
-            if (segment_duration >= 1):
-                result.append( {  'start': start_timestamp, 'end': end_timestamp } )
-
-            start_timestamp = end_timestamp
-
-        return result
-
-def get_audio_duration(file: str):
-    return float(ffmpeg.probe(file)["format"]["duration"])
-
-def load_audio(file: str, sample_rate: int = 16000, 
-               start_time: str = None, duration: str = None):
-    """
-    Open an audio file and read as mono waveform, resampling as necessary
-
-    Parameters
-    ----------
-    file: str
-        The audio file to open
-
-    sr: int
-        The sample rate to resample the audio if necessary
-
-    start_time: str
-        The start time, using the standard FFMPEG time duration syntax, or None to disable.
-    
-    duration: str
-        The duration, using the standard FFMPEG time duration syntax, or None to disable.
-
-    Returns
-    -------
-    A NumPy array containing the audio waveform, in float32 dtype.
-    """
-    try:
-        inputArgs = {'threads': 0}
-
-        if (start_time is not None):
-            inputArgs['ss'] = start_time
-        if (duration is not None):
-            inputArgs['t'] = duration
-
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        out, _ = (
-            ffmpeg.input(file, **inputArgs)
-            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate)
-            .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True)
-        )
-    except ffmpeg.Error as e:
-        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}")
-
-    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
\ No newline at end of file
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/picocolors/picocolors.browser.js b/spaces/younker/chatgpt-turbo/client/node_modules/picocolors/picocolors.browser.js
deleted file mode 100644
index 5eb9fbe8b5a0d60e91c88a2851b3b975d70261bf..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/picocolors/picocolors.browser.js
+++ /dev/null
@@ -1,4 +0,0 @@
-var x=String;
-var create=function() {return {isColorSupported:false,reset:x,bold:x,dim:x,italic:x,underline:x,inverse:x,hidden:x,strikethrough:x,black:x,red:x,green:x,yellow:x,blue:x,magenta:x,cyan:x,white:x,gray:x,bgBlack:x,bgRed:x,bgGreen:x,bgYellow:x,bgBlue:x,bgMagenta:x,bgCyan:x,bgWhite:x}};
-module.exports=create();
-module.exports.createColors = create;
diff --git a/spaces/yuangongfdu/ltu-2/README.md b/spaces/yuangongfdu/ltu-2/README.md
deleted file mode 100644
index a30f9f926964fef96bc6bd2b44e38b5f52fde4eb..0000000000000000000000000000000000000000
--- a/spaces/yuangongfdu/ltu-2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Ltu 2
-emoji: 📚
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: bsd-2-clause
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yuhanbo/chat-gpt/app/api/newbing/route.ts b/spaces/yuhanbo/chat-gpt/app/api/newbing/route.ts
deleted file mode 100644
index 7decbcd01e421daaeb45dec8b4acbcbfd4c60704..0000000000000000000000000000000000000000
--- a/spaces/yuhanbo/chat-gpt/app/api/newbing/route.ts
+++ /dev/null
@@ -1,81 +0,0 @@
-import { BingChat, ChatMessage } from "../../bing-chat/build/index";
-
-export async function POST(req: Request) {
-  try {
-    let cookies = process.env.COOKIES;
-    const api = new BingChat({
-      cookie: cookies,
-    });
-    let chat: ChatMessage = {
-      id: "",
-      text: "",
-      author: "bot",
-      conversationId: "",
-      clientId: "",
-      conversationSignature: "",
-    };
-    const res: any = await bingAiMessageSendWrapper(
-      api,
-      await req.json(),
-      chat,
-    );
-    // const res = await api.sendMessage(await req.json(), {
-    //   // print the partial response as the AI is "typing"
-    //   onProgress: (partialResponse) => {
-    //     console.log(partialResponse.text);
-    //   },
-    //   variant: "Precise",
-    // });
-    // console.log(res['text'])
-    return new Response(res["text"]);
-  } catch (e) {
-    console.error("[NewBing] ", e);
-    return new Response(JSON.stringify(e));
-  }
-}
-
-/**
- * @param { import("bing-chat").BingChat } client
- * @param { string } message
- * @param { import("bing-chat").ChatMessage } [session]
- * @returns { Promise<import("bing-chat").ChatMessage> }
- */
-function bingAiMessageSendWrapper(
-  client: BingChat,
-  message: string,
-  session: ChatMessage,
-) {
-  const TIMEOUT_THRESHOLD = 120 * 1000;
-  return new Promise((resolve, reject) => {
-    let response = {
-      text: "",
-    };
-    let responseText = "";
-    let temp = {
-      time: new Date().valueOf(),
-      response: response,
-    };
-    const verifyIfResponseChangedInterval = setInterval(() => {
-      if (new Date().valueOf() - temp.time > TIMEOUT_THRESHOLD) {
-        clearInterval(verifyIfResponseChangedInterval);
-        temp.response.text = responseText;
-        resolve(temp.response);
-      }
-    }, 500);
-    client
-      .sendMessage(message, {
-        ...session,
-        onProgress: (partialResponse) => {
-          temp.response = partialResponse;
-          responseText += partialResponse.text;
-          temp.time = new Date().valueOf();
-        },
-      })
-      .then((response) => {
-        resolve(response);
-      })
-      .catch((error) => {
-        reject(error);
-      });
-  });
-}
diff --git a/spaces/zhang-wei-jian/docker/node_modules/is-glob/README.md b/spaces/zhang-wei-jian/docker/node_modules/is-glob/README.md
deleted file mode 100644
index 740724b276e289aec14ee6ce99d42ab9a6eb4b48..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/is-glob/README.md
+++ /dev/null
@@ -1,206 +0,0 @@
-# is-glob [![NPM version](https://img.shields.io/npm/v/is-glob.svg?style=flat)](https://www.npmjs.com/package/is-glob) [![NPM monthly downloads](https://img.shields.io/npm/dm/is-glob.svg?style=flat)](https://npmjs.org/package/is-glob) [![NPM total downloads](https://img.shields.io/npm/dt/is-glob.svg?style=flat)](https://npmjs.org/package/is-glob) [![Build Status](https://img.shields.io/github/workflow/status/micromatch/is-glob/dev)](https://github.com/micromatch/is-glob/actions)
-
-> Returns `true` if the given string looks like a glob pattern or an extglob pattern. This makes it easy to create code that only uses external modules like node-glob when necessary, resulting in much faster code execution and initialization time, and a better user experience.
-
-Please consider following this project's author, [Jon Schlinkert](https://github.com/jonschlinkert), and consider starring the project to show your :heart: and support.
-
-## Install
-
-Install with [npm](https://www.npmjs.com/):
-
-```sh
-$ npm install --save is-glob
-```
-
-You might also be interested in [is-valid-glob](https://github.com/jonschlinkert/is-valid-glob) and [has-glob](https://github.com/jonschlinkert/has-glob).
-
-## Usage
-
-```js
-var isGlob = require('is-glob');
-```
-
-### Default behavior
-
-**True**
-
-Patterns that have glob characters or regex patterns will return `true`:
-
-```js
-isGlob('!foo.js');
-isGlob('*.js');
-isGlob('**/abc.js');
-isGlob('abc/*.js');
-isGlob('abc/(aaa|bbb).js');
-isGlob('abc/[a-z].js');
-isGlob('abc/{a,b}.js');
-//=> true
-```
-
-Extglobs
-
-```js
-isGlob('abc/@(a).js');
-isGlob('abc/!(a).js');
-isGlob('abc/+(a).js');
-isGlob('abc/*(a).js');
-isGlob('abc/?(a).js');
-//=> true
-```
-
-**False**
-
-Escaped globs or extglobs return `false`:
-
-```js
-isGlob('abc/\\@(a).js');
-isGlob('abc/\\!(a).js');
-isGlob('abc/\\+(a).js');
-isGlob('abc/\\*(a).js');
-isGlob('abc/\\?(a).js');
-isGlob('\\!foo.js');
-isGlob('\\*.js');
-isGlob('\\*\\*/abc.js');
-isGlob('abc/\\*.js');
-isGlob('abc/\\(aaa|bbb).js');
-isGlob('abc/\\[a-z].js');
-isGlob('abc/\\{a,b}.js');
-//=> false
-```
-
-Patterns that do not have glob patterns return `false`:
-
-```js
-isGlob('abc.js');
-isGlob('abc/def/ghi.js');
-isGlob('foo.js');
-isGlob('abc/@.js');
-isGlob('abc/+.js');
-isGlob('abc/?.js');
-isGlob();
-isGlob(null);
-//=> false
-```
-
-Arrays are also `false` (If you want to check if an array has a glob pattern, use [has-glob](https://github.com/jonschlinkert/has-glob)):
-
-```js
-isGlob(['**/*.js']);
-isGlob(['foo.js']);
-//=> false
-```
-
-### Option strict
-
-When `options.strict === false` the behavior is less strict in determining if a pattern is a glob. Meaning that
-some patterns that would return `false` may return `true`. This is done so that matching libraries like [micromatch](https://github.com/micromatch/micromatch) have a chance at determining if the pattern is a glob or not.
-
-**True**
-
-Patterns that have glob characters or regex patterns will return `true`:
-
-```js
-isGlob('!foo.js', {strict: false});
-isGlob('*.js', {strict: false});
-isGlob('**/abc.js', {strict: false});
-isGlob('abc/*.js', {strict: false});
-isGlob('abc/(aaa|bbb).js', {strict: false});
-isGlob('abc/[a-z].js', {strict: false});
-isGlob('abc/{a,b}.js', {strict: false});
-//=> true
-```
-
-Extglobs
-
-```js
-isGlob('abc/@(a).js', {strict: false});
-isGlob('abc/!(a).js', {strict: false});
-isGlob('abc/+(a).js', {strict: false});
-isGlob('abc/*(a).js', {strict: false});
-isGlob('abc/?(a).js', {strict: false});
-//=> true
-```
-
-**False**
-
-Escaped globs or extglobs return `false`:
-
-```js
-isGlob('\\!foo.js', {strict: false});
-isGlob('\\*.js', {strict: false});
-isGlob('\\*\\*/abc.js', {strict: false});
-isGlob('abc/\\*.js', {strict: false});
-isGlob('abc/\\(aaa|bbb).js', {strict: false});
-isGlob('abc/\\[a-z].js', {strict: false});
-isGlob('abc/\\{a,b}.js', {strict: false});
-//=> false
-```
-
-## About
-
-<details>
-<summary><strong>Contributing</strong></summary>
-
-Pull requests and stars are always welcome. For bugs and feature requests, [please create an issue](../../issues/new).
-
-</details>
-
-<details>
-<summary><strong>Running Tests</strong></summary>
-
-Running and reviewing unit tests is a great way to get familiarized with a library and its API. You can install dependencies and run tests with the following command:
-
-```sh
-$ npm install && npm test
-```
-
-</details>
-
-<details>
-<summary><strong>Building docs</strong></summary>
-
-_(This project's readme.md is generated by [verb](https://github.com/verbose/verb-generate-readme), please don't edit the readme directly. Any changes to the readme must be made in the [.verb.md](.verb.md) readme template.)_
-
-To generate the readme, run the following command:
-
-```sh
-$ npm install -g verbose/verb#dev verb-generate-readme && verb
-```
-
-</details>
-
-### Related projects
-
-You might also be interested in these projects:
-
-* [assemble](https://www.npmjs.com/package/assemble): Get the rocks out of your socks! Assemble makes you fast at creating web projects… [more](https://github.com/assemble/assemble) | [homepage](https://github.com/assemble/assemble "Get the rocks out of your socks! Assemble makes you fast at creating web projects. Assemble is used by thousands of projects for rapid prototyping, creating themes, scaffolds, boilerplates, e-books, UI components, API documentation, blogs, building websit")
-* [base](https://www.npmjs.com/package/base): Framework for rapidly creating high quality, server-side node.js applications, using plugins like building blocks | [homepage](https://github.com/node-base/base "Framework for rapidly creating high quality, server-side node.js applications, using plugins like building blocks")
-* [update](https://www.npmjs.com/package/update): Be scalable! Update is a new, open source developer framework and CLI for automating updates… [more](https://github.com/update/update) | [homepage](https://github.com/update/update "Be scalable! Update is a new, open source developer framework and CLI for automating updates of any kind in code projects.")
-* [verb](https://www.npmjs.com/package/verb): Documentation generator for GitHub projects. Verb is extremely powerful, easy to use, and is used… [more](https://github.com/verbose/verb) | [homepage](https://github.com/verbose/verb "Documentation generator for GitHub projects. Verb is extremely powerful, easy to use, and is used on hundreds of projects of all sizes to generate everything from API docs to readmes.")
-
-### Contributors
-
-| **Commits** | **Contributor** |  
-| --- | --- |  
-| 47 | [jonschlinkert](https://github.com/jonschlinkert) |  
-| 5  | [doowb](https://github.com/doowb) |  
-| 1  | [phated](https://github.com/phated) |  
-| 1  | [danhper](https://github.com/danhper) |  
-| 1  | [paulmillr](https://github.com/paulmillr) |  
-
-### Author
-
-**Jon Schlinkert**
-
-* [GitHub Profile](https://github.com/jonschlinkert)
-* [Twitter Profile](https://twitter.com/jonschlinkert)
-* [LinkedIn Profile](https://linkedin.com/in/jonschlinkert)
-
-### License
-
-Copyright © 2019, [Jon Schlinkert](https://github.com/jonschlinkert).
-Released under the [MIT License](LICENSE).
-
-***
-
-_This file was generated by [verb-generate-readme](https://github.com/verbose/verb-generate-readme), v0.8.0, on March 27, 2019._
\ No newline at end of file
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/components/chat-header.tsx b/spaces/zhaoys/wfms-kuiwenc/src/components/chat-header.tsx
deleted file mode 100644
index df755f39967e5e59fe477bae5ef085e61092135a..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/components/chat-header.tsx
+++ /dev/null
@@ -1,11 +0,0 @@
-import LogoIcon from '@/assets/images/bing.png'
-import Image from 'next/image'
-
-export function ChatHeader() {
-  return (
-    <div className="flex flex-col items-center justify-center">
-      <Image alt="logo" src={LogoIcon} width={60}/>
-      <div className="mt-4 mb-8 font-bold header-title">必应是 AI 支持的网上助手</div>
-    </div>
-  )
-}
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/pages/api/sydney.ts b/spaces/zhaoys/wfms-kuiwenc/src/pages/api/sydney.ts
deleted file mode 100644
index ece2dc5e7fdf9f8eb5f8e1afefb55162e989fadc..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-
-export const config = {
-  api: {
-    responseLimit: false,
-  },
-}
-const { WS_ENDPOINT = 'sydney.bing.com' } = process.env
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const conversationContext = req.body
-   const headers = createHeaders(req.cookies, Boolean(req.cookies['BING_HEADER1']))
-  const id = headers['x-forwarded-for']
-  // headers['x-forwarded-for'] = conversationContext?.userIpAddress || headers['x-forwarded-for']
-
-  debug(id, conversationContext, headers)
-  res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-  const uri = new URL(`wss://${req.headers['x-ws-endpoint'] || WS_ENDPOINT}/sydney/ChatHub`)
-  if ('encryptedconversationsignature' in conversationContext) {
-    uri.searchParams.set('sec_access_token', conversationContext['encryptedconversationsignature'])
-  }
-  debug(id, 'wss url', uri.toString())
-  const ws = new WebSocket(uri.toString(), {
-    headers,
-  })
-
-  const closeDog = new WatchDog()
-  const timeoutDog = new WatchDog()
-  ws.onmessage = (event) => {
-    timeoutDog.watch(() => {
-      debug(id, 'timeout')
-      ws.send(websocketUtils.packMessage({ type: 6 }))
-    }, 3000)
-    closeDog.watch(() => {
-      debug(id, 'timeout close')
-      ws.close()
-    }, 20000)
-    res.write(event.data)
-    if (/\{"type":([367])\b/.test(String(event.data))) {
-      const type = parseInt(RegExp.$1, 10)
-      debug(id, 'connection type', type)
-      if (type === 3) {
-        ws.close()
-      } else {
-        ws.send(websocketUtils.packMessage({ type }))
-      }
-    }
-  }
-
-  ws.onclose = () => {
-    timeoutDog.reset()
-    closeDog.reset()
-    debug(id, 'ws close')
-    res.end()
-  }
-
-  await new Promise((resolve) => ws.onopen = resolve)
-  ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
-  ws.send(websocketUtils.packMessage({ type: 6 }))
-  ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
-  req.socket.once('close', () => {
-    debug(id, 'connection close')
-    ws.close()
-    if (!res.closed) {
-      res.end()
-    }
-  })
-}
diff --git a/spaces/zhsso/roop/roop/utilities.py b/spaces/zhsso/roop/roop/utilities.py
deleted file mode 100644
index 433cb43b146c5c052ef212a90c61f8c448ce3d1f..0000000000000000000000000000000000000000
--- a/spaces/zhsso/roop/roop/utilities.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import glob
-import mimetypes
-import os
-import platform
-import shutil
-import ssl
-import subprocess
-import urllib
-from pathlib import Path
-from typing import List, Any
-from tqdm import tqdm
-
-import roop.globals
-
-TEMP_FILE = 'temp.mp4'
-TEMP_DIRECTORY = 'temp'
-
-# monkey patch ssl for mac
-if platform.system().lower() == 'darwin':
-    ssl._create_default_https_context = ssl._create_unverified_context
-
-
-def run_ffmpeg(args: List[str]) -> bool:
-    commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-loglevel', roop.globals.log_level]
-    commands.extend(args)
-    try:
-        subprocess.check_output(commands, stderr=subprocess.STDOUT)
-        return True
-    except Exception:
-        pass
-    return False
-
-
-def detect_fps(target_path: str) -> float:
-    command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=r_frame_rate', '-of', 'default=noprint_wrappers=1:nokey=1', target_path]
-    output = subprocess.check_output(command).decode().strip().split('/')
-    try:
-        numerator, denominator = map(int, output)
-        return numerator / denominator
-    except Exception:
-        pass
-    return 30.0
-
-
-def extract_frames(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
-
-
-def create_video(target_path: str, fps: float = 30.0) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.png'), '-c:v', roop.globals.video_encoder, '-crf', str(roop.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', temp_output_path])
-
-
-def restore_audio(target_path: str, output_path: str) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    done = run_ffmpeg(['-i', temp_output_path, '-i', target_path, '-c:v', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-y', output_path])
-    if not done:
-        move_temp(target_path, output_path)
-
-
-def get_temp_frame_paths(target_path: str) -> List[str]:
-    temp_directory_path = get_temp_directory_path(target_path)
-    return glob.glob((os.path.join(glob.escape(temp_directory_path), '*.png')))
-
-
-def get_temp_directory_path(target_path: str) -> str:
-    target_name, _ = os.path.splitext(os.path.basename(target_path))
-    target_directory_path = os.path.dirname(target_path)
-    return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
-
-
-def get_temp_output_path(target_path: str) -> str:
-    temp_directory_path = get_temp_directory_path(target_path)
-    return os.path.join(temp_directory_path, TEMP_FILE)
-
-
-def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
-    if source_path and target_path:
-        source_name, _ = os.path.splitext(os.path.basename(source_path))
-        target_name, target_extension = os.path.splitext(os.path.basename(target_path))
-        if os.path.isdir(output_path):
-            return os.path.join(output_path, source_name + '-' + target_name + target_extension)
-    return output_path
-
-
-def create_temp(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
-
-
-def move_temp(target_path: str, output_path: str) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    if os.path.isfile(temp_output_path):
-        if os.path.isfile(output_path):
-            os.remove(output_path)
-        shutil.move(temp_output_path, output_path)
-
-
-def clean_temp(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    parent_directory_path = os.path.dirname(temp_directory_path)
-    if not roop.globals.keep_frames and os.path.isdir(temp_directory_path):
-        shutil.rmtree(temp_directory_path)
-    if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
-        os.rmdir(parent_directory_path)
-
-
-def has_image_extension(image_path: str) -> bool:
-    return image_path.lower().endswith(('png', 'jpg', 'jpeg'))
-
-
-def is_image(image_path: str) -> bool:
-    if image_path and os.path.isfile(image_path):
-        mimetype, _ = mimetypes.guess_type(image_path)
-        return bool(mimetype and mimetype.startswith('image/'))
-    return False
-
-
-def is_video(video_path: str) -> bool:
-    if video_path and os.path.isfile(video_path):
-        mimetype, _ = mimetypes.guess_type(video_path)
-        return bool(mimetype and mimetype.startswith('video/'))
-    return False
-
-
-def conditional_download(download_directory_path: str, urls: List[str]) -> None:
-    if not os.path.exists(download_directory_path):
-        os.makedirs(download_directory_path)
-    for url in urls:
-        download_file_path = os.path.join(download_directory_path, os.path.basename(url))
-        if not os.path.exists(download_file_path):
-            request = urllib.request.urlopen(url) # type: ignore[attr-defined]
-            total = int(request.headers.get('Content-Length', 0))
-            with tqdm(total=total, desc='Downloading', unit='B', unit_scale=True, unit_divisor=1024) as progress:
-                urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
-
-
-def resolve_relative_path(path: str) -> str:
-    return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
diff --git a/spaces/zhtet/RegBotBeta/models/llamaCustom.py b/spaces/zhtet/RegBotBeta/models/llamaCustom.py
deleted file mode 100644
index d4ecf6cc7d9d4a87fa5d4c5b6c9f0ff28f4d26fa..0000000000000000000000000000000000000000
--- a/spaces/zhtet/RegBotBeta/models/llamaCustom.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import os
-import pickle
-from json import dumps, loads
-from typing import Any, List, Mapping, Optional
-
-import numpy as np
-import openai
-import pandas as pd
-import streamlit as st
-from dotenv import load_dotenv
-from huggingface_hub import HfFileSystem
-from langchain.llms.base import LLM
-from llama_index import (
-    Document,
-    GPTVectorStoreIndex,
-    LLMPredictor,
-    PromptHelper,
-    ServiceContext,
-    SimpleDirectoryReader,
-    StorageContext,
-    load_index_from_storage,
-)
-from llama_index.llms.base import llm_completion_callback
-from llama_index.llms import CompletionResponse, CustomLLM, LLMMetadata
-from llama_index.prompts import Prompt
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-from assets.prompts import custom_prompts
-
-load_dotenv()
-# openai.api_key = os.getenv("OPENAI_API_KEY")
-fs = HfFileSystem()
-
-# define prompt helper
-# set maximum input size
-CONTEXT_WINDOW = 2048
-# set number of output tokens
-NUM_OUTPUT = 525
-# set maximum chunk overlap
-CHUNK_OVERLAP_RATION = 0.2
-
-text_qa_template = Prompt(custom_prompts.text_qa_template_str)
-
-refine_template = Prompt(custom_prompts.refine_template_str)
-
-
-@st.cache_resource
-def load_model(model_name: str):
-    # llm_model_name = "bigscience/bloom-560m"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name, config="T5Config")
-
-    pipe = pipeline(
-        task="text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        # device=0, # GPU device number
-        # max_length=512,
-        do_sample=True,
-        top_p=0.95,
-        top_k=50,
-        temperature=0.7,
-    )
-
-    return pipe
-
-
-class OurLLM(CustomLLM):
-    def __init__(self):
-        self.model_name = ""
-        self.pipeline = None
-
-    @property
-    def modelName(self):
-        return self.model_name
-
-    @property
-    def pipeline(self):
-        return self.pipeline
-
-    @modelName.setter
-    def modelName(self, name: str):
-        self.model_name = name
-
-    @pipeline.setter
-    def pipeline(self, value):
-        self.pipeline = value
-
-    @property
-    def metadata(self) -> LLMMetadata:
-        """Get LLM metadata."""
-        return LLMMetadata(
-            context_window=CONTEXT_WINDOW,
-            num_output=NUM_OUTPUT,
-            # model_name=self.model_name,
-        )
-
-    @llm_completion_callback()
-    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
-        prompt_length = len(prompt)
-        response = self.pipeline(prompt, max_new_tokens=NUM_OUTPUT)[0]["generated_text"]
-
-        # only return newly generated tokens
-        text = response[prompt_length:]
-        return CompletionResponse(text=text)
-
-    @llm_completion_callback()
-    def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
-        raise NotImplementedError()
-
-
-class LlamaCustom:
-    def __init__(self, model_name: str) -> None:
-        self.vector_index = self.initialize_index(model_name=model_name)
-
-    def initialize_index(self, model_name: str):
-        index_name = model_name.split("/")[-1]
-
-        file_path = f"./vectorStores/{index_name}"
-
-        if os.path.exists(path=file_path):
-            # rebuild storage context
-            storage_context = StorageContext.from_defaults(persist_dir=file_path)
-
-            # local load index access
-            index = load_index_from_storage(storage_context)
-
-            # huggingface repo load access
-            # with fs.open(file_path, "r") as file:
-            #     index = pickle.loads(file.readlines())
-            return index
-        else:
-            prompt_helper = PromptHelper(
-                context_window=CONTEXT_WINDOW,
-                num_output=NUM_OUTPUT,
-                chunk_overlap_ratio=CHUNK_OVERLAP_RATION,
-            )
-
-            # define llm
-            pipe = load_model(model_name=model_name)
-            llm = OurLLM()
-            llm.model_name = model_name
-            llm.pipeline = pipe
-
-            llm_predictor = LLMPredictor(llm=llm)
-            service_context = ServiceContext.from_defaults(
-                llm_predictor=llm_predictor, prompt_helper=prompt_helper
-            )
-
-            # documents = prepare_data(r"./assets/regItems.json")
-            documents = SimpleDirectoryReader(input_dir="./assets/pdf").load_data()
-
-            index = GPTVectorStoreIndex.from_documents(
-                documents, service_context=service_context
-            )
-
-            # local write access
-            index.storage_context.persist(file_path)
-
-            # huggingface repo write access
-            # with fs.open(file_path, "w") as file:
-            #     file.write(pickle.dumps(index))
-            return index
-
-    def get_response(self, query_str):
-        print("query_str: ", query_str)
-        # query_engine = self.vector_index.as_query_engine()
-        query_engine = self.vector_index.as_query_engine(
-            text_qa_template=text_qa_template, refine_template=refine_template
-        )
-        response = query_engine.query(query_str)
-        print("metadata: ", response.metadata)
-        return str(response)
diff --git a/spaces/zideliu/styledrop/configs/imagenet256_base_vq_jax.py b/spaces/zideliu/styledrop/configs/imagenet256_base_vq_jax.py
deleted file mode 100644
index 81c5def95cf6341b178fa9ee2ffb9682a9bef72b..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/configs/imagenet256_base_vq_jax.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import ml_collections
-
-
-def d(**kwargs):
-    """Helper of creating a config dict."""
-    return ml_collections.ConfigDict(initial_dictionary=kwargs)
-
-
-def get_config():
-    config = ml_collections.ConfigDict()
-
-    config.seed = 1234
-    config.z_shape = (8, 16, 16)
-
-    config.autoencoder = d(
-        config_file='vq-f16-jax.yaml',
-    )
-
-    config.train = d(
-        n_steps=99999999,
-        batch_size=2048,
-        log_interval=10,
-        eval_interval=5000,
-        save_interval=5000,
-        fid_interval=50000,
-    )
-
-    config.eval = d(
-        n_samples=10000,
-        sample_steps=12,
-    )
-
-    config.optimizer = d(
-        name='adamw',
-        lr=0.0004,
-        weight_decay=0.03,
-        betas=(0.99, 0.99),
-    )
-
-    config.lr_scheduler = d(
-        name='customized',
-        warmup_steps=5000
-    )
-
-    config.nnet = d(
-        name='uvit_vq',
-        img_size=16,
-        codebook_size=1024,
-        in_chans=256,
-        patch_size=1,
-        embed_dim=768,
-        depth=12,
-        num_heads=12,
-        mlp_ratio=4,
-        qkv_bias=False,
-        num_classes=1001,
-        use_checkpoint=False,
-        skip=True,
-    )
-
-    config.muse = d(
-        ignore_ind=-1,
-        smoothing=0.1,
-        gen_temp=4.5
-    )
-
-    config.dataset = d(
-        name='imagenet256_features',
-        path='assets/datasets/imagenet256_vq_features/vq-f16-jax',
-        cfg=True,
-        p_uncond=0.15,
-    )
-
-    config.sample = d(
-        sample_steps=12,
-        n_samples=50000,
-        mini_batch_size=50,
-        cfg=True,
-        linear_inc_scale=True,
-        scale=3.,
-        path=''
-    )
-
-    return config
diff --git a/spaces/zideliu/styledrop/open_clip/coca_model.py b/spaces/zideliu/styledrop/open_clip/coca_model.py
deleted file mode 100644
index 039453af70d1c865dd7cc6016f732aff2f7dc3d2..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/open_clip/coca_model.py
+++ /dev/null
@@ -1,458 +0,0 @@
-from typing import Optional
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-import numpy as np
-from dataclasses import dataclass
-
-from .transformer import (
-    LayerNormFp32,
-    LayerNorm,
-    QuickGELU,
-    MultimodalTransformer,
-)
-from .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower
-
-try:
-    from transformers import (
-        BeamSearchScorer,
-        LogitsProcessorList,
-        TopPLogitsWarper,
-        TopKLogitsWarper,
-        RepetitionPenaltyLogitsProcessor,
-        MinLengthLogitsProcessor,
-        MaxLengthCriteria,
-        StoppingCriteriaList
-    )
-
-    GENERATION_TYPES = {
-        "top_k": TopKLogitsWarper,
-        "top_p": TopPLogitsWarper,
-        "beam_search": "beam_search"
-    }
-    _has_transformers = True
-except ImportError as e:
-    GENERATION_TYPES = {
-        "top_k": None,
-        "top_p": None,
-        "beam_search": "beam_search"
-    }
-    _has_transformers = False
-
-
-@dataclass
-class MultimodalCfg(CLIPTextCfg):
-    mlp_ratio: int = 4
-    dim_head: int = 64
-    heads: int = 8
-    n_queries: int = 256
-    attn_pooler_heads: int = 8
-
-
-def _build_text_decoder_tower(
-        embed_dim,
-        multimodal_cfg,
-        quick_gelu: bool = False,
-        cast_dtype: Optional[torch.dtype] = None,
-):
-    multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
-    act_layer = QuickGELU if quick_gelu else nn.GELU
-    norm_layer = (
-        LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
-    )
-
-    decoder = MultimodalTransformer(
-        context_length=multimodal_cfg.context_length,
-        width=multimodal_cfg.width,
-        heads=multimodal_cfg.heads,
-        layers=multimodal_cfg.layers,
-        ls_init_value=multimodal_cfg.ls_init_value,
-        output_dim=embed_dim,
-        act_layer=act_layer,
-        norm_layer=norm_layer,
-    )
-
-    return decoder
-
-
-class CoCa(nn.Module):
-    def __init__(
-            self,
-            embed_dim,
-            multimodal_cfg: MultimodalCfg,
-            text_cfg: CLIPTextCfg,
-            vision_cfg: CLIPVisionCfg,
-            quick_gelu: bool = False,
-            cast_dtype: Optional[torch.dtype] = None,
-            pad_id: int = 0,
-    ):
-        super().__init__()
-        multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
-        text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg
-        vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg
-
-        self.text = _build_text_tower(
-            embed_dim=embed_dim,
-            text_cfg=text_cfg,
-            quick_gelu=quick_gelu,
-            cast_dtype=cast_dtype,
-        )
-
-        vocab_size = (
-            text_cfg.vocab_size  # for hf models
-            if hasattr(text_cfg, "hf_model_name") and text_cfg.hf_model_name is not None
-            else text_cfg.vocab_size
-        )
-
-        self.visual = _build_vision_tower(
-            embed_dim=embed_dim,
-            vision_cfg=vision_cfg,
-            quick_gelu=quick_gelu,
-            cast_dtype=cast_dtype,
-        )
-
-        self.text_decoder = _build_text_decoder_tower(
-            vocab_size,
-            multimodal_cfg=multimodal_cfg,
-            quick_gelu=quick_gelu,
-            cast_dtype=cast_dtype,
-        )
-
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.pad_id = pad_id
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.visual.set_grad_checkpointing(enable)
-        self.text.set_grad_checkpointing(enable)
-        self.text_decoder.set_grad_checkpointing(enable)
-
-    def _encode_image(self, images, normalize=True):
-        image_latent, tokens_embs = self.visual(images)
-        image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent
-        return image_latent, tokens_embs
-
-    def _encode_text(self, text, normalize=True, embed_cls=True):
-        text = text[:, :-1] if embed_cls else text # make space for CLS token
-        text_latent, token_emb = self.text(text)
-        text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent
-        return text_latent, token_emb
-
-    def encode_image(self, images, normalize=True):
-        image_latent, _ = self._encode_image(images, normalize=normalize)
-        return image_latent
-
-    def encode_text(self, text, normalize=True, embed_cls=True):
-        text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls)
-        return text_latent
-
-    def forward(self, image, text, embed_cls=True, image_latent=None, image_embs=None):
-        text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls)
-        if image_latent is None or image_embs is None:
-            image_latent, image_embs = self._encode_image(image)
-
-        # TODO: add assertion to avoid bugs?
-        labels = text[:, -token_embs.shape[1]:]
-
-        logits = self.text_decoder(image_embs, token_embs)
-        return {
-            "image_features": image_latent,
-            "text_features": text_latent,
-            "logits": logits,
-            "labels": labels,
-            "logit_scale": self.logit_scale.exp()
-        }
-
-    def generate(
-        self,
-        image,
-        text=None,
-        seq_len=30,
-        max_seq_len=77,
-        temperature=1.,
-        generation_type="beam_search",
-        top_p=0.1,  # keep tokens in the 1 - top_p quantile
-        top_k=1,  # keeps the top_k most probable tokens
-        pad_token_id=None,
-        eos_token_id=None,
-        sot_token_id=None,
-        num_beams=6,
-        num_beam_groups=3,
-        min_seq_len=5,
-        stopping_criteria=None,
-        repetition_penalty=1.0,
-        fixed_output_length=False # if True output.shape == (batch_size, seq_len)
-    ):
-        # taking many ideas and components from HuggingFace GenerationMixin
-        # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation
-        assert _has_transformers, "Please install transformers for generate functionality. `pip install transformers`."
-        assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len"
-
-        with torch.no_grad():
-            sot_token_id = 49406 if sot_token_id is None else sot_token_id
-            eos_token_id = 49407 if eos_token_id is None else eos_token_id
-            pad_token_id = self.pad_id if pad_token_id is None else pad_token_id
-            logit_processor = LogitsProcessorList(
-                [
-                    MinLengthLogitsProcessor(min_seq_len, eos_token_id),
-                    RepetitionPenaltyLogitsProcessor(repetition_penalty),
-                ]
-            )
-
-            if stopping_criteria is None:
-                stopping_criteria = [MaxLengthCriteria(max_length=seq_len)]
-
-            stopping_criteria = StoppingCriteriaList(
-                stopping_criteria
-            )
-
-            device = image.device
-
-            if generation_type == "beam_search":
-                output = self._generate_beamsearch(
-                    image_inputs = image,
-                    pad_token_id=pad_token_id,
-                    eos_token_id=eos_token_id,
-                    sot_token_id=sot_token_id,
-                    num_beams=num_beams,
-                    num_beam_groups=num_beam_groups,
-                    min_seq_len=min_seq_len,
-                    stopping_criteria=stopping_criteria,
-                    logit_processor=logit_processor,
-                )
-                if fixed_output_length and output.shape[1] < seq_len:
-                    return torch.cat(
-                        (output, torch.ones(output.shape[0], seq_len-output.shape[1], device=device, dtype=output.dtype) * self.pad_id),
-                        dim=1
-                    )
-                return output
-
-            elif generation_type == "top_p":
-                logit_warper = GENERATION_TYPES[generation_type](top_p)
-            elif generation_type == "top_k":
-                logit_warper = GENERATION_TYPES[generation_type](top_k)
-            else:
-                raise ValueError(
-                    f"generation_type has to be one of "
-                    f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}."
-                )
-
-            image_latent, image_embs = self._encode_image(image)
-
-            if text is None:
-                text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id
-
-            was_training = self.training
-            num_dims = len(text.shape)
-
-            if num_dims == 1:
-                text = text[None, :]
-
-            cur_len = text.shape[1]
-            self.eval()
-            out = text
-
-            while True:
-                x = out[:, -max_seq_len:]
-                cur_len = x.shape[1]
-                logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)["logits"][:, -1]
-                mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id)
-                sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id
-
-                if mask.all():
-                    if not fixed_output_length:
-                        break
-                else:
-                    logits = logits[~mask, :]
-                    filtered_logits = logit_processor(x[~mask, :], logits)
-                    filtered_logits = logit_warper(x[~mask, :], filtered_logits)
-                    probs = F.softmax(filtered_logits / temperature, dim=-1)
-
-                    if (cur_len + 1 == seq_len):
-                        sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id
-                    else:
-                        sample[~mask, :] = torch.multinomial(probs, 1)
-
-                out = torch.cat((out, sample), dim=-1)
-
-                cur_len += 1
-
-                if stopping_criteria(out, None):
-                    break
-
-            if num_dims == 1:
-                out = out.squeeze(0)
-
-            self.train(was_training)
-            return out
-
-    def _generate_beamsearch(
-            self,
-            image_inputs,
-            pad_token_id=None,
-            eos_token_id=None,
-            sot_token_id=None,
-            num_beams=6,
-            num_beam_groups=3,
-            min_seq_len=5,
-            stopping_criteria=None,
-            logit_processor=None,
-            logit_warper=None,
-    ):
-        device = image_inputs.device
-        batch_size = image_inputs.shape[0]
-        image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0)
-        image_latent, image_embs = self._encode_image(image_inputs)
-
-        input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long)
-        input_ids = input_ids * sot_token_id
-        beam_scorer = BeamSearchScorer(
-            batch_size=batch_size,
-            num_beams=num_beams,
-            device=device,
-            num_beam_groups=num_beam_groups,
-        )
-        # instantiate logits processors
-        logits_processor = (
-            LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)])
-            if logit_processor is None
-            else logit_processor
-        )
-
-        batch_size = len(beam_scorer._beam_hyps)
-        num_beams = beam_scorer.num_beams
-        num_beam_groups = beam_scorer.num_beam_groups
-        num_sub_beams = num_beams // num_beam_groups
-        batch_beam_size, cur_len = input_ids.shape
-        beam_indices = None
-
-        if num_beams * batch_size != batch_beam_size:
-            raise ValueError(
-                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
-            )
-
-        beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
-        # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in
-        # the same group don't produce same tokens everytime.
-        beam_scores[:, ::num_sub_beams] = 0
-        beam_scores = beam_scores.view((batch_size * num_beams,))
-
-        while True:
-
-            # predicted tokens in cur_len step
-            current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
-
-            # indices which will form the beams in the next time step
-            reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
-
-            # do one decoder step on all beams of all sentences in batch
-            model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs)
-            outputs = self(
-                model_inputs['images'],
-                model_inputs['text'],
-                embed_cls=False,
-                image_latent=image_latent,
-                image_embs=image_embs
-            )
-
-            for beam_group_idx in range(num_beam_groups):
-                group_start_idx = beam_group_idx * num_sub_beams
-                group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
-                group_size = group_end_idx - group_start_idx
-
-                # indices of beams of current group among all sentences in batch
-                batch_group_indices = []
-
-                for batch_idx in range(batch_size):
-                    batch_group_indices.extend(
-                        [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
-                    )
-                group_input_ids = input_ids[batch_group_indices]
-
-                # select outputs of beams of currentg group only
-                next_token_logits = outputs['logits'][batch_group_indices, -1, :]
-                vocab_size = next_token_logits.shape[-1]
-
-                next_token_scores_processed = logits_processor(
-                    group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx
-                )
-                next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
-                next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
-
-                # reshape for beam search
-                next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
-
-                next_token_scores, next_tokens = torch.topk(
-                    next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
-                )
-
-                next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
-                next_tokens = next_tokens % vocab_size
-
-                # stateless
-                process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
-                beam_outputs = beam_scorer.process(
-                    group_input_ids,
-                    next_token_scores,
-                    next_tokens,
-                    next_indices,
-                    pad_token_id=pad_token_id,
-                    eos_token_id=eos_token_id,
-                    beam_indices=process_beam_indices,
-                )
-                beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
-                beam_next_tokens = beam_outputs["next_beam_tokens"]
-                beam_idx = beam_outputs["next_beam_indices"]
-
-                input_ids[batch_group_indices] = group_input_ids[beam_idx]
-                group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
-                current_tokens[batch_group_indices] = group_input_ids[:, -1]
-
-                # (beam_idx // group_size) -> batch_idx
-                # (beam_idx % group_size) -> offset of idx inside the group
-                reordering_indices[batch_group_indices] = (
-                    num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size)
-                )
-
-            input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
-
-            # increase cur_len
-            cur_len = cur_len + 1
-            if beam_scorer.is_done or stopping_criteria(input_ids, None):
-                break
-
-        final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
-        sequence_outputs = beam_scorer.finalize(
-            input_ids,
-            beam_scores,
-            next_tokens,
-            next_indices,
-            pad_token_id=pad_token_id,
-            eos_token_id=eos_token_id,
-            max_length=stopping_criteria.max_length,
-            beam_indices=final_beam_indices,
-        )
-        return sequence_outputs['sequences']
-
-
-def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs):
-    if past:
-        input_ids = input_ids[:, -1].unsqueeze(-1)
-
-    attention_mask = kwargs.get("attention_mask", None)
-    position_ids = kwargs.get("position_ids", None)
-
-    if attention_mask is not None and position_ids is None:
-        # create position_ids on the fly for batch generation
-        position_ids = attention_mask.long().cumsum(-1) - 1
-        position_ids.masked_fill_(attention_mask == 0, 1)
-    else:
-        position_ids = None
-    return {
-        "text": input_ids,
-        "images": image_inputs,
-        "past_key_values": past,
-        "position_ids": position_ids,
-        "attention_mask": attention_mask,
-    }
diff --git a/spaces/zomehwh/sovits-goldship/inference/__init__.py b/spaces/zomehwh/sovits-goldship/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/zomehwh/sovits-teio/onnxexport/model_onnx.py b/spaces/zomehwh/sovits-teio/onnxexport/model_onnx.py
deleted file mode 100644
index e28bae95ec1e53aa05d06fc784ff86d55f228d60..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-teio/onnxexport/model_onnx.py
+++ /dev/null
@@ -1,335 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.attentions as attentions
-import modules.commons as commons
-import modules.modules as modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-import utils
-from modules.commons import init_weights, get_padding
-from vdecoder.hifigan.models import Generator
-from utils import f0_to_coarse
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(self,
-                 channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 n_flows=4,
-                 gin_channels=0):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
-                                              gin_channels=gin_channels, mean_only=True))
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-
-class Encoder(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 gin_channels=0):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        # print(x.shape,x_lengths.shape)
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
-    def __init__(self,
-                 out_channels,
-                 hidden_channels,
-                 kernel_size,
-                 n_layers,
-                 gin_channels=0,
-                 filter_channels=None,
-                 n_heads=None,
-                 p_dropout=None):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-        self.f0_emb = nn.Embedding(256, hidden_channels)
-
-        self.enc_ = attentions.Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout)
-
-    def forward(self, x, x_mask, f0=None, z=None):
-        x = x + self.f0_emb(f0).transpose(1, 2)
-        x = self.enc_(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + z * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class F0Decoder(nn.Module):
-    def __init__(self,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 spk_channels=0):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.spk_channels = spk_channels
-
-        self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
-        self.decoder = attentions.FFT(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout)
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
-        self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
-
-    def forward(self, x, norm_f0, x_mask, spk_emb=None):
-        x = torch.detach(x)
-        if spk_emb is not None:
-            x = x + self.cond(spk_emb)
-        x += self.f0_prenet(norm_f0)
-        x = self.prenet(x) * x_mask
-        x = self.decoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x
-
-
-class SynthesizerTrn(nn.Module):
-    """
-  Synthesizer for Training
-  """
-
-    def __init__(self,
-                 spec_channels,
-                 segment_size,
-                 inter_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 resblock,
-                 resblock_kernel_sizes,
-                 resblock_dilation_sizes,
-                 upsample_rates,
-                 upsample_initial_channel,
-                 upsample_kernel_sizes,
-                 gin_channels,
-                 ssl_dim,
-                 n_speakers,
-                 sampling_rate=44100,
-                 **kwargs):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        self.ssl_dim = ssl_dim
-        self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-        self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
-
-        self.enc_p = TextEncoder(
-            inter_channels,
-            hidden_channels,
-            filter_channels=filter_channels,
-            n_heads=n_heads,
-            n_layers=n_layers,
-            kernel_size=kernel_size,
-            p_dropout=p_dropout
-        )
-        hps = {
-            "sampling_rate": sampling_rate,
-            "inter_channels": inter_channels,
-            "resblock": resblock,
-            "resblock_kernel_sizes": resblock_kernel_sizes,
-            "resblock_dilation_sizes": resblock_dilation_sizes,
-            "upsample_rates": upsample_rates,
-            "upsample_initial_channel": upsample_initial_channel,
-            "upsample_kernel_sizes": upsample_kernel_sizes,
-            "gin_channels": gin_channels,
-        }
-        self.dec = Generator(h=hps)
-        self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-        self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-        self.f0_decoder = F0Decoder(
-            1,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            spk_channels=gin_channels
-        )
-        self.emb_uv = nn.Embedding(2, hidden_channels)
-        self.predict_f0 = False
-
-    def forward(self, c, f0, mel2ph, uv, noise=None, g=None):
-
-        decoder_inp = F.pad(c, [0, 0, 1, 0])
-        mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
-        c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2)  # [B, T, H]
-
-        c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-        g = g.unsqueeze(0)
-        g = self.emb_g(g).transpose(1, 2)
-        x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
-        x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
-
-        if self.predict_f0:
-            lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
-            norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
-            pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
-            f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
-
-        z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise)
-        z = self.flow(z_p, c_mask, g=g, reverse=True)
-        o = self.dec(z * c_mask, g=g, f0=f0)
-        return o